title:
Sparse Fourier Transform over Lattices: A Unified Approach to Signal
  Reconstruction

abstract:
  We revisit the classical problem of band-limited signal reconstruction -- a
variant of the *Set Query* problem -- which asks to efficiently reconstruct (a
subset of) a $d$-dimensional Fourier-sparse signal ($\|\hat{x}(t)\|_0 \leq k$),
from minimum noisy samples of $x(t)$ in the time domain. We present a unified
framework for this problem, by developing a theory of sparse Fourier transforms
over *lattices*, which can be viewed as a "semi-continuous" version of SFT,
in-between discrete and continuous domains. Using this framework, we obtain the
following results:
  $\bullet$ *High-dimensional Fourier sparse recovery* We present a
sample-optimal discrete Fourier Set-Query algorithm with $O(k^{\omega+1})$
reconstruction time in one dimension, independent of the signal's length ($n$)
and $\ell_\infty$-norm ($R^* \approx \|\hat{x}\|_\infty$). This complements the
state-of-art algorithm of [Kap17], whose reconstruction time is $\tilde{O}(k
\log^2 n \log R^*)$, and is limited to low-dimensions. By contrast, our
algorithm works for arbitrary $d$ dimensions, mitigating the $\exp(d)$ blowup
in decoding time to merely linear in $d$.
  $\bullet$ *High-accuracy Fourier interpolation* We design a polynomial-time
$(1+ \sqrt{2} +\epsilon)$-approximation algorithm for continuous Fourier
interpolation. This bypasses a barrier of all previous algorithms [PS15,
CKPS16] which only achieve $>100$ approximation for this problem. Our algorithm
relies on several new ideas of independent interest in signal estimation,
including high-sensitivity frequency estimation and new error analysis with
sharper noise control.
  $\bullet$ *Fourier-sparse interpolation with optimal output sparsity* We give
a $k$-Fourier-sparse interpolation algorithm with optimal output signal
sparsity, improving on the approximation ratio, sample complexity and runtime
of prior works [CKPS16, CP19].


title: The Implicit Length Bias of Label Smoothing on Beam Search Decoding

abstract:
  Label smoothing is ubiquitously applied in Neural Machine Translation (NMT)
training. While label smoothing offers a desired regularization effect during
model training, in this paper we demonstrate that it nevertheless introduces
length biases in the beam search decoding procedure. Our analysis shows that
label smoothing implicitly applies a length penalty term to output sequence,
causing a bias towards shorter translations. We also show that for a model
fully optimized with label smoothing, translation length is implicitly upper
bounded by a fixed constant independent of input. We verify our theory by
applying a simple rectification function at inference time to restore the
unbiased distributions from the label-smoothed model predictions. This
rectification method led to consistent quality improvements on WMT
English-German, English-French, English-Czech and English-Chinese tasks, up to
+0.3 BLEU at beam size 4 and +2.8 BLEU at beam size 200.


title:
High-Sensitivity Characterization of Ultra-Thin Atomic Layers using
  Spin-Hall Effect of Light

abstract:
  Magnetic/non-magnetic/heterostructured ultra-thin films' characterisation is
highly demanding due to the emerging diverse applications of such films.
Diverse measurements are usually performed on such systems to infer their
electrical, optical and magnetic properties. We demonstrate that MOKE-based
spin-Hall effect of light (SHEL) is a versatile surface characterization tool
for studying materials' magnetic and dielectric ordering. Using this technique,
we measure magnetic field dependent complex Kerr angle and the coercivity in
ultra-thin films of permalloy (Py) and at molybdenum disulphide (MoS$_2$) -
permalloy (MSPy) hetero-structure interfaces. The measurements are compared
with standard magneto-optic Kerr effect (MOKE) studies to demonstrate that
SHEL-MOKE is a practical alternative to the conventional MOKE method, with
competitive sensitivity. A comprehensive theoretical model and simulation data
are provided to further strengthen the potential of this simple non-invasive
optical method. The theoretical model is applied to extract the optical
conductivity and susceptibility of non-magnetic ultra-thin layers such as
MoS$_2$ .


title: Giallar: Push-Button Verification for the Qiskit Quantum Compiler

abstract:
  This paper presents Giallar, a fully-automated verification toolkit for
quantum compilers. Giallar requires no manual specifications, invariants, or
proofs, and can automatically verify that a compiler pass preserves the
semantics of quantum circuits. To deal with unbounded loops in quantum
compilers, Giallar abstracts three loop templates, whose loop invariants can be
automatically inferred. To efficiently check the equivalence of arbitrary input
and output circuits that have complicated matrix semantics representation,
Giallar introduces a symbolic representation for quantum circuits and a set of
rewrite rules for showing the equivalence of symbolic quantum circuits. With
Giallar, we implemented and verified 44 (out of 56) compiler passes in 13
versions of the Qiskit compiler, the open-source quantum compiler standard,
during which three bugs were detected in and confirmed by Qiskit. Our
evaluation shows that most of Qiskit compiler passes can be automatically
verified in seconds and verification imposes only a modest overhead to
compilation performance.


title:
Skeptical binary inferences in multi-label problems with sets of
  probabilities

abstract:
  In this paper, we consider the problem of making distributionally robust,
skeptical inferences for the multi-label problem, or more generally for Boolean
vectors. By distributionally robust, we mean that we consider a set of possible
probability distributions, and by skeptical we understand that we consider as
valid only those inferences that are true for every distribution within this
set. Such inferences will provide partial predictions whenever the considered
set is sufficiently big. We study in particular the Hamming loss case, a common
loss function in multi-label problems, showing how skeptical inferences can be
made in this setting. Our experimental results are organised in three sections;
(1) the first one indicates the gain computational obtained from our
theoretical results by using synthetical data sets, (2) the second one
indicates that our approaches produce relevant cautiousness on those
hard-to-predict instances where its precise counterpart fails, and (3) the last
one demonstrates experimentally how our approach copes with imperfect
information (generated by a downsampling procedure) better than the partial
abstention [31] and the rejection rules.


title: An Application to Generate Style Guided Compatible Outfit

abstract:
  Fashion recommendation has witnessed a phenomenal growth of research,
particularly in the domains of shop-the-look, contextaware outfit creation,
personalizing outfit creation etc. Majority of the work in this area focuses on
better understanding of the notion of complimentary relationship between
lifestyle items. Quite recently, some works have realised that style plays a
vital role in fashion, especially in the understanding of compatibility
learning and outfit creation. In this paper, we would like to present the
end-to-end design of a methodology in which we aim to generate outfits guided
by styles or themes using a novel style encoder network. We present an
extensive analysis of different aspects of our method through various
experiments. We also provide a demonstration api to showcase the ability of our
work in generating outfits based on an anchor item and styles.


title:
Simple Techniques Work Surprisingly Well for Neural Network Test
  Prioritization and Active Learning (Replicability Study)

abstract:
  Test Input Prioritizers (TIP) for Deep Neural Networks (DNN) are an important
technique to handle the typically very large test datasets efficiently, saving
computation and labeling costs. This is particularly true for large-scale,
deployed systems, where inputs observed in production are recorded to serve as
potential test or training data for the next versions of the system. Feng et.
al. propose DeepGini, a very fast and simple TIP, and show that it outperforms
more elaborate techniques such as neuron- and surprise coverage. In a
large-scale study (4 case studies, 8 test datasets, 32'200 trained models) we
verify their findings. However, we also find that other comparable or even
simpler baselines from the field of uncertainty quantification, such as the
predicted softmax likelihood or the entropy of the predicted softmax
likelihoods perform equally well as DeepGini.


title:
Reducing the Cost of Training Security Classifier (via Optimized
  Semi-Supervised Learning)

abstract:
  Background: Most of the existing machine learning models for security tasks,
such as spam detection, malware detection, or network intrusion detection, are
built on supervised machine learning algorithms. In such a paradigm, models
need a large amount of labeled data to learn the useful relationships between
selected features and the target class. However, such labeled data can be
scarce and expensive to acquire. Goal: To help security practitioners train
useful security classification models when few labeled training data and many
unlabeled training data are available. Method: We propose an adaptive framework
called Dapper, which optimizes 1) semi-supervised learning algorithms to assign
pseudo-labels to unlabeled data in a propagation paradigm and 2) the machine
learning classifier (i.e., random forest). When the dataset class is highly
imbalanced, Dapper then adaptively integrates and optimizes a data oversampling
method called SMOTE. We use the novel Bayesian Optimization to search a large
hyperparameter space of these tuning targets. Result: We evaluate Dapper with
three security datasets, i.e., the Twitter spam dataset, the malware URLs
dataset, and the CIC-IDS-2017 dataset. Experimental results indicate that we
can use as low as 10% of original labeled data but achieve close or even better
classification performance than using 100% labeled data in a supervised way.
Conclusion: Based on those results, we would recommend using hyperparameter
optimization with semi-supervised learning when dealing with shortages of
labeled security data.


title:
(Private)-Retroactive Carbon Pricing [(P)ReCaP]: A Market-based Approach
  for Climate Finance and Risk Assessment

abstract:
  Insufficient Social Cost of Carbon (SCC) estimation methods and short-term
decision-making horizons have hindered the ability of carbon emitters to
properly correct for the negative externalities of climate change, as well as
the capacity of nations to balance economic and climate policy. To overcome
these limitations, we introduce Retrospective Social Cost of Carbon Updating
(ReSCCU), a novel mechanism that corrects for these limitations as empirically
measured evidence is collected. To implement ReSCCU in the context of carbon
taxation, we propose Retroactive Carbon Pricing (ReCaP), a market mechanism in
which polluters offload the payment of ReSCCU adjustments to insurers. To
alleviate systematic risks and minimize government involvement, we introduce
the Private ReCaP (PReCaP) prediction market, which could see real-world
implementation based on the engagement of a few high net-worth individuals or
independent institutions.


title:
Two-temperature activity induces liquid-crystal phases inaccessible in
  equilibrium

abstract:
  In equilibrium hard-rod fluids, and in effective hard-rod descriptions of
anisotropic soft-particle systems, the transition from the isotropic (I) phase
to the nematic phase (N) is observed above the rod aspect ratio L/D = 3.70 as
predicted by Onsager. We examine the fate of this criterion in a molecular
dynamics study of a system of soft repulsive spherocylinders rendered active by
coupling half the particles to a heat bath at a higher temperature than that
imposed on the other half. We show that the system phase separates and
self-organizes into various liquid-crystalline phases that are not observed in
equilibrium for the respective aspect ratios. In particular, we find a nematic
phase for L/D = 3 and a smectic phase for L/D = 2 above a critical activity.


title:
Revisiting Classical Multiclass Linear Discriminant Analysis with a
  Novel Prototype-based Interpretable Solution

abstract:
  Linear discriminant analysis (LDA) is a fundamental method for feature
extraction and dimensionality reduction. Despite having many variants,
classical LDA has its importance, as it is a keystone in human knowledge about
pattern recognition. For a dataset containing $C$ clusters, the classical
solution to LDA extracts at most $C-1$ features. In this paper, we introduce a
novel solution to classical LDA, called LDA++, that yields $C$ features, each
one interpretable as measuring similarity to one cluster. This novel solution
bridges between dimensionality reduction and multiclass classification.
Specifically, we prove that, under some mild conditions, the optimal weights of
a linear multiclass classifier for homoscedastic Gaussian data also make an
optimal solution to LDA. In addition, this novel interpretable solution reveals
some new facts about LDA and its relation with PCA. We provide a complete
numerical solution for our novel method, covering the cases 1) when the scatter
matrices can be constructed explicitly, 2) when constructing the scatter
matrices is infeasible, and 3) the kernel extension. The code is available at
https://github.com/k-ghiasi/LDA-plus-plus.


title:
Detecting momentum weak value: Shack-Hartmann versus a weak measurement
  wavefront sensor

abstract:
  The task of wavefront sensing is to measure the phase of the optical field.
Here, we demonstrate that the widely used Shack-Hartmann wavefront sensor
detects the weak value of transverse momentum, usually achieved by the method
of quantum weak measurement. We extend its input states to partially coherent
states and compare it with the weak measurement wavefront sensor, which has a
higher spatial resolution but a smaller dynamic range. Since weak values are
commonly used in investigating fundamental quantum physics and quantum
metrology, our work would find essential applications in these fields.


title: Parameter estimation for reflected OU processes

abstract:
  In this paper, we investigate the parameter estimation problem for reflected
OU processes. Both the estimates based on continuously observed processes and
discretely observed processes are considered. The explicit formulas for the
estimators are derived using the least squares method. Under some regular
conditions, we obtain the consistency and establish the asymptotic normality
for the estimators. Numerical results show that the proposed estimators perform
well with moderate sample sizes.


title:
Jack and Masters of All Trades: One-Pass Learning of a Set of Model Sets
  from Foundation Models

abstract:
  For deep learning, size is power. Massive neural nets trained on broad data
for a spectrum of tasks are at the forefront of artificial intelligence. These
foundation models or 'Jacks of All Trades' (JATs), when fine-tuned for
downstream tasks, are gaining importance in driving deep learning advancements.
However, environments with tight resource constraints, changing objectives and
intentions, or varied task requirements, could limit the real-world utility of
a singular JAT. Hence, in tandem with current trends towards building
increasingly large JATs, this paper conducts an initial exploration into
concepts underlying the creation of a diverse set of compact machine learning
model sets. Composed of many smaller and specialized models, we formulate the
Set of Sets to simultaneously fulfil many task settings and environmental
conditions. A means to arrive at such a set tractably in one pass of a
neuroevolutionary multitasking algorithm is presented for the first time,
bringing us closer to models that are collectively 'Masters of All Trades'.


title: SightSteeple: Agreeing to Disagree with Functional Blockchain Consensus

abstract:
  Classical and contemporary distributed consensus protocols, may they be for
binary agreement, state machine replication, or blockchain consensus, require
all protocol participants in a peer-to-peer system to agree on exactly the same
information as part of the consensus payload. Although this model of consensus
is extensively studied, and is useful for most consensus based decentralized
applications, it falls short of defining correct distributed systems which
mandate participant credential based privileged visibility into the consensus
payload, through the consensus protocol itself. We introduce a new paradigm for
distributed consensus, called functional blockchain consensus. Functional
blockchain consensus allows each blockchain protocol participant to agree on
some distinct sub-information of the list of transactions, as a function of the
credentials of the participant in the blockchain system, instead of agreeing on
the entire list of transactions. We motivate two adversary models, one with a
standard crash-fault adversary and another with a novel rational-fault
adversary, to compromise functional blockchain consensus. We then present two
versions of a blockchain protocol called SightSteeple, that achieves functional
blockchain consensus in the said fault models. SightSteeple relies on a novel
combination of standard blockchain consensus and functional encryption, among
other primitives, to achieve its goals of correctness. Finally, we discuss
practical uses of functional blockchain consensus based asymmetric distributed
ledgers, and motivate off-shoot constructions that can result from this new
consensus paradigm.


title: Microscopic theory of thermalization in 1D with nonlinear bath coupling

abstract:
  Using a non-perturbative classical model, we numerically investigate the
dynamics of mobile particles interacting with an infinite chain of harmonic
oscillators, an abstraction of ionic conduction through solid-state materials.
We show that coupling between the mobile particles and a single mass of the
chain is sufficient to induce dissipation of the mobile particles' energy over
a wide range of system parameters. When we introduce thermal fluctuations in
the position of the chain mass, the mobile particles exhibit thermalization,
eventually reaching the same temperature scale as the chain. This model
demonstrates how a minimal set of ingredients can exhibit a link between
microscopic motion and macroscopic observables, with computationally efficient
simulations. Finally, we suggest some experimental platforms that could realize
such a model.


title:
Theorem of resonance of Small Volume High Contrast multilayered
  materials

abstract:
  The need of mathematically formulate relations between composite materials'
properties and its resonance response is growing. This is due the fast
technological advancement in micro-material manufacturing, present in chips for
instance. In this paper two theorems are presented, providing formulas of
scattering resonance of double-layered and multilayered small volumes in terms
of the coefficient of susceptibility, being high, and the geometric
characteristics. Spectroscopy measurements of the composite medium can exploit
the formula to detect its dimension and susceptibility index.


title:
Using EAGLE simulations to study the effect of observational constraints
  on the determination of HI asymmetries in galaxies

abstract:
  We investigate the effect of observational constraints such as
signal-to-noise, resolution and column density level on the HI morphological
asymmetry ($\mathrm{A}_\mathrm{mod}$) and the effect of noise on the HI global
profile ($\mathrm{A}_\mathrm{flux}$) asymmetry indices. Using mock galaxies
from the EAGLE simulations we find an optimal combination of the observational
constraints that are required for robust measurement of the
$\mathrm{A}_\mathrm{mod}$ value of a galaxy: a column density threshold of
$5\times10^{19}cm^{-2}$ or lower at a minimal signal-to-noise of 3 and a galaxy
resolved with at least 11 beams. We also use mock galaxies to investigate the
effect of noise on the $\mathrm{A}_\mathrm{flux}$ values and conclude that a
global profile with signal-to-noise ratio greater than 5.5 is required to
achieve a robust measurement of asymmetry. We investigate the relation between
$\mathrm{A}_\mathrm{mod}$ and $\mathrm{A}_\mathrm{flux}$ indices and find them
to be uncorrelated which implies that $\mathrm{A}_\mathrm{flux}$ values cannot
be used to predict morphological asymmetries in galaxies.


title: Roughness of geodesics in Liouville quantum gravity

abstract:
  The metric associated with the Liouville quantum gravity (LQG) surface has
been constructed through a series of recent works and several properties of its
associated geodesics have been studied. In the current article we confirm the
folklore conjecture that the Euclidean Hausdorff dimension of LQG geodesics is
stirctly greater than 1 for all values of the so-called Liouville first passage
percolation (LFPP) parameter $\xi$. We deduce this from a general criterion due
to Aizenman and Burchard which in our case amounts to near-geometric bounds on
the probabilities of certain crossing events for LQG geodesics in the number of
crossings. We obtain such bounds using the axiomatic characterization of the
LQG metric after proving a special regularity property for the Gaussian free
field (GFF). We also prove an analogous result for the LFPP geodesics.


title:
Systematic lowering of the scaling of Monte Carlo calculations by
  partitioning andsubsampling

abstract:
  We propose to compute physical properties by Monte Carlo calculations using
conditional expectation values. The latter are obtained on top of the usual
Monte Carlo sampling by partitioning the physical space in several subspaces or
fragments, and subsampling each fragment (i.e., performing side-walks) while
freezing the environment. No bias is introduced and a zero-variance principle
holds in the limit of separability, i.e. when the fragments are independent. In
practice, the usual bottleneck of Monte Carlo calculations -- the scaling of
the statistical fluctuations as a function of the number of particles N -- is
relieved for extensive observables. We illustrate the method in variational
Monte Carlo on the 2D Hubbard model and on metallic hydrogen chains using
Jastrow-Slater wave functions. A factor O(N) is gained in numerical efficiency.


title:
Polarization-sensitive terahertz time-domain spectroscopy system without
  mechanical moving parts

abstract:
  We report on the measurement of terahertz electric-field vector waveforms by
using a system that contains no mechanical moving parts. It is known that two
phase-locked femtosecond lasers with different repetition rates can be used to
perform time-domain spectroscopy without using a mechanical delay stage.
Furthermore, an electro-optic modulator can be used to perform polarization
measurements without rotating any polarizers or waveplates. We experimentally
demonstrate the combination of these two methods and explain the analysis of
data obtained by such a system. Such a system provides a robust platform that
can promote the usage of polarization-sensitive terahertz time-domain
spectroscopy in basic science and practical applications. For the experimental
demonstration, we alter the polarization of a terahertz wave by a polarizer.


title: Mass-spectra of light-heavy tetraquarks

abstract:
  The mass spectra of light-heavy tetraquarks $cq\bar{c}\bar{q}$ (q= u, d) are
computed in a non-relativistic diquark model with one-gluon exchange plus
confining potential. In the diquark model, a $cq\bar{c}\bar{q}$ state is
regarded to be made of a light-heavy diquark (qc) and an antidiquark
$\bar{q}\bar{c}$ in triplet and antitriplet colour configuration respectively.
The masses of charm mesons were calculated in order to fit the model parameters
used to create the masses of tetraquarks and therefore enhance the model's
reliability. The masses of $(cq\bar{c}\bar{q})$ tetra-quark states are
determined to be in the range of 3.8 GeV - 4.7 GeV, which is consistent with
the experimentally reported charmonium-like states. In particular, the
$Z_{c}(3900)$, $Z_{c}(4430)$, and $\psi(4660)$ tetraquarks, which have been
seen experimentally, may all be described by our model.


title: Functions as Processes: The Case of Collapsing Non-determinism

abstract:
  This paper studies typed translations of $\lambda$-calculi into
$\pi$-calculi, both with non-determinism, informed by the Curry-Howard
isomorphism between linear logic and session types (propositions-as-sessions).
Prior work considered calculi with non-collapsing non-determinism, a
non-committal form of choice in which all alternatives are preserved, ensuring
confluence. A question left open is whether there is a correct translation for
calculi with the more traditional (and non-confluent) collapsing
non-determinism, which commits to one single alternative and discards the rest.
A session-typed $\pi$-calculi with collapsing non-determinism is proposed.
Next, (i) the key meta-theoretical properties of typed processes (type
preservation and deadlock-freedom) are proven following
propositions-as-sessions, and (ii) a correct translation of a resource
$\lambda$-calculus with non-determinism is given. An alternative semantics for
non-determinism is then shown to unlock stronger correctness results for the
translation.


title: Hyperk\"{a}hler varieties as Brill-Noether loci on curves

abstract:
  Consider the moduli space $M_C(r; K_C)$ of stable rank r vector bundles on a
curve $C$ with canonical determinant, and let $h$ be the maximum number of
linearly independent global sections of these bundles. If $C$ embeds in a K3
surface $X$ as a generator of $Pic(X)$ and the genus $g$ of $C$ is sufficiently
high, we show the Brill-Noether locus $BN_C \subset M_C(r; K_C)$ of bundles
with $h$ global sections is a smooth projective Hyperk\"{a}hler manifold of
dimension $2g -2r \lfloor \frac{g}{r}\rfloor$, isomorphic to a moduli space of
stable vector bundles on $X$.


title:
Measurements of the suppression and correlations of dijets in Pb+Pb
  collisions at $\sqrt{s_{_\text{NN}}}$ = 5.02 TeV

abstract:
  Studies of the correlations of the two highest transverse momentum (leading)
jets in individual Pb+Pb collision events can provide information about the
mechanism of jet quenching by the hot and dense matter created in such
collisions. In Pb+Pb and $pp$ collisions at $\sqrt{s_{_\text{NN}}}$ = 5.02 TeV,
measurements of the leading dijet transverse momentum ($p_{\mathrm{T}}$)
correlations are presented. Additionally, measurements in Pb+Pb collisions of
the dijet pair nuclear modification factors projected along leading and
subleading jet $p_{\mathrm{T}}$ are made. The measurements are performed using
the ATLAS detector at the LHC with 260 ${\mathrm {pb}^{-1}}$ of $pp$ data
collected in 2017 and 2.2 ${\mathrm {nb}^{-1}}$ of Pb+Pb data collected in 2015
and 2018. An unfolding procedure is applied to the two-dimensional leading and
subleading jet $p_{\mathrm{T}}$ distributions to account for experimental
effects in the measurement of both jets. Results are provided for dijets with
leading jet $p_{\mathrm{T}}$ greater than 100 GeV. Measurements of the
dijet-yield-normalized $x_{\mathrm{J}}$ distributions in Pb+Pb collisions show
an increased fraction of imbalanced jets compared to $pp$ collisions; these
measurements are in agreement with previous measurements of the same quantity
at 2.76 TeV in the overlapping kinematic range. Measurements of the
absolutely-normalized dijet rate in Pb+Pb and $pp$ collisions are also
presented, and show that balanced dijets are significantly more suppressed than
imbalanced dijets in Pb+Pb collisions. It is observed in the measurements of
the pair nuclear modification factors that the subleading jets are
significantly suppressed relative to leading jets with $p_{\mathrm{T}}$ between
100 and 316 GeV for all centralities in Pb+Pb collisions.


title:
MaLeFiSenta: Machine Learning for FilamentS Identification and
  orientation in the ISM

abstract:
  Filament identification became a key step to tackling fundamental problems in
various fields of Astronomy. Nevertheless, existing filament identification
algorithms are critically user-dependent and require individual
parametrization. In this study, we aimed at adapting the neural networks
approach to elaborate the best model for filament identification that would not
require fine-tuning for a given astronomical map. First, we created training
samples based on the most commonly used maps of the interstellar medium
obtained by Planck and Herschel space telescopes and the atomic hydrogen
all-sky survey HI4PI. We used the Rolling Hough Transform, a widely used
algorithm for filament identification, to produce training outputs. In the next
step, we trained different neural network models and discovered that a
combination of the Mask R-CNN and U-Net architecture is most appropriate for
filament identification and determination of their orientation angles. We
showed that neural network training might be performed efficiently on a
relatively small training sample of only around 100 maps. Our approach
eliminates the parametrization bias and facilitates filament identification and
angle determination on large data sets.


title:
Rational social distancing policy during epidemics with limited
  healthcare capacity

abstract:
  Epidemics of infectious diseases posing a serious risk to human health have
occurred throughout history. During the ongoing SARS-CoV-2 epidemic there has
been much debate about policy, including how and when to impose restrictions on
behavior. Under such circumstances policymakers must balance a complex spectrum
of objectives, suggesting a need for quantitative tools. Whether health
services might be 'overwhelmed' has emerged as a key consideration yet formal
modelling of optimal policy has so far largely ignored this. Here we show how
costly interventions, such as taxes or subsidies on behaviour, can be used to
exactly align individuals' decision making with government preferences even
when these are not aligned. We assume that choices made by individuals give
rise to Nash equilibrium behavior. We focus on a situation in which the
capacity of the healthcare system to treat patients is limited and identify
conditions under which the disease dynamics respect the capacity limit. In
particular we find an extremely sharp drop in peak infections as the maximum
infection cost in the government's objective function is increased. This is in
marked contrast to the gradual reduction without government intervention. The
infection costs at which this switch occurs depend on how costly the
intervention is to the government. We find optimal interventions that are quite
different to the case when interventions are cost-free. Finally, we identify a
novel analytic solution for the Nash equilibrium behavior for constant
infection cost.

categories: econ.TH cs.SY eess.SY math.OC physics.soc-ph


title:
Motion-selective coherent population trapping for subrecoil cooling of
  optically trapped atoms outside the Lamb-Dicke regime

abstract:
  We propose a scheme that combines velocity-selective coherent population
trapping (CPT) and Raman sideband cooling (RSC) for subrecoil cooling of
optically trapped atoms outside the Lamb-Dicke regime. This scheme is based on
an inverted $\mathsf{Y}$ configuration in an alkali-metal atom. It consists of
a $\Lambda$ formed by two Raman transitions between the ground hyperfine levels
and the $D$ transition, allowing RSC along two paths and formation of a CPT
dark state. Using state-dependent difference in vibration frequency of the atom
in a circularly polarized trap, we can tune the $\Lambda$ to make only the
motional ground state a CPT dark state. We call this scheme motion-selective
coherent population trapping (MSCPT). We write the master equations for RSC and
MSCPT and solve them numerically for a $^{87}$Rb atom in a one-dimensional
optical lattice when the Lamb-Dicke parameter is 1. Although MSCPT reaches the
steady state slowly compared with RSC, the former consistently produces colder
atoms than the latter. The numerical results also show that subrecoil cooling
by MSCPT outside the Lamb-Dicke regime is possible under a favorable, yet
experimentally feasible, condition. We explain this performance quantitatively
by calculating the relative darkness of each motional state. Finally, we
discuss on application of the MSCPT scheme to an optically trapped diatomic
polar molecule whose Stark shift and vibration frequency exhibit large
variations depending on the rotational quantum number.


title: A new thermal lattice Boltzmann model for liquid-vapor phase change

abstract:
  The lattice Boltzmann method is adopted to solve the liquid-vapor phase
change problems in this article. By modifying the collision term for the
temperature evolution equation, a new thermal lattice Boltzmann model is
constructed. As compared with previous studies, the most striking feature of
the present approach is that it could avoid the calculations of both the
Laplacian term of temperature [$\nabla \cdot \left( {\kappa \nabla T} \right)$]
and the gradient term of heat capacitance [$\nabla \left( {\rho {{\rm{c}}_v}}
\right)$]. In addition, since the present approach adopts a simple linear
equilibrium distribution function, it is possible to use the D2Q5 lattice for
the two dimensional cases consided here, making it is more efficiency than
previous works in which the lattice is usually limited to the D2Q9. This
approach is firstly validated by the problems of droplet evaporation in open
space and adroplet evaporation on heated surface, and the numerical results
show good agreement with the analytical results and the finite difference
method. Then it is used to model nucleate boiling problem, and the relationship
between detachment bubble diameter and gravity acceleration obtained with the
present approach fits well with the reported works.


title: Deep Video Harmonization with Color Mapping Consistency

abstract:
  Video harmonization aims to adjust the foreground of a composite video to
make it compatible with the background. So far, video harmonization has only
received limited attention and there is no public dataset for video
harmonization. In this work, we construct a new video harmonization dataset
HYouTube by adjusting the foreground of real videos to create synthetic
composite videos. Moreover, we consider the temporal consistency in video
harmonization task. Unlike previous works which establish the spatial
correspondence, we design a novel framework based on the assumption of color
mapping consistency, which leverages the color mapping of neighboring frames to
refine the current frame. Extensive experiments on our HYouTube dataset prove
the effectiveness of our proposed framework. Our dataset and code are available
at https://github.com/bcmi/Video-Harmonization-Dataset-HYouTube.


title:
Remark on the global regularity of 2D MHD equations with almost
  Laplacian magnetic diffusion

abstract:
  Whether or not the classical solutions of the two-dimensional (2D)
incompressible magnetohydrodynamics (MHD) equations with only Laplacian
magnetic diffusion (without velocity dissipation) are globally well-posed is a
difficult problem and remains completely open. In this paper, we establish the
global regularity of solutions to the 2D incompressible MHD equations with
almost Laplacian magnetic diffusion in the whole space. This result can be
regarded as a further improvement and generalization of the previous works.
Consequently, our result is more closer to the resolution of the global
regularity issue on the 2D MHD equations with standard Laplacian magnetic
diffusion.


title:
Zigzag magnetic order in a novel tellurate compound
  Na$_{4-\delta}$NiTeO$_{6}$ with $\mathit{S}$ = 1 chains

abstract:
  Na$_{4-\delta}$NiTeO$_{6}$ is a rare example in the transition-metal
tellurate family to realize a $\mathit{S}$ = 1 spin-chain structure. By
performing neutron powder diffraction measurements, the ground-state magnetic
structure of Na$_{4-\delta}$NiTeO$_{6}$ is determined. It is uncovered that
below $T_{\mathrm{N}}$ $\sim$ 6.8(2) K, the Ni$^{2+}$ moments form a screwed
ferromagnetic (FM) spin-chain structure running along the crystallographic
$\mathit{a}$ axis but these FM spin chains are coupled antiferromagnetically
along both $\mathit{b}$ and $\mathit{c}$ directions, giving rise to a magnetic
propagation vector of $\mathit{k}$ = (0, 1/2, 1/2). Such a zigzag magnetic
order is well supported by first-principles calculations. The moment size of
Ni$^{2+}$ spins is determined to be 2.1(1) $\mathit{\mu_{\mathrm{B}}}$ at 3 K,
suggesting a significant quenching of the orbital moment due to the crystalline
electric field (CEF) effect. The previously reported metamagnetic transition
around $H_{\mathrm{C}}$ $\sim$ 0.1 T can be understood as a field-induced
quantum phase transition from a three-dimensional antiferromagnet to a
quasi-one-dimensional FM spin-chain system. The relatively easy tunability of
the dimensionality of its magnetism by external parameters makes
Na$_{4-\delta}$NiTeO$_{6}$ a promising candidate for further explorations of
various types of novel spin-chain physics.


title:
From Noisy Prediction to True Label: Noisy Prediction Calibration via
  Generative Model

abstract:
  Noisy labels are inevitable yet problematic in machine learning society. It
ruins the generalization power of a classifier by making the classifier be
trained to be overfitted to wrong labels. Existing methods on noisy label have
focused on modifying classifier training procedure. It results in two possible
problems. First, these methods are not applicable to a pre-trained classifier
without further access into training. Second, it is not easy to train a
classifier and remove all of negative effects from noisy labels simultaneously.
From these problems, we suggests a new branch of approach, Noisy Prediction
Calibration (NPC) in learning with noisy labels. Through the introduction and
estimation of a new type of transition matrix via generative model, NPC
corrects the noisy prediction from the pre-trained classifier to the true label
as a post-processing scheme. We prove that NPC theoretically aligns with the
transition matrix based methods. Yet, NPC provides more accurate pathway to
estimate true label, even without involvement in classifier learning. Also, NPC
is applicable to any classifier trained with noisy label methods, if training
instances and its predictions are available. Our method, NPC, boosts the
classification performances of all baseline models on both synthetic and
real-world datasets.


title:
Initial Beamforming for Millimeter-Wave and Terahertz Communications in
  6G Mobile Systems

abstract:
  To meet the demand of supreme data rates in terabits-per-second, the
next-generation mobile system needs to exploit the abundant spectrum in the
millimeter-wave and terahertz bands. However, high-frequency transmission
heavily relies on large-scale antenna arrays to reap high beamforming gain,
used to compensate for severe propagation loss. It raises a problem of
omni-directional beamforming during the phase of initial access, where a base
station is required to broadcast synchronization signals and system information
to all users within its coverage. This paper proposes a novel initial
beamforming scheme, which provides instantaneous gain equally in all directions
by forming a pair of complementary beams. Numerical results verify that it can
achieve omni-directional coverage with the optimal performance that remarkably
outperforms the previous scheme called random beamforming. It is applicable for
any form of large-scale arrays, and all three architecture, i.e., digital,
analog, and hybrid beamforming.


title:
Energy-efficient Caching and Task offloading for Timely Status Updates
  in UAV-assisted VANETs

abstract:
  Intelligent edge network is maturing to enable smart and efficient
transportation systems. In this letter, we consider unmanned aerial vehicle
(UAV)-assisted vehicular networks where UAVs provide caching and computing
services in complement with base station (BS). One major challenge is that
vehicles need to obtain timely situational awareness via orchestration of
ubiquitous caching and computing resources. Note that cached data for vehicles'
perception tasks contains time-varying context information, thus freshness of
cached data should be considered in conjunction with task execution to
guarantee timeliness of obtained status updates. To this end, we propose a
two-stage performance metric to quantify the impact of cache refreshing and
computation offloading decisions on the age of status updates. We formulate an
energy minimization problem by jointly considering cache refreshing,
computation offloading and aging of status updates. To facilitate online
decision making, we propose a deep deterministic policy gradient(DDPG)-based
solution procedure and incorporate differentiated experience replay mechanism
to accelerate convergence. Simulation results show that the performance of
proposed solution is competitive in terms of energy consumption for obtaining
fresh status updates.


title:
Contrastive Learning for Improving ASR Robustness in Spoken Language
  Understanding

abstract:
  Spoken language understanding (SLU) is an essential task for machines to
understand human speech for better interactions. However, errors from the
automatic speech recognizer (ASR) usually hurt the understanding performance.
In reality, ASR systems may not be easy to adjust for the target scenarios.
Therefore, this paper focuses on learning utterance representations that are
robust to ASR errors using a contrastive objective, and further strengthens the
generalization ability by combining supervised contrastive learning and
self-distillation in model fine-tuning. Experiments on three benchmark datasets
demonstrate the effectiveness of our proposed approach.


title: A Multi-stage deep architecture for summary generation of soccer videos

abstract:
  Video content is present in an ever-increasing number of fields, both
scientific and commercial. Sports, particularly soccer, is one of the
industries that has invested the most in the field of video analytics, due to
the massive popularity of the game and the emergence of new markets. Previous
state-of-the-art methods on soccer matches video summarization rely on
handcrafted heuristics to generate summaries which are poorly generalizable,
but these works have yet proven that multiple modalities help detect the best
actions of the game. On the other hand, machine learning models with higher
generalization potential have entered the field of summarization of
general-purpose videos, offering several deep learning approaches. However,
most of them exploit content specificities that are not appropriate for sport
whole-match videos. Although video content has been for many years the main
source for automatizing knowledge extraction in soccer, the data that records
all the events happening on the field has become lately very important in
sports analytics, since this event data provides richer context information and
requires less processing. We propose a method to generate the summary of a
soccer match exploiting both the audio and the event metadata. The results show
that our method can detect the actions of the match, identify which of these
actions should belong to the summary and then propose multiple candidate
summaries which are similar enough but with relevant variability to provide
different options to the final editor. Furthermore, we show the generalization
capability of our work since it can transfer knowledge between datasets from
different broadcasting companies, different competitions, acquired in different
conditions, and corresponding to summaries of different lengths


title: Relativistic magnetic explosions

abstract:
  Many explosive astrophysical events, like magnetars' bursts and flares, are
magnetically driven. We consider dynamics of such magnetic explosions -
relativistic expansion of highly magnetized and highly magnetically
over-pressurized clouds. The corresponding dynamics is qualitatively different
from fluid explosions due to the topological constraint of the conservation of
the magnetic flux. Using analytical, relativistic MHD as well as force-free
calculations, we find that the creation of a relativistically expanding,
causally disconnected flow obeys a threshold condition: it requires
sufficiently high initial over-pressure and sufficiently quick decrease of the
pressure in the external medium (the pre-explosion wind). In the subcritical
case the magnetic cloud just "puffs-up" and quietly expands with the pre-flare
wind. We also find a compact analytical solution to the Prendergast's problem -
expansion of force-free plasma into vacuum.


title:
Learning stability guarantees for data-driven constrained switching
  linear systems

abstract:
  We consider stability analysis of constrained switching linear systems in
which the dynamics are unknown and whose switching signal is constrained by an
automaton. We propose a data-driven Lyapunov framework for providing
probabilistic stability guarantees based on data harvested from observations of
the system. By generalizing previous results on arbitrary switching linear
systems, we show that, by sampling a finite number of observations, we are able
to construct an approximate Lyapunov function for the underlying system.
Moreover, we show that the entropy of the language accepted by the automaton
allows to bound the number of samples needed in order to reach some
pre-specified accuracy.


title:
Impact of hydrogen on the dislocation structure upon tensile loading in
  a high manganese twinning induced plasticity steel

abstract:
  High-manganese twinning-induced plasticity (TWIP) steels exhibit mechanical
properties making them attractive for structural applications. However, they
are prone to hydrogen embrittlement (HE), and may undergo a brittle,
catastrophic failure upon hydrogen ingress in service conditions. We
investigated the he susceptibility of a Fe28Mn0.3C (wt.%) TWIP steel. We
observed a reduction in engineering strain from 75% to 10% and a 20% increase
in yield strength following the hydrogen-charging. The microstructural
evolution was examined at three different, lower tensile strain levels, i. E. ,
3%, 5% and 7% by using correlative electron backscatter diffraction (EBSD) and
electron channeling contrast imaging (ECCI). At 3% strain, dislocation cells
form in the hydrogen-charged region, which can be attributed to the increased
yield strength that promotes the slip activation on multiple slip systems. We
introduce an approach for image processing to quantify the cell size. At 7%
strain, we observed the formation of stacking faults and hexagonal close packed
-martensite in the hydrogen-charged region due to the reduction in stacking
fault energy (SFE) associated to the presence of hydrogen. In contrast, the
microstructure of the uncharged samples was composed of planar dislocation
arrangements at 3%, 5% and 7% strain, which is indicative of planar slip. We
discuss the details of how hydrogen modifies the dislocation structures and the
overall deformation mechanism, and the present study is expected to facilitate
the future design of steels resistant to HE.


title:
Unsupervised Denoising of Optical Coherence Tomography Images with
  Dual_Merged CycleWGAN

abstract:
  Nosie is an important cause of low quality Optical coherence tomography (OCT)
image. The neural network model based on Convolutional neural networks(CNNs)
has demonstrated its excellent performance in image denoising. However, OCT
image denoising still faces great challenges because many previous neural
network algorithms required a large number of labeled data, which might cost
much time or is expensive. Besides, these CNN-based algorithms need numerous
parameters and good tuning techniques, which is hardware resources consuming.
To solved above problems, We proposed a new Cycle-Consistent Generative
Adversarial Nets called Dual-Merged Cycle-WGAN for retinal OCT image
denoiseing, which has remarkable performance with less unlabeled traning data.
Our model consists of two Cycle-GAN networks with imporved generator,
descriminator and wasserstein loss to achieve good training stability and
better performance. Using image merge technique between two Cycle-GAN networks,
our model could obtain more detailed information and hence better training
effect. The effectiveness and generality of our proposed network has been
proved via ablation experiments and comparative experiments. Compared with
other state-of-the-art methods, our unsupervised method obtains best subjective
visual effect and higher evaluation objective indicators.


title:
Black-box stability analysis of hybrid systems with sample-based
  multiple Lyapunov functions

abstract:
  We present a framework based on multiple Lyapunov functions to find
probabilistic data-driven guarantees on the stability of unknown constrained
switching linear systems (CSLS), which are switching linear systems whose
switching signal is constrained by an automaton. The stability of a CSLS is
characterized by its constrained joint spectral radius (CJSR). Inspired by the
scenario approach and previous work on unconstrained switching systems, we
characterize the number of observations needed to find sufficient conditions on
the (in-)stability of a CSLS using the notion of CJSR. More precisely, our
contribution is the following: we derive a probabilistic upper bound on the
CJSR of an unknown CSLS from a finite number of observations. We also derive a
deterministic lower bound on the CJSR. From this we obtain a probabilistic
method to characterize the stability of an unknown CSLS.


title:
Twin interaction with $\Sigma$11 tilt grain boundaries in BCC Fe :
  Formation of new grain boundaries

abstract:
  It is well known that the twinning is an important mode of plastic
deformation in nanocrystalline materials. As a result, it is expected that the
twin can interact with different grain boundaries (GBs) during the plastic
deformation. Understanding these twin-GB interactions is crucial for our
understanding of mechanical behavior of materials. In this work, the twin
interaction with different $\Sigma$11 symmetric and asymmetric tilt GBs has
been investigated in BCC Fe using molecular dynamics (MD) simulations. The
results indicate that twin nucleate from the crack or GB and, its interaction
with $\Sigma$11 asymmetric tilt GBs leads to the formation of a new GB. This
new GB consist of $<$100$>$ Cottrell type immobile dislocations. The detailed
atomistic mechanisms responsible for this new GB formation have been revealed
using atomistic simulations. Interestingly, the new GB formation has not been
observed in the case of twin interaction with $\Sigma$11 symmetric tilt GBs.


title:
DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational
  Lensing Data

abstract:
  Gravitational lensing is the relativistic effect generated by massive bodies,
which bend the space-time surrounding them. It is a deeply investigated topic
in astrophysics and allows validating theoretical relativistic results and
studying faint astrophysical objects that would not be visible otherwise. In
recent years Machine Learning methods have been applied to support the analysis
of the gravitational lensing phenomena by detecting lensing effects in data
sets consisting of images associated with brightness variation time series.
However, the state-of-art approaches either consider only images and neglect
time-series data or achieve relatively low accuracy on the most difficult data
sets. This paper introduces DeepGraviLens, a novel multi-modal network that
classifies spatio-temporal data belonging to one non-lensed system type and
three lensed system types. It surpasses the current state of the art accuracy
results by $\approx$ 19% to $\approx$ 43%, depending on the considered data
set. Such an improvement will enable the acceleration of the analysis of lensed
objects in upcoming astrophysical surveys, which will exploit the petabytes of
data collected, e.g., from the Vera C. Rubin Observatory.

categories: astro-ph.IM astro-ph.CO cs.AI cs.CV cs.LG gr-qc


title: Foliations on Shimura varieties in positive characteristic

abstract:
  This paper is a continuation of [G-dS1]. We study foliations of two types on
Shimura varieties $S$ in characteristic $p$. The first, which we call
"tautological foliations", are defined on Hilbert modular varieties, and lift
to characteristic $0$. The second, the "$V$-foliations", are defined on unitary
Shimura varieties in characteristic $p$ only, and generalize the foliations
studied by us before, when the CM field in question was quadratic imaginary. We
determine when these foliations are $p$-closed, and the locus where they are
smooth. Where not smooth, we construct a "successive blow up" of our Shimura
variety to which they extend as smooth foliations. We discuss some integral
varieties of the foliations. We relate the quotient of $S$ by the foliation to
a purely inseparable map from a certain component of another Shimura variety of
the same type, with parahoric level structure at $p$, to $S.$


title: Highly twisted diagrams

abstract:
  We prove that the knots and links that admit a 3-highly twisted irreducible
diagram with more than two twist regions are hyperbolic. This should be
compared with a result of Futer-Purcell for 6-highly twisted diagrams. While
their proof uses geometric methods our proof is achieved by showing that the
complements of such knots or links are unannular and atoroidal. This is done by
using a new approach involving an Euler characteristic argument.


title:
Jam or Cream First? Modeling Ambiguity in Neural Machine Translation
  with SCONES

abstract:
  The softmax layer in neural machine translation is designed to model the
distribution over mutually exclusive tokens. Machine translation, however, is
intrinsically uncertain: the same source sentence can have multiple
semantically equivalent translations. Therefore, we propose to replace the
softmax activation with a multi-label classification layer that can model
ambiguity more effectively. We call our loss function Single-label Contrastive
Objective for Non-Exclusive Sequences (SCONES). We show that the multi-label
output layer can still be trained on single reference training data using the
SCONES loss function. SCONES yields consistent BLEU score gains across six
translation directions, particularly for medium-resource language pairs and
small beam sizes. By using smaller beam sizes we can speed up inference by a
factor of 3.9x and still match or improve the BLEU score obtained using
softmax. Furthermore, we demonstrate that SCONES can be used to train NMT
models that assign the highest probability to adequate translations, thus
mitigating the "beam search curse". Additional experiments on synthetic
language pairs with varying levels of uncertainty suggest that the improvements
from SCONES can be attributed to better handling of ambiguity.


title: 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone

abstract:
  State-of-the-art 3D detection methods rely on supervised learning and large
labelled datasets. However, annotating lidar data is resource-consuming, and
depending only on supervised learning limits the applicability of trained
models. Against this backdrop, here we propose using a self-supervised training
strategy to learn a general point cloud backbone model for downstream 3D vision
tasks. 3D scene flow can be estimated with self-supervised learning using cycle
consistency, which removes labelled data requirements. Moreover, the perception
of objects in the traffic scenarios heavily relies on making sense of the
sparse data in the spatio-temporal context. Our main contribution leverages
learned flow and motion representations and combines a self-supervised backbone
with a 3D detection head focusing mainly on the relation between the scene flow
and detection tasks. In this way, self-supervised scene flow training
constructs point motion features in the backbone, which help distinguish
objects based on their different motion patterns used with a 3D detection head.
Experiments on KITTI and nuScenes benchmarks show that the proposed
self-supervised pre-training increases 3D detection performance significantly.


title: FedDKD: Federated Learning with Decentralized Knowledge Distillation

abstract:
  The performance of federated learning in neural networks is generally
influenced by the heterogeneity of the data distribution. For a well-performing
global model, taking a weighted average of the local models, as done by most
existing federated learning algorithms, may not guarantee consistency with
local models in the space of neural network maps. In this paper, we propose a
novel framework of federated learning equipped with the process of
decentralized knowledge distillation (FedDKD) (i.e., without data on the
server). The FedDKD introduces a module of decentralized knowledge distillation
(DKD) to distill the knowledge of the local models to train the global model by
approaching the neural network map average based on the metric of divergence
defined in the loss function, other than only averaging parameters as done in
literature. Numeric experiments on various heterogeneous datasets reveal that
FedDKD outperforms the state-of-the-art methods with more efficient
communication and training in a few DKD steps, especially on some extremely
heterogeneous datasets.


title:
A multi-resolution SPH framework: Application to multi-phase
  fluid-structure interactions

abstract:
  In the previous work, Zhang et al. proposed a multi-resolution smoothed
particle hydrodynamics (SPH) method for fluid-structure interactions (FSI) with
achieving an optimized computational efficiency meanwhile maintaining good
numerical robustness and accuracy.
  In the present paper, this multi-resolution SPH framework where different
spatial-temporal discretizations are applied for different sub-systems is
extended to multi-phase flows involving large density ratio and interacting
with rigid or flexible structure. To this end, a simple and efficient
multi-phase model is introduced by exploiting different density
reinitialization strategies other than applying different formulations to
implement mass conservation to the light and heavy phases, respectively, to
realize the target of using same artificial speed of sound for the both. To
eliminate the unnatural voids and unrealistic phase separation meanwhile
decrease the numerical dissipation, the transport velocity formulation are
rewritten by applying temporal local flow state dependent background pressure.
  To handle the FSI coupling in both single- and multi-resolution scenarios in
the triple point, the one-sided Riemann-based solid boundary condition is
adopted. A set of examples involving multi-phase flows with high density ratio
and complex interface and multi-phase FSI are studied to demonstrate the
efficiency, accuracy and robustness of the present method. The validations
presented herein and those reported in the original paper of Zhang et al. where
single-phase FSI is studied put the present multi-resolution SPH framework in
good stead in terms of computational efficiency for multi-physics applications.


title: Anticoncentration and Berry--Esseen bounds for random tensors

abstract:
  We obtain estimates for the Kolmogorov distance to appropriately chosen
gaussians, of linear functions \[ \sum_{i\in [n]^d} \theta_i X_i \] of random
tensors $\boldsymbol{X}=\langle X_i:i\in [n]^d\rangle$ which are symmetric and
exchangeable, and whose entries have bounded third moment and vanish on
diagonal indices. These estimates are expressed in terms of intrinsic (and
easily computable) parameters associated with the random tensor
$\boldsymbol{X}$ and the given coefficients $\langle \theta_i:i\in
[n]^d\rangle$, and they are optimal in various regimes.
  The key ingredient -- which is of independent interest -- is a combinatorial
CLT for high-dimensional tensors which provides quantitative non-asymptotic
normality under suitable conditions, of statistics of the form \[
\sum_{(i_1,\dots,i_d)\in [n]^d}
\boldsymbol{\zeta}\big(i_1,\dots,i_d,\pi(i_1),\dots,\pi(i_d)\big) \] where
$\boldsymbol{\zeta}\colon [n]^d\times [n]^d\to\mathbb{R}$ is a deterministic
real tensor, and $\pi$ is a random permutation uniformly distributed on the
symmetric group $\mathbb{S}_n$. Our results extend, in any dimension $d$,
classical work of Bolthausen who covered the one-dimensional case, and more
recent work of Barbour/Chen who treated the two-dimensional case.


title:
Computationally efficient and data-adaptive changepoint inference in
  high dimension

abstract:
  High-dimensional changepoint inference that adapts to various change patterns
has received much attention recently. We propose a simple, fast yet effective
approach for adaptive changepoint testing. The key observation is that two
statistics based on aggregating cumulative sum statistics over all dimensions
and possible changepoints by taking their maximum and summation, respectively,
are asymptotically independent under some mild conditions. Hence we are able to
form a new test by combining the p-values of the maximum- and summation-type
statistics according to their limit null distributions. To this end, we develop
new tools and techniques to establish asymptotic distribution of the
maximum-type statistic under a more relaxed condition on componentwise
correlations among all variables than that in existing literature. The proposed
method is simple to use and computationally efficient. It is adaptive to
different sparsity levels of change signals, and is comparable to or even
outperforms existing approaches as revealed by our numerical studies.


title:
Electrically switchable tunneling across a graphene pn junction:
  evidence for canted antiferromagnetic phase in $\nu=0$ state

abstract:
  The ground state of a graphene sheet at charge neutrality in a perpendicular
magnetic field remains enigmatic, with various experiments supporting canted
antiferromagnetic, bond ordered, and even charge density wave phases. A
promising avenue to elucidating the nature of this state is to sandwich it
between regions of different filling factors, and study spin-dependent
tunneling across the edge modes at the interfaces. Here we report on tunnel
transport through a $\nu=0$ region in a graphite-gated, hexagonal boron nitride
($hBN$) encapsulated monolayer graphene device, with the $\nu=0$ strip
sandwiched by spin-polarized $\nu=\pm1$ quantum Hall states. We observe finite
tunneling ($t \sim 0.3-0.6$) between the $\nu=\pm1$ edges at not too small
magnetic fields ($B>3T$) and low tunnel bias voltage ($<30-60\mu V$), which is
surprising because electrons at the edge states nominally have opposite spins.
Hartree-Fock calculations elucidate these phenomena as being driven by the
formation of a CAF order parameter in the $\nu=0$ region at zero bias (for wide
enough junctions) leading to non-orthogonal spins at the edges. Remarkably,
this tunneling can be controllably switched off by increasing bias; bias
voltage leads to a pileup of charge at the junction, leading to a collapse of
the CAF order and a suppression of the tunneling.


title:
Electron emission from plasmonically induced Floquet bands at metal
  surfaces

abstract:
  We explore the possibility of existence of plasmonically generated electronic
Floquet bands at metal surfaces by studying the gauge transformed
electron-surface plasmon interaction in the prepumped plasmonic coherent state
environment. These bands may promote non-Einsteinian electron emission from
metal surfaces exposed to primary interactions with strong electromagnetic
fields. Resonant behaviour and scaling of emission yield with the parent
electronic structure and plasmonic state parameters are estimated for Ag(111)
surface. Relative yield intensities from non-Einsteinian emission channels in
photoelectron spectra offer the means to calibrate the mediating plasmonic
fields and therefrom ensuing surface Floquet bands.


title: Dark sector domain walls could explain the observed planes of satellites

abstract:
  The observed `planes of satellites' around the Milky Way and other nearby
galaxies are notoriously difficult to explain under the $\Lambda$CDM paradigm.
Here, we propose an alternative solution: domain walls arising in theories with
symmetry-breaking scalar fields coupled to matter. Because of the matter
coupling, satellite galaxies experience fifth forces as they pass through
domain walls, leading to a subset of satellites with orbits confined to the
domain wall plane. We demonstrate this effect using simple simulations of a toy
model comprising point-like satellites and an infinite domain wall, and explore
the efficacy of various planarity metrics in detecting this effect. We believe
this is the first potential `new physics' explanation for the observed planes
of satellites which does not do away with dark matter.


title: Connection formulae for trivariate $q$-polynomials

abstract:
  In this short paper, we establish connection formulae for trivariate
$q$-polynomials.


title:
Result and Congestion Aware Optimal Routing and Partial Offloading in
  Collaborative Edge Computing

abstract:
  Collaborative edge computing (CEC) is an emerging paradigm where
heterogeneous edge devices (stakeholders) collaborate to fulfill computation
tasks, such as model training or video processing, by sharing communication and
computation resources. Nevertheless, the optimal data/result routing and
computation offloading strategy in CEC with arbitrary topology still remains an
open problem. In this paper, we formulate a partial-offloading and multi-hop
routing model for arbitrarily divisible tasks. Each node individually decides
the computation of the received data and the forwarding of data/result traffic.
In contrast to most existing works, our model applies to tasks with
non-negligible result size, and enables separable data sources and result
destinations. We propose a network-wide cost minimization problem with
congestion-aware cost to jointly optimize routing and computation offloading.
This problem covers various performance metrics and constraints, such as
average queueing delay with limited processor capacity. Although the problem is
non-convex, we provide non-trivial necessary and sufficient conditions for the
global-optimal solution, and devise a fully distributed algorithm that
converges to the optimum in polynomial time, allows asynchronous individual
updating, and is adaptive to changes in network topology or task pattern.
Numerical evaluation shows that our proposed method significantly outperforms
other baseline algorithms in multiple network instances, especially in
congested scenarios.


title: Adjacency Spectra of Semigraphs

abstract:
  In this paper, we define the adjacency matrix of a semigraph. We give the
conditions for a matrix to be semigraphical and give an algorithm to construct
a semigraph from the semigraphical matrices. We derive lower and upper bounds
for largest eigenvalues. We study the eigenvalues of adjacency matrix of two
types of star semigraphs.


title:
A Library Perspective on Nearly-Unsupervised Information Extraction
  Workflows in Digital Libraries

abstract:
  Information extraction can support novel and effective access paths for
digital libraries. Nevertheless, designing reliable extraction workflows can be
cost-intensive in practice. On the one hand, suitable extraction methods rely
on domain-specific training data. On the other hand, unsupervised and open
extraction methods usually produce not-canonicalized extraction results. This
paper tackles the question how digital libraries can handle such extractions
and if their quality is sufficient in practice. We focus on unsupervised
extraction workflows by analyzing them in case studies in the domains of
encyclopedias (Wikipedia), pharmacy and political sciences. We report on
opportunities and limitations. Finally we discuss best practices for
unsupervised extraction workflows.


title: Meyer wavelets with scaling factor $N>2$

abstract:
  In this paper, Meyer wavelets with an arbitrary integer scaling factor $N>2$
are defined using wavelets with multiple scaling factors $MN>2$. Expressions
for frequency functions of wavelets and corresponding filters are obtained.


title:
What a Publication Tells You -- Benefits of Narrative Information Access
  in Digital Libraries

abstract:
  Knowledge bases allow effective access paths in digital libraries. Here users
can specify their information need as graph patterns for precise searches and
structured overviews (by allowing variables in queries). But especially when
considering textual sources that contain narrative information, i.e., short
stories of interest, harvesting statements from them to construct knowledge
bases may be a serious threat to the statements' validity. A piece of
information, originally stated in a coherent line of arguments, could be used
in a knowledge base query processing without considering its vital context
conditions. And this can lead to invalid results. That is why we argue to move
towards narrative information access by considering contexts in the query
processing step. In this way digital libraries can allow users to query for
narrative information and supply them with valid answers. In this paper we
define narrative information access, demonstrate its benefits for Covid 19
related questions, and argue on the generalizability for other domains such as
political sciences.


title:
Cations determine the mechanism and selectivity of alkaline ORR on
  Pt(111)

abstract:
  The proton-coupled electron transfer (PCET) mechanism of oxygen reduction
reaction (ORR) is a long-standing issue in electrocatalysis. Despite decades of
research, factors determining the ORR-PCET mechanism performance, even on the
prototypical Pt(111) surface, as a function of the pH, electrolyte and
electrode potential remain unknown. Herein, we integrate advanced experiments,
simulations, and theory to study alkaline ORR on a well-defined Pt(111) in the
presence of different cations. We unveil a dual-cation effect where the cations
simultaneously determine the active electrode surface, i.e. Pt-O/OH formation,
and the competition between inner- and outer-sphere PCET steps. This finding
provides direct evidence that the electrolyte affects the pathways, activity,
and selectivity which has important consequences for the practical design of
electrochemical systems and computational catalyst screening studies, for
example. Our work shows that all components of the electrochemical interface,
not the surface alone, are integral in electrocatalysis.


title:
Reply to W. T. Kranz on "Explicit analytical solution for random close
  packing in $d=2$ and $d=3$"

abstract:
  A recent Comment by W. T. Kranz is shown to be plagued by a serious
mathematical error which makes its conclusions invalid.


title:
Exponential moments for disk counting statistics of random normal
  matrices in the critical regime

abstract:
  We obtain large $n$ asymptotics for the $m$-point moment generating function
of the disk counting statistics of the Mittag-Leffler ensemble. We focus on the
critical regime where all disk boundaries are merging at speed
$n^{-\smash{\frac{1}{2}}}$, either in the bulk or at the edge. As corollaries,
we obtain two central limit theorems and precise large $n$ asymptotics of all
joint cumulants (such as the covariance) of the disk counting function. Our
results can also be seen as large $n$ asymptotics for $n\times n$ determinants
with merging planar discontinuities.


title: Fermionic semiclassical Lp estimates

abstract:
  We generalize the semiclassical Lp estimates of Koch, Tataru and Zworski in
the setting of Schr{\"o}dinger operators with confining potentials to density
matrices. This is motivated by the problem of the concentration of free
fermionic particles in a trapping potential. Our proof relies on semiclassical
and many-body tools. As an application, we provide bounds on spectral clusters.
We also discuss the optimality of the one-body and many-body bounds through
explicit examples of quasimodes.


title: Twisted algebras of geometric algebras

abstract:
  A twisting system is one of the major tools to study graded algebras,
however, it is often difficult to construct a (non-algebraic) twisting system
if a graded algebra is given by generators and relations. In this paper, we
show that a twisted algebra of a geometric algebra is determined by a certain
automorphism of its point variety. As an application, we classify twisted
algebras of $3$-dimensional geometric Artin-Schelter regular algebras up to
graded algebra isomorphism.


title:
Dynamic qubit allocation and routing for constrained topologies by CNOT
  circuit re-synthesis

abstract:
  Many quantum computers have constraints regarding which two-qubit operations
are locally allowed. To run a quantum circuit under those constraints, qubits
need to be allocated to different quantum registers, and multi-qubit gates need
to be routed accordingly. Recent developments have shown that Steiner-tree
based compiling strategies provide a competitive tool to route CNOT gates.
However, these algorithms require the qubit allocation to be decided before
routing. Moreover, the allocation is fixed throughout the computation, i.e. the
logical qubit will not move to a different qubit register. This is inefficient
with respect to the CNOT count of the resulting circuit. In this paper, we
propose the algorithm PermRowCol for routing CNOTs in a quantum circuit. It
dynamically reallocates logical qubits during the computation, and thus results
in fewer output CNOTs than the algorithms Steiner-Gauss[11] and RowCol [23].
Here we focus on circuits over CNOT only, but this method could be generalized
to a routing and allocation strategy on Clifford+T circuits by slicing the
quantum circuit into subcircuits composed of CNOTs and single-qubit gates.
Additionally, PermRowCol can be used in place of Steiner-Gauss in the synthesis
of phase polynomials as well as the extraction of quantum circuits from
ZX-diagrams.


title: Radiation damage uniformity in a SiPM

abstract:
  A dedicated single-cell SiPM structure is designed and measured to
investigate the radiation damage effects on the gain and breakdown voltage of
SiPMs exposed to a reactor neutron fluence up to $\Phi$ = 5e13 cm$^{-2}$. The
cell has a pitch of 15 $\mu$m. Results of the measurements and analysis of the
IV-curves are presented. Impact of the self-heating effect was investigated.
The radiation damage uniformity of 1 cell and 120 cells was checked up to
$U_\mathit{ov}$ = 1.7 V. Fluence dependence of the breakdown voltage from the
current measurements $U^{IV}_\mathit{bd}$ was extracted and compared to that of
the breakdown voltage from the gain measurements $U^{G}_\mathit{bd}$.


title:
In-situ calibration system for the measurement of the snow accumulation
  and the index-of-refraction profile for radio neutrino detectors

abstract:
  High-energy neutrinos ($E>10^{17}$eV) are detected cost-efficiently via the
Askaryan effect in ice, where a particle cascade induced by the neutrino
interaction produces coherent radio emission that can be picked up by antennas.
As the near-surface ice properties change rapidly within the upper 40m, a good
understanding of the ice properties is required to reconstruct the neutrino
properties. In particular, continuous monitoring of the snow accumulation
(which changes the depth of the antennas) and the index-of-refraction $n(z)$
profile are crucial for an accurate determination of the neutrino's direction
and energy. We present an in-situ calibration system that extends the radio
detector station with two radio emitters to continuously monitor the firn
properties within the upper 40m by measuring the time differences between
direct and reflected (off the surface) signals (D'n'R). We determine the
optimal positions of two transmitters at all three sites of current and future
radio detectors: Greenland, Moore's Bay, and the South Pole. We find that the
snow accumulation $\Delta h$ can be measured with a resolution of 5mm and the
parameters of an exponential $n(z)$ profile $\Delta n$ and $z_0$ with 0.03% and
0.2% precision respectively, which constitutes an improvement of more than a
factor of 10 as compared to the inference of the $n(z)$ profile from density
measurements. Additionally, as this technique is based on the measurement of
the signal propagation times we are not bound to the conversion of density to
index-of-refraction. We quantify the impact of these ice uncertainties on the
reconstruction of the neutrino vertex, direction, and energy and find that the
calibration device measures the ice properties to sufficient precision to have
negligible influence.


title:
Investigation on intense axial magnetic field shielding with a large
  melt cast processed Bi-2212 tube

abstract:
  The feasibility of shielding axial magnetic fields up to 1.4~T, using a
Bi-2212 hollow cylinder, is investigated at a temperature of 4.2~K. The
residual magnetic flux density along the axis of the tube is measured at
external fields of 1~T and 1.4~T. The shielding factor, defined as the ratio
between the applied and the residual magnetic flux densities at the center of
the tube, is measured to be $32\times 10^4$ at 1~T and $12\times 10^3$ at
1.4~T. The induced current density is evaluated from the measurements taking
the thickness of the tube into account. The stability of the measurements over
time is also addressed. Numerical simulations for the external and the residual
magnetic flux densities are performed and compared to the experimental results.
The study shows a high shielding performance of the Bi-2212 superconductor tube
at 4.2~K up to 1.4~T.


title:
Regarding the possibility to observe the LHCb hidden-charm strange
  pentaquark $P_{cs}(4459)^0$ in antikaon-induced $J/\psi$ meson production on
  protons and nuclei near the $J/\psi$$\Lambda$ production threshold

abstract:
  We study the near-threshold $J/\psi$ meson production on protons and nuclei
by considering incoherent direct non-resonant $K^-p \to {J/\psi}\Lambda$ and
two-step resonant ${K^-}p \to P_{cs}(4459)^0 \to {J/\psi}\Lambda$ charmonium
production processes. We calculate the absolute excitation functions, energy
and momentum distributions for the non-resonant, resonant and for the combined
(non-resonant plus resonant) production of $J/\psi$ mesons off protons as well
as off carbon and tungsten target nuclei at near-threshold incident antikaon
energies by considering these elementary production channels as well as by
assuming the spin-parity assignment of the hidden-charm resonance
$P_{cs}(4459)^0$ with strangeness as $J^P=(3/2)^-$ within six different
scenarios for the branching ratio $Br[P_{cs}(4459)^0 \to {J/\psi}\Lambda]$ of
the decay $P_{cs}(4459)^0 \to {J/\psi}\Lambda$. We show that the combined
observables considered reveal definite sensitivity to these scenarios, which
means that they may be an important tool to provide further evidence for the
existence of the pentaquark $P_{cs}(4459)^0$ resonance and to get valuable
information on its decay rate to the ${J/\psi}\Lambda$ final state. Their
measurements could be performed in the future at the J-PARC Hadron Experimental
Facility.


title: On the Analysis of a Generalised Rough Ait-Sahalia Interest Rate Model

abstract:
  Fractional Brownian motion with the Hurst parameter $H<\frac{1}{2}$ is used
widely, for instance, to describe a 'rough' stochastic volatility process in
finance. In this paper, we examine an Ait-Sahalia-type interest rate model
driven by a fractional Brownian motion with $H<\frac{1}{2}$ and establish
theoretical properties such as an existence-and-uniqueness theorem, regularity
in the sense of Malliavin differentiability and higher moments of the strong
solutions.


title: Sharp bounds on the height of K-semistable toric Fano varieties

abstract:
  Inspired by Fujita's algebro-geometric result that complex projective space
has maximal degree among all K-semistable Fano varieties, we conjecture that
the height of a K-semistable metrized arithmetic Fano variety X of relative
dimension n is maximal when X is the projective space over the integers,
endowed with the Fubini-Study metric. Our main result establishes the
conjecture for the canonical integral model of a toric Fano variety when n less
than or equal to 6 (the extension to higher dimensions is conditioned on a
conjectural "gap hypothesis" for the degree). Translated into toric Kahler
geometry this result yields a sharp lower bound on a toric invariant introduced
by Donaldson, defined as the minimum of the toric Mabuchi functional. We
furthermore reformulate our conjecture as an optimal lower bound on Odaka's
modular height. Along the way we show how to control the height of the
canonical toric model X, with respect to the Kahler-Einstein metric, by the
algebro-geometric degree of X in any dimension n. Logarithmic generalizations
of the results are also given.


title:
Logiformer: A Two-Branch Graph Transformer Network for Interpretable
  Logical Reasoning

abstract:
  Machine reading comprehension has aroused wide concerns, since it explores
the potential of model for text understanding. To further equip the machine
with the reasoning capability, the challenging task of logical reasoning is
proposed. Previous works on logical reasoning have proposed some strategies to
extract the logical units from different aspects. However, there still remains
a challenge to model the long distance dependency among the logical units.
Also, it is demanding to uncover the logical structures of the text and further
fuse the discrete logic to the continuous text embedding. To tackle the above
issues, we propose an end-to-end model Logiformer which utilizes a two-branch
graph transformer network for logical reasoning of text. Firstly, we introduce
different extraction strategies to split the text into two sets of logical
units, and construct the logical graph and the syntax graph respectively. The
logical graph models the causal relations for the logical branch while the
syntax graph captures the co-occurrence relations for the syntax branch.
Secondly, to model the long distance dependency, the node sequence from each
graph is fed into the fully connected graph transformer structures. The two
adjacent matrices are viewed as the attention biases for the graph transformer
layers, which map the discrete logical structures to the continuous text
embedding space. Thirdly, a dynamic gate mechanism and a question-aware
self-attention module are introduced before the answer prediction to update the
features. The reasoning process provides the interpretability by employing the
logical units, which are consistent with human cognition. The experimental
results show the superiority of our model, which outperforms the
state-of-the-art single model on two logical reasoning benchmarks.


title:
The general approach of weak-to-strong measurement transition for Fock
  state based pointers

abstract:
  Recently, the measurement transition between von Neumann's projective strong
measurement and Aharonov's weak measurement is investigated theoretically and
experimentally in many studies. In this work, the general formulas related to
the weak-to-strong measurement transition are presented for Fock state based
pointers and the coherent state based pointer case is illustrated as an
example. The ways to realize this proposal using the trapped ion system are
also discussed.


title:
Variational solutions to fermion-to-qubit mappings in two spatial
  dimensions

abstract:
  Through the introduction of auxiliary fermions, or an enlarged spin space,
one can map local fermion Hamiltonians onto local spin Hamiltonians, at the
expense of introducing a set of additional constraints. We present a
variational Monte-Carlo framework to study fermionic systems through
higher-dimensional (>1D) Jordan-Wigner transformations. We provide exact
solutions to the parity and Gauss-law constraints that are encountered in
bosonization procedures. We study the spinless Fermi-Hubbard ($t$-$V$) model in
2D and demonstrate how both the ground state and the low-energy excitation
spectra can be retrieved in combination with neural network quantum state
ansatze.


title: Deterministic pushdown automata can compress some normal sequences

abstract:
  In this paper, we give a deterministic pushdown transducer and a normal
sequence of digits compressed by it. This solves positively a question left
open in a previous paper by V. Becher, P. A. Heiber and the first author.


title:
Equilibrium and surviving species in a large Lotka-Volterra system of
  differential equations

abstract:
  Lotka-Volterra (LV) equations play a key role in the mathematical modeling of
various ecological, biological and chemical systems. When the number of species
(or, depending on the viewpoint, chemical components) becomes large, basic but
fundamental questions such as computing the number of surviving species still
lack theoretical answers. In this paper, we consider a large system of LV
equations where the interactions between the various species are a realization
of a random matrix. We provide conditions to have a unique equilibrium and
present a heuristics to compute the number of surviving species. This
heuristics combines arguments from Random Matrix Theory, mathematical
optimization (LCP), and standard extreme value theory. Numerical simulations,
together with an empirical study where the strength of interactions evolves
with time, illustrate the accuracy and scope of the results.


title: Simons type formulas for surfaces in Sol_3 and applications

abstract:
  We compute the Laplacian of the squared norm of the second fundamental form
of a surface in Sol_3 and then use this Simons type formula to obtain some gap
results for compact constant mean curvature surfaces of this space.


title: Moduli of Lie p-algebras

abstract:
  In this paper, we study moduli spaces of finite-dimensional Lie algebras with
flat center, proving that the forgetful map from Lie p-algebras to Lie algebras
is an affine fibration, and we point out a new case of existence of a
p-mapping. Then we illustrate these results for the special case of Lie
algebras of rank 3, whose moduli space we build and study over Z. We extend the
classical equivalence of categories between locally free Lie p-algebras of
finite rank with finite locally free group schemes of height 1, showing that
the centers of these objects correspond to each other. We finish by analysing
the smoothness of the moduli of p-Lie algebras of rank 3, in particular
identifying some smooth components.


title: Evocube: a Genetic Labeling Framework for Polycube-Maps

abstract:
  Polycube-maps are used as base-complexes in various fields of computational
geometry, including the generation of regular all-hexahedral meshes free of
internal singularities. However, the strict alignment constraints behind
polycube-based methods make their computation challenging for CAD models used
in numerical simulation via Finite Element Method (FEM). We propose a novel
approach based on an evolutionary algorithm to robustly compute polycube-maps
in this context. We address the labeling problem, which aims to precompute
polycube alignment by assigning one of the base axes to each boundary face on
the input. Previous research has described ways to initialize and improve a
labeling via greedy local fixes. However, such algorithms lack robustness and
often converge to inaccurate solutions for complex geometries. Our proposed
framework alleviates this issue by embedding labeling operations in an
evolutionary heuristic, defining fitness, crossover, and mutations in the
context of labeling optimization. We evaluate our method on a thousand smooth
and CAD meshes, showing Evocube converges to valid labelings on a wide range of
shapes. The limitations of our method are also discussed thoroughly.


title:
Computational Study of Magnetic Switching Mechanism and Magnetoresistive
  Behaviour of Nanoscale Spin Valve Elements

abstract:
  Investigation of the magnetic switching and magnetoresistive behaviour of
nanoscale spin valve elements of different sizes, shapes and arrays is of vital
importance for their application in future magnetic memory and storage devices.
To this end, we have inspected the magnetic switching mechanism and
magnetoresistive behaviour of nanoscale spin valve (Co/Cu/Ni80Fe20) elements of
two different shapes with varying lateral aspect ratios (ARs) by computational
micromagnetic simulation. Further, we have inspected the same for 2x2 and 3x3
arrays of the elliptical elements with AR = 1.25 of varying interelement
spacing. We have analysed how the shape of the hysteresis loop and its various
parameters such as the coercive field, remanence, saturation field were altered
by the variation in magnetic field. The magnetization reversal states were
simulated to explore the spatial coherence of magnetization switching. We
observe that the elements with higher AR show the Ni80Fe20 and Co layers
forming antiparallel states in the plateau similar to synthetic
antiferromagnets. As we reduce the AR, more complex quasi-uniform magnetic
states are observed which are even more complicated for elliptical elements.
The elliptical elements with the aspect ratio of 1.25 shows coherent and
predictable switching behaviour, showing its suitability for the application in
magnetic memory elements. We observe a gradual increase in magnetoresistance
(MR%) with the increase in AR of the spin valve elements, and the decrease in
interelement spacing between the spin valve elements in their arrays. The
magnetic flux density decreases, as we increase the cell spacing between the
elements. The magnetic hysteresis loops, magnetoresistance and spatial
coherence of magnetic switching give a guiding principle for selection of
geometric parameters of nanoscale spin valve arrays towards their application
in magnetic memory devices.


title:
Sharp estimates of the solutions to B{\'e}zout's polynomial equation and
  a corona theorem

abstract:
  In this paper, we obtain estimates for the solutions to the classical
B{\'e}zout equation that are analogous to Carleson's solution to the corona
theorem for the bounded analytic functions on the open unit disk. As an
application, we extend some results of Luo and obtain a corona theorem for the
multipliers of a class of de Branges--Rovnyak spaces.


title: Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor

abstract:
  In this paper, we investigate an online prediction strategy named as
Discounted-Normal-Predictor (Kapralov and Panigrahy, 2010) for smoothed online
convex optimization (SOCO), in which the learner needs to minimize not only the
hitting cost but also the switching cost. In the setting of learning with
expert advice, Daniely and Mansour (2019) demonstrate that
Discounted-Normal-Predictor can be utilized to yield nearly optimal regret
bounds over any interval, even in the presence of switching costs. Inspired by
their results, we develop a simple algorithm for SOCO: Combining online
gradient descent (OGD) with different step sizes sequentially by
Discounted-Normal-Predictor. Despite its simplicity, we prove that it is able
to minimize the adaptive regret with switching cost, i.e., attaining nearly
optimal regret with switching cost on every interval. By exploiting the
theoretical guarantee of OGD for dynamic regret, we further show that the
proposed algorithm can minimize the dynamic regret with switching cost in every
interval.


title: FirmTruss Community Search in Multilayer Networks

abstract:
  In applications such as biological, social, and transportation networks,
interactions between objects span multiple aspects. For accurately modeling
such applications, multilayer networks have been proposed. Community search
allows for personalized community discovery and has a wide range of
applications in large real-world networks. While community search has been
widely explored for single-layer graphs, the problem for multilayer graphs has
just recently attracted attention. Existing community models in multilayer
graphs have several limitations, including disconnectivity, free-rider effect,
resolution limits, and inefficiency. To address these limitations, we study the
problem of community search over large multilayer graphs. We first introduce
FirmTruss, a novel dense structure in multilayer networks, which extends the
notion of truss to multilayer graphs. We show that FirmTrusses possess nice
structural and computational properties and bring many advantages compared to
the existing models. Building on this, we present a new community model based
on FirmTruss, called FTCS, and show that finding an FTCS community is NP-hard.
We propose two efficient 2-approximation algorithms, and show that no
polynomial-time algorithm can have a better approximation guarantee unless P =
NP. We propose an index-based method to further improve the efficiency of the
algorithms. We then consider attributed multilayer networks and propose a new
community model based on network homophily. We show that community search in
attributed multilayer graphs is NP-hard and present an effective and efficient
approximation algorithm. Experimental studies on real-world graphs with
ground-truth communities validate the quality of the solutions we obtain and
the efficiency of the proposed algorithms.


title:
Machine-learned cloud classes from satellite data for process-oriented
  climate model evaluation

abstract:
  Clouds play a key role in regulating climate change but are difficult to
simulate within Earth system models (ESMs). Improving the representation of
clouds is one of the key tasks towards more robust climate change projections.
This study introduces a new machine-learning based evaluation method relying on
satellite observations to improve understanding of the representation of clouds
and their relevant processes in models. The proposed method is capable of
assigning distributions of established cloud types to coarse data. It
facilitates a more objective evaluation of clouds in ESMs and improves the
consistency of cloud process analysis. The method is built on satellite data
from the MODIS instrument labelled by deep neural networks with cloud types as
defined by the World Meteorological Organization (WMO), using cloud type labels
from CloudSat as ground truth. The method is applicable to datasets with
information about physical cloud variables comparable to MODIS satellite data
and at sufficiently high temporal resolution. We apply the method to
alternative satellite data from the Cloud_cci project (ESA Climate Change
Initiative), coarse grained to typical resolutions of climate models. The
resulting cloud type distributions are physically consistent and the horizontal
resolutions typical of ESMs are sufficient to apply our method. We recommend
outputting crucial variables required by our method for future ESM data
evaluation. This will enable the use of labelled satellite data for a more
systematic evaluation of clouds in climate models.


title: Smaller Progress Measures and Separating Automata for Parity Games

abstract:
  Calude et al. have recently shown that parity games can be solved in
quasi-polynomial time, a landmark result that has led to a number of approaches
with quasi-polynomial complexity. Jurdinski and Lasic have further improved the
precise complexity of parity games, especially when the number of priorities is
low (logarithmic in the number of positions). Both of these algorithms belong
to a class of game solving techniques now often called separating automata:
deterministic automata that can be used as witness automata to decide the
winner in parity games up to a given number of states and colours. We suggest a
number of adjustments to the approach of Calude et al. that lead to smaller
statespaces. These include and improve over those earlier introduced by
Fearnley et al.
  We identify two of them that, together, lead to a statespace of exactly the
same size Jurdzinski and Lasic's concise progress measures, which currently
hold the crown as smallest statespace. The remaining improvements, hence, lead
to a further reduction in the size of the statespace, making our approach the
most succinct progress measures available for parity games.


title:
Bitcoin P2P Network Measurements: A testbed study of the effect of peer
  selection on transaction propagation and confirmation times

abstract:
  Bitcoin is the first and the most extensive decentralized electronic
cryptocurrency system that uses blockchain technology. It uses a peer-to-peer
(P2P) network to operate without a central authority and propagate system
information such as transactions or blockchain updates. The communication
between participating nodes is highly relying on the underlying network
infrastructure to facilitate a platform. Understanding the impact of peer
formation strategies, peer list, and delay are vital on understanding node to
node communication. To this aim, we performed an extensive study on the
transaction characteristic of Bitcoin through a Testbed. The analysis shows
that peer selection strategies affect the transactions propagation and
confirmation time. Moreover, the default distance-based peer selection strategy
in Bitcoin performs less when there is high arrival intensity and creates high
number forks.