  Random column sampling is not guaranteed to yield data sketches that preserve
the underlying structures of the data and may not sample sufficiently from
less-populated data clusters. Also, adaptive sampling can often provide
accurate low rank approximations, yet may fall short of producing descriptive
data sketches, especially when the cluster centers are linearly dependent.
Motivated by that, this paper introduces a novel randomized column sampling
tool dubbed Spatial Random Sampling (SRS), in which data points are sampled
based on their proximity to randomly sampled points on the unit sphere. The
most compelling feature of SRS is that the corresponding probability of
sampling from a given data cluster is proportional to the surface area the
cluster occupies on the unit sphere, independently from the size of the cluster
population. Although it is fully randomized, SRS is shown to provide
descriptive and balanced data representations. The proposed idea addresses a
pressing need in data science and holds potential to inspire many novel
approaches for analysis of big data.


  It is shown that when properly analyzed using principles consistent with the
use of a Hilbert space to describe microscopic properties, quantum mechanics is
a local theory: one system cannot influence another system with which it does
not interact. Claims to the contrary based on quantum violations of Bell
inequalities are shown to be incorrect. A specific example traces a violation
of the CHSH Bell inequality in the case of a spin-3/2 particle to the
noncommutation of certain quantum operators in a situation where (non)locality
is not an issue. A consistent histories analysis of what quantum measurements
measure, in terms of quantum properties, is used to identify the basic problem
with derivations of Bell inequalities: the use of classical concepts (hidden
variables) rather than a probabilistic structure appropriate to the quantum
domain. A difficulty with the original Einstein-Podolsky-Rosen (EPR) argument
for the incompleteness of quantum mechanics is the use of a counterfactual
argument which is not valid if one assumes that Hilbert-space quantum mechanics
is complete; locality is not an issue. The quantum correlations that violate
Bell inequalities can be understood using local quantum common causes.
Wavefunction collapse and Schr\"odinger steering are calculational procedures,
not physical processes. A general Principle of Einstein Locality rules out
nonlocal influences between noninteracting quantum systems. Some suggestions
are made for changes in terminology that could clarify discussions of quantum
foundations and be less confusing to students.


  The tremendous expanse of search engines, dictionary and thesaurus storage,
and other text mining applications, combined with the popularity of readily
available scanning devices and optical character recognition tools, has
necessitated efficient storage, retrieval and management of massive text
databases for various modern applications. For such applications, we propose a
novel data structure, INSTRUCT, for efficient storage and management of
sequence databases. Our structure uses bit vectors for reusing the storage
space for common triplets, and hence, has a very low memory requirement.
INSTRUCT efficiently handles prefix and suffix search queries in addition to
the exact string search operation by iteratively checking the presence of
triplets. We also propose an extension of the structure to handle substring
search efficiently, albeit with an increase in the space requirements. This
extension is important in the context of trie-based solutions which are unable
to handle such queries efficiently. We perform several experiments portraying
that INSTRUCT outperforms the existing structures by nearly a factor of two in
terms of space requirements, while the query times are better. The ability to
handle insertion and deletion of strings in addition to supporting all kinds of
queries including exact search, prefix/suffix search and substring search makes
INSTRUCT a complete data structure.


  We numerically study the metastable states of the 2d Potts model. Both of
equilibrium and relaxation properties are investigated focusing on the finite
size effect. The former is investigated by finding the free energy extremal
point by the Wang-Landau sampling and the latter is done by observing the
Metropolis dynamics after sudden heating. It is explicitly shown that with
increasing system size the equilibrium spinodal temperature approaches the
bistable temperature in a power-law and the size-dependence of the nucleation
dynamics agrees with it. In addition, we perform finite size scaling of the
free energy landscape at the bistable point.


  Accurate calculation of the ion-ion recombination rate coefficient has been
of long-standing interest, as it controls the ion concentration in gas phase
systems and in aerosols. We describe the development of a hybrid
continuum-molecular dynamics approach to determine the ion-ion recombination
rate coefficient. The approach is based on the limiting sphere method
classically used for transition regime collision phenomena in aerosols. When
ions are sufficiently far from one another, ion-ion relative motion is
described by diffusion equations while within a critical distance, molecular
dynamics (MD) simulations are used to model ion-ion motion. MD simulations are
parameterized using the AMBER force-field as well as by considering partial
charges on atoms. Ion-neutral gas collisions are modeled in two mutually
exclusive cubic domains composed of 103 gas atoms each, which remain centered
on the recombining ions throughout calculations. Example calculations are
reported for NH4+ recombination with NO2- in He, across a pressure range from
10 kPa to 10,000 kPa. Excellent agreement is found in comparison of
calculations to literature values for the 100 kPa recombination rate
coefficient (1.0 x 10-12 m3 s-1) in He. We also recover the experimentally
observed increase in recombination rate coefficient with pressure at
sub-atmospheric pressures, and the observed decrease in recombination rate
coefficient in the high pressure continuum limit. We additionally find that
non-dimensionalized forms of rate coefficients are consistent with recently
developed equations for the dimensionless charged particle-ion collision rate
coefficient based on Langevin dynamics simulations.


  In Ref. \cite{Goyal:2019vsw}, we had considered simplified dark matter models
interacting gravitationally with the Standard Model particles in a
Randall-Sundrum frame work where models are considered with non-universal
couplings. In the top-philic graviton model the right-handed top quarks are
taken to interact strongly with the gravitons and in the lepton-philic model,
we assume that only the right-handed charged leptons interact strongly with the
gravitons. We extend the study to include not only the scalar, vector and
spin-1/2 fermions but also spin-3/2 fermionic dark matter. We find that there
is a large parameter space in these benchmark models where it is possible to
achieve the observed relic density consistent with the direct and indirect
searches, which are also consistent with the data from Large Hadron Collider.


  The decays $\rho^0\to 2\pi^+2\pi^-$ and $\rho^0\to 2\pi^0\pi^+\pi^-$ are
studied using various effective Lagrangians for $\pi$ and $\rho$ (and in some
cases $a_1$) mesons, all of which respect the approximate chiral symmetry of
the strong interaction. Partial widths of the order of 1 keV or less are found
in all cases. These are an order of magnitude smaller than recent predictions
based on non-chiral models.


  It is well-known that both the pathwidth and the outer-planarity of a graph
can be used to obtain lower bounds on the height of a planar straight-line
drawing of a graph. But both bounds fall short for some graphs. In this paper,
we consider two other parameters, the (simple) homotopy height and the (simple)
grid-major height. We discuss the relationship between them and to the other
parameters, and argue that they give lower bounds on the straight-line drawing
height that are never worse than the ones obtained from pathwidth and
outer-planarity.


  We use a novel non-equilibrium algorithm to simulate steady-state fluid
transport through a two-dimensional (2D) membrane due to a concentration
gradient by molecular dynamics (MD) for the first time. We confirm that, as
required by the Onsager reciprocal relations in the linear-response regime, the
solution flux obtained using this algorithm agrees with the excess solute flux
obtained from an established non-equilibrium MD algorithm for pressure-driven
flow. In addition, we show that the concentration-gradient solution flux in
this regime is quantified far more efficiently by explicitly applying a
transmembrane concentration difference using our algorithm than by applying
Onsager reciprocity to pressure-driven flow. The simulated fluid fluxes are
captured with reasonable quantitative accuracy by our previously derived
continuum theory of concentration-gradient-driven fluid transport through a 2D
membrane [J. Chem. Phys. 151, 044705 (2019)] for a wide range of solution and
membrane parameters even though the simulated pore sizes are only several times
the size of the fluid particles. The simulations deviate from the theory
especially for strong solute--membrane interactions relative to the thermal
energy, for which the theoretical approximations break down. Our findings will
be beneficial for molecular-level understanding of fluid transport driven by
concentration gradients through membranes made from 2D materials, which have
diverse applications in energy harvesting, molecular separations, and
biosensing.


  We investigate the performance of a downlink ultra-dense network (UDN) with
directional transmissions via stochastic geometry. Considering the dual-slope
path loss model and sectored beamforming pattern, we derive the expressions and
asymptotic characteristics of the coverage probability and constrained area
spectrum efficiency (ASE). Several special scenarios, namely the physically
feasible path loss model and adjustable beam pattern, are also analyzed.
Although signal-to-interference-plus-noise ratio collapsing still exists when
the path loss exponent in the near-field is no larger than 2, using strategies
like beam pattern adaption, can avoid the decrease of the coverage probability
and constrained ASE even when the base station density approaches infinity.


  Dropout as a regularizer in deep neural networks has been less effective in
convolutional layers than in fully connected layers. This is due to the fact
that dropout drops features randomly. When features are spatially correlated as
in the case of convolutional layers, information about the dropped pixels can
still propagate to the next layers via neighboring pixels. In order to address
this problem, more structured forms of dropout have been proposed. A drawback
of these methods is that they do not adapt to the data. In this work, we
introduce a novel structured regularization for convolutional layers, which we
call DropCluster. Our regularizer relies on data-driven structure. It finds
clusters of correlated features in convolutional layer outputs and drops the
clusters randomly at each iteration. The clusters are learned and updated
during model training so that they adapt both to the data and to the model
weights. Our experiments on the ResNet-50 architecture demonstrate that our
approach achieves better performance than DropBlock or other existing
structured dropout variants. We also demonstrate the robustness of our approach
when the size of training data is limited and when there is corruption in the
data at test time.


  We develop a general set of methods for computing the oblique electroweak
parameters S and T for bottom-up and top-down motivated scenarios in which the
Higgs weakly mixes with a superconformal extra sector. In addition to their
utility in phenomenological studies, the observables S and T are also of purely
theoretical interest as they are defined by correlation functions of broken
symmetries. We show that in the limit where the extra sector enjoys an
approximate custodial symmetry, the leading contributions to S and T can be
recast as calculable data of the theory in the conformal phase. Using this
result, we also obtain model-independent bounds on the sign and size of oblique
electroweak corrections from unitary superconformal theories.


  Toric topology emerged in the end of the 1990s on the borders of equivariant
topology, algebraic and symplectic geometry, combinatorics and commutative
algebra. It has quickly grown up into a very active area with many
interdisciplinary links and applications, and continues to attract experts from
different fields.
  The key players in toric topology are moment-angle manifolds, a family of
manifolds with torus actions defined in combinatorial terms. Their construction
links to combinatorial geometry and algebraic geometry of toric varieties via
the related notion of a quasitoric manifold. Discovery of remarkable geometric
structures on moment-angle manifolds led to seminal connections with the
classical and modern areas of symplectic, Lagrangian and non-Kaehler complex
geometry. A related categorical construction of moment-angle complexes and
their generalisations, polyhedral products, provides a universal framework for
many fundamental constructions of homotopical topology. The study of polyhedral
products is now evolving into a separate area of homotopy theory, with strong
links to other areas of toric topology. A new perspective on torus action has
also contributed to the development of classical areas of algebraic topology,
such as complex cobordism.
  The book contains lots of open problems and is addressed to experts
interested in new ideas linking all the subjects involved, as well as to
graduate students and young researchers ready to enter into a beautiful new
area.


  In this paper we deal with the problem of computing the exact crossing number
of almost planar graphs and the closely related problem of computing the exact
anchored crossing number of a pair of planar graphs. It was shown by [Cabello
and Mohar, 2013] that both problems are NP-hard; although they required an
unbounded number of high-degree vertices (in the first problem) or an unbounded
number of anchors (in the second problem) to prove their result. Somehow
surprisingly, only three vertices of degree greater than 3, or only three
anchors, are sufficient to maintain hardness of these problems, as we prove
here. The new result also improves the previous result on hardness of joint
crossing number on surfaces by [Hlin\v{e}n\'y and Salazar, 2015]. Our result is
best possible in the anchored case since the anchored crossing number of a pair
of planar graphs with two anchors each is trivial, and close to being best
possible in the almost planar case since the crossing number is efficiently
computable for almost planar graphs of maximum degree 3 [Riskin 1996, Cabello
and Mohar 2011].


  The paper collates a complete list of nodeless regular almost-everywhere
holomorphic (AEH) solutions for a subset of rational canonical Sturm-Liouville
equations (RCSLEs) exactly quantized on a finite interval by classical Jacobi
polynomials. The subset was constrained by the requirement that the appropriate
Liouville transformation results in the Schrodinger equation on the line. The
common remarkable feature of the selected nodeless solutions co-existent with
the discrete energy spectrum is that they can be used as seed functions for
multi-step 'canonical Liouville-Darboux transformations' (CLDTs) to convert the
Gauss-Reference (GRef) potential (appearing in the resultant Schrodinger
equation) into its isospectral rational SUSY partners conditionally exactly
quantized by the so-called 'Jacobi-Seed' Heine polynomials.


  Spatial heterogeneity plays an important role in complex ecosystem dynamics,
and therefore is also an important consideration in sustainable resource
management. However, little is known about how spatial effects can influence
management targets derived from a non-spatial harvest model. Here, we extended
the Schaefer model, a conventional non-spatial harvest model that is widely
used in resource management, to a spatially-explicit harvest model by
integrating environmental heterogeneities, as well as species exchange between
patches. By comparing the maximum sustainable yields (MSY), one of the central
management targets in resource management, obtained from the spatially extended
model with that of the conventional model, we examined the effect of spatial
heterogeneity. When spatial heterogeneity exists, we found that the Schaefer
model tends to overestimate the MSY, implying potential for causing
overharvesting. In addition, by assuming a well-mixed population in the
heterogeneous environment, we showed analytically that the Schaefer model
always overestimate the MSY, regardless of the number of patches existing. The
degree of overestimation becomes significant when spatial heterogeneity is
marked. Collectively, these results highlight the importance of integrating the
spatial structure to conduct sustainable resource management.


  We propose, within a perturbative string theory example, a cosmological way
to generate a large hierarchy between the observed Planck mass and the
fundamental string scale. Time evolution results in three large space
dimensions, one additional dimension transverse to our world and five small
internal dimensions with a very slow time evolution. The evolution of the
string coupling and internal space generate a large Planck mass. However due to
an exact compensation between the time evolution of the internal space and that
of the string coupling, the gauge and Yukawa couplings on our Universe are time
independent.


  Recent measurements of jet structure modifications at RHIC and LHC highlight
the importance of differential measurements to study the nature of jet
quenching. Since these jet structure observables are intimately dependent on
parton evolution in both the angular and energy scales, measurements are needed
to disentangle these two scales in order to probe the medium at different
length scales to study its characteristic properties such as the coherence
length. To that effect, the STAR collaboration presents fully unfolded results
of jet sub-structure observables designed to extract fundamental quantities
related to the parton shower via the SoftDrop shared momentum fraction, the
groomed jet radius, and the jet Mass in p+p collisions at 200 GeV as a function
of jet transverse momenta. We also showcase the first measurement of iterative
softdrop groomed observables for first, second and third splits with an
initiator prong transverse momenta ranging from 20-25 GeV. In comparing the
un-corrected data to our simulation, we are able to look at snapshots of the
jet clustering history leading towards an understand of the time evolution of
the parton shower. Having established the p+p baseline, we present the first
measurement of the jet's inherent angular structure in Au+Au collisions at 200
GeV via an experimentally robust observable the opening angle between the two
leading sub-jets ($\theta_{SJ}$). In Au+Au collisions at STAR, we utilize a
specific di-jet selection as introduced in our previous momentum imbalance
($A_{J}$) measurement and measure both the $A_{J}$ and the recoil jet spectra
differentially as a function of the angular classes based on the $\theta_{SJ}$
observable. With such measurements, we probe the medium response to jets at a
particular resolution scale and find no significant differences in quenching
for jets of different angular scales as given by $\theta_{SJ}$.


  We investigate the properties of damped Ly{\alpha} absorption systems (DLAs)
in semi-analytic models of galaxy formation, including partitioning of cold gas
in galactic discs into atomic, molecular, and ionized phases with a molecular
gas-based star formation recipe. We investigate two approaches for partitioning
gas into these constituents: a pressure-based and a metallicity-based recipe.
We identify DLAs by passing lines of sight through our simulations to compute
HI column densities. We find that models with "standard" gas radial profiles -
where the average specific angular momentum of the gas disc is equal to that of
the host dark matter halo - fail to reproduce the observed column density
distribution of DLAs. These models also fail to reproduce the distribution of
velocity widths {\Delta}v, overproducing low {\Delta}v relative to high
{\Delta}v systems. Models with "extended" radial gas profiles - corresponding
to gas discs with higher specific angular momentum - are able to reproduce
quite well the column density distribution of absorbers over the column density
range 19 < log NHI < 22.5 in the redshift range 2 < z < 3.5. The model with
pressure-based gas partitioning also reproduces the observed line density of
DLAs, HI gas density, and {\Delta}v distribution at z < 3 remarkably well.
However all of the models investigated here underproduce DLAs and the HI gas
density at z > 3. If this is the case, the flatness in the number of DLAs and
HI gas density over the redshift interval 0 < z < 5 may be due to a cosmic
coincidence where the majority of DLAs at z > 3 arise from intergalactic gas in
filaments while those at z < 3 arise predominantly in galactic discs. We
further investigate the dependence of DLA metallicity on redshift and
{\Delta}v, and find reasonably good agreement with the observations,
particularly when including the effects of metallicity gradients (abbrv.).


  In this paper we study practical numbers of some special forms. For any
integers $b\ge0$ and $c>0$, we show that if $n^2+bn+c$ is practical for some
integer $n>1$, then there are infinitely many nonnegative integers $n$ with
$n^2+bn+c$ practical. We also prove that there are infinitely many practical
numbers of the form $q^4+2$ with $q$ practical, and that there are infinitely
many practical Pythagorean triples $(a,b,c)$ with $\gcd(a,b,c)=6$ (or
$\gcd(a,b,c)=4$).


  The curvature of biological membranes at the nanometer scale is critically
important for vesicle trafficking, organelle morphology, and disease
propagation. This manuscript reports the development of Polarized Localization
Microscopy (PLM), a pointillist optical imaging technique for the detection of
nanoscale membrane curvature in correlation with single-molecule dynamics and
molecular sorting. PLM combines polarized total internal reflection
fluorescence microscopy (TIRFM) and single-molecule localization microscopy to
reveal membrane orientation with sub-diffraction-limited resolution without
reducing localization precision by point spread function (PSF) manipulation.
Further, membrane curvature detection with PLM requires fewer localization
events to detect curvature than 3D single-molecule localization microscopy
(e.g., PALM or STORM), which enables curvature detection 10x faster via PLM
than via 3D single-molecule localizations. With high sensitivity, PLM detects
curvature with provides super resolution images with >10x signal-to-noise
enhancement from diffraction-limited polarized TIRFM. With rotationally
confined lipophilic fluorophores and the polarized incident fluorescence
excitation, membrane-bending events are revealed with super-resolution.
Engineered hemispherical membrane curvature with a radius >= 24 nm was detected
with PLM with individual fluorophore have a localization precision of 13 +/- 5
nm. Further, deciphering molecular dynamics as a function of membrane topology
was enabled. The diffusion coefficient of individual DiI molecules was 7.6x
higher in planar supported lipid bilayers than within nanoscale membrane
curvature.


  We introduce the numerical linked cluster (NLC) expansion as a controlled
numerical tool for the study of the many-body localization (MBL) transition in
a disordered system with continuous non-perturbative disorder. Our approach
works directly in the thermodynamic limit, in any spatial dimension, and does
not rely on any finite size scaling procedure. We study the onset of many-body
delocalization through the breakdown of area-law entanglement in a generic
many-body eigenstate. By looking for initial signs of an instability of the
localized phase, we obtain a value for the critical disorder, which we believe
should be a lower bound for the true value, that is higher than current best
estimates from finite size studies. This implies that most current methods tend
to overestimate the extent of the localized phase due to finite size effects
making the localized phase appear stable at small length scales. We also study
the mobility edge in these systems as a function of energy density, and find
that our conclusion is the same at all examined energies.


  Snel is a relational database engine featuring Just-In-Time (JIT) compilation
of queries and columnar data representation. Snel is designed for fast on-line
analytics by leveraging the LLVM compiler infrastructure. It also has custom
special methods like resolving histograms as extensions to the SQL language.
"Snel" means "SQL Native Execution for LLVM".
  Unlike traditional database engines, it does not provide a client-server
interface. Instead, it exposes its interface as an extension to SQLite, for a
simple interactive usage from command line and for embedding in applications.
Since Snel tables are read-only, it does not provide features like transactions
or updates. This allows queries to be very fast since they don't have the
overhead of table locking or ensuring consistency.
  At its core, Snel is simply a dynamic library that can be used by client
applications. It has an SQLite extension for seamless integration with a
traditional SQL environment and simple interactive usage from command line.


  With the recent developments in machine learning, Carrasquilla and Melko have
proposed a paradigm that is complementary to the conventional approach for the
study of spin models. As an alternative to investigating the thermal average of
macroscopic physical quantities, they have used the spin configurations for the
classification of the disordered and ordered phases of a phase transition
through machine learning. We extend and generalize this method. We focus on the
configuration of the long-range correlation function instead of the spin
configuration itself, which enables us to provide the same treatment to
multi-component systems and the systems with a vector order parameter. We
analyze the Berezinskii-Kosterlitz-Thouless (BKT) transition with the same
technique to classify three phases: the disordered, the BKT, and the ordered
phases. We also present the classification of a model using the training data
of a different model.


  Fooji Inc. is a social media engagement platform that has created a
proprietary "Just-in-time" delivery network to provide prizes to social media
marketing campaign participants in real-time. In this paper, we prove the
efficacy of the "Just-in-time" delivery network through a cluster analysis that
extracts and presents the underlying drivers of campaign engagement.
  We utilize a machine learning methodology with a principal component analysis
to organize Fooji campaigns across these principal components. The arrangement
of data across the principal component space allows us to expose underlying
trends using a $K$-means clustering technique. The most important of these
trends is the demonstration of how the "Just-in-time" delivery network improves
social media engagement.


  Models in which the dark matter is produced at extremely low rates from the
annihilation of Standard Model particles in the early Universe allow us to
explain the current dark matter relic density while easily evading the
traditional experimental constraints. In scenarios where the dark matter
interacts with the Standard Model via a new physics mediator, the early
Universe dynamics of the dark sector can be particularly complex, as the dark
matter and the mediator could be in thermal and chemical equilibrium with each
other. This equilibration takes place via number-changing processes such as
double Compton scattering and bremsstrahlung, whose amplitudes are cumbersome
to calculate. In this paper, we show that in large regions of the parameter
space, these equilibration mechanisms do not significantly affect the final
dark matter relic density. In particular, for a model with a light dark photon
mediator, the relic density can be reasonably estimated by considering that the
dark matter is solely produced through the annihilation of Standard Model
particles. This result considerably simplifies the treatment of a large class
of dark matter theories, facilitating in particular the superimposition of the
relic density constraints on the current and future experimental bounds.


  In this article we introduce and study a motivic category in the arithmetic
of function fields, namely the category of motives over an algebraic closure
$L$ of a finite field with coefficients in a global function field over this
finite field. It is semi-simple, non-neutral Tannakian and possesses all the
expected fiber functors. This category generalizes the previous construction
due to Anderson and is more relevant for applications to the theory of
$G$-Shtukas, such as formulating the analog of the Langlands-Rapoport
conjecture over function fields. We further develop the analogy with the
category of motives over $L$ with coefficients in $\mathbb{Q}$ for which the
existence of the expected fiber functors depends on famous unproven
conjectures.


  According to current research in bibliometrics, percentiles (or percentile
rank classes) are the most suitable method for normalising the citation counts
of individual publications in terms of the subject area, the document type and
the publication year. Up to now, bibliometric research has concerned itself
primarily with the calculation of percentiles. This study suggests how
percentiles can be analysed meaningfully for an evaluation study. Publication
sets from four universities are compared with each other to provide sample
data. These suggestions take into account on the one hand the distribution of
percentiles over the publications in the sets (here: universities) and on the
other hand concentrate on the range of publications with the highest citation
impact - that is, the range which is usually of most interest in the evaluation
of scientific performance.


  Irradiation of cell monolayers by monoenergetic ions has made it possible to
establish survival curves at individual values of linear energy transfer. The
two-step model of radiobiological mechanism proposed recently by Judas and
Lokajicek (Judas L., Lokajicek M., 2001: Cell inactivation by ionizing
particles and the shapes of survival curves. J. Theor. Biol. 210 (1), 15-21.,
doi:10.1006/jtbi.2001.2283) has then enabled to show that some significant
deviations from the generally used linear-quadratic model should exist at
higher values of linear energy transfer, which has been also demonstrated
experimentally. However, the new model has been expressed in the form being
applicable rightfully to low-dose parts of survival curves only. It has been
now reformulated to be applicable in analyses of whole survival curves.
Inactivation probabilities after different numbers of particles traversing cell
nuclei (chromosomal systems) may be then derived from experimental data.
Analyses of published data obtained in irradiating by protons and deuterons
will be presented and discussed.


  Neural Cellular Automata (NCA) models are trainable variations of traditional
Cellular Automata (CA). Emergent motion in the patterns created by NCA has been
successfully applied to synthesize dynamic textures. However, the conditions
required for an NCA to display dynamic patterns remain unexplored. Here, we
investigate the relationship between the NCA architecture and the emergent
dynamics of the trained models. Specifically, we vary the number of channels in
the cell state and the number of hidden neurons in the MultiLayer Perceptron
(MLP), and draw a relationship between the combination of these two variables
and the motion strength between successive frames. Our analysis reveals that
the disparity and proportionality between these two variables have a strong
correlation with the emergent dynamics in the NCA output. We thus propose a
design principle for creating dynamic NCA.


  We investigate the applicability of curvilinear grids in the context of
astrophysical simulations and WENO schemes. With the non-smooth mapping
functions from Calhoun et al. (2008), we can tackle many astrophysical problems
which were out of scope with the standard grids in numerical astrophysics. We
describe the difficulties occurring when implementing curvilinear coordinates
into our WENO code, and how we overcome them. We illustrate the theoretical
results with numerical data. The WENO finite difference scheme works only for
high Mach number flows and smooth mapping functions whereas the finite volume
scheme gives accurate results even for low Mach number flows and on non-smooth
grids.


  The hybridization of magnetism and superconductivity has been an intriguing
playground for correlated electron systems, hosting various novel physical
phenomena. Usually, localized d- or f-electrons are central to magnetism. In
this study, by placing a PTCDA (3,4,9,10-perylene tetracarboxylic dianhydride)
molecular monolayer on ultra-thin Pb films, we built a hybrid
magnetism/superconductivity (M/SC) system consisting of only sp electronic
levels. The magnetic moments reside in the unpaired molecular orbital
originating from interfacial charge-transfers. We reported distinctive
tunneling spectroscopic features of such a Kondo screened pi-electron impurity
lattice on a superconductor in the regime of TK>>delta suggesting the formation
of a two-dimensional bound states band. Moreover, moir\'e superlattices with
tunable twist angle and the quantum confinement in the ultra-thin Pb films
provide easy and flexible implementations to tune the interplay between the
Kondo physics and the superconductivity, which are rarely present in M/SC
hybrid systems.


  In this paper we present a robust tracker to solve the multiple object
tracking (MOT) problem, under the framework of tracking-by-detection. As the
first contribution, we innovatively combine single object tracking (SOT)
algorithms with multiple object tracking algorithms, and our results show that
SOT is a general way to strongly reduce the number of false negatives,
regardless of the quality of detection. Another contribution is that we show
with a deep learning based appearance model, it is easy to associate detections
of the same object efficiently and also with high accuracy. This appearance
model plays an important role in our MOT algorithm to correctly associate
detections into long trajectories, and also in our SOT algorithm to discover
new detections mistakenly missed by the detector. The deep neural network based
model ensures the robustness of our tracking algorithm, which can perform data
association in a wide variety of scenes. We ran comprehensive experiments on a
large-scale and challenging dataset, the MOT16 benchmark, and results showed
that our tracker achieved state-of-the-art performance based on both public and
private detections.


  This article deals with Part family formation problem which is believed to be
moderately complicated to be solved in polynomial time in the vicinity of Group
Technology (GT). In the past literature researchers investigated that the part
family formation techniques are principally based on production flow analysis
(PFA) which usually considers operational requirements, sequences and time.
Part Coding Analysis (PCA) is merely considered in GT which is believed to be
the proficient method to identify the part families. PCA classifies parts by
allotting them to different families based on their resemblances in: (1) design
characteristics such as shape and size, and/or (2) manufacturing
characteristics (machining requirements). A novel approach based on simulated
annealing namely SAPFOCS is adopted in this study to develop effective part
families exploiting the PCA technique. Thereafter Taguchi's orthogonal design
method is employed to solve the critical issues on the subject of parameters
selection for the proposed metaheuristic algorithm. The adopted technique is
therefore tested on 5 different datasets of size 5 {\times} 9 to 27 {\times} 9
and the obtained results are compared with C-Linkage clustering technique. The
experimental results reported that the proposed metaheuristic algorithm is
extremely effective in terms of the quality of the solution obtained and has
outperformed C-Linkage algorithm in most instances.


  The past decade has seen tremendous progress in our understanding of the
behaviour of many probabilistic models at or near their "critical point". On
the 5th of July 2022, Hugo Duminil-Copin was awarded the Fields medal for the
crucial role he played in many of these developments. In this short review
article, we will try to put his work into context and present a small selection
of his results.


  Measurements of the top quark production cross section in proton-proton
collisions with the ATLAS detector at the Large Hadron Collider are presented.
The measurements require no, one or two electrons or muons in the final state
(single lepton, dilepton, hadronic channel). In addition, the decay modes with
tau leptons are tested (channels with tau leptons). The main focus is on
measurements of differential spectra of $t\bar{t}$ final states, in particular,
measurements that are able to constrain the modelling of additional parton
radiation like the jet multiplicity distribution.


  We establish a theoretical correspondence between spin-one antiferromagnetic
spinor condensates in an external magnetic field and quantum rotor models in an
external potential. We show that the rotor model provides a conceptually clear
picture of the possible phases and dynamical regimes of the antiferromagnetic
condensate. We also show that this mapping simplifies calculations of the
condensate's spectrum and wavefunctions. We use the rotor mapping to describe
the different dynamical regimes recently observed in $^{23}$Na condensates. We
also suggest a way to experimentally observe quantum mechanical effects
(collapse and revival) in spinor condensates.


  This paper investigates the average age of information (AoI) minimization in
status update systems in which the update packets are transmitted with fixed
rate. It is assumed that the source avoids queue-induced delay by generating
and sending a new status update packet in each time slot. Assuming that the
transmit power can be adapted based on the number of successive NACKs (negative
acknowledgements) received, a closed-form expression for the average AoI is
derived considering transmissions over block fading channel models. An
optimization problem for minimizing the average AoI under average power
constraints is formulated and a stochastic optimization algorithm for obtaining
a feasible solution is proposed. Numerical results on the proposed power
control policy show that more power is preferred to be allocated to packets
after a certain number of failure packets in the low-power regime and the
proposed power control reduces the average AoI compared to the constant power
policy. An alternative on-off power control policy is also proposed and shown
to achieve satisfactory performance.


  We study the synchronization of oscillators with inertias and phase shifts,
namely the second-order Kuramoto-Sakaguchi model. Using the self-consistent
method, we find that the effect of inertia is the introduction of effective
phase shifts. The discontinuous synchronization transition of the
Kuramoto-Sakaguchi model changes to a continuous one when the value of inertia
is small. In addition, we find a new synchronization process, in which with
increasing coupling strength the system reaches an oscillating state instead of
complete synchronization due to the cross-effect of phase shifts and inertias.
Through numerical simulations, the same type of synchronization process is also
found for oscillators in complex networks, including scale-free, small-world
and random networks.


  In this article we apply a discrete action principle for the Vlasov--Maxwell
equations in a structure-preserving particle-field discretization framework. In
this framework the finite-dimensional electromagnetic potentials and fields are
represented in a discrete de Rham sequence involving general finite element
spaces, and the particle-field coupling is represented by a set of projection
operators that commute with the differential operators. With a minimal number
of assumptions which allow for a variety of finite elements and shape functions
for the particles, we show that the resulting variational scheme has a general
discrete Poisson structure and thus leads to a semi-discrete Hamiltonian
system. By introducing discrete interior products we derive a second type of
space discretization which is momentum preserving, based on the same finite
elements and shape functions. We illustrate our method by applying it to spline
finite elements, and to a new spectral discretization where the particle-field
coupling relies on discrete Fourier transforms.


  Deep generative modelling for human body analysis is an emerging problem with
many interesting applications. However, the latent space learned by such
approaches is typically not interpretable, resulting in less flexibility. In
this work, we present deep generative models for human body analysis in which
the body pose and the visual appearance are disentangled. Such a
disentanglement allows independent manipulation of pose and appearance, and
hence enables applications such as pose-transfer without specific training for
such a task. Our proposed models, the Conditional-DGPose and the Semi-DGPose,
have different characteristics. In the first, body pose labels are taken as
conditioners, from a fully-supervised training set. In the second, our
structured semi-supervised approach allows for pose estimation to be performed
by the model itself and relaxes the need for labelled data. Therefore, the
Semi-DGPose aims for the joint understanding and generation of people in
images. It is not only capable of mapping images to interpretable latent
representations but also able to map these representations back to the image
space. We compare our models with relevant baselines, the ClothNet-Body and the
Pose Guided Person Generation networks, demonstrating their merits on the
Human3.6M, ChictopiaPlus and DeepFashion benchmarks.


  Addressing airport traffic jams is one of the most crucial and challenging
tasks in the remote sensing field, especially for the busiest airports. Several
solutions have been employed to address this problem depending on the airplane
detection process. The most effective solutions are through the use of
satellite images with deep learning techniques. Such solutions, however, are
significantly costly and require satellites and modern complicated technology
which may not be available in most countries worldwide. This paper provides a
universal, low cost and fast solution for airplane detection in airports. This
paper recommends the use of drones instead of satellites to feed the system
with drone images using a proposed deep learning model. Drone images are
employed as the dataset to train and evaluate a mask region convolution neural
network (RCNN) model. The Mask RCNN model applies faster RCNN as its base
configuration with critical modifications on its head neural network
constructions. The model detects whether or not an airplane is present and
includes mask estimations to approximate surface area and length, which will
help future works identify the airplane type. This solution can be easily
implemented globally as it is a low-cost and fast solution for airplane
detection at airports. The evaluation process reveals promising results
according to Microsoft Common Objects in Context (COCO) metrics.


  Dilaton black hole solutions which are neither asymptotically flat nor
(anti)-de Sitter but reduce to asymptotically flat solutions in some special
limits have been known for a Liouville type dilatonic potential. It is shown
how, by solving a pair of coupled differential equations, infinitesimally small
angular momentum can be added to these static solutions to produce rotating
black hole solutions.


  At chemical synapses, an arriving electric signal induces the fusion of
vesicles with the presynaptic membrane, thereby releasing neurotransmitters
into the synaptic cleft. After a fusion event, both the release site and the
vesicle undergo a recovery process before becoming available for reuse again.
Of central interest is the question which of the two restoration steps acts as
the limiting factor during neurotransmission under high-frequency sustained
stimulation. In order to investigate this question, we introduce a novel
non-linear reaction network which involves explicit recovery steps for both the
vesicles and the release sites, and includes the induced time-dependent output
current. The associated reaction dynamics are formulated by means of ordinary
differential equations (ODEs), as well as via the associated stochastic jump
process. While the stochastic jump model describes a single release site, the
average over many release sites is close to the ODE solution and shares its
periodic structure. The reason for this can be traced back to the insight that
recovery dynamics of vesicles and release sites are statistically almost
independent. A sensitivity analysis on the recovery rates based on the ODE
formulation reveals that neither the vesicle nor the release site recovery step
can be identified as the essential rate-limiting step but that the
rate-limiting feature changes over the course of stimulation. Under sustained
stimulation the dynamics given by the ODEs exhibit transient changes leading
from an initial depression of the postsynaptic response to an asymptotic
periodic orbit, while the individual trajectories of the stochastic jump model
lack the oscillatory behavior and asymptotic periodicity of the ODE-solution.


  Purpose: The standard clinical treatment of Twin-to-Twin Transfusion Syndrome
consists in the photo-coagulation of undesired anastomoses located on the
placenta which are responsible to a blood transfer between the two twins. While
being the standard of care procedure, fetoscopy suffers from a limited
field-of-view of the placenta resulting in missed anastomoses. To facilitate
the task of the clinician, building a global map of the placenta providing a
larger overview of the vascular network is highly desired. Methods: To overcome
the challenging visual conditions inherent to in vivo sequences (low contrast,
obstructions or presence of artifacts, among others), we propose the following
contributions: (i) robust pairwise registration is achieved by aligning the
orientation of the image gradients, and (ii) difficulties regarding long-range
consistency (e.g. due to the presence of outliers) is tackled via a bag-of-word
strategy, which identifies overlapping frames of the sequence to be registered
regardless of their respective location in time. Results: In addition to visual
difficulties, in vivo sequences are characterised by the intrinsic absence of
gold standard. We present mosaics motivating qualitatively our methodological
choices and demonstrating their promising aspect. We also demonstrate
semi-quantitatively, via visual inspection of registration results, the
efficacy of our registration approach in comparison to two standard baselines.
Conclusion: This paper proposes the first approach for the construction of
mosaics of placenta in in vivo fetoscopy sequences. Robustness to visual
challenges during registration and long-range temporal consistency are
proposed, offering first positive results on in vivo data for which standard
mosaicking techniques are not applicable.


  This paper presents a robust hierarchical MPC (H-MPC) for dynamic systems
with slow states subject to demand forecast uncertainty. The H-MPC has two
layers: (i) the scheduling MPC at the upper layer with a relatively long
prediction/planning horizon and slow update rate, and (ii) the piloting MPC at
the lower layer over a shorter prediction horizon with a faster update rate.
The scheduling layer MPC calculates the optimal slow states, which will be
tracked by the piloting MPC, while enforcing the system constraints according
to a long-range and approximate prediction of the future demand/load, e.g.,
traction power demand for driving a vehicle. In this paper, to enhance the
H-MPC robustness against the long-term demand forecast uncertainty, we propose
to use the high-quality preview information enabled by the connectivity
technology over the short horizon to modify the planned trajectories via a
constraint tightening approach at the scheduling layer. Simulation results are
presented for a simplified vehicle model to confirm the effectiveness of the
proposed robust H-MPC framework in handling demand forecast uncertainty.


  This paper explores small-scale departures from force-free electrodynamics
around a rotating neutron star, extending our treatment of resistive
instability in a quantizing magnetic field. A secondary, Cerenkov instability
is identified: relativistic particles flowing through thin current sheets
excite propagating charge perturbations that are localized near the sheets.
Growth is rapid at wavenumbers below the inverse ambient skin depth $k_{p,\rm
ex}$. Small-scale Alfv\'enic wavepackets are promising sources of coherent
curvature radiation. When the group Lorentz factor $\gamma_{\rm gr} \lesssim
(k_{p,\rm ex}R_c)^{1/3} \sim 100$, where $R_c$ is the magnetic curvature
radius, a fraction $\sim 10^{-3}$-$10^{-2}$ of the particle kinetic energy is
radiated into the extraordinary mode at a peak frequency $\sim 10^{-2}ck_{p,\rm
ex}$. Consistency with observations requires a high pair multiplicity ($\sim
10^{3-5}$) in the pulsar magnetosphere. Neither the primary, slow resistive
instability nor the secondary, Alfv\'enic instability depend directly on the
presence of magnetospheric `gaps', and may activate where the mean current is
fully supplied by outward drift of the corotation charge. The resistive mode is
overstable and grows at a rate comparable to the stellar spin frequency; the
model directly accommodates strong pulse-to-pulse radio flux variations and
coordinated subpulse drift. Alfv\'en mode growth can track the local plasma
conditions, allowing for lower-frequency emission from the outer magnetosphere.
Beamed radio emission from charged packets with $\gamma_{\rm gr} \sim 50-100$
also varies on sub-millisecond timescales. The modes identified here will be
excited inside the magnetosphere of a magnetar, and may mediate Taylor
relaxation of the magnetic twist.


  A new method for computing exact conformal partial wave expansions is
developed and applied to approach the problem of Hilbert space (Wightman)
positivity in a non-perturbative four-dimensional quantum field theory model.
The model is based on the assumption of global conformal invariance on
compactified Minkowski space. Bilocal fields arising in the harmonic
decomposition of the operator product expansion prove to be a powerful
instrument in exploring the field content. In particular, in the theory of a
field of dimension 4 which has the properties of a (gauge invariant)
Lagrangian, the scalar field contribution to the 6-point function of the twist
2 bilocal field is analyzed with the aim to separate the free field part from
the nontrivial part.


  Electroaerodynamic (EAD) propulsion, where thrust is produced by collisions
between electrostatically-accelerated ions and neutral air, is a potentially
transformative method for indoor flight owing to its silent and solid-state
nature. Like rotors, EAD thrusters exhibit changes in performance based on
proximity to surfaces. Unlike rotors, they have no fragile and quickly spinning
parts that have to avoid those surfaces; taking advantage of the efficiency
benefits from proximity effects may be a route towards longer-duration indoor
operation of ion-propelled fliers. This work presents the first empirical study
of ground proximity effects for EAD propulsors, both individually and as
quad-thruster arrays. It focuses on multi-stage ducted centimeter-scale
actuators suitable for use on small robots envisioned for deployment in
human-proximal and indoor environments. Three specific effects (ground,
suckdown, and fountain lift), each occurring with a different magnitude at a
different spacing from the ground plane, are investigated and shown to have
strong dependencies on geometric parameters including thruster-to-thruster
spacing, thruster protrusion from the fuselage, and inclusion of flanges or
strakes. Peak thrust enhancement ranging from 300 to 600% is found for certain
configurations operated in close proximity (0.2 mm) to the ground plane and as
much as a 20% increase is measured even when operated centimeters away.


  XY frustrated magnets exhibit an unsual critical behavior: they display
scaling laws accompanied by nonuniversal critical exponents and a negative
anomalous dimension. This suggests that they undergo weak first order phase
transitions. We show that all perturbative approaches that have been used to
investigate XY frustrated magnets fail to reproduce these features. Using a
nonperturbative approach based on the concept of effective average action, we
are able to account for this nonuniversal scaling and to describe qualitatively
and, to some extent, quantitatively the physics of these systems.


  We analytically find an approximate Bloch-Messiah reduction of a noncollinear
parametric amplifier pumped with a focused monochromatic beam. We consider type
I phase matching. The results are obtained using a perturbative expansion and
scaled to a high gain regime. They allow a straightforward maximization of the
signal gain and minimization of the parametric fluorescence noise. We find the
fundamental mode of the amplifier, which is an elliptic Gaussian defining the
optimal seed beam shape. We conclude that the output of the amplifier should be
stripped of higher order modes, which are approximately Hermite-Gaussian beams.
Alternatively, the pump waist can be adjusted such that the amount of noise
produced in the higher order modes is minimized.


  In this paper we propose a framework for solving constrained online convex
optimization problem. Our motivation stems from the observation that most
algorithms proposed for online convex optimization require a projection onto
the convex set $\mathcal{K}$ from which the decisions are made. While for
simple shapes (e.g. Euclidean ball) the projection is straightforward, for
arbitrary complex sets this is the main computational challenge and may be
inefficient in practice. In this paper, we consider an alternative online
convex optimization problem. Instead of requiring decisions belong to
$\mathcal{K}$ for all rounds, we only require that the constraints which define
the set $\mathcal{K}$ be satisfied in the long run. We show that our framework
can be utilized to solve a relaxed version of online learning with side
constraints addressed in \cite{DBLP:conf/colt/MannorT06} and
\cite{DBLP:conf/aaai/KvetonYTM08}. By turning the problem into an online
convex-concave optimization problem, we propose an efficient algorithm which
achieves $\tilde{\mathcal{O}}(\sqrt{T})$ regret bound and
$\tilde{\mathcal{O}}(T^{3/4})$ bound for the violation of constraints. Then we
modify the algorithm in order to guarantee that the constraints are satisfied
in the long run. This gain is achieved at the price of getting
$\tilde{\mathcal{O}}(T^{3/4})$ regret bound. Our second algorithm is based on
the Mirror Prox method \citep{nemirovski-2005-prox} to solve variational
inequalities which achieves $\tilde{\mathcal{\mathcal{O}}}(T^{2/3})$ bound for
both regret and the violation of constraints when the domain $\K$ can be
described by a finite number of linear constraints. Finally, we extend the
result to the setting where we only have partial access to the convex set
$\mathcal{K}$ and propose a multipoint bandit feedback algorithm with the same
bounds in expectation as our first algorithm.


  This memoir is devoted to the study of formal-analytic arithmetic surfaces.
These are arithmetic counterparts, in the context of Arakelov geometry, of
germs of smooth complex-analytic surfaces along a projective complex curve.
Formal-analytic surfaces provide a natural framework for arithmetic
algebraization theorems, old and new.
  Formal-analytic arithmetic surfaces admit a rich geometry which parallels the
geometry of complex analytic surfaces. Notably the dichotomy between
pseudoconvexity and pseudoconcavity plays a central role in their geometry.
  Our study of formal-analytic arithmetic surfaces relies crucially on the use
of real-valued invariants. Some of these are intersection-theoretic, in the
spirit of Arakelov intersection theory. Some other invariants involve
infinite-dimensional geometry of numbers.
  Relating our new intersection-theoretic invariants to more classical
invariants of Arakelov geometry leads us to investigate a new invariant, the
Archimedean overflow, attached to an analytic map from a pointed compact
Riemann surface with boundary to a Riemann surface. It is related to the
characteristic functions of Nevanlinna theory.
  Our results on the geometry of formal-analytic arithmetic surfaces admit
applications to concrete problems of arithmetic geometry. Notably we generalize
the arithmetic holonomicity theorem of Calegari-Dimitrov-Tang regarding the
dimension of spaces of power series with integral coefficients satisfying some
convergence conditions.
  We also establish an arithmetic counterpart of theorems of Lefschetz and Nori
by providing a bound on the index, in the \'etale fundamental group of an
arithmetic surface, of the closed subgroup generated by the \'etale fundamental
groups of some arithmetic curve and of some compact Riemann surfaces mapping to
the arithmetic surface.


  A novel treatment of non-adiabatic couplings is proposed. The derivation
starts from the long-known, but not well-known, fact that the wave function of
the complete system of elctrons and nuclei can be written, without
approximation, as a Born-Oppenheimer-type product of a nuclear wavefunction,
X(R), and an electronic one, Phi_R(r), which depends parametrically on the
nuclear configuration R. From the variational principle we deduce formally
exact equations for Phi_R(r) and X(R). The algebraic structure of the exact
nuclear equation coincides with the corresponding one in the adiabatic
approximation. The electronic equation, however, contains terms not appearing
in the adiabatic case, which couple the electronic and the nuclear
wavefunctions and account for the electron-nuclear correlation beyond the
Born-Oppenheimer level. It is proposed that these terms can be incorporated
using an optimized local effective potential.


  Recently, the progress of learning-by-synthesis has proposed a training model
for synthetic images, which can effectively reduce the cost of human and
material resources. However, due to the different distribution of synthetic
images compared to real images, the desired performance cannot be achieved.
Real images consist of multiple forms of light orientation, while synthetic
images consist of a uniform light orientation. These features are considered to
be characteristic of outdoor and indoor scenes, respectively. To solve this
problem, the previous method learned a model to improve the realism of the
synthetic image. Different from the previous methods, this paper try to purify
real image by extracting discriminative and robust features to convert outdoor
real images to indoor synthetic images. In this paper, we first introduce the
segmentation masks to construct RGB-mask pairs as inputs, then we design a
attention-guided style transfer network to learn style features separately from
the attention and bkgd(background) region , learn content features from full
and attention region. Moreover, we propose a novel region-level task-guided
loss to restrain the features learnt from style and content. Experiments were
performed using mixed studies (qualitative and quantitative) methods to
demonstrate the possibility of purifying real images in complex directions. We
evaluate the proposed method on three public datasets, including LPW, COCO and
MPIIGaze. Extensive experimental results show that the proposed method is
effective and achieves the state-of-the-art results.


  Let $(Y_t)_{t\geq 1}$ be a sequence of i.i.d.\ observations and
$\{f_\theta,\theta\in \mathbb{R}^d\}$ be a parametric model. We introduce a new
online algorithm for computing a sequence $(\hat{\theta}_t)_{t\geq 1}$ which is
shown to converge almost surely to $\text{argmax}_{\theta\in
\mathbb{R}^d}\mathbb{E}[\log f_\theta(Y_1)]$ at rate $ \mathcal{O}(\log
(t)^{(1+\varepsilon)/2}t^{-1/2})$, with $\varepsilon>0$ a user specified
parameter. This convergence result is obtained under standard conditions on the
statistical model and, most notably, we allow the mapping $\theta\mapsto
\mathbb{E}[\log f_\theta(Y_1)]$ to be multi-modal. However, the computational
cost to process each observation grows exponentially with the dimension of
$\theta$, which makes the proposed approach applicable to low or moderate
dimensional problems only. We also derive a version of the estimator
$\hat{\theta}_t$ which is well suited to Student-t linear regression models.
The corresponding estimator of the regression coefficients is robust to the
presence of outliers, as shown by experiments on simulated and real data, and
thus, as a by-product of this work, we obtain a new online and adaptive robust
estimation method for linear regression models.


  Chemistry has arrived on the shore of the Island of Stability with the first
chemical investigation of the superheavy elements Cn, 113, and 114. The results
of three experimental series leading to first measured thermodynamic data and
qualitatively evaluated chemical properties for these elements are described.
An interesting volatile compound class has been observed in the on-line
experiments for the elements Bi and Po. Hence, an exciting chemical study of
their heavier transactinide homologues, elements 115 and 116 is suggested.


  We prove several consistency results in choiceless set theory ZF+DC regarding
countable chromatic numbers of various algebraic hypergraphs on Euclidean
spaces.


  The Wigner function of a pure continuous-variable quantum state is
non-negative if and only if the state is Gaussian. Here we show that for the
canonical pair angle and angular momentum, the only pure states with
non-negative Wigner functions are the eigenstates of the angular momentum. Some
implications of this surprising result are discussed.


  We present schemes for the generation and evaluation of continuous variable
entanglement of bright optical beams and give a brief overview of the variety
of optical techniques and quantum communication applications on this basis. A
new entanglement-based quantum interferometry scheme with bright beams is
suggested. The performance of the presented schemes is independent of the
relative interference phase which is advantageous for quantum communication
applications.


  Quenched or frozen-in structural disorder is ubiquitous in real experimental
systems. Much of the progress is achieved in understanding the phase separation
of such systems using the diffusion-driven coarsening in Ising model with
quenched disorder. But there is a paucity of research in the phase separation
kinetics in fluids with quenched disorder. In this paper, we present results
from a detailed Molecular dynamics simulation, the effects of randomly placed
localized impurities on the phase separating kinetics of binary fluid mixture.
Two different models are offered for representing the impurities. We observe a
dramatic slowing down in the pattern formation with increasing impurity
concentration. This sluggish domain growth kinetics follows power-law with a
disorder-dependent exponent. The correlation function and structure factor show
a non-Porod behavior, indicating the roughening of domain interfaces. We have
also studied the effect of quenched disorder on the aging dynamics by
calculating the two-time order parameter auto correlation function and find
that the Fisher and Huse scaling law holds good in presence of quenched
disorder.


  In the first part of this paper [16], some results on how to compute the flat
spectra of Boolean constructions w.r.t. the transforms {I,H}^n, {H,N}^n and
{I,H,N}^n were presented, and the relevance of Local Complementation to the
quadratic case was indicated. In this second part, the results are applied to
develop recursive formulae for the numbers of flat spectra of some structural
quadratics. Observations are made as to the generalised Bent properties of
boolean functions of algebraic degree greater than two, and the number of flat
spectra w.r.t. {I,H,N}^n are computed for some of them.


  We argue that an excess in entanglement between the visible and hidden units
in a Quantum Neural Network can hinder learning. In particular, we show that
quantum neural networks that satisfy a volume-law in the entanglement entropy
will give rise to models not suitable for learning with high probability. Using
arguments from quantum thermodynamics, we then show that this volume law is
typical and that there exists a barren plateau in the optimization landscape
due to entanglement. More precisely, we show that for any bounded objective
function on the visible layers, the Lipshitz constants of the expectation value
of that objective function will scale inversely with the dimension of the
hidden-subsystem with high probability. We show how this can cause both
gradient descent and gradient-free methods to fail. We note that similar
problems can happen with quantum Boltzmann machines, although stronger
assumptions on the coupling between the hidden/visible subspaces are necessary.
We highlight how pretraining such generative models may provide a way to
navigate these barren plateaus.


  An implicit method for the ohmic dissipation is proposed. The proposed method
is based on the Crank-Nicolson method and exhibits second-order accuracy in
time and space. The proposed method has been implemented in the SFUMATO
adaptive mesh refinement (AMR) code. The multigrid method on the grids of the
AMR hierarchy converges the solution. The convergence is fast but depends on
the time step, resolution, and resistivity. Test problems demonstrated that
decent solutions are obtained even at the interface between fine and coarse
grids. Moreover, the solution obtained by the proposed method shows good
agreement with that obtained by the explicit method, which required many time
steps. The present method reduces the number of time steps, and hence the
computational costs, as compared with the explicit method.


  We propose a general physics-based deep learning architecture for wave-based
imaging problems. A key difficulty in imaging problems with a varying
background wave speed is that the medium "bends" the waves differently
depending on their position and direction. This space-bending geometry makes
the equivariance to translations of convolutional networks an undesired
inductive bias. We build an interpretable neural architecture inspired by
Fourier integral operators (FIOs) which approximate the wave physics. FIOs
model a wide range of imaging modalities, from seismology and radar to Doppler
and ultrasound. We focus on learning the geometry of wave propagation captured
by FIOs, which is implicit in the data, via a loss based on optimal transport.
The proposed FIONet performs significantly better than the usual baselines on a
number of imaging inverse problems, especially in out-of-distribution tests.


  We study the dynamics of a one-dimensional non-linear and non-local drift-di
usion equation set in the half-line, with the coupling involving the trace
value on the boundary. The initial mass M of the density determines the
behaviour of the equation: attraction to self similar pro le, to a steady state
of nite time blow up for supercritical mass. Using the logarithmic Sobolev and
the HWI inequalities we obtain a rate of convergence for the cases subcritical
and critical mass. Moreover, we prove a comparison principle on the equation
obtained after space integration. This concentration-comparison principle
allows proving blow-up of solutions for large initial data without any
monotonicity assumption on the initial data.


  A model for incomplete reconnection in sawtooth crashes is presented. The
reconnection inflow during the crash phase of sawteeth self-consistently
convects the high pressure core toward the reconnection site, raising the
pressure gradient there. Reconnection shuts off if the diamagnetic drift speed
at the reconnection site exceeds a threshold, which may explain incomplete
reconnection. The relaxation of magnetic shear after reconnection stops may
explain the destabilization of ideal interchange instabilities reported
previously. Proof-of-principle two-fluid simulations confirm this basic
picture. Predictions of the model compare favorably to data from the Mega
Ampere Spherical Tokamak. Applications to transport modeling of sawteeth are
discussed. The results should apply across tokamaks, including ITER.


  In this paper we present the first REST API for the generation and evaluation
of bijective S-boxes. We also present the first Single Page Application tool
for researchers and students that allows the use of a graphical interface. We
give a small dataset of classical S-boxes to test the properties evaluations.
We show how to define experiments and we include two local search experiments
into the proposed tool.


  Multilingual language models have shown impressive cross-lingual transfer
ability across a diverse set of languages and tasks. To improve the
cross-lingual ability of these models, some strategies include transliteration
and finer-grained segmentation into characters as opposed to subwords. In this
work, we investigate lexical sharing in multilingual machine translation (MT)
from Hindi, Gujarati, Nepali into English. We explore the trade-offs that exist
in translation performance between data sampling and vocabulary size, and we
explore whether transliteration is useful in encouraging cross-script
generalisation. We also verify how the different settings generalise to unseen
languages (Marathi and Bengali). We find that transliteration does not give
pronounced improvements and our analysis suggests that our multilingual MT
models trained on original scripts seem to already be robust to cross-script
differences even for relatively low-resource languages


  We present the detection of non-radial oscillations in a hot,
helium-atmosphere white dwarf using 78.7 d of nearly uninterrupted photometry
from the Kepler space telescope. With an effective temperature >30,000 K, PG
0112+104 becomes the hottest helium-atmosphere white dwarf known to pulsate.
The rich oscillation spectrum of low-order g-modes includes clear patterns of
rotational splittings from consecutive sequences of dipole and quadrupole
modes, which can be used to probe the rotation rate with depth in this highly
evolved stellar remnant. We also measure a surface rotation rate of 10.17404 hr
from an apparent spot modulation in the K2 data. With two independent measures
of rotation, PG 0112+104 provides a remarkable test of asteroseismic inference.


  The Dirac equation is extended for a relativistic electron in an
orthorhombically-anisotropic conduction band. Its covariance is established
with general proper and improper Lorentz transformations. In the
non-relativistic limit, the kinetic and Zeeman energy terms of the Hamiltonian
are both determined by the same three effective masses, and the quantum spin
Hall effect is derived. This has important consequences for magnetic
measurements of many classes of clean anisotropic semiconductors, metals, and
superconductors. The Zeeman energy is vanishingly small for magnetic fields
parallel to clean monolayers and in all directions in quasi-one-dimensional
materials.


  PSR B1259-63 is a $\gamma$-ray binary system, where the compact object is a
pulsar. The system has an orbital period of 1236.7 days and shows peculiar
$\gamma$-ray flares (in 100\,MeV--300\,GeV) after its periastron time. We
analyzed the \textit{Fermi}-LAT observation of PSR B1259-63 during its latest
periastron passage, as well as its previous three periastrons. The bright GeV
flares started about 60 days after the periastron epoch in 2021. This delay is
larger than that around the 2017 periastron and much larger than earlier
periastrons. The delay of the GeV flux peak time in each periastron passage is
apparent in our results. We discussed the possible origin of this delay and
made a prediction of the GeV flux peak time in next periastron passage, based
on observation of the previous delays.


  Smooth Schubert varieties in rational homogeneous manifolds of Picard number
1 are horospherical varieties. We characterize standard embeddings of smooth
Schubert varieties in rational homogeneous manifolds of Picard number 1 by
means of varieties of minimal rational tangents. In particular, we mainly
consider nonhomogeneous smooth Schubert varieties in symplectic Grassmannians
and in the 20-dimensional $F_4$-homogeneous manifold associated to a short
simple root.


  In a previous work (arXiv:2010.02027) we showed how the full configuration
interaction (FCI) ground state energy can be obtained as a functional of an
arbitrary reference wavefunction by means of a gradient descent or quasi-Newton
algorithm. Here, we extend this approach and consider the optimization of the
low-energy subspace of the Hamiltonian from an arbitrary reference subspace.
The energies along the optimization path are obtained in terms of transition
matrix elements among the states in the reference subspace. We show an
application of the algorithm with a reference subspace constructed from a
non-orthogonal configuration interaction (NOCI) formalism to describe the
avoided crossing in LiF and the low-lying singlet and triplet spectrum of
formaldehyde.


  We present the theoretical mean-field zero-temperature phase diagram of a
Bose-Einstein condensate (BEC) with dipolar interactions loaded into an optical
lattice with a staggered flux. Apart from uniform superfluid, checkerboard
supersolid and striped supersolid phases, we identify several supersolid phases
with staggered vortices, which can be seen as combinations of supersolid phases
found in earlier work on dipolar BECs and a staggered-vortex phase found for
bosons in optical lattices with staggered flux. By allowing for different
phases and densities on each of the four sites of the elementary plaquette,
more complex phase patterns are found.


  Traditional problem-based exams are not efficient instruments for assessing
the "structure" of physics students' conceptual knowledge or for providing
diagnostically detailed feedback to students and teachers. We present the Free
Term Entry task, a candidate assessment instrument for exploring the
connections between concepts in a student's understanding of a subject. In this
task, a student is given a general topic area and asked to respond with as many
terms from the topic area as possible in a given time; the "thinking time"
between each term-entry event is recorded along with the response terms. The
task was given to students from two different introductory physics classes.
Response term thinking times were found to correlate with the strength of the
association between two concepts. In addition, sets of thinking times from the
task show distinct, characteristic patterns which might prove valuable for
student assessment. We propose a quantitative dynamical model named the Matrix
Walk Model which is able to match many aspects of the observed data. One
particular feature of the data -- a distinct "spike" superimposed on the
otherwise log-normal distribution of most thinking time sets -- has not been
fit. The spike, other patterns observed in the data, and the proposed
phenomenological model could all benefit from a grounding in cognitive theory.


  Density functional calculations for K_{0.5}Li_{0.5}NbO_3 show strong A-site
driven ferroelectricity, even though the average tolerance factor is
significantly smaller than unity and there is no stereochemically active A-site
ion. This is due to the frustration of tilt instabilities by A-site disorder.
There are very large off-centerings of the Li ions, which contribute strongly
to the anisotropy between the tetragonal and rhombohedral ferroelectric states,
yielding a tetragonal ground state even without strain coupling.


  Platoons of autonomous vehicles are being investigated as a way to increase
road capacity and fuel efficiency. Cooperative Adaptive Cruise Control (CACC)
is an approach to achieve such platoons, in which vehicles collaborate using
wireless communication. While this collaboration improves performance, it also
makes the vehicles vulnerable to cyber-attacks. In this paper the performance
of a sliding mode observer (SMO) based approach to cyber-attack detection is
analysed, considering simultaneous attacks on the communication and local
sensors. To this end, the considered cyber-attacks are divided into three
classes for which relevant theoretical properties are proven. Furthermore, the
harm that attacks within each of these classes can do to the system while
avoiding detection is analysed based on simulation examples.


  A large amount of data on the WWW remains inaccessible to crawlers of Web
search engines because it can only be exposed on demand as users fill out and
submit forms. The Hidden web refers to the collection of Web data which can be
accessed by the crawler only through an interaction with the Web-based search
form and not simply by traversing hyperlinks. Research on Hidden Web has
emerged almost a decade ago with the main line being exploring ways to access
the content in online databases that are usually hidden behind search forms.
The efforts in the area mainly focus on designing hidden Web crawlers that
focus on learning forms and filling them with meaningful values. The paper
gives an insight into the various Hidden Web crawlers developed for the purpose
giving a mention to the advantages and shortcoming of the techniques employed
in each.


  Instabilities in 1D spatially extended systems are studied with the aid of
both temporal and spatial Lyapunov exponents. A suitable representation of the
spectra allows a compact description of all the possible disturbances in
tangent space. The analysis is carried out for chaotic and periodic
spatiotemporal patterns. Singularities of the spectra and localization
properties of the associated Lyapunov vectors are discussed.


  Subtle changes in white matter (WM) microstructure have been associated with
normal aging and neurodegeneration. To study these associations in more detail,
it is highly important that the WM tracts can be accurately and reproducibly
characterized from brain diffusion MRI. In addition, to enable analysis of WM
tracts in large datasets and in clinical practice it is essential to have
methodology that is fast and easy to apply. This work therefore presents a new
approach for WM tract segmentation: Neuro4Neuro, that is capable of direct
extraction of WM tracts from diffusion tensor images using convolutional neural
network (CNN). This 3D end-to-end method is trained to segment 25 WM tracts in
aging individuals from a large population-based study (N=9752, 1.5T MRI). The
proposed method showed good segmentation performance and high reproducibility,
i.e., a high spatial agreement (Cohen's kappa, k = 0.72 ~ 0.83) and a low
scan-rescan error in tract-specific diffusion measures (e.g., fractional
anisotropy: error = 1% ~ 5%). The reproducibility of the proposed method was
higher than that of a tractography-based segmentation algorithm, while being
orders of magnitude faster (0.5s to segment one tract). In addition, we showed
that the method successfully generalizes to diffusion scans from an external
dementia dataset (N=58, 3T MRI). In two proof-of-principle experiments, we
associated WM microstructure obtained using the proposed method with age in a
normal elderly population, and with disease subtypes in a dementia cohort. In
concordance with the literature, results showed a widespread reduction of
microstructural organization with aging and substantial group-wise
microstructure differences between dementia subtypes. In conclusion, we
presented a highly reproducible and fast method for WM tract segmentation that
has the potential of being used in large-scale studies and clinical practice.


  2018's Brazilian presidential elections highlighted the influence of
alternative media and social networks, such as Twitter. In this work, we
perform an analysis covering politically motivated discourses related to the
second round in Brazilian elections. In order to verify whether similar
discourses reinforce group engagement to personal beliefs, we collected a set
of tweets related to political hashtags at that moment. To this end, we have
used a combination of topic modeling approach with opinion mining techniques to
analyze the motivated political discourses. Using SentiLex-PT, a Portuguese
sentiment lexicon, we extracted from the dataset the top 5 most frequent group
of words related to opinions. Applying a bag-of-words model, the cosine
similarity calculation was performed between each opinion and the observed
groups. This study allowed us to observe an exacerbated use of passionate
discourses in the digital political scenario as a form of appreciation and
engagement to the groups which convey similar beliefs.


  We propose a flavor model where both CP and flavor symmetries are broken at
the supersymmetric level. The model is an effective SU(5) theory based on a
U(2) horizontal symmetry. The minimum of the supersymmetric scalar potential
can be solved to yield a realistic pattern of charged fermion masses. The Higgs
sector contains a symmetric, an antisymmetric and two vector fields, plus their
U(2) conjugates. These Higgs fields are the only fields strictly required to
break the flavor and CP symmetries and generate masses for all charged fermions
including the up-quark. The model predicts the existence of an absolute minimum
in the space of CP-phases. The value alpha ~ pi/2 is predicted in a particular
limit of the parameter space of the model.


  We compute the Euclidean actions of a $d$-dimensional charged rotating black
brane both in the canonical and the grand-canonical ensemble through the use of
the counterterms renormalization method, and show that the logarithmic
divergencies associated with the Weyl anomalies and matter field vanish. We
obtain a Smarr-type formula for the mass as a function of the entropy, the
angular momenta, and the electric charge, and show that these quantities
satisfy the first law of thermodynamics. Using the conserved quantities and the
Euclidean actions, we calculate the thermodynamics potentials of the system in
terms of the temperature, angular velocities, and electric potential both in
the canonical and grand-canonical ensembles. We also perform a stability
analysis in these two ensembles, and show that the system is thermally stable.
This is commensurate with the fact that there is no Hawking-Page phase
transition for a black object with zero curvature horizon. Finally, we obtain
the logarithmic correction of the entropy due to the thermal fluctuation around
the equilibrium.


  We propose a novel high-fidelity expressive speech synthesis model, UniTTS,
that learns and controls overlapping style attributes avoiding interference.
UniTTS represents multiple style attributes in a single unified embedding space
by the residuals between the phoneme embeddings before and after applying the
attributes. The proposed method is especially effective in controlling multiple
attributes that are difficult to separate cleanly, such as speaker ID and
emotion, because it minimizes redundancy when adding variance in speaker ID and
emotion, and additionally, predicts duration, pitch, and energy based on the
speaker ID and emotion. In experiments, the visualization results exhibit that
the proposed methods learned multiple attributes harmoniously in a manner that
can be easily separated again. As well, UniTTS synthesized high-fidelity speech
signals controlling multiple style attributes. The synthesized speech samples
are presented at
https://anonymous-authors2022.github.io/paper_works/UniTTS/demos/.


  Chaitin's work, in its depth and breadth, encompasses many areas of
scientific and philosophical interest. It helped establish the accepted
mathematical concept of randomness, which in turn is the basis of tools that I
have developed to justify and quantify what I think is clear evidence of the
algorithmic nature of the world. To illustrate the concept I will establish
novel upper bounds of algorithmic randomness for elementary cellular automata.
I will discuss how the practice of science consists in conceiving a model that
starts from certain initial values, running a computable instantiation, and
awaiting a result in order to determine where the system may be in a future
state--in a shorter time than the time taken by the actual unfolding of the
phenomenon in question. If a model does not comply with all or some of these
requirements it is traditionally considered useless or even unscientific, so
the more precise and faster the better. A model is thus better if it can
explain more with less, which is at the core of Chaitin's "compression is
comprehension". I will pursue these questions related to the random versus
possibly algorithmic nature of the world in two directions, drawing heavily on
the work of Chaitin. I will also discuss how the algorithmic approach is
related to the success of science at producing models of the world, allowing
computer simulations to better understand it and make more accurate predictions
and interventions.


  The problem of pairing in anisotropic electron systems possessing patches of
fermion condensate in the vicinity of the van Hove points is analyzed.
Attention is directed to opportunities for the occurrence of non-BCS pairing
correlations between the states belonging to the fermion condensate. It is
shown that the physical emergence of such pairing correlations would
drastically alter the behavior of the single-particle Green function, the
canonical pole of Fermi-liquid theory being replaced by a branch point.


  Machine learning techniques exhibit significant performance in discriminating
different phases of matter and provide a new avenue for studying phase
transitions. We investigate the phase transitions of three dimensional
$q$-state Potts model on cubic lattice by using a transfer learning approach,
Domain Adversarial Neural Network (DANN). With the unique neural network
architecture, it could evaluate the high-temperature (disordered) and
low-temperature (ordered) phases, and identify the first and second order phase
transitions. Meanwhile, by training the DANN with a few labeled configurations,
the critical points for $q=2,3,4$ and $5$ can be predicted with high accuracy,
which are consistent with those of the Monte Carlo simulations. These findings
would promote us to learn and explore the properties of phase transitions in
high-dimensional systems.


  Acceleration can change the ionization of X-ray irradiated gas to the point
that the gas becomes thermally unstable. Cloud formation, the expected outcome
of thermal instability (TI), will be suppressed in a dynamic flow, however, due
to the stretching of fluid elements that accompanies acceleration. It is
therefore unlikely that cloud formation occurs during the launching phase of a
supersonic outflow. In this paper, we show that the most favorable conditions
for dynamical TI in highly supersonic outflows are found at radii beyond the
acceleration zone, where the growth rate of entropy modes is set by the linear
theory rate for a static plasma. This finding implies that even mildly
relativistic outflows can become clumpy, and we explicitly demonstrate this
using hydrodynamical simulations of ultrafast outflows. We describe how the
continuity and heat equations can be used to appreciate another impediment
(beside mode disruption due to the stretching) to making an outflow clumpy:
background flow conditions may not allow the plasma to enter a TI zone in the
first place. The continuity equation reveals that both impediments are in fact
tightly coupled, yet one is easy to overcome. Namely, time variability in the
radiation field is found to be a robust means of placing gas in a TI zone. We
further show how the ratio of the dynamical and thermal timescales enters
linear theory; the heat equation reveals how this ratio depends on the two
processes that tend to remove gas from a TI zone -- adiabatic cooling and heat
advection.


  By using the piecewise method, Lagrange interpolation formula and Lucas'
theorem, we determine explicit expressions of the inverses of a class of
reversed Dickson permutation polynomials and some classes of generalized
cyclotomic mapping permutation polynomials over finite fields of characteristic
three.


  Given a group $G$ and a subgroup $H \leq G$, a set $\mathcal{F}\subset G$ is
called $H$\emph{-intersecting} if for any $g,g' \in \mathcal{F}$, there exists
$xH \in G/H$ such that $gxH=g'xH$. The \emph{intersection density} of the
action of $G$ on $G/H$ by (left) multiplication is the rational number
$\rho(G,H)$, equal to the maximum ratio $\frac{|\mathcal{F}|}{|H|}$, where
$\mathcal{F} \subset G$ runs through all $H$-intersecting sets of $G$. The
\emph{intersection spectrum} of the group $G$ is then defined to be the set
  $$
  \sigma(G) := \left\{ \rho(G,H) : H\leq G \right\}.
  $$ It was shown by Bardestani and Mallahi-Karai [{\it J. Algebraic Combin.},
42(1):111-128, 2015] that if $\sigma(G) = \{1\}$, then $G$ is necessarily
solvable. The natural question that arises is, therefore, which rational
numbers larger than $1$ belong to $\sigma(G)$, whenever $G$ is non-solvable. In
this paper, we study the intersection spectrum of the linear group
$\operatorname{PSL}_2(q)$. It is shown that $2 \in
\sigma\left(\operatorname{PSL}_2(q)\right)$, for any prime power $q\equiv 3
\pmod 4$. Moreover, when $q\equiv 1 \pmod 4$, it is proved that
$\rho(\operatorname{PSL}_2(q),H)=1$, for any odd index subgroup $H$ (containing
$\mathbb{F}_q$) of the Borel subgroup (isomorphic to $\mathbb{F}_q\rtimes
\mathbb{Z}_{\frac{q-1}{2}}$) consisting of all upper triangular matrices.


  We prove that the Riemann hypothesis is equivalent to the condition
$\int_{2}^x\left(\pi(t)-\text{li}(t)\right)\mathrm{d}t<0$ for all $x>2$. Here,
$\pi(t)$ is the prime-counting function and $\text{li}(t)$ is the logarithmic
integral. This makes explicit a claim of Pintz (1991). Moreover, we prove an
analogous result for the Chebyshev function $\theta(t)$ and discuss the extent
to which one can make related claims unconditionally.


  The internal evolution of disk galaxies like the Milky Way are driven by
non-axisymmetries (bars) and the implied angular momentum transfer of the
matter; baryons are essentially driven inwards to build a more concentrated
disk. This mass concentration may lead to the decoupling of a secondary bar,
since the orbit precessing frequency is then much enhanced. Vertical resonances
with the bar will form a box/peanut bulge in a Gyr time-scale. Gas flows due to
gravity torques can lead to a young nuclear disk forming stars, revealed by a
sigma-drop in velocity dispersion. These gas flows moderated by feedback
produce intermittent accretion of the super-massive black hole, and cycles of
AGN activity. The fountain effect due to nuclear star formation may lead to
inclined, and even polar nuclear disks.


  Exploiting a fundamental characteristic of galaxy assembly in the
{\Lambda}CDM paradigm, the observed spatial biasing and kinematics of
metal-poor globular star clusters are used to constrain the local reionization
epoch around individual galaxies. Selecting three galaxies located in different
environments, the first attempt at constraining the environmental propagation
of reionization in the local Universe is carried out. The joint constraint from
the three galaxies (z_reion = 10.5^{+1.0}_{-0.9}) agrees remarkably well with
the latest WMAP constraint on z_reion for a simple instantaneous reionization
model. More importantly, the range of z_reion values found here are consistent
with the global range of z_reion estimates from other observations. We
furthermore find a 1.7{\sigma} indication that reionization completed in
low-density environments before the intergalactic medium in high-density
environments was reionized. This is consistent with certain theoretical models
that predict that reionization was globally prolonged in duration, with neutral
hydrogen pockets surviving in high-density environments, even after the
surrounding regions were reionized. More generally, this work provides a useful
constraint on the formation history of galaxy stellar halos.


  While person Re-identification (Re-ID) has progressed rapidly due to its wide
real-world applications, it also causes severe risks of leaking personal
information from training data. Thus, this paper focuses on quantifying this
risk by membership inference (MI) attack. Most of the existing MI attack
algorithms focus on classification models, while Re-ID follows a totally
different training and inference paradigm. Re-ID is a fine-grained recognition
task with complex feature embedding, and model outputs commonly used by
existing MI like logits and losses are not accessible during inference. Since
Re-ID focuses on modelling the relative relationship between image pairs
instead of individual semantics, we conduct a formal and empirical analysis
which validates that the distribution shift of the inter-sample similarity
between training and test set is a critical criterion for Re-ID membership
inference. As a result, we propose a novel membership inference attack method
based on the inter-sample similarity distribution. Specifically, a set of
anchor images are sampled to represent the similarity distribution conditioned
on a target image, and a neural network with a novel anchor selection module is
proposed to predict the membership of the target image. Our experiments
validate the effectiveness of the proposed approach on both the Re-ID task and
conventional classification task.


  In previous publications we have proposed that Inflation can be realized in a
second minimum of the Standard Model Higgs potential at energy scales of about
$10^{16}$ GeV, if the minimum is not too deep and if a mechanism which allows a
transition to the radiation dominated era can be found. This is provided, {\it
e.g.}, by scalar-tensor gravity models or hybrid models. Using such ideas we
had predicted the Higgs boson mass to be of about $126\pm 3$ GeV, which has
been confirmed by the LHC, and that a possibly measurable amount of gravity
waves should be produced. Using more refined recent theoretical calculations of
the RGE we show that such scenario has the right scale of Inflation only for
small Higgs mass, lower than about 124 GeV, otherwise gravity waves are
overproduced. The precise value is subject to some theoretical error and to
experimental errors on the determination of the strong coupling constant. Such
an upper bound corresponds also to the recent claimed measurement by BICEP2 of
the scale of inflation through primordial tensor modes. Finally we show that
introducing a moderately large non-minimal coupling for the Higgs field the
bound can shift to larger values and be reconciled with the LHC measurements of
the Higgs mass.


  The cloud of cold atoms obtained from a magneto-optical trap is known to
exhibit two types of instabilities in the regime of high atomic densities:
stochastic instabilities and deterministic instabilities. In the present paper,
the experimentally observed deterministic dynamics is described extensively.
Three different behaviors are distinguished. All are cyclic, but not
necessarily periodic. Indeed, some instabilities exhibit a cyclic behavior with
an erratic return time. A one-dimensional stochastic model taking into account
the shadow effect is shown to be able to reproduce the experimental behavior,
linking the instabilities to a several bifurcations. Erraticity of some of the
regimes is shown to be induced by noise.


  We study the problem of parametric parallel complexity analysis of
concurrent, message-passing programs. To make the analysis local and
compositional, it is based on a conservative extension of binary session types,
which structure the type and direction of communication between processes and
stand in a Curry-Howard correspondence with intuitionistic linear logic. The
main innovation is to enrich session types with the temporal modalities next
($\bigcirc A$), always ($\Box A$), and eventually ($\Diamond A$), to
additionally prescribe the timing of the exchanged messages in a way that is
precise yet flexible. The resulting temporal session types uniformly express
properties such as the message rate of a stream, the latency of a pipeline, the
response time of a concurrent queue, or the span of a fork/join parallel
program. The analysis is parametric in the cost model and the presentation
focuses on communication cost as a concrete example. The soundness of the
analysis is established by proofs of progress and type preservation using a
timed multiset rewriting semantics. Representative examples illustrate the
scope and usability of the approach.