title: Mask scalar prediction for improving robust automatic speech recognition

abstract:
  Using neural network based acoustic frontends for improving robustness of
streaming automatic speech recognition (ASR) systems is challenging because of
the causality constraints and the resulting distortion that the frontend
processing introduces in speech. Time-frequency masking based approaches have
been shown to work well, but they need additional hyper-parameters to scale the
mask to limit speech distortion. Such mask scalars are typically hand-tuned and
chosen conservatively. In this work, we present a technique to predict mask
scalars using an ASR-based loss in an end-to-end fashion, with minimal increase
in the overall model size and complexity. We evaluate the approach on two
robust ASR tasks: multichannel enhancement in the presence of speech and
non-speech noise, and acoustic echo cancellation (AEC). Results show that the
presented algorithm consistently improves word error rate (WER) without the
need for any additional tuning over strong baselines that use hand-tuned
hyper-parameters: up to 16% for multichannel enhancement in noisy conditions,
and up to 7% for AEC.


title: Approach to Predicting News -- A Precise Multi-LSTM Network With BERT

abstract:
  Varieties of Democracy (V-Dem) is a new approach to conceptualizing and
measuring democracy and politics. It has information for 200 countries and is
one of the biggest databases for political science. According to the V-Dem
annual democracy report 2019, Taiwan is one of the two countries that got
disseminated false information from foreign governments the most. It also shows
that the "made-up news" has caused a great deal of confusion in Taiwanese
society and has serious impacts on global stability. Although there are several
applications helping distinguish the false information, we found out that the
pre-processing of categorizing the news is still done by human labor. However,
human labor may cause mistakes and cannot work for a long time. The growing
demands for automatic machines in the near decades show that while the machine
can do as good as humans or even better, using machines can reduce humans'
burden and cut down costs. Therefore, in this work, we build a predictive model
to classify the category of news. The corpora we used contains 28358 news and
200 news scraped from the online newspaper Liberty Times Net (LTN) website and
includes 8 categories: Technology, Entertainment, Fashion, Politics, Sports,
International, Finance, and Health. At first, we use Bidirectional Encoder
Representations from Transformers (BERT) for word embeddings which transform
each Chinese character into a (1,768) vector. Then, we use a Long Short-Term
Memory (LSTM) layer to transform word embeddings into sentence embeddings and
add another LSTM layer to transform them into document embeddings. Each
document embedding is an input for the final predicting model, which contains
two Dense layers and one Activation layer. And each document embedding is
transformed into 1 vector with 8 real numbers, then the highest one will
correspond to the 8 news categories with up to 99% accuracy.


title:
Bulk domain Meissner state in the ferromagnetic superconductor
  EuFe$_{2}$(As$_{0.8}$P$_{0.2}$)$_{2}$: Consequence of compromise between
  ferromagnetism and superconductivity

abstract:
  Small-angle neutron scattering (SANS) measurements are performed on the
ferromagnetic superconductor EuFe$_{2}$(As$_{0.8}$P$_{0.2}$)$_{2}$
($T\rm_{sc}=22.5\,$K) to probe the delicate interplay between ferromagnetism
and superconductivity. A clear signature of large ferromagnetic domains is
found below the ferromagnetic ordering temperature $T\rm_{C}$ = 18.5 K. In a
small temperature interval of $\sim$ 1.5 K below $T\rm_{C}$, additional SANS
signal is observed, of which the indirect Fourier transform reveals
characteristic length scales in between $\sim$ 80 nm to $\sim$ 160 nm. These
nanometer-scaled domain structures are identified to result from an
intermediate inhomogeneous Meissner effect denoted domain Meissner state, which
was recently observed on the surface of EuFe$_{2}$(As$_{0.79}$P$_{0.21}$)$_{2}$
crystals by means of magnetic force microscopy [V. S. Stolyarov $\mathit{et}$
$\mathit{al.}$, Sci. Adv. 4, 1061 (2018)], ascribing to the competition between
ferromagnetism and superconductivity. Our measurements clearly render the
domain Meissner state as a bulk phenomenon and provide a key solution to the
mystery regarding the intriguing coexistence of strong ferromagnetism and bulk
superconductivity in these compounds.


title: PyGOD: A Python Library for Graph Outlier Detection

abstract:
  PyGOD is an open-source Python library for detecting outliers on graph data.
As the first comprehensive library of its kind, PyGOD supports a wide array of
leading graph-based methods for node-, edge-, subgraph-, and graph-level
outlier detection, under a unified, well-documented API designed for use by
both researchers and practitioners. To overcome the scalability issue in large
graphs, we provide advanced functionalities for selected models, including
mini-batch and sampling. PyGOD is equipped with best practices to foster code
reliability and maintainability, including unit testing, continuous
integration, and code coverage. To foster accessibility, PyGOD is released
under a permissive BSD-license at https://github.com/pygod-team/pygod/ and the
Python Package Index (PyPI).


title:
Galactic seismology: joint evolution of stellar and gaseous disc
  corrugations triggered by a crossing satellite

abstract:
  Evidence for wave-like corrugations are well established in the Milky Way and
in nearby disc galaxies. These were originally detected as a displacement of
the interstellar medium about the midplane, either in terms of vertical
distance or vertical velocity. Over the past decade, similar patterns have
emerged in the Milky Way's stellar disc. We investigate how these vertical
waves are triggered by a passing satellite. For the first time, we use
high-resolution N-body/hydrodynamical simulations to study how the corrugations
set up and evolve jointly in the stellar and gaseous discs. We find that the
gas corrugations follow the stellar corrugations, i.e. they are initially in
phase although, after a few rotation periods (500-700 Myr), the distinct waves
separate and thereafter evolve in different ways. The spatial and kinematic
amplitudes (and thus the energy) of the corrugations dampen with time, with the
gaseous corrugation settling at a faster rate (~800 Myr vs. ~1 Gyr). In
contrast, the vertical energy of individual disc stars is fairly constant
throughout the galaxy's evolution. This difference arises because corrugations
are an emergent phenomenon supported by the collective, ordered motions of
co-spatial ensembles of stars. We show that the damping of the stellar
corrugations can be understood as a consequence of incomplete phase mixing,
while the damping of the gaseous corrugations is a natural consequence of the
dissipative nature of the gas. We suggest that the degree of correlation
between the stellar and gaseous waves may help to age-date the phenomenon.


title: Running vacuum cosmology with bulk viscous matter

abstract:
  We study the late acceleration of the universe by incorporating bulk viscous
matter with the running vacuum. The vacuum energy density varies as the squares
of the Hubble parameter ($\rho_{\Lambda}\propto H^2$), and the coefficient of
bulk viscosity of matter is proportional to the velocity of expansion ($\xi
\propto H$). We obtained an analytical solution to the Friedmann equations and
estimated the model parameters using the combined data set SN1a+CMB+BAO+OHD. We
have evaluated the universe's age as 14 Gyr, which is slightly higher than the
age-predicted by the $\Lambda$CDM model. However, it is an improved result
compared to the age-predicted by a class of bulk viscous matter-dominated
models. Interestingly, we have obtained the coefficient of bulk viscosity of
the matter component as $1.316\times 10^5$ kg $\textnormal m^{-1}$ $\textnormal
s^{-1}$ which is one to two orders of magnitude less than the value predicted
by most of the bulk viscous matter-dominated models and it falls in the range
of highly viscous materials found on the earth. The Hubble parameter is a
decreasing function of the scale factor, and it attains a constant value in the
far future that corresponds to an end deSitter phase of evolution. The
deceleration parameter shows a transition from matter-dominated decelerated
phase to vacuum energy-dominated accelerating phase, and the transition
redshift is obtained as $z_T = 0.73$. The statefinder analysis distinguishes
our model from the $\Lambda$CDM model at present, and the $r-s$ trajectory
reveals the quintessence behaviour of the vacuum energy. The phase space
analysis shows that the universe is evolving towards a mechanically stable
state in the far future. The entropy evolution satisfies the generalised second
law of thermodynamics, and the entropy is maximised in the far future
evolution.


title:
Prime Factorization of ideals in commutative rings, with a focus on
  Krull rings

abstract:
  Let $R$ be a commutative ring with identity. The structure theorem says that
$R$ is a PIR (resp., UFR, general ZPI-ring, $\pi$-ring) if and only if $R$ is a
finite direct sum of PIDs (resp., UFDs, Dedekind domains, $\pi$-domains) and
special primary rings. All of these four types of integral domains are Krull
domains, so motivated by the structure theorem, we study the prime
factorization of ideals in a ring that is a finite direct sum of Krull domains
and special primary rings. Such a ring will be called a general Krull ring. It
is known that Krull domains can be characterized by the star operations $v$ or
$t$ as follows: An integral domain $R$ is a Krull domain if and only if every
nonzero proper principal ideal of $R$ can be written as a finite $v$- or
$t$-product of prime ideals. However, this is not true of general Krull rings.
In this paper, we introduce a new star operation $u$ on $R$, so that $R$ is a
general Krull ring if and only if every proper principal ideal of $R$ can be
written as a finite $u$-product of prime ideals. We also study several
ring-theoretic properties of general Krull rings including Kaplansky-type
theorem, Mori-Nagata theorem, Nagata rings, and Noetherian property.


title:
LAMOST J045019.27+394758.7, with peculiar abundances of N, Na, V, Zn, is
  possibly a Sculptor dwarf galaxy escapee

abstract:
  We present the results of the high-resolution (R$\sim$60,000) spectroscopic
analysis of the star LAMOSTJ045019.27+394758.7 (hereafter J045) from the list
of carbon stars of LAMOST DR2. From our analysis, we find that J045 does not
exhibit the spectral characteristics of carbon stars. It is found to be a
metal-poor ( [Fe/H] = $-$1.05) giant that shows very unusual elemental
abundances, particularly for N, Na, V, and Zn. J045 shows ${\alpha}$-elements
(Mg, Si, Ca) with near-solar values ($<$[$\alpha$/Fe]$>$ = 0.09) in contrast to
Galactic stars that show [$\alpha$/Fe] in the range 0.2 to 0.3 dex. In J045, Sc
and Ti are under abundant with [X/Fe] $\le$ $-$0.25. Vanadium gives [V/Fe] =
0.51 and zinc is under-abundant with [Zn/Fe] = $-$0.62. The object exhibits
near-solar abundances for Sr, Y, Ba, Pr, and Sm. The La is marginally enhanced,
and Ce and Nd are marginally under-abundant in J045. With [Ba/Eu] = $-$0.38,
the object falls into the category of neutron-capture rich r-I stars. The
estimated abundances of various elements show that the observed abundance
pattern is not compatible with the abundances characteristic of Galactic
metal-poor stars but matches quite closely with the abundance pattern of
Sculptor Dwarf galaxy stars of similar metallicity. Based on the above
observational evidences, we suggest that the object is a possible Sculptor
Dwarf Galaxy escapee.


title: Convergence of neural networks to Gaussian mixture distribution

abstract:
  We give a proof that, under relatively mild conditions, fully-connected
feed-forward deep random neural networks converge to a Gaussian mixture
distribution as only the width of the last hidden layer goes to infinity. We
conducted experiments for a simple model which supports our result. Moreover,
it gives a detailed description of the convergence, namely, the growth of the
last hidden layer gets the distribution closer to the Gaussian mixture, and the
other layer successively get the Gaussian mixture closer to the normal
distribution.


title:
Formation of singularities in solutions to nonlinear hyperbolic systems
  with general sources

abstract:
  We consider nonlinear hyperbolic systems with a general source and prove that
for appropriately chosen smooth initial data the lifespan of the associated
$C^1$-solution $u$ cannot be infinite. We employ ideas of F. John (1974) and L.
H\"ormander (1987) to show that the derivative $u_x$ of $u$ becomes unbounded
in finite time.


title: The average Mordell-Weil rank of elliptic surfaces over number fields

abstract:
  Let $K$ be a finitely generated field over $\mathbb{Q}$. Let $\mathcal{X}\to
\mathcal{B}$ be a family of elliptic surfaces over $K$ such that each elliptic
fibration has the same configuration of singular fibers. Let $r$ be the minimum
of the Mordell-Weil rank in this family. Then we show that the locus inside
$|\mathcal{B}|$ where the Mordell-Weil rank is at least $r+1$ is a sparse
subset.
  In this way we prove Cowan's conjecture on the average Mordell-Weil rank of
elliptic surfaces over $\mathbb{Q}$ and prove a similar result for elliptic
surfaces over arbitrary number fields.


title:
Centimeter-level Positioning by Instantaneous Lidar-aided GNSS Ambiguity
  Resolution

abstract:
  High-precision vehicle positioning is key to the implementation of modern
driving systems in urban environments. Global Navigation Satellite System
(GNSS) carrier phase measurements can provide millimeter- to centimeter-level
positioning, provided that the integer ambiguities are correctly resolved.
Abundant code measurements are often used to facilitate integer ambiguity
resolution (IAR), however, they suffer from signal blockage and multipath in
urban canyons. In this contribution, a lidar-aided instantaneous ambiguity
resolution method is proposed. Lidar measurements, in the form of 3D keypoints,
are generated by a learning-based point cloud registration method using a
pre-built HD map and integrated with GNSS observations in a mixed measurement
model to produce precise float solutions, which in turn increase the ambiguity
success rate. Closed-form expressions of the ambiguity variance matrix and the
associated Ambiguity Dilution of Precision (ADOP) are developed to provide a
priori evaluation of such lidar-aided ambiguity resolution performance. Both
analytical and experimental results show that the proposed method enables
successful instantaneous IAR with limited GNSS satellites and frequencies,
leading to centimeter-level vehicle positioning.


title: The Jones Polynomial -- Diagrams and Categories

abstract:
  This paper is a memory of the work and influence of Vaughan Jones. It is an
exposition of the remarkable breakthroughs in knot theory and low dimensional
topology that were catalyzed by his work. The paper recalls the inception of
the Jones polynomial and the author's discovery of the bracket polynomial model
for the Jones polynomial. We then describe some of the developments in knot
theory that were inspired by the Jones polynomial and involve variations and
generalizations of this invariant. The paper is written in the form of a
personal odyssey and with the intent to show different mathematical themes that
arise in relation to the Jones polynomial. This invariant can be interpreted in
relation to combinatorial topology, statistical mechanics, Lie algebras, Hopf
algebras, quantum field theory, category theory and more. In each case the
Jones invariant appears as a key example for patterns and connections of these
mathematical and physical contexts. It is remarkable to what extent Vaughan
Jones' discovery of his polynomial has touched so much mathematics, physics,
natural science and so many of our mathematical lives.


title: Learning Dual-Pixel Alignment for Defocus Deblurring

abstract:
  It is a challenging task to recover all-in-focus image from a single defocus
blurry image in real-world applications. On many modern cameras, dual-pixel
(DP) sensors create two-image views, based on which stereo information can be
exploited to benefit defocus deblurring. Despite existing DP defocus deblurring
methods achieving impressive results, they directly take naive concatenation of
DP views as input, while neglecting the disparity between left and right views
in the regions out of camera's depth of field (DoF). In this work, we propose a
Dual-Pixel Alignment Network (DPANet) for defocus deblurring. Generally, DPANet
is an encoder-decoder with skip-connections, where two branches with shared
parameters in the encoder are employed to extract and align deep features from
left and right views, and one decoder is adopted to fuse aligned features for
predicting the all-in-focus image. Due to that DP views suffer from different
blur amounts, it is not trivial to align left and right views. To this end, we
propose novel encoder alignment module (EAM) and decoder alignment module
(DAM). In particular, a correlation layer is suggested in EAM to measure the
disparity between DP views, whose deep features can then be accordingly aligned
using deformable convolutions. And DAM can further enhance the alignment of
skip-connected features from encoder and deep features in decoder. By
introducing several EAMs and DAMs, DP views in DPANet can be well aligned for
better predicting latent all-in-focus image. Experimental results on real-world
datasets show that our DPANet is notably superior to state-of-the-art
deblurring methods in reducing defocus blur while recovering visually plausible
sharp structures and textures.


title: Razumikhin and Krasovskii Approaches for Safe Stabilization

abstract:
  This paper studies the stabilization and safety problems of nonlinear
time-delay systems. Following both Razumikhin and Krasovskii approaches, we
propose novel control Lyapunov functions/functionals for the stabilization
problem and novel control barrier functions/functionals for the safety problem.
The proposed control Lyapunov and barrier functions/functionals extend the
existing ones from the delay-free case to the time-delay case, and allow for
designing the stabilizing and safety controllers in closed-form. Since
analytical solutions to time-delay optimal control problems are hard to be
achieved, a sliding mode control based approach is developed to merge the
proposed control Lyapunov and barrier functions/functionals. Based on the
sliding surface functional, a feedback control law is established to
investigate the stabilization and safety objectives simultaneously. In
particular, the properties of the sliding surface functional are analyzed, and
further how to construct the sliding surface functional is discussed. Finally,
the proposed approaches are illustrated via two numerical examples from the
connected cruise control problem of automotive systems and the synchronization
problem of multi-agent systems.


title:
A comprehensive review on topological superconducting materials and
  interfaces

abstract:
  Superconductivity in topological materials has drawn a significant interest
of the scientific community as these materials provide a hint of the existence
of Majorana fermions conceived from the quantized thermal conductivity, a
zero-biased conduction peak and the anomalous Josephson effect. In this review,
we make a systematic study of recent advances in the field of topological
superconductivity. The article comprises of both bulk systems as well as
heterostructures. A brief description of Majorana fermions and their
relationship with topological superconductors and heterostructures is also
carried out. Also, this review consists of details of key experimental
techniques to characterize candidates of topological superconductivity.
Moreover, we summarize the potential material candidate that may demonstrate
topological superconductivity. We also consider some intrinsic odd-parity
superconductors, non-centrosymmetric, centrosymmetric superconductors, doped
topological insulators, doped topological crystalline insulators and some other
materials that are expected to show superconductivity along with topological
non-trivial states in bulk from. The effect of pressure, emergence of
superconductivity in topological materials and Muon Spin Rotation studies are
also summarized in this article.


title:
Toward Consistent and Efficient Map-based Visual-inertial Localization:
  Theory Framework and Filter Design

abstract:
  This paper focuses on designing a consistent and efficient filter for
map-based visual-inertial localization. First, we propose a new Lie group with
its algebra, based on which a novel invariant extended Kalman filter (invariant
EKF) is designed. We theoretically prove that, when we do not consider the
uncertainty of the map information, the proposed invariant EKF can naturally
maintain the correct observability properties of the system. To consider the
uncertainty of the map information, we introduce a Schmidt filter. With the
Schmidt filter, the uncertainty of the map information can be taken into
consideration to avoid over-confident estimation while the computation cost
only increases linearly with the size of the map keyframes. In addition, we
introduce an easily implemented observability-constrained technique because
directly combining the invariant EKF with the Schmidt filter cannot maintain
the correct observability properties of the system that considers the
uncertainty of the map information. Finally, we validate our proposed system's
high consistency, accuracy, and efficiency via extensive simulations and
real-world experiments.


title: Instance-Specific Feature Propagation for Referring Segmentation

abstract:
  Referring segmentation aims to generate a segmentation mask for the target
instance indicated by a natural language expression. There are typically two
kinds of existing methods: one-stage methods that directly perform segmentation
on the fused vision and language features; and two-stage methods that first
utilize an instance segmentation model for instance proposal and then select
one of these instances via matching them with language features. In this work,
we propose a novel framework that simultaneously detects the target-of-interest
via feature propagation and generates a fine-grained segmentation mask. In our
framework, each instance is represented by an Instance-Specific Feature (ISF),
and the target-of-referring is identified by exchanging information among all
ISFs using our proposed Feature Propagation Module (FPM). Our instance-aware
approach learns the relationship among all objects, which helps to better
locate the target-of-interest than one-stage methods. Comparing to two-stage
methods, our approach collaboratively and interactively utilizes both vision
and language information for synchronous identification and segmentation. In
the experimental tests, our method outperforms previous state-of-the-art
methods on all three RefCOCO series datasets.


title:
Stability and Bifurcation Analysis of a Fractional Order Delay
  Differential Equation Involving Cubic Nonlinearity

abstract:
  Fractional derivative and delay are important tools in modeling memory
properties in the natural system. This work deals with the stability analysis
of a fractional order delay differential equation \begin{equation*} D^\alpha
x(t)=\delta x(t-\tau)-\epsilon x(t-\tau)^3-px(t)^2+q x(t). \end{equation*} We
provide linearization of this system in a neighbourhood of equilibrium points
and propose linearized stability conditions. To discuss the stability of
equilibrium points, we propose various conditions on the parameters $\delta$,
$\epsilon$, $p$, $q$ and $\tau$. Even though there are five parameters involved
in the system, we are able to provide the stable region sketch in the
$q\delta-$plane for any positive $\epsilon$ and $p$. This provides the complete
analysis of stability of the system. Further, we investigate chaos in the
proposed model. This system exhibits chaos for a wide range of delay parameter.


title:
Function-words Enhanced Attention Networks for Few-Shot Inverse Relation
  Classification

abstract:
  The relation classification is to identify semantic relations between two
entities in a given text. While existing models perform well for classifying
inverse relations with large datasets, their performance is significantly
reduced for few-shot learning. In this paper, we propose a function words
adaptively enhanced attention framework (FAEA) for few-shot inverse relation
classification, in which a hybrid attention model is designed to attend
class-related function words based on meta-learning. As the involvement of
function words brings in significant intra-class redundancy, an adaptive
message passing mechanism is introduced to capture and transfer inter-class
differences.We mathematically analyze the negative impact of function words
from dot-product measurement, which explains why message passing mechanism
effectively reduces the impact. Our experimental results show that FAEA
outperforms strong baselines, especially the inverse relation accuracy is
improved by 14.33% under 1-shot setting in FewRel1.0.


title:
Reformulating Speaker Diarization as Community Detection With Emphasis
  On Topological Structure

abstract:
  Clustering-based speaker diarization has stood firm as one of the major
approaches in reality, despite recent development in end-to-end diarization.
However, clustering methods have not been explored extensively for speaker
diarization. Commonly-used methods such as k-means, spectral clustering, and
agglomerative hierarchical clustering only take into account properties such as
proximity and relative densities. In this paper we propose to view
clustering-based diarization as a community detection problem. By doing so the
topological structure is considered. This work has four major contributions.
First it is shown that Leiden community detection algorithm significantly
outperforms the previous methods on the clustering of speaker-segments. Second,
we propose to use uniform manifold approximation to reduce dimension while
retaining global and local topological structure. Third, a masked filtering
approach is introduced to extract "clean" speaker embeddings. Finally, the
community structure is applied to an end-to-end post-processing network to
obtain diarization results. The final system presents a relative DER reduction
of up to 70 percent. The breakdown contribution of each component is analyzed.


title:
Super-High-Frequency Low-Loss Sezawa Mode SAW Devices in a GaN/SiC
  Platform

abstract:
  This paper presents a comprehensive study of the performance of Sezawa
surface acoustic wave (SAW) devices in SweGaN QuanFINE ultrathin GaN/SiC
platform, reaching frequencies above 14 GHz for the first time. Sezawa mode
frequency scaling is achieved due to the elimination of the thick buffer layer
typically present in epitaxial GaN technology. Finite element analysis (FEA) is
first performed to find the range of frequencies over which the Sezawa mode is
supported in the grown structure. Transmission lines and resonance cavities
driven with Interdigital Transducers (IDTs) are designed, fabricated, and
characterized. Modified Mason circuit models are developed for each class of
devices to extract critical performance metrics. We observe a strong
correlation between measured and simulated dispersion of the phase velocity
(vp) and piezoelectric coupling coefficient (k^2). Maximum k^2 of 0.61% and
frequency-quality factor product (f.Qm) of 6x10^12 1/s are achieved for Sezawa
resonators at 11 GHz, with a minimum propagation loss of 0.26 dB/wavelength for
the two-port devices. Sezawa modes are observed at frequencies spanning up to
14.3 GHz, achieving a record high in GaN microelectromechanical systems (MEMS)
to the best of the authors' knowledge.


title:
Towards the correct microscopic structure of aqueous CsCl solutions with
  a comparison of classical interatomic potential models

abstract:
  The structure of aqueous CsCl solutions was investigated by classical
molecular dynamics simulations (MD) at three salt concentrations (1.5, 7.5, and
15 mol %). Thirty interatomic potential sets, based on the 12-6 Lennard-Jones
model, parametrized for non-polarizable water solvent molecules were collected
and tested. Some basic properties, such as density, static dielectric constant,
and self-diffusion coefficients, predicted by the force fields (FF), were
compared with available experimental data. The simulated particle
configurations were used to calculate the partial radial distribution functions
(PRDF) and the neutron and X-ray total scattering structure factors (TSSF). The
TSSFs were compared with experimental data from the literature, to find the
best FF models, which describe the structure correctly. It was found that,
though several of the thirty models failed in the tests, some models are
compatible with the measured data. Values of the structural parameters
consistent with the experiments were determined (such as water-ion distances,
the average number of water molecules around the ions, average number, and
distance between anion-cation contact ion pairs, water-water hydrogen bonds).
It was shown, that in addition to models in which the number of contact ion
pairs is too high, models in which this number is too low are also unable to
reproduce the experimental data.


title: Fast Successive-Cancellation Decoding of Polar Codes with Sequence Nodes

abstract:
  Due to the sequential nature of the successive-cancellation (SC) algorithm,
the decoding of polar codes suffers from significant decoding latencies. Fast
SC decoding is able to speed up the SC decoding process, by implementing
parallel decoders at the intermediate levels of the SC decoding tree for some
special nodes with specific information and frozen bit patterns. To further
improve the parallelism of SC decoding, this paper present a new class of
special node composed of a sequence of rate one or single-parity-check
(SR1/SPC) nodes, which envelops a wide variety of existing special node types.
Then, we analyse the parity constraints caused by the frozen bits in each
descendant node, such that the decoding performance of the SR1/SPC node can be
preserved once the parity constraints are satisfied. Finally, a generalized
fast SC decoding algorithm is proposed for the SR1/SPC node, where the
corresponding parity constraints are taken into consideration. Simulation
results show that compared with the state-of-the-art fast SC decoding
algorithms, the proposed decoding algorithm achieves a higher degree of
parallelism, especially for high-rate polar codes, without tangibly altering
the error-correction performance.


title:
Lipids and lipid-mixtures in boundary layers: from hydration lubrication
  to osteoarthritis

abstract:
  The hydration layer surrounding the phosphocholine headgroups of
single-component phosphatidylcholine (PC) lipids, or of lipid-mixtures,
assembled at an interface greatly modifies the interfacial properties and
interactions. As water molecules within the hydration layer are held tightly by
the headgroup but are nonetheless very fluid upon shear, the boundary lipid
layers, exposing the highly hydrated headgroup arrays, can provide efficient
boundary lubrication when sliding against an opposing surface, at
physiologically-high contact pressures. Additionally, any free lipids in the
surrounding liquid can heal defects which may form during sliding on the
boundary PC layer. Similar boundary lipid layers contribute to the lubricating,
pressure-bearing, and wear-protection functions of healthy articular joints.
This review presents a survey of the relationship between the molecular
composition of the interfacial complex and the lubrication behavior of the
lipid-based boundary layers, which could be beneficial for designing boundary
lubricants for intra-articular injection for the treatment of early OA.


title: On an Invariance Problem for Parameterized Concurrent Systems

abstract:
  We consider concurrent systems consisting of replicated finite-state
processes that synchronize via joint interactions in a network with
user-defined topology. The system is specified using a resource logic with a
multiplicative connective and inductively defined predicates, reminiscent of
Separation Logic. The problem we consider is if a given formula in this logic
defines an invariant, namely whether any model of the formula, following an
arbitrary firing sequence of interactions, is transformed into another model of
the same formula. This property, called \emph{havoc invariance}, is
quintessential in proving the correctness of reconfiguration programs that
change the structure of the network at runtime. We show that the havoc
invariance problem is many-one reducible to the entailment problem $\phi
\models \psi$, asking if any model of $\phi$ is also a model of $\psi$.
Although, in general, havoc invariance is found to be undecidable, this
reduction allows to prove that havoc invariance is in 2EXP, for a general
fragment of the logic, with a 2EXP entailment problem.


title:
Magnetovolume Effect on the First-Order Metamagnetic Transition in
  UTe$_2$

abstract:
  The link between the metamagnetic transition and novel spin-triplet
superconductivity of UTe$_2$ was discussed thermodynamically through
magnetostriction measurements in a pulsed-magnetic field. We revealed a
discontinuous magnetostriction across the metamagnetic transition at
$\mu_0H_{\rm m}\approx 35$~T for the applied magnetic fields along the
crystallographic $b$ axis in the orthorhombic structure. The resultanting
volume magnetostriction of $\Delta V/V \approx-5.9\times 10^{-4}$ gives the
initial pressure dependence of $H_{\rm m}$ by employing the
Clausius-Clapeyron's equation, which agrees with previous pressure experiments.
Further, significant anisotropic magnetostriction (AMS), derived by subtracting
the averaged linear magnetostriction, was revealed. Contrary to the weakly
field-dependent AMS along the $a$ axis, those along the $b$ and $c$ axes show
strong field dependences with a similar magnitude but with opposite signs,
indicating its lattice instability. The relationship between characteristic
energy scales of magnetic fields and temperatures was discussed in terms of the
Gr\"uneisen parameters compared to the other $f$-electron systems. The volume
shrinkage in UTe$_2$ at $H_{\rm m}$, contrary to the volume expansion in
typical heavy fermion metamagnets, pushes to invoke the link with the valence
instability related to the itinerant-localized dual nature of the U magnetism.


title:
Generalization of Doubly Nonnegative Cone: Focusing on
  Inner-Approximation for Generalized Copositive Cone

abstract:
  We aim to provide better relaxation for generalized completely positive
(copositive) programming. We first develop an inner-approximation hierarchy for
the generalized copositive cone over a symmetric cone. Exploiting this
hierarchy as well as the existing hierarchy proposed by Zuluaga et al. (SIAM J
Optim 16(4):1076--1091, 2006), we then propose two (NN and ZVP) generalized
doubly nonnegative (GDNN) cones. They are (if defined) always tractable, in
contrast to the existing (BD) GDNN cone proposed by Burer and Dong (Oper Res
Lett 40(3):203--206, 2012). We focus our investigation on the inclusion
relationship between the three GDNN cones over a direct product of a
nonnegative orthant and second-order cones or semidefinite cones. We find that
the NN GDNN cone is included in the ZVP one theoretically and in the BD one
numerically. Although there is no inclusion relationship between the ZVP and BD
GDNN cones theoretically, the result of solving GDNN programming relaxation
problems of mixed 0--1 second-order cone programming shows that the proposed
GDNN cones provide a tighter bound than the existing one in most cases. To sum
up, the proposed GDNN cones have theoretical and numerical superiority over the
existing one.


title:
Automated Generation of High-Performance Computational Fluid Dynamics
  Codes

abstract:
  Domain-Specific Languages (DSLs) improve programmers productivity by
decoupling problem descriptions from algorithmic implementations. However, DSLs
for High-Performance Computing (HPC) have two additional critical requirements:
performance and scalability. This paper presents the automated process of
generating, from abstract mathematical specifications of Computational Fluid
Dynamics (CFD) problems, optimised parallel codes that perform and scale as
manually optimised ones. We consciously combine within Saiph, a DSL for solving
CFD problems, low-level optimisations and parallelisation strategies, enabling
high-performance single-core executions which effectively scale to multi-core
and distributed environments. Our results demonstrate how high-level DSLs can
offer competitive performance by transparently leveraging state-of-the-art HPC
techniques.


title:
Cross-media Scientific Research Achievements Query based on Ranking
  Learning

abstract:
  With the advent of the information age, the scale of data on the Internet is
getting larger and larger, and it is full of text, images, videos, and other
information. Different from social media data and news data, scientific
research achievements information has the characteristics of many proper nouns
and strong ambiguity. The traditional single-mode query method based on
keywords can no longer meet the needs of scientific researchers and managers of
the Ministry of Science and Technology. Scientific research project information
and scientific research scholar information contain a large amount of valuable
scientific research achievement information. Evaluating the output capability
of scientific research projects and scientific research teams can effectively
assist managers in decision-making. In view of the above background, this paper
expounds on the research status from four aspects: characteristic learning of
scientific research results, cross-media research results query, ranking
learning of scientific research results, and cross-media scientific research
achievement query system.


title: Resonances in heavy meson - heavy baryon coupled-channel interactions

abstract:
  The interactions of $\bar{D}^{(*)} \Lambda_c - \bar{D}^{(*)}\Sigma_c^{(*)}$
are studied within the framework of a dynamical coupled-channel approach. A
series of bound states and resonances with different spin and parity are
dynamically generated in the hidden charm sector. Four $S$-wave bound states
are found in the mass range of 4.3 to 4.5 GeV, close to the pentaquark states
observed by LHCb. Two of the states have a spin parity of $J^P= 1/2^-$ and the
other two have $J^P=3/2^-$. In addition, several resonances with different spin
and parity in higher partial waves are predicted.


title:
Novel small band gap double perovskites comprising of silver and
  thallium: A DFT insight into structural, optoelectronic, and thermoelectric
  properties

abstract:
  Researchers have addressed the non-traditional power generation schemes as
alternatives to the traditional fossil-fuel methods enormously since the
scientific community has serious concerns about shortages of energy on our
planet for future generations. In this scenario, the innovative materials for
photovoltaic and thermoelectric device applications are required by addressing
current issues of instability and efficiency. Perovskites are very popular in
this regard particularly having higher power conversion efficiency of 25.2% in
the case of solar cells. In the current article, we investigated innovative
small direct band gap double perovskites (elapsolite) Cs$_2$AgTlX$_6$ (X= Cl,
Br) with a comprehensive discussion on structural, electronic, optical, and
thermoelectric properties using a first-principles approach. The compounds
under investigation are found stable, efficient, and economical with alluring
optical and thermoelectric properties. The higher absorption peaks in the
visible range, substantial optical conductivities (~10$^{16}$ sec$^{-1}$), and
a lower percentage of reflection in the visible range make these compounds
fascinating for solar cell applications. Whereas large values of Seebeck
coefficients, electrical conductivities, the figure of merits (greater than
unity), and small values of thermal conductivities suggest the applications of
these compounds in thermoelectric generators.


title:
Amplitude excitation and giant spin-lattice fluctuations in a pyrochlore
  ruthenate single crystal

abstract:
  We uncover a wide temperature regime of enhanced spin-lattice fluctuations in
single crystals of the pyrochlore ruthenate Nd$_2$Ru$_2$O$_7$. These
fluctuations, prominent in Raman spectroscopy, yet elusive to thermodynamic
probes, emerge below the N\'{e}el temperature and culminate in a new low-energy
amplitude excitation with an order-parameter-like behavior at 3 meV.
Concomitantly, an enormous phonon energy renormalization of $\Delta
\omega_{\mathrm{ph}} > 3$ meV -- an order of magnitude larger than those in
related pyrochlore oxides -- is observed. The unique two-fold symmetry of the
low-energy mode, incompatible with the underlying crystal structure, raises the
possibility of nematic order. Our study shines a light on instabilities in
pyrochlore systems and on resulting symmetries and amplitude excitations that
can be described within the Higgs mechanism.


title:
A Robust Contrastive Alignment Method For Multi-Domain Text
  Classification

abstract:
  Multi-domain text classification can automatically classify texts in various
scenarios. Due to the diversity of human languages, texts with the same label
in different domains may differ greatly, which brings challenges to the
multi-domain text classification. Current advanced methods use the
private-shared paradigm, capturing domain-shared features by a shared encoder,
and training a private encoder for each domain to extract domain-specific
features. However, in realistic scenarios, these methods suffer from
inefficiency as new domains are constantly emerging. In this paper, we propose
a robust contrastive alignment method to align text classification features of
various domains in the same feature space by supervised contrastive learning.
By this means, we only need two universal feature extractors to achieve
multi-domain text classification. Extensive experimental results show that our
method performs on par with or sometimes better than the state-of-the-art
method, which uses the complex multi-classifier in a private-shared framework.


title: Symmetry restoration methods

abstract:
  Symmetry techniques based on group theory play a prominent role in the
analysis of nuclear phenomena, and in particular in the understanding of
observed regular patterns in nuclear spectra and selection rules for
electromagnetic transitions. A variety of symmetry-based nuclear models have
been developed in nuclear physics, providing efficient tools of choice to
interpret nuclear spectroscopic data. This chapter provides a pedagogical
introduction to the basic idea of symmetry-breaking mechanism and
symmetry-restoration methods in modeling atomic nuclei.


title: Centrally Essential Rings and Semirings

abstract:
  This work is a review of results about centrally essential rings and
semirings. A ring (resp., semiring) is said to be centrally essential if it is
either commutative or satisfy the property that for any non-central element
$a$, there exist non-zero central elements $x$ and $y$ with $ax=y$. The class
of centrally essential rings is very large; many corresponding examples are
given in the work


title:
Interactions Between Bilayers of Phospholipids Mixture Extracted from
  Human Osteoarthritic Synovial Fluid

abstract:
  Duncan Dowson, whom this issue commemorates, was a world leader in the field
of biotribology, with prolific contributions both in fluid-based and boundary
lubrication of biological tissues, in particular articular cartilage, a central
issue in biotribology due to its importance for joint homeostasis. Here we
explore further the issue of cartilage boundary lubrication, which has been
attributed to phospholipid (PL)-exposing layers at the cartilage surface in
part. A surface force balance (SFB) with unique sensitivity is used to
investigate the normal and frictional interactions of the boundary layers
formed by PLs extracted from osteoarthritic (OA) human synovial fluid (hSF).
Our results reveal that vesicles of the OA-hSF lipids rupture spontaneously to
form bilayers on the mica substrate (which, like the in-vivo articular
cartilage surface in synovial joints, is negatively-charged) which then undergo
hemifusion at quite low pressures in the SFB, attributed to the large
heterogeneity of the hSF lipids. Nanometric friction measurements reveal
friction coefficients mu = ca. 0.03 across the hemi-fused bilayer of these
lipids, indicating residual hydration lubrication at the lipid-headgroup vs.
substrate interface. Addition of calcium ions causes an increase in friction to
mu = ca. 0.2, attributed either to calcium-bridging attraction of lipid
headgroups to the negatively-charged substrate, or a shift of the slip plane to
the more dissipative hydrophobic-tail vs. hydrophobic-tail interface. Our
results suggest that the heterogeneity and composition of the OA-hSF lipids may
be associated with higher friction at the cartilage boundary layers, and thus a
connection with greater wear and degradation, due to hemifusion of the exposed
lipid bilayers.


title: Mirror Games Against an Open Book Player

abstract:
  Mirror games were invented by Garg and Schnieder (ITCS 2019). Alice and Bob
take turns (with Alice playing first) in declaring numbers from the set {1,2,
...2n}. If a player picks a number that was previously played, that player
loses and the other player wins. If all numbers are declared without
repetition, the result is a draw. Bob has a simple mirror strategy that assures
he won't lose and requires no memory. On the other hand, Garg and Schenier
showed that every deterministic Alice needs memory of size linear in $n$ in
order to secure a draw.
  Regarding probabilistic strategies, previous work showed that a model where
Alice has access to a secret random perfect matching over {1,2, ...2n} allows
her to achieve a draw in the game w.p. a least 1-1/n and using only polylog
bits of memory.
  We show that the requirement for secret bits is crucial: for an `open book'
Alice with no secrets (Bob knows her memory but not future coin flips) and
memory of at most n/4c bits for any c>2, there is a Bob that wins w.p. close to
1-2^{-c/2}.


title:
LM-Debugger: An Interactive Tool for Inspection and Intervention in
  Transformer-Based Language Models

abstract:
  The opaque nature and unexplained behavior of transformer-based language
models (LMs) have spurred a wide interest in interpreting their predictions.
However, current interpretation methods mostly focus on probing models from
outside, executing behavioral tests, and analyzing salience input features,
while the internal prediction construction process is largely not understood.
In this work, we introduce LM-Debugger, an interactive debugger tool for
transformer-based LMs, which provides a fine-grained interpretation of the
model's internal prediction process, as well as a powerful framework for
intervening in LM behavior. For its backbone, LM-Debugger relies on a recent
method that interprets the inner token representations and their updates by the
feed-forward layers in the vocabulary space. We demonstrate the utility of
LM-Debugger for single-prediction debugging, by inspecting the internal
disambiguation process done by GPT2. Moreover, we show how easily LM-Debugger
allows to shift model behavior in a direction of the user's choice, by
identifying a few vectors in the network and inducing effective interventions
to the prediction process. We release LM-Debugger as an open-source tool and a
demo over GPT2 models.


title: A Spatial Deconstruction Behavioural Model for Pedestrian Dynamics

abstract:
  How to reproduce realistic motion in simulations has always been a
fundamental problem for pedestrian dynamics, and a critical challenge for
current studies is the natural correlation of the movement choices and the
human behaviours. To advance the study, we deconstruct the two-dimensional
space of pedestrian crowds with a behavioural motion network on basis of
Voronoi diagram and Delaunay diagram, and accordingly formulate a behavioural
model considering both the long-term route choice and the real-time velocity
choice. The comparisons between simulation results and real-world movements
suggest that the behavioural model can not only deal with traditional scenarios
but also be capable of describing well-agreed movements in challenging
conflicting situations. The spatial deconstruction idea provides a geometric
perspective to recognize and reproduce the pedestrian behaviours, and it can
further benefit the understanding and realization of pedestrian dynamics and
even robot navigation.


title: On the weakly Arf $(S_2)$-ifications of Noetherian rings

abstract:
  The weakly Arf $(S_2)$-ification of a commutative Noetherian ring $R$ is
considered to be a birational extension which is good next to the
normalization. The weakly Arf property (WAP for short) of $R$ was introduced in
1971 by J. Lipman with his famous paper [12], and recently rediscovered by [4],
being closely explored with further developments. The present paper aims at
constructing, for a given Noetherian ring $R$ which satisfies certain mild
conditions, the smallest module-finite birational extension of $R$ which
satisfies WAP and the condition $(S_2)$ of Serre. We shall call this extension
the weakly Arf $(S_2)$-ification, and develop the basic theory, including some
existence theorems.


title: Non-determinsitic algebraic rewriting as adjunction

abstract:
  We develop a general model theoretic semantics to rewriting beyond the usual
confluence and termination assumptions. This is based on preordered algebra
which is a model theory that extends many sorted algebra. In this framework we
characterise rewriting in arbitrary algebras rather than term algebras (called
algebraic rewriting) as a persistent adjunction and use this result, on the one
hand for proving the soundness and the completeness of an abstract
computational model of rewriting that underlies the non-deterministic
programming with Maude and CafeOBJ, and on the other hand for developing a
compositionality result for algebraic rewriting in the context of the
pushout-based modularisation technique.


title: Super-Bloch oscillations with parametric modulation of a parabolic trap

abstract:
  Super-Bloch oscillations are the outcome of a relative phase between Bloch
oscillations and modulations of the periodic lattice. We analyze the dynamics
for a model system in which such a relative phase is intrinsically present due
to the position-dependent force provided by a parabolic trap and therefore an
external detuning is not required. The relative phase is not unique and the
realized dynamics depends on the initial phase of the modulated parabolic
potential. We provide accurate explanations for the different obtained
oscillatory transport and spreading regimes by analyzing the spatio-temporal
dynamics in real space and by visualizing the relative phase in the k-space
dynamics. We also compare our numerical results to an approximate semiclassical
analytical expression for the group velocity for a modulated constant force
system and find good agreement for coherent oscillations but deviations for
oscillations with spreading dynamics which altogether supports the
interpretations of our findings.


title:
Robust Two-Layer Partition Clustering of Sparse Multivariate Functional
  Data

abstract:
  In this work, a novel elastic time distance for sparse multivariate
functional data is proposed. This concept serves as the foundation for
clustering functional data with various time measurements per subject.
Subsequently, a robust two-layer partition clustering is introduced. With the
proposed distance, our approach not only is applicable to both complete and
imbalanced multivariate functional data but also is resistant to outliers and
capable of detecting outliers that do not belong to any clusters. The classical
distance-based clustering methods such as K-medoids and agglomerative
hierarchical clustering are extended to the sparse multivariate functional case
based on our proposed distance. Numerical experiments on the simulated data
highlight that the performance of the proposed algorithm is superior to the
performances of the existing model-based and extended distance-based methods.
Using Northwest Pacific cyclone track data as an example, we demonstrate the
effectiveness of the proposed approach. The code is available online for
readers to apply our clustering method and replicate our analyses.


title:
Explicit minimal positive solution to linear coefficient non-homogeneous
  second order recurrence relation with constant non-homogenity

abstract:
  Second order recurrence relations of real numbers arise form various
applications in discrete time dynamical systems as well as in the context on
Markov chains. Solutions to the recurrence relations are fully defined by the
first two initial values. In particular, in the context of Markov chains, the
notion of minimal positive solutions arises, when fixing only the first initial
value to be 0 and leaving the second one unknown. We calculate in this work the
minimal positive solution $(a_i)_{i\in\mathbb{N}}$ to non-homogeneous second
order recurrence relation with affine coefficients when the non-homogeneity is
constant and negative, and the first initial value equals $a_0=0$. We obtain an
explicit expression of the initial value $a_1$ which leads to the minimal
positive solution as well as the minimal positive solution
$(a_i)_{i\in\mathbb{N}}$ itself. Additionally, we prove that this sequence
converges to $0$ and obtain the convergence speed in $O(i^{-1})$. We show in
the last section that a choice of rational parameters in the recurrence
relation imply that all a i are transcendental numbers and we make a link to
the impossibility of obtaining computer based visualizations of the minimal
positive solution $(a_i)_{i\in\mathbb{N}}$.


title: A gravity antenna based on quantum technologies: MIGA

abstract:
  We report the realization of a large scale gravity antenna based on
matter-wave interferometry, the MIGA project. This experiment consists in an
array of cold Rb sources correlated by a 150 m long optical cavity. MIGA is in
construction at the LSBB underground laboratory, a site that benefits from a
low background noise and is an ideal premise to carry out precision gravity
measurements. The MIGA facility will be a demonstrator for a new generation of
GW detector based on atom interferometry that could open the infrasound window
for the observation of GWs. We describe here the status of the instrument
construction, focusing on the infrastructure works at LSBB and the realization
of the vacuum vessel of the antenna.


title: Semilinear clannish algebras

abstract:
  We define a class of associative algebras generalizing `clannish algebras',
as introduced by the second author, but also incorporating semilinear
structure, like a skew polynomial ring. Clannish algebras generalize the well
known `string algebras' introduced by Butler and Ringel. Our main result is the
classification of finite-dimensional indecomposable modules for these new
algebras.


title:
Neural Maximum A Posteriori Estimation on Unpaired Data for Motion
  Deblurring

abstract:
  Real-world dynamic scene deblurring has long been a challenging task since
paired blurry-sharp training data is unavailable. Conventional Maximum A
Posteriori estimation and deep learning-based deblurring methods are restricted
by handcrafted priors and synthetic blurry-sharp training pairs respectively,
thereby failing to generalize to real dynamic blurriness. To this end, we
propose a Neural Maximum A Posteriori (NeurMAP) estimation framework for
training neural networks to recover blind motion information and sharp content
from unpaired data. The proposed NeruMAP consists of a motion estimation
network and a deblurring network which are trained jointly to model the
(re)blurring process (i.e. likelihood function). Meanwhile, the motion
estimation network is trained to explore the motion information in images by
applying implicit dynamic motion prior, and in return enforces the deblurring
network training (i.e. providing sharp image prior). The proposed NeurMAP is an
orthogonal approach to existing deblurring neural networks, and is the first
framework that enables training image deblurring networks on unpaired datasets.
Experiments demonstrate our superiority on both quantitative metrics and visual
quality over state-of-the-art methods. Codes are available on
https://github.com/yjzhang96/NeurMAP-deblur.


title:
The Pristine Inner Galaxy Survey (PIGS) IV: A photometric metallicity
  analysis of the Sagittarius dwarf spheroidal galaxy

abstract:
  We present a comprehensive metallicity analysis of the Sagittarius dwarf
spheroidal galaxy (Sgr dSph) using $Pristine\,CaHK$ photometry. We base our
member selection on $Gaia$ EDR3 astrometry applying a magnitude limit at $G_{0}
= 17.3$, and our population study on the metallicity-sensitive photometry from
the $Pristine$ Inner Galaxy Survey (PIGS). Working with photometric
metallicities instead of spectroscopic metallicities allows us to cover an
unprecedented large area ($\sim 100$ square degrees) of the dwarf galaxy, and
to study the spatial distribution of its members as function of metallicity
with little selection effects. Our study compares the spatial distributions of
a metal-poor population of 9719 stars with [Fe/H] $< -1.3$ and a metal rich one
of 30115 stars with [Fe/H] $> -1.0$. The photometric Sgr sample also allows us
to assemble the largest sample of 1150 very metal-poor Sgr candidates ([Fe/H]
$< -2.0$). By investigating and fitting the spatial properties of the
metal-rich and metal-poor population, we find a negative metallicity gradient
which extends up to 12 degrees from the Sgr center (or $\sim 5.5$ kpc at the
distance of Sgr), the limit of our footprint. We conclude that the relative
number of metal-poor stars increases in the outer areas of the galaxy, while
the central region is dominated by metal-rich stars. These finding suggest an
outside-in formation process and are an indication of the extended formation
history of Sgr, which has been affected by the tidal interaction between Sgr
and the Milky Way.


title:
The computational evidence for the crucial role of the dipole
  cross-correlations in the polar glass-forming liquids

abstract:
  In this letter, we analyze the dipole-dipole correlations obtained from the
molecular dynamics simulations for strongly- and weakly-polar model liquids. As
a result, we found that cross-correlations contribution to the systems total
dipole moment correlation function, which is directly measured in the
dielectric spectroscopy experiment, is negligible for weakly-polar liquids. In
contrast, the cross-correlations term dominates over the self-correlations one
for examined strongly polar-liquid. Consequently, our studies strongly support
the interpretation of the dielectric spectra nature of the glass forming
liquids, recently proposed by Pabst et al.


title: The LISA Data Challenges

abstract:
  The future space-based gravitational-wave detector LISA will deliver rich and
information-dense data by listening to the milliHertz Universe. The measured
time series will contain the imprint of tens of thousands of detectable
Galactic binaries constantly emitting, tens of supermassive black hole merger
events per year, tens of stellar-origin black holes, and possibly thousands of
extreme mass-ratio inspirals. On top of that, we expect to detect the presence
of stochastic gravitational wave backgrounds and bursts. Finding and
characterizing many such sources is a vast and unsolved task. The LISA Data
Challenges (LDCs) are an open and collaborative effort to tackle this exciting
problem. A new simulated data set, nicknamed Sangria, has just been released
with the purpose of tackling mild source confusion with idealized instrumental
noise. This presentation will describe the LDC strategy, showcase the available
datasets and analysis tools, and discuss future efforts to prepare LISA data
analysis.


title: Deeper Insights into ViTs Robustness towards Common Corruptions

abstract:
  Recent literature have shown design strategies from Convolutions Neural
Networks (CNNs) benefit Vision Transformers (ViTs) in various vision tasks.
However, it remains unclear how these design choices impact on robustness when
transferred to ViTs. In this paper, we make the first attempt to investigate
how CNN-like architectural designs and CNN-based data augmentation strategies
impact on ViTs' robustness towards common corruptions through an extensive and
rigorous benchmarking. We demonstrate that overlapping patch embedding and
convolutional Feed-Forward Network (FFN) boost performance on robustness.
Furthermore, adversarial noise training is powerful on ViTs while
fourier-domain augmentation fails. Moreover, we introduce a novel conditional
method enabling input-varied augmentations from two angles: (1) Generating
dynamic augmentation parameters conditioned on input images. It conduces to
state-of-the-art performance on robustness through conditional convolutions;
(2) Selecting most suitable augmentation strategy by an extra predictor helps
to achieve the best trade-off between clean accuracy and robustness.


title:
Motion Planning and Robust Tracking for the Heat Equation using Boundary
  Control

abstract:
  Robust output tracking is addressed in this paper for a heat equation with
Neumann boundary conditions and anti-collocated boundary input and output. The
desired reference tracking is solved using the well-known flatness and Lyapunov
approaches. The reference profile is obtained by solving the motion planning
problem for the nominal plant. To robustify the closed-loop system in the
presence of the disturbances and uncertainties, it is then augmented with PI
feedback plus a discontinuous component responsible for rejecting matched
disturbances with \textit{a priori} known magnitude bounds. Such control law
only requires the information of the system at the same boundary as the control
input is located. The resulting dynamic controller globally exponentially
stabilizes the error dynamics while also attenuating the influence of
Lipschitz-in-time external disturbances and parameter uncertainties. For the
case when the motion planning is performed over the uncertain plant, an
exponential Input-to-State Stability is obtained, preserving the boundedness of
the tracking error norm. The proposed controller relies on a discontinuous term
that however passes through an integrator, thereby minimizing the chattering
effect in the plant dynamics. The performance of the closed-loop system, thus
designed, is illustrated in simulations under different kinds of reference
trajectories in the presence of external disturbances and parameter
uncertainties.


title: Fractional Brownian Gyrator

abstract:
  When a physical system evolves in a thermal bath at a constant temperature,
it arrives eventually to an equilibrium state whose properties are independent
of the kinetic parameters and of the precise evolution scenario. This is
generically not the case for a system driven out of equilibrium which, on the
contrary, reaches a steady-state with properties that depend on the full
details of the dynamics such as the driving noise and the energy dissipation.
How the steady state depends on such parameters is in general a non-trivial
question. Here, we approach this broad problem using a minimal model of a
two-dimensional nano-machine, the Brownian gyrator, that consists of a trapped
particle driven by fractional Gaussian noises -- a family of noises with
long-ranged correlations in time and characterized by an anomalous diffusion
exponent $\alpha$. When the noise is different in the different spatial
directions, our fractional Brownian gyrator persistently rotates. Even if the
noise is non-trivial, with long-ranged time correlations, thanks to its
Gaussian nature we are able to characterize analytically the resulting
nonequilibrium steady state by computing the probability density function, the
probability current, its curl and the angular velocity and complement our study
by numerical results.


title:
General Kernel estimates of Schr\"odinger type operators with unbounded
  diffusion terms

abstract:
  We prove first that the realization $A_{\min}$ of
$A:=\mathrm{div}(Q\nabla)-V$ in $L^2(\mathbb{R}^d)$ with unbounded coefficients
generates a symmetric sub-Markovian and ultracontractive semigroup on
$L^2(\mathbb{R}^d)$ which coincides on $L^2(\mathbb{R}^d)\cap
C_b(\mathbb{R}^d)$ with the minimal semigroup generated by a realization of $A$
on $C_b(\mathbb{R}^d)$. Moreover, using time dependent Lyapunov functions, we
prove pointwise upper bounds for the heat kernel of $A$ and deduce some
spectral properties of $A_{\min}$ in the case of polynomially and exponentially
diffusion and potential coefficients.


title:
Quantum phase transitions from competing short- and long-range
  interactions on a $\pi$-flux lattice

abstract:
  Quantum phase transitions from the cluster-charge interaction, which is
composed of competing short- and long-range interactions, are investigated on a
$\pi$-flux lattice by using the mean-field theory and determinant quantum Monte
Carlo (DQMC) simulations. Both methods identify a plaquette-dimer phase, which
develops from a finite interaction strength. While its signature in DQMC is
relatively weak, a obvious antiferromagnetic transition is revealed in the spin
structure factor instead. The corresponding critical interaction and exponents
are readily obtained by finite-size scalings, with the plaquette-dimer
structure factor that can also be well scaled. These results suggest a possible
deconfined quantum critical point between the plaquette-dimer and
antiferromagnetic phases driven by the cluster-charge interaction on a
$\pi$-flux lattice.


title: Morest: Model-based RESTful API Testing with Execution Feedback

abstract:
  RESTful APIs are arguably the most popular endpoints for accessing Web
services. Blackbox testing is one of the emerging techniques for ensuring the
reliability of RESTful APIs. The major challenge in testing RESTful APIs is the
need for correct sequences of API operation calls for in-depth testing. To
build meaningful operation call sequences, researchers have proposed techniques
to learn and utilize the API dependencies based on OpenAPI specifications.
However, these techniques either lack the overall awareness of how all the APIs
are connected or the flexibility of adaptively fixing the learned knowledge. In
this paper, we propose Morest, a model-based RESTful API testing technique that
builds and maintains a dynamically updating RESTful-service Property Graph
(RPG) to model the behaviors of RESTful-services and guide the call sequence
generation. We empirically evaluated Morest and the results demonstrate that
Morest can successfully request an average of 152.66%-232.45% more API
operations, cover 26.16%-103.24% more lines of code, and detect 40.64%-215.94%
more bugs than state-of-the-art techniques. In total, we applied Morest to 6
real-world projects and found 44 bugs (13 of them cannot be detected by
existing approaches). Specifically, 2 of the confirmed bugs are from Bitbucket,
a famous code management service with more than 6 million users.


title: Wilson lines construction of $\mathfrak{osp}(1|2)$ conformal blocks

abstract:
  We study N=1 superconformal theory in the context of AdS/CFT correspondence
in the large central charge limit using Chern-Simons formulation of $3d$
gravity. In this limit conformal dimensions of a subclass of so-called light
primary superfields remain finite and are governed by $\mathfrak{osp}(1|2)$
subalgebra of N=1 super-Virasoro algebra. We describe the construction of
$\mathfrak{osp}(1|2)$ conformal blocks in terms of Wilson lines of the
Chern-Simons $3d$ gravity. We consider examples of two and three-point blocks
on the sphere and one-point torus blocks of light superprimary fields, which
belong to finite-dimensional representations of $\mathfrak{osp}(1|2)$. We study
the correlation function for lower and upper components of the primary
$\mathfrak{osp}(1|2)$ doublets and show that the associated conformal blocks
are obtained via Wilson line construction in Chern-Simons theory.


title: Where and What: Driver Attention-based Object Detection

abstract:
  Human drivers use their attentional mechanisms to focus on critical objects
and make decisions while driving. As human attention can be revealed from gaze
data, capturing and analyzing gaze information has emerged in recent years to
benefit autonomous driving technology. Previous works in this context have
primarily aimed at predicting "where" human drivers look at and lack knowledge
of "what" objects drivers focus on. Our work bridges the gap between
pixel-level and object-level attention prediction. Specifically, we propose to
integrate an attention prediction module into a pretrained object detection
framework and predict the attention in a grid-based style. Furthermore,
critical objects are recognized based on predicted attended-to areas. We
evaluate our proposed method on two driver attention datasets, BDD-A and
DR(eye)VE. Our framework achieves competitive state-of-the-art performance in
the attention prediction on both pixel-level and object-level but is far more
efficient (75.3 GFLOPs less) in computation.


title: ClothFormer:Taming Video Virtual Try-on in All Module

abstract:
  The task of video virtual try-on aims to fit the target clothes to a person
in the video with spatio-temporal consistency. Despite tremendous progress of
image virtual try-on, they lead to inconsistency between frames when applied to
videos. Limited work also explored the task of video-based virtual try-on but
failed to produce visually pleasing and temporally coherent results. Moreover,
there are two other key challenges: 1) how to generate accurate warping when
occlusions appear in the clothing region; 2) how to generate clothes and
non-target body parts (e.g. arms, neck) in harmony with the complicated
background; To address them, we propose a novel video virtual try-on framework,
ClothFormer, which successfully synthesizes realistic, harmonious, and
spatio-temporal consistent results in complicated environment. In particular,
ClothFormer involves three major modules. First, a two-stage anti-occlusion
warping module that predicts an accurate dense flow mapping between the body
regions and the clothing regions. Second, an appearance-flow tracking module
utilizes ridge regression and optical flow correction to smooth the dense flow
sequence and generate a temporally smooth warped clothing sequence. Third, a
dual-stream transformer extracts and fuses clothing textures, person features,
and environment information to generate realistic try-on videos. Through
rigorous experiments, we demonstrate that our method highly surpasses the
baselines in terms of synthesized video quality both qualitatively and
quantitatively.


title:
On two-body and three-body spin correlations in leptonic $t\bar{t}Z$
  production and anomalous couplings at the LHC

abstract:
  We study the anomalous $t\bar{t}Z$ couplings in the $t\bar{t}Z$ production in
$4l+2b+MET$ final state at the $13$ TeV LHC. We use the polarizations of top
quarks and $Z$ boson, two-body and three-body spin correlations among the top
quarks and $Z$ boson, and the cross section to probe the anomalous couplings
and estimate a limit on the couplings of the effective vertex as well as the
effective operators for a set of luminosities $150$ fb$^{-1}$, $300$ fb$^{-1}$,
and $3000$ fb$^{-1}$. The polarizations and the spin correlations are found to
be helpful on top of the cross section to better constrain the anomalous
couplings.


title:
Cybertwin-enabled 6G Space-air-ground Integrated Networks: Architecture,
  Open Issue, and Challenges

abstract:
  Space-air-ground integrated network (SAGIN) is considered as a core
requirement in emerging 6G networks, which integrates the terrestrial and
non-terrestrial networks to reach the full network coverage and ubiquitous
services. To envision the ubiquitous intelligence and the deep integration in
6G SAGIN, a paradigm of cybertwin-enabled 6G SAGIN is presented in this paper.
Specifically, a cybertwin-enabled SAGIN architecture is first presented, where
a novel five-dimension digital twin (DT) model is presented. Particularly,
three categories of critical technologies are presented based on the cybertwin
of SAGIN, i.e., cybertwin-based multi-source heterogeneous network integration,
cybertwin-based integrated cloud-edge-end, and cybertwin-based integrated
sensing-communication-computing. Besides, two open issues in the
cybertwin-enabled SAGIN are studied, i.e., the networking decision and
optimization and the cybertwin-enabled cross-layer privacy and security, where
the challenges are discussed and the potential solutions are directed. In
addition, a case study with federal learning is developed and open research
issues are discussed.


title:
Effects of attractive inter-particle interaction on cross-transport
  coefficient between mass and heat in binary fluids

abstract:
  A concentration gradient is induced by a temperature gradient for some binary
mixtures of fluids due to the mass transfer. This phenomenon is the so-called
thermodiffision or the Ludwig-Soret effect being characterized by the mass and
heat cross transport coefficient L1q. Naively, we expect that L1q is controlled
by the chemical potential. Then the static structure of a fluid would be
primalily important. This simple picture implies that the existence of the
attractive part in the interaction potential does not largely affect L1q.
Although many molecular dynamics simulations have been performed for L1q, the
effect of the attractive part on L1q seems not to be studied in detail. We
obtained L1q of lighter particles for binary fluids via equilibrium molecular
dynamics simulations for equimolar binary liquids, in which the inter-particle
interactions were common for all the particle pairs whereas the mass ratio was
varied. We had two sets of simulations with the Lennard-Jones (LJ) and the
Weeks-Chandler- Andersen (WCA) interactions to see the effect of the attractive
interaction between the particles on L1q. The simulations revealed that L1q
strongly depends on the employed interaction to exhibit L1q < 0 for LJ whereas
L1q > 0 for WCA. To analyze this significant difference, we decomposed the heat
flux into the kinetic and potential contributions, and calculated the
cross-correlation between the decomposed flux and the mass flux. We found that
the potential contribution dominates the behavior of L1q.


title: Bias-Variance Decompositions for Margin Losses

abstract:
  We introduce a novel bias-variance decomposition for a range of strictly
convex margin losses, including the logistic loss (minimized by the classic
LogitBoost algorithm), as well as the squared margin loss and canonical
boosting loss. Furthermore, we show that, for all strictly convex margin
losses, the expected risk decomposes into the risk of a "central" model and a
term quantifying variation in the functional margin with respect to variations
in the training data. These decompositions provide a diagnostic tool for
practitioners to understand model overfitting/underfitting, and have
implications for additive ensemble models -- for example, when our
bias-variance decomposition holds, there is a corresponding "ambiguity"
decomposition, which can be used to quantify model diversity.


title:
Source-independent quantum random number generator against detector
  blinding attacks

abstract:
  Randomness, mainly in the form of random numbers, is the fundamental
prerequisite for the security of many cryptographic tasks. Quantum randomness
can be extracted even if adversaries are fully aware of the protocol and even
control the randomness source. However, an adversary can further manipulate the
randomness via detector blinding attacks, which are a hacking attack suffered
by protocols with trusted detectors. Here, by treating no-click events as valid
error events, we propose a quantum random number generation protocol that can
simultaneously address source vulnerability and ferocious detector blinding
attacks. The method can be extended to high-dimensional random number
generation. We experimentally demonstrate the ability of our protocol to
generate random numbers for two-dimensional measurement with a generation speed
of 0.515 Mbps, which is two orders of magnitude higher than that of
device-independent protocols that can address both issues of imperfect sources
and imperfect detectors.


title:
Raman Spectroscopy and Machine Learning-based Optical Sensor for Rapid
  Tuberculosis Diagnosis via Sputum

abstract:
  Tuberculosis (TB) is a contagious disease that causes 1.5 million deaths per
year globally. Early diagnosis of TB patients is critical to control its
spread. However, standard TB diagnostic tests such as sputum culture take days
to weeks to produce results. Here, we demonstrate a quick, portable,
easy-to-use, and non-invasive optical sensor for TB detection based on sputum
samples. The probe uses Raman spectroscopy to detect TB in the supernatant of a
patient's sputum. We deploy a machine learning algorithm, principal component
analysis, on the acquired Raman data to enhance the detection sensitivity and
specificity. On testing 112 potential TB patients, our results show that the
developed sensor's accuracy is 100% for true-positive and 93.4% for
true-negative. Moreover, the probe correctly identifies patients on TB
medication. We anticipate that our work will lead to a viable and rapid TB
diagnostic platform.


title: Mixed Strategies for Security Games with General Defending Requirements

abstract:
  The Stackelberg security game is played between a defender and an attacker,
where the defender needs to allocate a limited amount of resources to multiple
targets in order to minimize the loss due to adversarial attack by the
attacker. While allowing targets to have different values, classic settings
often assume uniform requirements to defend the targets. This enables existing
results that study mixed strategies (randomized allocation algorithms) to adopt
a compact representation of the mixed strategies.
  In this work, we initiate the study of mixed strategies for the security
games in which the targets can have different defending requirements. In
contrast to the case of uniform defending requirement, for which an optimal
mixed strategy can be computed efficiently, we show that computing the optimal
mixed strategy is NP-hard for the general defending requirements setting.
However, we show that strong upper and lower bounds for the optimal mixed
strategy defending result can be derived. We propose an efficient
close-to-optimal Patching algorithm that computes mixed strategies that use
only few pure strategies. We also study the setting when the game is played on
a network and resource sharing is enabled between neighboring targets. Our
experimental results demonstrate the effectiveness of our algorithm in several
large real-world datasets.


title:
Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary
  Algorithm for Symbolic Regression

abstract:
  Currently, the genetic programming version of the gene-pool optimal mixing
evolutionary algorithm (GP-GOMEA) is among the top-performing algorithms for
symbolic regression (SR). A key strength of GP-GOMEA is its way of performing
variation, which dynamically adapts to the emergence of patterns in the
population. However, GP-GOMEA lacks a mechanism to optimize coefficients. In
this paper, we study how fairly simple approaches for optimizing coefficients
can be integrated into GP-GOMEA. In particular, we considered two variants of
Gaussian coefficient mutation. We performed experiments using different
settings on 23 benchmark problems, and used machine learning to estimate what
aspects of coefficient mutation matter most. We find that the most important
aspect is that the number of coefficient mutation attempts needs to be
commensurate with the number of mixing operations that GP-GOMEA performs. We
applied GP-GOMEA with the best-performing coefficient mutation approach to the
data sets of SRBench, a large SR benchmark, for which a ground-truth underlying
equation is known. We find that coefficient mutation can help re-discovering
the underlying equation by a substantial amount, but only when no noise is
added to the target variable. In the presence of noise, GP-GOMEA with
coefficient mutation discovers alternative but similarly-accurate equations.


title:
A Reduced Order Model for Joint Assemblies by Hyper-Reduction and
  Model-Driven Sampling

abstract:
  The dynamic behavior of jointed assemblies exhibiting friction nonlinearities
features amplitude-dependent dissipation and stiffness. To develop numerical
simulations for predictive and design purposes, macro-scale High Fidelity
Models (HFMs) of the contact interfaces are required. However, the high
computational cost of such HFMs impedes the feasibility of the simulations. To
this end, we propose a model-driven method for constructing hyper-reduced order
models of such assemblies. Focusing on steady-state analysis, we use the
Multi-Harmonic Balance Method (MHBM) to formulate the equations of motion in
frequency domain. The reduction basis is constructed through solving a set of
vibration problems corresponding to fictitious interface conditions.
Subsequently, a Galerkin projection reduces the order of the model.
Nonetheless, the necessary fine discretization of the interfaces represents a
bottleneck for achieving high speedups. For this reason, we implement an
adapted Energy Conserving Weighing and Sampling (ECSW) technique for Hyper
Reduction (HR), thereby allowing significant speedups for meshes of arbitrary
fineness. This feature is particularly advantageous since analysts typically
encounter a trade-off between accuracy and computational cost when deciding on
the mesh size, whose estimation is particularly challenging for problems of
this type. To assess the accuracy of our method without resorting to the HF
solution, we propose an error indicator with thresholds that have proven
reliable in our analyses. Finally, the accuracy and efficiency of the method
are demonstrated by two case studies.


title:
On extension of Calder\'on-Zygmund type singular integrals and their
  commutators

abstract:
  Motivated by the recent works [Huan Yu, Quansen Jiu, and Dongsheng Li, 2021]
and [Yanping Chen and Zihua Guo, 2021], we study the following extension of
Calder\'on-Zygmund type singular integrals $$ T_{\beta}f (x) = p.v.
\int_{\mathbb{R}^n} \frac{\Omega(y)}{|y|^{n-\beta}} f(x-y) \, dy, $$ for $0 <
\beta < n$, and their commutators. We establish estimates of these singular
integrals on Lipschitz spaces, Hardy spaces and Muckenhoupt $A_p$-weighted
$L^p$-spaces. We also establish Lebesgue and Hardy space estimates of their
commutators. Our estimates are uniform in small $\beta$, and therefore one can
pass onto the limits as $\beta \to 0$ to deduce analogous estimates for the
classical Calder\'on-Zygmund type singular integrals and their commutators.


title: Budgeted Out-tree Maximization with Submodular Prizes

abstract:
  We consider a variant of the prize collecting Steiner tree problem in which
we are given a \emph{directed graph} $D=(V,A)$, a monotone submodular prize
function $p:2^V \rightarrow \mathbb{R}^+ \cup \{0\}$, a cost function $c:V
\rightarrow \mathbb{Z}^{+}$, a root vertex $r \in V$, and a budget $B$. The aim
is to find an out-subtree $T$ of $D$ rooted at $r$ that costs at most $B$ and
maximizes the prize function. We call this problem \emph{Directed Rooted
Submodular Out-tree} (\textbf{DRSO}).
  Very recently, Ghuge and Nagarajan [SODA\ 2020] gave a quasi-polynomial-time
$O\left(\frac{\log n'}{\log \log n'}\right)$-approximation algorithm for the
case in which the costs are associated to the edges, where $n'$ is the number
of vertices in an optimal solution.
  In this paper we give a polynomial-time algorithm for \textbf{DRSO} that
guarantees an approximation factor of $O(\sqrt{B}/\epsilon^3)$ at the cost of a
budget violation of a factor $1+\epsilon$, for any $\epsilon \in (0,1]$. The
same result holds for the edge-cost case, to our knowledge this is the first
polynomial-time approximation algorithm for this case.
  We further show that the unrooted version of \textbf{DRSO} can be
approximated to a factor of $O(\sqrt{B})$ without budget violation, which is an
improvement over the factor $O(\Delta \sqrt{B})$ given in~[Kuo et al.\
IEEE/ACM\ Trans.\ Netw.\ 2015] for the undirected and unrooted case, where
$\Delta$ is the maximum degree of the graph. Finally, we provide some
new/improved approximation bounds for several related problems, including the
additive-prize version of \textbf{DRSO}, the maximum budgeted connected set
cover problem, and the budgeted sensor cover problem.


title:
Superconductivity, generalized random phase approximation and linear
  scaling methods

abstract:
  We review the Bardeen-Cooper-Schrieffer mean-field theory emphasizing its
origin as a variational approximation for the grand potential. This is done by
using the Bogoliubov inequality as the starting point. Then we write the
mean-field grand potential as an explicit function of the one-particle density
matrix, which turns out to be a natural generalization of the Mermin
functional. This result opens the way for the application to superconducting
systems of the linear scaling methods developed in the context of electronic
structure theory. Finally, we show that computing the superfluid weight from
the derivatives of the mean-field grand potential naturally leads to the
generalized random phase approximation. Our results showcase the advantage of a
density matrix-based approach and are potentially interesting for the study of
disordered superconductors and superconductors with large unit cell, such as
twisted bilayer graphene and other moir\'e materials.

categories: cond-mat.supr-con cond-mat.dis-nn cond-mat.quant-gas cond-mat.stat-mech


title: CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets

abstract:
  Over the course of the COVID-19 pandemic, large volumes of biomedical
information concerning this new disease have been published on social media.
Some of this information can pose a real danger to people's health,
particularly when false information is shared, for instance recommendations on
how to treat diseases without professional medical advice. Therefore, automatic
fact-checking resources and systems developed specifically for the medical
domain are crucial. While existing fact-checking resources cover
COVID-19-related information in news or quantify the amount of misinformation
in tweets, there is no dataset providing fact-checked COVID-19-related Twitter
posts with detailed annotations for biomedical entities, relations and relevant
evidence. We contribute CoVERT, a fact-checked corpus of tweets with a focus on
the domain of biomedicine and COVID-19-related (mis)information. The corpus
consists of 300 tweets, each annotated with medical named entities and
relations. We employ a novel crowdsourcing methodology to annotate all tweets
with fact-checking labels and supporting evidence, which crowdworkers search
for online. This methodology results in moderate inter-annotator agreement.
Furthermore, we use the retrieved evidence extracts as part of a fact-checking
pipeline, finding that the real-world evidence is more useful than the
knowledge indirectly available in pretrained language models.


title:
When do Contrastive Word Alignments Improve Many-to-many Neural Machine
  Translation?

abstract:
  Word alignment has proven to benefit many-to-many neural machine translation
(NMT). However, high-quality ground-truth bilingual dictionaries were used for
pre-editing in previous methods, which are unavailable for most language pairs.
Meanwhile, the contrastive objective can implicitly utilize automatically
learned word alignment, which has not been explored in many-to-many NMT. This
work proposes a word-level contrastive objective to leverage word alignments
for many-to-many NMT. Empirical results show that this leads to 0.8 BLEU gains
for several language pairs. Analyses reveal that in many-to-many NMT, the
encoder's sentence retrieval performance highly correlates with the translation
quality, which explains when the proposed method impacts translation. This
motivates future exploration for many-to-many NMT to improve the encoder's
sentence retrieval performance.


title: Probing the extragalactic mid-infrared background with HAWC

abstract:
  The extragalactic background light (EBL) contains all the radiation emitted
by nuclear and accretion processes in stars and compact objects since the epoch
of recombination. Measuring the EBL density directly is challenging, especially
in the near- to far-infrared waveband, mainly due to the zodiacal light
foreground. Instead, gamma-ray astronomy offers the possibility to indirectly
set limits on the EBL by studying the effects of gamma-ray absorption in the
very high energy (VHE:$>$100 GeV) spectra of distant blazars. The High Altitude
Water Cherenkov gamma ray observatory (HAWC) is one of the few instruments
sensitive to gamma rays with energies above 10 TeV. This offers the opportunity
to probe the EBL in the near/mid IR region: $\lambda$ = 1 $\mu$m - 100 $\mu$m.
In this study, we fit physically motivated emission models to Fermi Large Area
Telescope (LAT) GeV data to extrapolate the intrinsic TeV spectra of blazars.
We then simulate a large number of absorbed spectra for different randomly
generated EBL model shapes and calculate Bayesian credible bands in the EBL
intensity space by comparing and testing the agreement between the absorbed
spectra and HAWC extragalactic observations of two blazars. The resulting bands
are in agreement with current EBL lower and upper limits, showing a downward
trend towards higher wavelength values $\lambda>10\, \mu$m also observed in
previous measurements.


title:
Solid Phase Recrystallization in Arsenic Ion-Implanted
  Silicon-On-Insulator by Microsecond UV Laser Annealing

abstract:
  UV laser annealing (UV-LA) enables surface-localized high-temperature thermal
processing to form abrupt junctions in emerging monolithically stacked devices,
where the applicable thermal budget is restricted. In this work, UV-LA is
performed to regrow a silicon-on-insulator wafer partially amorphized by
arsenic ion implantation as well as to activate the dopants. In a microsecond
scale ( 10^-6 s to 10^-5 s) UV-LA process, monocrystalline solid phase
recrystallization and dopant activation without junction deepening are
evidenced, thus opening various applications in low thermal budget integration
flows. However, some concerns remain. First, the surface morphology is degraded
after the regrowth, possibly because of the non-perfect uniformity of the used
laser beam and/or the formation of defects near the surface involving the
excess dopants. Second, many of the dopants are inactive and seem to form deep
levels in the Si band gap, suggesting a further optimization of the ion
implantation condition to manage the initial crystal damage and the heating
profile to better accommodate the dopants into the substitutional sites.


title: Inexact Proximal Newton methods in Hilbert spaces

abstract:
  We consider Proximal Newton methods with an inexact computation of update
steps. To this end, we introduce two inexactness criteria which characterize
sufficient accuracy of these update step and with the aid of these investigate
global convergence and local acceleration of our method. The inexactness
criteria are designed to be adequate for the Hilbert space framework we find
ourselves in while traditional inexactness criteria from smooth Newton or
finite dimensional Proximal Newton methods appear to be inefficient in this
scenario. The performance of the method and its gain in effectiveness in
contrast to the exact case are showcased considering a simple model problem in
function space.


title:
Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary
  Features in the Analysis of Deaths from Hyperglycaemia

abstract:
  Lower-and-middle income countries are faced with challenges arising from a
lack of data on cause of death (COD), which can limit decisions on population
health and disease management. A verbal autopsy(VA) can provide information
about a COD in areas without robust death registration systems. A VA consists
of structured data, combining numeric and binary features, and unstructured
data as part of an open-ended narrative text. This study assesses the
performance of various machine learning approaches when analyzing both the
structured and unstructured components of the VA report. The algorithms were
trained and tested via cross-validation in the three settings of binary
features, text features and a combination of binary and text features derived
from VA reports from rural South Africa. The results obtained indicate
narrative text features contain valuable information for determining COD and
that a combination of binary and text features improves the automated COD
classification task.
  Keywords: Diabetes Mellitus, Verbal Autopsy, Cause of Death, Machine
Learning, Natural Language Processing


title: Damping of the isovector giant dipole resonance in $^{40,48}$Ca

abstract:
  The fine structure of the IsoVector Giant Dipole Resonance (IVGDR) in the
doubly-magic nuclei $^{40,48}$Ca observed in inelastic proton scattering
experiments under $0^\circ$ is used to investigate the role of different
mechanisms contributing to the IVGDR decay width. Characteristic energy scales
are extracted from the fine structure by means of wavelet analysis. The
experimental scales are compared to different theoretical approaches allowing
for the inclusion of complex configurations beyond the mean-field level.
Calculations are performed in the framework of RPA and second-RPA in a
relativistic approach based on an effective meson-exchange interaction, with
the UCOM effective interaction and, for the first time, with realistic two-
plus three-nucleon interactions from chiral effective field theory employing
the in-medium similarity renormalization group. All models highlight the role
of Landau fragmentation for the damping of the IVGDR, while the differences in
the coupling strength between one particle-one hole (1p-1h) and two
particle-two hole (2p-2h) configurations lead to very different pictures of the
importance of the spreading width resulting in wavelet scales being a sensitive
measure of their interplay. The relativistic approach, in particular, shows
impressive agreement with the number and absolute values of the scales
extracted from the experimental data.


title: Supernovae and photoionizing feedback in spiral arm molecular clouds

abstract:
  We explore the interplay between supernovae and the ionizing radiation of
their progenitors in star forming regions. The relative contributions of these
stellar feedback processes are not well understood, particularly on scales
greater than a single star forming cloud. We focus predominantly on how they
affect the interstellar medium. We re-simulate a 500 pc^2 region from previous
work that included photoionization and add supernovae. Over the course of 10
Myr more than 500 supernovae occur in the region. The supernovae remnants cool
very quickly in the absence of earlier photoionization, but form much larger
and more spherical hot bubbles when photoionization is present. Overall, the
photoionization has a significantly greater effect on gas morphology and the
sites of star formation. However, the two processes are comparable when looking
at their effect on velocity dispersion. When combined, the two feedback
processes increase the velocity dispersions by more than the sum of their
parts, particularly on scales above 5 pc.


title: Drag reduction on a transonic airfoil

abstract:
  Flow control for turbulent skin-friction drag reduction is applied to a
transonic airfoil to improve its aerodynamic performance. The study is based on
direct numerical simulations (with up to 1.8 billions cells) of the
compressible turbulent flow around a supercritical airfoil, at Reynolds and
Mach numbers of $Re_\infty= 3 \times 10^5$ and $M_\infty =0.7$. Control via
spanwise forcing is applied over a fraction of the suction side of the airfoil.
Besides locally reducing friction, the control modifies the shock wave and
significantly improves the aerodynamic efficiency of the airfoil by increasing
lift and decreasing drag. Hence, the airfoil can achieve the required lift at a
lower angle of attack and with a lower drag. Estimates at the aircraft level
indicate that substantial savings are possible; when control is active, its
energy cost becomes negligible thanks to the small application area. We suggest
that skin-friction drag reduction should be considered not only as a goal, but
also as a tool to improve the global aerodynamics of complex flows.