  Angstrom-scale fluidic channels are ubiquitous in nature, and play an
important role in regulating cellular traffic, signaling, and responding to
stimuli. Synthetic channels are now a reality with the emergence of several
cutting-edge bottom-up and top-down fabrication methods. In particular, the use
of atomically thin two dimensional (2D) materials and nanotubes as components
to build fluidic conduits has pushed the limits of fabrication to the
Angstrom-scale. Here, we provide an overview of the recent developments in the
fabrication methods for nano- and angstrofluidic channels while categorizing
them on the basis of dimensionality (0D pores, 1D tubes, 2D slits), along with
the latest advances in measurement techniques. We discuss the ionic transport
governed by various stimuli in these channels and draw comparison of ionic
mobility, streaming and osmotic power, with varying pore sizes across all the
dimensionalities. Towards the end of the review, we highlight the unique future
opportunities in the development of smart ionic devices.


  We study the complexity of solving the \emph{generalized MinRank problem},
i.e. computing the set of points where the evaluation of a polynomial matrix
has rank at most $r$. A natural algebraic representation of this problem gives
rise to a \emph{determinantal ideal}: the ideal generated by all minors of size
$r+1$ of the matrix. We give new complexity bounds for solving this problem
using Gr\"obner bases algorithms under genericity assumptions on the input
matrix. In particular, these complexity bounds allow us to identify families of
generalized MinRank problems for which the arithmetic complexity of the solving
process is polynomial in the number of solutions. We also provide an algorithm
to compute a rational parametrization of the variety of a 0-dimensional and
radical system of bi-degree $(D,1)$. We show that its complexity can be bounded
by using the complexity bounds for the generalized MinRank problem.


  We start from a quantum computational principle suggested for string theory
in a previous paper and discuss how it might lead to a dynamical principle
implying the correct classical gravitational limit. Besides this, we briefly
look at some structural properties of moduli space.


  This paper formulates an inverse power flow problem which is to infer a nodal
admittance matrix (hence the network structure of a power system) from voltage
and current phasors measured at a number of buses. We show that the admittance
matrix can be uniquely identified from a sequence of measurements corresponding
to different steady states when every node in the system is equipped with a
measurement device, and a Kron-reduced admittance matrix can be determined even
if some nodes in the system are not monitored (hidden nodes). Furthermore, we
propose effective algorithms based on graph theory to uncover the actual
admittance matrix of radial systems with hidden nodes. We provide theoretical
guarantees for the recovered admittance matrix and demonstrate that the actual
admittance matrix can be fully recovered even from the Kron-reduced admittance
matrix under some mild assumptions. Simulations on standard test systems
confirm that these algorithms are capable of providing accurate estimates of
the admittance matrix from noisy sensor data.


  We study the thermodynamics and kinetics of folding for a small peptide. Our
data rely on Monte Carlo simulations where the interactions among all atoms are
taken into account. Monte Carlo kinetics is used to study folding of the
peptide at suitable temperatures. The results of these canonical simulations
are compared with that of a generalized-ensemble simulation. Our work
demonstrates that concepts of folding which were developed in the past for
minimalist models hold also for this peptide when simulated with an all-atom
force field.


  Gradient-domain machine learning (GDML) is an accurate and efficient approach
to learn a molecular potential and associated force field based on the kernel
ridge regression algorithm. Here, we demonstrate its application to learn an
effective coarse-grained (CG) model from all-atom simulation data in a sample
efficient manner. The coarse-grained force field is learned by following the
thermodynamic consistency principle, here by minimizing the error between the
predicted coarse-grained force and the all-atom mean force in the
coarse-grained coordinates. Solving this problem by GDML directly is impossible
because coarse-graining requires averaging over many training data points,
resulting in impractical memory requirements for storing the kernel matrices.
In this work, we propose a data-efficient and memory-saving alternative. Using
ensemble learning and stratified sampling, we propose a 2-layer training scheme
that enables GDML to learn an effective coarse-grained model. We illustrate our
method on a simple biomolecular system, alanine dipeptide, by reconstructing
the free energy landscape of a coarse-grained variant of this molecule. Our
novel GDML training scheme yields a smaller free energy error than neural
networks when the training set is small, and a comparably high accuracy when
the training set is sufficiently large.


  We study the multiplier algebras $A(\mathcal{H})$ obtained as the closure of
the polynomials on certain reproducing kernel Hilbert spaces $\mathcal{H}$ on
the ball $\mathbb{B}_d$ of $\mathbb{C}^d$. Our results apply, in particular, to
the Drury-Arveson space, the Dirichlet space and the Hardy space on the ball.
We first obtain a complete description of the dual and second dual spaces of
$A(\mathcal H)$ in terms of the complementary bands of Henkin and totally
singular measures for $\operatorname{Mult}(\mathcal{H})$. This is applied to
obtain several definitive results in interpolation. In particular, we establish
a sharp peak interpolation result for compact
$\operatorname{Mult}(\mathcal{H})$-totally null sets as well as a Pick and peak
interpolation theorem. Conversely, we show that a mere interpolation set is
$\operatorname{Mult}(\mathcal{H})$-totally null.


  We show that the hypothesis of analyticity in the uniqueness theory of
vacuum, or electrovacuum, static black holes is not needed. More generally, we
show that prehorizons covering a closed set cannot occur in well-behaved
domains of outer communications.


  We present a high-resolution ($R \simeq 20,000$) near-infrared (9,100-13,500
$\AA$) long-slit spectrum of P Cygni obtained with the newly commissioned
WINERED spectrograph in Japan. In the obtained spectrum, we have found that the
velocity profiles of the [Fe II] emission lines are resolved into two peaks at
a velocity of $\simeq220$ km s$^{-1}$ with a moderate dip in between and with
additional sub-peaks at $\simeq\pm100$~km~s$^{-1}$. The sub-peak component is
confirmed with the long-slit echellogram to originate in the known shell with a
radius of $\simeq10^{\prime\prime}$, which was originally created by the
outburst in 1600 AD. On the other hand, the $\simeq220$ km s$^{-1}$ component,
which dominates the [Fe II] flux from P Cygni, is found to be concentrated
closer to the central star with an apparent spatial extent of
$\simeq3^{\prime\prime}$. The extent is much larger than the compact
($<0^{\prime\prime}.1$) regions traced with hydrogen, helium, and metal
permitted lines. The velocity, estimated mass, and dynamical time of the
extended emission-line region suggest that the region is an outer part of the
stellar wind region. We suggest that the newly-identified emission-line region
may trace a reverse shock due to the stellar wind overtaking the outburst
shell.


  We study a model for switching strategies in the Prisoner's Dilemma game on
adaptive networks of player pairings that coevolve as players attempt to
maximize their return. We use a node-based strategy model wherein each player
follows one strategy at a time (cooperate or defect) across all of its
neighbors, changing that strategy and possibly changing partners in response to
local changes in the network of player pairing and in the strategies used by
connected partners. We compare and contrast numerical simulations with existing
pair approximation differential equations for describing this system, as well
as more accurate equations developed here using the framework of approximate
master equations. We explore the parameter space of the model, demonstrating
the relatively high accuracy of the approximate master equations for describing
the system observations made from simulations. We study two variations of this
partner-switching model to investigate the system evolution, predict stationary
states, and compare the total utilities and other qualitative differences
between these two model variants.


  Two finely segmented and very compact calorimeters are foreseen for the
instrumentation of the very forward region of the ILC and the CLIC detectors; a
luminometer to measure the rate of low angle Bhabha scattering events with a
precision better than $10^{-3}$ and, at lower polar angles, a detector for fast
luminosity estimate. Both devices will improve the hermeticity of the detector
in searches for new particles. Due to the expected high occupancy, fast
front-end electronics is needed. The calorimeter closest to the beampipe will
be hit by a large amount of beamstrahlung remnants, so that radiation hard
sensors are required. Results on the performance of the sensor and readout
electronics prototypes are summarized.


  Statistical machine learning methods, especially nonparametric Bayesian
methods, have become increasingly popular to infer clonal population structure
of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant
process (CRP), a popular construction used in nonparametric mixture models, to
infer the phylogeny and genotype of major subclonal lineages represented in the
population of cancer cells. We also propose new split-merge updates tailored to
the subclonal reconstruction problem that improve the mixing time of Markov
chains. In comparisons with the tree-structured stick breaking prior used in
PhyloSub, we demonstrate superior mixing and running time using the treeCRP
with our new split-merge procedures. We also show that given the same number of
samples, TSSB and treeCRP have similar ability to recover the subclonal
structure of a tumor.


  Based on the Schrodinger equation, exact expressions for the non-relativistic
particle energy in the local external field and the external field potential
are derived as inhomogeneous density functionals. On this basis, it is shown
that, when considering more than two noninteracting electrons, the energy of
such a system cannot be an inhomogeneous density functional. This means that
the Hohenberg-Kohn lemma which assert that in the ground state to each
inhomogeneous density corresponds only one potential of the external field
cannot be a justification of the existence of the universal density functional
in the general case. At the same time, statements of the density functional
theory remain valid when considering any number of noninteracting ground-state
bosons due to the Bose condensation effect.


  Research on open-domain question answering (QA) has a long tradition. A
challenge in this domain is answering complex questions (CQA) that require
complex inference methods and large amounts of knowledge. In low resource
languages, such as Persian, there are not many datasets for open-domain complex
questions and also the language processing toolkits are not very accurate. In
this paper, we propose a knowledge-based approach for answering Persian complex
questions using Farsbase; the Persian knowledge graph, exploiting PeCoQ; the
newly created complex Persian question dataset. In this work, we handle
multi-constraint and multi-hop questions by building their set of possible
corresponding logical forms. Then Multilingual-BERT is used to select the
logical form that best describes the input complex question syntactically and
semantically. The answer to the question is built from the answer to the
logical form, extracted from the knowledge graph. Experiments show that our
approach outperforms other approaches in Persian CQA.


  We present the Massive Star-forming Regions (MSFRs) Omnibus X-ray Catalog
(MOXC), a compendium of X-ray point sources from {\em Chandra}/ACIS
observations of a selection of MSFRs across the Galaxy, plus 30 Doradus in the
Large Magellanic Cloud. MOXC consists of 20,623 X-ray point sources from 12
MSFRs with distances ranging from 1.7 kpc to 50 kpc. Additionally, we show the
morphology of the unresolved X-ray emission that remains after the catalogued
X-ray point sources are excised from the ACIS data, in the context of \Spitzer\
and {\em WISE} observations that trace the bubbles, ionization fronts, and
photon-dominated regions that characterize MSFRs. In previous work, we have
found that this unresolved X-ray emission is dominated by hot plasma from
massive star wind shocks. This diffuse X-ray emission is found in every MOXC
MSFR, clearly demonstrating that massive star feedback (and the
several-million-degree plasmas that it generates) is an integral component of
MSFR physics.


  We address the origin of the observed bimodal rotational distribution of
stars in massive young and intermediate age stellar clusters. This bimodality
is seen as split main sequences at young ages and also has been recently
directly observed in the $Vsini$ distribution of stars within massive young and
intermediate age clusters. Previous models have invoked binary interactions as
the origin of this bimodality, although these models are unable to reproduce
all of the observational constraints on the problem. Here we suggest that such
a bimodal rotational distribution is set up early within a cluster's life,
i.e., within the first few Myr. Observations show that the period distribution
of low-mass ($\la 2 M_\odot$) pre-main sequence (PMS) stars is bimodal in many
young open clusters and we present a series of models to show that if such a
bimodality exists for stars on the PMS that it is expected to manifest as a
bimodal rotational velocity (at fixed mass/luminosity) on the main sequence for
stars with masses in excess of $\sim1.5$~\msun. Such a bimodal period
distribution of PMS stars may be caused by whether stars have lost (rapid
rotators) or been able to retain (slow rotators) their circumstellar discs
throughout their PMS lifetimes. We conclude with a series of predictions for
observables based on our model.


  Zero-shot action recognition can recognize samples of unseen classes that are
unavailable in training by exploring common latent semantic representation in
samples. However, most methods neglected the connotative relation and
extensional relation between the action classes, which leads to the poor
generalization ability of the zero-shot learning. Furthermore, the learned
classifier incline to predict the samples of seen class, which leads to poor
classification performance. To solve the above problems, we propose a two-stage
deep neural network for zero-shot action recognition, which consists of a
feature generation sub-network serving as the sampling stage and a graph
attention sub-network serving as the classification stage. In the sampling
stage, we utilize a generative adversarial networks (GAN) trained by action
features and word vectors of seen classes to synthesize the action features of
unseen classes, which can balance the training sample data of seen classes and
unseen classes. In the classification stage, we construct a knowledge graph
(KG) based on the relationship between word vectors of action classes and
related objects, and propose a graph convolution network (GCN) based on
attention mechanism, which dynamically updates the relationship between action
classes and objects, and enhances the generalization ability of zero-shot
learning. In both stages, we all use word vectors as bridges for feature
generation and classifier generalization from seen classes to unseen classes.
We compare our method with state-of-the-art methods on UCF101 and HMDB51
datasets. Experimental results show that our proposed method improves the
classification performance of the trained classifier and achieves higher
accuracy.


  An intelligent reflecting surface (IRS) can adaptively adjust the phase
shifts of its reflecting units to strengthen the desired signal and/or suppress
the undesired signal. In this letter, we investigate an IRS-aided secure
wireless communication system where a multi-antenna access point (AP) sends
confidential messages to a single-antenna user in the presence of a
single-antenna eavesdropper. In particular, we consider the challenging
scenario where the eavesdropping channel is stronger than the legitimate
communication channel and they are also highly correlated in space. We maximize
the secrecy rate of the legitimate communication link by jointly designing the
AP's transmit beamforming and the IRS's reflect beamforming. While the
resultant optimization problem is difficult to solve, we propose an efficient
algorithm to obtain high-quality suboptimal solution for it by applying the
alternating optimization and semidefinite relaxation methods. Simulation
results show that the proposed design significantly improves the secrecy
communication rate for the considered setup over the case without using the
IRS, and outperforms a heuristic scheme.


  Gamma-ray bursts (GRBs) associated with gravitational wave events are, and
will likely continue to be, viewed at a larger inclination than GRBs without
gravitational wave detections. As demonstrated by the afterglow of GW170817,
this requires an extension of the common GRB afterglow models which typically
assume emission from an on-axis top hat jet. We present a characterization of
the afterglows arising from structured jets, providing a framework covering
both successful and choked jets. We compute new closure relations for
structured jets and compare with the established relations for energy injection
and refreshed shock models. The temporal slope before the jet break is found to
be a simple function of the ratio between the viewing angle and effective
opening angle of the jet. A numerical model to calculate synthetic light curves
and spectra is publicly available as the open source Python package
afterglowpy.


  Searches for primordial non-Gaussianity in cosmological perturbations are a
key means of revealing novel primordial physics. However, robustly extracting
signatures of primordial non-Gaussianity from non-linear scales of the
late-time Universe is an open problem. In this paper, we apply k-Nearest
Neighbor cumulative distribution functions, kNN-CDFs, to the
\textsc{quijote-png} simulations to explore the sensitivity of kNN-CDFs to
primordial non-Gaussianity. An interesting result is that for halo samples with
$M_h<10^{14}$ M$_\odot$/h, the kNN-CDFs respond to \textit{equilateral} PNG in
a manner distinct from the other parameters. This persists in the galaxy
catalogs in redshift space and can be differentiated from the impact of galaxy
modelling, at least within the halo occupation distribution (HOD) framework
considered here. kNN-CDFs are related to counts-in-cells and, through mapping a
subset of the kNN-CDF measurements into the count-in-cells picture, we show
that our results can be modeled analytically. A caveat of the analysis is that
we only consider the HOD framework, including assembly bias. It will be
interesting to validate these results with other techniques for modeling the
galaxy--halo connection, e.g., (hybrid) effective field theory or
semi-analytical methods.


  In our work we analyse the political disaffection or "the subjective feeling
of powerlessness, cynicism, and lack of confidence in the political process,
politicians, and democratic institutions, but with no questioning of the
political regime" by exploiting Twitter data through machine learning
techniques. In order to validate the quality of the time-series generated by
the Twitter data, we highlight the relations of these data with political
disaffection as measured by means of public opinion surveys. Moreover, we show
that important political news of Italian newspapers are often correlated with
the highest peaks of the produced time-series.


  Motivated by the work of Nojiri et al. (2019) [S. Nojiri, S. D. Odintsov and
E. N. Saridakis, Holographic inflation, Phys. Lett. B 797 (2019) 134829], the
present study reports a model of inflation under the consideration that the
inflationary regime is originated by a type of holographic energy density. The
infrared cutoff has been selected based on the modified holographic model that
is a particular case of Nojiri-Odintsov holographic dark energy \cite{odi1}
that unifies phantom inflation with the acceleration of the universe on
late-time. On getting an analytical solution for Hubble parameter we considered
the presence of bulk viscosity and the effective equation of state parameter
appeared to be consistent with the inflationary scenario with some constraints.
It has also been observed that in the inflationary scenario the contribution of
bulk viscosity is not of much significance and its influence is increasing with
the evolution of the universe. Inflationary observables have been computed for
the model and the slow-roll parameters have been computed. Finally, it has been
observed that the trajectories in $n_s - r$ are compatible with the
observational bound found by Planck. It has been concluded that the tensor to
scalar ratio for this model can explain the primordial fluctuation in the early
universe as well.


  Dengue is one of the major international public health concerns. Although
progress is underway, developing a vaccine against the disease is challenging.
Thus, the main approach to fight the disease is vector control. A model for the
transmission of Dengue disease is presented. It consists of eight mutually
exclusive compartments representing the human and vector dynamics. It also
includes a control parameter (insecticide) in order to fight the mosquito. The
model presents three possible equilibria: two disease-free equilibria (DFE) and
another endemic equilibrium. It has been proved that a DFE is locally
asymptotically stable, whenever a certain epidemiological threshold, known as
the basic reproduction number, is less than one. We show that if we apply a
minimum level of insecticide, it is possible to maintain the basic reproduction
number below unity. A case study, using data of the outbreak that occurred in
2009 in Cape Verde, is presented.


  The rapid uptake of mobile devices and the rising popularity of mobile
applications and services pose unprecedented demands on mobile and wireless
networking infrastructure. Upcoming 5G systems are evolving to support
exploding mobile traffic volumes, agile management of network resource to
maximize user experience, and extraction of fine-grained real-time analytics.
Fulfilling these tasks is challenging, as mobile environments are increasingly
complex, heterogeneous, and evolving. One potential solution is to resort to
advanced machine learning techniques to help managing the rise in data volumes
and algorithm-driven applications. The recent success of deep learning
underpins new and powerful tools that tackle problems in this space.
  In this paper we bridge the gap between deep learning and mobile and wireless
networking research, by presenting a comprehensive survey of the crossovers
between the two areas. We first briefly introduce essential background and
state-of-the-art in deep learning techniques with potential applications to
networking. We then discuss several techniques and platforms that facilitate
the efficient deployment of deep learning onto mobile systems. Subsequently, we
provide an encyclopedic review of mobile and wireless networking research based
on deep learning, which we categorize by different domains. Drawing from our
experience, we discuss how to tailor deep learning to mobile environments. We
complete this survey by pinpointing current challenges and open future
directions for research.


  Approximate Bayesian Computation (ABC) enables parameter inference for
complex physical systems in cases where the true likelihood function is
unknown, unavailable, or computationally too expensive. It relies on the
forward simulation of mock data and comparison between observed and synthetic
catalogues. Here we present cosmoabc, a Python ABC sampler featuring a
Population Monte Carlo (PMC) variation of the original ABC algorithm, which
uses an adaptive importance sampling scheme. The code is very flexible and can
be easily coupled to an external simulator, while allowing to incorporate
arbitrary distance and prior functions. As an example of practical application,
we coupled cosmoabc with the numcosmo library and demonstrate how it can be
used to estimate posterior probability distributions over cosmological
parameters based on measurements of galaxy clusters number counts without
computing the likelihood function. cosmoabc is published under the GPLv3
license on PyPI and GitHub and documentation is available at
http://goo.gl/SmB8EX


  We detected carbon-chain molecules (CCMs) HC2n+1N (n=1-3) and C3S in Ku band
as well as {high-energy excitation lines including C4H N=9-8, J=17/2-15/2,
19/2-17/2, and CH3CCH J=5-4, K=2 in the 3 mm band toward a starless core called
the eastern molecular core (EMC) of L1489 IRS. Maps of all the observed lines
were also obtained. Comparisons with a number of early starless cores and WCCC
source L1527 show that the column densities of C4H and CH3CCH are close to
those of L1527, and the CH3CCH column densities of the EMC and L1527 are
slightly higher than those of TMC-1. The EMC and L1527 have similar C3S column
densities, but they are much lower than those of all the starless cores, with
only 6.5% and 10% of the TMC-1 value, respectively. The emissions of the
N-bearing species of the EMC and L1527 are at the medium level of the starless
cores. These comparisons show that the CCM emissions in the EMC are similar to
those of L1527, though L1527 contains a protostar. Although dark and quiescent,
the EMC is warmer and at a later evolutionary stage than classical
carbon-chain--producing regions in the cold, dark, quiescent early phase. The
PACS, SPIRE, and SCUBA maps evidently show that the L1489 IRS seems to be the
heating source of the EMC. Although it is located at the margins of the EMC,
its bolometric luminosity and bolometric temperature are relatively high. Above
all, the EMC is a rather particular carbon-chain-producing region and is quite
significant for CCM science.


  We study almost periodic orbits of quantum systems and prove that for
periodic time-dependent Hamiltonians an orbit is almost periodic if, and only
if, it is precompact. In the case of quasiperiodic time-dependence we present
an example of a precompact orbit that is not almost periodic. Finally we
discuss some simple conditions assuring dynamical stability for nonautonomous
quantum system.


  In this review we present a theory of cosmological constant and Dark Energy
(DE), based on the topological structure of the vacuum. The Multiple Point
Principle (MPP) is reviewed. It demonstrates the existence of the two vacua
into the SM. The Froggatt-Nielsen's prediction of the top-quark and Higgs
masses is given in the assumption that there exist two degenerate vacua in the
SM. This prediction was improved by the next order calculations. We also
considered B.G. Sidharth's theory of cosmological constant based on the
non-commutative geometry of the Planck scale space-time, what gives an
extremely small DE density providing the accelerating expansion of the
Universe. Theory of two degenerate vacua - the Planck scale phase and
Electroweak (EW) phase - also is reviewed, topological defects in these vacua
are investigated, also the Compton wavelength phase suggested by B.G. Sidharth
was discussed. A general theory of the phase transition and the problem of the
vacuum stability in the SM is reviewed. Assuming that the recently discovered
at the LHC new resonance with mass $m_S \simeq 750$ GeV is a new scalar $S$
bound state $6t + 6\bar t$, earlier predicted by C.D. Froggatt, H.B. Nielsen
and L.V. Laperashvili, we try to provide the vacuum stability in the SM and
exact accuracy of the MPP.


  The counter-intuitive phenomenon of pressure-induced softening in materials
is likely to be caused by the same dynamical behaviour that produces negative
thermal expansion. Through a combination of molecular dynamics simulation on an
idealised model and neutron diffraction at variable temperature and pressure,
we show the existence of extraordinary and unprecedented pressure-induced
softening in the negative thermal expansion material scandium fluoride,
ScF$_3$, with values of the pressure-derivative of the bulk modulus $B$,
$B^\prime = \partial B / \partial P$, reaching as low as $-40 \pm 1$.


  Pulsed Bessel beams of light propagating in free-space experience diffraction
effects that resemble those of anomalous dispersion on pulse propagation. It is
then shown that a pulsed Bessel beam in a normally dispersive material can
remain diffraction- and dispersion-free due to mutual cancellation of
diffraction and group velocity dispersion. The size of the Bessel transversal
profile for localized transmission is determined by the dispersive properties
of the material at the pulse carrier frequency.


  Elliott and Kucerovsky stated that a non-unital extension of separable
$C^\ast$-algebras with a stable ideal, is nuclearly absorbing if and only if
the extension is purely large. However, their proof was flawed. We give a
counter example to their theorem as stated, but establish an equivalent
formulation of nuclear absorption under a very mild additional assumption to
being purely large. In particular, if the quotient algebra is non-unital, then
we show that the original theorem applies. We also examine how this effects
results in classification theory.


  This paper presents the setup for empirical validations of the Pair-based
Analytical model for Segmented Telescope Imaging from Space (PASTIS)
tolerancing model for segmented coronagraphy. We show the hardware
configuration of the High-contrast imager for Complex Aperture Telescopes
(HiCAT) testbed on which these experiments will be conducted at an intermediate
contrast regime between $10^{-6}$ and $10^{-8}$. We describe the optical
performance of the testbed with a classical Lyot coronagraph and describe the
recent hardware upgrade to a segmented mode, using an IrisAO segmented
deformable mirror. Implementing experiments on HiCAT is made easy through its
top-level control infrastructure that uses the same code base to run on the
real testbed, or to invoke the optical simulator. The experiments presented in
this paper are run on the HiCAT testbed emulator, which makes them ready to be
performed on actual hardware. We show results of three experiments with results
from the emulator, with the goal to demonstrate PASTIS on hardware next. We
measure the testbed PASTIS matrix, and validate the PASTIS analytical
propagation model by comparing its contrast predictions to simulator results.
We perform the tolerancing analysis on the optical eigenmodes (PASTIS modes)
and on independent segments, then validate these results in respective
experiments. This work prepares and enables the experimental validation of the
analytical segment-based tolerancing model for segmented aperture coronagraphy
with the specific application to the HiCAT testbed.


  A pump coupled to a conserved density generates long-range modulations,
resulting from the non-equilibrium nature of the dynamics. We study how these
modulations are modified at the critical point where the system exhibits
intrinsic long-range correlations. To do so, we consider a pump in a diffusive
fluid, which is known to generate a density profile in the form of an electric
dipole potential and a current in the form of a dipolar field above the
critical point. We demonstrate that while the current retains its form at the
critical point, the density profile changes drastically. At criticality, in
$d<4$ dimensions, the deviation of the density from the average is given by
${\rm sgn}(\cos(\theta))|\cos({\theta})/r^{(d-1)}|^{1/\delta}$ at large
distance $r$ from the pump and angle $\theta$ with respect to the pump's
orientation. At short distances, there is a crossover to a
$\cos({\theta})/r^{d-3+\eta}$ profile. Here $\delta$ and $\eta$ are Ising
critical exponents. The effect of the local pump on the domain wall structure
below the critical point is also considered.


  A number of micro-scale biological flows are characterized by spatio-temporal
chaos. These include dense suspensions of swimming bacteria, microtubule
bundles driven by motor proteins, and dividing and migrating confluent layers
of cells. A characteristic common to all of these systems is that they are
laden with active matter, which transforms free energy in the fluid into
kinetic energy. Because of collective effects, the active matter induces
multi-scale flow motions that bear strong visual resemblance to turbulence. In
this study, multi-scale statistical tools are employed to analyze direct
numerical simulations (DNS) of periodic two- (2D) and three-dimensional (3D)
active flows and compare them to classic turbulent flows. Statistical
descriptions of the flows and their variations with activity levels are
provided in physical and spectral spaces. A scale-dependent intermittency
analysis is performed using wavelets. The results demonstrate fundamental
differences between active and high-Reynolds number turbulence; for instance,
the intermittency is smaller and less energetic in active flows, and the work
of the active stress is spectrally exerted near the integral scales and
dissipated mostly locally by viscosity, with convection playing a minor role in
momentum transport across scales.


  We obtain explicit expressions for the thermoelectric transport coefficients
of a strongly coupled, planar medium in the presence of an orthogonal magnetic
field and momentum-dissipating processes. The computations are performed within
the gauge/gravity framework where the momentum dissipation mechanism is
introduced by including a mass term for the bulk graviton. Relying on the
structure of the computed transport coefficients and promoting the parameters
to become dynamical functions, we propose a holography inspired phenomenology
open to a direct comparison with experimental data from the cuprates.


  Supersonic turbulent motions are the remarkable properties of interstellar
medium. Previous numerical simulations have demonstrated that the thermal
instability in a shock-compressed layer produces the supersonic turbulent
motion that does not decay. In this paper we focus on two- and
three-dimensional numerical simulations of the non-linear development of simple
thermal instability incorporating physical viscosity but without any external
forcing, in order to isolate the effects of various processes responsible for
the long-lasting turbulent motion. As the initial condition for our
simulations, we set up spatially uniform gas with thermally unstable
temperature in a box with periodic boundaries. After the linear growth stage of
the thermal instability, two-phase medium forms where cold clumps are embedded
in warm medium, and turbulent fluid flow clearly visible as translational
motions of the cold clumps does not decay in a viscous dissipation timescale.
The amplitude of the turbulent velocity increases when we reduce the Prandtl
number that is the non-dimensional ratio of kinetic viscosity to thermal
conduction: the saturation amplitude does not change when we increase the
viscosity and thermal conduction coefficients simultaneously in order to keep
the Prandtl number. This shows that the thermal conduction plays an important
role in maintaining turbulent motions against viscous dissipation. The
amplitude also increases when we increase the ratio of the computational domain
length $L$ to the cooling length $\lambda_{\rm c}$ that is defined by the
product of the cooling time and the sound speed, as long as $L \la 100
\lambda_{\rm c}$.


  Soft, porous mechanical metamaterials exhibit pattern transformations that
may have important applications in soft robotics, sound reduction and
biomedicine. To design these innovative materials, it is important to be able
to simulate them accurately and quickly, in order to tune their mechanical
properties. Since conventional simulations using the finite element method
entail a high computational cost, in this article we aim to develop a machine
learning-based approach that scales favorably to serve as a surrogate model. To
ensure that the model is also able to handle various microstructures, including
those not encountered during training, we include the microstructure as part of
the network input. Therefore, we introduce a graph neural network that predicts
global quantities (energy, stress stiffness) as well as the pattern
transformations that occur (the kinematics). To make our model as accurate and
data-efficient as possible, various symmetries are incorporated into the model.
The starting point is an E(n)-equivariant graph neural network (which respects
translation, rotation and reflection) that has periodic boundary conditions
(i.e., it is in-/equivariant with respect to the choice of RVE), is scale
in-/equivariant, can simulate large deformations, and can predict scalars,
vectors as well as second and fourth order tensors (specifically energy, stress
and stiffness). The incorporation of scale equivariance makes the model
equivariant with respect to the similarities group, of which the Euclidean
group E(n) is a subgroup. We show that this network is more accurate and
data-efficient than graph neural networks with fewer symmetries. To create an
efficient graph representation of the finite element discretization, we use
only the internal geometrical hole boundaries from the finite element mesh to
achieve a better speed-up and scaling with the mesh size.


  In Randall-Sundrum single-brane (RS-II) model, it was conjectured that there
is no static large black hole localized on the brane based on adS/CFT
correspondence. Here we consider the phase diagram of black objects in the
models extended from the RS-II model. We propose a scenario for the phase
diagram consistent with the classical black hole evaporation conjecture. The
proposed scenario indicates the existence of a rich variety of the families of
black objects.


  Transfer learning plays a key role in advancing machine learning models, yet
conventional supervised pretraining often undermines feature transferability by
prioritizing features that minimize the pretraining loss. In this work, we
adapt a self-supervised learning regularization technique from the VICReg
method to supervised learning contexts, introducing Variance-Covariance
Regularization (VCReg). This adaptation encourages the network to learn
high-variance, low-covariance representations, promoting learning more diverse
features. We outline best practices for an efficient implementation of our
framework, including applying it to the intermediate representations. Through
extensive empirical evaluation, we demonstrate that our method significantly
enhances transfer learning for images and videos, achieving state-of-the-art
performance across numerous tasks and datasets. VCReg also improves performance
in scenarios like long-tail learning and hierarchical classification.
Additionally, we show its effectiveness may stem from its success in addressing
challenges like gradient starvation and neural collapse. In summary, VCReg
offers a universally applicable regularization framework that significantly
advances transfer learning and highlights the connection between gradient
starvation, neural collapse, and feature transferability.


  We present a hardware agnostic error mitigation algorithm for near term
quantum processors inspired by the classical Lanczos method. This technique can
reduce the impact of different sources of noise at the sole cost of an increase
in the number of measurements to be performed on the target quantum circuit,
without additional experimental overhead. We demonstrate through numerical
simulations and experiments on IBM Quantum hardware that the proposed scheme
significantly increases the accuracy of cost functions evaluations within the
framework of variational quantum algorithms, thus leading to improved
ground-state calculations for quantum chemistry and physics problems beyond
state-of-the-art results.


  For any polynomial ideal $I$, let the minimal triangular set contained in the
reduced Buchberger-Gr\"obner basis of $I$ with respect to the purely
lexicographical term order be called the W-characteristic set of $I$. In this
paper, we establish a strong connection between Ritt's characteristic sets and
Buchberger's Gr\"obner bases of polynomial ideals by showing that the
W-characteristic set $C$ of $I$ is a Ritt characteristic set of $I$ whenever
$C$ is an ascending set, and a Ritt characteristic set of $I$ can always be
computed from $C$ with simple pseudo-division when $C$ is regular. We also
prove that under certain variable ordering, either the W-characteristic set of
$I$ is normal, or irregularity occurs for the $j$th, but not the $(j+1)$th,
elimination ideal of $I$ for some $j$. In the latter case, we provide explicit
pseudo-divisibility relations, which lead to nontrivial factorizations of
certain polynomials in the Buchberger-Gr\"obner basis and thus reveal the
structure of such polynomials. The pseudo-divisibility relations may be used to
devise an algorithm to decompose arbitrary polynomial sets into normal
triangular sets based on Buchberger-Gr\"obner bases computation.


  We explore the effect of quantum gravity on matter within a Renormalization
Group framework. First, our results provide an explicit example of how
misleading conclusions can be drawn by analyzing the gravitational
contributions to beta functions, instead of analyzing universal quantities,
such as critical exponents, that can be extracted from the beta functions. This
could be key to explain differences between perturbative studies and Functional
Renormalization Group studies. Second, we strengthen the evidence that
asymptotically safe gravity could generate a predictive ultraviolet completion
for matter theories with gauge interactions, even in the limit of vanishing
dimensionful regulator function. We also find that the situation can be more
subtle with higher-order, gravity-induced matter interactions.


  Two major uncertainties, dataset bias and adversarial examples, prevail in
state-of-the-art AI algorithms with deep neural networks. In this paper, we
present an intuitive explanation for these issues as well as an interpretation
of the performance of deep networks in a natural-image space. The explanation
consists of two parts: the philosophy of neural networks and a hypothetical
model of natural-image spaces. Following the explanation, we 1) demonstrate
that the values of training samples differ, 2) provide incremental boost to the
accuracy of a CIFAR-10 classifier by introducing an additional "random-noise"
category during training, 3) alleviate over-fitting thereby enhancing the
robustness against adversarial examples by detecting and excluding illusive
training samples that are consistently misclassified. Our overall contribution
is therefore twofold. First, while most existing algorithms treat data equally
and have a strong appetite for more data, we demonstrate in contrast that an
individual datum can sometimes have disproportionate and counterproductive
influence and that it is not always better to train neural networks with more
data. Next, we consider more thoughtful strategies by taking into account the
geometric and topological properties of natural-image spaces to which deep
networks are applied.


  The nuclear spin-lattice relaxation rate ($1/T_{1}$) has been measured in the
single crystals of hole doped two-leg ladder compounds
Sr$_{14-x}$Ca$_{x}$Cu$_{24}$O$_{41}$ and in the undoped parent material
La$_6$Ca$_8$Cu$_{24}$O$_{41}$. Comparison of $1/T_{1}$ at the Cu and the two
distinct oxygen sites revealed that the major spectral weight of low frequency
spin fluctuations is located near $q \sim (\pi, \pi)$ for most of the
temperature and doping ranges investigated. Remarkable difference in the
temperature dependence of $1/T_1$ for the two oxygen sites in the heavily doped
$x$=12 sample revealed reduction of singlet correlations between two legs in
place of growing antiferromagnetic correlations along the leg direction with
increasing temperature. Such behavior is most likely caused by the dissociation
of bound hole pairs.


  Recent advances in language modeling using deep neural networks have shown
that these models learn representations, that vary with the network depth from
morphology to semantic relationships like co-reference. We apply pre-trained
language models to low-resource named entity recognition for Historic German.
We show on a series of experiments that character-based pre-trained language
models do not run into trouble when faced with low-resource datasets. Our
pre-trained character-based language models improve upon classical CRF-based
methods and previous work on Bi-LSTMs by boosting F1 score performance by up to
6%. Our pre-trained language and NER models are publicly available under
https://github.com/stefan-it/historic-ner .


  Athletics are a quintessential and universal expression of humanity. From
French monks who in the 12th century invented jeu de paume, the precursor to
modern lawn tennis, back to the K'iche' people who played the Maya Ballgame as
a form of religious expression over three thousand years ago, humans have
sought to train their minds and bodies to excel in sporting contests. Advances
in robotics are opening up the possibility of robots in sports. Yet, key
challenges remain, as most prior works in robotics for sports are limited to
pristine sensing environments, do not require significant force generation, or
are on miniaturized scales unsuited for joint human-robot play. In this paper,
we propose the first open-source, autonomous robot for playing regulation
wheelchair tennis. We demonstrate the performance of our full-stack system in
executing ground strokes and evaluate each of the system's hardware and
software components. The goal of this paper is to (1) inspire more research in
human-scale robot athletics and (2) establish the first baseline for a
reproducible wheelchair tennis robot for regulation singles play. Our paper
contributes to the science of systems design and poses a set of key challenges
for the robotics community to address in striving towards robots that can match
human capabilities in sports.


  The transport sector is a major contributor to greenhouse gas emissions in
Europe. Shifting to electric vehicles (EVs) powered by a low-carbon energy mix
would reduce carbon emissions. However, to support the development of electric
mobility, a better understanding of EV charging behaviours and more accurate
forecasting models are needed. To fill that gap, the Smarter Mobility Data
Challenge has focused on the development of forecasting models to predict EV
charging station occupancy. This challenge involved analysing a dataset of 91
charging stations across four geographical areas over seven months in
2020-2021. The forecasts were evaluated at three levels of aggregation
(individual stations, areas and global) to capture the inherent hierarchical
structure of the data. The results highlight the potential of hierarchical
forecasting approaches to accurately predict EV charging station occupancy,
providing valuable insights for energy providers and EV users alike. This open
dataset addresses many real-world challenges associated with time series, such
as missing values, non-stationarity and spatio-temporal correlations. Access to
the dataset, code and benchmarks are available at
https://gitlab.com/smarter-mobility-data-challenge/tutorials to foster future
research.


  Combinatorial neural codes are $0/1$ vectors that are used to model the
co-firing patterns of a set of place cells in the brain. One wide-open problem
in this area is to determine when a given code can be algorithmically drawn in
the plane as a Venn diagram-like figure. A sufficient condition to do so is for
the code to have a property called $k$-inductively pierced. Gross, Obatake, and
Youngs recently used toric algebra to show that a code on three neurons is
$1$-inductively pierced if and only if the toric ideal is trivial or generated
by quadratics. No result is known for additional neurons in the same
generality, part of the difficulty coming from the large number of codewords
possible when additional neurons are used.
  In this article, we study two infinite classes of combinatorial neural codes
in detail. For each code, we explicitly compute its universal Gr\"obner basis.
This is done for the first class by recognizing that the codewords form a
Lawrence-type matrix. With the second class, this is done by showing that the
matrix is totally unimodular. These computations allow one to compute the state
polytopes of the corresponding toric ideals, from which all distinct initial
ideals may be computed efficiently. Moreover, we show that the state polytopes
are combinatorially equivalent to well-known polytopes: the permutohedron and
the stellohedron.


  Phase-change memory (PCM) is a promising non-volatile solid-state memory
technology. A PCM cell stores data by using its amorphous and crystalline
states. The cell changes between these two states using high temperature.
However, since the cells are sensitive to high temperature, it is important,
when programming cells, to balance the heat both in time and space.
  In this paper, we study the time-space constraint for PCM, which was
originally proposed by Jiang et al. A code is called an
\emph{$(\alpha,\beta,p)$-constrained code} if for any $\alpha$ consecutive
rewrites and for any segment of $\beta$ contiguous cells, the total rewrite
cost of the $\beta$ cells over those $\alpha$ rewrites is at most $p$. Here,
the cells are binary and the rewrite cost is defined to be the Hamming distance
between the current and next memory states. First, we show a general upper
bound on the achievable rate of these codes which extends the results of Jiang
et al. Then, we generalize their construction for $(\alpha\geq 1,
\beta=1,p=1)$-constrained codes and show another construction for $(\alpha = 1,
\beta\geq 1,p\geq1)$-constrained codes. Finally, we show that these two
constructions can be used to construct codes for all values of $\alpha$,
$\beta$, and $p$.


  Tails used as inertial appendages induce body rotations of animals and
robots, a phenomenon that is governed largely by the ratio of the body and tail
moments of inertia. However, vertebrate tails have more degrees of freedom
(e.g., number of joints, rotational axes) than most current theoretical models
and robotic tails. To understand how morphology affects inertial appendage
function, we developed an optimization-based approach that finds the maximally
effective tail trajectory and measures error from a target trajectory. For
tails of equal total length and mass, increasing the number of equal-length
joints increased the complexity of maximally effective tail motions. When we
optimized the relative lengths of tail bones while keeping the total tail
length, mass, and number of joints the same, this optimization-based approach
found that the lengths match the pattern found in the tail bones of mammals
specialized for inertial maneuvering. In both experiments, adding joints
enhanced the performance of the inertial appendage, but with diminishing
returns, largely due to the total control effort constraint. This
optimization-based simulation can compare the maximum performance of diverse
inertial appendages that dynamically vary in moment of inertia in 3D space,
predict inertial capabilities from skeletal data, and inform the design of
robotic inertial appendages.


  Molecular motor proteins form the basis of cellular dynamics. Recently,
notable efforts have led to the creation of their DNA-based mimics, which can
carry out complex nanoscale motion. However, such functional analogues have not
yet been integrated or operated inside synthetic cells toward the goal of
realizing artificial biological systems entirely from the bottom-up. In this
Letter, we encapsulate and actuate DNA-assembled dynamic nanostructures inside
cell-sized microfluidic compartments. These encapsulated DNA nanostructures not
only exhibit structural reconfigurability owing to their pH-sensitive molecular
switches upon external stimuli but also possess optical feedback enabled by the
integrated plasmonic probes. In particular, we demonstrate the power of
microfluidic compartmentalization for achieving on-chip plasmonic enantiomer
separation and substrate filtration. Our work exemplifies that the two unique
tools, droplet-based microfluidics and DNA technology, offering high precision
on the microscale and nanoscale, respectively, can be brought together to
greatly enrich the complexity and diversity of functional synthetic systems.


  In the framework of Thomas-Fermi approximation, we study systematically the
EOSs and microscopic structures of neutron star matter in a vast density range
with $n_\mathrm{b}\approx 10^{-10}$-2 $\mathrm{fm}^{-3}$, where various
covariant density functionals are adopted, i.e., those with nonlinear self
couplings (NL3, PK1, TM1, GM1, MTVTC) and density-dependent couplings (DD-LZ1,
DDME-X, PKDD, DD-ME2, DD2, TW99). It is found that the EOSs generally coincide
with each other at $n_\mathrm{b}\lesssim 10^{-4}$ fm${}^{-3}$ and 0.1
fm${}^{-3}\lesssim n_\mathrm{b} \lesssim 0.3$ fm${}^{-3}$, while in other
density regions they are sensitive to the effective interactions between
nucleons. By adopting functionals with larger slope of symmetry energy $L$, the
curvature parameter $K_\mathrm{sym}$ and neutron drip density generally
increase, while the droplet size, proton number of nucleus, core-crust
transition density, and onset density of non-spherical nuclei decrease. All
functionals predict neutron stars with maximum masses exceeding the
two-solar-mass limit, while those of DD2, DD-LZ1, DD-ME2, and DDME-X predict
optimum neutron star radii according to the observational constraints.
Nevertheless, the corresponding skewness coefficients $J$ are much lager than
expected, while only the functionals MTVTC and TW99 meet the start-of-art
constraints on $J$. More accurate measurements on the radius of PSR J0740+6620
and the maximum mass of neutron stars are thus essential to identify the
functional that satisfies all constraints from nuclear physics and
astrophysical observations. Approximate linear correlations between neutron
stars' radii at $M=1.4 M_{\odot}$ and $2 M_{\odot}$, the slope $L$ and
curvature parameter $K_\mathrm{sym}$ of symmetry energy are observed as well,
which is mainly attributed to the curvature-slope correlations in the
functionals adopted here.


  Generically, massive gravity gives a non-unique gravitational field around a
star. For a special family of massive gravity theories, we show that the
stellar gravitational field is unique and observationally acceptable, that is
close to Einsteinian. The black hole solutions in this family of theories are
also studied and shown to be peculiar. Black holes have a near-horizon throat
and the curvature diverging at the horizon. We show that there exists a
sub-family of these massive gravity theories with non-singular at horizon black
holes.


  Recent work shows issues of consistency with explanations, with methods
generating local explanations that seem reasonable instance-wise, but that are
inconsistent across instances. This suggests not only that instance-wise
explanations can be unreliable, but mainly that, when interacting with a system
via multiple inputs, a user may actually lose confidence in the system. To
better analyse this issue, in this work we treat explanations as objects that
can be subject to reasoning and present a formal model of the interactive
scenario between user and system, via sequences of inputs, outputs, and
explanations. We argue that explanations can be thought of as committing to
some model behaviour (even if only prima facie), suggesting a form of
entailment, which, we argue, should be thought of as non-monotonic. This
allows: 1) to solve some considered inconsistencies in explanation, such as via
a specificity relation; 2) to consider properties from the non-monotonic
reasoning literature and discuss their desirability, gaining more insight on
the interactive explanation scenario.


  Using an artificial mouth with an accurate pressure control, the onset of the
pressure oscillations inside the mouthpiece of a simplified clarinet is studied
experimentally. Two time profiles are used for the blowing pressure: in a first
set of experiments the pressure is increased at constant rates, then decreased
at the same rate. In a second set of experiments the pressure rises at a
constant rate and is then kept constant for an arbitrary period of time. In
both cases the experiments are repeated for different increase rates. Numerical
simulations using a simplified clarinet model blown with a constantly
increasing mouth pressure are compared to the oscillating pressure obtained
inside the mouthpiece. Both show that the beginning of the oscillations appears
at a higher pressure values than the theoretical static threshold pressure, a
manifestation of bifurcation delay. Experiments performed using an interrupted
increase in mouth pressure show that the beginning of the oscillation occurs
close to the stop in the increase of the pressure. Experimental results also
highlight that the speed of the onset transient of the sound is roughly the
same, independently of the duration of the increase phase of the blowing
pressure.


  We describe the Dragonfly Telephoto Array, a robotic imaging system optimized
for the detection of extended ultra low surface brightness structures. The
array consists of eight Canon 400mm f/2.8 telephoto lenses coupled to eight
science-grade commercial CCD cameras. The lenses are mounted on a common
framework and are co-aligned to image simultaneously the same position on the
sky. The system provides an imaging capability equivalent to a 0.4m aperture
f/1.0 refractor with a 2.6 deg X 1.9 deg field of view. The system has no
obstructions in the light path, optimized baffling, and internal optical
surfaces coated with a new generation of anti-reflection coatings based on
sub-wavelength nanostructures. As a result, the array's point spread function
has a factor of ~10 less scattered light at large radii than well-baffled
reflecting telescopes. The Dragonfly Telephoto Array is capable of imaging
extended structures to surface brightness levels below 30 mag/arcsec^2 in 10h
integrations (without binning or foreground star removal). This is considerably
deeper than the surface brightness limit of any existing wide-field telescope.
At present no systematic errors limiting the usefulness of much longer
integration times has been identified. With longer integrations (50-100h),
foreground star removal and modest binning the Dragonfly Telephoto Array is
capable of probing structures with surface brightnesses below 32 mag/arcsec^2.
Detection of structures at these surface brightness levels may hold the key to
solving the "missing substructure" and "missing satellite" problems of
conventional hierarchical galaxy formation models. The Dragonfly Telephoto
Array is therefore executing a fully-automated multi-year imaging survey of a
complete sample of nearby galaxies in order to undertake the first census of
ultra-faint substructures in the nearby Universe.


  Most existing masked audio modeling (MAM) methods learn audio representations
by masking and reconstructing local spectrogram patches. However, the
reconstruction loss mainly accounts for the signal-level quality of the
reconstructed spectrogram and is still limited in extracting high-level audio
semantics. In this paper, we propose to enhance the semantic modeling of MAM by
distilling cross-modality knowledge from contrastive language-audio pretraining
(CLAP) representations for both masked and unmasked regions (MAM-CLAP) and
leveraging a multi-objective learning strategy with a supervised classification
branch (SupMAM), thereby providing more semantic knowledge for MAM and enabling
it to effectively learn global features from labels. Experiments show that our
methods significantly improve the performance on multiple downstream tasks.
Furthermore, by combining our MAM-CLAP with SupMAM, we can achieve new
state-of-the-art results on various audio and speech classification tasks,
exceeding previous self-supervised learning and supervised pretraining methods.


  We study the properties of polyelectrolyte chains under different solvent
conditions, using a variational technique. The free energy and the
conformational properties of a polyelectrolyte chain are studied minimizing the
free energy $F_N$, depending on $N(N-1)/2$ trial probabilities that
characterize the conformation of the chain. The Gaussian approximation is
considered for a ring of length $2^4<N<2^{16}$ and for an open chain of length
$2^4<N<2^9$ in poor and theta solvent conditions, including a Coulomb repulsion
between the monomers. In theta solvent conditions the blob size is measured and
found in agreement with scaling theory, including charge depletion effects,
expected for the case of an open chain. In poor solvent conditions, a globule
instability, driven by electrostatic repulsion, is observed. We notice also
inhomogeneous behavior of the monomer--monomer correlation function,
reminiscence of necklace formation in poor solvent polyelectrolyte solutions. A
global phase diagram in terms of solvent quality and inverse Bjerrum length is
presented.


  The nuclear matrix elements $M^{0\nu}$ of the neutrinoless double beta decay
($0\nu\beta\beta$) of most nuclei with known $2\nu\beta\beta$-decay rates are
systematically evaluated using the Quasiparticle Random Phase Approximation
(QRPA) and Renormalized QRPA (RQRPA). The experimental $2\nu\beta\beta$-decay
rate is used to adjust the most relevant parameter, the strength of the
particle-particle interaction. With such procedure the $M^{0\nu}$ values become
essentially independent on single-particle basis size, the axial vector
quenching factor, etc. Theoretical arguments in favor of the adopted way of
determining the interaction parameters are presented. It is suggested that most
of the spread among the published $M^{0\nu}$'s can be ascribed to the choices
of implicit and explicit parameters, inherent to the QRPA method.


  We use radial velocity observations to search for long-period gas giant
companions in systems hosting inner super-Earth (1-4 R_Earth, 1-10 M_Earth)
planets to constrain formation and migration scenarios for this population. We
consistently re-fit published RV datasets for 65 stars and find 9 systems with
statistically significant trends indicating the presence of an outer companion.
We combine these RV data with AO images to constrain the masses and semi-major
axes of these companions. We quantify our sensitivity to the presence of
long-period companions by fitting the sample with a power law distribution and
find an occurrence rate of 39+/-7% for companions 0.5-20 M_Jup and 1-20 AU.
Half of our systems were discovered by the transit method and half were
discovered by the RV method. While differences in RV baselines and number of
data points between the two samples lead to different sensitivities to distant
companions, we find that occurrence rates of gas giant companions in each
sample are consistent at the 0.5$\sigma$ level. We compare the frequency of
Jupiter analogs in these systems to the equivalent rate from field star surveys
and find that Jupiter analogs are more common around stars hosting
super-Earths. We conclude that the presence of outer gas giants does not
suppress the formation of inner super-Earths, and that these two populations of
planets instead appear to be correlated. We also find that the stellar
metallicities of systems with gas giant companions are higher than those
without companions, in agreement with the well-established metallicity
correlation from RV surveys of field stars.


  Focused ion beam (FIB) techniques are commonly used to machine, analyse and
image materials at the micro- and nanoscale. However, FIB modifies the
integrity of the sample by creating defects that cause lattice distortions.
Methods have been developed to reduce FIB-induced strain, however these
protocols need to be evaluated for their effectiveness. Here we use
non-destructive Bragg coherent X-ray diffraction imaging to study the in situ
annealing of FIB-milled gold microcrystals. We simultaneously measure two
non-collinear reflections for two different crystals during a single annealing
cycle, demonstrating the ability to reliably track the location of multiple
Bragg peaks during thermal annealing. The thermal lattice expansion of each
crystal is used to calculate the local temperature. This is compared to
thermocouple readings, which are shown to be substantially affected by thermal
resistance. To evaluate the annealing process, we analyse each reflection by
considering facet area evolution, cross-correlation maps of displacement field
and binarised morphology, and average strain plots. The crystal's strain and
morphology evolve with increasing temperature, which is likely to be caused by
the diffusion of gallium in gold below ~280{\deg}C and the self-diffusion of
gold above ~280{\deg}C. The majority of FIB-induced strains are removed by
380-410\degree C, depending on which reflection is being considered. Our
observations highlight the importance of measuring multiple reflections to
unambiguously interpret material behaviour.


  Single flux density measurements at observed-frame sub-millimeter and
millimeter wavelengths are commonly used to probe dust and gas masses in
galaxies. In this Letter, we explore the robustness of this method to infer
dust mass, focusing on quiescent galaxies, using a series of controlled
experiments on four massive haloes from the Feedback in Realistic Environments
(FIRE) project. Our starting point is four star-forming, central galaxies at
seven redshifts between z=1.5 and z=4.5. We generate modified quiescent
galaxies that have been quenched for 100Myr, 500Myr, or 1Gyr prior to each of
the studied redshifts by re-assigning stellar ages. We derive spectral energy
distributions for each fiducial and modified galaxy using radiative transfer.
We demonstrate that the dust mass inferred is highly dependent on the assumed
dust temperature, T_dust, which is often unconstrained observationally.
Motivated by recent work on quiescent galaxies that assumed T_dust~25K, we show
that the ratio between dust mass and 1.3mm flux density can be higher than
inferred by up to an order of magnitude, due to the considerably lower dust
temperatures seen in non star-forming galaxies. This can lead to an
underestimation of dust mass (and, when sub-mm flux density is used as a proxy
for molecular gas content, gas mass). This underestimation is most severe at
higher redshifts, where the observed-frame 1.3mm flux density probes rest-frame
wavelengths far from the Rayleigh-Jeans regime, and hence depends
super-linearly on dust temperature. We fit relations between ratios of
rest-frame far-infrared flux densities and mass-weighted dust temperature that
can be used to constrain dust temperatures from observations and hence derive
more reliable dust and molecular gas masses.


  As is well known, the fluctuations from a stable stationary nonequilibrium
state are described by a linearized nonhomogeneous Boltzmann-Langevin equation.
The stationary state itself may be described by a nonlinear Boltzmann equation.
The ways of its linearization sometimes seem to be not unique. We argue that
there is actually a unique way to obtain a linear equation for the
fluctuations. In the present paper we treat as an example an analytical theory
of nonequilibrium shot noise in a diffusive conductor under the space charge
limited regime. Our approach is compared with that of Schomerus, Mishchenko and
Beenakker [Phys. Rev. B 60, 5839 (1999)]. We find some difference between the
present theory and the approach of their paper and discuss a possible origin of
the difference. We believe that it is related to the fundamentals of the theory
of fluctuation phenomena in a nonequilibrium electron gas.


  We prove quantitative unique continuation results for the semiclassical
Schrodinger operator on smooth, compact domains. These take the form of
exponentially decreasing (in h) local L^{2} lower bounds for exponentially
precise quasimodes. We also show that these lower bounds are sharp in h, and
that, moreover, the hypothesized quasimode accuracy is also sharp.


  We prove that the energy gap of the model proposed by Zhang, Ahmadain, and
Klich [1] is exponentially small in the square of the system size. In [2] a
class of exactly solvable quantum spin chain models was proposed that have
integer spins ($s$), with a nearest neighbors Hamiltonian, and a unique ground
state. The ground state can be seen as a uniform superposition of all
$s-$colored Motzkin walks. The half-chain entanglement entropy provably
violates the area law by a square root factor in the system's size
($\sim\sqrt{n}$) for $s>1$. For $s=1$, the violation is logarithmic [3].
Moreover in [2] it was proved that the gap vanishes polynomially and is
$O(n^{-c})$ with $c\ge2$.
  Recently, a deformation of [2], which we call "weighted Motzkin quantum spin
chain" was proposed [1]. This model has a unique ground state that is a
superposition of the $s-$colored Motzkin walks weighted by
$t^{\text{area\{Motzkin walk\}}}$ with $t>1$. The most surprising feature of
this model is that it violates the area law by a factor of $n$. Here we prove
that the gap of this model is upper bounded by $8ns\text{ }t^{-n^{2}/3}$ for
$t>1$.


  With changing climatic conditions, we are already seeing an increase in
extreme weather events and their secondary consequences, including landslides.
Landslides threaten infrastructure, including roads, railways, buildings, and
human life. Hazard-based spatial planning and early warning systems are
cost-effective strategies to reduce the risk to society from landslides.
However, these both rely on data from previous landslide events, which is often
scarce. Many deep learning (DL) models have recently been applied for landside
mapping using medium- to high-resolution satellite images as input. However,
they often suffer from sensitivity problems, overfitting, and low mapping
accuracy. This study addresses some of these limitations by using a diverse
global landslide dataset, using different segmentation models, such as Unet,
Linknet, PSP-Net, PAN, and DeepLab and based on their performances, building an
ensemble model. The ensemble model achieved the highest F1-score (0.69) when
combining both Sentinel-1 and Sentinel-2 bands, with the highest average
improvement of 6.87 % when the ensemble size was 20. On the other hand,
Sentinel-2 bands only performed very well, with an F1 score of 0.61 when the
ensemble size is 20 with an improvement of 14.59 % when the ensemble size is
20. This result shows considerable potential in building a robust and reliable
monitoring system based on changes in vegetation index dNDVI only.


  We investigate the appearance of mobility edges in a one-dimensional
non-Hermitian tight-banding model with alternating hopping constants and slowly
varying quasi-periodic on-site potentials. Due to the presence of slowly
varying exponent, the parity-time (PT) symmetry of this model is broken and its
spectra is complex. It is found that the spectrum of this model can be divided
into three different types of patterns depending on the magnitude of the
quasi-periodic potential. As the amplitude of the potential increases from
small to large, the initially well defined mobility edges become blurred
gradually and then eventually disappear for large enough potential. This
behavior of the mobility edges is also confirmed by a detailed study of the
winding number of the complex spectra of this non-Hermitian model.


  In this paper, we present an eleven invariant isotropic irreducible function
basis of a third order three-dimensional symmetric tensor. This irreducible
function basis is a proper subset of the Olive-Auffray minimal isotropic
integrity basis of that tensor. The octic invariant and a sextic invariant in
the Olive-Auffray integrity basis are dropped out. This result is of
significance to the further research of irreducible function bases of higher
order tensors.


  The second in a two-part series, this paper extends the 3rd-order Spectral
Representation Method for simulation of ergodic multi-variate stochastic
processes according to a prescribed cross power spectral density and cross
bispectral density. The 2nd and 3rd order ensemble properties of the simulated
stochastic vector processes are shown to satisfy the target cross correlation
properties in expectation. A multi-indexed frequency discretization is
introduced to ensure ergodicity of the sample functions. This is first shown
for uni-variate processes and then the simulation formula for multi-variate
processes is provided. Ensemble properties and ergodicity of the sample
functions are proven. Additionally, it is shown that the simulations can be
implemented efficiently with the Fast Fourier Transform, which greatly reduces
computational effort. An example involving the simulation of turbulent wind
velocity fluctuations is presented to further highlight the features and
applications of the algorithm.


  We report specific heat, resistivity and susceptibility measurements at
different temperatures, magnetic fields, and pressures to provide solid
evidence of CoS2 being a marginal Fermi liquid. The presence of a tricritical
point in the phase diagram of the system provides an opportunity to test the
spin fluctuation theory with a high limit of accuracy. A magnetic field
suppresses the amplitude of the spin fluctuations and recovers conventional
Fermi liquid behavior, connecting both states continuously.


  Timing observations are crucial for determining the basic parameters of newly
discovered pulsars. Using the Five-hundred-meter Aperture Spherical radio
Telescope (FAST) with the L-band 19-beam receiver covering the frequency range
of 1.0--1.5 GHz, the FAST Galactic Plane Pulsar Snapshot (GPPS) Survey has
discovered more than 600 faint pulsars with flux densities of only a few or a
few tens of $\mu$Jy at 1.25 GHz. To obtain accurate position, spin parameters
and dispersion measure of a pulsar, and to calculate derived parameters such as
the characteristic age and surface magnetic field, we collect available FAST
pulsar data obtained either through targeted follow-up observations or through
coincidental survey observations with one of the 19 beams of the receiver. From
these data we obtain time of arrival (TOA) measurements for 30 newly discovered
pulsars as well as for 13 known pulsars. We demonstrate that the TOA
measurements acquired by the FAST from any beams of the receiver in any
observation mode (e.g. the tracking mode or the snapshot mode) can be combined
to get timing solutions. We update the ephemerides of 13 previously known
pulsars and obtain the first phase-coherent timing results for 30 isolated
pulsars discovered in the FAST GPPS Survey. Notably, PSR J1904+0853 is an
isolated millisecond pulsar, PSR J1906+0757 is a disrupted recycled pulsar, and
PSR J1856+0211 has a long period of 9.89 s that can constrain pulsar death
lines. Based on these timing solutions, all available FAST data have been added
together to obtain the best pulse profiles for these pulsars.


  Data aggregation has been widely implemented as an infrastructure of
data-driven systems. However, a centralized data aggregation model requires a
set of strong trust assumptions to ensure security and privacy. In recent
years, decentralized data aggregation has become realizable based on
distributed ledger technology. Nevertheless, the lack of appropriate
centralized mechanisms like identity management mechanisms carries risks such
as impersonation and unauthorized access. In this paper, we propose a novel
decentralized data aggregation framework by leveraging self-sovereign identity,
an emerging identity model, to lift the trust assumptions in centralized models
and eliminate identity-related risks. Our framework formulates the aggregation
protocol regarding data persistence and acquisition aspects, considering
security, efficiency, flexibility, and compatibility. Furthermore, we
demonstrate the applicability of our framework via a use case study where we
concretize and apply our framework in a decentralized neuroscience data
aggregation scenario.


  In real-world scenarios like traffic and energy, massive time-series data
with missing values and noises are widely observed, even sampled irregularly.
While many imputation methods have been proposed, most of them work with a
local horizon, which means models are trained by splitting the long sequence
into batches of fit-sized patches. This local horizon can make models ignore
global trends or periodic patterns. More importantly, almost all methods assume
the observations are sampled at regular time stamps, and fail to handle complex
irregular sampled time series arising from different applications. Thirdly,
most existing methods are learned in an offline manner. Thus, it is not
suitable for many applications with fast-arriving streaming data. To overcome
these limitations, we propose BayOTIDE: Bayesian Online Multivariate Time
series Imputation with functional decomposition. We treat the multivariate time
series as the weighted combination of groups of low-rank temporal factors with
different patterns. We apply a group of Gaussian Processes (GPs) with different
kernels as functional priors to fit the factors. For computational efficiency,
we further convert the GPs into a state-space prior by constructing an
equivalent stochastic differential equation (SDE), and developing a scalable
algorithm for online inference. The proposed method can not only handle
imputation over arbitrary time stamps, but also offer uncertainty
quantification and interpretability for the downstream application. We evaluate
our method on both synthetic and real-world datasets.We release the code at
{https://github.com/xuangu-fang/BayOTIDE}


  We develop a theory of projective Fraisse limits in the spirit of Irwin-
Solecki. The structures here will additionally support dual semantics as in
[Sl10, Sl12]. Let Y be a compact metrizable space and let G be a closed
subgroup of Homeo(Y). We show that there is always a projective Frasse limit K
and a closed definable equivalence relation r on K, so that the quotient of K
under r is homeomorphic to Y and the projection of K on Y induces a continuous
group embedding of Aut(K) into G with dense image.


  We give new necessary and sufficient conditions on the Weyl tensor for
generalized Robertson-Walker (GRW) space-times to be perfect-fluid space-times.
For GRW space-times, we determine the form of the Ricci tensor in all the
O(n)-invariant subspaces provided by Gray's decomposition of the gradient of
the Ricci tensor. In all but one, the Ricci tensor is Einstein or has the form
of perfect fluid. We discuss the corresponding equations of state that result
from the Einstein equation in dimension 4, where perfect-fluid GRW space-times
are Robertson-Walker.


  We present first results of our study on the Euclidean topological charge
density correlation function. In order to get a well defined topological charge
density and to improve the signal of the correlation function at large
separations we make use of the gradient flow. We investigate the flow-time
dependence on fine quenched lattices. The final goal of this study is to
perform a continuum extrapolation for the pure SU(3) plasma and to extract the
related transport coefficient, the sphaleron rate.


  The problem of two companies of agents with one-step memory playing game is
investigated in the context of the Iterated Prisoner's Dilemma under the
partial imitation rule, where a player can imitate only those moves that he has
observed in his games with his opponent. We limit our study to the special case
where the players in the two groups enjoy the same conditions on a fully
connected network, so that there are only two payoff matrices required: one for
players playing games with members of the same company, and the other one for
players playing games with members from a different company. We show that this
symmetric case of two companies of players can be reduced to the one-company
case with an effective payoff matrix, from which a phase diagram for the
players using the two dominant strategies, Pavlov and Grim Trigger can be
constructed. The phase diagram is computed by numerical integration of the
approximate mean value equations. The results are in good agreement with
simulations of the two-company model. The phase diagram leads to an interesting
conclusion that a player will more likely become a Grim Trigger, regardless of
their affiliated company, when the noise level increases so that he is more
irrational, or when the intra-group temptation to defect increases.


  A new supersymmetric D7-brane embedding in the Pilch-Warner gravitational
background is found exactly, by solving the supersymmetric condition. In the
dual holographic picture, our setting corresponds to adding a quenched
fundamental matter sector to N=2* super Yang-Mills theory, at zero temperature.
We show that previous results in the same setting are missing the Wess-Zumino
term in the D-brane action, and how our results complete the picture.


  The R\'enyi entropies of Coulomb systems $R_{p}[\rho], 0 < p < \infty$ are
logarithms of power functionals of the electron density $\rho(\vec{r})$ which
quantify most appropriately the electron uncertainty and describe numerous
physical observables. However, its analytical determination is a hard issue not
yet solved except for the first lowest-lying energetic states of some specific
systems. This is so even for the $D$-dimensional hydrogenic system, which is
the main prototype of the multidimensional Coulomb many-body systems. Recently,
the R\'enyi entropies of this system have been found in the two extreme
high-energy (Rydberg) and high-dimensional (pseudo-classical) cases. In this
work we determine the position and momentum R\'enyi entropies (with integer $p$
greater than 1) for all the discrete stationary states of the multidimensional
hydrogenic system directly in terms of the hyperquantum numbers which
characterize the states, the nuclear charge and the space dimensionality. We
have used a methodology based on linearization formulas for powers of the
orthogonal Laguerre and Gegenbauer polynomials which control the hydrogenic
states.


  In this paper, we give resolvent estimates for the linearized operator of the
Navier-Stokes equation in $\R^2$ around the Oseen vortices, in the fast
rotating limit $\alpha\to+\infty$.


  For a finite vector space $V$ and a non-negative integer $r\le\dim V$ we
estimate the smallest possible size of a subset of $V$, containing a translate
of every $r$-dimensional subspace. In particular, we show that if $K\subset V$
is the smallest subset with this property, $n$ denotes the dimension of $V$,
and $q$ is the size of the underlying field, then for $r$ bounded and $r<n\le
rq^{r-1}$ we have $|V\setminus K|=\Theta(nq^{n-r+1})$. This improves previously
known bounds $|V\setminus K|=\Omega(q^{n-r+1})$ and $|V\setminus
K|=O(n^2q^{n-r+1})$.


  The drag and diffusion coefficients of heavy quarks (HQs) have been evaluated
in the pre-equilibrium phase of the evolving fireball produced in heavy ion
collisions at RHIC and LHC energies. The KLN and classical Yang-Mills spectra
have been used for describing the momentum distributions of the gluons produced
just after the collisions but before they thermalize. The interaction of the
HQs with these gluons has been treated within the framework of perturbative
QCD. We have observed that the HQs are dragged almost equally by the
kinetically equilibrated and out-of-equilibrium gluonic systems. We have also
noticed that the HQs diffusion in the pre-equilibrium gluonic phase is as fast
as in the kinetically equilibrated gluons. Moreover, the diffusion is faster in
the pre-equilibrium phase than in the chemically equilibrated quark-gluon
plasma. These findings may have significant impact on the analysis of
experimental results on the elliptic flow and the high momentum suppression of
the open charm and beauty hadrons.


  The thermo-mechanical effect in superfluid helium is used to create an
initial chemical potential difference, $\Delta \mu_0$, across a solid $^4$He
sample. This $\Delta \mu_0$ causes a flow of helium atoms from one reservoir
filled with superfluid helium, through a sample cell filled with solid helium,
to another superfluid-filled reservoir until chemical potential equilibrium is
restored. The solid helium sample is separated from each of the reservoirs by
Vycor rods that allow only the superfluid component to flow. With an improved
technique, measurements of the flow, $F$, at several fixed solid helium
temperatures, $T$, have been made as function of $\Delta \mu$ in the pressure
range 25.5 - 26.1 bar. And, measurements of $F$ have been made as a function of
temperature in the range $180 < T < 545$~mK for several fixed values of $\Delta
\mu$. The temperature dependence of the flow above $100$~mK shows a reduction
of the flux with an increase in temperature that is well described by $F =
F_0^*[1 - a\exp(-E/T)]$. The non-linear functional dependence $F \sim (\Delta
\mu)^b$, with $b < 0.5$ independent of temperature but dependent on pressure,
documents in some detail the dissipative nature of the flow and suggests that
this system demonstrates Luttinger liquid-like one-dimensional behavior. The
mechanism that causes this flow behavior is not certain, but is consistent with
superflow on the cores of edge dislocations.


  The causal properties of curved spacetime, which underpin our sense of time
in gravitational theories, are defined by the null cones of the spacetime
metric. In classical general relativity, it is assumed that these coincide with
the light cones determined by the physical propagation of light rays. However,
the quantum vacuum acts as a dispersive medium for the propagation of light,
since vacuum polarisation in QED induces interactions which effectively violate
the strong equivalence principle (SEP). For low frequencies the phenomenon of
gravitational birefringence occurs and indeed, for some metrics and
polarisations, photons may acquire {\it superluminal} phase velocities. In this
article, we review some of the remarkable features of SEP violating
superluminal propagation in curved spacetime and discuss recent progress on the
issue of dispersion, explaining why it is the high-frequency limit of the phase
velocity that determines the characteristics of the effective wave equation and
thus the physical causal structure.


  Given a positive, decreasing sequence $a,$ whose sum is $L$, we consider all
the closed subsets of $[0,L]$ such that the lengths of their complementary open
intervals are in one to one correspondence with the sequence $a$. The aim of
this note is to investigate the possible values that Assouad-type dimensions
can attain for this class of sets. In many cases, the set of attainable values
is a closed interval whose endpoints we determine.


  Statistical static timing analysis deals with the increasing variations in
manufacturing processes to reduce the pessimism in the worst case timing
analysis. Because of the correlation between delays of circuit components,
timing model generation and hierarchical timing analysis face more challenges
than in static timing analysis. In this paper, a novel method to generate
timing models for combinational circuits considering variations is proposed.
The resulting timing models have accurate input-output delays and are about 80%
smaller than the original circuits. Additionally, an accurate hierarchical
timing analysis method at design level using pre-characterized timing models is
proposed. This method incorporates the correlation between modules by replacing
independent random variables to improve timing accuracy. Experimental results
show that the correlation between modules strongly affects the delay
distribution of the hierarchical design and the proposed method has good
accuracy compared with Monte Carlo simulation, but is faster by three orders of
magnitude.


  Molecular electronic devices are the upmost destiny of the miniaturization
trend of electronic components. Although not yet reproducible on large scale,
molecular devices are since recently subject of intense studies both
experimentally and theoretically, which agree in pointing out the extreme
sensitivity of such devices on the nature and quality of the contacts. This
chapter intends to provide a general theoretical framework for modelling
electronic transport at the molecular scale by describing the implementation of
a hybrid method based on Green function theory and density functional
algorithms. In order to show the presence of contact-dependent features in the
molecular conductance, we discuss three archetypal molecular devices, which are
intended to focus on the importance of the different sub-parts of a molecular
two-terminal setup.


  The flow semigroup, introduced by John Rhodes, is an invariant for digraphs
and a complete invariant for graphs. After collecting together previous partial
results, we refine and prove Rhodes's conjecture on the structure of the
maximal groups in the flow semigroup for finite, antisymmetric, strongly
connected digraphs.
  Building on this result, we investigate and fully describe the structure and
actions of the maximal subgroups of the flow semigroup acting on all but $k$
points for all finite digraphs and graphs for all $k\geq 1$. A linear algorithm
(in the number of edges) is presented to determine these so-called `defect $k$
groups' for any finite (di)graph.
  Finally, we prove that the complexity of the flow semigroup of a 2-vertex
connected (and strongly connected di)graph with $n$ vertices is $n-2$,
completely confirming Rhodes's conjecture for such (di)graphs.


  We present a method to constrain the temperature of astrophysical black holes
through detecting the inspiral phase of binary black hole coalescences. At
sufficient separation, inspiraling black holes can be regarded as isolated
objects, hence their temperature can still be defined. Due to their intrinsic
radiation, inspiraling black holes lose part of their masses during the
inspiral phase. As a result, coalescence speeds up, introducing a correction to
the orbital phase. We show that this dephasing may allow us to constrain the
temperature of inspiraling black holes through gravitational-wave detection.
Using the binary black-hole coalescences of the first two observing runs of the
Advanced LIGO and Virgo detectors, we constrain the temperature of parental
black holes to be less than about $ 10^9 $ K. Such a constraint corresponds to
luminosity of about $ 10^{-16} M_{\odot} \rm s^{-1} $ for a black hole of $ 20
M_{\odot} $, which is about 20 orders of magnitude below the peak luminosity of
the corresponding gravitational-wave event, indicating no evidence for strong
quantum-gravity effects through the detection of the inspiral phase.


  Stochastic Differential Equations (SDEs) serve as a powerful modeling tool in
various scientific domains, including systems science, engineering, and
ecological science. While the specific form of SDEs is typically known for a
given problem, certain model parameters remain unknown. Efficiently inferring
these unknown parameters based on observations of the state in discrete time
series represents a vital practical subject. The challenge arises in nonlinear
SDEs, where maximum likelihood estimation of parameters is generally unfeasible
due to the absence of closed-form expressions for transition and stationary
probability density functions of the states. In response to this limitation, we
propose a novel two-step parameter inference mechanism. This approach involves
a global-search phase followed by a local-refining procedure. The global-search
phase is dedicated to identifying the domain of high-value likelihood
functions, while the local-refining procedure is specifically designed to
enhance the surrogate likelihood within this localized domain. Additionally, we
present two simulation-based approximations for the transition density, aiming
to efficiently or accurately approximate the likelihood function. Numerical
examples illustrate the efficacy of our proposed methodology in achieving
posterior parameter estimation.


  We study a concentration problem on the unit sphere $\mathbb{S}^2$ for
band-limited spherical harmonics expansions using large sieve methods. We
derive upper bounds for concentration in terms of the maximum Nyquist density.
Our proof uses estimates of the spherical harmonics coefficients of certain
zonal filters. We also demonstrate an analogue of the classical large sieve
inequality for spherical harmonics expansions.


  Despite the availability of effective vaccines, the persistence of SARS-CoV-2
suggests that co-circulation with other pathogens and resulting multi-epidemics
may become increasingly frequent. To better forecast and control the risk of
such multi-epidemics, it is essential to elucidate the potential interactions
of SARS-CoV-2 with other pathogens; these interactions, however, remain poorly
defined. Here, we aimed to review the current body of evidence about SARS-CoV-2
interactions. To study pathogen interactions in a systematic way, we first
developed a general framework to capture their major components: sign,
strength, symmetry, duration, and mechanism. We then reviewed the experimental
evidence from animal models about SARS-CoV-2 interactions. Of the 14 studies
identified, 11 focused on the outcomes of co-infection with non-attenuated
influenza A viruses and generally demonstrated that co-infection increased
disease severity compared with either mono-infection. By contrast, the effect
of co-infection on the viral load of either virus was variable and inconsistent
across studies. Next, we reviewed the epidemiological evidence about SARS-CoV-2
interactions in human populations. Although numerous studies were identified,
only few were specifically designed to infer interaction and many were prone to
multiple biases, including confounding. Nevertheless, their results suggested
that influenza and pneumococcal conjugate vaccinations were associated with
reduced riskof SARS-CoV-2 infection. Finally, we formulated simple transmission
models of SARS-CoV-2 co-circulation with a viral or a bacterial pathogen,
showing how they can naturally incorporate the proposed framework. More
generally, we argue that such models, when designed with an integrative and
multidisciplinary perspective, will be invaluable tools to resolve the
substantial uncertainties that remain about SARS-CoV-2 interactions.


  We present a library for generic programming in OCaml, adapting some
techniques borrowed from other functional languages. The library makes use of
three recent additions to OCaml: generalised abstract datatypes are essential
to reflect types, extensible variants allow this reflection to be open for new
additions, and extension points provide syntactic sugar and generate boiler
plate code that simplify the use of the library. The building blocks of the
library can be used to support many approaches to generic programming through
the concept of view. Generic traversals are implemented on top of the library
and provide powerful combinators to write concise definitions of recursive
functions over complex tree types. Our case study is a type-safe
deserialisation function that respects type abstraction.


  To optimize the operation of a HVAC system with advanced techniques such as
artificial neural network, previous studies usually need forecast information
in their method. However, the forecast information inevitably contains errors
all the time, which degrade the performance of the HVAC operation. Hence, in
this study, we propose MILP-based imitation learning method to control a HVAC
system without using the forecast information in order to reduce energy cost
and maintain thermal comfort at a given level. Our proposed controller is a
deep neural network (DNN) trained by using data labeled by a MILP solver with
historical data. After training, our controller is used to control the HVAC
system with real-time data. For comparison, we also develop a second method
named forecast-based MILP which control the HVAC system using the forecast
information. The performance of the two methods is verified by using real
outdoor temperatures and real day-ahead prices in Detroit city, Michigan,
United States. Numerical results clearly show that the performance of the
MILP-based imitation learning is better than that of the forecast-based MILP
method in terms of hourly power consumption, daily energy cost, and thermal
comfort. Moreover, the difference between results of the MILP-based imitation
learning method and optimal results is almost negligible. These optimal results
are achieved only by using the MILP solver at the end of a day when we have
full information on the weather and prices for the day.


  Few-shot table-to-text generation is a task of composing fluent and faithful
sentences to convey table content using limited data. Despite many efforts
having been made towards generating impressive fluent sentences by fine-tuning
powerful pre-trained language models, the faithfulness of generated content
still needs to be improved. To this end, this paper proposes a novel approach
Attend, Memorize and Generate (called AMG), inspired by the text generation
process of humans. In particular, AMG (1) attends over the multi-granularity of
context using a novel strategy based on table slot level and traditional
token-by-token level attention to exploit both the table structure and natural
linguistic information; (2) dynamically memorizes the table slot allocation
states; and (3) generates faithful sentences according to both the context and
memory allocation states. Comprehensive experiments with human evaluation on
three domains (i.e., humans, songs, and books) of the Wiki dataset show that
our model can generate higher qualified texts when compared with several
state-of-the-art baselines, in both fluency and faithfulness.