title: Event Horizon of a Charged Black Hole Binary Merger

abstract:
  We analyse the formation and evolution of the event horizon of a black hole
binary merger when the black holes are charged. We find that the presence of
charge influences the properties of the merger and can be useful for
investigating the validity of various theories of modified gravity and several
proposals for dark matter candidates. It can moreover give insights into
various aspects of astrophysical phenomena involving black holes, such as
degeneracies in the gravitational wave parameter determination. We perform our
analysis both analytically and numerically, in $D=4$ dimensions, in the Extreme
Mass Ratio (EMR) limit and compare the results. The development of analytical
results in the EMR limit is of uttermost importance in view of the upcoming
observations of the LISA interferometer. We then use our analysis to describe
how the horizon evolves in time during the merger and to investigate the growth
in the area of the event horizon and the duration of the merger. We moreover
provide a numerical solution valid in arbitrary dimensions $D \geq 4$ which
could be of interest in the context of the AdS/CFT correspondence or for
examining possible extensions of general relativity.


title:
Characterizing the Advective Hot Flows of Nova-Like Cataclysmic
  Variables in the X-rays: The case of BZ Cam and V592 Cas

abstract:
  We present a joint spectral analysis of ROSAT PSPC, Swift XRT, and NuSTAR
FPMA/B data of the nova-like (NL) cataclysmic variables (CVs), BZ Cam and V592
Cas in the 0.1-78.0 keV band. Plasma models of collisional equilibrium fail to
model the 6.0-7.0 iron line complex and continuum with $\chi^2_\nu$ larger than
2.0. Our results show nonequilibrium ionization (NEI) conditions in the X-ray
plasma with temperatures of 8.2-9.4 keV and 10.0-12.9 keV for BZ Cam and V592
Cas, respectively. The centroids of He-like and H-like iron ionization lines
are not at their equilibrium values as expected from NEI conditions. We find
power law spectral components that reveal the existence of scattering and
Comptonization with a photon index of 1.50-1.87. We detect a P Cygni profile in
the H-like iron line of BZ Cam translating to outflows of 4500-8700 km s$^{-1}$
consistent with the fast winds in the optical and UV. This is the first time
such a fast collimated outflow is detected in the X-rays from an accreting CV.
An Iron K$\alpha$ line around 6.2-6.5 keV is found revealing the existence of
reflection effects in both sources. We study the broadband noise and find that
the optically thick disk truncates in BZ Cam and V592 Cas consistent with
transition to an advective hot flow structure. V592 Cas also exhibits a
quasi-periodic oscillation at 1.4$^{+2.6}_{-0.3}$ mHz. In general, we find that
the two NLs portray spectral and noise characteristics as expected from
advective hot accretion flows at low radiative efficiency.


title:
Robust electronic and tunable magnetic states in Sm$ _{2} $NiMnO$ _{6} $
  ferromagnetic insulator

abstract:
  Ferromagnetic insulators (FM-Is) are the materials of interest for new
generation quantum electronic applications. Here, we have investigated the
physical observables depicting FM-I ground states in epitaxial Sm$ _{2} $NiMnO$
_{6} $ (SNMO) double perovskite thin films fabricated under different
conditions to realize different level of Ni/Mn anti-site disorders (ASDs). The
presence of ASDs immensely influence the characteristic magnetic and anisotropy
behaviors in SNMO system by introducing short scale antiferromagnetic
interactions in predominant long range FM ordered host matrix. Charge
disproportion between cation sites in form of $ Ni^{2+}+Mn^{4+} \longrightarrow
Ni^{3+}+Mn^{3+} $, causes mixed valency in both Ni and Mn species, which is
found insensitive to ASD concentrations. Temperature dependent photo emission,
photo absorption measurements duly combined with cluster model configuration
interaction simulations, suggest that the eigenstates of Ni and Mn cations can
be satisfactorily described as a linear combination of the unscreened $ d^{n} $
and screened $ d^{n+1} \underline{L} $ ($ \underline{L} $: O 2\textit{p} hole)
states. The electronic structure across the Fermi level (E$ _{F} $) exhibits
closely spaced Ni $ 3d $, Mn $ 3d $ and O $ 2p $ states. From occupied and
unoccupied bands, estimated values of the Coulomb repulsion energy ($ U $) and
ligand to metal charge transfer energy ($ \Delta $), indicate charge transfer
insulating nature, where remarkable modification in Ni/Mn $ 3d $ - O $ 2p $
hybridization takes place across the FM transition temperature. Existence of
ASD broadens the Ni, Mn $ 3d $ spectral features, whereas spectral positions
are found to be unaltered. Hereby, present work demonstrates SNMO thin film as
a FM-I system, where FM state can be tuned by manipulating ASD in the crystal
structure, while I state remains intact.


title:
The charm-quark contribution to light-by-light scattering in the muon
  $(g-2)$ from lattice QCD

abstract:
  We compute the hadronic light-by-light scattering contribution to the muon
$g-2$ from the charm quark using lattice QCD. The calculation is performed on
ensembles generated with dynamical $(u,d,s)$ quarks at the SU(3)$_{\rm f}$
symmetric point with degenerate pion and kaon masses of around 415 MeV. It
includes the connected charm contribution, as well as the leading disconnected
Wick contraction, involving the correlation between a charm and a light-quark
loop. Cutoff effects turn out to be sizeable, which leads us to use
lighter-than-physical charm masses, to employ a broad range of lattice spacings
reaching down to 0.039 fm and to perform a combined charm-mass and continuum
extrapolation. We use the $\eta_c$ meson to define the physical charm-mass
point and obtain a final value of $a_\mu^{\rm HLbL,c} = (2.8\pm 0.5) \times
10^{-11}$, whose uncertainty is dominated by the systematics of the
extrapolation. Our result is consistent with the estimate based on a simple
charm-quark loop, whilst being free of any perturbative scheme dependence on
the charm mass. The mixed charm-light disconnected contraction contributes a
small negative amount to the final value.


title: Quantum Bayesian Statistical Inference

abstract:
  In this work a quantum analogue of Bayesian statistical inference is
considered. Based on the notion of instrument, we propose a sequential
measurement scheme from which observations needed for statistical inference are
obtained. We further put forward a quantum analogue of Bayes rule, which states
how the prior normal state of a quantum system updates under those
observations. We next generalize the fundamental notions and results of
Bayesian statistics according to the quantum Bayes rule. It is also note that
our theory retains the classical one as its special case. Finally, we
investigate the limit of posterior normal state as the number of observations
tends to infinity.


title:
Differentiating Network Flows for Priority-Aware Scheduling of Incoming
  Packets in Real-Time IoT Systems

abstract:
  When IP-packet processing is unconditionally carried out on behalf of an
operating system kernel thread, processing systems can experience overload in
high incoming traffic scenarios. This is especially worrying for embedded
real-time devices controlling their physical environment in industrial IoT
scenarios and automotive systems. We propose an embedded real-time aware IP
stack adaption with an early demultiplexing scheme for incoming packets and
subsequent per-flow aperiodic scheduling. By instrumenting existing embedded IP
stacks, rigid prioritization with minimal latency is deployed without the need
of further task resources. Simple mitigation techniques can be applied to
individual flows, causing hardly measurable overhead while at the same time
protecting the system from overload conditions. Our IP stack adaption is able
to reduce the low-priority packet processing time by over 86% compared to an
unmodified stack. The network subsystem can thereby remain active at a 7x
higher general traffic load before disabling the receive IRQ as a last resort
to assure deadlines.


title: Compressed Empirical Measures (in finite dimensions)

abstract:
  We study approaches for compressing the empirical measure in the context of
finite dimensional reproducing kernel Hilbert spaces (RKHSs).In this context,
the empirical measure is contained within a natural convex set and can be
approximated using convex optimization methods. Such an approximation gives
under certain conditions rise to a coreset of data points. A key quantity that
controls how large such a coreset has to be is the size of the largest ball
around the empirical measure that is contained within the empirical convex set.
The bulk of our work is concerned with deriving high probability lower bounds
on the size of such a ball under various conditions. We complement this
derivation of the lower bound by developing techniques that allow us to apply
the compression approach to concrete inference problems such as kernel ridge
regression. We conclude with a construction of an infinite dimensional RKHS for
which the compression is poor, highlighting some of the difficulties one faces
when trying to move to infinite dimensional RKHSs.


title: I still have Time(s): Extending HeidelTime for German Texts

abstract:
  HeidelTime is one of the most widespread and successful tools for detecting
temporal expressions in texts. Since HeidelTime's pattern matching system is
based on regular expression, it can be extended in a convenient way. We present
such an extension for the German resources of HeidelTime: HeidelTime-EXT . The
extension has been brought about by means of observing false negatives within
real world texts and various time banks. The gain in coverage is 2.7% or 8.5%,
depending on the admitted degree of potential overgeneralization. We describe
the development of HeidelTime-EXT, its evaluation on text samples from various
genres, and share some linguistic observations. HeidelTime ext can be obtained
from https://github.com/texttechnologylab/heideltime.


title:
Effects of spatial resolution on inferences of atmospheric quantities
  from simulations

abstract:
  Small scale processes are thought to be important for the dynamics of the
solar atmosphere. While numerical resolution fundamentally limits their
inclusion in MHD simulations, real observations at the same nominal resolution
should still contain imprints of sub-resolution effects. This means that the
synthetic observables from a simulation of given resolution might not be
directly comparable to real observables at the same resolution. It is thus of
interest to investigate how inferences based on synthetic spectra from
simulations with different numerical resolutions compare, and whether these
differences persist after the spectra have been spatially degraded to a common
resolution. We aim to compare synthetic spectra obtained from realistic 3D
radiative magnetohydrodynamic (rMHD) simulations run at three different
numerical resolutions from the same initial atmosphere, using very simple
methods for inferring line-of-sight velocities and magnetic fields.
Additionally we examine how the differing spatial resolution impacts the
results retrieved from the STiC inversion code. We find that while the simple
inferences for all three simulations reveal the same large-scale tendencies,
the higher resolutions yield more fine-grained structures and more extreme
line-of-sight velocities/magnetic fields in concentrated spots even after
spatial smearing. We also see indications that the imprints of sub-resolution
effects on the degraded spectra result in systematic errors in the inversions,
and that these errors increase with the amount of sub-resolution effects
included. Fortunately, however, we find that including successively more
sub-resolution yields smaller additional effects; i.e. there is a clear trend
of diminishing importance for progressively finer sub-resolution effects.


title:
Veronese subalgebras and Veronese morphisms for a class of Yang-Baxter
  algebras

abstract:
  We study $d$-Veronese subalgebras $A^{(d)}$ of quadratic algebras $A_X= A(K,
X, r)$ related to finite nondegenerate involutive set-theoretic solutions $(X,
r)$ of the Yang-Baxter equation, where $K$ is a field and $d\geq 2$ is an
integer. We find an explicit presentation of the $d$-Veronese $A^{(d)}$ in
terms of one-generators and quadratic relations. We introduce the notion of a
$d$-Veronese solution $(Y, r_Y)$, canonically associated to $(X,r)$ and use its
Yang-Baxter algebra $A_Y= A(K, Y, r_Y)$ to define a Veronese morphism
$v_{n,d}:A_Y \rightarrow A_X $. We prove that the image of $v_{n,d}$ is the
$d$-Veronese subalgebra $A^{(d)}$, and find explicitly a minimal set of
generators for its kernel. The results agree with their classical analogues in
the commutative case, which corresponds to the case when $(X,r)$ is the trivial
solution.


title:
X-raying the Birth of Binary Neutron Stars and Neutron Star-Black Hole
  Binaries

abstract:
  We consider fallback accretion after an ultra-stripped supernova (USSN) that
accompanies formation of a binary neutron star (BNS) or a neutron star-black
hole binary (NS-BH). The fallback matter initially accretes directly to the
nascent NS, while it starts to accrete to the circumbinary disk, typically
$0.1\mbox{-}1\, \mathrm{day}$ after the onset of the USSN explosion. The
circumbinary disk mass further accretes, forming mini disks around each compact
object, with a super-Eddington rate up to a few years. We show that such a
system constitutes a binary ultraluminous X-ray source (ULX), and a fraction of
the X rays can emerge through the USSN ejecta. We encourage follow-up
observations of USSNe within $\lesssim 100\,\rm Mpc$ and $\sim
100\mbox{-}1,000\,\mathrm{day}$ after the explosion using Chandra, XMM Newton
and NuSTAR, which could detect the X-ray counterpart with time variations
representing the properties of the nascent compact binary, e.g., the orbital
motion of the binary, the spin of the NS, and/or the quasiperiodic oscillation
of the mini disks.


title: Evolution of the $\gamma$-ray strength function in neodymium isotopes

abstract:
  The experimental gamma-ray strength functions (gamma-SFs) of 142,144-151Nd
have been studied for gamma-ray energies up to the neutron separation energy.
The results represent a unique set of gamma-SFs for an isotopic chain with
increasing nuclear deformation. The data reveal how the low-energy enhancement,
the scissors mode and the pygmy dipole resonance evolve with nuclear
deformation and mass number. The data indicate that the mechanisms behind the
low-energy enhancement and the scissors mode are decoupled from each other.


title:
Core Box Image Recognition and its Improvement with a New Augmentation
  Technique

abstract:
  Most methods for automated full-bore rock core image analysis (description,
colour, properties distribution, etc.) are based on separate core column
analyses. The core is usually imaged in a box because of the significant amount
of time taken to get an image for each core column. The work presents an
innovative method and algorithm for core columns extraction from core boxes.
The conditions for core boxes imaging may differ tremendously. Such differences
are disastrous for machine learning algorithms which need a large dataset
describing all possible data variations. Still, such images have some standard
features - a box and core. Thus, we can emulate different environments with a
unique augmentation described in this work. It is called template-like
augmentation (TLA). The method is described and tested on various environments,
and results are compared on an algorithm trained on both 'traditional' data and
a mix of traditional and TLA data. The algorithm trained with TLA data provides
better metrics and can detect core on most new images, unlike the algorithm
trained on data without TLA. The algorithm for core column extraction
implemented in an automated core description system speeds up the core box
processing by a factor of 20.


title: Conjectures on reductive homogeneous spaces

abstract:
  We address some conjectures and open problems in "analysis of symmetries"
which include the study of non-commutative harmonic analysis and discontinuous
groups for reductive homogeneous spaces beyond the classical framework: (1)
discrete series for non-symmetric homogeneous spaces $G/H$; (2) discontinuous
group $\Gamma$ for $G/H$ beyond the Riemannian setting; (3) analysis on
pseudo-Riemannian locally homogeneous spaces.


title: Molecular rotors to probe the local viscosity of a polymer glass

abstract:
  We investigate the local viscosity of a polymer glass around its glass
transition temperature using environment-sensitive fluorescent molecular rotors
embedded in the polymer matrix. The rotors' fluorescence depends on the local
viscosity, and measuring the fluorescence intensity and lifetime of the probe
therefore allows to measure the local free volume in the polymer glass when
going through the glass transition. This also allows us to study the local
viscosity and free volume when the polymer film is put under an external
stress. We find that the film does not flow homogeneously, but undergoes shear
banding that is visible as a spatially varying free volume and viscosity.


title:
Bulk and surface electronic structure of Bi$_4$Te$_3$ from $GW$
  calculations and photoemission experiments

abstract:
  We present a combined theoretical and experimental study of the electronic
structure of stoichiometric Bi$_4$Te$_3$, a natural superlattice of alternating
Bi$_2$Te$_3$ quintuple layers and Bi bilayers. In contrast to the related
semiconducting compounds Bi$_2$Te$_3$ and Bi$_1$Te$_1$, density functional
theory predicts Bi$_4$Te$_3$ to be a semimetal. In this work, we compute the
quasiparticle electronic structure of Bi$_4$Te$_3$ in the framework of the $GW$
approximation within many-body perturbation theory. The quasiparticle
corrections are found to modify the dispersion of the valence and conduction
bands in the vicinity of the Fermi energy, leading to the opening of a small
indirect band gap. Based on the analysis of the eigenstates, Bi$_4$Te$_3$ is
classified as a dual topological insulator with bulk topological invariants
$\mathbb{Z}_2$ (1;111) and magnetic mirror Chern number $n_M=1$. The bulk $GW$
results are used to build a Wannier-functions based tight-binding Hamiltonian
that is further applied to study the electronic properties of the (111)
surface. The comparison with our angle-resolved photoemission measurements
shows excellent agreement between the computed and measured surface states and
indicates the dual topological nature of Bi$_4$Te$_3$.


title:
$J/\psi$ near threshold in holographic QCD: A and D gravitational form
  factors

abstract:
  The diffractive photoproduction of $J/\Psi$ on a nucleon, is mostly due to
gluonic exchanges at all $\sqrt{s}$. In holographic QCD (large number of colors
and strong $^\prime$t Hooft coupling), these exchanges are captured by
gravitons near threshold, and their Reggeized Pomeron form asymptotically. We
revisit our holographic analysis of the A and D gravitational form factors in
light of the new lattice data, and use them to refine our predictions for the
photoproduction of $J/\Psi$ near threshold, and the comparison to the GlueX
data. We use these results to estimate the scalar and mass radii of the
nucleon, and describe the gravitational pressure and shear across a nucleon.


title:
An Investigation of Monotonic Transducers for Large-Scale Automatic
  Speech Recognition

abstract:
  The two most popular loss functions for streaming end-to-end automatic speech
recognition (ASR) are the RNN-Transducer (RNN-T) and the connectionist temporal
classification (CTC) objectives. Both perform an alignment-free training by
marginalizing over all possible alignments, but use different transition rules.
Between these two loss types we can classify the monotonic RNN-T (MonoRNN-T)
and the recently proposed CTC-like Transducer (CTC-T), which both can be
realized using the graph temporal classification-transducer (GTC-T) loss
function. Monotonic transducers have a few advantages. First, RNN-T can suffer
from runaway hallucination, where a model keeps emitting non-blank symbols
without advancing in time, often in an infinite loop. Secondly, monotonic
transducers consume exactly one model score per time step and are therefore
more compatible and unifiable with traditional FST-based hybrid ASR decoders.
However, the MonoRNN-T so far has been found to have worse accuracy than RNN-T.
It does not have to be that way, though: By regularizing the training - via
joint LAS training or parameter initialization from RNN-T - both MonoRNN-T and
CTC-T perform as well - or better - than RNN-T. This is demonstrated for
LibriSpeech and for a large-scale in-house data set.


title: On the Influence of Explainable AI on Automation Bias

abstract:
  Artificial intelligence (AI) is gaining momentum, and its importance for the
future of work in many areas, such as medicine and banking, is continuously
rising. However, insights on the effective collaboration of humans and AI are
still rare. Typically, AI supports humans in decision-making by addressing
human limitations. However, it may also evoke human bias, especially in the
form of automation bias as an over-reliance on AI advice. We aim to shed light
on the potential to influence automation bias by explainable AI (XAI). In this
pre-test, we derive a research model and describe our study design.
Subsequentially, we conduct an online experiment with regard to hotel review
classifications and discuss first results. We expect our research to contribute
to the design and development of safe hybrid intelligence systems.


title:
Efficient Monte Carlo Method for Integral Fractional Laplacian in
  Multiple Dimensions

abstract:
  In this paper, we develop a Monte Carlo method for solving PDEs involving an
integral fractional Laplacian (IFL) in multiple dimensions. We first construct
a new Feynman-Kac representation based on the Green function for the fractional
Laplacian operator on the unit ball in arbitrary dimensions. Inspired by the
"walk-on-spheres" algorithm proposed in [24], we extend our algorithm for
solving fractional PDEs in the complex domain. Then, we can compute the
expectation of a multi-dimensional random variable with a known density
function to obtain the numerical solution efficiently. The proposed algorithm
finds it remarkably efficient in solving fractional PDEs: it only needs to
evaluate the integrals of expectation form over a series of inside ball tangent
boundaries with the known Green function. Moreover, we carry out the error
estimates of the proposed method for the $n$-dimensional unit ball. Finally,
ample numerical results are presented to demonstrate the robustness and
effectiveness of this approach for fractional PDEs in unit disk and complex
domains, and even in ten-dimensional unit balls.


title:
The MAVERIC survey: A catalogue of radio sources in southern globular
  clusters from the Australia Telescope Compact Array

abstract:
  Radio continuum observations offer a new window on compact objects in
globular clusters compared to typical X-ray or optical studies. As part of the
MAVERIC survey, we have used the Australia Telescope Compact Array to carry out
a deep (median central noise level of approximately 4 microJy per beam) radio
continuum survey of 26 southern globular clusters at central frequencies of 5.5
and 9.0 GHz. This paper presents a catalogue of 1285 radio continuum sources in
the fields of these 26 clusters. Considering the surface density of background
sources, we find significant evidence for a population of radio sources in
seven of the 26 clusters, and also identify at least 11 previously known
compact objects (6 pulsars and 5 X-ray binaries). While the overall density of
radio continuum sources with 7.25-GHz flux densities greater than about 20
microJy in typical globular clusters is relatively low, the survey has already
led to the discovery of several exciting compact binaries, including a
candidate ultracompact black hole X-ray binary in 47 Tuc. Many of the
unclassified radio sources near the centres of the clusters are likely to be
true cluster sources, and multi-wavelength follow-up will be necessary to
classify these objects and better understand the demographics of accreting
compact binaries in globular clusters.


title:
The MAVERIC Survey: Radio catalogs and source counts from deep Very
  Large Array imaging of 25 Galactic globular clusters

abstract:
  The MAVERIC survey is the first deep radio continuum imaging survey of Milky
Way globular clusters, with a central goal of finding and classifying accreting
compact binaries, including stellar-mass black holes. Here we present radio
source catalogs for 25 clusters with ultra-deep Karl G. Jansky Very Large Array
observations. The median observing time was 10 hr per cluster, resulting in
typical rms sensitivities of 2.3 and 2.1 uJy per beam at central frequencies of
5.0 and 7.2 GHz, respectively. We detect nearly 1300 sources in our survey at 5
sigma, and while many of these are likely to be background sources, we also
find strong evidence for an excess of radio sources in some clusters. The radio
spectral index distribution of sources in the cluster cores differs from the
background, and shows a bimodal distribution. We tentatively classify the
steep-spectrum sources (those much brighter at 5.0 GHz) as millisecond pulsars
and the flat-spectrum sources as compact or other kinds of binaries. These
provisional classifications will be solidified with the future addition of
X-ray and optical data. The outer regions of our images represent a deep,
relatively wide field (~ 0.4/sq. deg) and high resolution C band background
survey, and we present source counts calculated for this area. We also release
radio continuum images for these 25 clusters to the community.


title:
Perpendicular and Parallel Phase Separation in Two Species Driven
  Diffusive Lattice Gases

abstract:
  We study three different lattice models in which two species of diffusing
particles are driven in opposite directions by an electric field. We focus on
dynamical phase transitions that involve phase separation into domains that may
be parallel or perpendicular to a driving field. In all cases, the
perpendicular state appears for weak driving, consistent with previous work.
For strong driving, we introduce two models that support the parallel state. In
one model, this state occurs because of the inclusion of dynamical rules that
enhance lateral diffusion during collisions; in the other, it is a result of a
nearest-neighbour attractive/repulsive interaction between particles of the
same/opposite species. We discuss the connections between these results and the
behaviour found in off-lattice systems, including laning and freezing by
heating.


title: Notes on cylinders in smooth projective surfaces

abstract:
  In this article, we determine the existing condition of cylinders in smooth
minimal geometrically rational surfaces over a perfect field. Furthermore, we
show that for any birational map between smooth projective surfaces, one
contains a cylinder if and only if so does the other.


title:
Revisiting the evidences for spectral anomalies in distant blazars: new
  data on the photon-ALP mixing

abstract:
  We re-examine possible dependencies on redshift of the spectral parameters of
blazars observed at very-high energies (VHEs) with Imaging Atmospheric
Cherenkov telescopes (IACTs). This is relevant to assess potential effects with
the source distance of the photon to axion-like particle (ALP) mixing, that
would deeply affect the propagation of VHE photons across the Universe. We
focus our spectral analysis on 38 BL Lac objects (32 high-peaked and 6
intermediate-peaked) up to redshift $z\simeq 0.5$, and a small sample of 5 Flat
Spectrum Radio Quasars up to $z=1$ treated independently to increase the
redshift baseline. The 78 independent spectra of these sources are first of all
carefully corrected for the gamma-gamma interaction with photons of the
Extragalactic Background Light, that are responsible for the major
redshift-dependent opacity effect. Then, the corrected spectra are fitted with
simple power-laws to infer the intrinsic spectral indices $\Gamma_{\rm em}$ at
VHE, to test the assumption that such spectral properties are set by the local
rather than the global cosmological environment. We find some systematic
anti-correlations with redshift of $\Gamma_{\rm em}$ that might indicate,
although with low-significance, a spectral anomaly potentially requiring a
revision of the photon propagation process. More conclusive tests with higher
statistical significance will require the observational improvements offered by
the forthcoming new generation of Cherenkov arrays (CTA, ASTRI, LHAASO).


title: Geometry-dependent skin effects in reciprocal photonic crystals

abstract:
  Skin effect that all eigenmodes within a frequency range become edge states
is dictated by the topological properties of complex eigenvalues unique in
non-Hermitian systems. The prevailing attempts to realize such a fascinating
effect are confined to either one-dimensional or nonreciprocal systems
exhibiting asymmetric couplings. Here, inspired by a recent model Hamiltonian
theory, we propose a realistic reciprocal two-dimensional (2D) photonic crystal
(PhC) system that shows the desired skin effect. Specifically, we establish a
routine for designing such non-Hermitian systems via revealing the inherent
connections between the non-trivial eigenvalue topology of order-2 exceptional
points (EPs) and the skin effects. Guided by the proposed strategy, we
successfully design a 2D PhC that possesses the EPs with nonzero eigenvalue
winding numbers. The spectral area along a specific wavevector direction is
then formed by leveraging the symmetry of the macroscopic geometry and the unit
cell. The projected-band-structure calculations are performed to demonstrate
that the desired skin effect exists at the specific crystalline interfaces. We
finally employ time-domain simulations to vividly illustrate this phenomenon by
exciting a pulse at the center of a finite-sized PhC. Our results form a solid
basis for further experimental confirmations and applications of the skin
effect.


title: The homotopy types of $Sp(n)$-gauge groups over $S^{4m}$

abstract:
  Let $m$ and $n$ be two positive integers such that $m < n$. Denote by
$P_{n,k}$ the principal $Sp(n)$-bundle over $S^{4m}$ and
$\mathcal{G}_{k,m}(Sp(n))$ be the gauge group of $P_{n,k}$ classified by
$k\varepsilon'$, where $\varepsilon'$ is a generator of
$\pi_{4m}(B(Sp(n)))\cong\mathbb{Z}$. In this article we partially classify the
homotopy types of $\mathcal{G}_{k,m}(Sp(n))$. Also we will study the order of
the Samelson product $S^{4m-1}\wedge Q_{n-m+1}\rightarrow Sp(n)$, where
$Q_{n-m+1}$ be the symplectic quasi-projective space of rank $n-m+1$.


title: The density conjecture for principal congruence subgroups

abstract:
  We prove Sarnak's density conjecture for the principal congruence subgroup of
SL_n(Z) of squarefree level and discuss various arithmetic applications. The
ingredients include new bounds for local Whittaker functions and Kloosterman
sums.


title: Stochastic Adaptive Linear Quadratic Differential Games

abstract:
  Game theory is playing more and more important roles in understanding complex
systems and in investigating intelligent machines with various uncertainties.
As a starting point, we consider the classical two-player zero-sum
linear-quadratic stochastic differential games, but in contrast to most of the
existing studies, the coefficient matrices of the systems are assumed to be
unknown to both players, and consequently it is necessary to study adaptive
strategies of the players, which may be termed as adaptive games and which has
rarely been explored in the literature. In this paper, it will be shown that
the adaptive strategies of both players can be constructed by the combined use
of a weighted least squares (WLS) estimation algorithm, a random regularization
method and a diminishing excitation method. Under almost the same structural
conditions as those in the traditional known parameters case, we will show that
the closed-loop adaptive game systems will be globally stable and
asymptotically reaches the Nash equilibrium as the time tends to infinity.


title: OpenGlue: Open Source Graph Neural Net Based Pipeline for Image Matching

abstract:
  We present OpenGlue: a free open-source framework for image matching, that
uses a Graph Neural Network-based matcher inspired by SuperGlue
\cite{sarlin20superglue}. We show that including additional geometrical
information, such as local feature scale, orientation, and affine geometry,
when available (e.g. for SIFT features), significantly improves the performance
of the OpenGlue matcher. We study the influence of the various attention
mechanisms on accuracy and speed. We also present a simple architectural
improvement by combining local descriptors with context-aware descriptors. The
code and pretrained OpenGlue models for the different local features are
publicly available.


title: On Sibuya-like distributions in branching and birth-and-death processes

abstract:
  We report some properties of heavy-tailed Sibuya-like distributions related
to thinning, self-decomposability and branching processes. Extension of the
thinning operation of on-negative integer-valued random variables to scaling by
arbitrary positive number leads to a new class of probability distributions
with generating function $Q(w)$ expressible as a Laplace transform
$\varphi(1-w)$ and probability mass function $p_n$ satisfying simple one step
recurrence relation between $p_{n+1}$ and $p_n$. We show that the compound
Poisson-Sibuya and the shifted Sibuya distributions belong to this class. Using
the fact that the same Markov property is present in stationary solutions of
the birth and death equations we identify the Sibuya distribution and some of
its variants as particular solutions of these equations. We also establish
condition when integer-valued non-negative heavy-tailed random variable has
finite $r$-th absolute moment ($0 < r < a < 1$).


title:
Two dark matter candidates in three-Higgs-doublet models with $S_3$
  symmetry

abstract:
  Models with an extended scalar electroweak sector can have vanishing vacuum
expectation values. Such behaviour is a result of an underlying symmetry. If a
symmetry prevents couplings between fermions and additional scalars, such
scalars could become viable dark matter candidates if some additional criteria
are satisfied. We catalogue $S_3$-symmetric three-Higgs-doublet models, also
allowing for softly broken $S_3$-symmetric scalar potential terms, based on
whether a specific model could possibly accommodate a dark matter candidate.
The variety of the $S_3$-symmetric family models arises due to different
possibilities to arrange vacuum expectation values. Such models can have vacua
with one or two vanishing vacuum expectation values. In our study we assume
that the dark matter candidate is stabilised by the $\mathbb{Z}_2$ symmetry.
The $\mathbb{Z}_2$ symmetry is a remnant of $S_3$ symmetry which survived
spontaneous symmetry breaking, and not superimposed over $S_3$. We explore two
models; with an without CP violation. These models have a single dark and two
active scalar sectors. The active sectors behave in many aspects like a Type-I
two-Higgs-doublet model. The dark matter candidate masses, in two cases, are
different from the known models with three scalar doublets. After investigating
the models in detail, identifying parameters compatible with both theoretical
and experimental constraints, we found that the dark matter candidate mass
could be within the range of $[52.5,\,89]~\text{GeV}$ or
$[6.5,\,44.5]~\text{GeV}$ for a model with CP violation.


title: Steering Edge Currents through a Floquet-Topological Insulator

abstract:
  Periodic driving may cause topologically protected, chiral transport along
edges of a 2D lattice that, without driving, would be topologically trivial. We
study what happens if one adds a different on-site potential along the diagonal
of such a 2D grid. In addition to the usual bulk and edge states, the system
then also exhibits doublon states, analogous to two interacting particles in
one dimension. A particle initially located at an edge propagates along the
system's boundary. Its wavefunction splits when it hits the diagonal and
continues propagating simultaneously along the edge and the diagonal. The
strength of the diagonal potential determines the ratio between both parts. We
show that for specific values of the diagonal potential, hopping onto the
diagonal is prohibited so that the system effectively separates into two
triangular lattices. For other values of the diagonal potential, we find a
temporal delay between the two contributions traveling around and through the
system. This behavior could enable the steering of topologically protected
transport of light along the edges and through the bulk of laser-inscribed
photonic waveguide arrays.


title: Less than Few: Self-Shot Video Instance Segmentation

abstract:
  The goal of this paper is to bypass the need for labelled examples in
few-shot video understanding at run time. While proven effective, in many
practical video settings even labelling a few examples appears unrealistic.
This is especially true as the level of details in spatio-temporal video
understanding and with it, the complexity of annotations continues to increase.
Rather than performing few-shot learning with a human oracle to provide a few
densely labelled support videos, we propose to automatically learn to find
appropriate support videos given a query. We call this self-shot learning and
we outline a simple self-supervised learning method to generate an embedding
space well-suited for unsupervised retrieval of relevant samples. To showcase
this novel setting, we tackle, for the first time, video instance segmentation
in a self-shot (and few-shot) setting, where the goal is to segment instances
at the pixel-level across the spatial and temporal domains. We provide strong
baseline performances that utilize a novel transformer-based model and show
that self-shot learning can even surpass few-shot and can be positively
combined for further performance gains. Experiments on new benchmarks show that
our approach achieves strong performance, is competitive to oracle support in
some settings, scales to large unlabelled video collections, and can be
combined in a semi-supervised setting.


title: ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs

abstract:
  As Abstract Meaning Representation (AMR) implicitly involves compound
semantic annotations, we hypothesize auxiliary tasks which are semantically or
formally related can better enhance AMR parsing. We find that 1) Semantic role
labeling (SRL) and dependency parsing (DP), would bring more performance gain
than other tasks e.g. MT and summarization in the text-to-AMR transition even
with much less data. 2) To make a better fit for AMR, data from auxiliary tasks
should be properly "AMRized" to PseudoAMR before training. Knowledge from
shallow level parsing tasks can be better transferred to AMR Parsing with
structure transform. 3) Intermediate-task learning is a better paradigm to
introduce auxiliary tasks to AMR parsing, compared to multitask learning. From
an empirical perspective, we propose a principled method to involve auxiliary
tasks to boost AMR parsing. Extensive experiments show that our method achieves
new state-of-the-art performance on different benchmarks especially in
topology-related scores.


title: Transparency and Policymaking with Endogenous Information Provision

abstract:
  How does a biased lobbyist disclose information in anticipation of a career
concerned politician? I develop a model to study this problem and show that the
answer depends on the design of lobbying protocol. If the protocol requires the
lobbyist's preference to be publicly known, career concerns improve the
public's welfare by inducing the lobbyist to provide more information. In
contrast, if the lobbyist's preference is not known, career concerns can
instead discourage the provision of information. I also explore how the
conventional transparency on decision consequences performs with different
designs of lobbying protocol. In particular, the paper highlights a novel
conflict that the transparency of the lobbyist's intent crowds out any
potential positive effect of the transparency on consequences.


title: Feshbach Spectroscopy of Cs Atom Pairs in Optical Tweezers

abstract:
  We prepare pairs of $^{133}$Cs atoms in a single optical tweezer and perform
Feshbach spectroscopy for collisions of atoms in the states $(f=3, m_f=\pm3)$.
We detect enhancements in pair loss using a detection scheme where the optical
tweezers are repeatedly subdivided. For atoms in the state $(3,-3)$, we
identify resonant features by performing inelastic loss spectroscopy. We carry
out coupled-channel scattering calculations and show that at typical
experimental temperatures the loss features are mostly centred on zeroes in the
scattering length, rather than resonance centres. We measure the number of
atoms remaining after a collision, elucidating how the different loss processes
are influenced by the tweezer depth. These measurements probe the energy
released during an inelastic collision, and thus give information on the states
of the collision products. We also identify resonances with atom pairs prepared
in the absolute ground state $(f=3, m_f=3)$, where two-body radiative loss is
engineered by an excitation laser blue-detuned from the Cs D$_2$ line. These
results demonstrate optical tweezers to be a versatile tool to study two-body
collisions with number-resolved detection sensitivity.


title:
MAT-free graphic arrangements and a characterization of strongly chordal
  graphs by edge-labeling

abstract:
  Ideal subarrangements of a Weyl arrangement are proved to be free by the
multiple addition theorem (MAT) due to Abe-Barakat-Cuntz-Hoge-Terao (2016).
They form a significant class among Weyl subarrangements that are known to be
free so far. The concept of MAT-free arrangements was introduced recently by
Cuntz-M{\"u}cksch (2020) to capture a core of the MAT, which enlarges the ideal
subarrangements from the perspective of freeness. The aim of this paper is to
give a precise characterization of the MAT-freeness in the case of type $A$
Weyl subarrangements (or graphic arrangements). It is known that the ideal and
free graphic arrangements correspond to the unit interval and chordal graphs
respectively. We prove that a graphic arrangement is MAT-free if and only if
the underlying graph is strongly chordal. In particular, it affirmatively
answers a question of Cuntz-M{\"u}cksch that MAT-freeness is closed under
taking localization in the case of graphic arrangements.


title:
Stochastic Galerkin method for cloud simulation. Part II: a fully random
  Navier-Stokes-cloud model

abstract:
  This paper is a continuation of the work presented in [Chertock et al., Math.
Cli. Weather Forecast. 5, 1 (2019), 65--106]. We study uncertainty propagation
in warm cloud dynamics of weakly compressible fluids. The mathematical model is
governed by a multiscale system of PDEs in which the macroscopic fluid dynamics
is described by a weakly compressible Navier-Stokes system and the microscopic
cloud dynamics is modeled by a convection-diffusion-reaction system. In order
to quantify uncertainties present in the system, we derive and implement a
generalized polynomial chaos stochastic Galerkin method. Unlike the first part
of this work, where we restricted our consideration to the partially stochastic
case in which the uncertainties were only present in the cloud physics
equations, we now study a fully random Navier-Stokes-cloud model in which we
include randomness in the macroscopic fluid dynamics as well. We conduct a
series of numerical experiments illustrating the accuracy and efficiency of the
developed approach.


title: Antipatterns in Software Classification Taxonomies

abstract:
  Empirical results in software engineering have long started to show that
findings are unlikely to be applicable to all software systems, or any domain:
results need to be evaluated in specified contexts, and limited to the type of
systems that they were extracted from. This is a known issue, and requires the
establishment of a classification of software types.
  This paper makes two contributions: the first is to evaluate the quality of
the current software classifications landscape. The second is to perform a case
study showing how to create a classification of software types using a curated
set of software systems.
  Our contributions show that existing, and very likely even new,
classification attempts are deemed to fail for one or more issues, that we
named as the `antipatterns' of software classification tasks. We collected 7 of
these antipatterns that emerge from both our case study, and the existing
classifications.
  These antipatterns represent recurring issues in a classification, so we
discuss practical ways to help researchers avoid these pitfalls. It becomes
clear that classification attempts must also face the daunting task of
formulating a taxonomy of software types, with the objective of establishing a
hierarchy of categories in a classification.


title: Detecting electric charge with Extreme Mass Ratio Inspirals

abstract:
  We consider extreme mass ratio inspirals during which an electrically charged
compact object with mass $m_p$ and the charge to mass ratio $q$ inspirals
around a Schwarzschild black hole of mass $M$. Using the Teukolsky and
generalized Sasaki-Nakamura formalisms for the gravitational and
electromagnetic perturbations around a Schwarzschild black hole, we numerically
calculate the energy flux of both gravitational and electromagnetic waves
induced by a charged particle moving in circular orbits. With one year
observation of these extreme mass ratio inspirals, we show that space-based
gravitational wave detector such as the Laser Interferometer Space Antenna can
detect the charge to mass ratio as small as $q\sim 10^{-3}$.


title: Calibrating distribution models from PELVE

abstract:
  The Value-at-Risk (VaR) and the Expected Shortfall (ES) are the two most
popular risk measures in banking and insurance regulation. To bridge between
the two regulatory risk measures, the Probability Equivalent Level of VaR-ES
(PELVE) was recently proposed to convert a level of VaR to that of ES. It is
straightforward to compute the value of PELVE for a given distribution model.
In this paper, we study the converse problem of PELVE calibration, that is, to
find a distribution model that yields a given PELVE, which may either be
obtained from data or from expert opinion. We discuss separately the cases when
one-point, two-point and curve constraints are given. In the most complicated
case of a curve constraint, we convert the calibration problem to that of an
advanced differential equation. We further study some technical properties of
PELVE by offering a few new results on monotonicity and convergence.


title:
Event-triggered Approximate Byzantine Consensus with Multi-hop
  Communication

abstract:
  In this paper, we consider a resilient consensus problem for the multi-agent
network where some of the agents are subject to Byzantine attacks and may
transmit erroneous state values to their neighbors. In particular, we develop
an event-triggered update rule to tackle this problem as well as reduce the
communication for each agent. Our approach is based on the mean subsequence
reduced (MSR) algorithm with agents being capable to communicate with multi-hop
neighbors. Since delays are critical in such an environment, we provide
necessary graph conditions for the proposed algorithm to perform well with
delays in the communication. We highlight that through multi-hop communication,
the network connectivity can be reduced especially in comparison with the
common onehop communication case. Lastly, we show the effectiveness of the
proposed algorithm by a numerical example.


title:
Globally time-reversible fluid simulations with smoothed particle
  hydrodynamics

abstract:
  This paper describes an energy-preserving and globally time-reversible code
for weakly compressible smoothed particle hydrodynamics (SPH). We do not add
any additional dynamics to the Monaghan's original SPH scheme at the level of
ordinary differential equation, but we show how to discretize the equations by
using a corrected expression for density and by invoking a symplectic
integrator. Moreover, to achieve the global-in-time reversibility, we have to
correct the initial state, implement a conservative fluid-wall interaction, and
use the fixed-point arithmetic. Although the numerical scheme is reversible
globally in time (solvable backwards in time while recovering the initial
conditions), we observe thermalization of the particle velocities and growth of
the Boltzmann entropy. In other words, when we do not see all the possible
details, as in the Boltzmann entropy, which depends only on the one-particle
distribution function, we observe the emergence of the second law of
thermodynamics (irreversible behavior) from purely reversible dynamics.


title:
Channel estimation for double IRS assisted broadband single-user SISO
  communication

abstract:
  In this paper, two Intelligent reflecting surfaces (double IRS) assisted
single-user single input single output (SISO) communication system is
considered. The cascaded channels (mobile user
(MU)$\rightarrow$IRS-1$\rightarrow$base station (BS),
MU$\rightarrow$IRS-2$\rightarrow$BS and
MU$\rightarrow$IRS-1$\rightarrow$IRS-2$\rightarrow$BS channels) are estimated
under Bayesian setting. Here, the goal is to evaluate the performance of the
estimator in case of MU$\rightarrow$IRS-1$\rightarrow$BS and
MU$\rightarrow$IRS-2$\rightarrow$BS channel links using Bayesian Cramer-Rao
lower bound (CRLB). Without the knowledge of closed form pdf of inner product
of circularly symmetric complex Gaussian (CSCG) random vectors, we cannot
obtain the fisher information. Hence, by numerical computation we obtain the
Bayesian CRLB. In the simulation results, we show that we can approximate the
pdf of the inner product of CSCG random vectors by a Rayleigh distribution by
increasing the number of elements on the IRS, which is analogous to Central
Limit Theorem (CLT). Also, the results convey that the mean squared error (MSE)
almost matches with the Bayesian CRLB.


title:
Cell barrier characterization in transwell inserts by electrical
  impedance spectroscopy

abstract:
  We describe an impedance-based method for cell barrier integrity testing. A
four-electrode electrical impedance spectroscopy (EIS) setup can be realized by
simply connecting a commercial chopstick-like electrode (STX-1) to a
potentiostat allowing monitoring cell barriers cultivated in transwell inserts.
Subsequent electric circuit modeling of the electrical impedance results the
capacitive properties of the barrier next to the well-known transepithelial
electrical resistance (TEER). The versatility of the new method was analyzed by
the EIS analysis of a Caco-2 monolayer in response to (a) different membrane
coating materials, (b) two different permeability enhancers ethylene
glycol-bis(2-aminoethylether)-N,N,N',N'-tetraacetic acid (EGTA) and saponin,
and (c) sonoporation. For the different membrane coating materials, the TEERs
of the standard and new protocol coincide and increase during cultivation,
while the capacitance shows a distinct maximum for three different surface
materials (no coating, Matrigel, and collagen I). The permeability enhancers
cause a decline in the TEER value, but only saponin alters the capacitance of
the cell layer by two orders of magnitude. Hence, cell layer capacitance and
TEER represent two independent properties characterizing the monolayer. The use
of commercial chopstick-like electrodes to access the impedance of a barrier
cultivated in transwell inserts enables remarkable insight into the behavior of
the cellular barrier with no extra work for the researcher. This simple method
could evolve into a standard protocol used in cell barrier research.


title: Cross-Lingual Phrase Retrieval

abstract:
  Cross-lingual retrieval aims to retrieve relevant text across languages.
Current methods typically achieve cross-lingual retrieval by learning
language-agnostic text representations in word or sentence level. However, how
to learn phrase representations for cross-lingual phrase retrieval is still an
open problem. In this paper, we propose XPR, a cross-lingual phrase retriever
that extracts phrase representations from unlabeled example sentences.
Moreover, we create a large-scale cross-lingual phrase retrieval dataset, which
contains 65K bilingual phrase pairs and 4.2M example sentences in 8
English-centric language pairs. Experimental results show that XPR outperforms
state-of-the-art baselines which utilize word-level or sentence-level
representations. XPR also shows impressive zero-shot transferability that
enables the model to perform retrieval in an unseen language pair during
training. Our dataset, code, and trained models are publicly available at
www.github.com/cwszz/XPR/.


title:
Using a Semantic Knowledge Base to Improve the Management of Security
  Reports in Industrial DevOps Projects

abstract:
  Integrating security activities into the software development lifecycle to
detect security flaws is essential for any project. These activities produce
reports that must be managed and looped back to project stakeholders like
developers to enable security improvements. This so-called Feedback Loop is a
crucial part of any project and is required by various industrial security
standards and models. However, the operation of this loop presents a variety of
challenges. These challenges range from ensuring that feedback data is of
sufficient quality over providing different stakeholders with the information
they need to the enormous effort to manage the reports. In this paper, we
propose a novel approach for treating findings from security activity reports
as belief in a Knowledge Base (KB). By utilizing continuous logical inferences,
we derive information necessary for practitioners and address existing
challenges in the industry. This approach is currently evaluated in industrial
DevOps projects, using data from continuous security testing.


title:
A Re-analysis of Repeatability and Reproducibility in the Ames-USDOE-FBI
  Study

abstract:
  Forensic firearms identification, the determination by a trained firearms
examiner as to whether or not bullets or cartridges came from a common weapon,
has long been a mainstay in the criminal courts. Reliability of forensic
firearms identification has been challenged in the general scientific
community, and, in response, several studies have been carried out aimed at
showing that firearms examination is accurate, that is, has low error rates.
Less studied has been the question of consistency, of. whether two examinations
of the same bullets or cartridge cases come to the same conclusion, carried out
by an examiner on separate occasions -- intrarater reliability or repeatability
-- or by two examiners -- interrater reliability or reproducibility. One
important study, described in a 2020 Report by the Ames Laboratory-USDOE to the
Federal Bureau of Investigation, went beyond considerations of accuracy to
investigate firearms examination repeatability and reproducibility. The
Report's conclusions were paradoxical. The observed agreement of examiners with
themselves or with other examiners appears mediocre. However, the study
concluded repeatability and reproducibility are satisfactory, on grounds that
the observed agreement exceeds a quantity called the expected agreement. We
find that appropriately employing expected agreement as it was intended does
not suggest satisfactory repeatability and reproducibility, but the opposite.


title: Membrane-electrode assemblies for flow-electrode capacitive deionization

abstract:
  Scale-up of flow-electrode capacitive deionization is hindered due to the
reliance on thick brittle graphite current collectors. Inspired by developments
of electrochemical technologies we present the use of flexible membrane
electrode assemblies (MEA) to solve these limitations. We tested different
carbon-fiber fabrics as current collectors and laminated them successfully with
ion-exchange membranes. The use of thinner ion-exchange membranes is now
possible due to the reinforcement with the carbon fiber fabric. Desalination
experiments reveal that a MEA setup can achieve salt transfer rates equal to
standard setups. Hence, we deduce that charge percolation also acts outside the
electric field. In a single point of contact, ionic and electric charges are
exchanged at the carbon surface of the MEA. The use of thinner membranes leads
to a reduced potential drop. Together with a more homogeneous electric field
across the feed water section, this can compensate for the reduction of contact
surface between flow electrode and current collector.


title: Distributional Transform Based Information Reconciliation

abstract:
  In this paper we present an information reconciliation protocol designed for
Continuous-Variable QKD using the Distributional Transform. By combining tools
from copula and information theories, we present a method for extracting
independent symmetric Bernoulli bits for Gaussian modulated CVQKD protocols,
which we called the Distributional Transform Expansion (DTE). We derived the
expressions for the maximum reconciliation efficiency for both homodyne and
heterodyne measurement, which, for the last one, efficiency greater than 0.9 is
achievable at signal to noise ratio lower than -3.6 dB


title: A survey on improving NLP models with human explanations

abstract:
  Training a model with access to human explanations can improve data
efficiency and model performance on in- and out-of-domain data. Adding to these
empirical findings, similarity with the process of human learning makes
learning from explanations a promising way to establish a fruitful
human-machine interaction. Several methods have been proposed for improving
natural language processing (NLP) models with human explanations, that rely on
different explanation types and mechanism for integrating these explanations
into the learning process. These methods are rarely compared with each other,
making it hard for practitioners to choose the best combination of explanation
type and integration mechanism for a specific use-case. In this paper, we give
an overview of different methods for learning from human explanations, and
discuss different factors that can inform the decision of which method to
choose for a specific use-case.


title: A mini-module with built-in spacers for high-throughput ultrafiltration

abstract:
  Ultrafiltration membrane modules suffer from a permeate flow decrease arising
during filtration and caused by concentration polarization and fouling in,
e.g., fermentation broth purification. Such performance losses are frequently
mitigated by manipulating the hydrodynamic conditions at the membrane-fluid
interface using, e.g., mesh spacers acting as static mixers. This additional
element increases manufacturing complexity while improving mass transport in
general, yet accepting their known disadvantages such as less transport in dead
zones. However, the shape of such spacers is limited to the design of
commercially available spacer geometries. Here, we present a methodology to
design an industrially relevant mini-module with an optimized built-in 3D
spacer structure in a flat-sheet ultrafiltration membrane module to eliminate
the spacer as a separate part. Therefore, the built-in structures have been
conceptually implemented through an in-silico design in compliance with the
specifications for an injection molding process. Ten built-in structures were
investigated in a digital twin of the mini-module by 3D-CFD simulations to
select two options, which were then compared to the empty feed channel
regarding mass transfer. Subsequently, the simulated flux increase was
experimentally verified during bovine serum albumin (BSA) filtration. The new
built-in sinusoidal corrugation outperforms conventional mesh spacer inlays by
up to 30% higher permeation rates. The origin of these improvements is
correlated to the flow characteristics inside the mini-module as visualized
online and in-situ by low-field and high-field magnetic resonance imaging
velocimetry (flow-MRI) during pure water permeation.


title: GestureLens: Visual Analysis of Gestures in Presentation Videos

abstract:
  Appropriate gestures can enhance message delivery and audience engagement in
both daily communication and public presentations. In this paper, we contribute
a visual analytic approach that assists professional public speaking coaches in
improving their practice of gesture training through analyzing presentation
videos. Manually checking and exploring gesture usage in the presentation
videos is often tedious and time-consuming. There lacks an efficient method to
help users conduct gesture exploration, which is challenging due to the
intrinsically temporal evolution of gestures and their complex correlation to
speech content. In this paper, we propose GestureLens, a visual analytics
system to facilitate gesture-based and content-based exploration of gesture
usage in presentation videos. Specifically, the exploration view enables users
to obtain a quick overview of the spatial and temporal distributions of
gestures. The dynamic hand movements are firstly aggregated through a heatmap
in the gesture space for uncovering spatial patterns, and then decomposed into
two mutually perpendicular timelines for revealing temporal patterns. The
relation view allows users to explicitly explore the correlation between speech
content and gestures by enabling linked analysis and intuitive glyph designs.
The video view and dynamic view show the context and overall dynamic movement
of the selected gestures, respectively. Two usage scenarios and expert
interviews with professional presentation coaches demonstrate the effectiveness
and usefulness of GestureLens in facilitating gesture exploration and analysis
of presentation videos.


title: Invertible Mask Network for Face Privacy-Preserving

abstract:
  Face privacy-preserving is one of the hotspots that arises dramatic interests
of research. However, the existing face privacy-preserving methods aim at
causing the missing of semantic information of face and cannot preserve the
reusability of original facial information. To achieve the naturalness of the
processed face and the recoverability of the original protected face, this
paper proposes face privacy-preserving method based on Invertible "Mask"
Network (IMN). In IMN, we introduce a Mask-net to generate "Mask" face firstly.
Then, put the "Mask" face onto the protected face and generate the masked face,
in which the masked face is indistinguishable from "Mask" face. Finally, "Mask"
face can be put off from the masked face and obtain the recovered face to the
authorized users, in which the recovered face is visually indistinguishable
from the protected face. The experimental results show that the proposed method
can not only effectively protect the privacy of the protected face, but also
almost perfectly recover the protected face from the masked face.


title: Model Checking Strategic Abilities in Information-sharing Systems

abstract:
  We introduce a subclass of concurrent game structures (CGS) with imperfect
information in which agents are endowed with private data-sharing capabilities.
Importantly, our CGSs are such that it is still decidable to model-check these
CGSs against a relevant fragment of ATL. These systems can be thought as a
generalisation of architectures allowing information forks, in the sense that,
in the initial states of the system, we allow information forks from agents
outside a given set A to agents inside this A. For this reason, together with
the fact that the communication in our models underpins a specialised form of
broadcast, we call our formalism A-cast systems. To underline, the fragment of
ATL for which we show the model-checking problem to be decidable over A-cast is
a large and significant one; it expresses coalitions over agents in any subset
of the set A. Indeed, as we show, our systems and this ATL fragments can encode
security problems that are notoriously hard to express faithfully:
terrorist-fraud attacks in identity schemes.


title:
Flow-electrode capacitive deionization enables continuous and
  energy-efficient brine concentration

abstract:
  Many industrial and agricultural applications require the treatment of water
streams containing high concentrations of ionic species for closing material
cycles. High concentration factors are often desired, but hard to achieve with
established thermal or membrane-based water treatment technologies at low
energy consumptions. Capacitive deionization processes are normally assumed as
relevant for the treatment of low salinity solutions only. Flow-electrode
capacitive deionization (FCDI), on the other hand, is an electrically driven
water desalination technology, which allows the continuous desalination and
concentration of saline water streams even at elevated salinities. Ions are
adsorbed electrostatically in pumpable carbon flow electrodes, which enable a
range of new process designs.
  In this article, it is shown that continuously operated FCDI systems can be
applied for the treatment of salt brines. Concentrations of up to 291.5~g/L
NaCl were reached in the concentrate product stream. Based on this, FCDI is a
promising technology for brine treatment and salt recovery. Additionally, a
reduction of the energy demand by more than 70% is demonstrated by introducing
multiple cell pairs into a continuous FCDI system. While the economic
feasibility is not investigated here, the results show that FCDI systems may
compete with established technologies regarding their energy demand.


title:
Complexity phase transitions in instantaneous quantum polynomial-time
  circuits

abstract:
  We study a subclass of the Instantaneous Quantum Polynomial-time (IQP)
circuit with a varying density of two-qubit gates. We identify two phase
transitions as a function of the gate density. At the first transition, the
coherent Gibbs representation of the output state transitions from quasilocal
to extended, and becomes completely non-local after the second transition,
resembling that of Haar random states. We introduce several new diagnostics to
argue that learning the distribution after the first transition already becomes
classically intractable, even though its output distribution is far from
random. Our work thus opens a novel pathway for exploring quantum advantage in
more practical setups whose outcome distributions are more structured than
random states.


title: Towards Efficient Single Image Dehazing and Desnowing

abstract:
  Removing adverse weather conditions like rain, fog, and snow from images is a
challenging problem. Although the current recovery algorithms targeting a
specific condition have made impressive progress, it is not flexible enough to
deal with various degradation types. We propose an efficient and compact image
restoration network named DAN-Net (Degradation-Adaptive Neural Network) to
address this problem, which consists of multiple compact expert networks with
one adaptive gated neural. A single expert network efficiently addresses
specific degradation in nasty winter scenes relying on the compact architecture
and three novel components. Based on the Mixture of Experts strategy, DAN-Net
captures degradation information from each input image to adaptively modulate
the outputs of task-specific expert networks to remove various adverse winter
weather conditions. Specifically, it adopts a lightweight Adaptive Gated Neural
Network to estimate gated attention maps of the input image, while different
task-specific experts with the same topology are jointly dispatched to process
the degraded image. Such novel image restoration pipeline handles different
types of severe weather scenes effectively and efficiently. It also enjoys the
benefit of coordinate boosting in which the whole network outperforms each
expert trained without coordination.
  Extensive experiments demonstrate that the presented manner outperforms the
state-of-the-art single-task methods on image quality and has better inference
efficiency. Furthermore, we have collected the first real-world winter scenes
dataset to evaluate winter image restoration methods, which contains various
hazy and snowy images snapped in winter. Both the dataset and source code will
be publicly available.


title:
Vacancy-mediated suppression of phase separation in a model
  two-dimensional surface alloy by the difference of the atomic jump rates

abstract:
  A vacancy-mediated collective diffusion model is used to compute a
thermally-induced (spinodal) phase separation in a typical fcc bi-metallic
surface alloy at low median concentration of vacancies, focusing on the effect
of the ratio, $\Gamma=\Gamma_A/\Gamma_B$, of the jump rates to the vacant sites
of the two types of atoms. The model is formulated for the diffusion of one
atomic species and vacancies, employing the kinetic transport coefficients
derived by Moleko \emph{et al.} (Phil. Mag. A 59, 141 (1989)). It is
demonstrated that for A$_{0.7}$B$_{0.3}$ alloy, $\Gamma \sim 50$ results in
suppression of phase separation, whereas very small $\Gamma$ values result in
mild phase separation (i.e., the suppression is incomplete).

categories: cond-mat.mtrl-sci cond-mat.mes-hall math.DS nlin.PS physics.atm-clus


title:
Inferring Epidemics from Multiple Dependent Data via Pseudo-Marginal
  Methods

abstract:
  Health-policy planning requires evidence on the burden that epidemics place
on healthcare systems. Multiple, often dependent, datasets provide a noisy and
fragmented signal from the unobserved epidemic process including transmission
and severity dynamics. This paper explores important challenges to the use of
state-space models for epidemic inference when multiple dependent datasets are
analysed. We propose a new semi-stochastic model that exploits deterministic
approximations for large-scale transmission dynamics while retaining
stochasticity in the occurrence and reporting of relatively rare severe events.
This model is suitable for many real-time situations including large seasonal
epidemics and pandemics. Within this context, we develop algorithms to provide
exact parameter inference and test them via simulation. Finally, we apply our
joint model and the proposed algorithm to several surveillance data on the
2017-18 influenza epidemic in England to reconstruct transmission dynamics and
estimate the daily new influenza infections as well as severity indicators as
the case-hospitalisation risk and the hospital-intensive care risk.


title: Families of non-tiling domains satisfying P\'olya's conjecture

abstract:
  We show the existence of classes of non-tiling domains satisfying P\'{o}lya's
conjecture, including some two- and three-dimensional examples. This is a
consequence of a more general observation asserting that if a domain satisfies
P\'olya's conjecture eventually, that is, for a sufficiently large order of the
eigenvalues, and may be partioned into $p$ non-overlapping isometric
sub-domains, with $p$ arbitrarily large, then there exists an order $p_{0}$
such that for $p$ larger than $p_{0}$ all such sub-domains satisfy P\'olya's
conjecture. In particular, this allows us to show that families of thin
cylinders satisfy P\'olya's conjecture. We also improve the Berezin-Li-Yau
constant for general cylinders.


title:
Integrable Models From Non-Commutative Geometry With Applications to 3D
  Dualities

abstract:
  We discuss a new class of strong homotopy algebras constructed via inner
deformations. Such deformations have a number of remarkable properties. In the
simplest case, every one-parameter family of associative algebras leads to an
$L_\infty$-algebra that can be used to construct a classical integrable model.
Another application of this class of $L_\infty$-algebras is related with the
three-dimensional bosonization duality in Chern--Simons vector models, where it
implements the idea of the slightly-broken higher spin symmetry. One large
class of associative algebras originates from Deformation Quantization of
Poisson Manifolds. Applications to the $3d$-bosonization duality require,
however, an extension to deformation quantization of Poisson Orbifolds, which
is an open problem. The $3d$-bosonization duality can be proven by showing that
there is a unique class of invariants of the $L_\infty$-algebra that can serve
as correlation functions.


title:
Investigation of superconductivity in Ce-doped (La,Pr)OBiS2 single
  crystals

abstract:
  Single crystals of Ce-doped (La,Pr)OBiS2 superconductors, multinary
rare-earth elements substituted ROBiS2, were successfully grown. The grown
crystals typically had a size of 1-2 mm and a plate-like shape with a
well-developed c-plane. The c-axis lattice constants of the obtained
(La,Ce,Pr)OBiS2 single crystals were approximately 13.6-13.7 A, and the
superconducting transition temperature was 1.23-2.18 K. Valence fluctuations of
Ce and Pr were detected through X-ray absorption spectroscopy analysis. In
contrast to (Ce,Pr)OBiS2 and (La,Ce)OBiS2, the superconducting transition
temperature of (La,Ce,Pr)OBiS2 increased with increasing concentrations of the
tetravalent state at the R-site.


title: The Coming Decades of Quantum Simulation

abstract:
  Contemporary quantum technologies face major difficulties in fault tolerant
quantum computing with error correction, and focus instead on various shades of
quantum simulation (Noisy Intermediate Scale Quantum, NISQ) devices, analogue
and digital quantum simulators and quantum annealers. There is a clear need and
quest for such systems that, without necessarily simulating quantum dynamics of
some physical systems, can generate massive, controllable, robust, entangled,
and superposition states. This will, in particular, allow the control of
decoherence, enabling the use of these states for quantum communications (e.g.
to achieve efficient transfer of information in a safer and quicker way),
quantum metrology, sensing and diagnostics (e.g. to precisely measure phase
shifts of light fields, or to diagnose quantum materials). In this Chapter we
present a vision of the golden future of quantum simulators in the decades to
come.


title: Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing

abstract:
  We present PHORHUM, a novel, end-to-end trainable, deep neural network
methodology for photorealistic 3D human reconstruction given just a monocular
RGB image. Our pixel-aligned method estimates detailed 3D geometry and, for the
first time, the unshaded surface color together with the scene illumination.
Observing that 3D supervision alone is not sufficient for high fidelity color
reconstruction, we introduce patch-based rendering losses that enable reliable
color reconstruction on visible parts of the human, and detailed and plausible
color estimation for the non-visible parts. Moreover, our method specifically
addresses methodological and practical limitations of prior work in terms of
representing geometry, albedo, and illumination effects, in an end-to-end model
where factors can be effectively disentangled. In extensive experiments, we
demonstrate the versatility and robustness of our approach. Our
state-of-the-art results validate the method qualitatively and for different
metrics, for both geometric and color reconstruction.


title:
Multifractal analysis and large deviations of homological growth rates
  for hyperbolic surfaces

abstract:
  We perform a multifractal analysis of homological growth rates of oriented
geodesics on a hyperbolic surface. Our main result provides a formula for the
Hausdorff dimension of level sets of prescribed growth rates in terms of a
generalized Poincar\'e exponent of the Fuchsian group. We employ symbolic
dynamics developed by Bowen and Series, ergodic theory and thermodynamic
formalism to prove the analyticity of the dimension spectrum and the large
deviations rate function.


title:
Wireless powering efficiency assessment for deep-body implantable
  devices

abstract:
  Several frequency-dependent mechanisms restrict the maximum achievable
efficiency for wireless powering implantable bioelectric devices. Similarly,
many mathematical formulations have been proposed to evaluate the effect of
these mechanisms as well as predict this maximum efficiency and the
corresponding optimum frequency. However, most of these methods consider a
simplified model, and they cannot tackle some realistic aspects of implantable
wireless power transfer. Therefore, this paper proposed a novel approach that
can analyze the efficiency in anatomical models and provide insightful
information on achieving this optimum operation. First, this approach is
validated with a theoretical spherical wave expansion analysis, and the results
for a simplified spherical model and a bidimensional human pectoral model are
compared. Results have shown that even though a magnetic receiver outperforms
an electric one for near-field operation and both sources could be equally
employed in far-field range, it is in mid-field that the maximum efficiency is
achieved, with an optimum frequency between 1-5 GHz, depending on the
implantation depth. In addition, the receiver orientation is another factor
that affects the efficiency, with a maximum difference between the best and
worst-case scenarios around five times for an electric source and over 13 times
for the magnetic one. Finally, this approach is used to analyze the case of a
wirelessly powered deep-implanted pacemaker by an on-body transmitter and to
establish the parameters that lead to the maximum achievable efficiency.


title: Feynman Rules for Scalar Conformal Blocks

abstract:
  We complete the proof of "Feynman rules" for constructing $M$-point conformal
blocks with external and internal scalars in any topology for arbitrary $M$ in
any spacetime dimension by combining the rules for the blocks (based on their
Witten diagram interpretation) with the rules for the construction of conformal
cross ratios (based on OPE flow diagrams). The full set of Feynman rules leads
to blocks as power series of the hypergeometric type in the conformal cross
ratios. We then provide a proof by recursion of the Feynman rules which relies
heavily on the first Barnes lemma and the decomposition of the topology of
interest in comb-like structures. Finally, we provide a nine-point example to
illustrate the rules.


title:
Adaptable Semantic Compression and Resource Allocation for Task-Oriented
  Communications

abstract:
  Task-oriented communication is a new paradigm that aims at providing
efficient connectivity for accomplishing intelligent tasks rather than the
reception of every transmitted bit. In this paper, a deep learning-based
task-oriented communication architecture is proposed where the user extracts,
compresses and transmits semantics in an end-to-end (E2E) manner. Furthermore,
an approach is proposed to compress the semantics according to their importance
relevant to the task, namely, adaptable semantic compression (ASC). Assuming a
delay-intolerant system, supporting multiple users indicates a problem that
executing with the higher compression ratio requires fewer channel resources
but leads to the distortion of semantics, while executing with the lower
compression ratio requires more channel resources and thus may lead to a
transmission failure due to delay constraint. To solve the problem, both
compression ratio and resource allocation are optimized for the task-oriented
communication system to maximize the success probability of tasks.
Specifically, due to the nonconvexity of the problem, we propose a compression
ratio and resource allocation (CRRA) algorithm by separating the problem into
two subproblems and solving iteratively to obtain the convergent solution.
Furthermore, considering the scenarios where users have various service levels,
a compression ratio, resource allocation, and user selection (CRRAUS) algorithm
is proposed to deal with the problem. In CRRAUS, users are adaptively selected
to complete the corresponding intelligent tasks based on branch and bound
method at the expense of higher algorithm complexity compared with CRRA.
Simulation results show that the proposed CRRA and CRRAUS algorithms can obtain
at least 15% and 10% success gains over baseline algorithms, respectively.


title:
Highly Tunable and Strong Bound Exciton in MoSi2N4 via Strain
  Engineering

abstract:
  Motivated by the recently synthesized layered material MoSi2N4, we
investigated excitonic response of quasiparticle of monolayer MoSi2N4 by using
G0W0 and Bethe-Salpeter equation (BSE) calculations. With a dually sandwiched
structure consisting of a central MoN2 layer analogue of 2H-MoS2 capped with
silicon-nitrogen (SiN) honeycomb outer layers, MoSi2N4 possesses frontier
orbitals confined at the central MoN2 layer with similar sub-valley at K-point
as 2H-MoS2. The valley splitting (~130 meV) due to the spin-orbital coupling
(SOC) gives rise to a doublet in the spectrum. Excitons in MoSi2N4 shows a
strong binding energy up to 0.95 eV with the optical bandgap of 2.44 eV. Both
electronic and optical gaps are highly sensitive to tensile strains and become
redshift albeit a marginal change of exciton binding energy. With the
protection of capped SiN layers, quantum confined excitons in MoSi2N4 without
the need of additional passivation layer like BN would provide a bright new
platform for robust emission with partially screened disturbance from
environment.


title: Noncommutative Novikov algebras

abstract:
  The class of Novikov algebras is a popular object of study among classical
nonassociative algebras. The generic example of a Novikov algebra may be
obtained from a differential associative and commutative algebra. We consider a
more general class of linear algebras which may be obtained in the same way
from not necessarily commutative associative algebras with a derivation.


title: Self-Calibrated Efficient Transformer for Lightweight Super-Resolution

abstract:
  Recently, deep learning has been successfully applied to the single-image
super-resolution (SISR) with remarkable performance. However, most existing
methods focus on building a more complex network with a large number of layers,
which can entail heavy computational costs and memory storage. To address this
problem, we present a lightweight Self-Calibrated Efficient Transformer (SCET)
network to solve this problem. The architecture of SCET mainly consists of the
self-calibrated module and efficient transformer block, where the
self-calibrated module adopts the pixel attention mechanism to extract image
features effectively. To further exploit the contextual information from
features, we employ an efficient transformer to help the network obtain similar
features over long distances and thus recover sufficient texture details. We
provide comprehensive results on different settings of the overall network. Our
proposed method achieves more remarkable performance than baseline methods. The
source code and pre-trained models are available at
https://github.com/AlexZou14/SCET.


title: Gravitomagnetic induction in the field of a gravitational wave

abstract:
  The interaction of a plane gravitational wave with test masses can be
described in the proper detector frame, using Fermi coordinates, in terms of a
gravitoelectric and a gravitomagnetic field. We use this approach to calculate
the displacements produced by gravitational waves up to second order in the
distance parameter and, in doing so, we emphasize the relevance of the
gravitomagnetic contribution related to gravitational induction. In addition,
we show how this approach can be generalized to calculate displacements up to
arbitrary order.


title: Bots, Disinformation, and the First Trump Impeachment

abstract:
  We study the behavior of bots and supporters of the Qanon conspiracy theory
on Twitter during the first impeachment of U.S. President Donald Trump. During
a 103 day period covering the impeachment, we collect over 67.7 million tweets
from 3.6 million users, along with their 53.6 million edge follower network. We
find that bots represent 1% of all users, but have a prevalence near 10% among
Qanon supporters. Despite their small numbers, bots generate over 31% of all
impeachment related tweets. We also find that bots share more disinformation,
but use less toxic language than other users. The follower network of Qanon
supporters exhibits a hierarchical structure, with bots acting as central hubs
surrounded by isolated humans. We quantify the daily impact of bots on the
impeachment discussion using the generalized harmonic influence centrality
measure. Overall, we find that due to their greater numbers, pro-Trump bots
have more influence on most days. On a per bot basis, anti-Trump and pro-Trump
bots have similar impact, while Qanon bots have less impact than non-Qanon
bots.


title: Heterogeneous Feature Augmentation for Ponzi Detection in Ethereum

abstract:
  While blockchain technology triggers new industrial and technological
revolutions, it also brings new challenges. Recently, a large number of new
scams with a "blockchain" sock-puppet continue to emerge, such as Ponzi
schemes, money laundering, etc., seriously threatening financial security.
Existing fraud detection methods in blockchain mainly concentrate on manual
feature and graph analytics, which first construct a homogeneous transaction
graph using partial blockchain data and then use graph analytics to detect
anomaly, resulting in a loss of pattern information. In this paper, we mainly
focus on Ponzi scheme detection and propose HFAug, a generic Heterogeneous
Feature Augmentation module that can capture the heterogeneous information
associated with account behavior patterns and can be combined with existing
Ponzi detection methods. HFAug learns the metapath-based behavior
characteristics in an auxiliary heterogeneous interaction graph, and aggregates
the heterogeneous features to corresponding account nodes in the homogeneous
one where the Ponzi detection methods are performed. Comprehensive experimental
results demonstrate that our HFAug can help existing Ponzi detection methods
achieve significant performance improvement on Ethereum datasets, suggesting
the effectiveness of heterogeneous information on detecting Ponzi schemes.


title: Global-and-Local Collaborative Learning for Co-Salient Object Detection

abstract:
  The goal of co-salient object detection (CoSOD) is to discover salient
objects that commonly appear in a query group containing two or more relevant
images. Therefore, how to effectively extract inter-image correspondence is
crucial for the CoSOD task. In this paper, we propose a global-and-local
collaborative learning architecture, which includes a global correspondence
modeling (GCM) and a local correspondence modeling (LCM) to capture
comprehensive inter-image corresponding relationship among different images
from the global and local perspectives. Firstly, we treat different images as
different time slices and use 3D convolution to integrate all intra features
intuitively, which can more fully extract the global group semantics. Secondly,
we design a pairwise correlation transformation (PCT) to explore similarity
correspondence between pairwise images and combine the multiple local pairwise
correspondences to generate the local inter-image relationship. Thirdly, the
inter-image relationships of the GCM and LCM are integrated through a
global-and-local correspondence aggregation (GLA) module to explore more
comprehensive inter-image collaboration cues. Finally, the intra- and
inter-features are adaptively integrated by an intra-and-inter weighting fusion
(AEWF) module to learn co-saliency features and predict the co-saliency map.
The proposed GLNet is evaluated on three prevailing CoSOD benchmark datasets,
demonstrating that our model trained on a small dataset (about 3k images) still
outperforms eleven state-of-the-art competitors trained on some large datasets
(about 8k-200k images).


title: The evolution of high-density cores of the BOSS Great Wall superclusters

abstract:
  High-density cores (HDCs) of galaxy superclusters that embed rich clusters
and groups of galaxies are the earliest large objects to form in the cosmic
web, and the largest objects that may collapse in the present or future. We
study the dynamical state and possible evolution of the HDCs in the BOSS Great
Wall (BGW) superclusters at redshift $z \approx 0.5$ in order to understand the
growth and evolution of structures in the Universe. We derived the density
contrast values for the spherical collapse model in a wide range of redshifts
and used these values to study the dynamical state and possible evolution of
the HDCs of the BGW superclusters. The masses of the HDCs were calculated using
stellar masses of galaxies in them. We found the masses and radii of the
turnaround and future collapse regions in the HDCs and compared them with those
of local superclusters. We determined eight HDCs in the BGW superclusters. The
masses of their turnaround regions are in the range of $M_{\mathrm{T}} \approx
0.4 - 3.3\times~10^{15}h^{-1}M_\odot,$ and radii are in the range of
$R_{\mathrm{T}} \approx 3.5 - 7 h^{-1}$Mpc. The radii of their future collapse
regions are in the range of $R_{\mathrm{FC}} \approx 4 - 8h^{-1}$Mpc. Distances
between individual cores in superclusters are much larger: of the order of $25
- 35h^{-1}$Mpc. The richness and sizes of the HDCs are comparable with those of
the HDCs of the richest superclusters in the local Universe. The BGW
superclusters will probably evolve to several poorer superclusters with masses
similar to those of the local superclusters. This may weaken the tension with
the $\Lambda$CDM model, which does not predict a large number of very rich and
large superclusters in our local cosmic neighbourhood, and explains why there
are no superclusters as elongated as those in the BGW in the local Universe.


title: Symmetry of intrinsically singular solutions of double phase problems

abstract:
  We continue the study of positive singular solutions of PDEs arising from
double phase functionals started in [6]. In particular, we consider the case
$p<q < 2$, and we relax the assumption on the capacity of the singular set
using an intrinsic notion of capacity.


title:
Blockwise Streaming Transformer for Spoken Language Understanding and
  Simultaneous Speech Translation

abstract:
  Although Transformers have gained success in several speech processing tasks
like spoken language understanding (SLU) and speech translation (ST), achieving
online processing while keeping competitive performance is still essential for
real-world interaction. In this paper, we take the first step on streaming SLU
and simultaneous ST using a blockwise streaming Transformer, which is based on
contextual block processing and blockwise synchronous beam search. Furthermore,
we design an automatic speech recognition (ASR)-based intermediate loss
regularization for the streaming SLU task to improve the classification
performance further. As for the simultaneous ST task, we propose a
cross-lingual encoding method, which employs a CTC branch optimized with target
language translations. In addition, the CTC translation output is also used to
refine the search space with CTC prefix score, achieving joint CTC/attention
simultaneous translation for the first time. Experiments for SLU are conducted
on FSC and SLURP corpora, while the ST task is evaluated on Fisher-CallHome
Spanish and MuST-C En-De corpora. Experimental results show that the blockwise
streaming Transformer achieves competitive results compared to offline models,
especially with our proposed methods that further yield a 2.4% accuracy gain on
the SLU task and a 4.3 BLEU gain on the ST task over streaming baselines.


title:
{\L}ojasiewicz-Simon inequalities for minimal networks: stability and
  convergence

abstract:
  We investigate stability properties of the motion by curvature of planar
networks. We prove Lojasiewicz-Simon gradient inequalities for the length
functional of planar networks with triple junctions. In particular, such an
inequality holds for networks with junctions forming angles equal to
$\tfrac23\pi$ that are close in $H^2$-norm to minimal networks, i.e., networks
whose edges also have vanishing curvature. The latter inequality bounds a
concave power of the difference between length of a minimal network $\Gamma_*$
and length of a triple junctions network $\Gamma$ from above by the $L^2$-norm
of the curvature of the edges of $\Gamma$. We apply this result to prove the
stability of minimal networks in the sense that a motion by curvature starting
from a network sufficiently close in $H^2$-norm to a minimal one exists for all
times and smoothly converges. We further rigorously construct an example of a
motion by curvature having uniformly bounded curvature that smoothly converges
to a degenerate network in infinite time.


title: Feature Structure Distillation for BERT Transferring

abstract:
  Knowledge distillation is an approach to transfer information on
representations from a teacher to a student by reducing their difference. A
challenge of this approach is to reduce the flexibility of the student's
representations inducing inaccurate learning of the teacher's knowledge. To
resolve it in BERT transferring, we investigate distillation of structures of
representations specified to three types: intra-feature, local inter-feature,
global inter-feature structures. To transfer them, we introduce \textit{feature
structure distillation} methods based on the Centered Kernel Alignment, which
assigns a consistent value to similar features structures and reveals more
informative relations. In particular, a memory-augmented transfer method with
clustering is implemented for the global structures. In the experiments on the
nine tasks for language understanding of the GLUE dataset, the proposed methods
effectively transfer the three types of structures and improve performance
compared to state-of-the-art distillation methods. Indeed, the code for the
methods is available in https://github.com/maroo-sky/FSD


title: Study of optical and gamma-ray long-term variability in blazars

abstract:
  Blazars, a subset of powerful active galactic nuclei, feature relativistic
jets that shine in a broadband electromagnetic radiation, e. g. from radio to
TeV emission. Here I present the results of the studies that explore gamma-ray
and optical variability properties of a sample of gamma-ray bright sources
Several methods of time-series analyses are performed on the decade-long
optical and Fermi/LAT observations. The main results are as follows: The
sources are found highly variable in both the bands, and the gamma-ray power
spectral density is found to be consistent with flicker noise suggesting
long-memory processes at work. While comparing two emission, not only the
overall optical and the $\gamma$-ray emission are highly correlated but also
both the observation distributions exhibit heavy tailed log-normal distribution
and linear RMS-flux relation. In addition, in some of the sources indications
of quasi-periodic oscillation were revealed with similar characteristic
timescales in both the bands. We discuss the results in light of current blazar
models with relativistic shocks propagating down the jet viewed close to the
line of sight.


title: Light curves of BSM-induced neutrino echoes in the optically-thin limit

abstract:
  High-energy neutrinos from astrophysical transients serve as a probe of
neutrino physics beyond the Standard Model. In particular, nonstandard
interaction of neutrinos with the cosmic neutrino background or dark matter
(DM) may have imprints on not only their spectra but also the arrival and
time-delay distributions. Assuming that the interaction occurs at most once
during the neutrino propagation, we provide analytic formulas for light curves
of the neutrino echoes induced by BSM. The formulas can be used for
constraining neutrino-neutrino coupling and neutrino-DM coupling.


title: Electronic DC-SQUID Emulator

abstract:
  Pulsed readout of Direct Current (DC) SUperconducting Quantum Interference
Device (SQUID) is crucial for experiments which need to be performed at
millikelvin temperatures, such as the readout of superconducting and electron
spin based qubits. Pulsed readout algorithms used in these experiments are
usually specific to the experimental setup and require some optimization. We
present a circuit that emulates the behavior of a DC-SQUID in order to allow
the development and evaluation of pulsed readout algorithms at room temperature
without the need of a running dilution refrigerator. This novel circuit also
constitutes a low cost device which can be used to teach the principles of a
DC-SQUID in courses aimed at training the next generation of quantum engineers.


title:
Two dimensional LiMgAs; a novel Topological Quantum Catalyst for
  Hydrogen Evolution Reaction

abstract:
  Quantum materials such as Topological Insulators (TI) have been promising due
to diverse applications of their robust surface/edge states in the bulk (3D)
and two-dimensional (2D) regime. Such conducting surface states in 3D systems,
host "\textit{electron bath}" which are known to facilitate catalysis. However,
the analogous effects in 2D scenarios wherein, conducting helical edge states
giving rise to Fermionic accumulation has been scarcely addressed. Using
density functional theory based \textit{first-principles} calculations, we
demonstrate that, the conducting edge states in 2D TI such as LiMgAs can be
exploited to facilitate excellent catalytic response towards Hydrogen evolution
reactions. The Gibbs free energy in such cases was found to be as low as
$-$0.02 eV which is quite superior as compared to other materials reported in
literature. The concept presented herein can be extended to other well known 2D
TI and used to realise novel topological quantum catalysts for ultra-high
performance and efficient catalytic applications.


title: Vector bundles on elliptic surfaces and logarithmic transformations

abstract:
  In this paper, we study how certain vector bundles on an elliptic surface are
changed under logarithmic transformations.