  We study a black hole in an expanding Universe during the radiation-dominated
stage. In the case when the black hole radius is much smaller than the
cosmological horizon, we present a solution of the Einstein equations for the
metric, the matter density and velocity distributions. At distances much
smaller than the cosmological horizon the solution features quasi-stationary
accretion, while at large distances the solution asymptotes the homogeneous
radiation dominated FLRW. We discuss how our solution is related to the
McVittie solution. The obtained results can be applied, in particular, for the
formation of dark matter density spikes around primordial black holes, and for
the evolution of dark matter clumps during the radiation-dominated stage.


  A unified framework for the Chevalley and equitable presentation of
$U_q(sl_2)$ is introduced. It is given in terms of a system of Freidel-Maillet
type equations satisfied by a pair of quantum K-operators ${\mathcal K}^\pm$,
whose entries are expressed in terms of either Chevalley or equitable
generators. The Hopf algebra structure is reconsidered in light of this unified
framework, and interwining relations for each pair of ${\mathcal K}^\pm$ are
obtained. A K-operator solving a spectral parameter dependent Freidel-Maillet
type equation is also considered. Different specializations of this K-operator
are shown to admit a decomposition in terms of ${\mathcal K}^\pm$ of Chevalley
or equitable type. Explicit examples of K-matrices without/with spectral
parameter are derived by specializing the K-operators previously obtained.


  A complete analytical and numerical treatment of all magnetohydrodynamic
waves and instabilities for radially stratified, magnetized accretion disks is
presented. The instabilities are a possible source of anomalous transport.
While recovering results on known hydrodynamicand both weak- and strong-field
magnetohydrodynamic perturbations, the full magnetohydrodynamic spectra for a
realistic accretion disk model demonstrates a much richer variety of
instabilities accessible to the plasma than previously realized. We show that
both weakly and strongly magnetized accretion disks are prone to strong
non-axisymmetric instabilities.The ability to characterize all waves arising in
accretion disks holds great promise for magnetohydrodynamic spectroscopic
analysis.


  We provide global and semi-global controllability results for hyperbolic
conservation laws on a bounded domain, with a general (not necessarily
convex)flux and a time-dependent source term acting as a control. The results
are achieved for, possibly critical, both continuously differentiable states
and BV states. The proofs are based on a combination of the return method and
on the analysis of the Riccati equaiton for the space derivative of the
solution.


  We study a quenched SU(2) lattice gauge theory in which, in an attempt to
distinguish between timelike and spacelike gauge fields, the gauge ensemble
{U_mu} is generated from a 3 dimensional gauge-Higgs model, the timelike link
variables being "reconstructed" from the Higgs fields. The resulting ensemble
is used to study quenched quark propagation with non-zero chemical potential
mu; in particular, the quark density, chiral and superfluid condensates, meson,
baryon and gauge-fixed quark propagators are all studied as functions of mu.
While it proves possible to alter the strength of the inter-quark interaction
by changing the parameters of the dimensionally reduced model, there is no
evidence for any region of parameter space where quarks exhibit deconfined
behaviour or thermodynamic observables scale as if there were a Fermi surface.


  We study the asymptotics of Hankel determinants constructed using the values
$\zeta(an+b)$ of the Riemann zeta function at positive integers in an
arithmetic progression. Our principal result is a Diophantine application of
the asymptotics.


  The carbon, nitrogen, and oxygen abundances and trends in the bulge are
discussed in the context of our recent analysis of these elements in an
on-going project based on near-IR spectra (Ryde et al. 2009). We obtained these
using the CRIRES spectrometer on the VLT. The formation and evolution of the
Milky Way bulge can be constrained by studying elemental abundances of bulge
stars. Due to the large and variable visual extinction in the line-of-sight
towards the bulge, an analysis in the near-IR is preferred.


  If $A$ is an $n \times n$ Hermitian matrix with eigenvalues
$\lambda_1(A),\dots,\lambda_n(A)$ and $i,j = 1,\dots,n$, then the
$j^{\mathrm{th}}$ component $v_{i,j}$ of a unit eigenvector $v_i$ associated to
the eigenvalue $\lambda_i(A)$ is related to the eigenvalues
$\lambda_1(M_j),\dots,\lambda_{n-1}(M_j)$ of the minor $M_j$ of $A$ formed by
removing the $j^{\mathrm{th}}$ row and column by the formula $$
|v_{i,j}|^2\prod_{k=1;k\neq
i}^{n}\left(\lambda_i(A)-\lambda_k(A)\right)=\prod_{k=1}^{n-1}\left(\lambda_i(A)-\lambda_k(M_j)\right)\,.$$
We refer to this identity as the \emph{eigenvector-eigenvalue identity} and
show how this identity can also be used to extract the relative phases between
the components of any given eigenvector. Despite the simple nature of this
identity and the extremely mature state of development of linear algebra, this
identity was not widely known until very recently. In this survey we describe
the many times that this identity, or variants thereof, have been discovered
and rediscovered in the literature (with the earliest precursor we know of
appearing in 1834). We also provide a number of proofs and generalizations of
the identity.


  Current interferometric gravitational wave detectors use test masses with
mirror coatings formed from multiple layers of dielectric materials, most
commonly alternating layers of SiO2 (silica) and Ta2O5 (tantala). However,
mechanical loss in the Ta2O5/SiO2 coatings may limit the design sensitivity for
advanced detectors. We have investigated sources of mechanical loss in the
Ta2O5/SiO2 coatings, including loss associated with the coating-substrate
interface, with the coating-layer interfaces, and with the bulk material. Our
results indicate that the loss is associated with the bulk coating materials
and that the loss of Ta2O5 is substantially larger than that of SiO2.


  The Open Access Citation Advantage (OACA) has been a major topic of
discussion in the literature over the past twenty years. In this paper, we
propose a method to constitute a control group to isolate the OACA effect.
Thus, we compared citation impact (MNCS) of 2,458,378 publications in fully OA
journals to that (weighted MNCS) of a control group of non-OA publications
(\#10,310,842). Similarly, we did the same exercise for OA publications in
hybrid journals (\#1,024,430) and their control group (\#11,533,001), over the
period 2010-2020. The results showed that there is no OACA for publications in
fully OA journals, and that there is rather a disadvantage. Conversely, the
OACA seems to be a reality in hybrid journals, suggesting that a better
accessibility in this context tends to improve the visibility of publications.
The lack of OACA for publications in fully OA journals is to be expected, as a
great proportion of OA journals are newly created and less attractive to
high-impact senior researchers.Another striking result of this paper is the
fall of the OACA from 2016. The citation advantage fell from 70% to 9% between
2016 and 2020 (for publications in hybrid journals). We wonder if this fall is
linked to the increase in the notoriety of pirate sites (eg Sci-Hub) from 2016.
In other words, the democratization of pirate sites instantly cancels the
positive effect of OA publication insofar as the question of access to
scientific content no longer arises.


  The Exascale Computing Project (ECP) is invested in co-design to assure that
key applications are ready for exascale computing. Within ECP, the Co-design
Center for Particle Applications (CoPA) is addressing challenges faced by
particle-based applications across four sub-motifs: short-range
particle-particle interactions (e.g., those which often dominate molecular
dynamics (MD) and smoothed particle hydrodynamics (SPH) methods), long-range
particle-particle interactions (e.g., electrostatic MD and gravitational
N-body), particle-in-cell (PIC) methods, and linear-scaling electronic
structure and quantum molecular dynamics (QMD) algorithms. Our crosscutting
co-designed technologies fall into two categories: proxy applications (or apps)
and libraries. Proxy apps are vehicles used to evaluate the viability of
incorporating various types of algorithms, data structures, and
architecture-specific optimizations and the associated trade-offs; examples
include ExaMiniMD, CabanaMD, CabanaPIC, and ExaSP2. Libraries are modular
instantiations that multiple applications can utilize or be built upon; CoPA
has developed the Cabana particle library, PROGRESS/BML libraries for QMD, and
the SWFFT and fftMPI parallel FFT libraries. Success is measured by
identifiable lessons learned that are translated either directly into parent
production application codes or into libraries, with demonstrated performance
and/or productivity improvement. The libraries and their use in CoPA's ECP
application partner codes are also addressed.


  Filaments are one of the most common features in the solar atmosphere, and
are of significance in solar, stellar and laboratory plasma physics. Using data
from the Chinese H$\alpha$ Solar Explorer, the Solar Upper Transition Region
Imager and the Solar Dynamics Observatory, we report on multiwavelength imaging
and spectral observations of the activation of a small filament. The filament
activation produces several localized dynamic brightenings, which are probably
produced by internal reconnections of the braided magnetic fields in the
filament. The filament expands during the activation and its threads reconnect
with the ambient magnetic fields, which leads to the formation of hot arcades
or loops overlying the filament. The thermal energy of each of these localized
brightenings is estimated in the order of $10^{25}-10^{27} erg$ and the total
energy is estimated to be $\sim1.77 \times 10^{28} erg$. Our observations
demonstrate that the internal magnetic reconnections in the filament can lead
to localized heating to the filament threads and prompt external reconnections
with ambient corona structures, and thus could contribute to the energy and
mass transferring into the corona.


  We study the finite temperature properties of the SO(3) lattice gauge theory
using mean field theory. The main result is the calculation of the effective
action at finite temperature. The form of the effective action is used to
explain the behaviour of the adjoint Wilson line in numerical simulations.
Numerical simulations of the SO(3) lattice gauge theory show that the adjoint
Wilson line has a very small value at low temperatures; at high temperatures,
metastable states are observed in which the adjoint Wilson line takes positive
or negative values. The effective action is able to explain the origin of these
metastable states. A comparison of the effective actions of the SU(2) and the
SO(3) lattice gauge theories explains their different behaviour at high
temperatures. The mean field theory also predicts a finite temperature phase
transition in the SO(3) lattice gauge theory.


  The aim of this paper is to explore the complexity factor (CF) for those
self-gravitating relativistic spheres whose evolution proceeds non-dynamically.
We are adopting the definition of CF mentioned in \cite{PhysRevD.97.044010},
modifying it to the static spherically symmetric case, within the framework of
a modified gravity theory (the Palatini $f(R)$ theory). In this respect, we
have considered radial dependent anisotropic matter content coupled with
spherical geometry and determined the complexity factor involved in the
patterns of radial evolution. We shall explore the field and a well-known
Tolman-Oppenheimer-Volkoff equations. After introducing structure scalars from
the orthogonal decomposition of the Riemann tensor, we shall calculate
complexity factor. An exact analytical model is presented by considering
firstly ansatz provided by Gokhroo and Mehra. The role of matter variables and
$f(R)$ terms are analyzed in the structure formation as well as their evolution
through a complexity factor.


  A recently designed hyperspectral imaging device enables multiplexed
acquisition of an entire data volume in a single snapshot thanks to
monolithically-integrated spectral filters. Such an agile imaging technique
comes at the cost of a reduced spatial resolution and the need for a
demosaicing procedure on its interleaved data. In this work, we address both
issues and propose an approach inspired by recent developments in compressed
sensing and analysis sparse models. We formulate our superresolution and
demosaicing task as a 3-D generalized inpainting problem. Interestingly, the
target spatial resolution can be adjusted for mitigating the compression level
of our sensing. The reconstruction procedure uses a fast greedy method called
Pseudo-inverse IHT. We also show on simulations that a random arrangement of
the spectral filters on the sensor is preferable to regular mosaic layout as it
improves the quality of the reconstruction. The efficiency of our technique is
demonstrated through numerical experiments on both synthetic and real data as
acquired by the snapshot imager.


  In the study of the capacity problem for multiple access channels (MACs), a
lower bound on the error probability obtained by Han plays a crucial role in
the converse parts of several kinds of channel coding theorems in the
information-spectrum framework. Recently, Yagi and Oohama showed a tighter
bound than the Han bound by means of Polyanskiy's converse. In this paper, we
give a new bound which generalizes and strengthens the Yagi-Oohama bound, and
demonstrate that the bound plays a fundamental role in deriving extensions of
several known bounds. In particular, the Yagi-Oohama bound is generalized to
two different directions; i.e, to general input distributions and to general
encoders. In addition we extend these bounds to the quantum MACs and apply them
to the converse problems for several information-spectrum settings.


  We present cosmological constraints based on the cosmic microwave background
(CMB) lensing potential power spectrum measurement from the recent 500 deg$^2$
SPTpol survey, the most precise CMB lensing measurement from the ground to
date. We fit a flat $\Lambda$CDM model to the reconstructed lensing power
spectrum alone and in addition with other data sets: baryon acoustic
oscillations (BAO) as well as primary CMB spectra from Planck and SPTpol. The
cosmological constraints based on SPTpol and Planck lensing band powers are in
good agreement when analysed alone and in combination with Planck full-sky
primary CMB data. With weak priors on the baryon density and other parameters,
the CMB lensing data alone provide a 4\% constraint on $\sigma_8\Omega_m^{0.25}
= 0.0593 \pm 0.025$.. Jointly fitting with BAO data, we find $\sigma_8=0.779
\pm 0.023$, $\Omega_m = 0.368^{+0.032}_{-0.037}$, and $H_0 =
72.0^{+2.1}_{-2.5}\,\text{km}\,\text{s}^{-1}\,\text{Mpc}^{-1} $, up to
$2\,\sigma$ away from the central values preferred by Planck lensing + BAO.
However, we recover good agreement between SPTpol and Planck when restricting
the analysis to similar scales. We also consider single-parameter extensions to
the flat $\Lambda$CDM model. The SPTpol lensing spectrum constrains the spatial
curvature to be $\Omega_K = -0.0007 \pm 0.0025$ and the sum of the neutrino
masses to be $\sum m_{\nu} < 0.23$ eV at 95\% C.L. (with Planck primary CMB and
BAO data), in good agreement with the Planck lensing results. With the
differences in the $S/N$ of the lensing modes and the angular scales covered in
the lensing spectra, this analysis represents an important independent check on
the full-sky Planck lensing measurement.


  The Lie-Trotter formula $e^{\hat{A}+\hat{B}} = \lim_{N\to \infty}
(e^{\hat{A}/N} e^{\hat{B}/N})^N$ is of great utility in a variety of quantum
problems ranging from the theory of path integrals and Monte Carlo methods in
theoretical chemistry, to many-body and thermostatistical calculations. We
generalize it for the q-exponential function $e_q (x) = [1+ (1-q)
x]^{(1/(1-q))}$ (with $e_1(x)=e^x$), and prove $e_q(\hat{A}+\hat{B}+(1-q)
[\hat{A}\hat{B}+\hat{B}\hat{A}] /2) = \lim_{N\to \infty}
{[e_{1-(1-q)N}(\hat{A}/N)] [e_{1-(1-q)N}(\hat{B}/N)]}^N$. This extended formula
is expected to be similarly useful in the nonextensive situations


  In this paper, we consider the generalized stationary Stokes system with
$p$-growth and Dini-$\operatorname{BMO}$ regular coefficients. The main purpose
is to establish pointwise estimates for the shear rate and the associated
pressure to such Stokes system in terms of an unconventional nonlinear
Havin-Maz'ya-Wolff type potential of the nonhomogeneous term in the plane. As a
consequence, a symmetric gradient $L^{\infty}$ estimate is obtained. Moreover,
we derive potential estimates for the weak solution to the Stokes system
without additional regularity assumptions on the coefficients in higher
dimensional space.


  The xView2 competition and xBD dataset spurred significant advancements in
overhead building damage detection, but the competition's pixel level scoring
can lead to reduced solution performance in areas with tight clusters of
buildings or uninformative context. We seek to advance automatic building
damage assessment for disaster relief by proposing an auxiliary challenge to
the original xView2 competition. This new challenge involves a new dataset and
metrics indicating solution performance when damage is more local and limited
than in xBD. Our challenge measures a network's ability to identify individual
buildings and their damage level without excessive reliance on the buildings'
surroundings. Methods that succeed on this challenge will provide more
fine-grained, precise damage information than original xView2 solutions. The
best-performing xView2 networks' performances dropped noticeably in our new
limited/local damage detection task. The common causes of failure observed are
that (1) building objects and their classifications are not separated well, and
(2) when they are, the classification is strongly biased by surrounding
buildings and other damage context. Thus, we release our augmented version of
the dataset with additional object-level scoring metrics
(https://drive.google.com/drive/folders/1VuQZuAg6-Yo8r5J4OCx3ZRpa_fv9aaDX?usp=sharing)
to test independence and separability of building objects, alongside the
pixel-level performance metrics of the original competition. We also experiment
with new baseline models which improve independence and separability of
building damage predictions. Our results indicate that building damage
detection is not a fully-solved problem, and we invite others to use and build
on our dataset augmentations and metrics.


  The goal is to review the notion of a complete Segal space and how certain
categorical notions behave in this context. In particular, we study
functoriality in complete Segal spaces via fibrations. Then we use it to define
limits and adjunctions in a complete Segal space. This is mostly expository and
the focus is on examples and intuition.


  Pulsars inside binary systems can accrete matter that arrives up to the
pulsar surface provided that its period is long enough. During the accretion
process, matter has to be accelerated to the rotational velocity of the pulsar
magnetosphere at the distance where the balance between the pressure of matter
and the magnetic field is achieved. At this distance, a very turbulent and
magnetized region is formed in which hadrons can be accelerated to relativistic
energies. These hadrons can interact with the very strong radiation field
coming from the hot polar cap on the neutron star surface created by the
in-falling matter. We calculate the neutrino event rates produced in an km$^2$
detector that can be expected from accreting millisecond and classical X-ray
pulsars at a typical distance within our own Galaxy.


  An analysis of the source position differences between VLBI-based ICRF and
$Gaia$-CRF catalogues is a key step in assessing their systematic errors and
determining their mutual orientation. One of the main factors that limits the
accuracy of determination of the orientation parameters between two frames is
the impact of outliers. To mitigate this effect, a new method is proposed based
on pixelization data over the equal-area cells, followed by median filtering of
the data in each cell. After this, a new data set is formed, consisting of data
points near-uniformly distributed over the sphere. The vector spherical
harmonics (VSH) decomposition is then applied to this data to finally compute
the orientation parameters between ICRF and $Gaia$ frames. To validate the
proposed approach, a comparison was made of the ICRF3-SX and $Gaia$~DR2
catalogues using several methods for outliers removal. The results of this work
showed that the proposed method is practically insensitive to outliers and thus
provides much more robust results of catalogues comparison than the methods
used so far. This conclusion was confirmed by analogous test comparison of the
$Gaia$~DR2 and OCARS catalogues.


  We quantitatively identify the origin of anomalous transport in a
representative model of a heterogeneous system---tracer migration in the
complex flow patterns of a lognormally distributed hydraulic conductivity ($K$)
field. The transport, determined by a particle tracking technique, is
characterized by breakthrough curves; the ensemble averaged curves document
anomalous transport in this system, which is entirely accounted for by a
truncated power-law distribution of local transition times $\psi(t)$ within the
framework of a continuous time random walk. Unique to this study is the linking
of $\psi(t)$ directly to the system heterogeneity. We assess the statistics of
the dominant preferred pathways by forming a particle-visitation weighted
histogram $\{wK\}$. Converting the ln($K$) dependence of $\{wK\}$ into time
yields the equivalence of $\{wK\}$ and $\psi(t)$, and shows the part of
$\{wK\}$ that forms the power-law of $\psi(t)$, which is the origin of
anomalous transport. We also derive an expression defining the power law
exponent in terms of the $\{wK\}$ parameters. This equivalence is a remarkable
result, particularly given the correlated $K$-field, the complexity of the flow
field and the statistics of the particle transitions.


  We study a competition between two growth models with long-range correlations
on the torus $\mathbb T_n^d$ of size $n$ in dimension $d$. We append the edge
set of the torus $\mathbb T_n^d$ by including all non-nearest-neighbour edges,
and from two source vertices, two first-passage percolation (FPP) processes
start flowing on $\mathbb T_n^d$ and compete to cover the sites. The FPP
processes we consider are long-range first-passage percolation processes, as
studied by Chaterjee and Dey. Here, we have two types, say Type-$1$ and
Type-$2$, and the Type-$i$ transmission time of an edge $e$ equals
$\lambda_i^{-1} \|e\|^{\alpha_i}E_e$ for $i\in\{1,2\}$, where $(E_e)_e$ is a
family of i.i.d.\ rate-one exponential random variables,
$\lambda_1,\lambda_2>0$ are the global rate parameters, and
$\alpha_1,\alpha_2\geq 0$ are the long-range parameters. In particular, we
consider the instantaneous percolation regime, where
$\alpha_1,\alpha_2\in[0,d)$, and we allow all parameters to depend on $n$. We
study \emph{coexistence}, the event that both types reach a positive proportion
of the graph, and identify precisely when coexistence occurs. In the case of
absence of coexistence, we outline several phase transitions in the size of the
losing type, depending on the relation between the rates of both types. One of
the consequences of our results is that for constant intensity competition,
i.e.\ when the long-range parameters of the two processes are the same, while
their rates differ by a constant multiplicative factor, coexistence of the two
processes at the scale of the torus volume happen if and only if their global
rates are equal. On the other hand, when the long-range parameters differ, it
is possible for one of the types, e.g.\ Type-$2$, to reach a significant number
of vertices, even when its global rate parameter $\lambda_2$ is much smaller
than $\lambda_1$.


  The reflection spectrum of the torus around AGN is characterized by X-ray
fluorescent lines, which are most prominent for type II AGN. A clumpy torus
allows photons reflected from the back-side of the torus to leak through the
front free-of-obscuration regions. Therefore, the observed X-ray fluorescent
lines are sensitive to the clumpiness of the torus. We analyse a sample of type
II AGN observed with Chandra HETGS, and measure the fluxes for the Si Ka and Fe
Ka lines. The measured Fe Ka/Si Ka ratios, spanning a range between $5-60$, are
far smaller than the ratios predicted from simulations of smooth tori,
indicating that the tori of the studied sources have clumpy distributions
rather than smooth ones. Compared with simulation results of clumpy tori with a
half-opening angle of 60$^{\circ}$, the Circinus galaxy has a Fe Ka/Si Ka ratio
of $\sim60$, which is close to the simulation results for $N=5$, where $N$ is
the average number of clumps along the line of sight. The Fe Ka/Si Ka ratios of
the other sources are all below the simulation results for $N=2$. Overall, it
shows that the non-Fe fluorescent lines in the soft X-ray band are a
potentially powerful probe of the clumpiness of the torus around AGN.


  Strong and fast "bang-bang" (BB) pulses have been recently proposed as a
means for reducing decoherence in a quantum system. So far theoretical analysis
of the BB technique relied on model Hamiltonians. Here we introduce a method
for empirically determining the set of required BB pulses, that relies on
quantum process tomography. In this manner an experimenter may tailor his or
her BB pulses to the quantum system at hand, without having to assume a model
Hamiltonian.


  Differentially private GNNs (Graph Neural Networks) have been recently
studied to provide high accuracy in various tasks on graph data while strongly
protecting user privacy. In particular, a recent study proposes an algorithm to
protect each user's feature vector in an attributed graph, which includes
feature vectors along with node IDs and edges, with LDP (Local Differential
Privacy), a strong privacy notion without a trusted third party. However, this
algorithm does not protect edges (friendships) in a social graph, hence cannot
protect user privacy in unattributed graphs, which include only node IDs and
edges. How to provide strong privacy with high accuracy in unattributed graphs
remains open. In this paper, we propose a novel LDP algorithm called the DPRR
(Degree-Preserving Randomized Response) to provide LDP for edges in GNNs. Our
DPRR preserves each user's degree hence a graph structure while providing edge
LDP. Technically, our DPRR uses Warner's RR (Randomized Response) and strategic
edge sampling, where each user's sampling probability is automatically tuned
using the Laplacian mechanism to preserve the degree information under edge
LDP. We also propose a privacy budget allocation method to make the noise in
both Warner's RR and the Laplacian mechanism small. We focus on graph
classification as a task of GNNs and evaluate the DPRR using three social graph
datasets. Our experimental results show that the DPRR significantly outperforms
three baselines and provides accuracy close to a non-private algorithm in all
datasets with a reasonable privacy budget, e.g., epsilon=1. Finally, we
introduce data poisoning attacks to our DPRR and a defense against the attacks.
We evaluate them using the three social graph datasets and discuss the
experimental results.


  We present an efficient method to generate large simulations of the Epoch of
Reionization (EoR) without the need for a full 3-dimensional radiative transfer
code. Large dark-matter-only simulations are post-processed to produce maps of
the redshifted 21cm emission from neutral hydrogen. Dark matter haloes are
embedded with sources of radiation whose properties are either based on
semi-analytical prescriptions or derived from hydrodynamical simulations. These
sources could either be stars or power-law sources with varying spectral
indices. Assuming spherical symmetry, ionized bubbles are created around these
sources, whose radial ionized fraction and temperature profiles are derived
from a catalogue of 1-D radiative transfer experiments. In case of overlap of
these spheres, photons are conserved by redistributing them around the
connected ionized regions corresponding to the spheres. The efficiency with
which these maps are created allows us to span the large parameter space
typically encountered in reionization simulations. We compare our results with
other, more accurate, 3-D radiative transfer simulations and find excellent
agreement for the redshifts and the spatial scales of interest to upcoming 21cm
experiments. We generate a contiguous observational cube spanning redshift 6 to
12 and use these simulations to study the differences in the reionization
histories between stars and quasars. Finally, the signal is convolved with the
LOFAR beam response and its effects are analyzed and quantified. Statistics
performed on this mock data set shed light on possible observational strategies
for LOFAR.


  It has been well-known that for two-way contingency tables with fixed row
sums and column sums the set of square-free moves of degree two forms a Markov
basis. However when we impose an additional constraint that the sum of a
subtable is also fixed, then these moves do not necessarily form a Markov
basis. Thus, in this paper, we show a necessary and sufficient condition on a
subtable so that the set of square-free moves of degree two forms a Markov
basis.


  Myopia, or nearsightedness, is the most common eye disorder, resulting
primarily from excess elongation of the eye. The etiology of myopia, although
known to be complex, is poorly understood. Here we report the largest ever
genome-wide association study (43,360 participants) on myopia in Europeans. We
performed a survival analysis on age of myopia onset and identified 19
significant associations (p < 5e-8), two of which are replications of earlier
associations with refractive error. These 19 associations in total explain 2.7%
of the variance in myopia age of onset, and point towards a number of different
mechanisms behind the development of myopia. One association is in the gene
PRSS56, which has previously been linked to abnormally small eyes; one is in a
gene that forms part of the extracellular matrix (LAMA2); two are in or near
genes involved in the regeneration of 11-cis-retinal (RGR and RDH5); two are
near genes known to be involved in the growth and guidance of retinal ganglion
cells (ZIC2, SFRP1); and five are in or near genes involved in neuronal
signaling or development. These novel findings point towards multiple genetic
factors involved in the development of myopia and suggest that complex
interactions between extracellular matrix remodeling, neuronal development, and
visual signals from the retina may underlie the development of myopia in
humans.


  Distinctive orderings and phase diagram structures are found, from
renormalization-group theory, for odd q-state clock spin-glass models in d=3
dimensions. These models exhibit asymmetric phase diagrams, as is also the case
for quantum Heisenberg spin-glass models. No finite-temperature spin-glass
phase occurs. For all odd $q\geqslant 5$, algebraically ordered
antiferromagnetic phases occur. One such phase is dominant and occurs for all
$q\geqslant 5$. Other such phases occupy small low-temperature portions of the
phase diagrams and occur for $5 \leqslant q \leqslant 15$. All algebraically
ordered phases have the same structure, determined by an attractive
finite-temperature sink fixed point where a dominant and a subdominant pair
states have the only non-zero Boltzmann weights. The phase transition critical
exponents quickly saturate to the high q value.


  Cosmic-ray energy densities in central regions of starburst galaxies, as
inferred from radio and gamma-ray measurements of, respectively, non-thermal
synchrotron and neutral pion decay emission, are typically U_p = O(100)eV/cm3,
i.e. typically at least an order of magnitude larger than near the Galactic
center and in other non-very-actively star-forming galaxies. We first show that
these very different energy-density levels reflect a similar disparity in the
respective supernova rates in the two environments, which is not unexpected
given the supernova origin of (Galactic) energetic particles. As a consequence
of this correspondence, we then demonstrate that there is partial quantitative
evidence that the stellar initial mass function (IMF) in starburst nuclei has a
low-mass truncation at ~2M_sun, as predicted by theoretical models of turbulent
media, in contrast with the much smaller value of 0.1M_sun that characterizes
the low-mass cutoff of the stellar IMF in `normal' galactic environments.


  Support Vector Data Description (SVDD) is a machine-learning technique used
for single class classification and outlier detection. SVDD formulation with
kernel function provides a flexible boundary around data. The value of kernel
function parameters affects the nature of the data boundary. For example, it is
observed that with a Gaussian kernel, as the value of kernel bandwidth is
lowered, the data boundary changes from spherical to wiggly. The spherical data
boundary leads to underfitting, and an extremely wiggly data boundary leads to
overfitting. In this paper, we propose empirical criterion to obtain good
values of the Gaussian kernel bandwidth parameter. This criterion provides a
smooth boundary that captures the essential geometric features of the data.


  We investigate the use of iterated function system (IFS) models for data
analysis. An IFS is a discrete dynamical system in which each time step
corresponds to the application of one of a finite collection of maps. The maps,
which represent distinct dynamical regimes, may act in some pre-determined
sequence or may be applied in random order. An algorithm is developed to detect
the sequence of regime switches under the assumption of continuity. This method
is tested on a simple IFS and applied to an experimental computer performance
data set. This methodology has a wide range of potential uses: from
change-point detection in time-series data to the field of digital
communications.


  Motif discovery is a fundamental step in data mining tasks for time-series
data such as clustering, classification and anomaly detection. Even though many
papers have addressed the problem of how to find motifs in time-series by
proposing new motif discovery algorithms, not much work has been done on the
exploration of the motifs extracted by these algorithms. In this paper, we
argue that visually exploring time-series motifs computed by motif discovery
algorithms can be useful to understand and debug results. To explore the output
of motif discovery algorithms, we propose the use of an adapted Self-Organizing
Map, the DTW-SOM, on the list of motif's centers. In short, DTW-SOM is a
vanilla Self-Organizing Map with three main differences, namely (1) the use the
Dynamic Time Warping distance instead of the Euclidean distance, (2) the
adoption of two new network initialization routines (a random sample
initialization and an anchor initialization) and (3) the adjustment of the
Adaptation phase of the training to work with variable-length time-series
sequences. We test DTW-SOM in a synthetic motif dataset and two real
time-series datasets from the UCR Time Series Classification Archive. After an
exploration of results, we conclude that DTW-SOM is capable of extracting
relevant information from a set of motifs and display it in a visualization
that is space-efficient.


  We show how macroscopically distinct quantum superposition states
(Schroedinger cat states) may be used as logical qubit encodings for the
correction of spontaneous emission errors. Spontaneous emission causes a bit
flip error which is easily corrected by a standard error correction circuit.
The method works arbitrarily well as the distance between the amplitudes of the
superposed coherent states increases.


  Every programmer has a characteristic style, ranging from preferences about
identifier naming to preferences about object relationships and design
patterns. Coding conventions define a consistent syntactic style, fostering
readability and hence maintainability. When collaborating, programmers strive
to obey a project's coding conventions. However, one third of reviews of
changes contain feedback about coding conventions, indicating that programmers
do not always follow them and that project members care deeply about adherence.
Unfortunately, programmers are often unaware of coding conventions because
inferring them requires a global view, one that aggregates the many local
decisions programmers make and identifies emergent consensus on style. We
present NATURALIZE, a framework that learns the style of a codebase, and
suggests revisions to improve stylistic consistency. NATURALIZE builds on
recent work in applying statistical natural language processing to source code.
We apply NATURALIZE to suggest natural identifier names and formatting
conventions. We present four tools focused on ensuring natural code during
development and release management, including code review. NATURALIZE achieves
94% accuracy in its top suggestions for identifier names and can even transfer
knowledge about conventions across projects, leveraging a corpus of 10,968 open
source projects. We used NATURALIZE to generate 18 patches for 5 open source
projects: 14 were accepted.


  We propose a new method to overcome the nodal plane problem for the
tomographic reconstruction of molecular orbitals with twofold mirror
antisymmetry in the length form based on high-order harmonic generation. It is
shown that, by carrying out the reconstruction procedure in the rotating
laboratory frame using the component of the dipole moment parallel to the
electron recollision direction, the nodal plane problem is avoided and the
target orbital is successfully reconstructed. Moreover, it is found that, the
proposed method can completely avoid the additional artificial lobes found in
the results from the traditional method in the velocity form and therefore
provides a more reliable reproduction of the target orbital.


  Natural language explanations promise to offer intuitively understandable
explanations of a neural network's decision process in complex vision-language
tasks, as pursued in recent VL-NLE models. While current models offer
impressive performance on task accuracy and explanation plausibility, they
suffer from a range of issues: Some models feature a modular design where the
explanation generation module is poorly integrated with a separate module for
task-answer prediction, employ backbone models trained on limited sets of
tasks, or incorporate ad hoc solutions to increase performance on single
datasets. We propose to evade these limitations by applying recent advances in
large-scale multi-task pretraining of generative Transformer models to the
problem of VL-NLE tasks. Our approach outperforms recent models by a large
margin, with human annotators preferring the generated explanations over the
ground truth in two out of three evaluated datasets. As a novel challenge in
VL-NLE research, we propose the problem of multi-task VL-NLE and show that
jointly training on multiple tasks can increase the explanation quality. We
discuss the ethical implications of high-quality NLE generation and other
issues in recent VL-NLE research.


  We have investigated the dispersion renormalization $Z_{disp}$ in
La$_{2-x}$Sr$_x$CuO$_4$ (LSCO) over the wide doping range of $x=0.03-0.30$, for
binding energies extending to several hundred meV's. Strong correlation effects
conspire in such a way that the system exhibits an LDA-like dispersion which
essentially `undresses' ($Z_{disp}\to 1$) as the Mott insulator is approached.
Our finding that the Mott insulator contains `nascent' or `preformed' metallic
states with a vanishing spectral weight offers a challenge to existing
theoretical scenarios for cuprates.


  The structure of social contact networks strongly influences the dynamics of
epidemic diseases. In particular the scale-free structure of real-world social
networks allows unlikely diseases with low infection rates to spread and become
endemic. However, in particular for potentially fatal diseases, also the impact
of the disease on the social structure cannot be neglected, leading to a
complex interplay. Here, we consider the growth of a network by preferential
attachment from which nodes are simultaneously removed due to an SIR epidemic.
We show that increased infectiousness increases the prevalence of the disease
and simultaneously causes a transition from scale-free to exponential topology.
Although a transition to a degree distribution with finite variance takes
place, the network still exhibits no epidemic threshold in the thermodynamic
limit. We illustrate these results using agent-based simulations and
analytically tractable approximation schemes.


  PINSKY is a system for open-ended learning through neuroevolution in
game-based domains. It builds on the Paired Open-Ended Trailblazer (POET)
system, which originally explored learning and environment generation for
bipedal walkers, and adapts it to games in the General Video Game AI (GVGAI)
system. Previous work showed that by co-evolving levels and neural network
policies, levels could be found for which successful policies could not be
created via optimization alone. Studied in the realm of Artificial Life as a
potentially open-ended alternative to gradient-based fitness, minimal criteria
(MC)-based selection helps foster diversity in evolutionary populations. The
main question addressed by this paper is how the open-ended learning actually
works, focusing in particular on the role of transfer of policies from one
evolutionary branch ("species") to another. We analyze the dynamics of the
system through creating phylogenetic trees, analyzing evolutionary trajectories
of policies, and temporally breaking down transfers according to species type.
Furthermore, we analyze the impact of the minimal criterion on generated level
diversity and inter-species transfer. The most insightful finding is that
inter-species transfer, while rare, is crucial to the system's success.


  Modelling of early language acquisition aims to understand how infants
bootstrap their language skills. The modelling encompasses properties of the
input data used for training the models, the cognitive hypotheses and their
algorithmic implementations being tested, and the evaluation methodologies to
compare models to human data. Recent developments have enabled the use of more
naturalistic training data for computational models. This also motivates
development of more naturalistic tests of model behaviour. A crucial step
towards such an aim is to develop representative speech datasets consisting of
speech heard by infants in their natural environments. However, a major
drawback of such recordings is that they are typically noisy, and it is
currently unclear how the sound quality could affect analyses and modelling
experiments conducted on such data. In this paper, we explore this aspect for
the case of infant-directed speech (IDS) and adult-directed speech (ADS)
analysis. First, we manually and automatically annotated audio quality of
utterances extracted from two corpora of child-centred long-form recordings (in
English and French). We then compared acoustic features of IDS and ADS in an
in-lab dataset and across different audio quality subsets of naturalistic data.
Finally, we assessed how the audio quality and recording environment may change
the conclusions of a modelling analysis using a recent self-supervised learning
model. Our results show that the use of modest and high audio quality
naturalistic speech data result in largely similar conclusions on IDS and ADS
in terms of acoustic analyses and modelling experiments. We also found that an
automatic sound quality assessment tool can be used to screen out useful parts
of long-form recordings for a closer analysis with comparable results to that
of manual quality annotation.


  We have measured the spin-dependent structure function $g_1^p$ in inclusive
deep-inelastic scattering of polarized muons off polarized protons, in the
kinematic range $0.003 < x < 0.7$ and $1 GeV^2 < Q^2 < 60 GeV^2$. A
next-to-leading order QCD analysis is used to evolve the measured
$g_1^p(x,Q^2)$ to a fixed $Q^2_0$. The first moment of $g_1^p$ at $Q^2_0 = 10
GeV^2$ is $\Gamma^p = 0.136\pm 0.013(stat.) \pm 0.009(syst.)\pm 0.005(evol.)$.
This result is below the prediction of the Ellis-Jaffe sum rule by more than
two standard deviations. The singlet axial charge $a_0$ is found to be $0.28
\pm 0.16$. In the Adler-Bardeen factorization scheme, $\Delta g \simeq 2$ is
required to bring $\Delta \Sigma$ in agreement with the Quark-Parton Model. A
combined analysis of all available proton and deuteron data confirms the
Bjorken sum rule.


  The magnetic behavior of bcc iron nanoclusters, with diameters between 2 and
8 nm, is investigated by means of spin dynamics (SD) simulations coupled to
molecular dynamics (MD-SD), using a distance-dependent exchange interaction.
Finite-size effects in the total magnetization as well as the influence of the
free surface and the surface/core proportion of the nanoclusters are analyzed
in detail for a wide temperature range, going beyond the cluster and bulk Curie
temperatures. Comparison is made with experimental data and with theoretical
models based on the mean-field Ising model adapted to small clusters, and
taking into account the influence of low coordinated spins at free surfaces.
Our results for the temperature dependence of the average magnetization per
atom M(T), including the thermalization of the transnational lattice degrees of
freedom, are in very good agreement with available experimental measurements on
small Fe nanoclusters. In contrast, significant discrepancies with experiment
are observed if the translational degrees of freedom are artificially frozen.
The finite-size effects on M(T) are found to be particularly important near the
cluster Curie temperature. Simulated magnetization above the Curie temperature
scales with cluster size as predicted by models assuming short-range magnetic
ordering (SRMO). Analytical approximations to the magnetization as a function
of temperature and size are proposed.


  For decades, the conventional view is that an s-wave BCS superfluid can not
support uniform spin polarization due to a gap $\Delta$ in the quasiparticle
excitation spectrum. We show that this is an artifact of the dismissal of
quasiparticle interactions $V_{qp}^{}$ in the conventional approach at the
outset. Such interactions can cause triplet fluctuations in the ground state
and hence non-zero spin polarization at "magnetic field" $h<\Delta$. The
resulting ground state is a pairing state of quasiparticles on the ``BCS
vacuum". For sufficiently large $V_{qp}$, the spin polarization of at unitarity
has the simple form $m\propto \mu^{1/2}$. Our study is motivated by the recent
experiments at Rice which found evidence of a homogenous superfluid state with
uniform spin polarization.


  An efficient simulation-based methodology is proposed for the rolling window
estimation of state space models, called particle rolling Markov chain Monte
Carlo (MCMC) with double block sampling. In our method, which is based on
Sequential Monte Carlo (SMC), particles are sequentially updated to approximate
the posterior distribution for each window by learning new information and
discarding old information from observations. Th particles are refreshed with
an MCMC algorithm when the importance weights degenerate. To avoid degeneracy,
which is crucial for reducing the computation time, we introduce a block
sampling scheme and generate multiple candidates by the algorithm based on the
conditional SMC. The theoretical discussion shows that the proposed methodology
with a nested structure is expressed as SMC sampling for the augmented space to
provide the justification. The computational performance is evaluated in
illustrative examples, showing that the posterior distributions of the model
parameters are accurately estimated. The proofs and additional discussions
(algorithms and experimental results) are provided in the Supplementary
Material.


  The homogeneous isotropic Boltzmann equation (HIBE) is a fundamental dynamic
model for many applications in thermodynamics, econophysics and sociodynamics.
Despite recent hardware improvements, the solution of the Boltzmann equation
remains extremely challenging from the computational point of view, in
particular by deterministic methods (free of stochastic noise). This work aims
to improve a deterministic direct method recently proposed [V.V. Aristov,
Kluwer Academic Publishers, 2001] for solving the HIBE with a generic
collisional kernel and, in particular, for taking care of the late dynamics of
the relaxation towards the equilibrium. Essentially (a) the original problem is
reformulated in terms of particle kinetic energy (exact particle number and
energy conservation during microscopic collisions) and (b) the computation of
the relaxation rates is improved by the DVM-like correction, where DVM stands
for Discrete Velocity Model (ensuring that the macroscopic conservation laws
are exactly satisfied). Both these corrections make possible to derive very
accurate reference solutions for this test case. Moreover this work aims to
distribute an open-source program (called HOMISBOLTZ), which can be
redistributed and/or modified for dealing with different applications, under
the terms of the GNU General Public License. The program has been purposely
designed in order to be minimal, not only with regards to the reduced number of
lines (less than 1,000), but also with regards to the coding style (as simple
as possible).


  Using analytical arguments and the numerical renormalization group method we
investigate the spin-thermopower of a quantum dot in a magnetic field. In the
particle-hole symmetric situation the temperature difference applied across the
dot drives a pure spin current without accompanying charge current. For
temperatures and fields at or above the Kondo temperature, but of the same
order of magnitude, the spin-Seebeck coefficient is large, of the order of
k_B/e. Via a mapping, we relate the spin-Seebeck coefficient to the
charge-Seebeck coefficient of a negative-U quantum dot where the corresponding
result was recently reported by Andergassen et al. in Phys. Rev. B 84, 241107
(2011). For several regimes we provide simplified analytical expressions. In
the Kondo regime, the dependence of the spin-Seebeck coefficient on the
temperature and the magnetic field is explained in terms of the shift of the
Kondo resonance due to the field and its broadening with the temperature and
the field. We also consider the influence of breaking the particle-hole
symmetry and show that a pure spin current can still be realized provided a
suitable electric voltage is applied across the dot. Then, except for large
asymmetries, the behavior of the spin-Seebeck coefficient remains similar to
that found in the particle-hole symmetric point.


  Solecki has shown that a broad natural class of $G_{\delta}$ ideals of
compact sets can be represented through the ideal of nowhere dense subsets of a
closed subset of the hyperspace of compact sets. In this note we show that the
closed subset in this representation can be taken to be closed upwards.


  We consider models of Extended Gravity and in particular, generic models
containing scalar-tensor and higher-order curvature terms, as well as a model
derived from noncommutative spectral geometry. Studying, in the weak-field
approximation, the geodesic and Lense-Thirring processions, we impose
constraints on the free parameters of such models by using the recent
experimental results of the Gravity Probe B and LARES satellites.


  Objective: Researchers often use model-based multiple imputation to handle
missing at random data to minimize bias while making the best use of all
available data. However, there are sometimes constraints within the data that
make model-based imputation difficult and may result in implausible values. In
these contexts, we describe how to use random hot deck imputation to allow for
plausible multiple imputation in longitudinal studies.
  Study Design and Setting: We illustrate random hot deck multiple imputation
using The Childhood Health, Activity, and Motor Performance School Study
Denmark (CHAMPS-DK), a prospective cohort study that measured weekly sports
participation for 1700 Danish schoolchildren. We matched records with missing
data to several observed records, generated probabilities for matched records
using observed data, and sampled from these records based on the probability of
each occurring. Because imputed values are generated randomly, multiple
complete datasets can be created and analyzed similar to model-based multiple
imputation.
  Conclusion: Multiple imputation using random hot deck imputation is an
alternative method when model-based approaches are infeasible, specifically
where there are constraints within and between covariates.


  We give conditions under which general bipartite entangled nonorthogonal
states become maximally entangled states. By the conditions we construct a
large class of entangled nonorthogonal states with exact one ebit of
entanglement in both bipartite and multipartite systems. One remarkable
property is that the amount of entanglement in this class of states is
independent on the parameters involved in the states. Finally we discuss how to
generate the bipartite maximally entangled nonorthogonal states.


  Non-adiabatic motion of Bose-Einstein condensates of rubidium atoms arising
from the dynamical nature of a time-orbiting-potential (TOP) trap was observed
experimentally. The orbital micromotion of the condensate in velocity space at
the frequency of the rotating bias field of the TOP was detected by a
time-of-flight method. A dependence of the equilibrium position of the atoms on
the sense of rotation of the bias field was observed. We have compared our
experimental findings with numerical simulations. The nonadiabatic following of
the atomic spin in the trap rotating magnetic field produces geometric forces
acting on the trapped atoms.


  The entropic uncertainty relation with quantum side information (EUR-QSI)
from [Berta et al., Nat. Phys. 6, 659 (2010)] is a unifying principle relating
two distinctive features of quantum mechanics: quantum uncertainty due to
measurement incompatibility, and entanglement. In these relations, quantum
uncertainty takes the form of preparation uncertainty where one of two
incompatible measurements is applied. In particular, the "uncertainty witness"
lower bound in the EUR-QSI is not a function of a post-measurement state. An
insightful proof of the EUR-QSI from [Coles et al., Phys. Rev. Lett. 108,
210405 (2012)] makes use of a fundamental mathematical consequence of the
postulates of quantum mechanics known as the non-increase of quantum relative
entropy under quantum channels. Here, we exploit this perspective to establish
a tightening of the EUR-QSI which adds a new state-dependent term in the lower
bound, related to how well one can reverse the action of a quantum measurement.
As such, this new term is a direct function of the post-measurement state and
can be thought of as quantifying how much disturbance a given measurement
causes. Our result thus quantitatively unifies this feature of quantum
mechanics with the others mentioned above. We have experimentally tested our
theoretical predictions on the IBM Quantum Experience and find reasonable
agreement between our predictions and experimental outcomes.


  Effective Lagrangians for Quantum Chromodynamics (QCD) especially suited for
understanding deconfinement and chiral symmetry restoration at nonzero
temperature and matter density are reviewed. These effective theories allow one
to study generic properties of phase transitions using non-order parameter
fields without loosing the information encoded in the true order parameter.
{}For the pure gauge theory we demonstrate that, near the deconfining phase
transition, the center group symmetry is naturally linked to the conformal
anomaly. Another relevant outcome is that when the theory contains also quarks
we can explain the intertwining of chiral symmetry restoration and
deconfinement for QCD with matter fields either in the fundamental or in the
adjoint representation of the gauge group. As a test of our general approach we
show our results to be applicable also at non zero baryon chemical potential.
We also predict new testable substructures to be present in the phase diagram
of quarks in the adjoint representation of the gauge group. Here we provide
some new insights on the large $N$ limit of gauge theories by investigating the
hadronic world. We propose that the world of infinite $N$ should already be
well described when N=6 for QCD with two and three light flavors. Finally, we
suggest possible future applications of our results for heavy ions collisions.


  We study the transport dynamics of momenta deposited from jets in
ultrarelativistic heavy-ion collisions. Assuming that the high-energy partons
traverse expanding quark-gluon fluids and are subject to lose their energy and
momentum, we simulate dijet asymmetric events by solving relativistic
hydrodynamic equations numerically without linearization in the fully
(3+1)-dimensional coordinate. Mach cones are formed and strongly broadened by
radial flow of the background medium. As a result, the yield of low-$p_{T}$
particles increases at large angles from the jet axis and compensates the dijet
momentum imbalance inside the jet-cone. This provides an intimate link between
the medium excitation by jets and results in dijet asymmetric events observed
by the CMS Collaboration.


  Evaluating the degree of partisan districting (Gerrymandering) in a
statistical framework typically requires an ensemble of districting plans which
are drawn from a prescribed probability distribution that adheres to a
realistic and non-partisan criteria. In this article we introduce novel
non-reversible Markov chain Monte-Carlo (MCMC) methods for the sampling of such
districting plans which have improved mixing properties in comparison to
previously used (reversible) MCMC algorithms. In doing so we extend the current
framework for construction of non-reversible Markov chains on discrete sampling
spaces by considering a generalization of skew detailed balance. We provide a
detailed description of the proposed algorithms and evaluate their performance
in numerical experiments.


  The distinguishing number (index) $D(G)$ ($D'(G)$) of a graph $G$ is the
least integer $d$ such that $G$ has an vertex (edge) labeling with $d$ labels
that is preserved only by the trivial automorphism. It is known that for every
graph $G$ we have $D'(G) \leq D(G) + 1$. In this note we characterize trees for
which this inequality is sharp. We also show that if $G$ is a connected
unicyclic graph, then $D'(G) = D(G)$.


  Context: Modern software systems (e.g., Apache Spark) are usually written in
multiple programming languages (PLs). There is little understanding on the
phenomenon of multi-programming-language commits (MPLCs), which involve
modified source files written in multiple PLs. Objective: This work aims to
explore MPLCs and their impacts on development difficulty and software quality.
Methods: We performed an empirical study on eighteen non-trivial Apache
projects with 197,566 commits. Results: (1) the most commonly used PL
combination consists of all the four PLs, i.e., C/C++, Java, JavaScript, and
Python; (2) 9% of the commits from all the projects are MPLCs, and the
proportion of MPLCs in 83% of the projects goes to a relatively stable level;
(3) more than 90% of the MPLCs from all the projects involve source files in
two PLs; (4) the change complexity of MPLCs is significantly higher than that
of non-MPLCs; (5) issues fixed in MPLCs take significantly longer to be
resolved than issues fixed in non-MPLCs in 89% of the projects; (6) MPLCs do
not show significant effects on issue reopen; (7) source files undergoing MPLCs
tend to be more bug-prone; and (8) MPLCs introduce more bugs than non-MPLCs.
Conclusions: MPLCs are related to increased development difficulty and
decreased software quality.


  The Euler-MacLaurin summation formula relates a sum of a function to a
corresponding integral, with a remainder term. The remainder term has an
asymptotic expansion, and for a typical analytic function, it is a divergent
(Gevrey-1) series. Under some decay assumptions of the function in a half-plane
(resp. in the vertical strip containing the summation interval), Hardy (resp.
Abel-Plana) prove that the asymptotic expansion is a Borel summable series, and
give an exact Euler-MacLaurin summation formula.
  Using a mild resurgence hypothesis for the function to be summed, we give a
Borel summable transseries expression for the remainder term, as well as a
Laplace integral formula, with an explicit integrand which is a resurgent
function itself. In particular, our summation formula allows for resurgent
functions with singularities in the vertical strip containing the summation
interval.
  Finally, we give two applications of our results. One concerns the
construction of solutions of linear difference equations with a small
parameter. And another concerns the problem of proving resurgence of formal
power series associated to knotted objects.


  We present evidence for interaction between the supernova remnant (SNR)
G357.7+0.3 and nearby molecular clouds, leading to the formation of wind-swept
structures and bright emission rims. These features are not observed at visual
wavelengths, but are clearly visible in mid-infrared (MIR) mapping undertaken
using the Spitzer Space Telescope (SST). Analysis of one of these clouds, the
bright cometary structure G357.46+0.60, suggests that it contains strong
polycyclic aromatic hydrocarbon (PAH) emission features in the 5.8 and 8.0
microns photometric bands, and that these are highly variable over relatively
small spatial scales. This source also appears to contain a YSO within the
bright rim structure, with a steeply rising spectrum between 1.25 and 24
microns.
  Finally, it is noted that a further, conical emission region appears to be
associated with the Mira V1139 Sco, and it is suggested that this may represent
the case of a Mira outflow interacting with a SNR.


  Young, massive stars in the Galactic halo are widely supposed to be the
result of an ejection event from the Galactic disk forcing some stars to leave
their place of birth as so-called runaway stars. Here, we present a detailed
spectroscopic and kinematic analysis of the runaway B-star HIP 60350 to
determine which runaway scenario - a supernova explosion disrupting a binary
system or dynamical interaction in star clusters - may be responsible for HIP
60350's peculiar orbit. Based on a non-local thermodynamic equilibrium
approach, a high-resolution optical echelle spectrum was examined to revise
spectroscopic quantities and for the first time to perform a differential
chemical abundance analysis with respect to the B-type star 18 Peg. The results
together with proper motions from the Hipparcos Catalog further allowed the
three-dimensional kinematics of the star to be studied numerically. The
abundances derived for HIP 60350 are consistent with a slightly supersolar
metallicity agreeing with the kinematically predicted place of birth ca. 6 kpc
away from the Galactic center. However, they do not exclude the possibility of
an alpha-enhanced abundance pattern expected in the case of the supernova
scenario. Its outstanding high Galactic rest frame velocity of 530 plus-minus
35 km/s is a consequence of ejection in the direction of Galactic rotation and
slightly exceeds the local Galactic escape velocity in a standard Galactic
potential. Hence HIP 60350 may be unbound to the Galaxy.


  We prove that QMA where the verifier may also make a single non-collapsing
measurement is equal to NEXP, resolving an open question of Aaronson. We show
this is a corollary to a modified proof of QMA+ = NEXP [arXiv:2306.13247]. At
the core of many results inspired by Blier and Tapp [arXiv:0709.0738] is an
unphysical property testing problem deciding whether a quantum state is close
to an element of a fixed basis.


  In a composite superconductor in uniform rotation, the London field strength
at equilibrium is given by the usual expression B_L = 2m Omega/e; here m
corresponds to the bare electron mass, although the effective mass m* can be
different in different layers. In the presence of strong angular accelerations,
however, transient phases and differences in m* can become relevant. In
particular, if the layers are orthogonal to the rotation axis and one of them
is much thinner than the others, the superconductor may enter metastable states
characterized by intense "slide" supercurrents in this layer. We investigate
this phenomenon theoretically and with reference to current experiments.


  Since with massive data growth, the need for autonomous and generic anomaly
detection system is increased. However, developing one stand-alone generic
anomaly detection system that is accurate and fast is still a challenge. In
this paper, we propose conventional time-series analysis approaches, the
Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Seasonal
Trend decomposition using Loess (STL), to detect complex and various anomalies.
Usually, SARIMA and STL are used only for stationary and periodic time-series,
but by combining, we show they can detect anomalies with high accuracy for data
that is even noisy and non-periodic. We compared the algorithm to Long Short
Term Memory (LSTM), a deep-learning-based algorithm used for anomaly detection
system. We used a total of seven real-world datasets and four artificial
datasets with different time-series properties to verify the performance of the
proposed algorithm.


  We propose the implementation of a switch of particle statistics with an
embedding quantum simulator. By encoding both Bose-Einstein and Fermi-Dirac
statistics into an enlarged Hilbert space, the statistics of quantum particles
may be changed in situ during the time evolution, from bosons to fermions and
from fermions to bosons, as many times as desired. We illustrate our proposal
with few-qubit examples, although the protocol is straightforwardly extendable
to larger numbers of particles. This proposal can be implemented on different
quantum platforms such as trapped ions, quantum photonics, and superconducting
circuits, among others. The possibility to implement permutation symmetrization
and antisymmetrization of quantum particles enhances the toolbox of quantum
simulations, for unphysical operations as well as for symmetry transformations.


  COLA is a software library for lattice QCD, written in a combination of
modern Fortran and C/C++. Intel and NVIDIA have dominated the HPC domain in the
years leading up to the exascale era, but the status quo has changed with the
arrival of Frontier and other AMD-based systems in the supercomputing Top 500.
Setonix is a next generation HPE Cray EX system hosted at the Pawsey
Supercomputing Centre in Perth, Australia. Setonix features AMD EPYC CPUs and
AMD Instinct GPUs. This report describes some of my experiences in evolving
COLA to adapt to the current hardware landscape.


  Subpulse modulation has been regarded as one of the most insightful and
intriguing aspects of pulsar radio emission. This phenomenon is generally
explained by the presence of a carousel of sparks in the polar acceleration gap
region that rotates around the magnetic axis due to the E$\times$B drift. While
there have been extensive single pulse studies, geometric signatures of the
underlying carousel, or lack thereof, in simultaneous multi-frequency
observations have remained largely unexplored. This work presents a theoretical
account of such expected signatures, particularly that of a geometry induced
phase-offset in subpulse modulation, including various formulae that can be
readily applied to observations. A notable result is a method to resolve
aliasing in the measured subpulse modulation period without relying on
knowledge of the viewing geometry parameters. It is also shown in detail that
the geometry induced phase-offset enables critical tests of various observed
phenomena as well as proposed hypotheses, e.g., multi-altitude emission,
magnetic field twisting, pseudo-nulls, etc., in addition to that of the
carousel model itself. Finally, a detailed analysis of a 327 MHz pulse-sequence
of PSR B1237+25 is presented as a case study to test the single-frequency
multi-altitude emission scenario. The analysis provides a firm evidence of
inner and outer conal components of this pulsar to have originated from the
same carousel of subbeams and emitted at different heights.


  After a hundred years of searching for the origin of cosmic rays, where and
how they are made has finally become clear. Here we briefly trace that odyssey
through both astronomical observations and cosmic ray measurements.


  We report a triangular neural network implementation of neural autoregressive
flow (NAF). Unlike many universal autoregressive density models, our design is
highly modular, parameter economy, computationally efficient, and applicable to
density estimation of data with high dimensions. It achieves state-of-the-art
bits-per-dimension indices on MNIST and CIFAR-10 (about 1.1 and 3.7,
respectively) in the category of general-purpose density estimators.


  We revisit the study of string theory close to the Hagedorn temperature with
the aim towards cosmological applications. We consider interactions of open and
closed strings in a gas of D$p-$branes, and/or one isolated D$p$-brane, in an
arbitrary number $d$ of flat non-compact dimensions and general compact
dimensions. Leading order string perturbation theory is used to obtain the
basic interaction rates in a flat background, which are shown to be consistent
with the random walk picture of highly excited strings that should apply in
more general backgrounds. Using the random walk interpretation we infer the
structure of more general semi-inclusive string scattering rates and then write
down the corresponding Boltzmann equations describing ensembles of highly
excited closed and open strings. We organise the interaction terms in Boltzmann
equations so that detailed balance becomes manifest. We obtain the equilibrium
solutions and show that they reduce to previously computed solutions for $d=0$.
We further study the behaviour of non-equlibrium fluctuations and find explicit
analytic expressions for the equilibration rates (and for the number of open
strings in $d=0$). Potential implications for an early universe with strings at
high temperatures are outlined.


  The present study investigated the effect of a passive lower-limb exoskeleton
on lumbar load and verified the effectiveness of biomechanical analysis for
evaluating the physical burden while wearing the exoskeleton. Twelve healthy
male participants performed an assembly task under three conditions: standing
and high and low sitting while wearing the exoskeleton. We mainly analyzed the
joint compression force computed using the musculoskeletal model based on
measurements obtained from a motion capture system and force platform. While
wearing the exoskeleton, the lumbar joint compression force increased. Analysis
of the joint compression force can determine the increase in the lumbar load
when wearing an exoskeleton, which is not possible using the traditional
analysis of posture and muscular activity. Therefore, biomechanical analysis
based on a full-body musculoskeletal model provides valuable information in
evaluating the effect of the exoskeleton that attempts to prevent
musculoskeletal disorders.


  In this paper, we first introduce the Ray-Knight identity and percolation
Ray-Knight identity related to loop soup with intensity $\alpha (\ge 0)$ on
trees. Then we present the inversions of the above identities, which are
expressed in terms of repelling jump processes. In particular, the inversion in
the case of $\alpha=0$ gives the conditional law of a Markov jump process given
its local time field. We further show that the fine mesh limits of these
repelling jump processes are the self-repelling diffusions \cite{Aidekon}
involved in the inversion of the Ray-Knight identity on the corresponding
metric graph. This is a generalization of results in
\cite{2016Inverting,lupu2019inverting,LupuEJP657}, where the authors explore
the case of $\alpha=1/2$ on a general graph. Our construction is different from
\cite{2016Inverting,lupu2019inverting} and based on the link between random
networks and loop soups.


  Integer compositions, integer partitions, Fibonacci numbers, and
generalizations of these have recently been shown to be interconnected via
two-toned tilings of horizontal grids. In this article, we present refinements
of two-toned tilings, describe functions which analyze them, and apply these to
generalizations of integer compositions and partitions which interpolate
between the two.


  In this paper, we propose an analytical framework for internal hydraulic
jumps. Density jumps or internal hydraulic jumps occur when a supper critical
flow of water discharges into a stagnant layer of water with slightly different
density. The approach used here is control volume method which is also used to
analyze ordinary hydraulic jumps. The important difference here is that
entrainment is taken into account. Using conservation equations with the aid of
some simplifying assumptions we come to an equation that gives jump downstream
height as function of jump upstream characteristics and the entrainment. To
determine the magnitude of downstream height we use an experimental equation
for calculating the entrainment. Finally we verify our framework by comparing
the height that we gain from the derived equation with some experimental data.


  During recent years with the increase of data and data analysis needs,
privacy preserving data analysis methods have become of great importance.
Researchers have proposed different methods for this purpose. Secure
multi-party computation is one of such techniques that allows a group of
parties to evaluate a function on their data without revealing the data. This
is done by secret sharing approach, in which parties share a piece of their
data using polynomials and after doing function evaluation on shares of data
finally they do a Lagrange interpolation to get the result. Two approaches have
been proposed in secure multi-party computation for evaluating a function,
arithmetic gates and logical gates. In both of them and since communication is
an important step in multi-party computation, errors may happen. So, being able
to detect and correct errors is important. Moreover, as adversaries may
interrupt communication or manipulate the data, either in communication or
during computation, this error detection and correction provide participating
parties with a technique to detect such errors. Hence, in this paper we present
a secure multi-party computation error correcting technique that has the
ability to detect and correct errors on players shares. This technique is based
on Berlekamp-Welch error correcting codes and we assume that players shares are
generated using Reed-Solomon codes.


  Accurately predicting conditional average treatment effects (CATEs) is
crucial in personalized medicine and digital platform analytics. Since often
the treatments of interest cannot be directly randomized, observational data is
leveraged to learn CATEs, but this approach can incur significant bias from
unobserved confounding. One strategy to overcome these limitations is to seek
latent quasi-experiments in instrumental variables (IVs) for the treatment, for
example, a randomized intent to treat or a randomized product recommendation.
This approach, on the other hand, can suffer from low compliance, i.e., IV
weakness. Some subgroups may even exhibit zero compliance meaning we cannot
instrument for their CATEs at all. In this paper we develop a novel approach to
combine IV and observational data to enable reliable CATE estimation in the
presence of unobserved confounding in the observational data and low compliance
in the IV data, including no compliance for some subgroups. We propose a
two-stage framework that first learns biased CATEs from the observational data,
and then applies a compliance-weighted correction using IV data, effectively
leveraging IV strength variability across covariates. We characterize the
convergence rates of our method and validate its effectiveness through a
simulation study. Additionally, we demonstrate its utility with real data by
analyzing the heterogeneous effects of 401(k) plan participation on wealth.


  The Migdal effect has received much attention from the dark matter direct
detection community, in particular due to its power in setting limits on
sub-GeV particle dark matter. Currently, there is no experimental confirmation
of the Migdal effect through nuclear scattering using Standard Model probes. In
this work, we extend existing calculations of the Migdal effect to the case of
neutron-nucleus scattering, with a particular focus on neutron scattering angle
distributions in silicon. We identify kinematic regimes wherein the assumptions
present in current calculations of the Migdal effect hold for neutron
scattering, and demonstrate that these include viable neutron calibration
schemes. We then apply this framework to propose an experimental strategy to
measure the Migdal effect in cryogenic silicon detectors using an upgrade to
the NEXUS facility at Fermilab.


  We address the aircraft conflict resolution problem under trajectory
prediction uncertainty. We consider that aircraft velocity vectors may be
perturbed due to weather effects, such as wind, or measurement errors. Such
perturbations may affect aircraft trajectory prediction which plays a key role
in ensuring flight safety in air traffic control. Our goal is to solve the
aircraft conflict resolution problem in the presence of such perturbations and
guarantee that aircraft are separated for any realization of the uncertain
data. We propose an uncertainty model wherein aircraft velocities are
represented as random variables and the uncertainty set is assumed to be
polyhedral. We consider a robust optimization approach and embed the proposed
uncertainty model within state-of-the-art mathematical programming formulations
for aircraft conflict resolution. We then adopt the approach of Bertsimas and
Sim (2004) to formulate the robust counterpart. We use the complex number
reformulation and the constraint generation algorithm proposed by Dias et al.
(2020a) to solve the resulting nonconvex optimization problem on benchmarking
instances of the literature. Our numerical experiments reveal that
perturbations of the order of $\pm 5\%$ on aircraft velocities can be accounted
for without significantly impacting the objective function compared to the
deterministic case. Our tests also show that for greater levels of uncertainty,
several instances fail to admit conflict-free solutions, thus highlighting
existing risk factors in aircraft conflict resolution. We attempt to explain
this behavior by further analyzing pre- and post-optimization aircraft
trajectories. Our findings show that most infeasible instances have both a
relatively low total aircraft pairwise minimal distance and a high number of
conflicts.


  Near infrared slitless spectroscopy with the Wide Field Camera 3, onboard the
Hubble Space Telescope, offers a unique opportunity to study low-mass galaxy
populations at high-redshift ($z\sim$1-2). While most high$-z$ surveys are
biased towards massive galaxies, we are able to select sources via their
emission lines that have very-faint continua. We investigate the star formation
rate (SFR)-stellar mass ($M_{\star}$) relation for about 1000 emission-line
galaxies identified over a wide redshift range of $0.3 \lesssim z \lesssim
2.3$. We use the H$_{\alpha}$ emission as an accurate SFR indicator and correct
the broadband photometry for the strong nebular contribution to derive accurate
stellar masses down to $M_{\star} \sim 10^{7} M_{\odot}$. We focus here on a
subsample of galaxies that show extremely strong emission lines (EELGs) with
rest-frame equivalent widths ranging from 200 to 1500 \AA. This population
consists of outliers to the normal SFR-$M_{\star}$ sequence with much higher
specific SFRs ($> 10$ Gyr$^{-1}$). While on-sequence galaxies follow a
continuous star formation process, EELGs are thought to be caught during an
extreme burst of star formation that can double their stellar mass in less than
$100$ Myr. The contribution of starbursts to the total star formation density
appears to be larger than what has been reported for more massive galaxies in
previous studies. In the complete mass range $8.2 <$ log($M_{\star}/M_{\odot}$)
$< 10$ and a SFR lower completeness limit of about 2 $M_{\odot}$ yr$^{-1}$ (10
$M_{\odot}$ yr$^{-1}$) at $z\sim1$ ($z \sim 2$), we find that starbursts having
EW$_{rest}$(H$_{\alpha}$)$>$ 300, 200, and 100 A contribute up to $\sim13$, 18,
and 34 %, respectively, to the total SFR of emission-line selected sample at
$z\sim1-2$. The comparison with samples of massive galaxies shows an increase
in the contribution of starbursts towards lower masses.


  In this survey paper we present known results about reflexive subspace
lattices. We show that every nest and every atomic Boolean subspace lattice in
a complex Banach space is reflexive, even strongly reflexive. Our main tool is
Ringrose's Lemma about presence of rank-one operators in a reflexive algebra of
operators. We consider realizations of small lattices as subspace lattices in
Banach spaces. We exhibit some realizations of the pentagon and prove that some
of these realizations are reflexive. On the other hand, we show that double
triangle subspace lattices in finite-dimensional Banach spaces are
non-reflexive.


  We propose a social network-aware approach to studying socio-economic
segregation. The key question that we address is whether patterns of
segregation are more pronounced in social networks than the common spatial
neighborhood-focused manifestations of segregation. We, therefore, conduct a
population-scale social network analysis to study socio-economic segregation at
a comprehensive and highly granular social network level: 17.2 million
registered residents of the Netherlands that are connected through around 1.3
billion ties distributed over four distinct tie types. We take income
assortativity as a measure of socio-economic segregation, compare a social
network and spatial neighborhood approach, and find that the social network
structure exhibits two times as much segregation. As such, this work challenges
the dominance of the spatial perspective on segregation in both literature and
policymaking. While at a particular scale of spatial aggregation (e.g., the
geographical neighborhood), patterns of socio-economic segregation may appear
relatively minimal, they may in fact persist in the underlying social network
structure. Furthermore, we discover higher socio-economic segregation in larger
cities, shedding a different light on the common view of cities as hubs for
diverse socio-economic mixing. A population-scale social network perspective
hence offers a way to uncover hitherto 'hidden' segregation that extends beyond
spatial neighborhoods and infiltrates multiple aspects of human life.


  Multirotor aerial robots are becoming widely used for the inspection of
powerlines. To enable continuous, robust inspection without human intervention,
the robots must be able to perch on the powerlines to recharge their batteries.
Highly versatile perching capabilities are necessary to adapt to the variety of
configurations and constraints that are present in real powerline systems. This
paper presents a novel perching trajectory generation framework that computes
perception-aware, collision-free, and dynamically-feasible maneuvers to guide
the robot to the desired final state. Trajectory generation is achieved via
solving a Nonlinear Programming problem using the Primal-Dual Interior Point
method. The problem considers the full dynamic model of the robot down to its
single rotor thrusts and minimizes the final pose and velocity errors while
avoiding collisions and maximizing the visibility of the powerline during the
maneuver. The generated maneuvers consider both the perching and the posterior
recovery trajectories. The framework adopts costs and constraints defined by
efficient mathematical representations of powerlines, enabling online onboard
execution in resource-constrained hardware. The method is validated on-board an
agile quadrotor conducting powerline inspection and various perching maneuvers
with final pitch values of up to 180 degrees. The developed code is available
online at: https://github.com/grvcPerception/pa_powerline_perching


  In the supersymmetric standard model of particle interactions, R-parity
nonconservation is often invoked to obtain nonzero neutrino masses. We point
out here that such interactions of the supersymmetric particles would erase any
pre-existing lepton or baryon asymmetry of the universe before the electroweak
phase transition through the $B + L$ violating sphaleron processes. We then
show how neutrino masses may be obtained in supersymmetry (assuming R-parity
conservation) together with successful leptogenesis and predict the possible
existence of new observable particles.


  This paper is devoted to show a discrete adaptive finite element
approximation result for the isotropic two-dimensional Griffith energy arising
in fracture mechanics. The problem is addressed in the geometric measure
theoretic framework of generalized special functions of bounded deformation
which corresponds to the natural energy space for this functional. It is proved
to be approximated in the sense of $\Gamma$-convergence by a sequence of
discrete integral functionals defined on continuous piecewise affine functions.
The main feature of this result is that the mesh is part of the unknown of the
problem, and it gives enough flexibility to recover isotropic surface energies.


  The new excluded volume hadron gas model by Singh et al. [1-7] is critically
discussed. We demonstrate that in this model the results obtained from
relations between thermodynamical quantities disagree with the corresponding
results obtained by statistical ensemble averaging. Thus, the model does not
satisfy the requirements of thermodynamical consistency.


  Urban areas are negatively impacted by Carbon Dioxide (CO2 ) and Nitrogen
Oxide (NOx) emissions. In order to achieve a cost-effective reduction of
greenhouse gas emissions and to combat climate change, the European Union (EU)
introduced an Emissions Trading System (ETS) where organizations can buy or
receive emission allowances as needed. The current ETS is a centralized one,
consisting of a set of complex rules. It is currently administered at the
organizational level and is used for fixed-point sources of pollution such as
factories, power plants, and refineries. However, the current ETS cannot
efficiently cope with vehicle mobility, even though vehicles are one of the
primary sources of CO2 and NOx emissions. In this study, we propose a new
distributed Blockchain-based emissions allowance trading system called B-ETS.
This system enables transparent and trustworthy data exchange as well as
trading of allowances among vehicles, relying on vehicle-to-vehicle
communication. In addition, we introduce an economic incentive-based mechanism
that appeals to individual drivers and leads them to modify their driving
behavior in order to reduce emissions. The efficiency of the proposed system is
studied through extensive simulations, showing how increased vehicle
connectivity can lead to a reduction of the emissions generated from those
vehicles. We demonstrate that our method can be used for full life-cycle
monitoring and fuel economy reporting. This leads us to conjecture that the
proposed system could lead to important behavioral changes among the drivers


  Using 2D and 3D simulation, we study the "robustness" of the double
detonation scenario for Type Ia supernovae, in which a detonation in the helium
shell of a carbon-oxygen white dwarf induces a secondary detonation in the
underlying core. We find that a helium detonation cannot easily descend into
the core unless it commences (artificially) well above the hottest layer
calculated for the helium shell in current presupernova models. Compressional
waves induced by the sliding helium detonation, however, robustly generate hot
spots which trigger a detonation in the core. Our simulations show that this is
true even for non-axisymmetric initial conditions. If the helium is ignited at
multiple points, the internal waves can pass through one another or be
reflected, but this added complexity does not defeat the generation of the hot
spot. The ignition of very low-mass helium shells depends on whether a
thermonuclear runaway can simultaneously commence in a sufficiently large
region.


  We construct the N=2, D=9 supergravity theory up to the quartic fermionic
terms and derive the supersymmetry transformation rules for the fields modulo
cubic fermions. We consider a class of p-brane solutions of this theory, the
stainless p-branes, which cannot be isotropically oxidized into higher
dimensions. The new stainless elementary membrane and elementary particle
solutions are found. It is explicitly verified that these solutions preserve
half of the supersymmetry.


  Motivated by the recent experiments suggesting the importance of nematicity
in the phase diagrams of ironbased and cuprate high-Tc superconductors, we
study the influence of nematicity on the collective modes inside the
superconducting state in a non-equilibrium. In particular, we consider the
signatures of collective modes in short-time dynamics of a system with
competing nematic and s- and d-wave superconducting orders. In the rotationally
symmetric state, we show that the Bardasis-Schrieffer mode, corresponding to
the subdominant pairing, hybridizes with the nematic collective mode and merges
into a single in-gap mode, with the mixing vanishing only close to the phase
boundaries. For the d-wave ground state, we find that nematic interaction
suppresses the damping of the collective oscillations in the short-time
dynamics. Additionally, we find that even inside the nematic s+d-wave
superconducting state, a Bardasis-Schrieffer-like mode leads to order parameter
oscillations that strongly depend on the competition between the two pairing
symmetries. We discuss the connection of our results to the recent pump-probe
experiments on high-Tc superconductors.


  Probabilistic models analyze data by relying on a set of assumptions. Data
that exhibit deviations from these assumptions can undermine inference and
prediction quality. Robust models offer protection against mismatch between a
model's assumptions and reality. We propose a way to systematically detect and
mitigate mismatch of a large class of probabilistic models. The idea is to
raise the likelihood of each observation to a weight and then to infer both the
latent variables and the weights from data. Inferring the weights allows a
model to identify observations that match its assumptions and down-weight
others. This enables robust inference and improves predictive accuracy. We
study four different forms of mismatch with reality, ranging from missing
latent groups to structure misspecification. A Poisson factorization analysis
of the Movielens 1M dataset shows the benefits of this approach in a practical
scenario.


  We investigate the relationship between the generalization of program
completion defined in 1984 by Lloyd and Topor and the generalization of the
stable model semantics introduced recently by Ferraris et al. The main theorem
can be used to characterize, in some cases, the general stable models of a
logic program by a first-order formula. The proof uses Truszczynski's stable
model semantics of infinitary propositional formulas.


  Learning graphs from data automatically has shown encouraging performance on
clustering and semisupervised learning tasks. However, real data are often
corrupted, which may cause the learned graph to be inexact or unreliable. In
this paper, we propose a novel robust graph learning scheme to learn reliable
graphs from real-world noisy data by adaptively removing noise and errors in
the raw data. We show that our proposed model can also be viewed as a robust
version of manifold regularized robust PCA, where the quality of the graph
plays a critical role. The proposed model is able to boost the performance of
data clustering, semisupervised classification, and data recovery
significantly, primarily due to two key factors: 1) enhanced low-rank recovery
by exploiting the graph smoothness assumption, 2) improved graph construction
by exploiting clean data recovered by robust PCA. Thus, it boosts the
clustering, semi-supervised classification, and data recovery performance
overall. Extensive experiments on image/document clustering, object
recognition, image shadow removal, and video background subtraction reveal that
our model outperforms the previous state-of-the-art methods.


  A computer model was developed for estimation of the kinetics of microbial
inactivation by pulsed electric field. The model is based on the
electroporation theory of individual membrane damage, where spherical cell
geometry and distribution of cell sizes are assumed. The variation of microbial
cell sizes was assumed to follow a statistical probability distribution of the
Gaussian type. Surviving kinetics was approximated by Weibull equation. The
dependencies of two Weibull parameters (shape \textit{n} and time $\tau $,
respectively) versus electric field intensity E and width of cell diameters
distribution was studied.


  A hypercomplex structure on a differentiable manifold consists of three
integrable almost complex structures that satisfy quaternionic relations. If,
in addition, there exists a metric on the manifold which is Hermitian with
respect to the three structures, and such that the corresponding Hermitian
forms are closed, the manifold is said to be hyperkaehler. In the paper
"Non-Hermitian Yang-Mills connections", Kaledin and Verbitsky proved that the
twistor space of a hyperkaehler manifold admits a balanced metric; these were
first studied in the article "On the existence of special metrics in complex
geometry" by Michelsohn. In the present article, we review the proof of this
result and then generalize it and show that twistor spaces of general compact
hypercomplex manifolds are balanced.


  Pre-trained language models (PLMs) have achieved remarkable success on
various natural language understanding tasks. Simple fine-tuning of PLMs, on
the other hand, might be suboptimal for domain-specific tasks because they
cannot possibly cover knowledge from all domains. While adaptive pre-training
of PLMs can help them obtain domain-specific knowledge, it requires a large
training cost. Moreover, adaptive pre-training can harm the PLM's performance
on the downstream task by causing catastrophic forgetting of its general
knowledge. To overcome such limitations of adaptive pre-training for PLM
adaption, we propose a novel domain adaption framework for PLMs coined as
Knowledge-Augmented Language model Adaptation (KALA), which modulates the
intermediate hidden representations of PLMs with domain knowledge, consisting
of entities and their relational facts. We validate the performance of our KALA
on question answering and named entity recognition tasks on multiple datasets
across various domains. The results show that, despite being computationally
efficient, our KALA largely outperforms adaptive pre-training. Code is
available at: https://github.com/Nardien/KALA/.


  We study winding angles of oriented polymers with orientation-dependent
interaction in two dimensions. Using exact analytical calculations, computer
simulations, and phenomenological arguments, we succeed in finding the variance
of the winding angle for most of the phase diagram. Our results suggest that
the winding angle distribution is a universal quantity, and that the
$\theta$--point is the point where the three phase boundaries between the
swollen, the normal collapsed, and the spiral collapsed phase meet. The
transition between the normal collapsed phase and the spiral phase is shown to
be continuous.


  In health related machine learning applications, the training data often
corresponds to a non-representative sample from the target populations where
the learners will be deployed. In anticausal prediction tasks, selection biases
often make the associations between confounders and the outcome variable
unstable across different target environments. As a consequence, the
predictions from confounded learners are often unstable, and might fail to
generalize in shifted test environments. Stable prediction approaches aim to
solve this problem by producing predictions that are stable across unknown test
environments. These approaches, however, are sometimes applied to the training
data alone with the hope that training an unconfounded model will be enough to
generate stable predictions in shifted test sets. Here, we show that this is
insufficient, and that improved stability can be achieved by deconfounding the
test set features as well. We illustrate these observations using both
synthetic data and real world data from a mobile health study.


  We study a quasilinear Schr\"odinger equation with nonzero conditions at
infinity. In previous works, we obtained a continuous branch of traveling
waves, given by dark solitons indexed by their speed. Neglecting the
quasilinear term, one recovers the Gross--Pitaevskii equation, for which the
branch of dark solitons is stable. Moreover, Z.~Lin showed that the
Vakhitov--Kolokolov~(VK) stability criterion (in terms of the momentum of
solitons) holds for general semilinear equations with nonvanishing conditions
at infinity.
  In the quasilinear case, we prove that the VK stability criterion still
applies, by generalizing Lin's arguments. Therefore, we deduce that the branch
of dark solitons is stable for weak quasilinear interactions. For stronger
quasilinear interactions, a cusp appears in the energy-momentum diagram,
implying the stability of fast waves and the instability of slow waves.