Abstract:
  Research on heat waves (periods of excessively hot weather, which may be
accompanied by high humidity) is a newly emerging research topic within the
field of climate change research with high relevance for the whole of society.
In this study, we analyzed the rapidly growing scientific literature dealing
with heat waves. No summarizing overview has been published on this literature
hitherto. We developed a suitable search query to retrieve the relevant
literature covered by the Web of Science (WoS) as complete as possible and to
exclude irrelevant literature (n = 8,011 papers). The time-evolution of the
publications shows that research dealing with heat waves is a highly dynamic
research topic, doubling within about 5 years. An analysis of the thematic
content reveals the most severe heat wave events within the recent decades
(1995 and 2003), the cities and countries/regions affected (United States,
Europe, and Australia), and the ecological and medical impacts (drought, urban
heat islands, excess hospital admissions, and mortality). Risk estimation and
future strategies for adaptation to hot weather are major political issues. We
identified 104 citation classics which include fundamental early works of
research on heat waves and more recent works (which are characterized by a
relatively strong connection to climate change).


Abstract:
  Graph algorithms can be expressed in terms of linear algebra. GraphBLAS is a
library of low-level building blocks for such algorithms that targets algorithm
developers. LAGraph builds on top of the GraphBLAS to target users of graph
algorithms with high-level algorithms common in network analysis. In this
paper, we describe the first release of the LAGraph library, the design
decisions behind the library, and performance using the GAP benchmark suite.
LAGraph, however, is much more than a library. It is also a project to document
and analyze the full range of algorithms enabled by the GraphBLAS. To that end,
we have developed a compact and intuitive notation for describing these
algorithms. In this paper, we present that notation with examples from the GAP
benchmark suite.


Abstract:
  We propose a deep learning system for attention-guided dual-layer image
compression (AGDL). In the AGDL compression system, an image is encoded into
two layers, a base layer and an attention-guided refinement layer. Unlike the
existing ROI image compression methods that spend an extra bit budget equally
on all pixels in ROI, AGDL employs a CNN module to predict those pixels on and
near a saliency sketch within ROI that are critical to perceptual quality. Only
the critical pixels are further sampled by compressive sensing (CS) to form a
very compact refinement layer. Another novel CNN method is developed to jointly
decode the two compression layers for a much refined reconstruction, while
strictly satisfying the transmitted CS constraints on perceptually critical
pixels. Extensive experiments demonstrate that the proposed AGDL system
advances the state of the art in perception-aware image compression.


Abstract:
  Recently, using the assumption that the string theory effective action at the
critical dimension is background independent, the classical on-shell effective
action of the bosonic string theory at order $\alpha'$ in a spacetime manifold
without boundary has been reproduced, up to an overall parameter, by imposing
the $O(1,1)$ symmetry when the background has a circle. In the presence of the
boundary, we consider a background which has boundary and a circle such that
the unit normal vector of the boundary is independent of the circle. Then the
$O(1,1)$ symmetry can fix the bulk action without using the lowest order
equation of motion. Moreover, the above constraints and the constraint from the
principle of the least action in the presence of boundary can fix the boundary
action, up to five boundary parameters. In the least action principle, we
assume that not only the values of the massless fields but also the values of
their first derivatives are arbitrary on the boundary.
  We have also observed that the cosmological/one-dimensional reduction of the
leading order action in the presence of the Hawking-Gibbons boundary term,
produces zero boundary action. Imposing this as another constraint on the
boundary couplings at order $\alpha'$, we find the boundary action up to two
parameters. For a specific value for these two parameters, the gravity
couplings in the boundary become the Chern-Simons gravity plus another term
which has the Laplacian of the extrinsic curvature.


Abstract:
  We analyse the coronal elemental abundances during a small flare using
Hinode/EIS observations. Compared to the pre-flare elemental abundances, we
observed a strong increase in coronal abundance of Ca XIV 193.84 {\AA}, an
emission line with low first ionisation potential (FIP < 10 eV), as quantified
by the ratio Ca/Ar during the flare. This is in contrast to the unchanged
abundance ratio observed using Si X 258.38 {\AA}/S X 264.23 {\AA}. We propose
two different mechanisms to explain the different composition results. Firstly,
the small flare-induced heating could have ionised S, but not the noble gas Ar,
so that the flare-driven Alfv\'en waves brought up Si, S and Ca in tandem via
the ponderomotive force which acts on ions. Secondly, the location of the flare
in strong magnetic fields between two sunspots may suggest fractionation
occurred in the low chromosphere, where the background gas is neutral H. In
this region, high-FIP S could behave more like a low-FIP than a high-FIP
element. The physical interpretations proposed generate new insights into the
evolution of plasma abundances in the solar atmosphere during flaring, and
suggests that current models must be updated to reflect dynamic rather than
just static scenarios.


Abstract:
  The reliable {\it ab-initio} description of strongly correlated materials is
a long-sought capability in condensed matter physics. The $GW$+EDMFT method is
a promising scheme, which provides a self-consistent description of
correlations and screening, and does not require user-provided parameters. In
order to test the reliability of this approach we apply it to the
experimentally well characterized perovskite compound Ca$_2$RuO$_4$, in which a
temperature-dependent structural deformation drives a paramagnetic
metal-insulator transition. Our results demonstrate that the nonlocal
polarization and self-energy components introduced by $GW$ are essential for
setting the correct balance between interactions and bandwidths, and that the
$GW$+EDMFT scheme produces remarkably accurate predictions of the electronic
properties of this strongly correlated material.


Abstract:
  X-ray and gamma-ray emissions observed in lightning and long sparks are
usually connected with the bremsstrahlung of high-energy runaway electrons.
Here, an alternative physical mechanism for producing X-ray and gamma-ray
emissions caused by the polarization current and associated electromagnetic
field moving with relativistic velocity along a curved discharge channel has
been proposed. It is pointed out that lightning and spark discharges should
also produce a coherent radio-frequency radiation. The influence of the
conductivity and the radius of the lightning channel on the propagation
velocity of electromagnetic waves, taking into account the absorption, have
been investigated. The existence of fast electromagnetic surface waves
propagating along the lightning discharge channel at a speed close to the speed
of light in vacuum is shown. The possibility of the production of microwave,
X-ray and gamma-ray emissions by a polarization current pulse moving along a
curved path via synchrotron radiation mechanism during the lightning leader
steps formation and the very beginning of the return stroke stage is pointed
out. The existence of long tails in the power spectrum is shown, which explains
observations of photon energies in the range of 10-100 MeV in the TGF, as well
as measured power spectrum of laboratory spark discharge.


Abstract:
  Distance metric learning algorithms aim to appropriately measure similarities
and distances between data points. In the context of clustering, metric
learning is typically applied with the assist of side-information provided by
experts, most commonly expressed in the form of cannot-link and must-link
constraints. In this setting, distance metric learning algorithms move closer
pairs of data points involved in must-link constraints, while pairs of points
involved in cannot-link constraints are moved away from each other. For these
algorithms to be effective, it is important to use a distance metric that
matches the expert knowledge, beliefs, and expectations, and the
transformations made to stick to the side-information should preserve
geometrical properties of the dataset. Also, it is interesting to filter the
constraints provided by the experts to keep only the most useful and reject
those that can harm the clustering process. To address these issues, we propose
to exploit the dual information associated with the pairwise constraints of the
semi-supervised clustering problem. Experiments clearly show that distance
metric learning algorithms benefit from integrating this dual information.


Abstract:
  The elliptic flow coefficient ($v_{2}$) of $\phi$ mesons at mid-rapidity as a
function of transverse momentum ($p_{T}$) is measured for Au+Au collisions at
centre of mass energy ($\sqrt{s_{NN}}$) 27 and 54.4 GeV. The $v_{2}$
measurement is done using event plane from Time Projection Chamber (TPC,
$|\eta|$ $<$1.0) and Event Plane Detectors (EPD, 2.1$<$$|\eta|$$<$5.1) for 54.4
and 27 GeV, respectively. A high precision test of the number of constituent
quark scaling of $\phi$ meson $v_{2}$ (by including measurements for other
hadrons) has been shown. The results are compared to transport model (AMPT)
calculations.


Abstract:
  The ionic conduction properties of the technologically important
two-dimensional (2D) layered battery material Na2Mn3O7, with exceptional
small-voltage hysteresis between charge and discharge curves, have been
investigated as a function of temperature and frequency by an impedance
spectroscopy. The detailed analyses of the impedance data in the form of
dc-conductivity, ac-conductivity, electrical modulus, dielectric constant and
complex polarizability reveal a long-range Na-ionic conductivity with
negligible contribution from a local dipole relaxation. A significant
enhancement (~10^4 times) of the Na-ion conductivity has been found with the
increasing temperature from 353 K to 713 K. The temperature dependent
conductivity reveals thermally activated conduction process with activation
energies of 0.161 and 0.377 eV over the two temperature regions of 383-518 K
and 518-713 K, respectively. AC conductivity study reveals a long-range hopping
process for the conduction of charge carriers with a sharp increase of the
hopping range at 518 K. The scaling study of the ac-conductivity reveals that
the frequency-activated conductivity (above 10^4 Hz at 353 K) is mainly
controlled by the critical frequency that increases with the increasing
temperature. The Na-ion conduction in Na2Mn3O7 occurs predominantly by a
correlated barrier hopping process. Besides, a correlation between ionic
conduction and crystal structure has been established by x-ray and neutron
diffraction study. We have further shown that the conductivity of Na2Mn3O7 can
be enhanced by reduction of the stacking faults in the crystal structure. Our
study facilitates the understanding of the microscopic ionic conduction
mechanism in the highly efficient 2D battery material Na2Mn3O7 having high
energy storage capacity and high structural stability, paving way for the
discovery of materials for battery applications.


Abstract:
  Scientific literature tends to grow as a function of funding and interest in
a given field. Mining such literature can reveal trends that may not be
immediately apparent. The CORD-19 corpus represents a growing corpus of
scientific literature associated with COVID-19. We examined the intersection of
a set of candidate therapeutics identified in a drug-repurposing study with
temporal instances of the CORD-19 corpus to determine if it was possible to
find and measure changes associated with them over time. We propose that the
techniques we used could form the basis of a tool to pre-screen new candidate
therapeutics early in the research process.


Abstract:
  Linear-response time-dependent density-functional theory (TDDFT) can describe
excitonic features in the optical spectra of insulators and semiconductors,
using exchange-correlation (xc) kernels behaving as $-1/k^{2}$ to leading
order. We show how excitons can be modeled in real-time TDDFT, using an xc
vector potential constructed from approximate, long-range corrected xc kernels.
We demonstrate for various materials that this real-time approach is consistent
with frequency-dependent linear response, gives access to femtosecond exciton
dynamics following short-pulse excitations, and can be extended with some
caution into the nonlinear regime.


Abstract:
  Very recently, Window-based Transformers, which computed self-attention
within non-overlapping local windows, demonstrated promising results on image
classification, semantic segmentation, and object detection. However, less
study has been devoted to the cross-window connection which is the key element
to improve the representation ability. In this work, we revisit the spatial
shuffle as an efficient way to build connections among windows. As a result, we
propose a new vision transformer, named Shuffle Transformer, which is highly
efficient and easy to implement by modifying two lines of code. Furthermore,
the depth-wise convolution is introduced to complement the spatial shuffle for
enhancing neighbor-window connections. The proposed architectures achieve
excellent performance on a wide range of visual tasks including image-level
classification, object detection, and semantic segmentation. Code will be
released for reproduction.


Abstract:
  Classical random walks on finite graphs have an underrated property: a walk
from any vertex can reach every other vertex in finite time, provided they are
connected. Discrete-time quantum walks on finite connected graphs however, can
have infinite hitting times. This phenomenon is related to graph symmetry, as
previously characterized by the group of direction-preserving graph
automorphisms that trivially affect the coin Hilbert space. If a graph is
symmetric enough (in a particular sense) then the associated quantum walk
unitary will contain eigenvectors that do not overlap a set of target vertices,
for any coin flip operator. These eigenvectors span the Infinite Hitting Time
(IHT) subspace. Quantum states in the IHT subspace never reach the target
vertices, leading to infinite hitting times. However, this is not the whole
story: the graph of the 3D cube does not satisfy this symmetry constraint, yet
quantum walks on this graph with certain symmetric coins can exhibit infinite
hitting times. We study the effect of coin symmetry by analyzing the group of
coin-permutation symmetries (CPS): graph automorphisms that act nontrivially on
the coin Hilbert space but leave the coin operator invariant. Unitaries using
highly symmetric coins with large CPS groups, such as the permutation-invariant
Grover coin, are associated with higher probabilities of never arriving, as a
result of their larger IHT subspaces.


Abstract:
  Ontology alignment process is overwhelmingly cited in Knowledge Engineering
as a key mechanism aimed at bypassing heterogeneity and reconciling various
data sources, represented by ontologies, i.e., the the Semantic Web
cornerstone. In such infrastructures and environments, it is inconceivable to
assume that all ontologies covering a particular domain of knowledge are
aligned in pairs. Moreover, the high performance of alignment approaches is
closely related to two factors, i.e., time consumption and machine resource
limitations. Thus, good quality alignments are valuable and it would be
appropriate to exploit them. Based on this observation, this article introduces
a new method of indirect alignment of ontologies in a cross-lingual context.
Indeed, the proposed method deals with alignments of multilingual ontologies
and implements an indirect ontology alignment strategy based on a composition
and reuse of effective direct alignments. The trigger of the proposed method
process is based on alignment algebra which governs the semantics composition
of relationships and confidence values. The obtained results, after a thorough
and detailed experiment are very encouraging and highlight many positive
aspects about the new proposed method.


Abstract:
  Optimization-based state estimation is useful for handling of constrained
linear or nonlinear dynamical systems. It has an ideal form, known as full
information estimation (FIE) which uses all past measurements to perform state
estimation, and also a practical counterpart, known as moving-horizon
estimation (MHE) which uses most recent measurements of a limited length to
perform the estimation. Due to the theoretical ideal, conditions for robust
stability of FIE are relatively easier to establish than those for MHE, and
various sufficient conditions have been developed in literature. This work
reveals a generic link from robust stability of FIE to that of MHE, showing
that the former implies at least a weaker robust stability of MHE which
implements a long enough horizon. The implication strengthens to strict robust
stability of MHE if the system satisfies a mild Lipschitz continuity or
equivalently a robust exponential stability condition. The revealed
implications are then applied to derive new sufficient conditions for robust
stability of MHE, which further reveal an intrinsic relation between the
existence of a robustly stable FIE/MHE and the system being incrementally
input/output-to-state stable.


Abstract:
  Previous studies have shown that high-quality code comments assist developers
in program comprehension and maintenance tasks. However, the semi-structured
nature of comments, unclear conventions for writing good comments, and the lack
of quality assessment tools for all aspects of comments make their evaluation
and maintenance a non-trivial problem. To achieve high-quality comments, we
need a deeper understanding of code comment characteristics and the practices
developers follow. In this thesis, we approach the problem of assessing comment
quality from three different perspectives: what developers ask about commenting
practices, what they write in comments, and how researchers support them in
assessing comment quality.
  Our preliminary findings show that developers embed various kinds of
information in class comments across programming languages. Still, they face
problems in locating relevant guidelines to write consistent and informative
comments, verifying the adherence of their comments to the guidelines, and
evaluating the overall state of comment quality. To help developers and
researchers in building comment quality assessment tools, we provide: (i) an
empirically validated taxonomy of comment convention-related questions from
various community forums, (ii) an empirically validated taxonomy of comment
information types from various programming languages, (iii) a
language-independent approach to automatically identify the information types,
and (iv) a comment quality taxonomy prepared from a systematic literature
review.


Abstract:
  The Lighthouse system was originally developed as tracking system for virtual
reality applications. Due to its affordable price, it has also found attractive
use-cases in robotics in the past. However, existing works frequently rely on
the centralized official tracking software, which make the solution less
attractive for UAV swarms. In this work, we consider an open-source tracking
software that can run onboard small Unmanned Aerial Vehicles (UAVs) in
real-time and enable distributed swarming algorithms. We provide a dataset
specifically for the use cases i) flight; and ii) as ground truth for other
commonly-used distributed swarming localization systems such as ultra-wideband.
We then use this dataset to analyze both accuracy and precision of the
Lighthouse system in different use-cases. To our knowledge, we are the first to
compare two different Lighthouse hardware versions with a motion capture system
and the first to analyze the accuracy using tracking software that runs onboard
a microcontroller.


Abstract:
  In this paper some well-known tests based on empirical distribution functions
(EDF) with estimated parameters for testing composite normality hypothesis are
revisited, and some new results on asymptotic properties are provided. In
particular, the approximate Bahadur slopes are obtained -- in the case of close
alternatives -- for the EDF-based tests as well as the likelihood ratio test.
The local approximate efficiencies are calculated for several close
alternatives. The obtained results could serve as a benchmark for evaluation of
the quality of recent and future normality tests.


Abstract:
  We analyze a theory known as extended DBI, which interpolates between DBI and
the $U(N)\times U(N)/U(N)$ non-linear sigma model and represents a nontrivial
example of theories with mixed power counting. We discuss symmetries of the
action and their geometrical origin; the special case of SU(2) extended DBI
theory is treated in great detail. The revealed symmetries lead to a new type
of graded soft theorem that allows us to prove on-shell constructibility of the
tree-level S-matrix. It turns out that the on-shell constructibility of the
full extended DBI remains valid, even if its DBI sub-theory is modified in such
a way to preserve its own on-shell constructibility. We thus propose a slight
generalization of the DBI sub-theory, which we call 2-scale DBI theory. Gluing
it back to the rest of the extended DBI theory gives a new set of on-shell
reconstructible theories -- the 2-scale extended DBI theory and its
descendants. The uniqueness of the parent theory is confirmed by the bottom-up
approach that uses on-shell amplitude methods exclusively.


Abstract:
  Neutron stars offer the opportunity to study the behaviour of matter at
densities and temperatures inaccessible to terrestrial experiments.
Gravitational-wave observations of binary neutron star coalescences can
constrain the neutron-star equation of state before and after merger. After the
neutron star binary merges, hyperons can form in the remnant, changing the
behaviour of the neutron-star equation of state. In this study, we use
finite-entropy equations of state to show that a post-merger remnant can spin
up due to cooling. The magnitude of the spin-up depends on the neutron-star
equation of state. If hyperons are present, the post-merger spin-up changes the
peak gravitational-wave frequency by $\sim 540$ Hz, when the entropy per baryon
drops from $s=2$ $k_B$ to $s=0$ $k_B$. If hyperons are not present, the
post-merger spin-up changes by $\sim 360$ Hz, providing a gravitational-wave
signature for exotic matter. We expect the same qualitative behaviour whenever
temperature dependent phase transitions are triggered.


Abstract:
  Intelligent reflecting surface (IRS) has recently been emerged as an
effective way for improving the performance of wireless networks by
reconfiguring the propagation environment through a large number of passive
reflecting elements. This game-changing technology is especially important for
stepping into the Internet of Everything (IoE) era, where high performance is
demanded with very limited available resources. In this paper, we study a
backscatter-assisted wireless powered communication network (BS-WPCN), in which
a number of energy-constrained users, powered by a power station (PS), transmit
information to an access point (AP) via backscatter and active wireless
information transfer, with their communication being aided by an IRS. Using a
practical energy harvesting (EH) model which is able to capture the
characteristics of realistic energy harvesters, we investigate the maximization
of total network throughput. Specifically, IRS reflection coefficients, PS
transmit and AP receive beamforming vectors, power and time allocation are
designed through a two-stage algorithm, assuming minimum mean square error
(MMSE) receiver at the AP. The effectiveness of the proposed algorithm is
confirmed via extensive numerical simulations. We also show that our proposed
scheme is readily applicable to practical IRS-aided networks with discrete
phase shift values.


Abstract:
  Spatio-temporal models are widely used in many research areas including
ecology. The recent proliferation of the use of in-situ sensors in streams and
rivers supports space-time water quality modelling and monitoring in near
real-time. In this paper, we introduce a new family of dynamic spatio-temporal
models, in which spatial dependence is established based on stream distance and
temporal autocorrelation is incorporated using vector autoregression
approaches. We propose several variations of these novel models using a
Bayesian framework. Our results show that our proposed models perform well
using spatio-temporal data collected from real stream networks, particularly in
terms of out-of-sample RMSPE. This is illustrated considering a case study of
water temperature data in the northwestern United States.


Abstract:
  Technology companies have produced varied responses to concerns about the
effects of the design of their conversational AI systems. Some have claimed
that their voice assistants are in fact not gendered or human-like -- despite
design features suggesting the contrary. We compare these claims to user
perceptions by analysing the pronouns they use when referring to AI assistants.
We also examine systems' responses and the extent to which they generate output
which is gendered and anthropomorphic. We find that, while some companies
appear to be addressing the ethical concerns raised, in some cases, their
claims do not seem to hold true. In particular, our results show that system
outputs are ambiguous as to the humanness of the systems, and that users tend
to personify and gender them as a result.


Abstract:
  We analyze the rest-optical emission-line ratios of z~1.5 galaxies drawn from
the MOSFIRE Deep Evolution Field (MOSDEF) survey. Using composite spectra we
investigate the mass-metallicity relation (MZR) at z~1.5 and measure its
evolution to z=0. When using gas-phase metallicities based on the N2 line
ratio, we find that the MZR evolution from z~1.5 to z=0 depends on stellar
mass, evolving by $\Delta\rm log(\rm O/H)\sim0.25$ dex at
$M_*<10^{9.75}M_{\odot}$ down to $\Delta\rm log(\rm O/H)\sim0.05$ at
$M_*>10^{10.5}M_{\odot}$. In contrast, the O3N2-based MZR shows a constant
offset of $\Delta\rm log(\rm O/H)\sim0.30$ across all masses, consistent with
previous MOSDEF results based on independent metallicity indicators, and
suggesting that O3N2 provides a more robust metallicity calibration for our
z~1.5 sample. We investigated the secondary dependence of the MZR on SFR by
measuring correlated scatter about the mean $M_*$-specific SFR and
$M_*-\log(\rm O3N2)$ relations. We find an anti-correlation between $\log(\rm
O/H)$ and sSFR offsets, indicating the presence of a $M_*$-SFR-Z relation,
though with limited significance. Additionally, we find that our z~1.5 stacks
lie along the z=0 metallicity sequence at fixed
$\mu=\log(M_*/M_{\odot})-0.6\times\log(\rm SFR / M_{\odot} yr^{-1})$ suggesting
that the z~1.5 stacks can be described by the z=0 fundamental metallicity
relation (FMR). However, using different calibrations can shift the calculated
metallicities off of the local FMR, indicating that appropriate calibrations
are essential for understanding metallicity evolution with redshift. Finally,
understanding how [NII]/H$\alpha$ scales with galaxy properties is crucial to
accurately describe the effects of blended [NII] and H$\alpha$ on redshift and
H$\alpha$ flux measurements in future large surveys utilizing low-resolution
spectra such as with Euclid and the Roman Space Telescope.


Abstract:
  The interplay between band topology and magnetism can give rise to exotic
states of matter. For example, magnetically doped topological insulators can
realize a Chern insulator that exhibits quantized Hall resistance at zero
magnetic field. While prior works have focused on ferromagnetic systems, little
is known about band topology and its manipulation in antiferromagnets. Here, we
report that MnBi$_2$Te$_4$ is a rare platform for realizing a
canted-antiferromagnetic (cAFM) Chern insulator with electrical control. We
show that the Chern insulator state with Chern number $C = 1$ appears as soon
as the AFM to canted-AFM phase transition happens. The Chern insulator state is
further confirmed by observing the unusual transition of the $C = 1$ state in
the cAFM phase to the $C = 2$ orbital quantum Hall states in the magnetic field
induced ferromagnetic phase. Near the cAFM-AFM phase boundary, we show that the
Chern number can be toggled on and off by applying an electric field alone. We
attribute this switching effect to the electrical field tuning of the exchange
gap alignment between the top and bottom surfaces. Our work paves the way for
future studies on topological cAFM spintronics and facilitates the development
of proof-of-concept Chern insulator devices.


Abstract:
  Classical T Tauri stars are young low-mass systems still accreting material
from their disks. These systems are dynamic on timescales of hours to years.
The observed variability can help us infer the physical processes that occur in
the circumstellar environment. We aim at understanding the dynamics of the
magnetic interaction between the star and the inner accretion disk in young
stellar objects. We present the case of the young stellar system V2129 Oph,
which is a well-known T Tauri star. We performed a time series analysis of this
star using high-resolution spectroscopic data at optical and infrared
wavelengths from CFHT/ESPaDOnS, ESO/HARPS and CFHT/SPIRou. The new data sets
allowed us to characterize the accretion-ejection structure in this system and
to investigate its evolution over a timescale of a decade via comparisons to
previous observational data. We measure radial velocity variations and recover
a stellar rotation period of 6.53d. However, we do not recover the stellar
rotation period in the variability of various circumstellar lines, such as
H$\alpha$ and H$\beta$ in the optical or HeI 1083nm and Pa$\beta$ in the
infrared. Instead, we show that the optical and infrared line profile
variations are consistent with a magnetospheric accretion scenario that shows
variability with a period of about 6.0d, shorter than the stellar rotation
period. Additionally, we find a period of 8.5d in H$\alpha$ and H$\beta$ lines,
probably due to a structure located beyond the corotation radius, at a distance
of 0.09au. We investigate whether this could be accounted for by a wind
component, twisted or multiple accretion funnel flows, or an external
disturbance in the inner disk. We conclude that the dynamics of the
accretion-ejection process can vary significantly on a timescale of just a few
years, presumably reflecting the evolving magnetic field topology at the
stellar surface.


Abstract:
  This paper is devoted to proposing a general weighted low-rank recovery model
and designs a fast SVD-free computational scheme to solve it. First, our
generic weighted low-rank recovery model unifies several existing approaches in
the literature.~Moreover, our model readily extends to the non-convex setting.
Algorithm-wise, most first-order proximal algorithms in the literature for
low-rank recoveries require computing singular value decomposition (SVD). As
SVD does not scale properly with the dimension of the matrices, these
algorithms becomes slower when the problem size becomes larger. By
incorporating the variational formulation of the nuclear norm into the
sub-problem of proximal gradient descent, we avoid to compute SVD which results
in significant speed-up. Moreover, our algorithm preserves the {\em rank
identification property} of nuclear norm [33] which further allows us to design
a rank continuation scheme that asymptotically achieves the minimal iteration
complexity. Numerical experiments on both toy example and real-world problems
including structure from motion (SfM) and photometric stereo, background
estimation and matrix completion, demonstrate the superiority of our proposed
algorithm.


Abstract:
  This paper is the continuation of the previous one \cite{Cui2021}, where we
re-proved the area-minimization of cones over Grassmannians of $n$-planes
$G(n,m;\mathbb{F})(\mathbb{F}=\mathbb{R},\mathbb{C},\mathbb{H})$, Cayley plane
$\mathbb{O}P^2$ from the point view of Hermitian orthogonal projectors, and
gave area-minimizing cones associated to oriented real Grassmannians
$\widetilde{G}(n,m;\mathbb{R})$.
  In this paper, we make a further step on showing that the cones, of dimension
no less than $\mathbf{8}$, over minimal products of $G(n,m;\mathbb{F})$ are
area-minimizing. Moreover, those cones are very similar to the classical cones
over products of spheres, and for the critical situation -- the cones of
dimension $\mathbf{7}$ \cite{lawlor1991sufficient}, we gain more
area-minimizing cones by carefully computing the Jacobian
$inf_{v}det(I-tH^{v}_{ij})$. Certain minimizing cones among them had been found
from the perspective of $R$-spaces\cite{Ohno2021area}, or isoparametric
theory\cite{tang2020minimizing}, and others are completely new.
  We also prove that the cones over minimal product of
$\widetilde{G}(n,m;\mathbb{R})$ are area-minimizing.


Abstract:
  Coronal Mass Ejections (CMEs) are highly dynamic events originating in the
solar atmosphere, that show a wide range of kinematic properties and are the
major drivers of the space weather. The angular width of the CMEs is a crucial
parameter in the study of their kinematics. The fact that whether slow and fast
CMEs (as based on their relative speed to the average solar wind speed) are
associated with different processes at the location of their ejection is still
debatable. Thus, in this study, we investigate their angular width to
understand the differences between the slow and fast CMEs. We study the width
distribution of slow and fast CMEs and find that they follow different power
law distributions, with a power law indices ($\alpha$) of -1.1 and -3.7 for
fast and slow CMEs respectively. To reduce the projection effects, we further
restrict our analysis to only limb events as derived from manual catalog and we
find similar results. We then associate the slow and fast CMEs to their source
regions, and classified the sources as Active Regions (ARs) and Prominence
Eruptions (PEs). We find that slow and fast CMEs coming from ARs and PEs, also
follow different power laws in their width distributions. This clearly hints
towards a possibility that different mechanisms might be involved in the width
expansion of slow and fast CMEs coming from different sources.These results are
also crucial from the space weather perspective since the width of the CME is
an important factor in that aspect.


Abstract:
  Understanding multi-node vehicular wireless communication channels is crucial
for future time-sensitive safety applications for human-piloted as well as
partly autonomous vehicles on roads, railways and in the air. These highly
dynamic wireless communication channels are characterized by rapidly changing
channel statistics. In this paper we present the first fully mobile multi-node
vehicular wireless channel sounding system, which is capable of simultaneously
capturing multiple channel frequency responses, ensuring that measurement
conditions are identical for all observed links. We use it to analyze road
scenarios with multiple vehicles and a large obstructing double-decker bus. The
empirical measurement data is used to parametrize a model for the large
obstructing vehicle within a geometry-based stochastic channel model. We
compare the time-variant channel statistics obtained from our channel model
with the ones from the measurement campaign. By means of a channel emulator we
obtain the packet error rates of commercial modems for the measured and the
simulated wireless communication channels and compare them, in order to
validate the model at the link level. We find that the path loss, the root mean
square (RMS) delay spread, and the RMS Doppler spread deviate by less than
$3.6\,\mathrm{dB}$, $78\,\mathrm{ns}$, and $52\,\mathrm{Hz}$, respectively, for
$80\%$ of the total simulation duration. The PER obtained from measured data is
within the maximum and minimum bounds of our model for $86\%$ of the simulation
duration.


Abstract:
  Graph Neural Network (GNN) is a variant of Deep Neural Networks (DNNs)
operating on graphs. However, GNNs are more complex compared to traditional
DNNs as they simultaneously exhibit features of both DNN and graph
applications. As a result, architectures specifically optimized for either DNNs
or graph applications are not suited for GNN training. In this work, we propose
a 3D heterogeneous manycore architecture for on-chip GNN training to address
this problem. The proposed architecture, ReGraphX, involves heterogeneous ReRAM
crossbars to fulfill the disparate requirements of both DNN and graph
computations simultaneously. The ReRAM-based architecture is complemented with
a multicast-enabled 3D NoC to improve the overall achievable performance. We
demonstrate that ReGraphX outperforms conventional GPUs by up to 3.5X (on an
average 3X) in terms of execution time, while reducing energy consumption by as
much as 11X.


Abstract:
  Connected and automated vehicles (CAVs) provide the most intriguing
opportunity to reduce pollution, energy consumption, and travel delays. In this
paper, we address the problem of vehicle platoon formation in a traffic network
with partial CAV penetration rates. We investigate the interaction between CAV
and human-driven vehicle (HDV) dynamics, and provide a rigorous control
framework that enables platoon formation with the HDVs by only controlling the
CAVs within the network. We present a complete analytical solution of the CAV
control policy and the conditions under which a platoon formation is feasible.
We evaluate the solution and demonstrate the efficacy of the proposed framework
using numerical simulation.


Abstract:
  The last few years have seen tremendous progress in the observation of the
global properties of neutron stars (NSs), e.g. masses, radii and tidal
deformabilities. Such properties provide information about possible phase
transitions in the inner cores of NSs, provided the connection between observed
masses and radii and the equation of state (EoS) is well understood. We focus
the present study on first-order phase transitions, which often softens the EoS
and consequently reduces the maximum mass as well as the radii of NSs. Here, we
challenge this conventional expectation by constructing explicit examples of
EoSs undergoing a first-order phase transition, but which are much stiffer that
their purely hadronic counterparts. We also provide comparisons with the
recently proposed quarkyonic EoS which suggests a strong repulsion in the core
of NSs, and we show that their stiffness can be realistically masqueraded by
first-order phase transitions to exotic matter.


Abstract:
  In this paper, the circle formation control problem is addressed for a group
of cooperative underactuated fish-like robots involving unknown nonlinear
dynamics and disturbances. Based on the reinforcement learning and cognitive
consistency theory, we propose a decentralized controller without the knowledge
of the dynamics of the fish-like robots. The proposed controller can be
transferred from simulation to reality. It is only trained in our established
simulation environment, and the trained controller can be deployed to real
robots without any manual tuning. Simulation results confirm that the proposed
model-free robust formation control method is scalable with respect to the
group size of the robots and outperforms other representative RL algorithms.
Several experiments in the real world verify the effectiveness of our RL-based
approach for circle formation control.


Abstract:
  We introduce the notion of Krull super-dimension of supermodules over certain
super-commutative Noetherian super-rings. We investigate how this notion
relates to the notion of odd regular sequence introduced by T.Schmitt and how
it behaves with respect to the transition to the graded and bigraded
supermodules and super-rings associated with the original ones. We also apply
these results to the super-dimension theory of superschemes of finite type and
their morphisms.


Abstract:
  We outline and review the computations of polarized and unpolarized nucleon
structure functions within the bosonized Nambu-Jona-Lasinio chiral soliton
model. We focus on a consistent regularization prescription for the Dirac sea
contribution and present numerical results from that formulation. We also
reflect on previous calculations on quark distributions in chiral quark soliton
models and attempt to put them into perspective.


Abstract:
  In this paper, we introduce a new benchmark dataset for the challenging
writing in the air (WiTA) task -- an elaborate task bridging vision and NLP.
WiTA implements an intuitive and natural writing method with finger movement
for human-computer interaction (HCI). Our WiTA dataset will facilitate the
development of data-driven WiTA systems which thus far have displayed
unsatisfactory performance -- due to lack of dataset as well as traditional
statistical models they have adopted. Our dataset consists of five sub-datasets
in two languages (Korean and English) and amounts to 209,926 video instances
from 122 participants. We capture finger movement for WiTA with RGB cameras to
ensure wide accessibility and cost-efficiency. Next, we propose spatio-temporal
residual network architectures inspired by 3D ResNet. These models perform
unconstrained text recognition from finger movement, guarantee a real-time
operation by processing 435 and 697 decoding frames-per-second for Korean and
English, respectively, and will serve as an evaluation standard. Our dataset
and the source codes are available at https://github.com/Uehwan/WiTA.


Abstract:
  Face recognition has been one of the most relevant and explored fields of
Biometrics. In real-world applications, face recognition methods usually must
deal with scenarios where not all probe individuals were seen during the
training phase (open-set scenarios). Therefore, open-set face recognition is a
subject of increasing interest as it deals with identifying individuals in a
space where not all faces are known in advance. This is useful in several
applications, such as access authentication, on which only a few individuals
that have been previously enrolled in a gallery are allowed. The present work
introduces a novel approach towards open-set face recognition focusing on small
galleries and in enrollment detection, not identity retrieval. A Siamese
Network architecture is proposed to learn a model to detect if a face probe is
enrolled in the gallery based on a verification-like approach. Promising
results were achieved for small galleries on experiments carried out on
Pubfig83, FRGCv1 and LFW datasets. State-of-the-art methods like HFCN and HPLS
were outperformed on FRGCv1. Besides, a new evaluation protocol is introduced
for experiments in small galleries on LFW.


Abstract:
  Through inhalation of, e.g., hyperpolarized $^3$He, it is possible to acquire
gas diffusion magnetic resonance measurements that depend on the local geometry
in the vast network of microscopic airways that form the respiratory zone of
the human lung. Here, we demonstrate that this can be used to determine the
dimensions (length and radius) of these airways noninvasively. Specifically,
the above technique allows measurement of the weighted time-dependent diffusion
coefficient (also called the apparent diffusion coefficient), which we here
derive in analytic form using symmetries in the airway network. Agreement with
experiment is found for the full span of published hyperpolarized $^3$He
diffusion magnetic resonance measurements (diffusion times from milliseconds to
seconds) and published invasive airway dimension measurements.


Abstract:
  The Metric Dimension problem asks for a minimum-sized resolving set in a
given (unweighted, undirected) graph $G$. Here, a set $S \subseteq V(G)$ is
resolving if no two distinct vertices of $G$ have the same distance vector to
$S$. The complexity of Metric Dimension in graphs of bounded treewidth remained
elusive in the past years. Recently, Bonnet and Purohit [IPEC 2019] showed that
the problem is W[1]-hard under treewidth parameterization. In this work, we
strengthen their lower bound to show that Metric Dimension is NP-hard in graphs
of treewidth 24.


Abstract:
  To reduce the stress concentration and ensure the structural safety for
lattice structure designs, in this paper, a new optimization framework is
developed for the optimal design of graded lattice structures, innovatively
integrating fillet designs as well as yield and elastic buckling constraints.
Both strut and fillet radii are defined as design variables. Homogenization
method is employed to characterize the effective elastic constants and yield
stresses of the lattice metamaterials. Metamaterial models are developed to
represent the relationships between the metamaterial effective properties and
lattice geometric variables. A yield constraint, based on the modified Hills
yield criterion, is developed as a function of relative strut radii and fillet
parameters. An elastic buckling constraint, based on the Euler buckling formula
and the Johnson formula, is developed as a function of relative strut radii.
Both yield and buckling constraints are integrated into an optimization problem
formulation; a new optimization framework is proposed and a case study of
minimizing the compliance of a Messerschmitt-Bolkow-Blohm beam is conducted.
The yield and buckling constraints guarantee the safety of the optimized beams
composed of BCC and PC lattices. Reductions in compliance and stress
concentration are achieved by the optimized MBB beams.


Abstract:
  In this paper, we study the elliptic spectral sequence computing
$tmf_*(\mathbb{R} P^2)$ and $tmf_* (\mathbb{R} P^2 \wedge \mathbb{C} P^2)$.
Specifically, we compute all differentials and resolve exotic extensions by 2,
$\eta$, and $\nu$. For $tmf_* (\mathbb{R} P^2 \wedge \mathbb{C} P^2)$, we also
compute the effect of the $v_1$-self maps of $\mathbb{R} P^2 \wedge \mathbb{C}
P^2$ on $tmf$-homology.


Abstract:
  Quantum battery is an emerging subject in the field of quantum
thermodynamics, which is applied to charge, store and dispatch energy in
quantum systems. In this work, we propose a fast and stable charging protocol
based on the adiabatic evolution for the dark state of a three-level quantum
battery. It combines the conventional stimulated Raman adiabatic passage
(STIRAP) and the quantum transitionless driving technique. The charging process
can be accelerated up to nearly one order in magnitude even under constraint of
the strength of counter-diabatic driving. To perform the charging protocol in
the Rydberg atomic system as a typical platform for STIRAP, the prerequisite
driving pulses are modified to avoid the constraint of the forbidden
transition. Moreover, our protocol is found to be more robust against the
environmental dissipation and dephasing than the conventional STIRAP.


Abstract:
  The goal of this study is to analyze the fine structure of nonlinear modal
interactions in different 1D Burgers and 3D Navier-Stokes flows. This analysis
is focused on preferential alignments characterizing the phases of Fourier
modes participating in triadic interactions, which are key to determining the
nature of energy fluxes between different scales. We develop novel diagnostic
tools designed to probe the level of coherence among triadic interactions
realizing different flow scenarios. We consider extreme 1D viscous Burgers
flows and 3D Navier-Stokes flows which are complemented by singularity-forming
inviscid Burgers flows as well as viscous Burgers flows and Navier-Stokes flows
corresponding to generic turbulent and simple unimodal initial data, such as
the Taylor-Green vortex. The main finding is that while the extreme viscous
Burgers and Navier-Stokes flows reveal the same relative level of enstrophy
amplification by nonlinear effects, this behaviour is realized via modal
interactions with vastly different levels of coherence. In the viscous Burgers
flows the flux-carrying triads have phase values which saturate the
nonlinearity thereby maximizing the energy flux towards small scales. On the
other hand, in 3D Navier-Stokes flows with the extreme initial data the energy
flux to small scales is realized by a very small subset of helical triads. The
second main finding concerns the role of initial coherence. Comparison of the
flows resulting from the extreme and generic initial conditions shows striking
similarities between these two types of flows, for the 1D viscous Burgers
equation as well as the 3D Navier-Stokes equation.


Abstract:
  Digitization, i.e., the process of converting information into a digital
format, may provide various opportunities (e.g., increase in productivity,
disaster recovery, and environmentally friendly solutions) and challenges for
businesses. In this context, one of the main challenges would be to accurately
classify numerous scanned documents uploaded every day by customers as usual
business processes. For example, processes in banking (e.g., applying for
loans) or the Government Registry of BDM (Births, Deaths, and Marriages)
applications may involve uploading several documents such as a driver's license
and passport. There are not many studies available to address the challenge as
an application of image classification. Although some studies are available
which used various methods, a more accurate model is still required. The
current study has proposed a robust fusion model to define the type of identity
documents accurately. The proposed approach is based on two different methods
in which images are classified based on their visual features and text
features. A novel model based on statistics and regression has been proposed to
calculate the confidence level for the feature-based classifier. A fuzzy-mean
fusion model has been proposed to combine the classifier results based on their
confidence score. The proposed approach has been implemented using Python and
experimentally validated on synthetic and real-world datasets. The performance
of the proposed model is evaluated using the Receiver Operating Characteristic
(ROC) curve analysis.


Abstract:
  The sunspot umbra harbors the coolest plasma on the solar surface due to the
presence of strong magnetic fields. The routinely used atomic lines to observe
the photosphere have weak signals in the umbra and are often swamped by
molecular lines. This makes it harder to infer the properties of the umbra,
especially in the darkest regions. The lines of the Ti I multiplet at 2.2
$\mu$m are formed mainly at temperatures $\le\!4500$ K and are not known to be
affected by molecular blends in sunspots. Since the first systematic
observations in the 1990's, these lines have been seldom observed due to the
instrumental challenges involved at these longer wavelengths. We revisit these
lines and investigate their formation in different solar features. We
synthesize the Ti I multiplet using a snapshot from 3D MHD simulation of a
sunspot and explore the properties of two of its lines in comparison with two
commonly used iron lines at 630.25 nm and $1.5648\,\mu$m. We find that the Ti I
lines have stronger signals than the Fe I lines in both intensity and
polarization in the sunspot umbra and in penumbral spines. They have little to
no signal in the penumbral filaments and the quiet Sun, at $\mu=1$. Their
strong and well-split profiles in the dark umbra are less affected by stray
light. Consequently, inside the sunspot it is easier to invert these lines and
to infer the atmospheric properties, compared to the iron lines. The Cryo-NIRSP
instrument at the DKIST will provide the first ever high resolution
observations in this wavelength range. In this preparatory study, we
demonstrate the unique temperature and magnetic sensitivities of the Ti
multiplet, by probing the Sun's coolest regions which are not favourable for
the formation of other commonly used spectral lines. We thus expect such
observations to advance our understanding of sunspot properties.


Abstract:
  Document-level relation extraction (DocRE) aims at extracting the semantic
relations among entity pairs in a document. In DocRE, a subset of the sentences
in a document, called the evidence sentences, might be sufficient for
predicting the relation between a specific entity pair. To make better use of
the evidence sentences, in this paper, we propose a three-stage
evidence-enhanced DocRE framework consisting of joint relation and evidence
extraction, evidence-centered relation extraction (RE), and fusion of
extraction results. We first jointly train an RE model with a simple and
memory-efficient evidence extraction model. Then, we construct pseudo documents
based on the extracted evidence sentences and run the RE model again. Finally,
we fuse the extraction results of the first two stages using a blending layer
and make a final prediction. Extensive experiments show that our proposed
framework achieves state-of-the-art performance on the DocRED dataset,
outperforming the second-best method by 0.76/0.82 Ign F1/F1. In particular, our
method significantly improves the performance on inter-sentence relations by
1.23 Inter F1.


Abstract:
  While the Kerr-Schild double copy of the Coulomb solution in dimensions
higher than three is the Schwarzschild black hole, it is known that it should
be a non-vacuum solution in three dimensions. We show that the static black
hole solution of Einstein-Maxwell theory (with one ghost sign in the action) is
the double copy with the correct Newtonian limit, which provides an improvement
over the previous construction with a free scalar field that does not vanish at
infinity. By considering a negative cosmological constant, we also study the
charged Ba\~nados-Teitelboim-Zanelli black hole and find that the single copy
gauge field is the Coulomb solution modified by a term which describes an
electric field linearly increasing with the radial coordinate, which is the
usual behaviour of the Schwarzschild-AdS black hole in higher dimensions when
written around a flat background metric.


Abstract:
  Simultaneous wireless information and power transfer (SWIPT) technique is a
popular strategy to convey both information and RF energy for harvesting at
receivers. In this regard, we consider a two-way relay system with multiple
users and a multi-antenna relay employing SWIPT strategy, where splitting the
received signal leads to a rate-energy trade-off. In literature, the works on
transceiver design have been studied using computationally intensive and
suboptimal convex relaxation based schemes. In this paper, we study the
balanced precoder design using chordal distance (CD) decomposition, which
incurs much lower complexity, and is flexible to dynamic energy requirements.
It is analyzed that given a non-negative value of CD, the achieved harvested
energy for the proposed balanced precoder is higher than that for the perfect
interference alignment (IA) precoder. The corresponding loss in sum rates is
also analyzed via an upper bound. Simulation results add that the IA schemes
based on mean-squared error are better suited for the SWIPT maximization than
the subspace alignment-based methods.


Abstract:
  In this paper we extend the combinatorial theory of poly-Bernoulli numbers.
We prove combinatorially some identities involving poly-Bernoulli polynomials.
We introduce a combinatorial model for poly-Euler numbers and provide
combinatorial proofs of some identities. We redefine $r$-Eulerian polynomials
and verify Stephan's conjecture concerning poly-Bernoulli numbers and the
central binomial series.


Abstract:
  Training sparse networks to converge to the same performance as dense neural
architectures has proven to be elusive. Recent work suggests that
initialization is the key. However, while this direction of research has had
some success, focusing on initialization alone appears to be inadequate. In
this paper, we take a broader view of training sparse networks and consider the
role of regularization, optimization, and architecture choices on sparse
models. We propose a simple experimental framework, Same Capacity Sparse vs
Dense Comparison (SC-SDC), that allows for a fair comparison of sparse and
dense networks. Furthermore, we propose a new measure of gradient flow,
Effective Gradient Flow (EGF), that better correlates to performance in sparse
networks. Using top-line metrics, SC-SDC and EGF, we show that default choices
of optimizers, activation functions and regularizers used for dense networks
can disadvantage sparse networks. Based upon these findings, we show that
gradient flow in sparse networks can be improved by reconsidering aspects of
the architecture design and the training regime. Our work suggests that
initialization is only one piece of the puzzle and taking a wider view of
tailoring optimization to sparse networks yields promising results.


Abstract:
  A continuous-time multi-state history is semi-Markovian, if an intensity to
migrate from one state into another, depends on the duration in the first
state. Such duration can be formalised as covariate, entering the intensity
process of the transition counts. We derive the integrated intensity process,
prove its predictability and the martingale property of the residual. In
particular, we verify the usual conditions for the respective filtration. As a
consequence, according to Nielsen and Linton (1995), a kernel estimator of the
transition intensity, including the duration dependence, converges point-wise
at a slow rate, compared to the Markovian kernel estimator, i.e when ignoring
dependence. By using the rate discrepancy, we follow Gozalo (1993) and show
that the (properly scaled) maximal difference of the two kernel estimators on a
random grid of points is asymptotically chi-square-1-distributed. As a data
example, for a sample of 130,000 German women observed over a period of nine
years, we model the mortality after dementia onset, potentially dependent on
the disease duration. As usual, the models under both hypotheses need to be
enlarged to allow for independent right-censoring. We find a significant effect
of dementia duration, nearly independent of the bandwidth.


Abstract:
  On-device Deep Neural Networks (DNNs) have recently gained more attention due
to the increasing computing power of the mobile devices and the number of
applications in Computer Vision (CV), Natural Language Processing (NLP), and
Internet of Things (IoTs). Unfortunately, the existing efficient convolutional
neural network (CNN) architectures designed for CV tasks are not directly
applicable to NLP tasks and the tiny Recurrent Neural Network (RNN)
architectures have been designed primarily for IoT applications. In NLP
applications, although model compression has seen initial success in on-device
text classification, there are at least three major challenges yet to be
addressed: adversarial robustness, explainability, and personalization. Here we
attempt to tackle these challenges by designing a new training scheme for model
compression and adversarial robustness, including the optimization of an
explainable feature mapping objective, a knowledge distillation objective, and
an adversarially robustness objective. The resulting compressed model is
personalized using on-device private training data via fine-tuning. We perform
extensive experiments to compare our approach with both compact RNN (e.g.,
FastGRNN) and compressed RNN (e.g., PRADO) architectures in both natural and
adversarial NLP test settings.


Abstract:
  Photoluminescence (PL) intermittency is a ubiquitous phenomenon detrimentally
reducing the temporal emission intensity stability of single colloidal quantum
dots (CQDs) and the emission quantum yield of their ensembles. Despite efforts
for blinking reduction via chemical engineering of the QD architecture and its
environment, blinking still poses barriers to the application of QDs,
particularly in single-particle tracking in biology or in single-photon
sources. Here, we demonstrate the first deterministic all-optical suppression
of quantum dot blinking using a compound technique of visible and mid-infrared
(MIR) excitation. We show that moderate-field ultrafast MIR pulses (5.5 $\mu$m,
150 fs) can switch the emission from a charged, low quantum yield 'grey' trion
state to the 'bright' exciton state in CdSe/CdS core-shell quantum dots
resulting in a significant reduction of the QD intensity flicker.
Quantum-tunneling simulations suggest that the MIR fields remove the excess
charge from trions with reduced emission quantum yield to restore higher
brightness exciton emission. Our approach can be integrated with existing
single-particle tracking or super-resolution microscopy techniques without any
modification to the sample and translates to other emitters presenting
charging-induced PL intermittencies, such as single-photon emissive defects in
diamond and two-dimensional materials.


Abstract:
  We study the existence of fully nontrivial solutions to the system $$-\Delta
u_i+ \lambda_iu_i = \sum\limits_{j=1}^\ell \beta_{ij}|u_j|^p|u_i|^{p-2}u_i\
\hbox{in}\ \Omega, \qquad
  i=1,\ldots,\ell,$$ in a bounded or unbounded domain $\Omega$ in $\mathbb
R^N,$ $N\ge 3$. The $\lambda_i$'s are real numbers, and the nonlinear term may
have subcritical ($1<p<\frac{N}{N-2}$), critical ($p=\frac{N}{N-2}$), or
supercritical growth ($p>\frac{N}{N-2}$). The matrix $(\beta_{ij})$ is
symmetric and admits a block decomposition such that the diagonal entries
$\beta_{ii}$ are positive, the interaction forces within each block are
attractive (i.e., all entries $\beta_{ij}$ in each block are non-negative) and
the interaction forces between different blocks are repulsive (i.e., all other
entries are non-positive). We obtain new existence and multiplicity results of
fully nontrivial solutions, i.e., solutions where every component $u_i$ is
nontrivial. We also find fully synchronized solutions (i.e., $u_i=c_i u_1$ for
all $i=2,\ldots,\ell$) in the purely cooperative case whenever $p\in(1,2).$


Abstract:
  The rare event search experiments using germanium detectors are performed in
the underground laboratories to prevent cosmic rays. However, the cosmogenic
activation of the cupreous detector components on the ground will generate long
half-life radioisotopes and contribute continually to the expected background
level. We present a study on the cosmogenic activation of copper after 504 days
of exposure at an altitude of 2469.4 m outside the China Jinping Underground
Laboratory (CJPL). The specific activities of the cosmogenic nuclides produced
in the copper bricks were measured using a low background germanium gamma-ray
spectrometer at CJPL. The production rates at sea level, in units of
nuclei/kg/day, are 18.6 \pm 2.0 for Mn-54, 9.9 \pm 1.3 for Co-56, 48.3 \pm 5.5
for Co-57, 51.8 \pm 2.5 for Co-58 and 39.7 \pm 5.7 for Co-60, respectively.
Given the expected exposure history of the germanium detectors, a Monte Carlo
simulation is conducted to assess the cosmogenic background contributions of
the detectors' cupreous components.


Abstract:
  Random lasers (RLs) are intriguing devices with promising applications as
light sources for imaging, sensing, super resolution spectral analysis or
complex networks engineering. RLs can be obtained from optically pumped dyes,
optical fibers and crystals, or electrically pumped semiconductor
heterostructures. Semicon-ductor RLs are usually fabricated by introducing
scattering defects into the active layer, adding a degree of complexity to the
fabrication process and losing the ease of realization potentially offered by
disordered structures. Ready availability of electrically pumped RLs, avoiding
costly fabrication approach, would boost the use of these devices in research
and applications. Here, we realize an incoherent semiconductor RL by simply
processing the output mirror of an off-the-shelf Fabry-Perot laser diode via
controlled laser ablation. Optical feedback provided by the intact back mirror
and the ablated front mirror results in multi-mode ran-dom lasing with low
spatial coherence and speckled output emission profile.


Abstract:
  Detection of rare traits or diseases in a large population is challenging.
Pool testing allows covering larger swathes of population at a reduced cost,
while simplifying logistics. However, testing precision decreases as it becomes
unclear which member of a pool made the global test positive.
  In this paper we discuss testing strategies that provably approach
best-possible strategy - optimal in the sense that no other strategy can give
exact results with fewer tests. Our algorithms guarantee that they provide a
complete and exact result for every individual, without exceeding $1/0.99$
times the number of tests the optimal strategy would require.
  This threshold is arbitrary: algorithms closer to the optimal bound can be
described, however their complexity increases, making them less practical.
  Moreover, the way the algorithms process input samples leads to some
individuals' status to be known sooner, thus allowing to take urgency into
account when assigning individuals to tests.


Abstract:
  In this note, we study a family of subgraphs of the Farey graph, denoted as
$\mathcal{F}_N$ for every $N\in\mathbb{N}.$
  We show that $\mathcal{F}_N$ is connected if and only if $N$ is either equal
to one or a prime power. We introduce a class of continued fractions referred
to as $\mathcal{F}_N$-continued fractions for each $N>1.$ We establish a
relation between $\mathcal{F}_N$-continued fractions and certain paths from
infinity in the graph $\mathcal{F}_N.$ We discuss existence and uniqueness of
$\mathcal{F}_N$-continued fraction expansions of real numbers.


Abstract:
  This work is a theoretical study of the speed at which the material of an
impacted target is ejected during the formation of an impact crater. Our model,
starting from the first principle of thermodynamics, can describes the speed of
the ejecta recursing to considerations that include complex process in simple
calculations. The fit of the model with observations shows that the many
complex details implicit in an impact process could be included in some few
parameters. Ejecta speed could be described independent of impactor parameters.
The model is compared with subsonic and supersonic speed experiments showing
coincidence in several cases. The model works with subsonic and supersonic
impacts. We do not compare the model with hypersonic impacts (> 5 km/s),
however, as the model derivation no depends on the impactor velocity it is
likely that also work with this kind of impacts.


Abstract:
  The task of long-form question answering (LFQA) involves retrieving documents
relevant to a given question and using them to generate a paragraph-length
answer. While many models have recently been proposed for LFQA, we show in this
paper that the task formulation raises fundamental challenges regarding
evaluation and dataset creation that currently preclude meaningful modeling
progress. To demonstrate these challenges, we first design a new system that
relies on sparse attention and contrastive retriever learning to achieve
state-of-the-art performance on the ELI5 LFQA dataset. While our system tops
the public leaderboard, a detailed analysis reveals several troubling trends:
(1) our system's generated answers are not actually grounded in the documents
that it retrieves; (2) ELI5 contains significant train / validation overlap, as
at least 81% of ELI5 validation questions occur in paraphrased form in the
training set; (3) ROUGE-L is not an informative metric of generated answer
quality and can be easily gamed; and (4) human evaluations used for other text
generation tasks are unreliable for LFQA. We offer suggestions to mitigate each
of these issues, which we hope will lead to more rigorous LFQA research and
meaningful progress in the future.


Abstract:
  Consensus protocols have traditionally been studied in a setting where all
participants are known to each other from the start of the protocol execution.
In the parlance of the 'blockchain' literature, this is referred to as the
permissioned setting. What differentiates Bitcoin from these previously studied
protocols is that it operates in a permissionless setting, i.e. it is a
protocol for establishing consensus over an unknown network of participants
that anybody can join, with as many identities as they like in any role. The
arrival of this new form of protocol brings with it many questions. Beyond
Bitcoin, what can we prove about permissionless protocols in a general sense?
How does recent work on permissionless protocols in the blockchain literature
relate to the well-developed history of research on permissioned protocols in
distributed computing?
  To answer these questions, we describe a formal framework for the analysis of
both permissioned and permissionless systems. Our framework allows for
"apples-to-apples" comparisons between different categories of protocols and,
in turn, the development of theory to formally discuss their relative merits. A
major benefit of the framework is that it facilitates the application of a rich
history of proofs and techniques in distributed computing to problems in
blockchain and the study of permissionless systems. Within our framework, we
then address the questions above. We consider the Byzantine Generals Problem as
a formalisation of the problem of reaching consensus, and address a programme
of research that asks, "Under what adversarial conditions, and for what types
of permissionless protocol, is consensus possible?" We prove a number of
results for this programme, our main result being that deterministic consensus
is not possible for decentralised permissionless protocols. To close, we give a
list of eight open questions.


Abstract:
  Coorbit theory is a powerful machinery that constructs a family of Banach
spaces, the so-called coorbit spaces, from well-behaved unitary representations
of locally compact groups. A core feature of coorbit spaces is that they can be
discretized in a way that reflects the geometry of the underlying locally
compact group. Many established function spaces such as modulation spaces,
Besov spaces, Sobolev-Shubin spaces, and shearlet spaces are examples of
coorbit spaces. The goal of this survey is to give an overview of coorbit
theory with the aim of presenting the main ideas in an accessible manner.
Coorbit theory is generally seen as a complicated theory, filled with both
technicalities and conceptual difficulties. Faced with this obstacle, we feel
obliged to convince the reader of the theory's elegance. As such, this survey
is a showcase of coorbit theory and should be treated as a stepping stone to
more complete sources.


Abstract:
  Variational inequalities with thin obstacles and Signorini-type boundary
conditions are classical problems in the calculus of variations, arising in
numerous applications. In the linear case many refined results are known, while
in the nonlinear setting our understanding is still at a preliminary stage.
  In this paper we prove $C^1$ regularity for the solutions to a general class
of quasi-linear variational inequalities with thin obstacles and $C^{1,
\alpha}$ regularity for variational inequalities under Signorini-type
conditions on the boundary of a domain.


Abstract:
  To process data more efficiently, big data frameworks provide data
abstractions to developers. However, due to the abstraction, there may be many
challenges for developers to understand and debug the data processing code. To
uncover the challenges in using big data frameworks, we first conduct an
empirical study on 1,000 Apache Spark-related questions on Stack Overflow. We
find that most of the challenges are related to data transformation and API
usage. To solve these challenges, we design an approach, which assists
developers with understanding and debugging data processing in Spark. Our
approach leverages statistical sampling to minimize performance overhead, and
provides intermediate information and hint messages for each data processing
step of a chained method pipeline. The preliminary evaluation of our approach
shows that it has low performance overhead and we receive good feedback from
developers.


Abstract:
  We prove an extension of a seminal result of Bertini and Giacomin. Namely we
consider weakly asymmetric exclusion processes with several distinct initial
data simultaneously, then run according to the basic coupling, and we show
joint convergence to the solution of the KPZ equation with the same driving
noise in the limiting equation. Along the way, we analyze fine properties of
nontrivially coupled solutions-in-law of KPZ-type equations.


Abstract:
  We consider the optimization of a two-hop relay network based on an
amplify-and-forward Multiple-Input Multiple-Output (MIMO) relay. The relay is
assumed to derive the output signal by a Relay Transform Matrix (RTM) applied
to the input signal. Assuming perfect channel state information about the
network at the relay, the RTM is optimized according to two different criteria:
{\bf\em i)} network capacity; {\bf\em ii)} network capacity based on Orthogonal
Space--Time Block Codes. The two assumptions have been addressed in part in the
literature. The optimization problem is reduced to a manageable convex form,
whose KKT equations are explicitly solved. Then, a parametric solution is
given, which yields the power constraint and the capacity achieved with
uncorrelated transmitted data as functions of a positive indeterminate. The
solution for a given average power constraint at the relay is amenable to a
\emph{water-filling-like} algorithm, and extends earlier literature results
addressing the case without the direct link. Simulation results are reported
concerning a Rayleigh relay network and the role of the direct link SNR is
precisely assessed.


Abstract:
  Path integral contour deformations have been shown to mitigate sign and
signal-to-noise problems associated with phase fluctuations in lattice field
theories. We define a family of contour deformations applicable to $SU(N)$
lattice gauge theory that can reduce sign and signal-to-noise problems
associated with complex actions and complex observables. For observables, these
contours can be used to define deformed observables with identical expectation
value but different variance. As a proof-of-principle, we apply machine
learning techniques to optimize the deformed observables associated with Wilson
loops in two dimensional $SU(2)$ and $SU(3)$ gauge theory. We study loops
consisting of up to 64 plaquettes and achieve variance reduction of up to 4
orders of magnitude.


Abstract:
  Gas metallicity $Z$ and the related dust-to-gas ratio $f_\textrm{d}$ can
influence the growth of HII regions via metal line cooling and UV absorption.
We model these effects in star-forming regions containing massive stars. We
compute stellar feedback from photoionization and radiation pressure (RP) using
Monte Carlo radiative transfer coupled with hydrodynamics, including stellar
and diffuse radiation fields. We follow a $10^5$ M$_\odot$ turbulent cloud with
$Z/$Z$_\odot$ = 2, 1, 0.5, 0.1 and $f_\textrm{d} = 0.01 Z/$Z$_\odot$ with a
cluster-sink particle method for star formation. The models evolve for at least
1.5 Myr under feedback. Lower $Z$ results in higher temperatures and therefore
larger HII regions. For $Z \ge$Z$_\odot$, radiation pressure $P_\textrm{rad}$
can dominate locally over the gas pressure $P_\textrm{gas}$ in the inner
half-parsec around sink particles. Globally, the ratio of
$P_\textrm{rad}/P_\textrm{gas}$ is around 1 (2 Z$_\odot$), 0.3 (Z$_\odot$), 0.1
(0.5 Z$_\odot$), and 0.03 (0.1 Z$_\odot$). In the solar model, excluding RP
results in an ionized volume several times smaller than the fiducial model with
both mechanisms. Excluding RP and UV attenuation by dust results in a larger
ionized volume than the fiducial case. That is, UV absorption hinders growth
more than RP helps it. The radial expansion velocity of ionized gas reaches
$+$15 km/s outwards, while neutral gas has inward velocities for most of the
runtime, except for 0.1 Z$_\odot$ which exceeds $+$4 km/s. $Z$ and
$f_\textrm{d}$ do not significantly alter the star formation efficiency, rate,
or cluster half-mass radius, with the exception of 0.1 Z$_\odot$ due to the
earlier expulsion of neutral gas.


Abstract:
  We give a generalization of Meeks-Yau's celebrated embeddedness result for
the solutions of the Plateau problem for extreme curves.


Abstract:
  In this paper, we study a diffusive Lotka-Volterra competition model under
homogeneous Dirichlet boundary conditions. We shall discuss the effects of
dispersal rate and spatial heterogeneity on population dynamics. More
precisely, we establish the main results about the global asymptotic stability
of semitrivial as well as coexistence steady states.


Abstract:
  We study the dynamics of electroweak phase transition in a simple extension
of the Standard Model where the Higgs sector is extended by adding an
$SU(2)_L$-triplet with hypercharge Y=2. By making random scans over the
parameters of the model, we show that there are regions consistent with
constraints from collider experiments and the requirement for a strong
first-order electroweak phase transition which is needed for electroweak
baryogenesis. Further, we also study the power spectrum of the gravitational
waves which can be generated due to the first-order phase transitions.
Moreover, the detectability of these gravitational waves, via future
space-based detectors, is discussed.


Abstract:
  A major challenge in high-precision light-pulse atom interferometric
experiments such as in tests of the weak equivalence principle is the
uncontrollable dependency of the phase on initial velocity and position of the
atoms in the presence of inhomogeneous gravitational fields. To overcome this
limitation, mitigation strategies have been proposed, however, valid only for
harmonic potentials or only for small branch separations in more general
situations. Here we provide a mitigation formula for anharmonic perturbation
potentials including local gravitational effects that vary on length scales
much smaller than the spatial extent probed by the atoms and originate, e.g.,
from buildings that surround the experiment. Furthermore, our results are
applicable to general interferometer geometries with arbitrary branch
separation and allow for compensation of Coriolis effects in rotating reference
frames.


Abstract:
  Here we perform a Kaluza-Klein dimensional reduction of Vasiliev's
first-order description of massless spin-s particles from $D=3+1$ to $D=2+1$
and derive first-order self-dual models describing particles with helicities
$\pm s$ for the cases $s=1,2,3$. In the first two cases we recover known
(parity singlets) self-dual models. In the spin-3 case we derive a new first
order self-dual model with a local Weyl symmetry which lifts the traceless
restriction on the rank-3 tensor. A gauge fixed version of this model
corresponds to a known spin-3 self-dual model. We conjecture that our procedure
can be generalized to arbitrary integer spins.


Abstract:
  Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine
learning method based on evolutionary computation. GSGP performs search
operations directly at the level of program semantics, which can be done more
efficiently then operating at the syntax level like most GP systems. Efficient
implementations of GSGP in C++ exploit this fact, but not to its full
potential. This paper presents GSGP-CUDA, the first CUDA implementation of GSGP
and the most efficient, which exploits the intrinsic parallelism of GSGP using
GPUs. Results show speedups greater than 1,000X relative to the
state-of-the-art sequential implementation.


Abstract:
  In this article, we study bounded solutions of Euler-type equations on
$\mathbb{R}^d$ which have no integrability at $|x| \rightarrow +\infty$. As has
been previously noted, such solutions fail to achieve uniqueness in an initial
value problem, even under strong smoothness conditions. This contrasts with
well-posedness results that have been obtained by using the Leray projection
operator in these equations. This apparent paradox is solved by noting that
using the Leray projector requires an extra condition the solutions must
fulfill at $|x| \rightarrow + \infty$. We find this condition to be necessary
and sufficient. We deduce a full well-posedness result for the Euler equations
in the space $C^0_T(B^1_{\infty, 1})$. We also apply the methods developed for
the Euler system to other hydrodynamic models: the Navier-Stokes equations and
magnetohydrodynamics.


Abstract:
  We introduce a type of zero-dimensional dynamical system (a pair consisting
of a totally disconnected compact metrizable space along with a homeomorphism
of that space), which we call "fiberwise essentially minimal", and we prove
that the associated crossed product $ C^* $-algebra of such a system is an
AT-algebra. Under the additional assumption that the system has no periodic
points, we prove that the associated crossed product $ C^* $-algebra has real
rank zero, which tells us that such $ C^* $-algebras are classifiable by $ K
$-theory. We show that the definition of fiberwise essentially minimality
allows one to produce many nontrivial examples of such systems (ones that are
neither minimal nor essentially minimal). The associated crossed product $ C^*
$-algebras to these nontrivial examples are of particular interest because they
are non-simple (unlike in the minimal case).


Abstract:
  Next generation neutrino oscillation experiments like DUNE and T2HK are
multi-purpose observatories, with a rich physics program beyond oscillation
measurements. A special role is played by their near detector facilities, which
are particularly well-suited to search for weakly coupled dark sector particles
produced in the primary target. In this paper, we demonstrate this by
estimating the sensitivity of the DUNE near detectors to the scattering of
sub-GeV DM particles and to the decay of sub-GeV sterile neutrinos ("heavy
neutral leptons"). We discuss in particular the importance of the DUNE-PRISM
design, which allows some of the near detectors to be moved away from the beam
axis. At such off-axis locations, the signal-to-background ratio improves for
many new physics searches. We find that this leads to a dramatic boost in the
sensitivity to boosted DM particles interacting mainly with hadrons, while for
boosted DM interacting with leptons, data taken on-axis leads to marginally
stronger exclusion limits. Searches for heavy neutral leptons perform equally
well in both configurations.


Abstract:
  We use the technique of invariant frames to study a left invariant spray
structure on a Lie group, and calculate its S-curvature and Riemann curvature,
which generalizes the corresponding formulae in homogeneous Finsler geometry.
Using the canonical bi-invariant Berwald spray structure as the reference, any
left invariant spray structure can be associated with a spray vector field on
the Lie algebra. We find the correspondence between geodesics for a left
invariant spray structure and the inverse integral curves of its spray vector
field. As an application for this correspondence, we provide an alternative
proof for Landsberg Conjecture in the case of homogeneous Finsler surfaces.


Abstract:
  Structure factors describe how incident radiation is scattered from materials
such as silicon and germanium and characterize the physical interaction between
the material and scattered particles. We use neutron pendell\"{o}sung
interferometry to make precision measurements of the (220) and (400)
neutron-silicon structure factors, and achieve a factor of four improvement in
the (111) structure factor uncertainty. These data provide measurements of the
silicon Debye-Waller factor at room temperature and the mean square neutron
charge radius $\langle r_n^2 \rangle = -0.1101 \pm 0.0089 \, \mathrm{fm}^2$.
Combined with existing measurements of the Debye-Waller factor and charge
radius, the measured structure factors also improve constraints on the strength
of a Yukawa-modification to gravity by an order of magnitude over the 20 pm to
10 nm length scale range.


Abstract:
  Using a dataset of 6.32 fb$^{-1}$ of $e^+ e^-$ annihilation data collected
with the BESIII detector at center-of-mass energies between 4178 and 4226 MeV,
we have measured the absolute branching fraction of the leptonic decay $D_s^+
\to \tau^+ \nu_{\tau}$ via $\tau^+ \to e^+ \nu_e \bar{\nu}_{\tau}$, and find
$\mathcal{B}_{D_s^+ \to \tau^+ \nu_{\tau}}=(5.27\pm0.10\pm0.12)\times10^{-2}$,
where the first uncertainty is statistical and the second is systematic. The
precision is improved by a factor of 2 compared to the previous best
measurement. Combining with $f_{D_s^+}$ from Lattice quantum chromodynamics
calculations or the $|V_{cs}|$ from the CKMfitter group, we extract
$|V_{cs}|=0.978\pm0.009\pm0.012$ and $f_{D_s^+}= (251.1\pm2.4\pm3.0)$ MeV,
respectively. Combining our result with the world averages of
$\mathcal{B}_{D_s^+ \to \tau^+ \nu_{\tau}}$ and $\mathcal{B}_{D_s^+ \to \mu^+
\nu_{\mu}}$, we obtain the ratio of the branching fractions $\mathcal{B}_{D_s^+
\to \tau^+ \nu_{\tau}} / \mathcal{B}_{D_s^+ \to \mu^+ \nu_{\mu}} =
9.72\pm0.37$, which is consistent with the standard model prediction of lepton
flavor universality.


Abstract:
  We show that $3$-graphs on $n$ vertices whose codegree is at least $(2/3 +
o(1))n$ can be decomposed into tight cycles and admit Euler tours, subject to
the trivial necessary divisibility conditions. We also provide a construction
showing that our bounds are best possible up to the $o(1)$ term. All together,
our results answer in the negative some recent questions of Glock, Joos,
K\"uhn, and Osthus.


Abstract:
  Motivated by two identities published with Ramanujan's lost notebook and
connected, respectively, with the Gauss circle problem and the Dirichlet
divisor problem, in an earlier paper, three of the present authors derived
representations for certain sums of products of trigonometric functions as
double series of Bessel functions. These series are generalized in the present
paper by introducing the novel notion of balanced derivatives, leading to
further theorems. As we will see below, the regions of convergence in the
unbalanced case are entirely different than those in the balanced case. From
this viewpoint, it is remarkable that Ramanujan had the intuition to formulate
entries that are, in our new terminology, "balanced". If $x$ denotes the number
of products of the trigonometric functions appearing in our sums, in addition
to proving the identities mentioned above, theorems and conjectures for upper
and lower bounds for the sums as $x\to\infty$ are established.


Abstract:
  The performance of federated learning (FL) over wireless networks depend on
the reliability of the client-server connectivity and clients' local
computation capabilities. In this article we investigate the problem of client
scheduling and resource block (RB) allocation to enhance the performance of
model training using FL, over a pre-defined training duration under imperfect
channel state information (CSI) and limited local computing resources. First,
we analytically derive the gap between the training losses of FL with clients
scheduling and a centralized training method for a given training duration.
Then, we formulate the gap of the training loss minimization over client
scheduling and RB allocation as a stochastic optimization problem and solve it
using Lyapunov optimization. A Gaussian process regression-based channel
prediction method is leveraged to learn and track the wireless channel, in
which, the clients' CSI predictions and computing power are incorporated into
the scheduling decision. Using an extensive set of simulations, we validate the
robustness of the proposed method under both perfect and imperfect CSI over an
array of diverse data distributions. Results show that the proposed method
reduces the gap of the training accuracy loss by up to 40.7% compared to
state-of-theart client scheduling and RB allocation methods.


Abstract:
  We report the observation of gravitational waves from two compact binary
coalescences in LIGO's and Virgo's third observing run with properties
consistent with neutron star-black hole (NSBH) binaries. The two events are
named GW200105_162426 and GW200115_042309, abbreviated as GW200105 and
GW200115; the first was observed by LIGO Livingston and Virgo, and the second
by all three LIGO-Virgo detectors. The source of GW200105 has component masses
$8.9^{+1.2}_{-1.5}\,M_\odot$ and $1.9^{+0.3}_{-0.2}\,M_\odot$, whereas the
source of GW200115 has component masses $5.7^{+1.8}_{-2.1}\,M_\odot$ and
$1.5^{+0.7}_{-0.3}\,M_\odot$ (all measurements quoted at the 90% credible
level). The probability that the secondary's mass is below the maximal mass of
a neutron star is 89%-96% and 87%-98%, respectively, for GW200105 and GW200115,
with the ranges arising from different astrophysical assumptions. The source
luminosity distances are $280^{+110}_{-110}$ Mpc and $300^{+150}_{-100}$ Mpc,
respectively. The magnitude of the primary spin of GW200105 is less than 0.23
at the 90% credible level, and its orientation is unconstrained. For GW200115,
the primary spin has a negative spin projection onto the orbital angular
momentum at 88% probability. We are unable to constrain spin or tidal
deformation of the secondary component for either event. We infer a NSBH merger
rate density of $45^{+75}_{-33}\,\mathrm{Gpc}^{-3} \mathrm{yr}^{-1}$ when
assuming GW200105 and GW200115 are representative of the NSBH population, or
$130^{+112}_{-69}\,\mathrm{Gpc}^{-3} \mathrm{yr}^{-1}$ under the assumption of
a broader distribution of component masses.


Abstract:
  We show that for every positive integer $k$, any tournament can be
partitioned into at most $2^{ck}$ $k$-th powers of paths. This result is tight
up to the exponential constant. Moreover, we prove that for every
$\varepsilon>0$ and every integer $k$, any tournament on $n\ge
\varepsilon^{-Ck}$ vertices which is $\varepsilon$-far from being transitive
contains the $k$-th power of a cycle of length $\Omega(\varepsilon n)$; both
bounds are tight up to the implied constants.


Abstract:
  In this paper, we focus on normative systems for online communities. The
paper addresses the issue that arises when different community members
interpret these norms in different ways, possibly leading to unexpected
behavior in interactions, usually with norm violations that affect the
individual and community experiences. To address this issue, we propose a
framework capable of detecting norm violations and providing the violator with
information about the features of their action that makes this action violate a
norm. We build our framework using Machine Learning, with Logistic Model Trees
as the classification algorithm. Since norm violations can be highly
contextual, we train our model using data from the Wikipedia online community,
namely data on Wikipedia edits. Our work is then evaluated with the Wikipedia
use case where we focus on the norm that prohibits vandalism in Wikipedia
edits.


Abstract:
  The dust content of normal galaxies and the dust mass density (DMD) at high-z
(z>4) are unconstrained given the source confusion and the sensitivity
limitations of previous observations. The ALMA Large Program to INvestigate
[CII] at Early Times (ALPINE), which targeted 118 UV-selected star-forming
galaxies at 4.4<z<5.9, provides a new opportunity to tackle this issue for the
first time with a statistically robust dataset. We have exploited the
rest-frame far-infrared (FIR) fluxes of the 23 continuum individually detected
galaxies and stacks of continuum images to measure the dust content of the 118
UV-selected ALPINE galaxies. We have focused on the dust scaling relations and,
by comparing them with predictions from chemical evolution models, we have
probed the evolutionary stage of UV-selected galaxies at high-z. By using the
observed correlation between the UV-luminosity and the dust mass, we have
estimated the DMD of UV-selected galaxies at z~5, weighting the galaxies by
means of the UV-luminosity function (UVLF). The derived DMD has been compared
with the value we have estimated from the 10 ALPINE far-IR continuum blindly
detected galaxies at the redshift of the ALPINE targets. The comparison of the
observed dust scaling relations with chemical evolution models suggests that
ALPINE galaxies are not likely progenitors of disc galaxies, but of
intermediate and low mass proto-spheroids, resulting in present-day bulges of
spiral or elliptical galaxies. Interestingly, this conclusion is in line with
the independent morphological analysis, that shows that the majority (~70\%) of
the dust-continuum detected galaxies have a disturbed morphology. The DMD
obtained at z~5 from UV-selected sources is ~30% of the value obtained from
blind far-IR selected sources, showing that the UV-selection misses the most
dust-rich, UV-obscured galaxies.


Abstract:
  Non-autoregressive mechanisms can significantly decrease inference time for
speech transformers, especially when the single step variant is applied.
Previous work on CTC alignment-based single step non-autoregressive transformer
(CASS-NAT) has shown a large real time factor (RTF) improvement over
autoregressive transformers (AT). In this work, we propose several methods to
improve the accuracy of the end-to-end CASS-NAT, followed by performance
analyses. First, convolution augmented self-attention blocks are applied to
both the encoder and decoder modules. Second, we propose to expand the trigger
mask (acoustic boundary) for each token to increase the robustness of CTC
alignments. In addition, iterated loss functions are used to enhance the
gradient update of low-layer parameters. Without using an external language
model, the WERs of the improved CASS-NAT, when using the three methods, are
3.1%/7.2% on Librispeech test clean/other sets and the CER is 5.4% on the
Aishell1 test set, achieving a 7%~21% relative WER/CER improvement. For the
analyses, we plot attention weight distributions in the decoders to visualize
the relationships between token-level acoustic embeddings. When the acoustic
embeddings are visualized, we find that they have a similar behavior to word
embeddings, which explains why the improved CASS-NAT performs similarly to AT.


Abstract:
  Cherenkov radiation (CR) generated by a charge moving through a hollow
conical target made of dielectric material is analyzed. We consider two cases:
the charge moves from the base of the cone to its top (``straight'' cone) or
from the top to the base (``inverted'' cone). Unlike previous papers, a nonzero
shift of the charge trajectory from the symmetry axis is taken into account
which leads to generation of asymmetric CR. The most interesting effect is the
phenomenon of ``Cherenkov spotlight'' which has been reported earlier for
axially symmetric problems. This effect allows essential enhancement of the CR
intensity in the far-field region by proper selection of the target's
parameters and charge velocity. Here we describe the influence of charge shift
on CR far-field patterns paying the main attention to the ``Cherenkov
spotlight'' regime. Influence of variation of the charge speed on this
phenomenon is also investigated.


Abstract:
  During its two-year mission at comet 67P, Rosetta nearly continuously
monitored the inner coma plasma environment for gas production rates varying
over three orders of magnitude, at distances to the nucleus from a few to a few
hundred km. To achieve the best possible measurements, cross-calibration of the
plasma instruments is needed. We construct with two different physical models
to cross-calibrate the electron density as measured by the Mutual Impedance
Probe (MIP) to the ion current and spacecraft potential as measured by the
Rosetta Langmuir Probe (LAP), the latter validated with the Ion Composition
Analyser (ICA). We retrieve a continuous plasma density dataset for the entire
cometary mission with a much improved dynamical range compared to any plasma
instrument alone and, at times, improve the temporal resolution from
0.24-0.74~Hz to 57.8~Hz. The new density dataset is consistent with the
existing MIP density dataset and covers long time periods where densities were
too low to be measured by MIP. The physical model also yields, at 3~hour time
resolution, ion flow speeds as well as a proxy for the solar EUV flux from the
photoemission from the Langmuir Probes. We report on two independent
mission-wide estimates of the ion flow speed which are consistent with the bulk
H$_2$O$^+$ ion velocities as measured by ICA. We find the ion flow to
consistently be much faster than the neutral gas over the entire mission,
lending further evidence that the ions are collisionally decoupled from the
neutrals in the coma. RPC measurements of ion speeds are therefore not
consistent with the assumptions made in previously published plasma density
models of the comet ionosphere at the start and end of the mission. Also, the
measured EUV flux is perfectly consistent with independently derived values
previously published from Johansson et al. (2017) and lends support for the
conclusions drawn therein.