title:
Higher finiteness properties of arithmetic approximate lattices: The
  Rank Theorem for number fields

abstract:
  We introduce geometric and homological finiteness properties for countable
approximate groups via coarse geometry and then study these finiteness
properties for S-arithmetic reductive approximate groups. For S-arithmetic
approximate groups without infinite places we show that the finiteness length
is finite and compute this finiteness length explicitly. In the simple case it
is one less than the sum of the local ranks. This extends the Rank Theorem of
Bux, K\"ohl and the second author from positive characteristic to
characteristic zero. Our proof is based on a geometric version of their proof,
but except for some input from reduction theory it is characteristic free. This
indicates that the apparent differences between arithmetic groups in
characteristic zero and positive characteristic concerning finiteness
properties are entirely due to the presence of infinite places.


title:
The Dumont Ansatz for the Eulerian Polynomials, Peak Polynomials and
  Derivative Polynomials

abstract:
  We observe that three context-free grammars of Dumont can be brought to a
common ground, via the idea of transformations of grammars, proposed by
Ma-Ma-Yeh. Then we develop a unified perspective to investigate several
combinatorial objects in connection with the bivariate Eulerian polynomials. We
call this approach the Dumont ansatz.
  As applications, we provide grammatical treatments, in the spirit of the
symbolic method, of relations on the Springer numbers, the Euler numbers, the
three kinds of peak polynomials, an identity of Petersen, and the two kinds of
derivative polynomials, introduced by Knuth-Buckholtz and Carlitz-Scoville, and
later by Hoffman in a broader context. We obtain a convolution formula on the
left peak polynomials, leading to the Gessel formula. In this framework, we are
led to the combinatorial interpretations of the derivative polynomials due to
Josuat-Verg\`es.


title:
Primary and secondary invariants of Dirac operators on $G$-proper
  manifolds

abstract:
  In this article, we survey the recent constructions of cyclic cocycles on the
Harish-Chandra Schwartz algebra of a connected real reductive Lie group $G$ and
their applications to higher index theory for proper cocompact $G$-actions.


title: FedRecAttack: Model Poisoning Attack to Federated Recommendation

abstract:
  Federated Recommendation (FR) has received considerable popularity and
attention in the past few years. In FR, for each user, its feature vector and
interaction data are kept locally on its own client thus are private to others.
Without the access to above information, most existing poisoning attacks
against recommender systems or federated learning lose validity. Benifiting
from this characteristic, FR is commonly considered fairly secured. However, we
argue that there is still possible and necessary security improvement could be
made in FR. To prove our opinion, in this paper we present FedRecAttack, a
model poisoning attack to FR aiming to raise the exposure ratio of target
items. In most recommendation scenarios, apart from private user-item
interactions (e.g., clicks, watches and purchases), some interactions are
public (e.g., likes, follows and comments). Motivated by this point, in
FedRecAttack we make use of the public interactions to approximate users'
feature vectors, thereby attacker can generate poisoned gradients accordingly
and control malicious users to upload the poisoned gradients in a well-designed
way. To evaluate the effectiveness and side effects of FedRecAttack, we conduct
extensive experiments on three real-world datasets of different sizes from two
completely different scenarios. Experimental results demonstrate that our
proposed FedRecAttack achieves the state-of-the-art effectiveness while its
side effects are negligible. Moreover, even with small proportion (3%) of
malicious users and small proportion (1%) of public interactions, FedRecAttack
remains highly effective, which reveals that FR is more vulnerable to attack
than people commonly considered.


title: Which Tricks are Important for Learning to Rank?

abstract:
  Nowadays, state-of-the-art learning-to-rank (LTR) methods are based on
gradient-boosted decision trees (GBDT). The most well-known algorithm is
LambdaMART that was proposed more than a decade ago. Recently, several other
GBDT-based ranking algorithms were proposed. In this paper, we conduct a
thorough analysis of these methods in a unified setup. In particular, we
address the following questions. Is direct optimization of a smoothed ranking
loss preferable over optimizing a convex surrogate? How to properly construct
and smooth surrogate ranking losses? To address these questions, we compare
LambdaMART with YetiRank and StochasticRank methods and their modifications. We
also improve the YetiRank approach to allow for optimizing specific ranking
loss functions. As a result, we gain insights into learning-to-rank approaches
and obtain a new state-of-the-art algorithm.


title:
X-Fault: Impact of Faults on Binary Neural Networks in
  Memristor-Crossbar Arrays with Logic-in-Memory Computation

abstract:
  Memristor-based crossbar arrays represent a promising emerging memory
technology to replace conventional memories by offering a high density and
enabling computing-in-memory (CIM) paradigms. While analog computing provides
the best performance, non-idealities and ADC/DAC conversion limit
memristor-based CIM. Logic-in-Memory (LIM) presents another flavor of CIM, in
which the memristors are used in a binary manner to implement logic gates.
Since binary neural networks (BNNs) use binary logic gates as the dominant
operation, they can benefit from the massively parallel execution of binary
operations and better resilience to variations of the memristors. Although
conventional neural networks have been thoroughly investigated, the impact of
faults on memristor-based BNNs remains unclear. Therefore, we analyze the
impact of faults on logic gates in memristor-based crossbar arrays for BNNs. We
propose a simulation framework that simulates different traditional faults to
examine the accuracy loss of BNNs on memristive crossbar arrays. In addition,
we compare different logic families based on the robustness and feasibility to
accelerate AI applications.


title:
Estimates for the Kolmogorov widths of weighted Sobolev classes with
  conditions on the $0$th and the greatest derivatives

abstract:
  In this article, we obtain the order estimates for the Kolmogorov widths of
sets with conditions on the norm in the weighted Sobolev space $W^r_{p_1}$ and
in the weighted space $L_{p_0}$.


title:
A Method for Random Packing of Spheres with Application to Bonding
  Modeling in Powder Bed 3D Printing Process

abstract:
  A Matlab-based computational procedure is proposed to fill a given volume
with spheres whose radii are randomly picked from any specified probability
distribution supported by \verb|Matlab|. The general program sequence and
examples of filling a unit cube, a parallelepiped, and a concave domain between
two hemispherical surfaces, with spheres whose radii are drawn from the Weibull
and Gamma distributions, are presented. A sample application to the numerical
modeling of bond formation between particles heated by a laser beam in powder
bed 3D printing process is considered.


title: Microtubule organization and cell geometry

abstract:
  We present a systematic study of the influence of cell geometry on the
orientational distribution of microtubules (MTs) nucleated from a single
microtubule organizing center (MTOC). For simplicity we consider an elliptical
cell geometry, a setting appropriate to a generic non-spherical animal cell.
Within this context we introduce four models of increasing complexity, in each
case introducing additional mechanisms that govern the interaction of the MTs
with the cell boundary. In order, we consider the cases: MTs that can bind to
the boundary with a fixed mean residence time (M0), force-producing MTs that
can slide on the boundary towards the cell poles (MS), MTs that interact with a
generic polarity factor that is transported and deposited at the boundary, and
which in turn stabilizes the MTs at the boundary (MP), and a final model in
which both sliding and stabilization by polarity factors is taken into account
(MSP). In the baseline model (M0), the exponential length distribution of MTs
causes most of the interactions at the cell boundary to occur along the shorter
transverse direction in the cell, leading to transverse biaxial order. MT
sliding (MS) is able to reorient the main axis of this biaxial order along the
longitudinal axis. The polarization mechanism introduced in MP and MSP
overrules the geometric bias towards bipolar order observed in M0 and MS, and
allows the establishment of unipolar order either along the short- (MP) or the
long cell axis (MSP). The behavior of the latter two models can be
qualitatively reproduced by a very simple toy model with discrete MT
orientations.


title:
Adaptive Network Combination for Single-Image Reflection Removal: A
  Domain Generalization Perspective

abstract:
  Recently, multiple synthetic and real-world datasets have been built to
facilitate the training of deep single image reflection removal (SIRR) models.
Meanwhile, diverse testing sets are also provided with different types of
reflection and scenes. However, the non-negligible domain gaps between training
and testing sets make it difficult to learn deep models generalizing well to
testing images. The diversity of reflections and scenes further makes it a
mission impossible to learn a single model being effective to all testing sets
and real-world reflections. In this paper, we tackle these issues by learning
SIRR models from a domain generalization perspective. Particularly, for each
source set, a specific SIRR model is trained to serve as a domain expert of
relevant reflection types. For a given reflection-contaminated image, we
present a reflection type-aware weighting (RTAW) module to predict expert-wise
weights. RTAW can then be incorporated with adaptive network combination
(AdaNEC) for handling different reflection types and scenes, i.e., generalizing
to unknown domains. Two representative AdaNEC methods, i.e., output fusion (OF)
and network interpolation (NI), are provided by considering both adaptation
levels and efficiency. For images from one source set, we train RTAW to only
predict expert-wise weights of other domain experts for improving
generalization ability, while the weights of all experts are predicted and
employed during testing. An in-domain expert (IDE) loss is presented for
training RTAW. Extensive experiments show the appealing performance gain of our
AdaNEC on different state-of-the-art SIRR networks. Source code and pre-trained
models will available at https://github.com/csmliu/AdaNEC.


title:
Site-dependent Local Spin Susceptibility and Low-energy Excitation in a
  Weyl Semimetal WTe$_2$

abstract:
  Site-dependent local spin susceptibility is investigated with $^{125}$Te
nuclear magnetic resonance in a Weyl semimetal WTe$_2$. The nuclear
spin-lattice relaxation rate $1/T_1T$ shows a dependence of the square of
temperature $T$ at high temperatures, followed by a constant behavior below 50
K. The temperature dependence features Weyl fermions appearing around the
linearly crossing bands. The Knight shift $K$ scales to the square root of
$1/T_1T$, corroborating a predominant spin contribution in low-lying
excitation. The observed dependence of $K$ and $1/T_1T$ on the four Te sites
shows the site-dependent electron correlation and density of states. The
angular profile of the NMR spectrum gives the anisotropic hyperfine coupling
tensor, consistent with $5p$ hole occupations on Te sites.


title:
Space-charge-limited current density for nonplanar diodes with
  monoenergetic emission using Lie-point symmetries

abstract:
  Understanding space-charge limited current density (SCLCD) is fundamentally
and practically important for characterizing many high-power and high-current
vacuum devices. Despite this, no analytic equations for SCLCD with nonzero
monoenergetic initial velocity have been derived for nonplanar diodes from
first principles. Obtaining analytic equations for SCLCD for nonplanar
geometries is often complicated by the nonlinearity of the problem and over
constrained boundary conditions. In this letter, we use the canonical
coordinates obtained by identifying Lie-point symmetries to linearize the
governing differential equations to derive SCLCD for any orthogonal diode.
Using this method, we derive exact analytic equations for SCLCD with a
monoenergetic injection velocity for one-dimensional cylindrical, spherical,
tip-to-tip (t-t), and tip-to-plate (t-p) diodes. We specifically demonstrate
that the correction factor from zero initial velocity to monoenergetic emission
depends only on the initial kinetic and electric potential energies and not on
the diode geometry and that SCLCD is universal when plotted as a function of
the canonical gap size. We also show that SCLCD for a t-p diode is a factor of
four larger than a t-t diode independent of injection velocity. The results
reduce to previously derived results for zero initial velocity using
variational calculus and conformal mapping.


title:
Exploration of Near-Horizon CFT Duality and $AdS_2/CFT_1$ in Conformal
  Weyl Gravity

abstract:
  We compute near horizon black hole entropy via the N\"other current method
within the conformal Weyl gravity paradigm for vacuum and non-vacuum
spacetimes. We do this, in the vacuum case, for the near horizon near extremal
Kerr metric and for the non-vacuum case we couple the conformal Weyl gravity
field equations to a near horizon (linear) $U(1)$ gauge potential and analyze
the respective found solutions. We highlight the non-universality of black hole
entropy between black hole solutions of varying symmetries, yet their
congruence with Wald's entropy formula for the respective gravity theory.
Finally, we implement an $AdS_2/CFT_1$ construction to compute the full
asymptotic symmetry group of one of the non-vacuum conformal Weyl black holes.
We do this by performing a Robinson-Wilczek two dimensional reduction, thus
enabling the construction of an effective quantum theory of the remaining field
content. The effective stress energy tensor generates an asymptotic Virasoro
algebra, to $s$-wave approximation, whose center in conjunction with their
proper regularized lowest Virasoro eigen-mode is implemented to compute black
hole entropy via the statistical Cardy formula. We additionally implement
quantum holomorphic fluxes (of the dual CFT) in the near horizon to compute the
Hawking temperature of the respective black hole spacetime. We conclude with a
discussion and outlook for future work.


title: The Group Loss++: A deeper look into group loss for deep metric learning

abstract:
  Deep metric learning has yielded impressive results in tasks such as
clustering and image retrieval by leveraging neural networks to obtain highly
discriminative feature embeddings, which can be used to group samples into
different classes. Much research has been devoted to the design of smart loss
functions or data mining strategies for training such networks. Most methods
consider only pairs or triplets of samples within a mini-batch to compute the
loss function, which is commonly based on the distance between embeddings. We
propose Group Loss, a loss function based on a differentiable label-propagation
method that enforces embedding similarity across all samples of a group while
promoting, at the same time, low-density regions amongst data points belonging
to different groups. Guided by the smoothness assumption that "similar objects
should belong to the same group", the proposed loss trains the neural network
for a classification task, enforcing a consistent labelling amongst samples
within a class. We design a set of inference strategies tailored towards our
algorithm, named Group Loss++ that further improve the results of our model. We
show state-of-the-art results on clustering and image retrieval on four
retrieval datasets, and present competitive results on two person
re-identification datasets, providing a unified framework for retrieval and
re-identification.


title:
Joint Initial Access and Localization in Millimeter Wave Vehicular
  Networks: a Hybrid Model/Data Driven Approach

abstract:
  High resolution compressive channel estimation provides information for
vehicle localization when a hybrid mmWave MIMO system is considered. Complexity
and memory requirements can, however, become a bottleneck when high accuracy
localization is required. An additional challenge is the need of path order
information to apply the appropriate geometric relationships between the
channel path parameters and the vehicle, RSU and scatterers position. In this
paper, we propose a low complexity channel estimation strategy of the angle of
departure and time difference of arrival based on multidimensional orthogonal
matching pursuit. We also design a deep neural network that predicts the order
of the channel paths so only the LoS and first order reflections are used for
localization. Simulation results obtained with realistic vehicular channels
generated by ray tracing show that sub-meter accuracy can be achieved for 50%
of the users, without resorting to perfect synchronization assumptions or
unfeasible all-digital high resolution MIMO architectures.


title: Explicit examples of resonances for Anosov maps of the torus

abstract:
  In [23], Slipantschuk, Bandtlow and Just gave concrete examples of Anosov
diffeomorphisms of the two-torus for which their resonances could be completely
described. Their approach was based on composition operators acting on analytic
anisotropic Hilbert spaces, and in this note we present a construction of
alternative anisotropic Hilbert spaces which helps to simplify parts of their
analysis and gives scope for constructing further examples.


title:
LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of
  Attacks in Arguments

abstract:
  In argumentative discourse, persuasion is often achieved by refuting or
attacking others arguments. Attacking is not always straightforward and often
comprise complex rhetorical moves such that arguers might agree with a logic of
an argument while attacking another logic. Moreover, arguer might neither deny
nor agree with any logics of an argument, instead ignore them and attack the
main stance of the argument by providing new logics and presupposing that the
new logics have more value or importance than the logics present in the
attacked argument. However, no existing studies in the computational
argumentation capture such complex rhetorical moves in attacks or the
presuppositions or value judgements in them. In order to address this gap, we
introduce LPAttack, a novel annotation scheme that captures the common modes
and complex rhetorical moves in attacks along with the implicit presuppositions
and value judgements in them. Our annotation study shows moderate
inter-annotator agreement, indicating that human annotation for the proposed
scheme is feasible. We publicly release our annotated corpus and the annotation
guidelines.


title: Context-aware Visual Tracking with Joint Meta-updating

abstract:
  Visual object tracking acts as a pivotal component in various emerging video
applications. Despite the numerous developments in visual tracking, existing
deep trackers are still likely to fail when tracking against objects with
dramatic variation. These deep trackers usually do not perform online update or
update single sub-branch of the tracking model, for which they cannot adapt to
the appearance variation of objects. Efficient updating methods are therefore
crucial for tracking while previous meta-updater optimizes trackers directly
over parameter space, which is prone to over-fit even collapse on longer
sequences. To address these issues, we propose a context-aware tracking model
to optimize the tracker over the representation space, which jointly
meta-update both branches by exploiting information along the whole sequence,
such that it can avoid the over-fitting problem. First, we note that the
embedded features of the localization branch and the box-estimation branch,
focusing on the local and global information of the target, are effective
complements to each other. Based on this insight, we devise a
context-aggregation module to fuse information in historical frames, followed
by a context-aware module to learn affinity vectors for both branches of the
tracker. Besides, we develop a dedicated meta-learning scheme, on account of
fast and stable updating with limited training samples. The proposed tracking
method achieves an EAO score of 0.514 on VOT2018 with the speed of 40FPS,
demonstrating its capability of improving the accuracy and robustness of the
underlying tracker with little speed drop.


title:
Integer quantum Hall effect of two-component hardcore bosons in
  topological triangular lattice

abstract:
  We study the many-body ground states of two-component hardcore bosons in
topological triangular lattice models. Utilizing exact diagonalization and
density-matrix renormalization group calculations, we demonstrate that at
commensurate two-third filling per lattice site, two-component bosonic integer
quantum hall (BIQH) effect emerges with the associated
$\mathbf{K}=\begin{pmatrix} 0 & 1\\ 1 & 0\\ \end{pmatrix}$ matrix under strong
intercomponent Hubbard repulsion. The topological nature is further elucidated
by (i) the unique ground state degeneracy with a robust spectrum gap, (ii)
quantized topological Chern number matrix $\mathbf{C}=\mathbf{K}^{-1}$, and
(iii) two counterpropagating edge branches. Moreover, with increasing
nearest-neighbor repulsions, the ground state undergoes a first-order
transition from a BIQH liquid to a commensurate solid order.


title:
Modeling the GRB jet properties with 3D general relativistic simulations
  of magnetically arrested accretion flows

abstract:
  We investigate the dependence of the GRB jet structure and its evolution on
the properties of the accreting torus in the central engine. Our models
numerically evolve the accretion disk around a Kerr black hole using 3D general
relativistic magnetohydrodynamic simulations. We use two different analytical
hydrodynamical models of the accretion disk, based on the Fishbone-Moncrief and
Chakrabarti solutions, as our initial states for the structure of the collapsar
disk and the remnant after a binary neutron star merger, respectively. We
impose poloidal magnetic fields of two different geometries upon the initial
stable solutions. We study the formation and evolution of the magnetically
arrested disk state and its effect on the properties of the emitted jet. The
jets produced in our models are structured and have a relatively hollow core
and reach higher Lorentz factors at an angle $\gtrsim 9{^\circ}$ from the axis.
The jet in our short GRB model has an opening angle of up to $\sim 25^{\circ}$
while our long GRB engine produces a narrower jet, of up to $\sim 11^{\circ}$.
We also study the time variability of the jets and provide an estimate of the
minimum variability timescale in our models. The application of our models to
the GRB jets in the binary neutron star post-merger system and to the
ultra-relativistic jets launched from collapsing stars are briefly discussed.


title: SAUSAGE: Security Analysis of Unix domain Socket Usage in Android

abstract:
  The Android operating system is currently the most popular mobile operating
system in the world. Android is based on Linux and therefore inherits its
features including its Inter-Process Communication (IPC) mechanisms. These
mechanisms are used by processes to communicate with one another and are
extensively used in Android. While Android-specific IPC mechanisms have been
studied extensively, Unix domain sockets have not been examined
comprehensively, despite playing a crucial role in the IPC of highly privileged
system daemons. In this paper, we propose SAUSAGE, an efficient novel static
analysis framework to study the security properties of these sockets. SAUSAGE
considers access control policies implemented in the Android security model, as
well as authentication checks implemented by the daemon binaries. It is a fully
static analysis framework, specifically designed to analyze Unix domain socket
usage in Android system daemons, at scale. We use this framework to analyze 200
Android images across eight popular smartphone vendors spanning Android
versions 7-9. As a result, we uncover multiple access control misconfigurations
and insecure authentication checks. Our notable findings include a permission
bypass in highly privileged Qualcomm system daemons and an unprotected socket
that allows an untrusted app to set the scheduling priority of other processes
running on the system, despite the implementation of mandatory SELinux
policies. Ultimately, the results of our analysis are worrisome; all vendors
except the Android Open Source Project (AOSP) have access control issues,
allowing an untrusted app to communicate to highly privileged daemons through
Unix domain sockets introduced by hardware manufacturer or vendor
customization.


title:
Exact percolation probabilities for a square lattice: Site percolation
  on a plane, cylinder, and torus

abstract:
  We have found analytical expressions (polynomials) of the percolation
probability for site percolation on a square lattice of size $L \times L$ sites
when considering a plane (the crossing probability in a given direction), a
cylinder (spanning probability), and a torus (wrapping probability along one
direction). Since some polynomials are extremely cumbersome, they are presented
as separate files in Supplemental material. The system sizes for which this was
feasible varied up to $L=17$ for a plane, up to $L=16$ for a cylinder, and up
to $L=12$ for a torus. To obtain a percolation probability polynomial, all
possible combinations of occupied and empty sites have to be taken into
account. However, using dynamic programming along with some ideas related to
the topology, we offer an algorithm which allows a significant reduction in the
number of configurations requiring consideration. A rigorous formal description
of the algorithm is presented. Divisibility properties of the polynomials have
been rigorously proved. Reliability of the polynomials obtained have been
confirmed by the divisibility tests. The wrapping probability polynomials on a
torus provide a better estimate of the percolation threshold than that from the
spanning probability polynomials. Surprisingly, even a naive finite size
scaling analysis allows an estimate to be obtained of the percolation threshold
$p_c = 0.59269$.


title:
Artificial Intelligence: Framework of driving triggers to past, present
  and future applications and influencers of industry sector adoption

abstract:
  To gain a sense of the development of Artificial Intelligence (AI), this
research analyzes what has been done in the past, presently in the last decade
and what is predicted for the next several decades. The paper will highlight
the biggest changes in AI and give examples of how these technologies are
applied in several key industry sectors along with influencers that can affect
adoption speed. Lastly, the research examines the driving triggers such as
cost, speed, accuracy, diversity/inclusion and interdisciplinary
research/collaboration that propel AI into an essential transformative
technology.


title:
On the free rotation of a polarized spinning-top as a test of the
  correct radiation reaction torque

abstract:
  The formula for dipole radiation reaction torque acting on a system of
charges, and the Larmor-like formula for the angular momentum loss by this
system, differ in the time derivative term which is the analogue of the Schott
term in the energy loss problem. In the well-known textbooks this discrepancy
is commonly avoided via neglect of the Schott term, and the Larmor-like formula
is preferred. In the present paper both formulae are used to derive two
different equations of motion of a polarized spinning-top. Both equations are
integrable for the symmetric top and lead to quite different solutions. That
one following from the Larmor-like formula is physically unplausible, in
contrast to another one. This result is accorded with the reinterpretation of
Larmor's formula discussed recently in the pedagogical literature. It is
appeared, besides, that the Schott term is of not only academic significance,
but it may determine the behavior of polarized micro- and nanoparticles in
nature or future experiments.


title:
Sampling Lov\'asz Local Lemma For General Constraint Satisfaction
  Solutions In Near-Linear Time

abstract:
  We give a fast algorithm for sampling uniform solutions of general constraint
satisfaction problems (CSPs) in a local lemma regime. The expected running time
of our algorithm is near-linear in $n$ and a fixed polynomial in $\Delta$,
where $n$ is the number of variables and $\Delta$ is the max degree of
constraints. Previously, up to similar conditions, sampling algorithms with
running time polynomial in both $n$ and $\Delta$, only existed for the almost
atomic case, where each constraint is violated by a small number of forbidden
local configurations.
  Our sampling approach departs from all previous fast algorithms for sampling
LLL, which were based on Markov chains. A crucial step of our algorithm is a
recursive marginal sampler that is of independent interests. Within a local
lemma regime, this marginal sampler can draw a random value for a variable
according to its marginal distribution, at a local cost independent of the size
of the CSP.


title: Validations and corrections of the SFD and Planck maps

abstract:
  Precise correction of dust reddening is fundamental to obtain the intrinsic
parameters of celestial objects. The Schlegel et al. (SFD) and the Planck 2D
extinction maps are widely used for the reddening correction. In this work,
using accurate reddening determinations of about two million stars from the
Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) data release
5 (DR5) and Gaia DR2, we check and calibrate the SFD and Planck maps in the
middle and high Galactic latitudes. The maps show similar precision in
reddening correction. We find small yet significant spatially dependent biases
for the four maps, which are similar between the SFD and Planck2014-R maps, and
between the Planck2014-Tau and Planck2019-Tau maps. The biases show a clear
dependence on the dust temperature and extinction for the SFD and Planck2014-R
maps. While those of the Planck2014-Tau and Planck2019-Tau maps have a weak
dependence on the dust temperature, they both strongly depend on the dust
spectral index. Finally, we present corrections of the SFD and Planck
extinction maps within the LAMOST footprint, along with empirical relations for
corrections outside the LAMOST footprint. Our results provide important clues
for the further improvement of the Galactic all-sky extinction maps and lay an
significant foundation for the accurate extinction correction in the era of
precision astronomy.


title: On the Suppression and Enhancement of Thermal Chemical Rates in a Cavity

abstract:
  The observed modification of thermal chemical rates in Fabry-Perot cavities
remains a poorly understood effect theoretically. Recent breakthroughs explain
some of the observations through the Grote-Hynes theory, where the cavity
introduces friction with the reaction coordinate, thus reducing the
transmission coefficient and the rate. The regime of rate enhancement, the
observed sharp resonances at varying cavity frequencies, and the survival of
these effects in the collective regime remain mostly unexplained. In this
paper, we consider the \emph{cis}-\emph{trans} isomerization of HONO
atomistically using an \emph{ab-initio} potential energy surface. We evaluate
the transmission coefficient using the reactive flux method and identify the
conditions for rate acceleration. In the underdamped, low-friction regime of
the reaction coordinate, the cavity coupling enhances the rate with increasing
coupling strength until reaching the Kramers turnover point. Sharp resonances
in this regime are related to cavity-enabled energy redistribution channels.


title:
Stable Charged Gravastar Model in ${f}(\mathfrak{R},\textbf{T}^{2})$
  Gravity with Conformal Motion

abstract:
  This paper investigates the influence of charge on physical features of
gravastars in the framework of energy-momentum squared gravity. A gravastar is
an alternate model to a black hole that comprises of three distinct regions,
namely the intermediate shell, inner and outer sectors. Different values of the
barotropic equation of state parameter provide the mathematical formulation for
these regions. We construct the structure of a gravastar admitting conformal
motion for a specific model of energy-momentum squared gravity. The field
equations are formulated for a spherically symmetric spacetime with charged
perfect matter distribution. For the smooth matching of external and internal
spacetimes, we use Israel matching criteria. Various physical attributes of
gravastars such as the equation of state parameter, proper length, entropy and
energy are investigated (in the presence of charge) versus thickness of the
shell. The charge in the inner core of gravastars preserves the state of
equilibrium by counterbalancing the inward gravitational force. It is concluded
that the non-singular solutions of charged gravastar are physically viable in
the background of energy-momentum squared gravity.


title: Bi-directional Loop Closure for Visual SLAM

abstract:
  A key functional block of visual navigation system for intelligent autonomous
vehicles is Loop Closure detection and subsequent relocalisation.
State-of-the-Art methods still approach the problem as uni-directional along
the direction of the previous motion. As a result, most of the methods fail in
the absence of a significantly similar overlap of perspectives. In this study,
we propose an approach for bi-directional loop closure. This will, for the
first time, provide us with the capability to relocalize to a location even
when traveling in the opposite direction, thus significantly reducing long-term
odometry drift in the absence of a direct loop. We present a technique to
select training data from large datasets in order to make them usable for the
bi-directional problem. The data is used to train and validate two different
CNN architectures for loop closure detection and subsequent regression of 6-DOF
camera pose between the views in an end-to-end manner. The outcome packs a
considerable impact and aids significantly to real-world scenarios that do not
offer direct loop closure opportunities. We provide a rigorous empirical
comparison against other established approaches and evaluate our method on both
outdoor and indoor data from the FinnForest dataset and PennCOSYVIO dataset.


title:
Low Frequency Oscillations of Plasma Injected in Open Magnetic Trap from
  Independent Ultra-high Frequency Source

abstract:
  Methods of plasma heating by electromagnetic waves, interaction of
electro-magnetic waves with magnetoactive plasma, plasma turbulence and
transport processes are investigated in the trap of the linear plasma device
OMT2. An ultrahigh frequency (UHF) contactless method is used to fill the open
magnetic trap by plasma. We propose a new method of open magnetic trap filling
with plasma whereby plasma is injected in the trap along the magnetic field
from an independent stationary UHF source. The source is located outside the
trap and plasma formation in it takes place in the strongly nonuniform magnetic
field, in the electron cyclotron resonance (ECR) regime by means of the UHF
power. In this paper we study the efficiency of open magnetic trap filling by
plasma and plasma low frequency (LF) oscillations in the trap.


title:
Deviation of Mercury's spin axis from an exact Cassini state induced by
  dissipation

abstract:
  We compute predictions of the deviation of Mercury's spin axis from an exact
Cassini state caused by tidal dissipation, and viscous and electromagnetic (EM)
friction at the core-mantle boundary (CMB) and inner core boundary (ICB).
Viscous friction at the CMB generates a phase lead, viscous and EM friction at
the ICB produce a phase lag; the magnitude of the deviation depends on the
inner core size, kinematic viscosity and magnetic field strength, but cannot
exceed an upper bound. For a small inner core, viscous friction at the CMB
results in a maximum phase lead of 0.027 arcsec. For a large inner core (radius
$>1000$ km), EM friction at the ICB generates the largest phase lag, but it
does not exceed 0.1 arcsec. Elastic deformations induced by the misaligned
fluid and solid cores play a first order role in the phase lead/lag caused by
viscous and EM coupling, and contribute to a perturbation in mantle obliquity
on par with that caused by tidal deformations. Tidal dissipation results in a
phase lag and its magnitude (in units of arcsec) is given by the empirical
relation (80/Q), where Q is the quality factor; Q=80 results in a phase lag of
~1 arcsec. A large inner core with a low viscosity of the order of 10^{17} Pa s
or lower can significantly affect $Q$ and thus the resulting phase lag. The
limited mantle phase lag suggested by observations (<10 arcsec) implies a lower
limit on the bulk mantle viscosity of approximately 10^{17} Pa s.


title:
TMD Evolution Study of the $\cos 2 \phi$ Azimuthal Asymmetry in
  Unpolarized $J/\psi$ Production at EIC

abstract:
  Semi-inclusive $J/\psi$ production in electron-proton collisions is a
promising process to study gluon transverse momentum distributions (TMDs) at
the future Electron-Ion Collider. In this article, we improve on previous
studies of the $\cos 2 \phi$ azimuthal asymmetry that arises from the linear
polarization of gluons inside unpolarized protons by including TMD evolution.
We find that in the TMD regime the asymmetry grows monotonically with
increasing transverse momentum of the outgoing $J/\psi$, in contrast to tree
level calculations with Gaussian TMDs. We include an estimate of the
nonperturbative uncertainty and note a problem with the positivity bound for a
commonly applied approach to deal with the very small $b$ region. Our
predictions for the asymmetry at EIC can reach up to 20% in some cases, but
generally depend on $x$ and $Q^2$ and on the choice of Color Octet
Long-Distance Matrix Elements which forms the largest source of uncertainty.


title:
Back-to-back azimuthal correlations in Z+jet events at high transverse
  momentum in the TMD parton branching method at next-to-leading order

abstract:
  Azimuthal correlations in Z+jet production at large transverse momenta are
computed by matching Parton - Branching (PB) TMD parton distributions and
showers with NLO calculations via MCatNLO. The predictions are compared with
those for dijet production in the same kinematic range. The azimuthal
correlations $\Delta\phi$ between the Z boson and the leading jet are steeper
compared to those in dijet production at transverse momenta ${\cal O}(100)$ GeV
, while they become similar for very high transverse momenta ${\cal O}(1000)$
GeV. The different patterns of Z+jet and dijet azimuthal correlations can be
used to search for potential factorization - breaking effects in the
back-to-back region, which depend on the different color and spin structure of
the final states and their interferences with the initial states. In order to
investigate these effects experimentally, we propose to measure the ratio of
the distributions in $\Delta\phi$ for Z+jet - and multijet production at low
and at high transverse momenta, and compare the results to predictions obtained
assuming factorization. We examine the role of theoretical uncertainties by
performing variations of the factorization scale, renormalization scale and
matching scale. In particular, we present a comparative study of matching scale
uncertainties in the cases of PB -TMD and collinear parton showers.


title: Characterizing the Reproducibility of Noisy Quantum Circuits

abstract:
  The ability of a quantum computer to reproduce or replicate the results of a
quantum circuit is a key concern for verifying and validating applications of
quantum computing. Statistical variations in circuit outcomes that arise from
ill-characterized fluctuations in device noise may lead to computational errors
and irreproducible results. While device characterization offers a direct
assessment of noise, an outstanding concern is how such metrics bound the
reproducibility of a given quantum circuit. Here, we first directly assess the
reproducibility of a noisy quantum circuit, in terms of the Hellinger distance
between the computational results, and then we show that device
characterization offers an analytic bound on the observed variability. We
validate the method using an ensemble of single qubit test circuits, executed
on a superconducting transmon processor with well-characterized readout and
gate error rates. The resulting description for circuit reproducibility, in
terms of a composite device parameter, is confirmed to define an upper bound on
the observed Hellinger distance, across the variable test circuits. This
predictive correlation between circuit outcomes and device characterization
offers an efficient method for assessing the reproducibility of noisy quantum
circuits.


title: Matrix Completion with Sparse Noisy Rows

abstract:
  Exact matrix completion and low rank matrix estimation problems has been
studied in different underlying conditions. In this work we study exact
low-rank completion under non-degenerate noise model. Non-degenerate random
noise model has been previously studied by many researchers under given
condition that the noise is sparse and existing in some of the columns. In this
paper, we assume that each row can receive random noise instead of columns and
propose an interactive algorithm that is robust to this noise. We show that we
use a parametrization technique to give a condition when the underlying matrix
could be recoverable and suggest an algorithm which recovers the underlying
matrix.


title:
Internet-of-Things Architectures for Secure Cyber-Physical Spaces: the
  VISOR Experience Report

abstract:
  Internet of things (IoT) technologies are becoming a more and more widespread
part of civilian life in common urban spaces, which are rapidly turning into
cyber-physical spaces. Simultaneously, the fear of terrorism and crime in such
public spaces is ever-increasing. Due to the resulting increased demand for
security, video-based IoT surveillance systems have become an important area
for research. Considering the large number of devices involved in the illicit
recognition task, we conducted a field study in a Dutch Easter music festival
in a national interest project called VISOR to select the most appropriate
device configuration in terms of performance and results. We iteratively
architected solutions for the security of cyber-physical spaces using IoT
devices. We tested the performance of multiple federated devices encompassing
drones, closed-circuit television, smart phone cameras, and smart glasses to
detect real-case scenarios of potentially malicious activities such as
mosh-pits and pick-pocketing. Our results pave the way to select optimal IoT
architecture configurations -- i.e., a mix of CCTV, drones, smart glasses, and
camera phones in our case -- to make safer cyber-physical spaces' a reality.


title: Survey of Matrix Completion Algorithms

abstract:
  Matrix completion problem has been investigated under many different
conditions since Netflix announced the Netflix Prize problem. Many research
work has been done in the field once it has been discovered that many real life
dataset could be estimated with a low-rank matrix. Since then compressed
sensing, adaptive signal detection has gained the attention of many
researchers. In this survey paper we are going to visit some of the matrix
completion methods, mainly in the direction of passive and adaptive directions.
First, we discuss passive matrix completion methods with convex optimization,
and the second active matrix completion techniques with adaptive signal
detection methods. Traditionally many machine learning problems are solved in
passive environment. However, later it has been observed that adaptive sensing
algorithms many times performs more efficiently than former algorithms. Hence
algorithms in this setting has been extensively studied. Therefore, we are
going to present some of the latest adaptive matrix completion algorithms in
this paper meanwhile providing passive methods.


title:
Real-world K-Anonymity Applications: the \textsc{KGen} approach and its
  evaluation in Fraudulent Transactions

abstract:
  K-Anonymity is a property for the measurement, management, and governance of
the data anonymization. Many implementations of k-anonymity have been described
in state of the art, but most of them are not able to work with a large number
of attributes in a "Big" dataset, i.e., a dataset drawn from Big Data. To
address this significant shortcoming, we introduce and evaluate \textsc{KGen}
an approach to K-anonymity featuring Genetic Algorithms. \textsc{KGen} promotes
such a meta-heuristic approach since it can solve the problem by finding a
pseudo-optimal solution in a reasonable time over a considerable load of input.
\textsc{KGen} allows the data manager to guarantee a high anonymity level while
preserving the usability and preventing loss of information entropy over the
data. Differently from other approaches that provide optimal global solutions
catered for small datasets, \textsc{KGen} works properly also over Big datasets
while still providing a good-enough solution. Evaluation results show how our
approach can still work efficiently on a real world dataset, provided by Dutch
Tax Authority, with 47 attributes (i.e., the columns of the dataset to be
anonymized) and over 1.5K+ observations (i.e., the rows of that dataset), as
well as on a dataset with 97 attributes and over 3942 observations.


title:
Magnetic hot-spots generation at optical frequencies in all-dielectric
  mesoscale Janus particles

abstract:
  At optical frequencies due to the small value of the magnetic permeability of
natural materials, the magnetic effects are week. To this end, the natural
dielectric materials are unemployable for practical magnetic applications in
optics. We have shown that it is possible to induce the intense magnetic hot
spots in a Janus dielectric mesoscale particle. The basic idea of the Janus
particle based on a combination of the effects of a photonic jet, whispering
gallery waves and the concept of solid immersion. Simulations show that H^2/E^2
contrast maybe more 10 and maximal magnetic field intensity enhancement is more
than 1000 for a wavelength-scaled particle with refractive index less than 2.


title:
Retail Central Bank Digital Currencies (CBDC), Disintermediation and
  Financial Privacy: The Case of the Bahamian Sand Dollar

abstract:
  The fast-growing, market-driven demand for cryptocurrencies worries central
banks, as their monetary policy could be completely undermined. Central bank
digital currencies (CBDCs) could offer a solution, yet our understanding of
their design and consequences is in its infancy. This non-technical paper
examines how The Bahamas has designed the Sand Dollar, the first real-world
instance of a retail CBDC. It contrasts the Sand Dollar with definition-based
specifications. I then develop a scenario analysis to illustrate commercial
bank risks. In this process, the central bank becomes a deposit monopolist,
leading to high funding risks, disintermediation risks, and solvency risks for
the com-mercial banking sector. I argue that restrictions and caps will be the
new specifications of a regulatory framework for CBDCs if disintermediation in
the banking sector is to be prevented. I identify the anonymity of CBDCs as a
comparative disadvantage that will affect their adoption. These findings
provide insight into governance problems facing central banks, and coherently
lead to the design of the Sand Dollar. I conclude by suggesting that combating
cryptocurrencies is a task that cannot be solved by a CBDC.


title: Deep Learning for Spectral Filling in Radio Frequency Applications

abstract:
  Due to the Internet of Things (IoT) proliferation, Radio Frequency (RF)
channels are increasingly congested with new kinds of devices, which carry
unique and diverse communication needs. This poses complex challenges in modern
digital communications, and calls for the development of technological
innovations that (i) optimize capacity (bitrate) in limited bandwidth
environments, (ii) integrate cooperatively with already-deployed RF protocols,
and (iii) are adaptive to the ever-changing demands in modern digital
communications. In this paper we present methods for applying deep neural
networks for spectral filling. Given an RF channel transmitting digital
messages with a pre-established modulation scheme, we automatically learn novel
modulation schemes for sending extra information, in the form of additional
messages, "around" the fixed-modulation signals (i.e., without interfering with
them). In so doing, we effectively increase channel capacity without increasing
bandwidth. We further demonstrate the ability to generate signals that closely
resemble the original modulations, such that the presence of extra messages is
undetectable to third-party listeners. We present three computational
experiments demonstrating the efficacy of our methods, and conclude by
discussing the implications of our results for modern RF applications.


title: Helium line emissivities for nebular astrophysics

abstract:
  We present the results of several collisional-radiative models describing
optically-thin emissivities of the main lines in neutral helium formed by
recombination, for a grid of electron temperatures and densities, typical of H
II regions and Planetary Nebulae. Accurate emissivities are required for
example to measure the helium abundance in nebulae and as a consequence its
primordial value. We compare our results with those obtained by previous
models, finding significant differences, well above the target accuracy of one
percent. We discuss in some detail our chosen set of atomic rates and the
differences with those adopted by previous models. The main differences lie in
the treatment of electron and proton collision rates and we discuss which
transitions are least sensitive to the choice of these rates and therefore best
suited to high precision abundance determinations. We have focused our
comparisons on the case B approximation where only He and He$^+$ are
considered, but also present results of full models including the bare nuclei,
photo-excitation and photo-ionisation and either black-body or observed
illuminating spectrum in the case of the Orion nebula, to indicate which
spectral lines are affected by opacity. For those transitions, accurate
radiative transfer calculations should be performed. We provide tables of
emissivities for all transitions within $n \le 5$ and all those between the $n
\le 5$ and $n' \le 25$ states, in the log $T_{\rm e}$ [K]=10$^{3.0(0.1)4.6}$
and log $N_{\rm e}$ [cm$^{-3}$]=10$^{2(0.5)6}$ ranges, and a FORTRAN code to
interpolate to any $T_{\rm e}, N_{\rm e}$ within these ranges.


title: The Giant Spin Hall Effect at Optical Frequencies

abstract:
  We generalize the spin Hall angle to laser pulses of finite frequencies in
the linear response regime and predict a giant optical spin Hall effect.
Namely, for certain transition metal elements, at particular frequencies, the
spin current can be a significant fraction of the charge current, and even
exceed it for XUV frequencies. By maximizing spin current while minimizing the
charge current, we thus minimize a major source of heating in spintronic
devices. We employ {\it ab-initio} time-dependent density functional theory
(TDDFT), and with real-time simulations calculate the conductivity and
transverse spin conductivity for all $3$d, $4$d, and $5$d transition metals for
frequencies up to $50$ eV. In the XUV frequency range we find values greater
than $1$ for the spin Hall angle, indicating spin currents larger than the
charge current can be generated.


title:
An implicit symplectic solver for high-precision long term integrations
  of the Solar System

abstract:
  Compared to other symplectic integrators (the Wisdom and Holman map and its
higher order generalizations) that also take advantage of the hierarchical
nature of the motion of the planets around the central star, our methods
require solving implicit equations at each time-step. We claim that, despite
this disadvantage, FCIRK16 is more efficient than explicit symplectic
integrators for high precision simulations thanks to: (i) its high order of
precision, (ii) its easy parallelization, and (iii) its efficient
mixed-precision implementation which reduces the effect of round-off errors. In
addition, unlike typical explicit symplectic integrators for near Keplerian
problems, FCIRK16 is able to integrate problems with arbitrary perturbations
(non necessarily split as a sum of integrable parts). We present a novel
analysis of the effect of close encounters in the leading term of the local
discretization errors of our integrator. Based on that analysis, a mechanism to
detect and refine integration steps that involve close encounters is
incorporated in our code. That mechanism allows FCIRK16 to accurately resolve
close encounters of arbitrary bodies. We illustrate our treatment of close
encounters with the application of FCIRK16 to a point mass Newtonian 15-body
model of the Solar System (with the Sun, the eight planets, Pluto, and five
main asteroids) and a 16-body model treating the Moon as a separate body. We
also present some numerical comparisons of FCIRK16 with a state-of-the-art high
order explicit symplectic scheme for 16-body model that demonstrate the
superiority of our integrator when very high precision is required.

categories: physics.comp-ph astro-ph.EP astro-ph.IM cs.NA math.NA


title: Teaching for large-scale Reproducibility Verification

abstract:
  We describe a unique environment in which undergraduate students from various
STEM and social science disciplines are trained in data provenance and
reproducible methods, and then apply that knowledge to real, conditionally
accepted manuscripts and associated replication packages. We describe in detail
the recruitment, training, and regular activities. While the activity is not
part of a regular curriculum, the skills and knowledge taught through explicit
training of reproducible methods and principles, and reinforced through
repeated application in a real-life workflow, contribute to the education of
these undergraduate students, and prepare them for post-graduation jobs and
further studies.


title: Non-Hermitian laser arrays with tunable phase locking

abstract:
  Inspired by the idea of non-Hermitian spectral engineering and non-Hermitian
skin effect, a novel design for stable emission of coupled laser arrays with
tunable phase locking and strong supermode competition suppression is
suggested. We consider a linear array of coupled resonators with asymmetric
mode coupling displaying the non-Hermitian skin effect and show that, under
suitable tailoring of complex frequencies of the two edge resonators, the laser
array can stably emit in a single extended supermode with tunable phase locking
and with strong suppression of all other skin supermodes. The proposed laser
array design offers strong robustness against both structural imperfections of
the system and dynamical instabilities typical of semiconductor laser arrays.


title:
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in
  Federated Learning

abstract:
  In a practical setting towards better generalization abilities of client
models for realizing robust personalized Federated Learning (FL) systems,
efficient model aggregation methods have been considered as a critical research
objective. It is a challenging issue due to the consequences of non-i.i.d.
properties of client's data, often referred to as statistical heterogeneity and
small local data samples from the various data distributions. Therefore, to
develop robust generalized global and personalized models, conventional FL
methods need redesigning the knowledge aggregation from biased local models
while considering huge divergence of learning parameters due to skewed client
data. In this work, we demonstrate that the knowledge transfer mechanism is a
de facto technique to achieve these objectives and develop a novel knowledge
distillation-based approach to study the extent of knowledge transfer between
the global model and local models. Henceforth, our method considers the
suitability of transferring the outcome distribution and (or) the embedding
vector of representation from trained models during cross-device knowledge
transfer using a small proxy dataset in heterogeneous FL. In doing so, we
alternatively perform cross-device knowledge transfer following general
formulations as 1) global knowledge transfer and 2) on-device knowledge
transfer. Through simulations on four federated datasets, we show the proposed
method achieves significant speedups and high personalized performance of local
models. Furthermore, the proposed approach offers a more stable algorithm than
FedAvg during the training, with minimal communication data load when
exchanging the trained model's outcomes and representation.


title:
Causality, Causal Discovery, and Causal Inference in Structural
  Engineering

abstract:
  Much of our experiments are designed to uncover the cause(s) and effect(s)
behind a data generating mechanism (i.e., phenomenon) we happen to be
interested in. Uncovering such relationships allows us to identify the true
working of a phenomenon and, most importantly, articulate a model that may
enable us to further explore the phenomenon on hand and/or allow us to predict
it accurately. Fundamentally, such models are likely to be derived via a causal
approach (as opposed to an observational or empirical mean). In this approach,
causal discovery is required to create a causal model, which can then be
applied to infer the influence of interventions, and answer any hypothetical
questions (i.e., in the form of What ifs? Etc.) that we might have. This paper
builds a case for causal discovery and causal inference and contrasts that
against traditional machine learning approaches; all from a civil and
structural engineering perspective. More specifically, this paper outlines the
key principles of causality and the most commonly used algorithms and packages
for causal discovery and causal inference. Finally, this paper also presents a
series of examples and case studies of how causal concepts can be adopted for
our domain.


title: Automated generalisation of buildings using CartAGen platform

abstract:
  In this paper, we present a methodology to automatically derive the
generalised representations of buildings at scales 1:25K, 1:50K, and to
delineate the urban area for 1:250K scale representation. These generalised
representations are derived from 1:10K scale. The automatic generalisation
processes are realised using the specific algorithms and the generalisation
models available in the CartAGen (CARTographic Agent GENeralisation) platform.
The CartAGen is an open source map generalisation platform developed by IGN
France. The proposed methodology in this paper is evaluated using the data
products available from the Ordnance Survey, UK, and the Survey of India,
India. This study investigates the applicability of the CartAGen platform for
generalising the data products which have been excluded from the investigations
by IGN France. This paper discusses the modifications required for such data
products.


title: A General Construction of Permutation Polynomials of $\Bbb F_{q^2}$

abstract:
  Let $r$ be a positive integer, $h(X)\in\Bbb F_{q^2}[X]$, and $\mu_{q+1}$ be
the subgroup of order $q+1$ of $\Bbb F_{q^2}^*$. It is well known that
$X^rh(X^{q-1})$ permutes $\Bbb F_{q^2}$ if and only if $\text{gcd}(r,q-1)=1$
and $X^rh(X)^{q-1}$ permutes $\mu_{q+1}$. There are many ad hoc constructions
of permutation polynomials of $\Bbb F_{q^2}$ of this type such that
$h(X)^{q-1}$ induces monomial functions on the cosets of a subgroup of
$\mu_{q+1}$. We give a general construction that can generate, through an
algorithm, {\em all} permutation polynomials of $\Bbb F_{q^2}$ with this
property, including many which are not known previously. The construction is
illustrated explicitly for permutation binomials and trinomials.


title:
Inductively Coupled In-Circuit Impedance Measurement and Its EMC
  Applications

abstract:
  In-circuit impedance provides key information for many EMC applications. The
inductive coupling approach is a promising method for in-circuit impedance
measurement because its measurement setups have no direct electrical contact
with the energized system under test, thus greatly simplifying the on-site
implementation. This paper presents and summaries the latest research on the
inductive coupling approach and its EMC applications. First of all, three
common measurement setups for this approach and their respective pros and cons
are discussed. Subsequently, their EMC applications are introduced. Finally,
recommendations for future research are listed.


title: An Isolated Gate Driver for Multi-Active Bridges with Soft Switching

abstract:
  The design of gate drivers is an important topic in power converter
topologies that can help reduce switching losses and increase power density.
Gate driving techniques that offer zero-voltage switching and/or zero current
switching have recently been successfully proposed for different modular
multilevel converters such as the cascaded H bridge. Previous papers on other
multilevel converters such as the multi-active bridge, however, do not
sufficiently assess the topics of gate driver design for this topology. This
work presents a novel isolated gate driver architecture tailored to the
multi-active bridge topology. Zero voltage switching is then achieved using two
multi-winding transformers. The advantages of the proposed topology are not
only a reduction of switching losses but also reduced component count.
  The topology is evaluated on a prototype using experimental results.
  It was shown using simulation and experiments that the proposed topology has
a high efficiency while providing compact power packaging.
  Especially for converters with many levels, the proposed topology is
therefore advantageous compared to existing solutions.


title: Algorithm design and approximation analysis on distributed robust game

abstract:
  We design a distributed algorithm to seek generalized Nash equilibria of a
robust game with uncertain coupled constraints. Due to the uncertainty of
parameters in set constraints, we aim to find a generalized Nash equilibrium in
the worst case. However, it is challenging to obtain the exact equilibria
directly because the parameters are from general convex sets, which may not
have analytic expressions or are endowed with high-dimensional nonlinearities.
To solve this problem, we first approximate parameter sets with inscribed
polyhedrons, and transform the approximate problem in the worst case into an
extended certain game with resource allocation constraints by robust
optimization. Then we propose a distributed algorithm for this certain game and
prove that an equilibrium obtained from the algorithm induces an
$\epsilon$-generalized Nash equilibrium of the original game, followed by
convergence analysis. Moreover, resorting to the metric spaces and the analysis
on nonlinear perturbed systems, we estimate the approximation accuracy related
to $\epsilon$ and point out the factors influencing the accuracy of $\epsilon$.


title:
Distributed Anomaly Detection and Estimation over Sensor Networks:
  Observational-Equivalence and Q-Redundant Observer Design

abstract:
  In this paper, we study stateless and stateful physics-based anomaly
detection scenarios via distributed estimation over sensor networks. In the
stateful case, the detector keeps track of the sensor residuals (i.e., the
difference of estimated and true outputs) and reports an alarm if certain
statistics of the recorded residuals deviate over a predefined threshold, e.g.,
\chi^2 (Chi-square) detector. Instead, only instantaneous deviation of the
residuals raises the alarm in the stateless case without considering the
history of the sensor outputs and estimation data. Given (approximate)
false-alarm rate for both cases, we propose a probabilistic threshold design
based on the noise statistics. We show by simulation that increasing the window
length in the stateful case may not necessarily reduce the false-alarm rate. On
the other hand, it adds unwanted delay to raise the alarm. The distributed
aspect of the proposed detection algorithm enables local isolation of the
faulty sensors with possible recovery solutions by adding redundant
observationally-equivalent sensors. We, then, offer a mechanism to design
Q-redundant distributed observers, robust to failure (or removal) of up to Q
sensors over the network.


title:
Physical Properties of the Supernova Remnant Population in the Small
  Magellanic Cloud

abstract:
  The X-ray emission from a supernova remnant is a powerful diagnostic of the
state of its shocked plasma. The temperature and the emission measure are
related to the energy of the explosion, the age of the remnant, and the density
of the surrounding medium. Here we present the results of a study of the
remnant population of the Small Magellanic Cloud. Progress in X-ray
observations of remnants has resulted in a sample of 20 remnants in the Small
Magellanic Clound with measured temperatures and emission measures. We apply
spherically symmetric supernova remnant evolution models to this set of
remnants, to estimate ages, explosion energies, and circumstellar medium
densities. The distribution of ages yields a remnant birthrate of $\sim$1/1200
yr. The energies and densities are well fit with log-normal distributions, with
means of 1.6$\times10^{51}$ erg and 0.14 cm$^{-3}$, and 1$\sigma$ dispersions
of a factor of 1.87 in energy and 3.06 in density, respectively.


title: Hole-doping induced ferromagnetism in 2D materials

abstract:
  Two-dimensional (2D) ferromagnetic materials are considered as promising
candidates for the future generations of spintronic devices. Yet, 2D materials
with intrinsic ferromagnetism are scarce. High-throughput first-principles
simulations are performed in order to screen 2D materials that present a
non-magnetic to a ferromagnetic transition upon hole doping. A global
evolutionary search is subsequently performed, in order to identify alternative
possible atomic structures of the eligible candidates, and 122 materials
exhibiting a hole-doping induced ferromagnetism are identified. Their energetic
and dynamic stability, as well as their magnetic properties under hole doping
are investigated systematically. Half of these 2D materials are metal halides,
followed by chalcogenides, oxides and nitrides, some of them having predicted
Curie temperatures above 300 K. The exchange interactions responsible for the
ferromagnetic order in these 2D materials are also discussed. This work not
only provides theoretical insights into hole-doped 2D ferromagnetic materials,
but also enriches the family of 2D magnetic materials for possible spintronic
applications.


title:
Continuity of some non-local functionals with respect to a convergence
  of the underlying measures

abstract:
  We study some non-local functionals on the Sobolev space $W^{1,p}_0(\Omega)$
involving a double integral on $\Omega\times\Omega$ with respect to a measure
$\mu$. We introduce a suitable notion of convergence of measures on product
spaces which implies a stability property in the sense of $\Gamma$-convergence
of the corresponding functionals.


title:
Numerical Prediction and Post-Test Numerical Analysis of the ASDMAD Wind
  Tunnel Tests in ETW

abstract:
  This paper presents numerical results in comparison with experimental data of
nominally static aeroelastic polars of the second ASDMAD (Aero-Structural
Dynamics Methods for Airplane Design) campaign conducted in the European
Transonic Windtunnel. For the simulation the modular solver package SOFIA
combined with the DLR solver TAU on the fluid side and the in-house solver
FEAFA on the structural side is applied. First the experimental setup and the
aeroelastic solver are introduced, followed by a detailed analysis of the
results. Comparisons of pressure distributions and the global lift coefficients
are presented for variations of angle of attack, Mach number and aerodynamic
loading factor. Additionally, the paper contains preliminary investigations of
dynamic results for excited vibration applying periodical inner force couples
in the wing root area.


title:
Inter- to Intra-Layer Resistivity Anisotropy of NdFeAs(O,H) with Various
  Hydrogen Concentrations

abstract:
  With molecular beam epitaxy and topotactic chemical reaction, we prepared
NdFeAs(O,H) epitaxial thin films with various hydrogen concentrations on
5{\deg} vicinal cut MgO substrates. By measuring the resistivities along the
longitudinal and transversal directions, the ab plane and the c axis
resistivities (\{rho}_ab and \{rho}_c) were obtained. The resistivity
anisotropy {\gamma}_\{rho}=\{rho}_c \ \{rho}_ab of NdFeAs(O,H) with various
hydrogen concentrations was compared with that of NdFeAs(O,F). At the H
concentrations which led to superconducting transition temperatures Tc over 40
K, {\gamma}_\r{ho} recorded ~100-150 at 50 K. On the other hand, a low
{\gamma}_\{rho} value of 9 was observed with the mostly doped sample. The
exponent \{beta} of the ab plane resistivity obtained by fitting a power law
expression \{rho}_{ab}(T)=\{rho}_0+AT^\{beta} to the data was close to unity
down to low temperature in the vicinity where the second antiferromagnetic
phase locates, which may be related to the quantum critical point discussed at
the over-doped side of the phase diagram.


title: Polynomial mapped bases: theory and applications

abstract:
  In this paper, we collect the basic theory and the most important
applications of a novel technique that has shown to be suitable for scattered
data interpolation, quadrature, bio-imaging reconstruction. The method relies
on polynomial mapped bases allowing, for instance, to incorporate data or
function discontinuities in a suitable mapping function. The new technique
substantially mitigates the Runge's and Gibbs effects.


title:
Topological susceptibility in finite temperature QCD with physical
  $(u/d, s, c)$ domain-wall quarks

abstract:
  We perform hybrid Monte-Carlo (HMC) simulation of lattice QCD with
$N_f=2+1+1$ domain-wall quarks at the physical point, on the $64^3 \times
(64,20,16,12,10,8,6)$ lattices, each with three lattice spacings. The lattice
spacings and the bare quark masses are determined on the $64^4$ lattices. The
resulting gauge ensembles provide a basis for studying finite temperature QCD
with $N_f=2+1+1 $ domain-wall quarks at the physical point. In this paper, we
determine the topological susceptibility of the QCD vacuum for $T > T_c \sim
150 $ MeV. The topological charge of each gauge configuration is measured by
the clover charge in the Wilson flow at the same flow time in physical units,
and the topological susceptibility $ \chi_t(a,T) $ is determined for each
ensemble with lattice spacing $a$ and temperature $T$. Using the topological
susceptibility $\chi_t(a,T) $ of 15 gauge ensembles with three lattice spacings
and different temperatures in the range $T \sim 155-516 $ MeV, we extract the
topological susceptibility $\chi_t(T)$ in the continuum limit. Moreover, a
detailed discussion on the reweighting method for domain-wall fermion is
presented.


title: The Josefson--Nissenzweig theorem and filters on $\omega$

abstract:
  For a free filter $F$ on $\omega$, endow the space $N_F=\omega\cup\{p_F\}$,
where $p_F\not\in\omega$, with the topology in which every element of $\omega$
is isolated whereas all open neighborhoods of $p_F$ are of the form
$A\cup\{p_F\}$ for $A\in F$. Spaces of the form $N_F$ constitute the class of
the simplest non-discrete Tychonoff spaces. The aim of this paper is to study
them in the context of the celebrated Josefson--Nissenzweig theorem from Banach
space theory. We prove, e.g., that a space $N_F$ carries a sequence
$\langle\mu_n\colon n\in\omega\rangle$ of normalized finitely supported signed
measures such that $\mu_n(f)\to 0$ for every bounded continuous real-valued
function $f$ on $N_F$ if and only if there is a density submeasure $\varphi$ on
$\omega$ such that the dual ideal $F^*$ is contained in the exhaustive ideal
$\mbox{Exh}(\varphi)$. Consequently, we get that if $F$ is a free filter on
$\omega$ contained in the filter dual to a density ideal, then: (1) if $X$ is a
Tychonoff space and $N_F$ is homeomorphic to a subspace of $X$, then the space
$C_p^*(X)$ of bounded continuous real-valued functions on $X$ contains a
complemented copy of the space $c_0$ endowed with the pointwise topology, (2)
if $K$ is a compact Hausdorff space and $N_F$ is homeomorphic to a subspace of
$K$, then the Banach space $C(K)$ of continuous real-valued functions on $K$ is
not a Grothendieck space. The latter result generalizes the well-known fact
stating that if a compact Hausdorff space $K$ contains a non-trivial convergent
sequence, then the space $C(K)$ is not Grothendieck. We also prove that the set
of filters which can be covered by filters dual to density ideals contains a
family of $2^\omega$ many mutually non-isomorphic $\mathbb{F}_\sigma$ P-filters
and a family of $2^{2^\omega}$ many mutually non-isomorphic non-Borel filters.


title:
Con$^{2}$DA: Simplifying Semi-supervised Domain Adaptation by Learning
  Consistent and Contrastive Feature Representations

abstract:
  In this work, we present Con$^{2}$DA, a simple framework that extends recent
advances in semi-supervised learning to the semi-supervised domain adaptation
(SSDA) problem. Our framework generates pairs of associated samples by
performing stochastic data transformations to a given input. Associated data
pairs are mapped to a feature representation space using a feature extractor.
We use different loss functions to enforce consistency between the feature
representations of associated data pairs of samples. We show that these learned
representations are useful to deal with differences in data distributions in
the domain adaptation problem. We performed experiments to study the main
components of our model and we show that (i) learning of the consistent and
contrastive feature representations is crucial to extract good discriminative
features across different domains, and ii) our model benefits from the use of
strong augmentation policies. With these findings, our method achieves
state-of-the-art performances in three benchmark datasets for SSDA.


title: An invitation to the Drury-Arveson space

abstract:
  This is an extended version of a three part mini course on the Drury-Arveson
space given as part of the Focus Program on Analytic Function Spaces and their
Applications, hosted by the Fields Institute and held remotely. The
Drury-Arveson space, also known as symmetric Fock space, is a natural
generalization of the classical Hardy space on the unit disc to the unit ball
in higher dimensions. It plays a universal role both in operator theory and in
function theory. These notes give an introduction to the Drury-Arveson space.
They are not intended to give a comprehensive overview of the entire subject,
but rather aim to explain some of the contexts in which the Drury-Arveson space
makes an appearance and to showcase the different approaches to this space.


title:
RobustSense: Defending Adversarial Attack for Secure Device-Free Human
  Activity Recognition

abstract:
  Deep neural networks have empowered accurate device-free human activity
recognition, which has wide applications. Deep models can extract robust
features from various sensors and generalize well even in challenging
situations such as data-insufficient cases. However, these systems could be
vulnerable to input perturbations, i.e. adversarial attacks. We empirically
demonstrate that both black-box Gaussian attacks and modern adversarial
white-box attacks can render their accuracies to plummet. In this paper, we
firstly point out that such phenomenon can bring severe safety hazards to
device-free sensing systems, and then propose a novel learning framework,
RobustSense, to defend common attacks. RobustSense aims to achieve consistent
predictions regardless of whether there exists an attack on its input or not,
alleviating the negative effect of distribution perturbation caused by
adversarial attacks. Extensive experiments demonstrate that our proposed method
can significantly enhance the model robustness of existing deep models,
overcoming possible attacks. The results validate that our method works well on
wireless human activity recognition and person identification systems. To the
best of our knowledge, this is the first work to investigate adversarial
attacks and further develop a novel defense framework for wireless human
activity recognition in mobile computing research.


title:
Exploring jet transport coefficients in the strongly interacting
  quark-gluon plasma

abstract:
  We study the interaction of leading jet partons in a strongly interacting
quark-gluon plasma (sQGP) medium based on the effective dynamical
quasi-particle model (DQPM). The DQPM describes the non-perturbative nature of
the sQGP at finite temperature $T$ and baryon chemical potential $\mu_B$ based
on a propagator representation of massive off-shell partons (quarks and gluons)
whose properties (characterized by spectral functions with $T,\mu_B$ dependent
masses and widths) are adjusted to reproduce the lQCD EoS for the QGP in
thermodynamic equilibrium. We present the results for the jet transport
coefficients, i.e. the transverse momentum transfer squared per unit length
$\hat{q}$ as well as the energy loss per unit length $\Delta E =dE/dx$, in the
QGP and investigate their dependence on the temperature $T$ and baryon chemical
potential $\mu_B$ as well as on jet properties such as the leading jet parton
momentum, mass, flavor, and the choice of the strong coupling constant. In this
first study only elastic scattering processes of a leading jet parton with the
sQGP partons are explored discarding presently the radiative processes (such as
gluon Bremsstrahlung) which are expected to be suppressed for the emission of
massive gluons. We present a comparison of our results for the elastic energy
loss in the sQGP medium with the pQCD results obtained by the BAMPS and LBT
models as well as with other theoretical approaches such as lattice QCD and the
LO-HTL and also with estimates of $\hat{q}$ by the color string percolation
model (CSPM) and the JET and JETSCAPE Collaborations based on a comparison of
hydrodynamical calculations with experimental heavy-ion data.


title:
Reply to comment on `Scrutinizing $\pi\pi$ scattering in light of recent
  lattice phase shifts'

abstract:
  We reply to the comment [arxiv:2202.08809] by E. van Beveren and G. Rupp on
our recent work [arxiv:2202.03124], by further clarifying the reason why bound-
and virtual-state poles for the $\sigma$ are necessary in our work in
describing the lattice phase shifts at $m_\pi$=391MeV.


title:
Ground-state properties of metallic solids from ab initio
  coupled-cluster theory

abstract:
  Metallic solids are a challenging target for wavefunction-based electronic
structure theories and have not been studied in great detail by such methods.
Here, we use coupled-cluster theory with single and double excitations (CCSD)
to study the structure of solid lithium and aluminum using optimized Gaussian
basis sets. We calculate the equilibrium lattice constant, bulk modulus, and
cohesive energy and compare them to experimental values, finding accuracy
comparable to common density functionals. Because the quantum chemical "gold
standard" CCSD(T) (CCSD with perturbative triple excitations) is inapplicable
to metals in the thermodynamic limit, we test two approximate improvements to
CCSD, which are found to improve the predicted cohesive energies.


title: Introducing ECAPA-TDNN and Wav2Vec2.0 Embeddings to Stuttering Detection

abstract:
  The adoption of advanced deep learning (DL) architecture in stuttering
detection (SD) tasks is challenging due to the limited size of the available
datasets. To this end, this work introduces the application of speech
embeddings extracted with pre-trained deep models trained on massive audio
datasets for different tasks. In particular, we explore audio representations
obtained using emphasized channel attention, propagation, and
aggregation-time-delay neural network (ECAPA-TDNN) and Wav2Vec2.0 model trained
on VoxCeleb and LibriSpeech datasets respectively. After extracting the
embeddings, we benchmark with several traditional classifiers, such as a
k-nearest neighbor, Gaussian naive Bayes, and neural network, for the
stuttering detection tasks. In comparison to the standard SD system trained
only on the limited SEP-28k dataset, we obtain a relative improvement of 16.74%
in terms of overall accuracy over baseline. Finally, we have shown that
combining two embeddings and concatenating multiple layers of Wav2Vec2.0 can
further improve SD performance up to 1% and 2.64% respectively.


title:
HiT-DVAE: Human Motion Generation via Hierarchical Transformer Dynamical
  VAE

abstract:
  Studies on the automatic processing of 3D human pose data have flourished in
the recent past. In this paper, we are interested in the generation of
plausible and diverse future human poses following an observed 3D pose
sequence. Current methods address this problem by injecting random variables
from a single latent space into a deterministic motion prediction framework,
which precludes the inherent multi-modality in human motion generation. In
addition, previous works rarely explore the use of attention to select which
frames are to be used to inform the generation process up to our knowledge. To
overcome these limitations, we propose Hierarchical Transformer Dynamical
Variational Autoencoder, HiT-DVAE, which implements auto-regressive generation
with transformer-like attention mechanisms. HiT-DVAE simultaneously learns the
evolution of data and latent space distribution with time correlated
probabilistic dependencies, thus enabling the generative model to learn a more
complex and time-varying latent space as well as diverse and realistic human
motions. Furthermore, the auto-regressive generation brings more flexibility on
observation and prediction, i.e. one can have any length of observation and
predict arbitrary large sequences of poses with a single pre-trained model. We
evaluate the proposed method on HumanEva-I and Human3.6M with various
evaluation methods, and outperform the state-of-the-art methods on most of the
metrics.


title: On universal subspaces for Lie groups

abstract:
  Let $U$ be a finite dimentional vector space over $\mathbb R$ or $\mathbb C$,
and let $\rho:G\to GL(U)$ be a representation of a connected Lie group $G$. A
linear subspace $V\subset U$ is called universal if every orbit of $G$ meets
$V$. We study universal subspaces for Lie groups, especially compact Lie
groups.
  Jinpeng and Dokovi\'{c} approached universality for compact groups through a
certain topological obstruction. They showed that the non-vanishing of the
obstruction class is sufficient for the universality of $V$, and asked whether
it is also necessary under certain conditions.
  In this article, we show that the answer to the question is negative in
general, but we discuss some important situations where the answer is positive.
We show that if $G$ is solvable and $\rho:G\to GL(U)$ is a complex
representation, then the only universal complex subspace is $U$ itself. We also
investigate the question of universality for a Levi subgroup.


title: Threshold solutions for the focusing generalized Hartree equations

abstract:
  We study the global behavior of solutions to the focusing generalized Hartree
equation with $H^1$ data at mass-energy threshold in the inter-range case. In
the earlier works of Arora-Roudenko [Arora-Roudenko 2021], the behavior of
solutions below the mass-energy threshold was classified. In this paper, we
first exhibit three special solutions: $e^{it} Q$, $Q^\pm$, where $Q^\pm$
exponentially approach to the $e^{it} Q$ in the positive time direction, $Q^+$
blows up and $Q^-$ scatters in the negative time direction. Then we classify
solutions at this threshold, showing that they behave exactly as the above
three special solutions up to symmetries, or scatter or blow up in both time
directions. The argument relies on the uniqueness and non-degeneracy of ground
state, which we regard as an assumption for the general case.


title: DAD: Data-free Adversarial Defense at Test Time

abstract:
  Deep models are highly susceptible to adversarial attacks. Such attacks are
carefully crafted imperceptible noises that can fool the network and can cause
severe consequences when deployed. To encounter them, the model requires
training data for adversarial training or explicit regularization-based
techniques. However, privacy has become an important concern, restricting
access to only trained models but not the training data (e.g. biometric data).
Also, data curation is expensive and companies may have proprietary rights over
it. To handle such situations, we propose a completely novel problem of
'test-time adversarial defense in absence of training data and even their
statistics'. We solve it in two stages: a) detection and b) correction of
adversarial samples. Our adversarial sample detection framework is initially
trained on arbitrary data and is subsequently adapted to the unlabelled test
data through unsupervised domain adaptation. We further correct the predictions
on detected adversarial samples by transforming them in Fourier domain and
obtaining their low frequency component at our proposed suitable radius for
model prediction. We demonstrate the efficacy of our proposed technique via
extensive experiments against several adversarial attacks and for different
model architectures and datasets. For a non-robust Resnet-18 model pre-trained
on CIFAR-10, our detection method correctly identifies 91.42% adversaries.
Also, we significantly improve the adversarial accuracy from 0% to 37.37% with
a minimal drop of 0.02% in clean accuracy on state-of-the-art 'Auto Attack'
without having to retrain the model.


title: On cosymplectic Lie Algebras

abstract:
  We give some properties of cosymplectic Lie algebras, we show, in particular,
that they support a left symmetric product. We also give some constructions of
cosymplectic Lie algebras, as well as a classification in three and
five-dimensional cosymplectic Lie algebras.


title:
Quasinormal modes for massive charged scalar fields in
  Reissner-Nordstr\"om dS black holes: anomalous decay rate

abstract:
  The anomalous decay rate of the quasinormal modes occurs when the
longest-lived modes are the ones with higher angular number. Such behaviour has
been recently studied in different static spacetimes, for uncharged scalar and
fermionic perturbations, being observed in both cases. In this work we consider
the propagation of charged massive scalar fields in the background of
Reissner-Nordstr\"om-de Sitter black holes and we mainly study the effect of
the scalar field charge in the spectrum of quasinormal frequencies, as well as,
its effect on the anomalous decay rate. Mainly, we show that the anomalous
behaviour is present for massive charged scalar fields as well, and a critical
value of scalar field mass exists, beyond which the behaviour is inverted.
However, there is also a critical value of the parameter $qQ$ of the charge of
the scalar field and of the charge of the black hole, which increases when the
cosmological constant increases, and beyond the critical value the anomalous
behaviour of the decay rate could be avoided for the fundamental mode.


title: Coarse-to-Fine Q-attention with Learned Path Ranking

abstract:
  We propose Learned Path Ranking (LPR), a method that accepts an end-effector
goal pose, and learns to rank a set of goal-reaching paths generated from an
array of path generating methods, including: path planning, Bezier curve
sampling, and a learned policy. The core idea being that each of the path
generation modules will be useful in different tasks, or at different stages in
a task. When LPR is added as an extension to C2F-ARM, our new system,
C2F-ARM+LPR, retains the sample efficiency of its predecessor, while also being
able to accomplish a larger set of tasks; in particular, tasks that require
very specific motions (e.g. opening toilet seat) that need to be inferred from
both demonstrations and exploration data. In addition to benchmarking our
approach across 16 RLBench tasks, we also learn real-world tasks, tabula rasa,
in 10-15 minutes, with only 3 demonstrations.


title: Metahouse: noise-insulating chamber based on periodic structures

abstract:
  Noise pollution remains a challenging problem requiring the development of
novel systems for noise insulation. Extensive work in the field of acoustic
metamaterials has led to occurrence of various ventilated structures which,
however, are usually demonstrated for rather narrow regions of the audible
spectrum. In this work, we further extend the idea of metamaterial-based
systems developing a concept of a metahouse chamber representing a ventilated
structure for broadband noise insulation. Broad stop bands originate from
strong coupling between pairs of Helmholtz resonators constituting the
structure. We demonstrate numerically and experimentally the averaged
transmission -43 dB within the spectral range from 1500 to 16500 Hz. The
sparseness of the structure together with the possibility to use optically
transparent materials suggest that the chamber may be also characterized by
partial optical transparency depending on the mutual position of structural
elements. The obtained results are promising for development of novel
noise-insulating structures advancing urban science.


title: Low Tree-Rank Bayesian Vector Autoregression Model

abstract:
  Vector autoregressions have been widely used for modeling and analysis of
multivariate time series data. In high-dimensional settings, model parameter
regularization schemes inducing sparsity have achieved good forecasting
performances. However, in many data applications such as those in neuroscience,
the graph estimates from existing methods still tend to be quite dense and
difficult to interpret, unless we made compromise in the goodness-of-fit. To
address this dilemma, in this article we propose to incorporate a commonly used
structural assumption -- that the ground-truth graph should be largely
connected, in the sense that it should only contain at most a few components.
We take a Bayesian approach and develop a novel tree-rank prior for the
regression coefficients. Specifically, this prior forces the non-zero
coefficients to appear only on the union of a few spanning trees. Since each
spanning tree connects $p$ nodes with only $(p-1)$ edges, this prior
effectively achieves both high connectivity and high sparsity. In analyzing
test-retest functional magnetic resonance imaging data, our model produces a
much more interpretable graph estimate, compared to popular existing
approaches. In addition, we show appealing properties of this new method, such
as differentiable computation, mild stability conditions and posterior
consistency.


title: Temporal Discontinuity for Splitting Polarization States of Light

abstract:
  Recently, time-varying electromagnetic structures have been extensively
investigated to unveil new physical phenomena. In this direction, one of the
important and historical topics is studying temporal discontinuities in these
structures. Here, we consider fast changes of bianisotropic media.
Specifically, we focus on introducing a temporal interface between isotropic
chiral and dielectric media. We show that due to the discontinuity in time,
interestingly, a linearly polarized electromagnetic wave is decomposed into
forward right-handed and forward left-handed circularly polarized waves having
different angular frequencies and the same phase velocities. This salient
effect allows splitting light to two different polarization states with high
efficiency. Hopefully, our findings will be useful as a possibility to control
polarization states of light.


title:
Spin ice in a general applied magnetic field: Kasteleyn transition,
  magnetic torque and rotational magnetocaloric effect

abstract:
  Spin ice is a paradigmatic frustrated system famous for the emergence of
magnetic monopoles and a large magnetic entropy at low temperatures. It
exhibits unusual behavior in the presence of an external magnetic field as a
result of the competition between the spin ice entropy and the Zeeman energy.
Studies of this have generally focused on fields applied along high symmetry
directions: [111], [001], and [110]. Here we consider a model of spin ice with
external field in an arbitrary direction. We find that the Kasteleyn transition
known for $[001]$ fields, appears also for general field directions and
calculate the associated Kasteleyn temperature $T_K$ as a function of field
direction. $T_K$ is found to vanish, with a logarithmic dependence on field
angle, approaching certain lines of special field directions. We further
investigate the thermodynamic properties of spin ice for $T>T_K$, using a
Husimi cactus approximation. As the system is cooled towards $T_K$ a large
magnetic torque appears, tending to align the $[001]$ crystal axis with the
external field. The model also exhibits a rotational magnetocaloric effect:
significant temperature changes can be obtained by adabiatically rotating the
crystal relative to a fixed field.


title:
Donors, Acceptors, and a Bit of Aromatics: Electronic Interactions of
  Molecular Adsorbates on hBN and MoS$_2$ Monolayers

abstract:
  The design of low-dimensional organic-inorganic interfaces for the next
generation of opto-electronic applications requires an in-depth understanding
of the microscopic mechanisms ruling electronic interactions in these systems.
In this work, we present a first-principles study based on density-functional
theory inspecting the structural, energetic, and electronic properties of five
molecular donors and acceptors adsorbed on freestanding hexagonal boron nitride
(hBN) and molybdenum disulfide (MoS$_2$) monolayers. All considered
heterostructures are stable, due to the crucial contribution of dispersion
interactions, which are maximized by the overall flat arrangement of the
physisorbed molecules on both substrates. The level alignment of the hybrid
systems depends on the characteristics of the constituents. On hBN, both type-I
and type-II heterostructures may form, depending on the relative energies of
the frontier orbitals with respect to the vacuum level. On the other hand, all
MoS$_2$-based hybrid systems exhibit a type-II level alignment, with the
molecular frontier orbitals positioned across the energy gap of the
semiconductor. The electronic structure of the hybrid materials is further
determined by the formation of interfacial dipole moments and by the
wave-function hybridization between the organic and inorganic constituents.
These results provide important indications for the design of novel
low-dimensional hybrid materials with suitable characteristics for
opto-electronics.


title: Geometric Versions of Schwarz's Lemma for Spherically Convex Functions

abstract:
  We prove several sharp distortion and monotonicity theorems for spherically
convex functions defined on the unit disk involving geometric quantities such
as spherical length, spherical area and total spherical curvature. These
results can be viewed as geometric variants of the classical Schwarz lemma for
spherically convex functions.


title: The binary black hole spin distribution likely broadens with redshift

abstract:
  The population-level distributions of the masses, spins, and redshifts of
binary black holes (BBHs) observed using gravitational waves can shed light on
how these systems form and evolve. Because of the complex astrophysical
processes shaping the inferred BBH population, models allowing for correlations
among these parameters will be necessary to fully characterize these sources.
We hierarchically analyze the BBH population detected by LIGO and Virgo with a
model allowing for correlations between the effective aligned spin and the
primary mass and redshift. We find that the width of the effective spin
distribution grows with redshift at 98.6% credibility. We determine this trend
to be robust under the application of several alternative models and
additionally verify that such a correlation is unlikely to be spuriously
introduced using a simulated population. We discuss the possibility that this
correlation could be due to a change in the natal black hole spin distribution
with redshift.


title: On the Ribenboim-Landau Conjecture

abstract:
  We prove the Ribenboim hypothesis, which states that if, starting from some
integer $N$, consecutive prime numbers $p_ {n}$, $p_{n+1}$ satisfy the
inequality $\sqrt {p_ {n+1}}-\sqrt{p_{n}} <1$, then the Landau problem # 4
(1912) has a positive solution.


title:
Complementarity of direct detection experiments in search of light Dark
  Matter

abstract:
  Dark Matter experiments searching for Weakly interacting massive particles
(WIMPs) primarily use nuclear recoils (NRs) in their attempt to detect WIMPs.
Migdal-induced electronic recoils (ERs) provide additional sensitivity to light
Dark Matter with $\mathcal{O}(\text{GeV}/c^2)$ masses. In this work, we use
Bayesian inference to find the parameter space where future detectors like
XENONnT and SuperCDMS SNOLAB will be able to detect WIMP Dark Matter through
NRs, Migdal-induced ERs or a combination thereof. We identify regions where
each detector is best at constraining the Dark Matter mass and spin independent
cross-section and infer where two or more detection configurations are
complementary to constraining these Dark Matter parameters through a combined
analysis.