Abstract:
  The nonlinear optical response of materials is the foundation upon which
applications such as frequency conversion, all-optical signal processing,
molecular spectroscopy, and nonlinear microscopy are built. However, the
utility of all such parametric nonlinear optical processes is hampered by
phase-matching requirements. Quasi-phase-matching, birefringent phase matching,
and higher-order-mode phase matching have all been developed to address this
constraint, but the methods demonstrated to date suffer from the inconvenience
of only being phase-matched for a single, specific arrangement of beams,
typically co-propagating, resulting in cumbersome experimental configurations
and large footprints for integrated devices. Here, we experimentally
demonstrate that these phase-matching requirements may be satisfied in a
parametric nonlinear optical process for multiple, if not all, configurations
of input and output beams when using low-index media. Our measurement
constitutes the first experimental observation of direction-independent phase
matching for a medium sufficiently long for phase matching concerns to be
relevant. We demonstrate four-wave mixing from spectrally distinct co- and
counter-propagating pump and probe beams, the backward-generation of a
nonlinear signal, and excitation by an out-of-plane probe beam. These results
explicitly show that the unique properties of low-index media relax traditional
phase-matching constraints, which can be exploited to facilitate nonlinear
interactions and miniaturize nonlinear devices, thus adding to the established
exceptional properties of low-index materials.


Abstract:
  Three-dimensional geophysical fluids support both internal and
boundary-trapped waves. To obtain the normal modes in such fluids we must solve
a differential eigenvalue problem for the vertical structure (for simplicity,
we only consider horizontally periodic domains). If the boundaries are
dynamically inert (e.g., rigid boundaries in the Boussinesq internal wave
problem, flat boundaries in the quasigeostrophic Rossby wave problem) the
resulting eigenvalue problem typically has a Sturm-Liouville form and the
properties of such problems are well-known. However, when restoring forces are
also present at the boundaries, then the equations of motion contain a
time-derivative in the boundary conditions and this leads to an eigenvalue
problem where the eigenvalue correspondingly appears in the boundary
conditions. In certain cases, the eigenvalue problem can be formulated as an
eigenvalue problem in the Hilbert space $L^2\oplus \mathbb{C}$ and this theory
is well-developed. Less explored is the case when the eigenvalue problem takes
place in a Pontryagin space, as in the Rossby wave problem over sloping
topography. This article develops the theory of such problems and explores the
properties of wave problems with dynamically-active boundaries. The theory
allows us to solve the initial value problem for quasigeostrophic Rossby waves
in a region with sloping bottom (we also apply the theory to two Boussinesq
problems with a free-surface). For a step-function perturbation at a
dynamically-active boundary, we find that the resulting time-evolution consists
of waves present in proportion to their projection onto the dynamically-active
boundary.


Abstract:
  Light front wave functions motivated by holographic constructions are used to
study Bloom-Gilman duality of deep inelastic scattering. Separate expressions
for structure functions in terms of quark and hadronic degrees of freedom are
presented, with a goal of relating the two expressions. A two-parton model is
defined and resonance transition form factors are computed using previously
derived light front wave functions. A new form of global duality is derived
from the valence quark-number sum rule. Using a complete set of hadronic states
is necessary for this new global duality to be achieved. Previous original work
does not provide such a set. This is remedied by amending the model to include
a longitudinal confining potential, and the resulting complete set is
sufficient to carry out the study of Bloom-Gilman duality. Expressions for
transition form factors are obtained and all are shown to fall asymptotically
as 1/Q2. The Feynman mechanism dominates the asymptotic behavior of the model.
These transition form factors are used to assess the validity of the global and
local duality sum rules, with the result that both neither are satisfied.
Evaluations of the hadronic expression for q(x,Q2) provide more details about
this lack. This result shows that the observed validity of both global and
local forms of duality for deep inelastic scattering must be related to a
feature of QCD that is deeper than completeness. Our simple present model
suggests a prediction that Bloom-Gilman duality would not be observed if deep
inelastic scattering experiments were to be made on the pion. The underlying
origin of the duality phenomenon in deep inelastic scattering is deeply buried
within the confinement aspects of QCD, and remains a mystery.


Abstract:
  This paper deals with Poisson approximation to weighted sums of
double-indexed independent random variables using Stein's method. The obtained
results improved the existing bounds. As applications, limit theorems for sums
of weighted Bernoulli and geometric random variables are discussed.


Abstract:
  In this work, we introduce DGO, a new MATLAB toolbox for derivative-free
global optimization. DGO collects various deterministic derivative-free
DIRECT-type algorithms for box-constrained, generally-constrained, and problems
with hidden constraints. Each sequential algorithm is implemented in two
different ways: using static and dynamic data structures for more efficient
information storage and organization. Furthermore, parallel schemes are applied
to some promising algorithms within DGO. The toolbox is equipped with a
graphical user interface (GUI), which ensures the user-friendly use of all
functionalities available in DGO. Available features are demonstrated in
detailed computational studies using a created comprehensive library of global
optimization problems. Additionally, eleven classical engineering design
problems are used to illustrate the potential of DGO to solve challenging
real-world problems.


Abstract:
  We construct a proof of the second law of thermodynamics in an arbitrary
diffeomorphism invariant theory of gravity working within the approximation of
linearized dynamical fluctuations around stationary black holes. We achieve
this by establishing the existence of an entropy current defined on the horizon
of the dynamically perturbed black hole in such theories. By construction, this
entropy current has non-negative divergence, suggestive of a mechanism for the
dynamical black hole to approach a final equilibrium configuration via entropy
production as well as the spatial flow of it on the null horizon. This enables
us to argue for the second law in its strongest possible form, which has a
manifest locality at each space-time point. We explicitly check that the form
of the entropy current that we construct in this paper exactly matches with
previously reported expressions computed considering specific four derivative
theories of higher curvature gravity. Using the same set up we also provide an
alternative proof of the physical process version of the first law applicable
to arbitrary higher derivative theories of gravity.


Abstract:
  Presheaves on a small category are well-known to correspond via a category of
elements construction to ordinary discrete fibrations over that same small
category. Work of R. Par\'e proposes that presheaves on a small double category
are certain lax functors valued in the double category of sets with spans. This
paper isolates the discrete fibration concept corresponding to this presheaf
notion and shows that the category of elements construction introduced by
Par\'e leads to an equivalence of virtual double categories.


Abstract:
  We present the results of a study of the prospect of detecting habitable
Trojan planets in the Kepler Habitable Zone circumbinary planetary systems
(Kepler-16, -47, -453, -1647, -1661). We integrated the orbits of 10,000
separate N-body systems (N=4,6), each with a one Earth-mass body in a randomly
selected orbit near the L4 and L5 Lagrangian points of the host HZ circumbinary
planet. We find that stable Trojan planets are restricted to a narrow range of
semimajor axes in all five systems and limited to small eccentricities in
Kepler-16, -47, and -1661. To assess the prospect of the detection of these
habitable Trojan planets, we calculated the amplitudes of the variations they
cause in the transit timing of their host bodies. Results show that the mean
amplitudes of the transit timing variations (TTVs) correlate with the mass of
the transiting planet and range from 70 minutes for Kepler-16b to 390 minutes
for Kepler-47c. Our analysis indicates that the TTVs of the circumbinary
planets caused by these Trojan bodies fall within the detectable range of
timing precision obtained from the Kepler telescope's long-cadence data. The
latter points to Kepler data as a viable source to search for habitable Trojan
planets.


Abstract:
  Randomized methods are becoming increasingly popular in numerical linear
algebra. However, few attempts have been made to use them in developing
preconditioners. Our interest lies in solving large-scale sparse symmetric
positive definite linear systems of equations where the system matrix is
preordered to doubly bordered block diagonal form (for example, using a nested
dissection ordering). We investigate the use of randomized methods to construct
high quality preconditioners. In particular, we propose a new and efficient
approach that employs Nystr\"om's method for computing low rank approximations
to develop robust algebraic two-level preconditioners. Construction of the new
preconditioners involves iteratively solving a smaller but denser symmetric
positive definite Schur complement system with multiple right-hand sides.
Numerical experiments on problems coming from a range of application areas
demonstrate that this inner system can be solved cheaply using block conjugate
gradients and that using a large convergence tolerance to limit the cost does
not adversely affect the quality of the resulting Nystr\"om--Schur two-level
preconditioner.


Abstract:
  In this work, we consider the smoothing effect of Vlasov-Poisson-Landau
system for both hard and soft potential. In particular, we prove that any
classical solutions becomes immediately smooth with respect to all variables.
We also give a proof on the global existence to Vlasov-Poisson-Landau system
with optimal large time decay. These results give the regularity to
Vlasov-Poisson-Landau system. The proof is based on the time-weighted energy
method building upon the pseudo-differential calculus.


Abstract:
  In this paper, we perform an analysis of 13 outflows in the Cygnus X
star-forming region. We use the James Clerk Maxwell Telescope observations of
$^{13}$CO(3-2) and C$^{18}$O(3-2) molecular emission lines combined with
archival $^{12}$CO(3-2) data. Using these new observations, we measure the
mechanical properties of the outflows, and identify the associated protostars,
finding their properties consistent with previous surveys of outflows
throughout the Milky Way. Finally, we develop and test a method to measure the
same properties using the existing $^{12}$CO(3-2) line data alone, finding the
properties agree to within a factor of 2.


Abstract:
  Fekete, Jord\'an and Kaszanitzky [4] characterised the graphs which can be
realised as 2-dimensional, infinitesimally rigid, bar-joint frameworks in which
two given vertices are coincident. We formulate a conjecture which would extend
their characterisation to an arbitrary set T of vertices and verify our
conjecture when |T| = 3.


Abstract:
  In this article, we investigate meandric systems having one shallow side: the
arch configuration on that side has depth at most two. This class of meandric
systems was introduced and extensively examined by I. P. Goulden, A. Nica, and
D. Puder in 2020. Shallow arch configurations are in bijection with the set of
interval partitions. We study meandric systems by using moment-cumulant
transforms for non-crossing and interval partitions, corresponding to the
notions of free and boolean independence, respectively, in non-commutative
probability. We obtain formulas for the generating series of different classes
of meandric systems with one shallow side, by explicitly enumerating the
simpler, irreducible objects. In addition, we propose random matrix models for
the corresponding meandric polynomials, which can be described in the language
of quantum information theory, in particular that of quantum channels.


Abstract:
  Just-in-time adaptive interventions (JITAIs) are time-varying adaptive
interventions that use frequent opportunities for the intervention to be
adapted--weekly, daily, or even many times a day. The micro-randomized trial
(MRT) has emerged for use in informing the construction of JITAIs. MRTs can be
used to address research questions about whether and under what circumstances
JITAI components are effective, with the ultimate objective of developing
effective and efficient JITAI. The purpose of this article is to clarify why,
when, and how to use MRTs; to highlight elements that must be considered when
designing and implementing an MRT; and to review primary and secondary analyses
methods for MRTs. We briefly review key elements of JITAIs and discuss a
variety of considerations that go into planning and designing an MRT. We
provide a definition of causal excursion effects suitable for use in primary
and secondary analyses of MRT data to inform JITAI development. We review the
weighted and centered least-squares (WCLS) estimator which provides consistent
causal excursion effect estimators from MRT data. We describe how the WCLS
estimator along with associated test statistics can be obtained using standard
statistical software such as R (R Core Team, 2019). Throughout we illustrate
the MRT design and analyses using the HeartSteps MRT, for developing a JITAI to
increase physical activity among sedentary individuals. We supplement the
HeartSteps MRT with two other MRTs, SARA and BariFit, each of which highlights
different research questions that can be addressed using the MRT and
experimental design considerations that might arise.


Abstract:
  For a prime number $\ell$ we say that an oriented pro-$\ell$ group
$(G,\theta)$ has the Bogomolov-Positselski property if the kernel of the
canonical projection on its maximal $\theta$-abelian quotient
$\pi^{ab}_{G,\theta}\colon G\to G(\theta)$ is a free pro-$\ell$ group contained
in the Frattini subgroup of $G$. We show that oriented pro-$\ell$ groups of
elementary type have the Bogomolov-Positselski property. This shows that
Efrat's Elementary Type Conjecture implies a positive answer to Positselski's
version of Bogomolov's Conjecture on maximal pro-$\ell$ Galois groups of a
field $K$ in case that $K^\times/(K^\times)^\ell$ is finite. Secondly, it is
shown that for an $H^\bullet$-quadratic oriented pro-$\ell$ group $(G,\theta)$
the Bogomolov-Positselski property can be expressed by the injectivity of the
transgression map $d_2^{2,1}$ in the Hochschild-Serre spectral sequence.


Abstract:
  Aditya-L1 is India's first solar mission with Visible Emission Line
Coronagraph (VELC) consisting of three spectral channels taking high-resolution
spectroscopic observations of the inner corona up to 1.5 Rsun at 5303 \AA, 7892
\AA, and 10747 \AA. In this work, we present the strategy for the slit width
optimization for the VELC using synthetic line profiles by taking into account
the instrument characteristics and coronal conditions for log(T) varying from 6
to 6.5. The synthetic profiles are convolved with simulated instrumental
scattered light and noise to estimate the signal-to-noise ratio (SNR), which
will be crucial to design the future observation plans. We find that the
optimum slit width for VELC turns out to be 50 microns providing sufficient SNR
for observations in different solar conditions. We also analyzed the effect of
plasma temperature on the SNR at different heights in the VELC field of view
for the optimized slit width. We also studied the expected effect of the
presence of a CME on the spectral channel observations. This analysis will help
to plan the science observations of VELC in different solar conditions.


Abstract:
  We study the lattice QCD $\Lambda_c N$ phase shifts for the $^3S_1-{}^3D_1$
coupled channel using both the leading order covariant chiral effective theory
and the next-to-leading order non-relativistic chiral effective field theory.
We show that although it is possible to describe simultaneously the $^3S_1$ and
$^3D_1$ phase shifts and the inelasticity $\eta_1$, the fitted energy range is
pretty small, only up to $E_\mathrm{c.m.}=5$ MeV. This raises concerns
regarding the consistency between leading/next-to-leading order chiral
effective field theory and the lattice QCD simulations.


Abstract:
  Mesons comprising a beauty quark and a strange quark can oscillate between
particle (B0s) and antiparticle (B0sbar) flavour eigenstates, with a frequency
given by the mass difference between heavy and light mass eigenstates, deltams.
Here we present a measurement of deltams using B0s2DsPi decays produced in
proton-proton collisions collected with the LHCb detector at the Large Hadron
Collider. This measurement improves upon the current deltams precision by a
factor of two. The oscillation frequency is found to be deltams = 17.7683 +-
0.0051 +- 0.0032 ps-1 where the first uncertainty is statistical and the second
systematic. We combine this result with previous LHCb measurements to determine
deltams = 17.7656 +- 0.0057 ps-1.


Abstract:
  We present an empirical investigation of a new mapping system based on a
graph of panoramic depth images. Panoramic images efficiently capture range
measurements taken by a spinning lidar sensor, recording fine detail on the
order of a few centimeters within maps of expansive scope on the order of tens
of millions of cubic meters. The flexibility of the system is demonstrated by
running the same mapping software against data collected by hand-carrying a
sensor around a laboratory space at walking pace, moving it outdoors through a
campus environment at running pace, driving the sensor on a small wheeled
vehicle on- and off-road, flying the sensor through a forest, carrying it on
the back of a legged robot navigating an underground coal mine, and mounting it
on the roof of a car driven on public roads. The full 3D maps are built online
with a median update time of less than ten milliseconds on an embedded NVIDIA
Jetson AGX Xavier system.


Abstract:
  We aim to reveal the nature of the reddest known substellar companion HD
206893 B by studying its near-infrared colors and spectral morphology and by
investigating its orbital motion. We fit atmospheric models for giant planets
and brown dwarfs and perform spectral retrievals with petitRADTRANS and ATMO on
the observed GRAVITY, SPHERE, and GPI spectra of HD 206893 B. To recover its
unusual spectral features, we include additional extinction by high-altitude
dust clouds made of enstatite grains in the atmospheric model fits. We also
infer the orbital parameters of HD 206893 B by combining the $\sim
100~\mu\text{as}$ precision astrometry from GRAVITY with data from the
literature and constrain the mass and position of HD 206893 C based on the Gaia
proper motion anomaly of the system. The extremely red color and the very
shallow $1.4~\mu\text{m}$ water absorption feature of HD 206893 B can be fit
well with the adapted atmospheric models and spectral retrievals. Altogether,
our analysis suggests an age of $\sim 3$-$300~\text{Myr}$ and a mass of $\sim
5$-$30~\text{M}_\text{Jup}$ for HD 206893 B, which is consistent with previous
estimates but extends the parameter space to younger and lower-mass objects.
The GRAVITY astrometry points to an eccentric orbit ($e =
0.29^{+0.06}_{-0.11}$) with a mutual inclination of $< 34.4~\text{deg}$ with
respect to the debris disk of the system. While HD 206893 B could in principle
be a planetary-mass companion, this possibility hinges on the unknown influence
of the inner companion on the mass estimate of
$10^{+5}_{-4}~\text{M}_\text{Jup}$ from radial velocity and Gaia as well as a
relatively small but significant Argus moving group membership probability of
$\sim 61\%$. However, we find that if the mass of HD 206893 B is $<
30~\text{M}_\text{Jup}$, then the inner companion HD 206893 C should have a
mass between $\sim 8$-$15~\text{M}_\text{Jup}$.


Abstract:
  We mimic random nanowire networks by the homogeneous, isotropic, and random
deposition of conductive zero-width sticks onto an insulating substrate. The
number density (the number of objects per unit area of the surface) of these
sticks is supposed to exceed the percolation threshold, i.e., the system under
consideration is a conductor. To identify any current-carrying part (the
backbone) of the percolation cluster, we have proposed and implemented a
modification of the well-known wall follower algorithm -- one type of maze
solving algorithm. The advantage of the modified algorithm is its
identification of the whole backbone without visiting all the edges. The
complexity of the algorithm depends significantly on the structure of the graph
and varies from $O\left(\sqrt{N_\text{V}}\right)$ to $\Theta(N_\text{V})$. The
algorithm has been applied to backbone identification in networks with
different number densities of conducting sticks. We have found that (i) for
number densities of sticks above the percolation threshold, the strength of the
percolation cluster quickly approaches unity as the number density of the
sticks increases; (ii) simultaneously, the percolation cluster becomes
identical to its backbone plus simplest dead ends, i.e., edges that are
incident to vertices of degree 1. This behavior is consistent with the
presented analytical evaluations.


Abstract:
  Equational reasoning is among the most important tools that functional
programming provides us. Curiously, relatively less attention has been paid to
reasoning about monadic programs.
  In this report we derive a backtracking algorithm for problem specifications
that use a monadic unfold to generate possible solutions, which are filtered
using a $\mathit{scanl}$-like predicate. We develop theorems that convert a
variation of $\mathit{scanl}$ to a $\mathit{foldr}$ that uses the state monad,
as well as theorems constructing hylomorphism. The algorithm is used to solve
the $n$-queens puzzle, our running example. The aim is to develop theorems and
patterns useful for the derivation of monadic programs, focusing on the
intricate interaction between state and non-determinism.


Abstract:
  We consider compactifications of rank $Q$ E-string theory on a genus zero
surface with no punctures but with flux for various subgroups of the
$\text{E}_8\times \text{SU}(2)$ global symmetry group of the six dimensional
theory. We first construct a simple Wess-Zumino model in four dimensions
corresponding to the compactification on a sphere with one puncture and a
particular value of flux, the cap model. Using this theory and theories
corresponding to two punctured spheres with flux, one can obtain a large number
of models corresponding to spheres with a variety of fluxes. These models
exhibit interesting IR enhancements of global symmetry as well as duality
properties. As an example we will show that constructing sphere models
associated to specific fluxes related by an action of the Weyl group of
$\text{E}_8$ leads to the S-confinement duality of the $\text{USp}(2Q)$ gauge
theory with six fundamentals and a traceless antisymmetric field. Finally, we
show that the theories we discuss possess an $\text{SU}(2)_{\text{ISO}}$
symmetry in four dimensions that can be naturally identified with the isometry
of the two-sphere. We give evidence in favor of this identification by
computing the `t Hooft anomalies of the $\text{SU}(2)_{\text{ISO}}$ in 4d and
comparing them with the predicted anomalies from 6d.


Abstract:
  Non-Hermitian systems with aperiodic order display phase transitions that are
beyond the paradigm of Hermitian physics. Unfortunately, owing to the
incommensurability of the potential most of known non-Hermitian models are not
integrable. This motivates the search for exactly solvable models, where
localization/delocalization phase transitions, mobility edges in complex plane
and their topological nature can be unraveled. Here we present an exactly
solvable model of quasi crystal, which is a non-pertrurbative non-Hermitian
extension of a famous integrable model of quantum chaos proposed by Grempel
{\it at al.} [Phys. Rev. Lett. {\bf 49}, 833 (1982)] and dubbed the Maryland
model. Contrary to the Hermitian Maryland model, its non-Hermitian extension
shows a richer scenario, with a localization-delocalization phase transition
via topological mobility edges in complex energy plane.


Abstract:
  We use example of the Davey-Stewartson hierarchy to show that in addition to
the standard equations given by Lax operator and evolutions of times with
positive numbers, one can consider time evolutions with negative numbers and
the same Lax operator. We derive corresponding Lax pairs and integrable
equations.


Abstract:
  Besides the well-known classification task, these days neural networks are
frequently being applied to generate or transform data, such as images and
audio signals. In such tasks, the conventional loss functions like the mean
squared error (MSE) may not give satisfactory results. To improve the
perceptual quality of the generated signals, one possibility is to increase
their similarity to real signals, where the similarity is evaluated via a
discriminator network. The combination of the generator and discriminator nets
is called a Generative Adversarial Network (GAN). Here, we evaluate this
adversarial training framework in the articulatory-to-acoustic mapping task,
where the goal is to reconstruct the speech signal from a recording of the
movement of articulatory organs. As the generator, we apply a 3D convolutional
network that gave us good results in an earlier study. To turn it into a GAN,
we extend the conventional MSE training loss with an adversarial loss component
provided by a discriminator network. As for the evaluation, we report various
objective speech quality metrics such as the Perceptual Evaluation of Speech
Quality (PESQ), and the Mel-Cepstral Distortion (MCD). Our results indicate
that the application of the adversarial training loss brings about a slight,
but consistent improvement in all these metrics.


Abstract:
  Our Milky Way (MW) has witnessed a series of major accretion events. One of
the later additions, Gaia-Enceladus, has contributed a considerable mass to the
inner Galaxy, but also generously donated to the outer halo. So far,
associations with present-day MW globular clusters (GCs) have been chiefly
based on their kinematics and ages. Here, we present a chemical abundance study
of the outer halo (R$_{\rm GC}$=18 kpc) GC NGC 1261, which has been suggested
to be an accreted object. We measured 31 species of 29 elements in two stars
from high-resolution Magellan/MIKE spectra and find that the cluster is
moderately metal poor, at [Fe/H]=-1.26. NGC 1261 is moderately
$\alpha$-enhanced to the 0.3-dex level. While from the small sample alone it is
difficult to assert any abundance correlations, the light elements Na,O,Mg, and
Al differ significantly between the two stars in contrast to the majority of
other elements with smaller scatter; this argues in favour of multiple
generations of stars coexisting in this GC. Intriguingly for its metallicity,
NGC 1261 shows heavy element abundances that are consistent with $r$-process
nucleosynthesis and we discuss their origin in various sites. In particular the
Eu overabundance quantitatively suggests that one single $r$-process event,
such as a neutron-star neutron-star merger or a rare kind of supernova, can be
responsible for the stellar enhancement or even the enrichment of the cluster
with the excess $r$-material. Its heavy element pattern makes NGC 1261 resemble
the moderately enhanced r-I stars that are commonly found in the halo and have
been detected in Gaia-Enceladus as well. Therefore, combining all kinematical,
age, and chemical evidence we conclude that NGC 1261 is a chemically intriguing
GC that was born in Gaia-Enceladus and has been subsequently accreted into the
MW halo. [abridged]


Abstract:
  Given a topological ring $R$, we study semitopological $R$-modules, construct
their completions, Bohr and borno modifications. For every topological space
$X$, we construct the free (semi)topological $R$-module over $X$ and prove that
for a $k$-space $X$ its free semitopological $R$-module is a topological
$R$-module. Also we construct a Tychonoff space $X$ whose free semitopological
$R$-module is not a topological $R$-module.


Abstract:
  Let $G$ be a classical linear algebraic group over an algebraically closed
field, and let $\mathfrak{n}$ denote the subset of nilpotent elements in its
Lie algebra. In this paper we study a partial order on the $G$-orbits in
$\mathfrak{n}$ given by taking limits along cocharacters of $G$. This gives
rise to the so-called accessibility order on the nilpotent orbits. Our main
results show that for general and special linear algebras, this new order
coincides with the usual dominance order on nilpotent orbits, but for
symplectic and orthogonal algebras this is not the case.


Abstract:
  We present measurements of the electron helicity asymmetry in quasi-elastic
proton knockout from $^{2}$H and $^{12}$C nuclei by polarized electrons. This
asymmetry depends on the fifth structure function, is antisymmetric with
respect to the scattering plane, and vanishes in the absence of final-state
interactions, and thus it provides a sensitive tool for their study. Our
kinematics cover the full range in off-coplanarity angle $\phi_{pq}$, with a
polar angle $\theta_{pq}$ coverage up to about 8 degrees. The missing energy
resolution enabled us to determine the asymmetries for knock-out resulting in
different states of the residual $^{11}$B system. We find that the helicity
asymmetry for $p$-shell knockout from $^{12}$C depends on the final state of
the residual system and is relatively large (up to $\approx 0.16$), especially
at low missing momentum. It is considerably smaller (up to $\approx 0.01$) for
$s$-shell knockout from both $^{12}$C and $^2$H. The data for $^2$H are in very
good agreement with theoretical calculations, while the predictions for
$^{12}$C exhibit differences with respect to the data.


Abstract:
  We analyze quantum fluctuation effects at the onset of charge or spin density
wave order in two-dimensional metals with an incommensurate nesting ($2k_F$)
wave vector connecting two pairs of hot spots on the Fermi surface. We first
compute the momentum and frequency dependence of the fermion self-energy near
the hot spots to leading order in a perturbation expansion (one loop).
Non-Fermi liquid behavior with a linear (in energy) quasi-particle decay rate
and a logarithmically vanishing quasi-particle weight is obtained. The momentum
dependence of the self-energy entails only finite renormalizations of the Fermi
velocity and the Fermi surface curvature at the hot spots. The perturbative
one-loop result is not self-consistent and casts doubt on the stability of the
$2k_F$ quantum critical point. We construct a self-consistent solution of the
one-loop equations with self-energy feedback, where the quantum critical point
is stabilized rather than being destroyed by fluctuations, while the non-Fermi
liquid behavior as found in the perturbative one-loop calculation is confirmed.


Abstract:
  We consider the fundamental problems of determining the rooted and global
edge and vertex connectivities (and computing the corresponding cuts) in
directed graphs. For rooted (and hence also global) edge connectivity with
small integer capacities we give a new randomized Monte Carlo algorithm that
runs in time $\tilde{O}(n^2)$. For rooted edge connectivity this is the first
algorithm to improve on the $\Omega(n^3)$ time bound in the dense-graph
high-connectivity regime. Our result relies on a simple combination of sampling
coupled with sparsification that appears new, and could lead to further
tradeoffs for directed graph connectivity problems.
  We extend the edge connectivity ideas to rooted and global vertex
connectivity in directed graphs. We obtain a $(1 + \epsilon)$-approximation for
rooted vertex connectivity in $\tilde{O}(nW/\epsilon)$ time where $W$ is the
total vertex weight (assuming integral vertex weights); in particular this
yields an $\tilde{O}(n^2/\epsilon)$ time randomized algorithm for unweighted
graphs. This translates to a $\tilde{O}(\kappa nW)$ time exact algorithm where
$\kappa$ is the rooted connectivity. We build on this to obtain similar bounds
for global vertex connectivity.
  Our results complement the known results for these problems in the low
connectivity regime due to work of Gabow [9] for edge connectivity from 1991,
and the very recent work of Nanongkai et al. [24] and Forster et al. [7] for
vertex connectivity.


Abstract:
  We prove that every positively-weighted tree T can be realized as the cut
locus C(x) of a point x on a convex polyhedron P, with T weights matching C(x)
lengths. If T has n leaves, P has (in general) n+1 vertices. We show there are
in fact a continuum of polyhedra P each realizing T for some x on P. Three main
tools in the proof are properties of the star unfolding of P, Alexandrov's
gluing theorem, and a cut-locus partition lemma. The construction of P from T
is surprisingly simple.


Abstract:
  The evolution of the atomic structures of the combinatorial library of
Sm-substituted thin film BiFeO3 along the phase transition boundary from the
ferroelectric rhombohedral phase to the non-ferroelectric orthorhombic phase is
explored using scanning transmission electron microscopy (STEM). Localized
properties including polarization, lattice parameter, and chemical composition
are parameterized from atomic-scale imaging and their causal relationships are
reconstructed using a linear non-Gaussian acyclic model (LiNGAM). This approach
is further extended toward exploring the spatial variability of the causal
coupling using the sliding window transform method, which revealed that new
causal relationships emerged both at the expected locations, such as domain
walls and interfaces, but also at additional regions forming clusters in the
vicinity of the walls or spatially distributed features. While the exact
physical origins of these relationships are unclear, they likely represent
nanophase separated regions in the morphotropic phase boundaries. Overall, we
pose that an in-depth understanding of complex disordered materials away from
thermodynamic equilibrium necessitates understanding not only of the generative
processes that can lead to observed microscopic states, but also the causal
links between multiple interacting subsystems.


Abstract:
  We prove distality of quantifier-free relations on valued fields with finite
residue field. By a result of Chernikov-Galvin-Starchenko, this yields
Szemer\'edi-Trotter-like incidence bounds for function fields over finite
fields. We deduce a version of the Elekes-Szab\'o theorem for such fields.


Abstract:
  Time-series is ubiquitous across applications, such as transportation,
finance and healthcare. Time-series is often influenced by external factors,
especially in the form of asynchronous events, making forecasting difficult.
However, existing models are mainly designated for either synchronous
time-series or asynchronous event sequence, and can hardly provide a synthetic
way to capture the relation between them. We propose Variational Synergetic
Multi-Horizon Network (VSMHN), a novel deep conditional generative model. To
learn complex correlations across heterogeneous sequences, a tailored encoder
is devised to combine the advances in deep point processes models and
variational recurrent neural networks. In addition, an aligned time coding and
an auxiliary transition scheme are carefully devised for batched training on
unaligned sequences. Our model can be trained effectively using stochastic
variational inference and generates probabilistic predictions with Monte-Carlo
simulation. Furthermore, our model produces accurate, sharp and more realistic
probabilistic forecasts. We also show that modeling asynchronous event
sequences is crucial for multi-horizon time-series forecasting.


Abstract:
  In this paper it presents, develops and discusses the existence of a process
with long scope memory structure, representing of the independence between the
degree of randomness of the traffic generated by the sources and flow pattern
exhibited by the network. The process existence is presented in term of a new
algorithmic that is a variant of the maximum likelihood estimator (MLE) of
Whittle, for the calculation of the Hurst exponent (H) of self-similar
stationary second order time series of the flows of the individual sources and
their aggregation. Also, it is discussed the additional problems introduced by
the phenomenon of the locality of the Hurst exponent, that appears when the
traffic flows consist of diverse elements with different Hurst exponents. The
instance is exposed with the intention of being considered as a new and
alternative approach for modeling and simulating traffic in existing computer
networks.


Abstract:
  In the present work we have studied the dynamics of dipolar atom-molecular
Bose Einstein Condensates coupled via Feshbach Resonance in a double well
potential. We have numerically solved four coupled GP like equations, two for
left well and two for right well for this atom molecular coupled system. Our
numerical results show that both the long-range dipole-dipole interaction
(chosen to be positive) and the coherent coupling interaction (which is
positive for bosons) facilitate the transmission of atoms and molecules from
left well to the right well when the population in the right well dominates
over that in the left well and is trapped for a period of time. Whereas in
absence of any one of these interactions probability of transient transmission
decreases. However in absence of both the interactions (dipole-dipole and
coherent coupling) i.e. when only the repulsive contact interaction is present,
it leads to self trapping in the left well for a period of time. It is also
shown that the signature of coherent coupling between atoms and molecules on
the density distribution of atoms in the double well potential is present both
in absence and presence of dipole-dipole interaction.


Abstract:
  Like ESG investing, climate change is an important concern for asset managers
and owners, and a new challenge for portfolio construction. Until now,
investors have mainly measured carbon risk using fundamental approaches, such
as with carbon intensity metrics. Nevertheless, it has not been proven that
asset prices are directly impacted by these fundamental-based measures. In this
paper, we focus on another approach, which consists in measuring the
sensitivity of stock prices with respect to a carbon risk factor. In our
opinion, carbon betas are market-based measures that are complementary to
carbon intensities or fundamental-based measures when managing investment
portfolios, because carbon betas may be viewed as an extension or
forward-looking measure of the current carbon footprint. In particular, we show
how this new metric can be used to build minimum variance strategies and how
they impact their portfolio construction.


Abstract:
  Developers need to perform adequate testing to ensure the quality of
Automatic Speech Recognition (ASR) systems. However, manually collecting
required test cases is tedious and time-consuming. Our recent work proposes
CrossASR, a differential testing method for ASR systems. This method first
utilizes Text-to-Speech (TTS) to generate audios from texts automatically and
then feed these audios into different ASR systems for cross-referencing to
uncover failed test cases. It also leverages a failure estimator to find
failing test cases more efficiently. Such a method is inherently
self-improvable: the performance can increase by leveraging more advanced TTS
and ASR systems. So in this accompanying tool demo paper, we devote more
engineering and propose CrossASR++, an easy-to-use ASR testing tool that can be
conveniently extended to incorporate different TTS and ASR systems, and failure
estimators. We also make CrossASR++ chunk texts from a given corpus dynamically
and enable the estimator to work in a more effective and flexible way. We
demonstrate that the new features can help CrossASR++ discover more failed test
cases. Using the same TTS and ASR systems, CrossASR++ can uncover 26.2% more
failed test cases for 4 ASRs than the original tool. Moreover, by simply adding
one more ASR for cross-referencing, we can increase the number of failed test
cases uncovered for each of the 4 ASR systems by 25.07%, 39.63%, 20.9\% and
8.17% respectively. We also extend CrossASR++ with 5 additional failure
estimators. Compared to worst estimator, the best one can discover 10.41% more
failed test cases within the same amount of time.


Abstract:
  Existing compartmental models in epidemiology are limited in terms of
optimizing the resource allocation to control an epidemic outbreak under
disease growth uncertainty. In this study, we address this core limitation by
presenting a multi-stage stochastic programming compartmental model, which
integrates the uncertain disease progression and resource allocation to control
an infectious disease outbreak. The proposed multi-stage stochastic program
involves various disease growth scenarios and optimizes the distribution of
treatment centers and resources while minimizing the total expected number of
new infections and funerals. We define two new equity metrics, namely infection
and capacity equity, and explicitly consider equity for allocating treatment
funds and facilities over multiple time stages. We also study the multi-stage
value of the stochastic solution (VSS), which demonstrates the superiority of
the proposed stochastic programming model over its deterministic counterpart.
We apply the proposed formulation to control the Ebola Virus Disease (EVD) in
Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and
fair resource-allocation strategies. Our model balances the proportion of
infections over all regions, even without including the infection equity or
prevalence equity constraints. Model results also show that allocating
treatment resources proportional to population is sub-optimal, and enforcing
such a resource allocation policy might adversely impact the total number of
infections and deaths, and thus resulting in a high cost that we have to pay
for the fairness. Our multi-stage stochastic epidemic-logistics model is
practical and can be adapted to control other infectious diseases in
meta-populations and dynamically evolving situations.


Abstract:
  The deformation behaviour of the intermetallic Al$_{2}$Cu-phase was
investigated using atomistic simulations and micropillar compression, where
slip on the unexpected {211} and {022} slip planes was revealed. Additionally,
all possible slip systems for the intermetallic phases were further evaluated
and a preference for the activation of slip systems based on their effective
interplanar distances as well as the effective Burgers vector is proposed. The
effective interplanar distance corresponds to the manually determined
interplanar distance, whereas the effective Burgers vector takes a potential
dislocation dissociation into account. This new order is: {211}1/2<111>,
{022}1/2<111> and {022}<100>, {110}<001>, {310}<001>, {022}<011>,
{110}1/2<111>, {112}<110> and {112}1/2<111> from high to low ratio of
deff/beff. Also, data on the critical resolved shear stresses of several of
these slip systems were measured.


Abstract:
  Many processes in psychology are complex, such as dyadic interactions between
two interacting partners (e.g. patient-therapist, intimate relationship
partners). Nevertheless, many basic questions about interactions are difficult
to investigate because dyadic processes can be within a person and between
partners, they are based on multimodal aspects of behavior and unfold rapidly.
Current analyses are mainly based on the behavioral coding method, whereby
human coders annotate behavior based on a coding schema. But coding is
labor-intensive, expensive, slow, focuses on few modalities. Current approaches
in psychology use LIWC for analyzing couples' interactions. However, advances
in natural language processing such as BERT could enable the development of
systems to potentially automate behavioral coding, which in turn could
substantially improve psychological research. In this work, we train machine
learning models to automatically predict positive and negative communication
behavioral codes of 368 German-speaking Swiss couples during an 8-minute
conflict interaction on a fine-grained scale (10-seconds sequences) using
linguistic features and paralinguistic features derived with openSMILE. Our
results show that both simpler TF-IDF features as well as more complex BERT
features performed better than LIWC, and that adding paralinguistic features
did not improve the performance. These results suggest it might be time to
consider modern alternatives to LIWC, the de facto linguistic features in
psychology, for prediction tasks in couples research. This work is a further
step towards the automated coding of couples' behavior which could enhance
couple research and therapy, and be utilized for other dyadic interactions as
well.


Abstract:
  Although rapid progress in-vehicle automated technology has sped up the
possibility of using fully automated technology for public use, little research
has been done on the possible influences of autonomous vehicles (AVs)
technology on long-distance travel. This technology has the potential to have a
significant effect on intercity trips. This study analyzed a travel survey to
anticipate the impact of this technology on long-distance trips. We have
divided trips into two different categories including trips for pleasure and
trips for business. Different hypotheses based on the authors' knowledge and
assisted by existing literature have been defined for each type of trip. By
using the Pearson method these hypotheses have been tested and the positive or
negative responses from respondents have been evaluated. The findings show that
using AVs for pleasure trips can increase the number of travelers and stimulate
people to choose longer distances for their trips. In addition, people enjoy
more and will be interested to travel more frequently. For business trips, AV
technology can reduce travel costs and job-related stress. Unlike pleasure
trips for which people are not interested in traveling at night, business
travelers prefer to travel at night.


Abstract:
  The production of axionlike particles (ALPs) with small masses in
ultraperipheral $Pb - p$ and $Pb - Pb$ collisions at the LHC is investigated.
The cross section and kinematical distributions associated to the diphoton
final state produced in the $\gamma \gamma \rightarrow a \rightarrow \gamma
\gamma$ subprocesses are estimated considering a realistic set of kinematical
cuts. A detailed analysis of the backgrounds is performed and the expected
sensitivity to the ALP production is derived. Our results demonstrate that a
future experimental analysis of the exclusive diphoton production for the
forward rapidities probed by the LHCb detector can improve the existing
exclusion limits on the ALP - photon coupling in the mass range 2 GeV $\le m_a
\le$ 5 GeV.


Abstract:
  Let $G$ be a graph with $m$ edges and spectral radius $\lambda_{1}$. Let
$bk\left( G\right) $ stand for the maximal number of triangles with a common
edge in $G$.
  In 1970 Nosal proved that if $\lambda_{1}^{2}>m,$ then $G$ contains a
triangle. In this paper we show that the same premise implies that \[ bk\left(
G\right) >\frac{1}{12}\sqrt[4]{m}. \] This result settles a conjecture of Zhai,
Lin, and Shu.
  Write $\lambda_{2}$ for the second largest eigenvalue of $G$. Recently, Lin,
Ning, and Wu showed that if $G$ is a triangle-free graph of order at least
three, then \[ \lambda_{1}^{2}+\lambda_{2}^{2}\leq m, \] thereby settling the
simplest case of a conjecture of Bollob\'{a}s and the author. We give a simpler
proof of their result.


Abstract:
  The origin of weakly bound objects like clusters and hypernuclei, observed in
heavy-ion collisions, is of theoretical and experimental interest. It is in the
focus of the experiments at RHIC and LHC since it is not evident how such
weakly bound objects can survive in an environment whose hadronic decay
products point to a temperature of the order of 150 MeV. It is as well one of
the key research topics in the future facilities of FAIR and NICA which are
under construction in Darmstadt (Germany) and Dubna (Russia), respectively. We
present here first results on the cluster dynamics within the model-independent
cluster recognition library "phase-space Minimum Spanning Tree" (psMST) applied
to different transport approaches: PHQMD, PHSD, SMASH and UrQMD. The psMST is
based on the "Minimum Spanning Tree" (MST) algorithm for the cluster
recognition which exploits correlations in coordinate space, and it is extended
to correlations of baryons in the clusters in momentum space. We show the
sensitivity of the cluster formation on the microscopic realization of the
n-body dynamics and on the potential interactions in heavy-ion collisions.


Abstract:
  Due to their large mass and small aspect ratio, icebergs pose a threat to
boats and offshore structures. Small icebergs and bergy bits can cause harm to
platform hulls and are more difficult to discover remotely. As icebergs are
dynamic mediums, the study of icebergs in relation to safe human operations
requires the rigorous analysis of the ice-ocean interaction, in particular with
waves and currents. In this paper, we present iceberg towing experiments and
analyze iceberg stability from GPS tracks and inertial motion unit data. The
towline tension as well as the boat motion relative to the iceberg was
measured. Different scenarios were investigated by changing the towing strategy
with regards to towing speed, direction (straight or curved trajectory) and
acceleration. Large amplitude roll oscillations with period of approximately 30
s were observed immediately after the load dropped and the iceberg returned to
a stable static position. In two of the cases, the iceberg flipped over partly
or entirely after some towing time. From the load cell, we observed
oscillations in the system with periods of approximately 6 s, which were
attributed to the rope elastic properties and the iceberg response. The load
oscillations increased when the towing direction was against the waves as
opposed to perpendicular to the waves.


Abstract:
  Teaching an anthropomorphic robot from human example offers the opportunity
to impart humanlike qualities on its movement. In this work we present a
reinforcement learning based method for teaching a real world bipedal robot to
perform movements directly from human motion capture data. Our method
seamlessly transitions from training in a simulation environment to executing
on a physical robot without requiring any real world training iterations or
offline steps. To overcome the disparity in joint configurations between the
robot and the motion capture actor, our method incorporates motion re-targeting
into the training process. Domain randomization techniques are used to
compensate for the differences between the simulated and physical systems. We
demonstrate our method on an internally developed humanoid robot with movements
ranging from a dynamic walk cycle to complex balancing and waving. Our
controller preserves the style imparted by the motion capture data and exhibits
graceful failure modes resulting in safe operation for the robot. This work was
performed for research purposes only.


Abstract:
  A short review of scalar curvature invariants in gravity theories is
presented. We introduce how these invariants are constructed and discuss the
minimal number of invariants required for a given spacetime. We then discuss
applications of these invariants and focus on three topics that are of
particular interest in modern gravity theories.


Abstract:
  Navigation guided by natural language instructions is particularly suitable
for Domestic Service Robots that interacts naturally with users. This task
involves the prediction of a sequence of actions that leads to a specified
destination given a natural language navigation instruction. The task thus
requires the understanding of instructions, such as ``Walk out of the bathroom
and wait on the stairs that are on the right''. The Visual and Language
Navigation remains challenging, notably because it requires the exploration of
the environment and at the accurate following of a path specified by the
instructions to model the relationship between language and vision. To address
this, we propose the CrossMap Transformer network, which encodes the linguistic
and visual features to sequentially generate a path. The CrossMap transformer
is tied to a Transformer-based speaker that generates navigation instructions.
The two networks share common latent features, for mutual enhancement through a
double back translation model: Generated paths are translated into instructions
while generated instructions are translated into path The experimental results
show the benefits of our approach in terms of instruction understanding and
instruction generation.


Abstract:
  Approximate computing (AC) leverages the inherent error resilience and is
used in many big-data applications from various domains such as multimedia,
computer vision, signal processing, and machine learning to improve systems
performance and power consumption. Like many other approximate circuits and
algorithms, the memory subsystem can also be used to enhance performance and
save power significantly. This paper proposes an efficient and effective
systematic methodology to construct an approximate non-volatile
magneto-resistive RAM (MRAM) framework using consumer-off-the-shelf (COTS) MRAM
chips. In the proposed scheme, an extensive experimental characterization of
memory errors is performed by manipulating the write latency of MRAM chips
which exploits the inherent (intrinsic/extrinsic process variation) stochastic
switching behavior of magnetic tunnel junctions (MTJs). The experimental
results and error-resilient image application reveal that the proposed AC
framework provides a significant performance improvement and demonstrates a
maximum reduction in MRAM write current of ~66% on average with negligible or
no loss in output quality.


Abstract:
  In practical optimisation the dominant characteristics of the problem are
often not known prior. Therefore, there is a need to develop general solvers as
it is not always possible to tailor a specialised approach to each application.
The hybrid form of Multi-Level Selection Genetic Algorithm (MLSGA) already
shows good performance on range of problems due to its diversity-first
approach, which is rare among Evolutionary Algorithms. To increase the
generality of its performance this paper proposes a distinct set of
co-evolutionary mechanisms, which defines co-evolution as competition between
collectives rather than individuals. This distinctive approach to
co-evolutionary provides less regular communication between sub-populations and
different fitness definitions between individuals and collectives. This
encourages the collectives to act more independently creating a unique
sub-regional search, leading to the development of co-evolutionary MLSGA
(cMLSGA). To test this methodology nine genetic algorithms are selected to
generate several variants of cMLSGA, which incorporates these approaches at the
individual level. The new mechanisms are tested on over 100 different functions
and benchmarked against the 9 state-of-the-art competitors in order to find the
best general solver. The results show that the diversity of co-evolutionary
approaches is more important than their individual performances. This allows
the selection of two competing algorithms that improve the generality of
cMLSGA, without large loss of performance on any specific problem type. When
compared to the state-of-the-art, the proposed methodology is the most
universal and robust, leading to an algorithm more likely to solve complex
problems with limited knowledge about the search space.


Abstract:
  Context. The physical processes driving the formation of Galactic spiral arms
are still under debate. Studies using open clusters favour the description of
the Milky Way spiral arms as long-lived structures following the classical
density wave theory. Current studies comparing the Gaia DR2 field stars
kinematic information of the Solar neighbourhood to simulations, find a better
agreement with short-lived arms with a transient behaviour. Aims. Our aim is to
provide an observational, data-driven view of the Milky Way spiral structure
and its dynamics using open clusters as the main tracers, and to contrast it
with simulation-based approaches. We use the most complete catalogue of Milky
Way open clusters, with astrometric Gaia EDR3 updated parameters, estimated
astrophysical information and radial velocities, to re-visit the nature of the
spiral pattern of the Galaxy. Methods. We use a Gaussian mixture model to
detect overdensities of open clusters younger than 30 Myr that correspond to
the Perseus, Local, Sagittarius and Scutum spiral arms, respectively. We use
the birthplaces of the open cluster population younger than 80 Myr to trace the
evolution of the different spiral arms and compute their pattern speed. We
analyse the age distribution of the open clusters across the spiral arms to
explore the differences in the rotational velocity of stars and spiral arms.
Results. We are able to increase the range in Galactic azimuth where
present-day spiral arms are described, better estimating its parameters by
adding 264 young open clusters to the 84 high-mass star-forming regions used so
far, thus increasing by a 314% the number of tracers. We use the evolution of
the open clusters from their birth positions to find that spiral arms nearly
co-rotate with field stars at any given radius, discarding a common spiral
pattern speed for the spiral arms explored. [abridged]


Abstract:
  This study analyses the dose difference for a variety of phantom materials
that can be used for Leksell Gamma Knife. These materials have properties that
are very similar to the human tissue not including the skull bone. Geant4 was
employed in the analysis the dose distributions for collimator helmet sizes of
4mm and 8mm. The phantom had a radius of 80mm. Water, brain, PMMA (Poly-methyl
methacrylate) and polystyrene were used as the material types. Results showed
no considerable differences for radiation dosimetries depending on the material
types. In addition, the polystyrene and PMMA (Poly-methyl methacrylate) phantom
are also suitable for measuring the dose profiles of the Gamma Knife unit.


Abstract:
  In satellite-based free-space continuous-variable QKD (CV-QKD), the parameter
estimation for the atmospheric channel fluctuations due to the turbulence
effects and attenuation is crucial for analyzing and improving the protocol
performance. In this paper, compressive sensing (CS) theory is applied to
free-space CV-QKD to achieve the channel parameter estimation with low
computational complexity and small amount of data. According to CS theory, the
possibility of the sparse representation for free-space channel is analyzed and
the two types of sparse reconstruction models for the channel parameters are
constructed combining with the stability of the sub-channels. The most part of
variable for parameter estimation is saved by using the model relying on the
variables in the quantum signals, while all the variables can be used to
generate the secret key by using the model relying on the second-order
statistics of the variables. The methods are well adapted for the cases with
the limited communication time since a little or no variable is sacrificed for
parameter estimation. Finally, simulation results are given to verify the
effectiveness of the proposed methods.


Abstract:
  We estimate chemical abundances and ionization parameters in the nuclear
region of a sample of 143 galaxies from the Palomar Spectroscopic Survey,
composed by Star-Forming Galaxies (87), Seyferts 2 (16) and LINERs (40) using
the \textsc{Hii-Chi-mistry} code. We also study for each spectral type the
correlation of the derived quantities with other different properties of the
host galaxies, such as morphology, stellar mass, luminosity and mass of their
Supermassive Black Holes. The results obtained for Star-Forming Galaxies are
used to check the soundness of our methodology. Then, we replicate a similar
study for our sample of AGN, distinguishing between Seyferts 2 and LINERs. We
report a saturation of Oxygen abundances for the nuclear regions of SFG. The
correlations between chemical abundances and their host galaxy properties for
SFG are in good agreement with previous studies. We find that Seyferts 2
present slightly higher chemical abundances but this result must be reexamined
in larger samples of Seyfert galaxies. In contrast, we obtain lower chemical
abundances for LINERs than for SFG. We confirm these relatively lower
abundances for another sample of infrared luminous LINERs in the same stellar
mass range. Our analysis of AGNs (both LINERs and Seyferts) shows that their
host galaxy properties are not correlated with our estimated chemical
abundances.


Abstract:
  We investigate critical properties of the stacked-$J_1$-$J_2$ Ising model on
a cubic lattice. Using Monte Carlo simulations and renormalization group, we
find a single phase transition of the first order for $J_2/J_1>1/2$. The
renormgroup approach predicts that a transition can be of the second order from
the universality class of the $O(2)$ model, but the Monte Carlo results show
another set of critical exponents: exponents continuously vary form the values
typical for a first-order transition in the finite-size scaling theory at
$1/2<J_2/J_1<1$ to the Ising values in the limit $J_2/J_1\to\infty$. We also
exclude the pseudo-first-order behavior observed in the $J_1$-$J_2$ Ising model
on a square lattice for $0.67\lesssim J_2/J_1\lesssim0.9$.


Abstract:
  Given a pair of translation surfaces it is very difficult to determine
whether they are supported on the same algebraic curve. In fact, there are very
few examples of such pairs. In this note we present infinitely many examples of
finite collections of translation surfaces supported on the same algebraic
curve.
  The underlying curves are hyperelliptic curves with many automorphisms. For
each curve, the automorphism of maximal order acts on the space of holomorphic
1-forms. We present a translation surface corresponding to each of the
eigenforms of this action.


Abstract:
  In many urban areas, traffic load and noise pollution are constantly
increasing. Automated systems for traffic monitoring are promising
countermeasures, which allow to systematically quantify and predict local
traffic flow in order to to support municipal traffic planning decisions. In
this paper, we present a novel open benchmark dataset, containing 2.5 hours of
stereo audio recordings of 4718 vehicle passing events captured with both
high-quality sE8 and medium-quality MEMS microphones. This dataset is well
suited to evaluate the use-case of deploying audio classification algorithms to
embedded sensor devices with restricted microphone quality and hardware
processing power. In addition, this paper provides a detailed review of recent
acoustic traffic monitoring (ATM) algorithms as well as the results of two
benchmark experiments on vehicle type classification and direction of movement
estimation using four state-of-the-art convolutional neural network
architectures.


Abstract:
  In this paper, we propose a whole-body planning framework that unifies
dynamic locomotion and manipulation tasks by formulating a single multi-contact
optimal control problem. We model the hybrid nature of a generic multi-limbed
mobile manipulator as a switched system, and introduce a set of constraints
that can encode any pre-defined gait sequence or manipulation schedule in the
formulation. Since the system is designed to actively manipulate its
environment, the equations of motion are composed by augmenting the robot's
centroidal dynamics with the manipulated-object dynamics. This allows us to
describe any high-level task in the same cost/constraint function. The
resulting planning framework could be solved on the robot's onboard computer in
real-time within a model predictive control scheme. This is demonstrated in a
set of real hardware experiments done in free-motion, such as base or
end-effector pose tracking, and while pushing/pulling a heavy resistive door.
Robustness against model mismatches and external disturbances is also verified
during these test cases.


Abstract:
  IoT devices have become popular targets for various network attacks due to
their lack of industry-wide security standards. In this work, we focus on smart
home IoT device identification and defending them against Distributed Denial of
Service (DDoS) attacks. The proposed framework protects smart homes by using
VLAN-based network isolation. This architecture has two VLANs: one with
non-verified devices and the other with verified devices, both of which are
managed by the SDN controller. Lightweight stateless flow-based features,
including ICMP, TCP, and UDP protocol percentage, packet count and size, and IP
diversity ratio, are proposed for efficient feature collections. Further
analysis is performed to minimize training data to run on resource-constrained
edge devices in smart home networks. Three popular machine learning algorithms,
including K-Nearest-Neighbors, Random Forest, and Support Vector Machines, are
used to classify IoT devices and detect different types of DDoS attacks,
including TCP-SYN, UDP, and ICMP. The system's effectiveness and efficiency are
evaluated by emulating a network consisting of an Open vSwitch, Faucet SDN
controller, and several IoT device traces from two different testbeds.


Abstract:
  This paper extends the project initiated in arXiv:2002.00201 and studies a
lifecycle portfolio choice problem with borrowing constraints and finite
retirement time in which an agent receives labor income that adjusts to
financial market shocks in a path dependent way. The novelty here, with respect
to arXiv:2002.00201, is the fact that we have a finite retirement time, which
makes the model more realistic, but harder to solve. The presence of both
path-dependency, as in arXiv:2002.00201, and finite retirement, leads to a
two-stages infinite dimensional stochastic optimal control problem, a family of
problems which, to our knowledge, has not yet been treated in the literature.
We solve the problem completely, and find explicitly the optimal controls in
feedback form. This is possible because we are able to find an explicit
solution to the associated infinite dimensional Hamilton-Jacobi-Bellman (HJB)
equation, even if state constraints are present. Note that, differently from
arXiv:2002.00201 , here the HJB equation is of parabolic type, hence the work
to identify the solutions and optimal feedbacks is more delicate, as it
involves, in particular, time-dependent state constraints, which, as far as we
know, have not yet been treated in the infinite dimensional literature. The
explicit solution allows us to study the properties of optimal strategies and
discuss their financial implications.


Abstract:
  The normalization of brain recordings from multiple subjects responding to
the natural stimuli is one of the key challenges in auditory neuroscience. The
objective of this normalization is to transform the brain data in such a way as
to remove the inter-subject redundancies and to boost the component related to
the stimuli. In this paper, we propose a deep learning framework to improve the
correlation of electroencephalography (EEG) data recorded from multiple
subjects engaged in an audio listening task. The proposed model extends the
linear multi-way canonical correlation analysis (CCA) for audio-EEG analysis
using an auto-encoder network with a shared encoder layer. The model is trained
to optimize a combined loss involving correlation and reconstruction. The
experiments are performed on EEG data collected from subjects listening to
natural speech and music. In these experiments, we show that the proposed deep
multi-way CCA (DMCCA) based model significantly improves the correlations over
the linear multi-way CCA approach with absolute improvements of 0.08 and 0.29
in terms of the Pearson correlation values for speech and music tasks
respectively.


Abstract:
  To what extent can the Planck satellite observations be interpreted as
confirmation of the quantum part of the inflationary paradigm? Has it "seen"
the Bunch-Davies state? We compare and contrast the Bunch-Davies interpretation
with one using a so-called entangled state in which the fluctuations of a
spectator scalar field are entangled with those of the metric perturbations
$\zeta$. We first show how a spectator scalar field $\Sigma$, with an
expectation value $\sigma(t)$ that evolves in time, will generically generate
such a state. We then use this state to compute the power spectrum
$P_{\zeta}(k)$ and thence the temperature anisotropies $C_l$ in the Cosmic
Microwave Background (CMB). We find interesting differences from the standard
calculations using the Bunch-Davies (BD) state. We argue that existing data may
already be used to place interesting bounds on this class of deviations from
the BD state and that, for some values of the parameters of the state, the
power spectra may be consistent with the Planck satellite data.


Abstract:
  We study notions of complexity for link complement states in Chern Simons
theory with compact gauge group $G$. Such states are obtained by the Euclidean
path integral on the complement of $n$-component links inside a 3-manifold
$M_3$. For the Abelian theory at level $k$ we find that a natural set of
fundamental gates exists and one can identify the complexity as differences of
linking numbers modulo $k$. Such linking numbers can be viewed as coordinates
which embeds all link complement states into $\mathbb{Z}_k ^{\otimes n(n-1)/2}$
and the complexity is identified as the distance with respect to a particular
norm. For non-Abelian Chern Simons theories, the situation is much more
complicated. We focus here on torus link states and show that the problem can
be reduced to defining complexity for a single knot complement state. We
suggest a systematic way to choose a set of minimal universal generators for
single knot complement states and then evaluate the complexity using such
generators. A detailed illustration is shown for $SU(2)_k$ Chern Simons theory
and the results can be extended to general compact gauge group.


Abstract:
  Dialogue systems in the form of chatbots and personal assistants are being
increasingly integrated into people's lives. Modern dialogue systems may
consider adopting anthropomorphic personas, mimicking societal demographic
groups to appear more approachable and trustworthy to users. However, the
adoption of a persona can result in the adoption of biases. In this paper, we
present the first large-scale study on persona biases in dialogue systems and
conduct analyses on personas of different social classes, sexual orientations,
races, and genders. We define persona biases as harmful differences in
responses (e.g., varying levels of offensiveness, agreement with harmful
statements) generated from adopting different demographic personas.
Furthermore, we introduce an open-source framework, UnitPersonaBias, to explore
and aggregate persona biases in dialogue systems. By analyzing the Blender and
DialoGPT dialogue systems, we observe that adopting personas can actually
decrease harmful responses, compared to not using any personas. Additionally,
we find that persona choices can affect the degree of harms in generated
responses and thus should be systematically evaluated before deployment. We
also analyze how personas can result in different amounts of harm towards
specific demographics.


Abstract:
  Recent researches have suggested that the predictive accuracy of neural
network may contend with its adversarial robustness. This presents challenges
in designing effective regularization schemes that also provide strong
adversarial robustness. Revisiting Vicinal Risk Minimization (VRM) as a
unifying regularization principle, we propose Adversarial Labelling of
Perturbed Samples (ALPS) as a regularization scheme that aims at improving the
generalization ability and adversarial robustness of the trained model. ALPS
trains neural networks with synthetic samples formed by perturbing each
authentic input sample towards another one along with an adversarially assigned
label. The ALPS regularization objective is formulated as a min-max problem, in
which the outer problem is minimizing an upper-bound of the VRM loss, and the
inner problem is L$_1$-ball constrained adversarial labelling on perturbed
sample. The analytic solution to the induced inner maximization problem is
elegantly derived, which enables computational efficiency. Experiments on the
SVHN, CIFAR-10, CIFAR-100 and Tiny-ImageNet datasets show that the ALPS has a
state-of-the-art regularization performance while also serving as an effective
adversarial training scheme.


Abstract:
  Remote sensing for archaeological investigations using surface response is
reasonably well established, however, remote subsurface exploration is limited
by depth and penetration and ground resolution. Furthermore, the conservation
of archaeological sites requires constant monitoring capability, which is often
not feasible between annual field seasons, but may be provided by modern
satellite coverage. Here we develop an approach using Sentinel-1 C-band radar
backscatter, and Sentinel-2 multispectral data, to map and characterise the
site of Qubbet el-Hawa, Egypt. The multispectral bands analysed show similar
sensitivity to satellite imagery. However, the radar backscatter is sensitive
to exposed known structures, as well as disturbances to soil
textural/composition profile due to excavation/erosion. Sub-resolution features
such as causeways manifest as a 'radar-break' in the backscatter - a
discontinuity in otherwise continuous radar units. Furthermore, the finite
subsurface response in the backscatter under the arid conditions of the site
means we are able to delineate some shallow subsurface structures and map their
orientation beneath the surface in areas not yet excavated. The sensitivity of
Sentinel-1 backscatter to soil disturbance and human activity at Qubbet
el-Hawa, and the short (~12 day) recurrence time of the satellites, makes it an
important tool in heritage conservation.


Abstract:
  The $24^{\circ}$ N jet borders the North Tropical Belt and North Tropical
Zone, and is the fastest prograde jet on Jupiter, reaching speeds above $170$
m/s. In this region, observations have shown several periodic convective
plumes, likely from latent heat release from water condensation, which affect
the cloud and zonal wind structure of the jet. We model this region with the
Explicit Planetary hybrid-Isentropic Coordinate model using its active
microphysics scheme to study the phenomenology of water and ammonia clouds
within the jet region. On perturbing the atmosphere, we find that an upper
tropospheric wave develops that directly influences the cloud structure within
the jet. This wave travels at $\sim75$ m/s in our model, and leads to periodic
chevron-shaped features in the ammonia cloud deck. These features travel with
the wave speed, and are subsequently much slower than the zonal wind at the
cloud deck. The cloud structure, and the slower drift rate, were both observed
following the convective outbreak in this region in 2016 and 2020. We find that
an upper level circulation is responsible for these cloud features in the
aftermath of the convective outbursts. The comparatively slower observed drift
rates of these features, relative to the wind speed of the jet, provides
constraints on the vertical wind shear above the cloud tops, and we suggest
that wind velocities determined from cloud tracking should correspond to a
different altitude compared to the $680$ hPa pressure level. We also diagnose
the convective potential of the atmosphere due to water condensation, and find
that it is strongly coupled to the wave.


Abstract:
  Over an arbitrary compact complex space or an arbitrary germ of complex space
$X$, we provide fine resolutions of pure Hodge modules with strict supports
$IC_X(\mathbb{V})$ via differential forms with locally $L^2$ boundary
conditions. When $\mathbb{V}=\mathbb{C}_{X_{\rm reg}}$ is the trivial variation
of Hodge structure, we give a solution to a Cheeger-Goresky-MacPherson type
conjecture: For any compact complex space $X$, there is a complete hermitian
metric $ds^2$ on $X_{\rm reg}$ such that there is a canonical isomorphism
  $$H^i_{(2)}(X_{\rm reg},ds^2)\simeq IH^i(X),\quad \forall i.$$
  Such metric $ds^2$ could be K\"ahler if $X$ is a K\"ahler space.
  As an application, we give a differential geometrical proof of the K\"ahler
package of the hypercohomology of pure Hodge modules. We also prove the
K\"ahler version of Kashiwara's conjecture in the absolute case.


Abstract:
  We report a theoretical description of the synthetic momentum-state lattices
with a 3D Gross-Pitaevskii equation (GPE), where both the external trap
potential and the mean-field spatial-density-dependent many-body interactions
are naturally included and exactly treated. The GPE models exhibit better
performance than the tight-binding model to depict the experimental
observations. Since the trap modifies the dispersion relation for free
particles and shapes the spatial density distribution that leads to
inhomogeneous interactions, decoherences (damping oscillation) appear even for
a short-time evolution. Our parametric calculations for the two-state
oscillation suggest that we should work with a relatively shallow trap in the
weakly interacting regime, especially when the long-term dynamics are
concerned. The impact of the mean-field interaction, i.e., the self-trapping
behavior, on the transport dynamics and the topological phase transition in a
finite multiple-state lattice chain is also specifically investigated. Such an
accurate treatment of the inhomogeneous interactions allows for further
investigations on the interplay with disorder, the pair correlation dynamics,
and the thermalization process in momentum space.


Abstract:
  We present LAMOST J0140355+392651 (hereafter J0140), a close ($P_{\rm orb} =
3.81$ hours) binary containing a bloated, low-mass ($M \approx 0.15 M_{\odot}$)
proto-white dwarf (WD) and a massive ($M\approx 0.95\,M_{\odot}$) WD companion.
The system's optical light curve is dominated by large-amplitude ellipsoidal
variability but also exhibits additional scatter, likely driven by pulsations.
The proto-WD is cooler ($T_{\rm eff} = 6800\pm 100$ K) and more puffy
($\log\left[g/\left({\rm cm\,s^{-2}}\right)\right]=4.74\pm0.07$) than any known
extremely low mass (ELM) WD, but hotter than any known cataclysmic variable
(CV) donor. It either completely or very nearly fills its Roche lobe
($R/R_{{\rm Roche\,lobe}}=0.99\pm0.01$), suggesting ongoing or recently
terminated mass transfer. No dwarf nova-like outbursts have been observed. The
spectrum is dominated by the proto-WD but shows tentative hints of H$\alpha$
emission, perhaps due to accretion onto the massive WD. The properties of the
system are well-matched by MESA binary evolution models of CVs with donors that
underwent significant nuclear evolution before the onset of mass transfer. In
these models, the bloated proto-WD is either still losing mass via stable Roche
lobe overflow or was doing so until very recently. In either case, it is
evolving toward higher temperatures at near-constant luminosity to become an
ELM WD. If the system is detached, mass transfer likely ended when the donor
became too hot for magnetic braking to remain efficient. Evolutionary models
predict that the binary will shrink to $P_{\rm orb}\lesssim 10$ minutes within
a few Gyr, when it will either merge or become an AM CVn binary. J0140 provides
an observational link between the formation channels of CVs, ELM WDs, detached
ultracompact WD binaries, and AM CVn systems.


Abstract:
  Using determinant Quantum Monte Carlo, we compare three methods of evaluating
the dc Hall coefficient $R_H$ of the Hubbard model: the direct measurement of
the off-diagonal current-current correlator $\chi_{xy}$ in a system coupled to
a finite magnetic field (FF), $\chi_{xy}^{\text{FF}}$; the three-current linear
response to an infinitesimal field as measured in the zero-field (ZF) Hubbard
Hamiltonian, $\chi_{xy}^{\text{ZF}}$; and the leading order of the recurrent
expansion $R_H^{(0)}$ in terms of thermodynamic susceptibilities. The two
quantities $\chi_{xy}^{\text{FF}}$ and $\chi_{xy}^{\text{ZF}}$ can be compared
directly in imaginary time. Proxies for $R_H$ constructed from the
three-current correlator $\chi_{xy}^{\text{ZF}}$ can be determined under
different simplifying assumptions and compared with $R_H^{(0)}$. We find these
different quantities to be consistent with one another, validating previous
conclusions about the close correspondence between Fermi surface topology and
the sign of $R_H$, even for strongly correlated systems. These various
quantities also provide a useful set of numerical tools for testing theoretical
predictions about the full behavior of the Hall conductivity for strong
correlations.


Abstract:
  Pooling is a critical operation in convolutional neural networks for
increasing receptive fields and improving robustness to input variations. Most
existing pooling operations downsample the feature maps, which is a lossy
process. Moreover, they are not invertible: upsampling a downscaled feature map
can not recover the lost information in the downsampling. By adopting the
philosophy of the classical Lifting Scheme from signal processing, we propose
LiftPool for bidirectional pooling layers, including LiftDownPool and
LiftUpPool. LiftDownPool decomposes a feature map into various downsized
sub-bands, each of which contains information with different frequencies. As
the pooling function in LiftDownPool is perfectly invertible, by performing
LiftDownPool backward, a corresponding up-pooling layer LiftUpPool is able to
generate a refined upsampled feature map using the detail sub-bands, which is
useful for image-to-image translation challenges. Experiments show the proposed
methods achieve better results on image classification and semantic
segmentation, using various backbones. Moreover, LiftDownPool offers better
robustness to input corruptions and perturbations.


Abstract:
  Modern radio telescopes produce unprecedented amounts of data, which are
passed through many processing pipelines before the delivery of scientific
results. Hyperparameters of these pipelines need to be tuned by hand to produce
optimal results. Because many thousands of observations are taken during a
lifetime of a telescope and because each observation will have its unique
settings, the fine tuning of pipelines is a tedious task. In order to automate
this process of hyperparameter selection in data calibration pipelines, we
introduce the use of reinforcement learning. We test two reinforcement learning
techniques, twin delayed deep deterministic policy gradient (TD3) and soft
actor-critic (SAC), to train an autonomous agent to perform this fine tuning.
For the sake of generalization, we consider the pipeline to be a black-box
system where the summarized state of the performance of the pipeline is used by
the autonomous agent. The autonomous agent trained in this manner is able to
determine optimal settings for diverse observations and is therefore able to
perform 'smart' calibration, minimizing the need for human intervention.


Abstract:
  One of the most important aims of grain boundary modeling is to predict the
evolution of a large collection of grains in phenomena such as abnormal grain
growth, coupled grain boundary motion, and recrystallization that occur under
extreme thermomechanical loads. A unified framework to study the coevolution of
grain boundaries with bulk plasticity has recently been developed by Admal et
al. (2018), which is based on modeling grain boundaries as continuum
dislocations governed by an energy based on the Kobayashi--Warren--Carter (KWC)
model (Kobayashi et al., 1998, 2000). While the resulting unified model
demonstrates coupled grain boundary motion and polygonization (seen in
recrystallization), it is restricted to grain boundary energies of the
Read--Shockley type, which applies only to small misorientation angles. In
addition, the implementation of the unified model using finite elements
inherits the computational challenges of the KWC model that originate from the
singular diffusive nature of its governing equations. The main goal of this
study is to generalize the KWC functional to grain boundary energies beyond the
Read--Shockley-type that respect the bicrystallography of grain boundaries. The
computational challenges of the KWC model are addressed by developing a
thresholding method that relies on a primal dual algorithm and the fast
marching method, resulting in an O(NlogN) algorithm, where N is the number of
grid points. We validate the model by demonstrating the Herring angle relation,
followed by a study of the grain microstructure evolution in a two-dimensional
face-centered cubic copper polycrystal with crystal symmetry-invariant grain
boundary energy data obtained from the lattice matching method of Runnels et
al. (2016a,b).


Abstract:
  3D face reconstruction plays a very important role in many real-world
multimedia applications, including digital entertainment, social media,
affection analysis, and person identification. The de-facto pipeline for
estimating the parametric face model from an image requires to firstly detect
the facial regions with landmarks, and then crop each face to feed the deep
learning-based regressor. Comparing to the conventional methods performing
forward inference for each detected instance independently, we suggest an
effective end-to-end framework for multi-face 3D reconstruction, which is able
to predict the model parameters of multiple instances simultaneously using
single network inference. Our proposed approach not only greatly reduces the
computational redundancy in feature extraction but also makes the deployment
procedure much easier using the single network model. More importantly, we
employ the same global camera model for the reconstructed faces in each image,
which makes it possible to recover the relative head positions and orientations
in the 3D scene. We have conducted extensive experiments to evaluate our
proposed approach on the sparse and dense face alignment tasks. The
experimental results indicate that our proposed approach is very promising on
face alignment tasks without fully-supervision and pre-processing like
detection and crop. Our implementation is publicly available at
\url{https://github.com/kalyo-zjl/WM3DR}.


Abstract:
  We propose a successive convex approximation based off-policy optimization
(SCAOPO) algorithm to solve the general constrained reinforcement learning
problem, which is formulated as a constrained Markov decision process (CMDP) in
the context of average cost. The SCAOPO is based on solving a sequence of
convex objective/feasibility optimization problems obtained by replacing the
objective and constraint functions in the original problems with convex
surrogate functions. At each iteration, the convex surrogate problem can be
efficiently solved by Lagrange dual method even the policy is parameterized by
a high-dimensional function. Moreover, the SCAOPO enables to reuse old
experiences from previous updates, thereby significantly reducing the
implementation cost when deployed in the real-world engineering systems that
need to online learn the environment. In spite of the time-varying state
distribution and the stochastic bias incurred by the off-policy learning, the
SCAOPO with a feasible initial point can still provably converge to a
Karush-Kuhn-Tucker (KKT) point of the original problem almost surely.


Abstract:
  This paper presents our solution for the ICDAR 2021 Competition on Scientific
Table Image Recognition to LaTeX. This competition has two sub-tasks: Table
Structure Reconstruction (TSR) and Table Content Reconstruction (TCR). We treat
both sub-tasks as two individual image-to-sequence recognition problems. We
leverage our previously proposed algorithm MASTER \cite{lu2019master}, which is
originally proposed for scene text recognition. We optimize the MASTER model
from several perspectives: network structure, optimizer, normalization method,
pre-trained model, resolution of input image, data augmentation, and model
ensemble. Our method achieves 0.7444 Exact Match and 0.8765 Exact Match @95\%
on the TSR task, and obtains 0.5586 Exact Match and 0.7386 Exact Match 95\% on
the TCR task.


Abstract:
  The QCD axion or axion-like particles are candidates of dark matter of the
universe. On the other hand, axion-like excitations exist in certain condensed
matter systems, which implies that there can be interactions of dark matter
particles with condensed matter axions. We discuss the relationship between the
condensed matter axion and a collective spin-wave excitation in an
anti-ferromagnetic insulator at the quantum level. The conversion rate of the
light dark matter, such as the elementary particle axion or hidden photon, into
the condensed matter axion is estimated for the discovery of the dark matter
signals.


Abstract:
  We present practical methods to show a Brunnian link is hyperbolic. The
methods, belonging to geometric topology, are based on observation, worked by
hand and generally applicable. Typical examples illustrate their efficiency. We
thus determine hyperbolicity for all of the known Brunnian links in literatures
except two series. Especially, we discover 6 infinite series of hyperbolic
Brunnian links.


Abstract:
  We introduce DELFT, a factoid question answering system which combines the
nuance and depth of knowledge graph question answering approaches with the
broader coverage of free-text. DELFT builds a free-text knowledge graph from
Wikipedia, with entities as nodes and sentences in which entities co-occur as
edges. For each question, DELFT finds the subgraph linking question entity
nodes to candidates using text sentences as edges, creating a dense and high
coverage semantic graph. A novel graph neural network reasons over the
free-text graph-combining evidence on the nodes via information along edge
sentences-to select a final answer. Experiments on three question answering
datasets show DELFT can answer entity-rich questions better than machine
reading based models, bert-based answer ranking and memory networks. DELFT's
advantage comes from both the high coverage of its free-text knowledge
graph-more than double that of dbpedia relations-and the novel graph neural
network which reasons on the rich but noisy free-text evidence.


Abstract:
  High-entropy alloys (HEAs) composed of multiple principal elements have been
shown to offer improved radiation resistance over their elemental or
dilute-solution counterparts. Using NiCoFeCrMn HEA as a model, here we
introduce carbon and nitrogen interstitial alloying elements to impart chemical
heterogeneities in the form of the local chemical order (LCO) and associated
compositional variations. Density functional theory simulations predict
chemical short-range order (CSRO) (nearest neighbors and the next couple of
atomic shells) surrounding C and N, due to the chemical affinity of C with (Co,
Fe) and N with (Cr, Mn). Atomic-resolution chemical mapping of the elemental
distribution confirms marked compositional variations well beyond statistical
fluctuations. Ni+ irradiation experiments at elevated temperatures demonstrate
a remarkable reduction in void swelling by at least one order of magnitude
compared to the base HEA without C and N alloying. The underlying mechanism is
that the interstitial-solute-induced chemical heterogeneities roughen the
lattice as well as the energy landscape, impeding the movements of, and
constraining the path lanes for, the normally fast-moving self-interstitials
and their clusters. The irradiation-produced interstitials and vacancies
therefore recombine more readily, delaying void formation. Our findings thus
open a promising avenue towards highly radiation-tolerant alloys.


Abstract:
  Deoxyribonucleic acid (DNA) has shown great promise in enabling computational
applications, most notably in the fields of DNA digital data storage and DNA
computing. Information is encoded as DNA strands, which will naturally bind in
solution, thus enabling search and pattern-matching capabilities. Being able to
control and predict the process of DNA hybridisation is crucial for the
ambitious future of Hybrid Molecular-Electronic Computing. Current tools are,
however, limited in terms of throughput and applicability to large-scale
problems. We present the first comprehensive study of machine learning methods
applied to the task of predicting DNA hybridisation. For this purpose, we
introduce an in silico-generated hybridisation dataset of over 2.5 million data
points, enabling the use of deep learning. Depending on hardware, we achieve a
reduction in inference time ranging from one to over two orders of magnitude
compared to the state-of-the-art, while retaining high fidelity. We then
discuss the integration of our methods in modern, scalable workflows.


Abstract:
  The Mg-Zn and Al-Zn binary alloys have been investigated theoretically under
static isotropic pressure. The stable phases of these binaries on both
initially hexagonal-close-packed (HCP) and face-centered-cubic (FCC) lattices
have been determined by utilizing an iterative approach that uses a
configurational cluster expansion method, Monte Carlo search algorithm, and
density functional theory (DFT) calculations. Based on 64-atom models, it is
shown that the most stable phases of the Mg-Zn binary alloy under ambient
condition are $\rm MgZn_3$, $\rm Mg_{19}Zn_{45}$, $\rm MgZn$, and $\rm
Mg_{34}Zn_{30}$ for the HCP, and $\rm MgZn_3$ and $\rm MgZn$ for the FCC
lattice, whereas the Al-Zn binary is energetically unfavorable throughout the
entire composition range for both the HCP and FCC lattices under all
conditions. By applying an isotropic pressure in the HCP lattice, $\rm
Mg_{19}Zn_{45}$ turns into an unstable phase at P$\approx$$10$~GPa, a new
stable phase $\rm Mg_{3}Zn$ appears at P$\gtrsim$$20$~GPa, and $\rm
Mg_{34}Zn_{30}$ becomes unstable for P$\gtrsim$$30$~GPa. For FCC lattice, the
$\rm Mg_{3}Zn$ phase weakly touches the convex hull at P$\gtrsim$$20$~GPa while
the other stable phases remain intact up to $\approx$$120$~GPa. Furthermore,
making use of the obtained DFT results, bulk modulus has been computed for
several compositions up to pressure values of the order of $\approx$$120$~GPa.
The findings suggest that one can switch between $\rm Mg$-rich and $\rm
Zn$-rich early-stage clusters simply by applying external pressure. $\rm
Zn$-rich alloys and precipitates are more favorable in terms of stiffness and
stability against external deformation.


Abstract:
  Convolutional layers in CNNs implement linear filters which decompose the
input into different frequency bands. However, most modern architectures
neglect standard principles of filter design when optimizing their model
choices regarding the size and shape of the convolutional kernel. In this work,
we consider the well-known problem of spectral leakage caused by windowing
artifacts in filtering operations in the context of CNNs. We show that the
small size of CNN kernels make them susceptible to spectral leakage, which may
induce performance-degrading artifacts. To address this issue, we propose the
use of larger kernel sizes along with the Hamming window function to alleviate
leakage in CNN architectures. We demonstrate improved classification accuracy
on multiple benchmark datasets including Fashion-MNIST, CIFAR-10, CIFAR-100 and
ImageNet with the simple use of a standard window function in convolutional
layers. Finally, we show that CNNs employing the Hamming window display
increased robustness against various adversarial attacks.


Abstract:
  The anomalies recently reported in lepton pair transitions of $^8$Be$^*$ and
$^4$He nuclei may be attributed to the existence of a feebly interacting light
vector boson $X17$. We study the effects of this hypothetical particle in the
semileptonic $H^* \to H e^+e^-$ decays ($H$ a $Q\bar{q}$ meson) in the
framework of the HQET+VMD model. Using current bounds and the universality
assumption of the $X17$ boson to quarks, we find that decays of $D^{*+}$ and
$D_s^{*+}$ mesons can be importantly enhanced relative to the dominant
photon-mediated contributions. Dedicated experimental searches at current heavy
meson factories may confirm the existence of this light boson or set stronger
bounds of their couplings to ordinary matter.