  Low frequency radio waves, while challenging to observe, are a rich source of
information about pulsars. The LOw Frequency ARray (LOFAR) is a new radio
interferometer operating in the lowest 4 octaves of the ionospheric "radio
window": 10-240MHz, that will greatly facilitate observing pulsars at low radio
frequencies. Through the huge collecting area, long baselines, and flexible
digital hardware, it is expected that LOFAR will revolutionize radio astronomy
at the lowest frequencies visible from Earth. LOFAR is a next-generation radio
telescope and a pathfinder to the Square Kilometre Array (SKA), in that it
incorporates advanced multi-beaming techniques between thousands of individual
elements. We discuss the motivation for low-frequency pulsar observations in
general and the potential of LOFAR in addressing these science goals. We
present LOFAR as it is designed to perform high-time-resolution observations of
pulsars and other fast transients, and outline the various relevant observing
modes and data reduction pipelines that are already or will soon be implemented
to facilitate these observations. A number of results obtained from
commissioning observations are presented to demonstrate the exciting potential
of the telescope. This paper outlines the case for low frequency pulsar
observations and is also intended to serve as a reference for upcoming
pulsar/fast transient science papers with LOFAR.


  HST has so far provided Cepheid distances to nine galaxies. Although not
sufficient yet to determine the distance of the extended Virgo cluster, they
are decisive for the distance scale in two ways. (1) Seven of the galaxies
contribute to a much improved calibration of the Tully-Fisher relation.
Applying this to a complete sample of Virgo spirals one obtains a cluster
distance of (m-M)=31.79 +/- 0.09. Other distance indicators support this value.
The adopted linear distance of 22.0 +/- 0.8 Mpc combined with the cluster
velocity of 1178 +/- 32 km s^-1 (in the CMB frame) gives H_0=54 +/- 2 (internal
error). (2) Six of the galaxies have been the site of seven SNe Ia with well
observed maxima. Their resulting calibration in absolute magnitudes gives
M_B(max)=-19.53 +/- 0.07 and M_V(max)=-19.49 +/- 0.07 with negligible intrinsic
scatter. If this calibration is used to determine the distances of all distant
SNe Ia with known maxima and with 1100 < v < 30'000 km s^-1, H_0 becomes 56 +/-
3 (internal error). Systematic errors tend to make this an upper limit; in
particular the case H_0 >= 70 can be excluded.
  The conclusion is that the large-scale value of the Hubble constant is H_0=55
+/- 10 (external error).


  The problem of a spinless particle subject to a general mixing of vector and
scalar screened Coulomb potentials in a two-dimensional world is analyzed and
its bounded solutions are found. Some unusual results, including the existence
of a bona fide solitary zero-eigenmode solution, are revealed for the
Klein-Gordon equation. The cases of pure vector and scalar potentials, already
analyzed in previous works, are obtained as particular cases.


  We prove that strongly homotopy algebras (such as $A_\infty$, $C_\infty$, sh
Lie, $B_\infty$, $G_\infty$,...) are homotopically invariant in the category of
chain complexes. An important consequence is a rigorous proof that `strongly
homotopy structures transfer over chain homotopy equivalences.'


  The self-duality equations for the Riemann tensor are studied using the
Ashtekar Hamiltonian formulation for general relativity. These equations may be
written as dynamical equations for three divergence free vector fields on a
three dimensional surface in the spacetime. A simplified form of these
equations, describing metrics with a one Killing field symmetry are written
down, and it shown that a particular sector of these equations has a
Hamiltonian form where the Hamiltonian is an arbitrary function on a
two-surface. In particular, any element of the $w_\infty$ algebra may be chosen
as a the Hamiltonian. For a special choice of this Hamiltonian, an infinite set
of solutions of the self-duality equations are given. These solutions are
parametrized by elements of the $w_\infty$ algebra, which in turn leads to an
explicit form of four dimensional complex self-dual metrics that are in one to
one correspondence with elements of this algebra.


  Acoustic-prosodic entrainment describes the tendency of humans to align or
adapt their speech acoustics to each other in conversation. This alignment of
spoken behavior has important implications for conversational success. However,
modeling the subtle nature of entrainment in spoken dialogue continues to pose
a challenge. In this paper, we propose a straightforward definition for local
entrainment in the speech domain and operationalize an algorithm based on this:
acoustic-prosodic features that capture entrainment should be maximally
different between real conversations involving two partners and sham
conversations generated by randomly mixing the speaking turns from the original
two conversational partners. We propose an approach for measuring local
entrainment that quantifies alignment of behavior on a turn-by-turn basis,
projecting the differences between interlocutors' acoustic-prosodic features
for a given turn onto a discriminative feature subspace that maximizes the
difference between real and sham conversations. We evaluate the method using
the derived features to drive a classifier aiming to predict an objective
measure of conversational success (i.e., low versus high), on a corpus of
task-oriented conversations. The proposed entrainment approach achieves 72%
classification accuracy using a Naive Bayes classifier, outperforming three
previously established approaches evaluated on the same conversational corpus.


  In this paper, we propose a general method for testing inequality
restrictions on nonparametric functions. Our framework includes many
nonparametric testing problems in a unified framework, with a number of
possible applications in auction models, game theoretic models, wage
inequality, and revealed preferences. Our test involves a one-sided version of
$L_{p}$ functionals of kernel-type estimators $(1\leq p <\infty )$ and is easy
to implement in general, mainly due to its recourse to the bootstrap method.
The bootstrap procedure is based on nonparametric bootstrap applied to
kernel-based test statistics, with estimated "contact sets." We provide
regularity conditions under which the bootstrap test is asymptotically valid
uniformly over a large class of distributions, including the cases that the
limiting distribution of the test statistic is degenerate. Our bootstrap test
is shown to exhibit good power properties in Monte Carlo experiments, and we
provide a general form of the local power function. As an illustration, we
consider testing implications from auction theory, provide primitive conditions
for our test, and demonstrate its usefulness by applying our test to real data.
We supplement this example with the second empirical illustration in the
context of wage inequality.


  Monocular depth estimation (MDE) is a fundamental topic of geometric computer
vision and a core technique for many downstream applications. Recently, several
methods reframe the MDE as a classification-regression problem where a linear
combination of probabilistic distribution and bin centers is used to predict
depth. In this paper, we propose a novel concept of iterative elastic bins
(IEBins) for the classification-regression-based MDE. The proposed IEBins aims
to search for high-quality depth by progressively optimizing the search range,
which involves multiple stages and each stage performs a finer-grained depth
search in the target bin on top of its previous stage. To alleviate the
possible error accumulation during the iterative process, we utilize a novel
elastic target bin to replace the original target bin, the width of which is
adjusted elastically based on the depth uncertainty. Furthermore, we develop a
dedicated framework composed of a feature extractor and an iterative optimizer
that has powerful temporal context modeling capabilities benefiting from the
GRU-based architecture. Extensive experiments on the KITTI, NYU-Depth-v2 and
SUN RGB-D datasets demonstrate that the proposed method surpasses prior
state-of-the-art competitors. The source code is publicly available at
https://github.com/ShuweiShao/IEBins.


  We describe a post-Minkowskii approximation of general relativity as a power
series expansion in G, Newton's gravitational constant. Material sources are
hidden behind boundaries, and only the vacuum Einstein equations are
considered. An iterative procedure is described which, in one complete step,
takes any approximate solution of the Einstein equations and produces a new
approximation which has the error decreased by a factor of G. Each step in the
procedure consists of three parts: first the equations of motion are used to
update the trajectories of the boundaries; then the field equations are solved
using a retarded Green's function for Minkowskii space; finally a gauge
transformation is performed which makes the geometry well behaved at future
null infinity. Differences between this approach to the Einstein equations and
similar ones are that we use a general (non-harmonic) gauge and formulate the
procedure in a constructive manner which emphasizes its suitability for
implementation on a computer.


  We consider the problem of "algebraic reconstruction" of linear combinations
of shifts of several known signals $f_1,\ldots,f_k$ from the Fourier samples.
Following \cite{Bat.Sar.Yom2}, for each $j=1,\ldots,k$ we choose sampling set
$S_j$ to be a subset of the common set of zeroes of the Fourier transforms
${\cal F}(f_\ell), \ \ell \ne j$, on which ${\cal F}(f_j)\ne 0$. It was shown
in \cite{Bat.Sar.Yom2} that in this way the reconstruction system is
"decoupled" into $k$ separate systems, each including only one of the signals
$f_j$. The resulting systems are of a "generalized Prony" form.
  However, the sampling sets as above may be non-uniform/not "dense enough" to
allow for a unique reconstruction of the shifts and amplitudes. In the present
paper we study uniqueness and robustness of non-uniform Fourier sampling of
signals as above, investigating sampling of exponential polynomials with purely
imaginary exponents. As the main tool we apply a well-known result in Harmonic
Analysis: the Tur\'an-Nazarov inequality (\cite{Naz}), and its generalization
to discrete sets, obtained in \cite{Fri.Yom}. We illustrate our general
approach with examples, and provide some simulation results.


  We examined the past history of the three most detached TransNeptunian
Objects (TNOs) -- Sedna, 2012 VP113, and Leleakuhonua (2015 TG387) -- the three
clearest members of the dynamical class known as sednoids, with high perihelia
distances $q$. By integrating backward their nominal (and a set of cloned)
orbits for the Solar System's age, we surprisingly find that the only time all
their apsidal lines tightly cluster was 4.5 Gyr ago, at perihelion longitude
$\varpi$ of $200^\circ$. This "primordial alignment" is independent of the
observational biases that contribute to the current on-sky clustering in the
large-semimajor axis Kuiper Belt. If future sednoid discoveries confirm these
findings, this strongly argues for an initial event during the planet formation
epoch which imprinted this particular apsidal orientation on the early detached
TNO population. Their apsidal orientations were then subsequently modified only
by the simple precession from the 4 giant planets (and weakly by the galactic
tide). If other sednoids also cluster around the same primordial value, various
models suggesting a still present planet in the outer Solar System would be
incompatible with this alignment. We inspected two scenarios that could
potentially explain the primordial alignment. First, a rogue planet model
(where another massive planet raises perihelia near its own longitude until
ejection) naturally produces this signature. Alternatively, a close stellar
passage early in Solar System history raises perihelia, but it is poor at
creating strong apsidal clustering. We show that all other known $35<q<55$ au
TNOs are either too perturbed or orbits are still too uncertain to provide
evidence for or against this paradigm.


  The purpose of this paper is to provide an algorithm for detecting and
tracking astral MTs in a fully automated way and supply a description of their
dynamic behaviour. For the algorithm testing, a dataset of stacks (i.e.
time-lapse image sequences), acquired with a confocal microscope, has been
employed. Cells were treated with two different drugs, nocodazole and taxol, in
order to explore their effect on microtubule dynamic instability.


  Any etale Lie groupoid G is completely determined by its associated
convolution algebra C_c(G) equipped with the natural Hopf algebroid structure.
We extend this result to the generalized morphisms between etale Lie groupoids:
we show that any principal H-bundle P over G is uniquely determined by the
associated C_c(G)-C_c(H)-bimodule C_c(P) equipped with the natural coalgebra
structure. Furthermore, we prove that the functor C_c gives an equivalence
between the Morita category of etale Lie groupoids and the Morita category of
locally grouplike Hopf algebroids.


  We present the results of fits to recent asqtad data in the light
pseudoscalar sector using SU(2) partially-quenched staggered chiral
perturbation theory. Superfine (a~0.06 fm) and ultrafine (a~0.045 fm) ensembles
are used, where light sea quark masses and taste splittings are small compared
to the strange quark mass. Our fits include continuum NNLO chiral logarithms
and analytic terms. We give preliminary results for the pion decay constant,
SU(2) low-energy constants and the chiral condensate in the two-flavor chiral
limit.


  The purpose of this paper is to adapt the empirical characteristic function
(ECF) method to stable, but possibly not inverse stable linear stochastic
system driven by the increments of a Levy-process. A remarkable property of the
ECF method for i.i.d. data is that, under an ideal setting, it gives an
efficient estimate of the unknown parameters of a given parametric family of
distributions. Variants of the ECF method for special classes of dependent data
has been suggested in several papers using the joint characteristic function of
blocks of unprocessed data. However, the latter may be unavailable for
Levy-systems. We introduce a new, computable score that is essentially a kind
of output error. The feasibility of the procedure is based on a result of
Devroye on the generation of r.v.-s with given c.f. Two special cases are
considered in detail, and the asymptotic covariance matrices of the estimators
are given. The present work extends our previous work on the ECF identification
of stable and inverse stable linear stochastic Levy-systems.


  In this paper, we focus on a class of robust vector polynomial optimization
problems (RVPOP in short) without any convex assumptions. By
combining/improving the utopia point method (a nonlinear scalarization) for
vector optimization and "joint+marginal" relaxation method for polynomial
optimization, we solve the RVPOP successfully. Both theoratical and
computational aspects are considered.


  This paper presents a novel semantics for a quantum programming language by
operator algebras, which are known to give a formulation for quantum theory
that is alternative to the one by Hilbert spaces. We show that the opposite
category of the category of W*-algebras and normal completely positive
subunital maps is an elementary quantum flow chart category in the sense of
Selinger. As a consequence, it gives a denotational semantics for Selinger's
first-order functional quantum programming language QPL. The use of operator
algebras allows us to accommodate infinite structures and to handle classical
and quantum computations in a unified way.


  Orbital angular momentum (OAM) radars are able to estimate the azimuth angle
and the rotation velocity of multiple targets without relative motion or beam
scanning. Moreover, OAM wireless communications can achieve high spectral
efficiency (SE) by utilizing a set of information-bearing modes on the same
frequency channel. Benefitting from the above advantages, in this paper, we
design a novel radar-centric joint OAM radar-communication (RadCom) scheme
based on uniform circular arrays (UCAs), which modulates information signals on
the existing OAM radar waveform. In details, we first propose an OAM-based
three-dimensional (3-D) super-resolution position estimation and rotation
velocity detection method, which can accurately estimate the 3-D position and
rotation velocity of multiple targets. Then, we derive the posterior Cramer-Rao
bound (PCRB) of the OAM-based estimates and, finally, we analyze the
transmission rate of the integrated communication system. To achieve the best
trade-off between imaging and communication, the transmitted integrated OAM
beams are optimized by means of an exhaustive search method. Both mathematical
analysis and simulation results show that the proposed radar-centric joint OAM
RadCom scheme can accurately estimate the 3-D position and rotation velocity of
multiple targets while ensuring the transmission rate of the communication
receiver, which can be regarded as an effective supplement to existing joint
RadCom schemes.


  In recent years, attention-based transformers have achieved tremendous
success across a variety of disciplines including natural languages. A key
ingredient behind their success is the generative pretraining procedure, during
which these models are trained on a large text corpus in an auto-regressive
manner. To shed light on this phenomenon, we propose a new framework that
allows both theory and systematic experiments to study the sequential modeling
capabilities of transformers through the lens of Markov chains. Inspired by the
Markovianity of natural languages, we model the data as a Markovian source and
utilize this framework to systematically study the interplay between the
data-distributional properties, the transformer architecture, the learnt
distribution, and the final model performance. In particular, we theoretically
characterize the loss landscape of single-layer transformers and show the
existence of global minima and bad local minima contingent upon the specific
data characteristics and the transformer architecture. Backed by experiments,
we demonstrate that our theoretical findings are in congruence with the
empirical results. We further investigate these findings in the broader context
of higher order Markov chains and deeper architectures, and outline open
problems in this arena. Code is available at
\url{https://github.com/Bond1995/Markov}.


  In this article, the existence of the spectrum (the eigenvalues) for the
nonlinear continuous operators acting in the Banach spaces is investigated. For
the study, this question is used a different approach that allows the studying
of all eigenvalues of the nonlinear operator relative to another nonlinear
operator. Here shows that in nonlinear operators, case is necessary to seek the
spectrum of the given nonlinear operator relative to another nonlinear operator
satisfying certain conditions. The different examples, for which eigenvalues
can be found are provided. Moreover, the nonlinear problems including
parameters are studied.


  Improvements in computer systems have historically relied on two well-known
observations: Moore's law and Dennard's scaling. Today, both these observations
are ending, forcing computer users, researchers, and practitioners to abandon
the general-purpose architectures' comforts in favor of emerging post-Moore
systems. Among the most salient of these post-Moore systems is the
Field-Programmable Gate Array (FPGA), which strikes a convenient balance
between complexity and performance. In this paper, we study modern FPGAs'
applicability in accelerating the Spectral Element Method (SEM) core to many
computational fluid dynamics (CFD) applications. We design a custom SEM
hardware accelerator operating in double-precision that we empirically evaluate
on the latest Stratix 10 GX-series FPGAs and position its performance (and
power-efficiency) against state-of-the-art systems such as ARM ThunderX2,
NVIDIA Pascal/Volta/Ampere Tesla-series cards, and general-purpose manycore
CPUs. Finally, we develop a performance model for our SEM-accelerator, which we
use to project future FPGAs' performance and role to accelerate CFD
applications, ultimately answering the question: what characteristics would a
perfect FPGA for CFD applications have?


  Mixtures of bosonic and fermionic atoms in optical lattices provide a
promising arena to study strongly correlated systems. In experiments realizing
such mixtures in the quantum degenerate regime the temperature is a key
parameter. In this work, we investigate the intrinsic heating and cooling
effects due to an entropy-preserving raising of the optical lattice potential.
We analyze this process, identify the generic behavior valid for a wide range
of parameters, and discuss it quantitatively for the recent experiments with
87Rb and 40K atoms. In the absence of a lattice, we treat the bosons in the
Hartree-Fock-Bogoliubov-Popov-approximation, including the fermions in a
self-consistent mean field interaction. In the presence of the full
three-dimensional lattice, we use a strong coupling expansion. As a result of
the presence of the fermions, the temperature of the mixture after the lattice
ramp-up is always higher than for the pure bosonic case. This sheds light onto
a key point in the analysis of recent experiments.


  We present three designs for planar superconducting microwave resonators for
electron spin resonance (ESR) experiments. We implement finite element
simulations to calculate the resonance frequency and quality factors as well as
the three-dimensional microwave magnetic field distribution of the resonators.
One particular resonator design offers an increased homogeneity of the
microwave magnetic field while the other two show a better confinement of the
mode volume. We extend our model simulations to calculate the collective
coupling rate between a spin ensemble and a microwave resonator in the presence
of an inhomogeneous magnetic resonator field. Continuous-wave ESR experiments
of phosphorus donors in $^\mathrm{nat}$Si demonstrate the feasibility of our
resonators for magnetic resonance experiments. We extract the collective
coupling rate and find a good agreement with our simulation results,
corroborating our model approach. Finally, we discuss specific application
cases for the different resonator designs.


  This review article discusses and compares various techniques for fabricating
metal nanowires. We begin by defining what we mean by a nanowire, and why such
nanostructures are of scientific and technological interest. We then present
different fabrication methodologies, describing in some detail the advantages
of each. "Top-down" techniques discussed include: electron beam lithography;
scanned probe lithography; step-edge and molecular beam epitaxy templating; and
nanotube templating. "Bottom-up" methodologies covered include:
electrodeposition into etched porous media; direct chemical synthesis;
step-edge decoration; and strain-mediated self-assembly. We conclude by
summarizing our observations and briefly discuss the future of metal nanowire
fabrication.


  Mediation analysis is appealing for its ability to improve understanding of
the mechanistic drivers of causal effects, but real-world data complexities
challenge its successful implementation, including: 1) the existence of
post-exposure variables that also affect mediators and outcomes (thus,
confounding the mediator-outcome relationship), that may also be 2)
multivariate, and 3) the existence of multivariate mediators. Interventional
direct and indirect effects (IDE/IIE) accommodate post-exposure variables that
confound the mediator-outcome relationship, but currently, no estimator for
IDE/IIE exists that allows for both multivariate mediators and multivariate
post-exposure intermediate confounders. This, again, represents a significant
limitation for real-world analyses. We address this gap by extending two
recently developed nonparametric estimators -- one that estimates the IDE/IIE
and another that estimates the IDE/IIE transported to a new, target population
-- to allow for multivariate mediators and multivariate intermediate
confounders simultaneously. We use simulation to examine finite sample
performance, and apply these estimators to longitudinal data from the Moving to
Opportunity trial. In the application, we walk through a strategy for
separating indirect effects into mediator- or mediator-group-specific indirect
effects, while appropriately accounting for other, possibly co-occurring
intermediate variables.


  We argue that the resolution to the black hole information paradox lies in a
proper accounting of the implications of diffeomorphism invariance for the
Hilbert space and observables of quantum gravity. The setting of asymptotically
Anti-de Sitter spacetime is adopted for most of the paper, but in the framework
of canonical quantum gravity, without invoking AdS/CFT duality. We present
Marolf's argument that boundary unitarity is a consequence of diffeomorphism
invariance, and show that its failure to apply in the classical limit results
from a lack of analyticity that has no quantum counterpart. We argue that
boundary unitarity leads to a {\it boundary information paradox}, which
generalizes the black hole information paradox and arises in virtually any
scattering process. We propose a resolution that involves operators of the
boundary algebra that redundantly encode information about physics in the bulk,
and explain why such redundancy need not violate the algebraic no cloning
theorem. We also argue that the infaller paradox, which has motivated the
firewall hypothesis for black hole horizons, is ill-posed in quantum gravity,
because it ignores essential aspects of the nature of the Hilbert space and
observables in quantum gravity.


  The acoustic nonlinearity parameter(beta) is sensitive to dislocation
parameters, which continuously change during plastic deformation.
Dislocation-based damage in structures/components is the source of the failure;
thus, beta has been studied as a metric for non-destructive evaluation. This
work consists of two parts: the development of an in situ experimental setup
for nonlinear Rayleigh wave measurements, and characterization of the
dependence of beta on applied stress at different levels of initial plastic
strain. First, we introduce an experimental setup and methods for repeatable in
situ nonlinear ultrasonic measurements. Details on design considerations and
measurement schemes are provided. In the second part, beta was measured in situ
during an incremental monotonic tensile test. The measured \beta monotonically
decreases with plastic strain, but it is relatively insensitive to the applied
stress during elastic deformation. This result highlights three aspects of the
evolution of beta, which have not been sufficiently emphasized in prior work:
the apparent insensitivity of beta to the applied stress during elastic
deformation, decreasing beta with plastic deformation, and the saturation of
beta. We attribute the trend of decreasing beta to a scaling of beta with
monopole loop length during plastic deformation, which depends on initial
microstructure. The saturation of beta at 1.8% coincides with a planar-to-wavy
transition of dislocation structures. The in situ nonlinear ultrasonic
experimental method presented in this work is significant as the in situ
results can provide broader insights on beta and dislocation-based damage
evolution than ex situ measurements alone.


  We provide a direct and elementary proof that the formula obtained in [MQR17]
for the TASEP transition probabilities for general (one-sided) initial data
solves the Kolmogorov backward equation. The same method yields the solution
for the related PushASEP particle system.


  Light carries energy and momentum. It can therefore alter the motion of
objects from atomic to astronomical scales. Being widely available, readily
controllable and broadly biocompatible, light is also an ideal tool to propel
microscopic particles, drive them out of thermodynamic equilibrium and make
them active. Thus, light-driven particles have become a recent focus of
research in the field of soft active matter. In this perspective, we discuss
recent advances in the control of soft active matter with light, which has
mainly been achieved using light intensity. We also highlight some first
attempts to utilize light's additional degrees of freedom, such as its
wavelength, polarization, and momentum. We then argue that fully exploiting
light with all of its properties will play a critical role to increase the
level of control over the actuation of active matter as well as the flow of
light itself through it. This enabling step will advance the design of soft
active matter systems, their functionalities and their transfer towards
technological applications.


  Neutrino oscillations have been established in solar and atmospheric
neutrinos, but a third signal from the LSND experiment is incompatible with
three Standard Model neutrinos. The MiniBooNE experiment can confirm or refute
the LSND signal with 1E21 protons on target. While working towards the
oscillation result, MiniBooNE will accumulate more than 1E6 neutrino
interactions in the 0 to 2 GeV range which will greatly increase the world's
knowledge of neutrino cross sections in this energy regime. Preliminary results
on the MiniBooNE muon neutrino charged current quasi-elastic analysis are
presented and compared to the NUANCE, NEUT, and NEUGEN cross section Monte
Carlos.


  We present low field thermoremanent magnetization (TRM) measurements in
granular CrO2 and composites of ferromagnetic (FM) CrO2 and antiferromagnetic
(AFM) Cr2O3. TRM in these samples is seen to display two distinct time scales.
A quasi static part of remanence, appearing only in low field regime exhibits a
peculiar field dependence. TRM is seen to first rise and then fall with
increasing cooling fields, eventually vanishing above a critical field. Similar
features in TRM have previously been observed in some antiferromagnets that
exhibit the phenomenon of piezomagnetism. Scaling analysis of the TRM data
suggest that presumably piezomoments generated in the AFM component drive the
FM magnetization dynamics in these granular systems in low field regime.


  Within a large database G containing graphs with labeled nodes and directed,
multi-edges; how can we detect the anomalous graphs? Most existing work are
designed for plain (unlabeled) and/or simple (unweighted) graphs. We introduce
CODETECT, the first approach that addresses the anomaly detection task for
graph databases with such complex nature. To this end, it identifies a small
representative set S of structural patterns (i.e., node-labeled network motifs)
that losslessly compress database G as concisely as possible. Graphs that do
not compress well are flagged as anomalous. CODETECT exhibits two novel
building blocks: (i) a motif-based lossless graph encoding scheme, and (ii)
fast memory-efficient search algorithms for S. We show the effectiveness of
CODETECT on transaction graph databases from three different corporations,
where existing baselines adjusted for the task fall behind significantly,
across different types of anomalies and performance metrics.


  We focus on the spreading properties of solutions of monostable equations
with non-linear diffusion. We consider both the porous medium diffusion and the
fast diffusion regimes. Initial data may have heavy tails, which tends to
accelerate the invasion phenomenon. On the other hand, the nonlinearity may
involve a weak Allee effect, which tends to slow down the process. We study the
balance between these three effects (nonlin-ear diffusion, initial tail, KPP
nonlinearity/Allee effect), revealing the separation between "no acceleration"
and "acceleration". In most of the cases where acceleration occurs, we also
give an accurate estimate of the position of the level sets.


  The powerful high-redshift quasar J2102+6015 (at z=4.575) may provide useful
information for studying supermassive black hole growth, galaxy evolution and
feedback in the early Universe. The source has so far been imaged with very
long baseline interferometry (VLBI) at 2/8 GHz (S/X) bands only, showing
complex compact structure. Its total radio spectrum peaks at ~6 GHz in the rest
frame. There is no sign of Doppler-boosted jet emission, and the separation of
the two major features in its east-west oriented structure spanning ~10
milliarcsec does not change significantly on a timescale longer than a decade.
However, VLBI astrometric monitoring observations suggest quasi-periodic (~3
yr) variation in its absolute position. J2102+6015 is presumably a young radio
source with jets misaligned with respect to the line of sight. Here we briefly
report on our new high-resolution imaging observations made with the European
VLBI Network (EVN) at 5 and 22 GHz frequencies in 2021 June, and give an
overview of what is currently known about this peculiar distant jetted active
galactic nucleus.


  Until recently, much of the microbial world was hidden from view. A global
research effort has changed this, unveiling and quantifying microbial diversity
across enormous range of critically-important contexts, from the human
microbiome, to plant-soil interactions, to marine life. Yet what has remained
largely hidden is the interplay of ecological and evolutionary processes that
led to the diversity we observe in the present day. We introduce a theoretical
framework to quantify the effect of ecological innovations in microbial
evolutionary history, using a new, coarse-grained approach that is robust to
the incompleteness and ambiguities in microbial community data. Applying this
methodology, we identify a balance of gradual, ongoing diversification and
rapid bursts across a vast range of microbial habitats. Moreover, we find
universal quantitative similarities in the tempo of diversification,
independent of habitat type.


  We have measured the linear thermal expansion and magnetostriction of the
Ising antiferromagnet TbNi2Ge2 along its c-axis from room temperature to 2 K
and in magnetic fields to 14 T. We find a magnetic phase diagram that agrees
with earlier work and estimate aspects of its uniaxial pressure dependence. We
also find a new high field feature near 10 T which may signal the onset of an
additional field-induced phase.


  We examine the transition from a fresh inflationary scenario to a radiation
dominated universe when the inflaton field is coupled to gravity. We show that
this transition is very natural when this coupling is $\xi=1/4$ at the end of
fresh inflation. This is a very important result that shows as fresh inflation
has a natural exit to the radiation dominated epoch.


  Cryptolysis is a framework that includes a collection of automated attacks on
the classical ciphers based on the article by Clark Dawson on Optimisation
heuristics for the automated cryptanalysis of classical ciphers from the
Journal of Combinatorial Mathematics and Combinatorial Computing, 1998.


  We compute the nonlinear optical response of an Fe monolayer placed on top of
1 to 4 monolayers of Cu(001). Our calculation is based on ab initio eigenstates
of the slab, which are obtained within the full-potential linearized augmented
plane-wave method. The ground-state spin-polarized electronic structure is
converged self-consistently to an accuracy better than 0.1 mRy. Subsequently,
we take the spin-orbit interaction into account within a second variational
treatment. The new set of eigenstates allows us to calculate the
magneto-optical transition matrix elements. The second-harmonic response is
determined in the reflection geometry with magnetization perpendicular to the
surface (the so-called polar configuration) using the surface-sheet model.
Adding layers of a noble metal (Cu) to the Fe monolayer gives a new degree of
freedom for the inclusion of nonmagnetic Cu d bands to the nonlinear
magneto-optical response of the slab, and the energy bands show that such an
addition converges essentially to an addition of d states and a small
broadening of the d band with growing number of Cu layers. The screened
nonlinear optical susceptibility is calculated and converges quite well with a
growing number of Cu layers. Our first-principles results confirm that the
magnetic tensor elements of the nonlinear optical response tensor are roughly
of the same order of magnitude as the nonmagnetic ones (in contrast to linear
optics, where the magnetic response is only a minor correction).


  The trigonometric su(n) spin chain with anti-periodic boundary condition
(su(n) spin torus) is demonstrated to be Yang-Baxter integrable. Based on some
intrinsic properties of the R-matrix, certain operator product identities of
the transfer matrix are derived. These identities and the asymptotic behavior
of the transfer matrix together allow us to obtain the exact eigenvalues in
terms of an inhomogeneous T-Q relation via the off-diagonal Bethe Ansatz.


  In this work, we incorporate decoy-state analysis into a well-established
numerical framework for key rate calculation, and apply the numerical framework
to decoy-state BB84 and measurement-device-independent (MDI) QKD protocols as
examples. Additionally, we combine with these decoy-state protocols what is
called "fine-grained statistics", which is a variation of existing QKD
protocols that makes use of originally discarded data to get a better key rate.
We show that such variations can grant protocols resilience against any unknown
and slowly changing rotation along one axis, similar to
reference-frame-independent QKD, but without the need for encoding physically
in an additional rotation-invariant basis. Such an analysis can easily be
applied to existing systems, or even data already recorded in previous
experiments, to gain significantly higher key rate when considerable
misalignment is present, extending the maximum distance for BB84 and MDI-QKD
and reducing the need for manual alignment in an experiment.


  The replica method has been used to calculate the interface free energy
associated with the change from periodic to anti-periodic boundary conditions
in finite-dimensional p-spin glass models in the phase which at mean-field
level has one-step replica symmetry breaking (1RSB). In any finite dimension
the interface free energy is exponentially small for a large system. This
result implies that in finite dimensions, the 1RSB state does not exist, as it
is destroyed by thermal excitation of arbitrarily large droplets. The
implications of this for the theory of structural glasses are discussed.


  The non-parametric bootstrap method is used to evaluate the uncertainties of
two $\alpha$ decay formulas, the universal decay law (UDL) and the new
Geiger-Nuttall law (NGNL). Such a method can simultaneously obtain the
uncertainty of each parameter, the correlation between each pair of parameters,
and the total, statistical, and systematic uncertainties of each formula. Both
even-even (ee) nuclei and odd-A (oA) nuclei are used in the analysis. The
collected data are separated into three parts: ee nuclei, oA nuclei without
spin or parity change (oA\_nc), and oA nuclei with spin and/or parity change
(oA\_c). Based on the residues between observed data and corresponding
calculations, the statistical and systematic uncertainties are decomposed from
the total uncertainty, from which one can clarify the effects from the shell
structure, pairing, and angular momentum change on describing $\alpha$ decay
half-life. If $N > 126$ and $N \leqslant 126$ nuclei are considered together,
the systematic uncertainty of residues between observed and predicted
half-lives are larger than if those groups are considered separately. Without
shell correction term, a much larger systematic uncertainty is found if
parameters obtained for $N \leqslant 126$ nuclei are used to describe the
half-lives of $N > 126$ nuclei. A global hindrance on the $\alpha$ decay
process is found in oA\_nc (oA\_c) nuclei comparing with ee (oA\_nc) nuclei. If
parameters obtained from ee (oA\_nc) nuclei are used, the half-lives of oA\_nc
(oA\_c) nuclei are generally underestimated with large systematic
uncertainties, which can be related to the contribution of pairing effect and
angular momentum. The recently observed superallowed decay from $^{104}$Te to
$^{100}$Sn is also discussed based on uncertainty analysis. (Abstract is not
fully presented because of length limitation)


  In this paper, I propose a technique for recovering quantum dynamical
information from imaginary-time data via the resolution of a one-dimensional
Hamburger moment problem. It is shown that the quantum autocorrelation
functions are uniquely determined by and can be reconstructed from their
sequence of derivatives at origin. A general class of reconstruction algorithms
is then identified, according to Theorem 3. The technique is advocated as
especially effective for a certain class of quantum problems in continuum
space, for which only a few moments are necessary. For such problems, it is
argued that the derivatives at origin can be evaluated by Monte Carlo
simulations via estimators of finite variances in the limit of an infinite
number of path variables. Finally, a maximum entropy inversion algorithm for
the Hamburger moment problem is utilized to compute the quantum rate of
reaction for a one-dimensional symmetric Eckart barrier.


  We report on the continuity equations for linear momentum and energy
associated to a recently introduced electromagnetic formulation based on
classical dipolar sources [Eur. Phys. J. Plus 138, 1034 (2023)]. When connected
to the mass-polariton quasi-particle dynamics, these equations provide a
consistent microscopic description of the local optical energy and momentum
transfer inside dielectric media, called microscopic mass-polariton
formulation. This procedure also unveils the true microscopic origin of the
long-known Abraham optical force density as an interplay between induced
dipoles and mechanical stresses generated within the material.


  We present a detailed study of the phase diagram of copper intercalated
TiSe$_2$ single crystals, combining local Hall-probe magnetometry, tunnel diode
oscillator technique (TDO), specific-heat, and angle-resolved photoemission
spectroscopy measurements. A series of the Cu$_x$TiSe$_2$ samples from three
different sources with various copper content $x$ and superconducting critical
temperatures $T_c$ have been investigated. We first show that the vortex
penetration mechanism is dominated by geometrical barriers enabling a precise
determination of the lower critical field, $H_{c1}$. We then show that the
temperature dependence of the superfluid density deduced from magnetic
measurements (both $H_{c1}$ and TDO techniques) clearly suggests the existence
of a small energy gap in the system, with a coupling strength $2\Delta_s \sim
[2.4-2.8]k_BT_c$, regardless of the copper content, in puzzling contradiction
with specific heat measurements which can be well described by one single large
gap $2\Delta_l \sim [3.7-3.9]k_BT_c$. Finally, our measurements reveal a
non-trivial doping dependence of the condensation energy, which remains to be
understood.


  The spectral flow is a well-known quantity in spectral theory that measures
the variation of spectra about $0$ along paths of selfadjoint Fredholm
operators. The aim of this work is twofold. Firstly, we consider homotopy
invariance properties of the spectral flow and establish a simple formula which
comprises its classical homotopy invariance and yields a comparison theorem for
the spectral flow under compact perturbations. We apply our result to the
existence of non-trivial solutions of boundary value problems of Hamiltonian
systems. Secondly, the spectral flow was axiomatically characterised by Lesch,
and by Ciriza, Fitzpatrick and Pejsachowicz under the assumption that the
endpoints of the paths of selfadjoint Fredholm operators are invertible. We
propose a different approach to the uniqueness of spectral flow which lifts
this additional assumption. As application of the latter result, we discuss the
relation between the spectral flow and the Maslov index in symplectic Hilbert
spaces.


  We consider magnetic fields on $\mathbb{R}^3$ which are parallel to a
conformal Killing field. When the latter generates a simple rotation we show
that a Weyl-Dirac operator with such a magnetic field cannot have a zero mode.
In particular this allows us to expand the class of non zero mode producing
magnetic fields to include examples of non-trivial smooth compactly supported
fields.


  This work focuses on object goal visual navigation, aiming at finding the
location of an object from a given class, where in each step the agent is
provided with an egocentric RGB image of the scene. We propose to learn the
agent's policy using a reinforcement learning algorithm. Our key contribution
is a novel attention probability model for visual navigation tasks. This
attention encodes semantic information about observed objects, as well as
spatial information about their place. This combination of the "what" and the
"where" allows the agent to navigate toward the sought-after object
effectively. The attention model is shown to improve the agent's policy and to
achieve state-of-the-art results on commonly-used datasets.


  We present a quantum interference approach to preserve coherence in the
external states of an atom trapped in an optical lattice. We show that this is
possible by suitably choosing the initial state of the atom. We demonstrate
this in context of decoherence due to spontaneous emission in an
one-dimensional optical double-well lattice.


  Clinical observations indicate that during critical care at the hospitals,
patients sleep positioning and motion affect recovery. Unfortunately, there is
no formal medical protocol to record, quantify, and analyze patient motion.
There is a small number of clinical studies, which use manual analysis of sleep
poses and motion recordings to support medical benefits of patient positioning
and motion monitoring. Manual processes are not scalable, are prone to human
errors, and strain an already taxed healthcare workforce. This study introduces
DECU (Deep Eye-CU): an autonomous mulitmodal multiview system, which addresses
these issues by autonomously monitoring healthcare environments and enabling
the recording and analysis of patient sleep poses and motion. DECU uses three
RGB-D cameras to monitor patient motion in a medical Intensive Care Unit (ICU).
The algorithms in DECU estimate pose direction at different temporal
resolutions and use keyframes to efficiently represent pose transition
dynamics. DECU combines deep features computed from the data with a modified
version of Hidden Markov Model to more flexibly model sleep pose duration,
analyze pose patterns, and summarize patient motion. Extensive experimental
results are presented. The performance of DECU is evaluated in ideal (BC:
Bright and Clear/occlusion-free) and natural (DO: Dark and Occluded) scenarios
at two motion resolutions in a mock-up and a real ICU. The results indicate
that deep features allow DECU to match the classification performance of
engineered features in BC scenes and increase the accuracy by up to 8% in DO
scenes. In addition, the overall pose history summarization tracing accuracy
shows an average detection rate of 85% in BC and of 76% in DO scenes. The
proposed keyframe estimation algorithm allows DECU to reach an average 78%
transition classification accuracy.


  Neutrinoless double beta decay (NDBD) is the only experiment that could probe
the Majorana nature of the neutrino. Here we study the theoretical implications
of NDBD for models yielding tri-bimaximal lepton mixing like A4 and S4.


  Revisions: reference added to: G. Gilbert, {\sl Nucl.Phys.} {\bf B328}, 159
(1989)


  We develop the Well's derivation for extensions realizing affine datum in
arbitrary varieties; in particular, we show there is an exact sequence
connecting the group of compatible automorphisms determined by the datum and
the subgroup of automorphisms of an extension which preserves the extension's
kernel. This implies a homomorphism between $2^{\mathrm{nd}}$-cohomology groups
which realizes a group of kernel-preserving automorphisms of an extension as
itself an extension of a subgroup of compatible automorphisms by the group of
derivations of the datum. The same results are obtained for nonabelian
extensions in any variety of $R$-modules expanded by multilinear operations.


  Helium atom scattering studies have the potential for making numerous
breakthroughs in the study of processes on surfaces. As this field remains
active, there will frequently be new young researchers entering the field. The
transition from student to researcher is often met with difficulty,
consequently wasting limited time available for a PhD or masters level
research. Addressing this issue, we present an educational package for emerging
research students in the field of helium atom scattering. We hope that this
package serves as sufficient material to significantly accelerate the progress
made by new postgraduate students.


  The power system frequency is important for the system overall stability.
However, there does not exist a single measurement point of the system
frequency due to the distributed nature of the system inertia and the small
inconsistency of different generator rotor electrical speeds in one
synchronized system. This paper proposed a new approach to calculate the system
center-of-inertia (COI) frequency and the rate-of-change-of-frequency (RoCoF)
more accurately using PMU data at multiple locations. The COI frequency and the
RoCoF value were further used to assist fast estimation of the imbalance MW
amount of a frequency event. Test results using actual measurements in the U.S.
Eastern Interconnection system validated the effectiveness of the proposed
method.


  We study many-class few-shot (MCFS) problem in both supervised learning and
meta-learning settings. Compared to the well-studied many-class many-shot and
few-class few-shot problems, the MCFS problem commonly occurs in practical
applications but has been rarely studied in previous literature. It brings new
challenges of distinguishing between many classes given only a few training
samples per class. In this paper, we leverage the class hierarchy as a prior
knowledge to train a coarse-to-fine classifier that can produce accurate
predictions for MCFS problem in both settings. The propose model,
"memory-augmented hierarchical-classification network (MahiNet)", performs
coarse-to-fine classification where each coarse class can cover multiple fine
classes. Since it is challenging to directly distinguish a variety of fine
classes given few-shot data per class, MahiNet starts from learning a
classifier over coarse-classes with more training data whose labels are much
cheaper to obtain. The coarse classifier reduces the searching range over the
fine classes and thus alleviates the challenges from "many classes". On
architecture, MahiNet firstly deploys a convolutional neural network (CNN) to
extract features. It then integrates a memory-augmented attention module and a
multi-layer perceptron (MLP) together to produce the probabilities over coarse
and fine classes. While the MLP extends the linear classifier, the attention
module extends the KNN classifier, both together targeting the "few-shot"
problem. We design several training strategies of MahiNet for supervised
learning and meta-learning. In addition, we propose two novel benchmark
datasets "mcfsImageNet" and "mcfsOmniglot" specially designed for MCFS problem.
In experiments, we show that MahiNet outperforms several state-of-the-art
models on MCFS problems in both supervised learning and meta-learning.


  We study the classification of ultrametric spaces based on their small scale
geometry (uniform homeomorphism), large scale geometry (coarse equivalence) and
both (all scale uniform equivalences). We prove that these equivalences can be
characterized with parallel constructions using a combinatoric tool called
common zig-zag chain.


  Ethereum has become a widely used platform to enable secure, Blockchain-based
financial and business transactions. However, a major concern in Ethereum is
the security of its smart contracts. Many identified bugs and vulnerabilities
in smart contracts not only present challenges to maintenance of blockchain,
but also lead to serious financial loses. There is a significant need to better
assist developers in checking smart contracts and ensuring their reliability.In
this paper, we propose a web service tool, named SmartEmbed, which can help
Solidity developers to find repetitive contract code and clone-related bugs in
smart contracts. Our tool is based on code embeddings and similarity checking
techniques. By comparing the similarities among the code embedding vectors for
existing solidity code in the Ethereum blockchain and known bugs, we are able
to efficiently identify code clones and clone-related bugs for any solidity
code given by users, which can help to improve the users' confidence in the
reliability of their code. In addition to the uses by individual developers,
SmartEmbed can also be applied to studies of smart contracts in a large scale.
When applied to more than 22K solidity contracts collected from the Ethereum
blockchain, we found that the clone ratio of solidity code is close to 90\%,
much higher than traditional software, and 194 clone-related bugs can be
identified efficiently and accurately based on our small bug database with a
precision of 96\%. SmartEmbed can be accessed at
\url{http://www.smartembed.net}. A demo video of SmartEmbed is at
\url{https://youtu.be/o9ylyOpYFq8}


  This paper surveys and unifies a number of recent contributions that have
collectively developed a metric for decentralized wireless network analysis
known as transmission capacity. Although it is notoriously difficult to derive
general end-to-end capacity results for multi-terminal or \adhoc networks, the
transmission capacity (TC) framework allows for quantification of achievable
single-hop rates by focusing on a simplified physical/MAC-layer model. By using
stochastic geometry to quantify the multi-user interference in the network, the
relationship between the optimal spatial density and success probability of
transmissions in the network can be determined, and expressed -- often fairly
simply -- in terms of the key network parameters. The basic model and
analytical tools are first discussed and applied to a simple network with path
loss only and we present tight upper and lower bounds on transmission capacity
(via lower and upper bounds on outage probability). We then introduce random
channels (fading/shadowing) and give TC and outage approximations for an
arbitrary channel distribution, as well as exact results for the special cases
of Rayleigh and Nakagami fading. We then apply these results to show how TC can
be used to better understand scheduling, power control, and the deployment of
multiple antennas in a decentralized network. The paper closes by discussing
shortcomings in the model as well as future research directions.


  We introduce a general method of constructing locally compact scattered
spaces from certain families of sets and then, with the help of this method, we
prove that if kappa^{<kappa}=kappa then there is such a space of height kappa^+
with only kappa many isolated points. This implies that there is a locally
compact scattered space of height omega_2 with omega_1 isolated points in ZFC,
solving an old problem of the first author.


  Multi-head self-attention is a key component of the Transformer, a
state-of-the-art architecture for neural machine translation. In this work we
evaluate the contribution made by individual attention heads in the encoder to
the overall performance of the model and analyze the roles played by them. We
find that the most important and confident heads play consistent and often
linguistically-interpretable roles. When pruning heads using a method based on
stochastic gates and a differentiable relaxation of the L0 penalty, we observe
that specialized heads are last to be pruned. Our novel pruning method removes
the vast majority of heads without seriously affecting performance. For
example, on the English-Russian WMT dataset, pruning 38 out of 48 encoder heads
results in a drop of only 0.15 BLEU.


  We present Spitzer Space Telescope 3.6 and 4.5 micron observations of the
binary neutron star merger GW170817 at 43, 74, and 264 days post-merger. Using
the final observation as a template, we uncover a source at the position of
GW170817 at 4.5 micron with a brightness of 22.9+/-0.3 AB mag at 43 days and
23.8+/-0.3 AB mag at 74 days (the uncertainty is dominated by systematics from
the image subtraction); no obvious source is detected at 3.6 micron to a
3-sigma limit of >23.3 AB mag in both epochs. The measured brightness is dimmer
by a factor of about 2-3 times compared to our previously published kilonova
model, which is based on UV, optical, and near-IR data at <30 days. However,
the observed fading rate and color (m_{3.6}-m_{4.5}> 0 AB mag) are consistent
with our model. We suggest that the discrepancy is likely due to a transition
to the nebular phase, or a reduced thermalization efficiency at such late time.
Using the Spitzer data as a guide, we briefly discuss the prospects of
observing future binary neutron star mergers with Spitzer (in LIGO/Virgo
Observing Run 3) and the James Webb Space Telescope (in LIGO/Virgo Observing
Run 4 and beyond).


  Driving a system out of equilibrium enriches the paradigm of spontaneous
symmetry breaking, which could then take place not only in space but also in
time. The interplay between temporal and spatial symmetries, as well as
symmetries from other internal degrees of freedom, can give rise to novel
nonequilibrium phases of matter. In this study, we investigate a
driven-dissipative superfluid model using holographic methods and reveal the
existence of a space-time supersolid (STS) phase which concomitantly breaks the
time translation, spatial translation, and the internal U(1) symmetry. The
holographic methods naturally include finite temperature effects, which enables
us to explore the complex phase diagram of this model and observe a cascade of
out-of-equilibrium phase transitions from the STS phase to a synchronized
superfluid phase, and finally to a normal fluid phase, by increasing the
temperature.


  We prove that the field equations of general relativity and other metric
theories can be derived from the conservation of energy-momentum without using
the assumption of least action principle. We show a new procedure for
perturbative derivation of symmetric pseudo-energy momentum for the gravity
field.


  There is a sequence of random numbers x1,x2, ..., xn and so on. Numbers are
independent of each other, but all numbers are from the same continuous
distribution. If x1 < x2 > x3, then x2 is a local maximum. Here, we show that
the probability mass function (PMF) of idstribution of distances between local
maxima is non-parametric and the same for any probability distribution of
random numbers in the sequence, and that the average distance is exactly 3. We
present a method of computation of this PMF and its table for distances betwen
2 and 29. This PMF is confirmed to match distance distributions of sample
random number sequences, which were created by pseudo-random number generators
or obtained from "true" random number sources.


  A version of Kontsevich Formality theorem is proven for smooth DG algebras.
As an application of this, it is proven that any quasiclassical datum of
noncommutative unfolding of an isolated surface singularity can be quantized.


  We determine exactly the short-distance leading behavior of the density
correlation functions of a two-dimensional two-component charge-symmetric
Coulomb gas composed of point particles, in the whole regime of stability where
the coulombic coupling $\beta<2$. More generally, we compute the short-distance
behavior of the effective interaction potential between two external arbitrary
charges $Q_1$ and $Q_2$ immersed in the plasma, for $\beta |Q_1|<2$ and $\beta
|Q_2|<2$. We also find the short-distance asymptotics of the density profiles
near a single external charge $Q$ immersed in the plasma for $\beta |Q|<2$.


  Recent advances in recording technology have allowed neuroscientists to
monitor activity from thousands of neurons simultaneously. Latent variable
models are increasingly valuable for distilling these recordings into compact
and interpretable representations. Here we propose a new approach to neural
data analysis that leverages advances in conditional generative modeling to
enable the unsupervised inference of disentangled behavioral variables from
recorded neural activity. Our approach builds on InfoDiffusion, which augments
diffusion models with a set of latent variables that capture important factors
of variation in the data. We apply our model, called Generating Neural
Observations Conditioned on Codes with High Information (GNOCCHI), to time
series neural data and test its application to synthetic and biological
recordings of neural activity during reaching. In comparison to a VAE-based
sequential autoencoder, GNOCCHI learns higher-quality latent spaces that are
more clearly structured and more disentangled with respect to key behavioral
variables. These properties enable accurate generation of novel samples (unseen
behavioral conditions) through simple linear traversal of the latent spaces
produced by GNOCCHI. Our work demonstrates the potential of unsupervised,
information-based models for the discovery of interpretable latent spaces from
neural data, enabling researchers to generate high-quality samples from unseen
conditions.


  We develop a novel convolutional architecture tailored for learning from data
defined over directed acyclic graphs (DAGs). DAGs can be used to model causal
relationships among variables, but their nilpotent adjacency matrices pose
unique challenges towards developing DAG signal processing and machine learning
tools. To address this limitation, we harness recent advances offering
alternative definitions of causal shifts and convolutions for signals on DAGs.
We develop a novel convolutional graph neural network that integrates learnable
DAG filters to account for the partial ordering induced by the graph topology,
thus providing valuable inductive bias to learn effective representations of
DAG-supported data. We discuss the salient advantages and potential limitations
of the proposed DAG convolutional network (DCN) and evaluate its performance on
two learning tasks using synthetic data: network diffusion estimation and
source identification. DCN compares favorably relative to several baselines,
showcasing its promising potential.


  Viscoelastic and thermodynamic properties of transient gels comprised of
telechelic polymers are theoretically studied. We extend classical theories of
transient networks so that correlations among polymer chains through the
network junctions are taken into account. This extension enables us to
investigate how rheological quantities, such as viscosity and elastic modulus,
are affected by the association equilibrium conditions, and how these
quantities are related to the aggregation number of junctions. We present a
theoretical model of transient networks with junctions comprised of variable
number of hydrophobic groups on the chain ends. Elastically effective chains
are defined as the chains whose both ends are associated with end groups on
other chains. It is shown that the dynamic shear moduli are well described in
terms of the Maxwell model characterized by a single relaxation time and the
high-frequency plateau modulus as in the classical theories, but the reduced
dynamic shear moduli depend on the polymer concentration and temperature
through the reduced concentration c given as a combination of the association
constant and the volume fraction of end groups. The plateau modulus and the
zero-shear viscosity rise nonlinearly with increasing c when c is small, but
they are proportional to c for higher c. The relaxation time also increases as
c increases due to the presence of pairwise junctions at small c.


  We report an empirical determination of the probability density functions
P(r) of the number r of earthquakes in finite space-time windows for the
California catalog, over fixed spatial boxes 5 x 5 km^2 and time intervals dt
=1, 10, 100 and 1000 days. We find a stable power law tail P(r) ~ 1/r^{1+mu}
with exponent mu \approx 1.6 for all time intervals. These observations are
explained by a simple stochastic branching process previously studied by many
authors, the ETAS (epidemic-type aftershock sequence) model which assumes that
each earthquake can trigger other earthquakes (``aftershocks''). An aftershock
sequence results in this model from the cascade of aftershocks of each past
earthquake. We develop the full theory in terms of generating functions for
describing the space-time organization of earthquake sequences and develop
several approximations to solve the equations. The calibration of the theory to
the empirical observations shows that it is essential to augment the ETAS model
by taking account of the pre-existing frozen heterogeneity of spontaneous
earthquake sources. This seems natural in view of the complex multi-scale
nature of fault networks, on which earthquakes nucleate. Our extended theory is
able to account for the empirical observation satisfactorily. In particular,
the adjustable parameters are determined by fitting the largest time window
$dt=1000$ days and are then used as frozen in the formulas for other time
scales, with very good agreement with the empirical data.


  We propose a feature for action recognition called Trajectory-Set (TS), on
top of the improved Dense Trajectory (iDT). The TS feature encodes only
trajectories around densely sampled interest points, without any appearance
features. Experimental results on the UCF50, UCF101, and HMDB51 action datasets
demonstrate that TS is comparable to state-of-the-arts, and outperforms many
other methods; for HMDB the accuracy of 85.4%, compared to the best accuracy of
80.2% obtained by a deep method. Our code is available on-line at
https://github.com/Gauffret/TrajectorySet .


  Recent work in fair machine learning has proposed dozens of technical
definitions of algorithmic fairness and methods for enforcing these
definitions. However, we still lack an understanding of how to develop machine
learning systems with fairness criteria that reflect relevant stakeholders'
nuanced viewpoints in real-world contexts. To address this gap, we propose a
framework for eliciting stakeholders' subjective fairness notions. Combining a
user interface that allows stakeholders to examine the data and the algorithm's
predictions with an interview protocol to probe stakeholders' thoughts while
they are interacting with the interface, we can identify stakeholders' fairness
beliefs and principles. We conduct a user study to evaluate our framework in
the setting of a child maltreatment predictive system. Our evaluations show
that the framework allows stakeholders to comprehensively convey their fairness
viewpoints. We also discuss how our results can inform the design of predictive
systems.


  We study an extension of standard bandit problem in which there are R layers
of experts. Multi-layered experts make selections layer by layer and only the
experts in the last layer can play arms. The goal of the learning policy is to
minimize the total regret in this hierarchical experts setting. We first
analyze the case that total regret grows linearly with the number of layers.
Then we focus on the case that all experts are playing Upper Confidence Bound
(UCB) strategy and give several sub-linear upper bounds for different
circumstances. Finally, we design some experiments to help the regret analysis
for the general case of hierarchical UCB structure and show the practical
significance of our theoretical results. This article gives many insights about
reasonable hierarchical decision structure.


  We study the monodomain (single-crystal) nematic elastomer materials, all
side-chain siloxane polymers with the same mesogenic groups and crosslinking
density, but differing in the type of crosslinking. Increasing the proportion
of long di-functional segments of main-chain nematic polymer, acting as network
crosslinking, results in dramatic changes in the uniaxial equilibrium thermal
expansion on cooling from isotropic phase. At higher concentration of main
chains their behaviour dominates the elastomer properties. At low concentration
of main-chain material, we detect two distinct transitions at different
temperatures, one attributed to the main-chain, the other to the side-chain
component. The effective uniaxial anisotropy of nematic rubber, r(T)
proportional to the effective nematic order parameter Q(T), is given by the
average of the two components and thus reflects the two-transition nature of
thermal expansion. The experimental data is compared with the theoretical model
of ideal nematic elastomers; applications in high-amplitude thermal actuators
are discussed in the end.


  Solid understanding of current induced torques is key to the development of
current and voltage controlled magnetization dynamics in ultrathin magnetic
heterostructures. To evaluate the size and direction of such torques, or
effective fields, a number of methods have been employed. Here we examine the
adiabatic (low frequency) harmonic Hall voltage measurement that has been used
to study the effective field. We derive an analytical formula for the harmonic
Hall voltages to evaluate the effective field for both out of plane and
in-plane magnetized systems. The formula agrees with numerical calculations
based on a macrospin model. Two different in-plane magnetized films,
Pt|CoFeB|MgO and CuIr|CoFeB|MgO are studied using the formula developed. The
effective field obtained for the latter system shows relatively good agreement
with that estimated using a spin torque switching phase diagram measurements
reported previously. Our results illustrate the versatile applicability of
harmonic Hall voltage measurement for studying current induced torques in
magnetic heterostructures.


  An optimal control problem on finite-dimensional positive cones is stated.
Under a critical assumption on the cone, the corresponding Bellman equation is
satisfied by a linear function, which can be computed by convex optimization. A
separate theorem relates the assumption on the cone to the existence of minimal
elements in certain subsets of the dual cone. Three special cases are derived
as examples. The first one, where the positive cone is the set of positive
semi-definite matrices, reduces to standard linear quadratic control. The
second one, where the positive cone is a polyhedron, reduces to a recent result
on optimal control of positive systems. The third special case corresponds to
linear quadratic control with additional structure, such as spatial invariance.


  Skin lesions are classified in benign or malignant. Among the malignant,
melanoma is a very aggressive cancer and the major cause of deaths. So, early
diagnosis of skin cancer is very desired. In the last few years, there is a
growing interest in computer aided diagnostic (CAD) using most image and
clinical data of the lesion. These sources of information present limitations
due to their inability to provide information of the molecular structure of the
lesion. NIR spectroscopy may provide an alternative source of information to
automated CAD of skin lesions. The most commonly used techniques and
classification algorithms used in spectroscopy are Principal Component Analysis
(PCA), Partial Least Squares - Discriminant Analysis (PLS-DA), and Support
Vector Machines (SVM). Nonetheless, there is a growing interest in applying the
modern techniques of machine and deep learning (MDL) to spectroscopy. One of
the main limitations to apply MDL to spectroscopy is the lack of public
datasets. Since there is no public dataset of NIR spectral data to skin
lesions, as far as we know, an effort has been made and a new dataset named
NIR-SC-UFES, has been collected, annotated and analyzed generating the
gold-standard for classification of NIR spectral data to skin cancer. Next, the
machine learning algorithms XGBoost, CatBoost, LightGBM, 1D-convolutional
neural network (1D-CNN) were investigated to classify cancer and non-cancer
skin lesions. Experimental results indicate the best performance obtained by
LightGBM with pre-processing using standard normal variate (SNV), feature
extraction providing values of 0.839 for balanced accuracy, 0.851 for recall,
0.852 for precision, and 0.850 for F-score. The obtained results indicate the
first steps in CAD of skin lesions aiming the automated triage of patients with
skin lesions in vivo using NIR spectral data.


  The origin of fluorine is a widely debated issue. Nevertheless, the
^{15}N({\alpha},{\gamma})^{19}F reaction is a common feature among the various
production channels so far proposed. Its reaction rate at relevant temperatures
is determined by a number of narrow resonances together with the DC component
and the tails of the two broad resonances at E_{c.m.} = 1323 and 1487 keV.
Measurement through the direct detection of the 19F recoil ions with the
European Recoil separator for Nuclear Astrophysics (ERNA) were performed. The
reaction was initiated by a 15N beam impinging onto a 4He windowless gas
target. The observed yield of the resonances at Ec.m. = 1323 and 1487 keV is
used to determine their widths in the {\alpha} and {\gamma} channels. We show
that a direct measurement of the cross section of the
^{15}N({\alpha},{\gamma})^{19}F reaction can be successfully obtained with the
Recoil Separator ERNA, and the widths {\Gamma}_{\gamma} and {\Gamma}_{\alpha}
of the two broad resonances have been determined. While a fair agreement is
found with earlier determination of the widths of the 1487 keV resonance, a
significant difference is found for the 1323 keV resonance {\Gamma}_{\alpha} .
The revision of the widths of the two more relevant broad resonances in the
15N({\alpha},{\gamma})19F reaction presented in this work is the first step
toward a more firm determination of the reaction rate. At present, the residual
uncertainty at the temperatures of the ^{19}F stellar nucleosynthesis is
dominated by the uncertainties affecting the Direct Capture component and the
364 keV narrow resonance, both so far investigated only through indirect
experiments.


  We study quasilinear evolutionary partial integro-differential equations of
second order which include time fractional $p$-Laplace equations of time order
less than one. By means of suitable energy estimates and De Giorgi's iteration
technique we establish results asserting the global boundedness of
appropriately defined weak solutions of these problems. We also show that a
maximum principle is valid for such equations.


  The lack of large labeled medical imaging datasets, along with significant
inter-individual variability compared to clinically established disease
classes, poses significant challenges in exploiting medical imaging information
in a precision medicine paradigm, where in principle dense patient-specific
data can be employed to formulate individual predictions and/or stratify
patients into finer-grained groups which may follow more homogeneous
trajectories and therefore empower clinical trials. In order to efficiently
explore the effective degrees of freedom underlying variability in medical
images in an unsupervised manner, in this work we propose an unsupervised
autoencoder framework which is augmented with a contrastive loss to encourage
high separability in the latent space. The model is validated on (medical)
benchmark datasets. As cluster labels are assigned to each example according to
cluster assignments, we compare performance with a supervised transfer learning
baseline. Our method achieves similar performance to the supervised
architecture, indicating that separation in the latent space reproduces expert
medical observer-assigned labels. The proposed method could be beneficial for
patient stratification, exploring new subdivisions of larger classes or
pathological continua or, due to its sampling abilities in a variation setting,
data augmentation in medical image processing.


  There has been strong interest recently in the so-called Cooper pair density
wave, subsequent to the proposition that such a state occurs in the hole-doped
cuprate superconductors. As of now there is no convincing demonstration of such
a state in the cuprate theoretical literature. We present here a brief but
complete review of our theoretical and computational work on the
paired-electron crystal (PEC), which has been also experimentally seen in the
insulating phase proximate to superconductivity (SC) in organic charge-transfer
solid (CTS) superconductors. Within our theory, SC in the CTS does indeed
evolve from the PEC. A crucial requirement for the finding of the PEC is that
the proper carrier density of one charge carrier per two sites is taken into
consideration at the outset. Following the discussion of CTS superconductors,
we briefly discuss how the theory can be extended to understand the phase
diagram of the cuprate superconductors that has remained mysterious after
nearly four decades of the discovery of SC in this family.


  Recurrent neural network (RNN)'s architecture is a key factor influencing its
performance. We propose algorithms to optimize hidden sizes under running time
constraint. We convert the discrete optimization into a subset selection
problem. By novel transformations, the objective function becomes submodular
and constraint becomes supermodular. A greedy algorithm with bounds is
suggested to solve the transformed problem. And we show how transformations
influence the bounds. To speed up optimization, surrogate functions are
proposed which balance exploration and exploitation. Experiments show that our
algorithms can find more accurate models or faster models than manually tuned
state-of-the-art and random search. We also compare popular RNN architectures
using our algorithms.


  A generalized form of 't Hooft-Nobbenhuis Complex space-time Transformation
is applied on momentum space from which a new model of Deformed Special
Relavity at Planck Scale is proposed. The model suggests an energy-dependent
Planck's "constant" that vary in space and time and Quantum Mechanics as a
low-energy approximation of perhaps a more fundamental theory at Planck Scale.


  A sphere packing bound (SPB) with a prefactor that is polynomial in the block
length $n$ is established for codes on a length $n$ product channel $W_{[1,n]}$
assuming that the maximum order $1/2$ Renyi capacity among the component
channels, i.e. $\max_{t\in[1,n]} C_{1/2,W_{t}}$, is $\mathit{O}(\ln n)$. The
reliability function of the discrete stationary product channels with feedback
is bounded from above by the sphere packing exponent. Both results are proved
by first establishing a non-asymptotic SPB. The latter result continues to hold
under a milder stationarity hypothesis.


  This work was motivated by a lacking general consensus in the exact form of
the boundary conditions (BCs) required on the solid surfaces for the continuum
modeling of RF plasmas. Various kinds of number and energy density BCs on solid
surfaces were surveyed and how they interacted with the electric potential BC
to affect the plasma was examined in two fundamental RF plasma reactor
configurations. A second-order local mean energy approximation with equations
governing the electron and ion number densities, and the electron energy
density was used to model the plasmas. Zero densities and various combinations
of drift, diffusion and thermal fluxes were considered to set up BCs. It was
shown that the choice of BC can have a significant impact on the sheath and
bulk plasma. The thermal and diffusion fluxes to the surface were found to be
important. A pure drift BC for dielectric walls failed to produce a sheath.


  Public support and political mobilization are two crucial factors for the
adoption of ambitious climate policies in line with the international
greenhouse gas reduction targets of the Paris Agreement. Despite their compound
importance, they are mainly studied separately. Using a random forest
machine-learning model, this article investigates the relative predictive power
of key established explanations for public support and mobilization for climate
policies. Predictive models may shape future research priorities and contribute
to theoretical advancement by showing which predictors are the most and least
important. The analysis is based on a pre-election conjoint survey experiment
on the Swiss CO2 Act in 2021. Results indicate that beliefs (such as the
perceived effectiveness of policies) and policy design preferences (such as for
subsidies or tax-related policies) are the most important predictors while
other established explanations, such as socio-demographics, issue salience (the
relative importance of issues) or political variables (such as the party
affiliation) have relatively weak predictive power. Thus, beliefs are an
essential factor to consider in addition to explanations that emphasize issue
salience and preferences driven by voters' cost-benefit considerations.


  We classify all fundamental integrable spin chains with two-dimensional local
Hilbert space which have regular R-matrices of difference form. This means that
the R-matrix underlying the integrable structures is of the form R(u,v)=R(u-v)
and reduces to the permutation operator at some particular point. We find a
total of 14 independent solutions, 8 of which correspond to well-known eight or
lower vertex models. The remaining 6 models appear to be new and some have
peculiar properties such as not being diagonalizable or being nilpotent.
Furthermore, for even R-matrices, we find a bijection between solutions of the
Yang-Baxter equation and the graded Yang-Baxter equation which extends our
results to the graded two-dimensional case.


  We explore spinning, precessing, unequal mass binary black holes to display
the long term orbital angular momentum, $\vec{L}$, flip dynamics. We study two
prototypical cases of binaries with mass ratios $q=1/7$ and $q=1/15$ and a
misaligned spin of the large black hole (with an intrinsic spin magnitude of
$S_2/m_2^2=0.85$). We conduct full numerical simulations, for nearly 14 and 18
orbits respectively, to evolve the binary down to merger and display a full
$L$-flip cycle. The pattern of radiation of such systems is particularly
interesting, displaying strong polarization-dependent variation of amplitudes
at precessional frequencies, leading to distinctive observational consequences
for ground, space, and pulsar timing based gravitational wave detectors. These
waveform features are strongly directional dependent and measurements of
gravitational waves polarizations can be exploited to disentangle the binary's
parameters in various astrophysical scenarios.


  Graph convolutional networks (GCNs) and their variants have achieved great
success in dealing with graph-structured data. Nevertheless, it is well known
that deep GCNs suffer from the over-smoothing problem, where node
representations tend to be indistinguishable as more layers are stacked up. The
theoretical research to date on deep GCNs has focused primarily on expressive
power rather than trainability, an optimization perspective. Compared to
expressivity, trainability attempts to address a more fundamental question:
Given a sufficiently expressive space of models, can we successfully find a
good solution via gradient descent-based optimizers? This work fills this gap
by exploiting the Graph Neural Tangent Kernel (GNTK), which governs the
optimization trajectory under gradient descent for wide GCNs. We formulate the
asymptotic behaviors of GNTK in the large depth, which enables us to reveal the
dropping trainability of wide and deep GCNs at an exponential rate in the
optimization process. Additionally, we extend our theoretical framework to
analyze residual connection-based techniques, which are found to be merely able
to mitigate the exponential decay of trainability mildly. Inspired by our
theoretical insights on trainability, we propose Critical DropEdge, a
connectivity-aware and graph-adaptive sampling method, to alleviate the
exponential decay problem more fundamentally. Experimental evaluation
consistently confirms using our proposed method can achieve better results
compared to relevant counterparts with both infinite-width and finite-width.


  Tracking calculations of charged particles in electromagnetic fields require
in principle the simultaneous solution of the equation of motion and of
Maxwell's equations. In many tracking codes a simpler and more efficient
approach is used: external fields like that of the accelerating structures are
provided as field maps, generated in separate computations and for the
calculation of self fields the model of a particle bunch in uniform motion is
used. We describe how an externally computed wake function can be approximated
by a table of Taylor coefficients and how the wake field kick can be calculated
for the particle distribution in a tracking calculation. The integrated kick,
representing the effect of a distributed structure, is applied at a discrete
time. As an example, we use our approach to calculate the emittance growth of a
bunch in an undulator beam pipe due to resitive wall wake field effects.


  The aim of this work is to define and perform a study of local times of all
Gaussian processes that have an integral representation over a real interval
(that maybe infinite). Very rich, this class of Gaussian processes, contains
Volterra processes (and thus fractional Brownian motion), multifractional
Brownian motions as well as processes, the regularity of which varies along the
time. Using the White Noise-based anticipative stochastic calculus with respect
to Gaussian processes developed in [Leb17], we first establish a Tanaka
formula. This allows us to define both weighted and non-weighted local times
and finally to provide occupation time formulas for both these local times. A
complete comparison of the Tanaka formula as well as the results on Gaussian
local times we present here, is made with the ones proposed in [MV05, LN12,
SV14].


  The numerical implementation of finite element discretization method for the
stream function formulation of a linearized Navier-Stokes equations is
considered. Algorithm 1 is applied using Argyris element. Three global
orderings of nodes are selected and registered in order to conclude the best
banded structure of matrix and a fluid flow calculation is considered to test a
problem which has a known solution. Visualization of global node orderings,
matrix sparsity patterns and stream function contours are displayed showing the
main features of the flow.


  This paper describes the methodology followed to build a neural machine
translation system in the biomedical domain for the English-Catalan language
pair. This task can be considered a low-resourced task from the point of view
of the domain and the language pair. To face this task, this paper reports
experiments on a cascade pivot strategy through Spanish for the neural machine
translation using the English-Spanish SCIELO and Spanish-Catalan El Peri\'odico
database. To test the final performance of the system, we have created a new
test data set for English-Catalan in the biomedical domain which is freely
available on request.


  The complex structure of a typical stratus cloud base height (or profile)
time series is analyzed with respect to the variability of its fluctuations and
their correlations at all experimentally observed temporal scales. Due to the
underlying processes that create these time series, they are expected to have
multiscaling properties. For obtaining reliable measures of these scaling
properties, different methods of statistical analysis are used herein : power
spectral density, detrended fluctuation analysis, and multifractal analysis.
This broad set of diagnostic techniques is applied to a typical stratus cloud
base height (CBH) data set; data were obtained from the Southern Great Plains
site of the Atmospheric Radiation Measurement Program of the Department of
Energy from a Belfort Laser Ceilometer. First, we demonstrate that this CBH
time series is a nonstationary signal with stationary increments. Further, two
scaling regimes are found, although the characteristic laws are quite similar
ones. Next, the multi-affine scaling properties are confirmed. The scaling
properties of the cloud base height profile of such a continental stratus are
found to be similar to those of the marine cloud base height profiles studied
by us previously. Some physical interpretation in terms of anomalous diffusion
(or fractional random walk) is given for the continental case.


  We present a new model to describe the star formation process in galaxies,
which includes the description of the different gas phases -- molecular,
atomic, and ionized -- together with its metal content. The model, which will
be coupled to cosmological simulations of galaxy formation, will be used to
investigate the relation between the star formation rate (SFR) and the
formation of molecular hydrogen. The model follows the time evolution of the
molecular, atomic and ionized phases in a gas cloud and estimates the amount of
stellar mass formed, by solving a set of five coupled differential equations.
As expected, we find a positive, strong correlation between the molecular
fraction and the initial gas density, which manifests in a positive correlation
between the initial gas density and the SFR of the cloud.


  The realization of global symmetries can depend on the geometry of the
underlying space. In particular, compactification can lead to spontaneous
breaking of such symmetries. Four-dimensional QCD with fundamental
representation fermions embedded in a space with one compact spatial dimension
has a critical length, at which the theory undergoes a phase transition and
develops a ground state that is no longer charge conjugation invariant. We show
this behavior with simulations of three color, four flavor QCD. We use unrooted
staggered fermion at two values of the lattice spacing and several quark
masses. We discuss the dependence of the transition on the dynamical fermion
mass as well as its connection to the finite temperature and chiral phase
transitions.


  Starting from the guiding principles of spacetime locality and
operationalism, a general framework for a probabilistic description of nature
is proposed. Crucially, no notion of time or metric is assumed, neither any
specific physical model. Remarkably, the emerging framework converges with a
recently proposed formulation of quantum theory, obtained constructively from
known quantum physics. At the same time the framework also admits statistical
theories of classical physics.


  We demonstrate that, with a fair comparison, the secret key rate of
discrete-variable measurement-device-independent quantum key distribution
(DV-MDI-QKD) with high-efficiency single-photon detectors and good system
alignment is typically rather high and thus highly suitable for not only long
distance communication but also metropolitan networks. The previous reservation
on the key rate and suitability of DV-MDI-QKD for metropolitan networks
expressed by Pirandola et al. [Nature Photon. 9, 397 (2015)] was based on an
unfair comparison with low-efficiency detectors and high quantum bit error
rate, and is, in our opinion, unjustified.


  The electronic structure and magnetic properties of the Jahn-Teller-distorted
perovskite KAgF3 have been investigated using the full-potential linerized aug-
mented plane-wave method. It is found that KAgF3 exhibits significant
quasi-one- dimensional antiferromagnetism with the ratio of exchange constant
jJ?j (perpen- dicular to the z axis) and J (along the z axis) about 0.04,
although the sublattice of magnetic ion is three-dimensional. The strong
quasi-one-dimensional antiferromag- netism originates from the
C-antiferro-distortive orbital ordering of the Ag2+ 4d9 ions. The orbital
ordered antiferromagnetic insulating state in KAgF3 is determined by on-site
Coulomb repulsion to a large extent.


  This is a translation of Heinz Bachmann's influential paper, wherein the
Bachmann-Howard ordinal is defined, and some general considerations given on
systems of ordinal functions. Permission to post has been granted by the
editors of Vierteljahrsschrift der Naturforschenden Gesellschaft in Zurich
(www.ngzh.ch)


  The current state of knowledge of genus g orientable crossing numbers of
complete graphs through K11 is reviewed. It is shown that cr3(K10)=3,
cr3(K11)<=14, and cr4(K11)=4. It is established with the aid of an algorithm
that there are precisely two non-isomorphic embeddings of K9 with a hexagon
with all its vertices distinct on a surface of genus 3.


  Let X_1,X_2,... be independent identically distributed random elements of a
compact group G. We discuss the speed of convergence of the law of the product
X_l*...*X_1 to the Haar measure. We give poly-log estimates for certain finite
groups and for compact semi-simple Lie groups. We improve earlier results of
Solovay, Kitaev, Gamburd, Shahshahani and Dinai.


  Within the context of structured networks, this paper introduces the concept
of the Fixed Strongly Structurally Controllable Subspace (FSSCS), enabling a
comprehensive characterization of controllable subspaces. From a
graph-theoretical viewpoint, the paper defines Fixed Strongly Structurally
Controllable (FSSC) nodes based on the FSSCS concept and establishes the
necessary and sufficient conditions for their identification. This paper
proposes a method for determining the exact dimension of the Strongly
Structurally Controllable Subspace (SSCS) in hierarchical directed acyclic
graphs, employing a blend of graph-theoretical approaches and controllability
matrix analyses. This approach not only facilitates the identification of FSSC
nodes but also enhances our understanding of the robustness of node
controllability against variations in network parameters within structured
networks, marking a significant advancement in the field of strong structural
controllability of individual nodes.


  D-finite functions and P-recursive sequences are defined in terms of linear
differential and recurrence equations with polynomial coefficients. In this
paper, we introduce a class of numbers closely related to D-finite functions
and P-recursive sequences. It consists of the limits of convergent P-recursive
sequences. Typically, this class contains many well-known mathematical
constants in addition to the algebraic numbers. Our definition of the class of
D-finite numbers depends on two subrings of the field of complex numbers. We
investigate how different choices of these two subrings affect the class.
Moreover, we show that D-finite numbers are essentially limits of D-finite
functions at the point one, and evaluating D-finite functions at non-singular
algebraic points typically yields D-finite numbers. This result makes it easier
to recognize certain numbers to be D-finite.


  Audio-visual automatic speech recognition (AV-ASR) models are very effective
at reducing word error rates on noisy speech, but require large amounts of
transcribed AV training data. Recently, audio-visual self-supervised learning
(SSL) approaches have been developed to reduce this dependence on transcribed
AV data, but these methods are quite complex and computationally expensive. In
this work, we propose replacing these expensive AV-SSL methods with a simple
and fast \textit{audio-only} SSL method, and then performing AV supervised
fine-tuning. We show that this approach is competitive with state-of-the-art
(SOTA) AV-SSL methods on the LRS3-TED benchmark task (within 0.5% absolute
WER), while being dramatically simpler and more efficient (12-30x faster to
pre-train). Furthermore, we show we can extend this approach to convert a SOTA
audio-only ASR model into an AV model. By doing so, we match SOTA AV-SSL
results, even though no AV data was used during pre-training.


  Heterogeneous graph neural architecture search (HGNAS) represents a powerful
tool for automatically designing effective heterogeneous graph neural networks.
However, existing HGNAS algorithms suffer from inefficient searches and
unstable results. In this paper, we present a new GPT-4 based HGNAS model to
improve the search efficiency and search accuracy of HGNAS. Specifically, we
present a new GPT-4 enhanced Heterogeneous Graph Neural Architecture Search
(GHGNAS for short). The basic idea of GHGNAS is to design a set of prompts that
can guide GPT-4 toward the task of generating new heterogeneous graph neural
architectures. By iteratively asking GPT-4 with the prompts, GHGNAS continually
validates the accuracy of the generated HGNNs and uses the feedback to further
optimize the prompts. Experimental results show that GHGNAS can design new
HGNNs by leveraging the powerful generalization capability of GPT-4. Moreover,
GHGNAS runs more effectively and stably than previous HGNAS models based on
reinforcement learning and differentiable search algorithms.


  In this paper we discuss some of the issues concerning the Memory and Content
aspects in the recent debate on the identification of a Standard Model of the
Mind (Laird, Lebiere, and Rosenbloom in press). In particular, we focus on the
representational models concerning the Declarative Memories of current
Cognitive Architectures (CAs). In doing so we outline some of the main problems
affecting the current CAs and suggest that the Conceptual Spaces, a
representational framework developed by Gardenfors, is worth-considering to
address such problems. Finally, we briefly analyze the alternative
representational assumptions employed in the three CAs constituting the current
baseline for the Standard Model (i.e. SOAR, ACT-R and Sigma). In doing so, we
point out the respective differences and discuss their implications in the
light of the analyzed problems.