Abstract:
  The gold standard for COVID-19 is RT-PCR, testing facilities for which are
limited and not always optimally distributed. Test results are delayed, which
impacts treatment. Expert radiologists, one of whom is a co-author, are able to
diagnose COVID-19 positivity from Chest X-Rays (CXR) and CT scans, that can
facilitate timely treatment. Such diagnosis is particularly valuable in
locations lacking radiologists with sufficient expertise and familiarity with
COVID-19 patients. This paper has two contributions. One, we analyse literature
on CXR based COVID-19 diagnosis. We show that popular choices of dataset
selection suffer from data homogeneity, leading to misleading results. We
compile and analyse a viable benchmark dataset from multiple existing
heterogeneous sources. Such a benchmark is important for realistically testing
models. Our second contribution relates to learning from imbalanced data.
Datasets for COVID X-Ray classification face severe class imbalance, since most
subjects are COVID -ve. Twin Support Vector Machines (Twin SVM) and Twin Neural
Networks (Twin NN) have, in recent years, emerged as effective ways of handling
skewed data. We introduce a state-of-the-art technique, termed as Twin
Augmentation, for modifying popular pre-trained deep learning models. Twin
Augmentation boosts the performance of a pre-trained deep neural network
without requiring re-training. Experiments show, that across a multitude of
classifiers, Twin Augmentation is very effective in boosting the performance of
given pre-trained model for classification in imbalanced settings.


Abstract:
  In this paper we study the time differential dual-phase-lag model of heat
conduction incorporating the microstructural interaction effect in the
fast-transient process of heat transport. We analyse the influence of the delay
times upon some qualitative properties of the solutions of the initial boundary
value problems associated to such a model. Thus, the uniqueness results are
established under the assumption that the conductivity tensor is positive
definite and the delay times $\tau_q$ and $\tau_T$ vary in the set $\{0\leq
\tau_q\leq 2\tau_T\}\cup \{0<2\tau_T< \tau_q\}$. For the continuous dependence
problem we establish two different estimates. The first one is obtained for the
delay times with $0\leq \tau_q \leq 2\tau_T$, which agrees with the
thermodynamic restrictions on the model in concern, and the solutions are
stable. The second estimate is established for the delay times with $0<2\tau_T<
\tau_q$ and it allows the solutions to have an exponential growth in time. The
spatial behavior of the transient solutions and the steady-state vibrations is
also addressed. For the transient solutions we establish a theorem of influence
domain, under the assumption that the delay times are in $\left\{0<\tau_q\leq
2\tau_T\right\}\cup \left\{0<2\tau_T<\tau_q\right\}$. While for the amplitude
of the harmonic vibrations we obtain an exponential decay estimate of
Saint-Venant type, provided the frequency of vibration is lower than a critical
value and without any restrictions upon the delay times.


Abstract:
  We prove an optimal $\Omega(n^{-1})$ lower bound on the spectral gap of
Glauber dynamics for anti-ferromagnetic two-spin systems with $n$ vertices in
the tree uniqueness regime. This spectral gap holds for all, including
unbounded, maximum degree $\Delta$. Consequently, we have the following mixing
time bounds for the models satisfying the uniqueness condition with a slack
$\delta\in(0,1)$:
  $\bullet$ $C(\delta) n^2\log n$ mixing time for the hardcore model with
fugacity $\lambda\le (1-\delta)\lambda_c(\Delta)= (1-\delta)\frac{(\Delta -
1)^{\Delta - 1}}{(\Delta - 2)^\Delta}$;
  $\bullet$ $C(\delta) n^2$ mixing time for the Ising model with edge activity
$\beta\in\left[\frac{\Delta-2+\delta}{\Delta-\delta},\frac{\Delta-\delta}{\Delta-2+\delta}\right]$;
where the maximum degree $\Delta$ may depend on the number of vertices $n$, and
$C(\delta)$ depends only on $\delta$.
  Our proof is built upon the recently developed connections between the
Glauber dynamics for spin systems and the high-dimensional expander walks. In
particular, we prove a stronger notion of spectral independence, called the
complete spectral independence, and use a novel Markov chain called the field
dynamics to connect this stronger spectral independence to the rapid mixing of
Glauber dynamics for all degrees.


Abstract:
  Future microcalorimeter X-ray observations will resolve spectral features in
unmatched detail. Understanding the line formation processes in the X-rays
deserves much attention. The purpose of this paper is to discuss such processes
in the presence of a photoionizing source. Line formation processes in one and
two-electron species are broadly categorized into four cases. Case A occurs
when the Lyman line optical depths are very small and photoexcitation does not
occur. Line photons escape the cloud without any scattering. Case B occurs when
the Lyman-line optical depths are large enough for photons to undergo multiple
scatterings. Case C occurs when a broadband continuum source strikes an
optically thin cloud. The Lyman lines are enhanced by induced radiative
excitation of the atoms/ions by continuum photons, also known as continuum
pumping. A fourth less-studied scenario, where the Case B spectrum is enhanced
by continuum pumping, is called Case D. Here, we establish the mathematical
foundation of Cases A, B, C, and D in an irradiated cloud with Cloudy. We also
show the total X-ray emission spectrum for all four cases within the energy
range 0.1 - 10 keV at the resolving power of XRISM around 6 keV. Additionally,
we show that a combined effect of electron scattering and partial blockage of
continuum pumping reduces the resonance line intensities. Such reduction
increases with column density and can serve as an important tool to measure the
column density/optical depth of the cloud.


Abstract:
  Little is known about the spin-flip diffusion length $l_{\rm sf}$, one of the
most important material parameters in the field of spintronics. We use a
density-functional-theory based scattering approach to determine values of
$l_{\rm sf}$ that result from electron-phonon scattering as a function of
temperature for all 5d transition metal elements. $l_{\rm sf}$ does not
decrease monotonically with the atomic number Z but is found to be inversely
proportional to the density of states at the Fermi level. By using the same
local current methodology to calculate the spin Hall angle $\Theta_{\rm sH}$
that characterizes the efficiency of the spin Hall effect, we show that the
products $\rho(T)l_{\rm sf}(T)$ and $\Theta_{\rm sH}(T)l_{\rm sf}(T)$ are
constant.


Abstract:
  We propose a predictive $Q_4$ flavored 2HDM model, where the scalar sector is
enlarged by the inclusion of several gauge singlet scalars and the fermion
sector by the inclusion of right handed Majorana neutrinos. In our model, the
$Q_4$ family symmetry is supplemented by several auxiliary cyclic symmetries,
whose spontaneous breaking produces the observed pattern of SM charged fermion
masses and quark mixing angles. The light active neutrino masses are generated
from an inverse seesaw mechanism at one loop level thanks to a remnant
preserved $Z_2$ symmetry. Our model succesfully reproduces the measured dark
matter relic abundance only for masses of the DM candidate below $\sim$ 0.8
TeV. Furthermore, our model is also consistent with the lepton and baryon
asymmetries of the Universe as well as with the muon anomalous magnetic moment.


Abstract:
  We use the modified Richardson-Lucy deconvolution algorithm to reconstruct
the Primordial Power Spectrum from the Weak Lensing Power spectrum
reconstructed from the CMB anisotropies. This provides an independent window to
observe and constrain the PPS $P_R(k)$ along different $k$ scales as compared
to CMB Temperature Power Spectrum. The Weak Lensing Power spectrum does not
contain secondary variations in power and hence is cleaner, unlike the
Temperature Power spectrum which suffers from lensing which is visible in its
PPS reconstructions. We demonstrate that the physical behaviour of the weak
lensing kernel is different from the temperature kernel and reconstructs broad
features over $k$. We provide an in-depth analysis of the error propagation
using simulated data and Monte-Carlo sampling, based on Planck best-fit
cosmological parameters to simulate the data and cosmic variance limited error
bars. The error and initial condition analysis provides a clear picture of the
optimal reconstruction region for the estimator and we provide and algorithm
for $P_R(k)$ sampling to be used based on the given data, errors and its
binning properties. Eventually we plan to use this method on actual mission
data and provide a cross reference to PPS reconstructed from other sectors and
any possible features in them.


Abstract:
  Data deduplication saves storage space by identifying and removing repeats in
the data stream. Compared with traditional compression methods, data
deduplication schemes are more time efficient and are thus widely used in large
scale storage systems. In this paper, we provide an information-theoretic
analysis on the performance of deduplication algorithms on data streams in
which repeats are not exact. We introduce a source model in which probabilistic
substitutions are considered. More precisely, each symbol in a repeated string
is substituted with a given edit probability. Deduplication algorithms in both
the fixed-length scheme and the variable-length scheme are studied. The
fixed-length deduplication algorithm is shown to be unsuitable for the proposed
source model as it does not take into account the edit probability. Two
modifications are proposed and shown to have performances within a constant
factor of optimal with the knowledge of source model parameters. We also study
the conventional variable-length deduplication algorithm and show that as
source entropy becomes smaller, the size of the compressed string vanishes
relative to the length of the uncompressed string, leading to high compression
ratios.


Abstract:
  We report multi-epoch radial velocities, rotational velocities, and
atmospheric parameters for 37 T-type brown dwarfs observed with Keck/NIRSPEC.
Using a Markov Chain Monte Carlo forward-modeling method, we achieve median
precisions of 0.5 km s$^{-1}$ and 0.9 km s$^{-1}$ for radial and rotational
velocities, respectively. All of the T dwarfs in our sample are thin disk brown
dwarfs. We confirm previously reported moving group associations for four T
dwarfs. However, the lack of spectral indicators of youth in two of these
sources suggests that these are chance alignments. We confirm two previously
un-resolved binary candidates, the T0+T4.5 2MASS J11061197+2754225 and the
L7+T3.5 2MASS J21265916+7617440, with orbital periods of 4 yr and 12 yr,
respectively. We find a kinematic age of 3.5$\pm$0.3 Gyr for local T dwarfs,
consistent with nearby late-M dwarfs (4.1$\pm$0.3 Gyr). Removal of thick disk L
dwarfs in the local ultracool dwarf sample gives a similar age for L dwarfs
(4.2$\pm$0.3 Gyr), largely resolving the local L dwarf age anomaly. The
kinematic ages of local late-M, L, and T dwarfs can be accurately reproduced
with population simulations incorporating standard assumptions of the mass
function, star formation rate, and brown dwarf evolutionary models. A kinematic
dispersion break is found at the L4$-$L6 subtypes, likely reflecting the
terminus of the stellar Main Sequence. We provide a compilation of precise
radial velocities for 172 late-M, L, and T dwarfs within $\sim$20 pc of the
Sun.


Abstract:
  One of the most intriguing phenomena in active matter has been the gas-liquid
like motility induced phase separation (MIPS) observed in repulsive active
particles. However, experimentally no particle can be a perfect sphere, and the
asymmetric shape, mass distribution or catalysis coating can induce an active
torque on the particle, which makes it a chiral active particle. Here using
computer simulations and dynamic mean-field theory, we demonstrate that the
large enough torque of circle active Brownian particles (cABPs) in two
dimensions generates a dynamical clustering state interrupting the conventional
MIPS. Multiple clusters arise from the combination of the conventional MIPS
cohesion, and the circulating current caused disintegration. The non-vanishing
current in non-equilibrium steady states microscopically originates from the
motility ``relieved'' by automatic rotation, which breaks the detailed balance
at the continuum level. This suggests that no equilibrium-like phase separation
theory can be constructed for chiral active colloids even with tiny active
torque, in which no visible collective motion exists. This mechanism also sheds
light on the understanding of dynamic clusters observed in a variety of active
matter systems.


Abstract:
  In this study we investigate the potential of parametric images formed from
ultrasound B-mode scans using the Nakagami distribution for non-invasive
classification of breast lesions. Through a sliding window technique, we
generated seven types of parametric images from each patient scan in our
dataset using basic and as well as derived parameters of the Nakagami
distribution. To determine the most suitable window size for image generation,
we conducted an empirical analysis using three windows, and selected the best
one for our study. From the parametric images formed for each patient, we
extracted a total of 72 features. Feature selection was performed to find the
optimum subset of features for the best classification performance.
Incorporating the selected subset of features with the Support Vector Machine
(SVM) classifier, and by tuning the decision threshold, we obtained a maximum
classification accuracy of 93.08%, an Area under the ROC Curve (AUC) of 0.9712,
a False Negative Rate of 0%, and a very low False Positive Rate of 8.65%. Our
results indicate that the high accuracy of such a procedure may assist in the
diagnostic process associated with detection of breast cancer, as well as help
to reduce false positive diagnosis.


Abstract:
  In a previous paper, we presented an extension of our reflection model
RELXILL_NK to include the finite thickness of the accretion disk following the
prescription in Taylor & Reynolds (2018). In this paper, we apply our model to
fit the 2013 simultaneous observations by NuSTAR and XMM-Newton of the
supermassive black hole in MCG-06-30-15 and the 2019 NuSTAR observation of the
Galactic black hole in EXO 1846-031. The high-quality data of these spectra had
previously led to precise black hole spin measurements and very stringent
constraints on possible deviations from the Kerr metric. We find that the disk
thickness does not change previous spin results found with a model employing an
infinitesimally thin disk, which confirms the robustness of spin measurements
in high radiative efficiency disks, where the impact of disk thickness is
minimal. Similar analysis on lower accretion rate systems will be an important
test for measuring the effect of disk thickness on black hole spin
measurements.


Abstract:
  Searches for new leptophobic resonances at high energy colliders usually
target their decay modes into pairs of light quarks, top quarks, or standard
model bosons. Additional decay modes may also be present, producing signatures
to which current searches are not sensitive. We investigate the performance of
generic searches that look for resonances decaying into two large-radius jets.
As benchmark for our analysis we use a supersymmetric $\text{U}(1)'$ extension
of the Standard Model, the so-called U$\mu\nu$SSM, where all the SM decay modes
of the $Z'$ boson take place, plus additional (cascade) decays into new
scalars. The generic searches use a generic multi-pronged jet tagger and take
advantage of the presence of $b$ quarks in the large-radius jets, and are
sensitive to all these $Z'$ decay modes (except into light quarks) at once. For
couplings that are well below current experimental constraints, these generic
searches are sensitive at the $3\sigma-4\sigma$ level with Run 2 LHC data.


Abstract:
  Load side participation can provide support to the power network by
appropriately adapting the demand when required. In addition, it enables an
economically improved power allocation. In this study, we consider the problem
of providing an optimal power allocation among generation and on-off loads
within the secondary frequency control timeframe. In particular, we consider a
mixed integer optimization problem which ensures that the secondary frequency
control objectives (i.e. generation/demand balance and the frequency attaining
its nominal value at steady state) are satisfied. We present analytical
conditions on the generation and on-off load profiles such that an
$\epsilon$-optimality interpretation of the steady state power allocation is
obtained, providing a non-conservative value for $\epsilon$. Moreover, we
develop a hierarchical control scheme that provides on-off load values that
satisfy the proposed conditions. We study the interaction of the proposed
control scheme with the physical dynamics of the power network and provide
analytic stability guarantees. Our results are verified with numerical
simulations on the Northeast Power Coordinating Council (NPCC) 140-bus system,
where it is demonstrated that the proposed algorithm yields a close to optimal
power allocation.


Abstract:
  Relational Hoare logics (RHL) provide rules for reasoning about relations
between programs. Several RHLs include a rule we call sequential product that
infers a relational correctness judgment from judgments of ordinary Hoare logic
(HL). Other rules embody sensible patterns of reasoning and have been found
useful in practice, but sequential product is relatively complete on its own
(with HL). As a more satisfactory way to evaluate RHLs, a notion of alignment
completeness is introduced, in terms of the inductive assertion method and
product automata. Alignment completeness results are given to account for
several different sets of rules. The notion may serve to guide the design of
RHLs and relational verifiers for richer programming languages and alignment
patterns.


Abstract:
  Antiferromagnetic PbMnTeO6, also known as mineral kuranakhite, has been
reported recently to have all three cations in trigonal prismatic coordination,
which is extremely unusual for both Mn(4+) and Te(6+). In this work, the phase
was reproduced with the same lattice parameters and N\'eel temperature TN = 20
K. However, powder neutron diffraction unambiguously determined octahedral
(trigonal antiprismatic) coordination for all cations within the chiral space
group P312. The same symmetry was proposed for SrMnTeO6 and PbGeTeO6, instead
of the reported space groups P-62m and P31m, respectively. PbMnTeO6 was found
to be a robust antiferromagnet with an assumingly substantial scale of exchange
interactions since the Neel temperature did not show any changes in external
magnetic fields up to 7 T. The determined effective magnetic moment meff = 3.78
mB was in excellent agreement with the numerical estimation using the effective
g-factor g = 1.95 directly measured here by electron spin resonance (ESR). Both
specific heat and ESR data indicated the two-dimensional character of magnetism
in the compound under study. The combination of chirality with magnetic order
makes PbMnTeO6 a promising material with possible multiferroic properties.


Abstract:
  Beam-induced ionization injection (B-III) is currently being explored as a
method for injecting an electron beam with a controlled density profile into a
plasma wakefield accelerator (PWFA). This process is initiated by the fields of
an unmatched drive beam where the slice envelope reaches its minimum value, the
'pinch'. To control the injected beam's qualities, it is crucial to study the
beam-slice envelope oscillations, especially size and the location of the
pinch. In this proceeding, an ansatz based on the harmonic motion is proposed
to find the analytical solution to beam-slice envelope evolution in the
nonlinear regime. The size of the pinch is then found through the application
of energy conservation in the transverse direction. The resulting analytical
expressions are shown to be in good agreement with numerical solutions.


Abstract:
  Topological superconductors (TSCs) are unconventional superconductors with
bulk superconducting gap and in-gap Majorana states on the boundary that may be
used as topological qubits for quantum computation. Despite their importance in
both fundamental research and applications, natural TSCs are very rare. Here,
combining state of the art synchrotron and laser-based angle-resolved
photoemission spectroscopy, we investigated a stoichiometric transition metal
dichalcogenide (TMD), 2M-WS2 with a superconducting transition temperature of
8.8 K (the highest among all TMDs in the natural form up to date) and observed
distinctive topological surface states (TSSs). Furthermore, in the
superconducting state, we found that the TSSs acquired a nodeless
superconducting gap with similar magnitude as that of the bulk states. These
discoveries not only evidence 2M-WS2 as an intrinsic TSC without the need of
sensitive composition tuning or sophisticated heterostructures fabrication, but
also provide an ideal platform for device applications thanks to its van der
Waals layered structure.


Abstract:
  Each knot invariant can be extended to singular knots according to the skein
rule. A Vassiliev invariant of order at most $n$ is defined as a knot invariant
that vanishes identically on knots with more than $n$ double points. A chord
diagram encodes the order of double points along a singular knot. A Vassiliev
invariant of order $n$ gives rise to a function on chord diagrams with $n$
chords. Such a function should satisfy some conditions in order to come from a
Vassiliev invariant. A weight system is a function on chord diagrams that
satisfies so-called 4-term relations. Given a Lie algebra $\mathfrak{g}$
equipped with a non-degenerate invariant bilinear form, one can construct a
weight system with values in the center of the universal enveloping algebra
$U(\mathfrak{g})$. In this paper, we calculate $\mathfrak{sl}_3$ weight system
for chord diagram whose intersection graph is complete bipartite graph
$K_{2,n}$.


Abstract:
  The interplay of interactions and disorder in low-dimensional superconductors
supports the formation of multiple quantum phases, as possible instabilities of
the Superconductor-Insulator Transition (SIT) at a singular quantum critical
point. We explore a one-dimensional model which exhibits such variety of phases
in the strongly quantum fluctuations regime. Specifically, we study the effect
of weak disorder on a two-leg Josephson ladder with comparable Josephson and
charging energies ($E_J$~$E_C$). An additional key feature of our model is the
requirement of perfect $\mathbb{Z}_2$-symmetry, respected by all parameters
including the disorder. Using a perturbative renormalization-group (RG)
analysis, we derive the phase diagram and identify at least one intermediate
phase between a full-fledged superconductor and a disorder-dominated insulator.
Most prominently, for repulsive interactions on the rungs we identify two
distinct mixed phases: in both of them the longitudinal charge mode is a
gapless superconductor, however one phase exhibits a dipolar charge density
order on the rungs, while the other is disordered. This latter phase is
characterized by coexisting superconducting (phase-locked) and charge-ordered
rungs, and encompasses the potential of evolving into a Griffith's phase
characteristic of the random-field Ising model in the strong disorder limit.


Abstract:
  The goal of person search is to localize and match query persons from scene
images. For high efficiency, one-step methods have been developed to jointly
handle the pedestrian detection and identification sub-tasks using a single
network. There are two major challenges in the current one-step approaches. One
is the mutual interference between the optimization objectives of multiple
sub-tasks. The other is the sub-optimal identification feature learning caused
by small batch size when end-to-end training. To overcome these problems, we
propose a decoupled and memory-reinforced network (DMRNet). Specifically, to
reconcile the conflicts of multiple objectives, we simplify the standard
tightly coupled pipelines and establish a deeply decoupled multi-task learning
framework. Further, we build a memory-reinforced mechanism to boost the
identification feature learning. By queuing the identification features of
recently accessed instances into a memory bank, the mechanism augments the
similarity pair construction for pairwise metric learning. For better encoding
consistency of the stored features, a slow-moving average of the network is
applied for extracting these features. In this way, the dual networks reinforce
each other and converge to robust solution states. Experimentally, the proposed
method obtains 93.2% and 46.9% mAP on CUHK-SYSU and PRW datasets, which exceeds
all the existing one-step methods.


Abstract:
  Sequence-to-sequence models have lead to significant progress in keyphrase
generation, but it remains unknown whether they are reliable enough to be
beneficial for document retrieval. This study provides empirical evidence that
such models can significantly improve retrieval performance, and introduces a
new extrinsic evaluation framework that allows for a better understanding of
the limitations of keyphrase generation models. Using this framework, we point
out and discuss the difficulties encountered with supplementing documents with
-- not present in text -- keyphrases, and generalizing models across domains.
Our code is available at https://github.com/boudinfl/ir-using-kg


Abstract:
  Despite the successes of deep neural networks on many challenging vision
tasks, they often fail to generalize to new test domains that are not
distributed identically to the training data. The domain adaptation becomes
more challenging for cross-modality medical data with a notable domain shift.
Given that specific annotated imaging modalities may not be accessible nor
complete. Our proposed solution is based on the cross-modality synthesis of
medical images to reduce the costly annotation burden by radiologists and
bridge the domain gap in radiological images. We present a novel approach for
image-to-image translation in medical images, capable of supervised or
unsupervised (unpaired image data) setups. Built upon adversarial training, we
propose a learnable self-attentive spatial normalization of the deep
convolutional generator network's intermediate activations. Unlike previous
attention-based image-to-image translation approaches, which are either
domain-specific or require distortion of the source domain's structures, we
unearth the importance of the auxiliary semantic information to handle the
geometric changes and preserve anatomical structures during image translation.
We achieve superior results for cross-modality segmentation between unpaired
MRI and CT data for multi-modality whole heart and multi-modal brain tumor MRI
(T1/T2) datasets compared to the state-of-the-art methods. We also observe
encouraging results in cross-modality conversion for paired MRI and CT images
on a brain dataset. Furthermore, a detailed analysis of the cross-modality
image translation, thorough ablation studies confirm our proposed method's
efficacy.


Abstract:
  Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden
(latent) neuronal states, based on measurements of brain activity. Since its
introduction in 2003 for functional magnetic resonance imaging data, DCM has
been extended to electrophysiological data, and several variants have been
developed. Their biophysically motivated formulations make these models
promising candidates for providing a mechanistic understanding of human brain
dynamics, both in health and disease. However, due to their complexity and
reliance on concepts from several fields, fully understanding the mathematical
and conceptual basis behind certain variants of DCM can be challenging. At the
same time, a solid theoretical knowledge of the models is crucial to avoid
pitfalls in the application of these models and interpretation of their
results. In this paper, we focus on one of the most advanced formulations of
DCM, i.e. conductance-based DCM for cross-spectral densities, whose components
are described across multiple technical papers. The aim of the present article
is to provide an accessible exposition of the mathematical background, together
with an illustration of the model's behavior. To this end, we include
step-by-step derivations of the model equations, point to important aspects in
the software implementation of those models, and use simulations to provide an
intuitive understanding of the type of responses that can be generated and the
role that specific parameters play in the model. Furthermore, all code utilized
for our simulations is made publicly available alongside the manuscript to
allow readers an easy hands-on experience with conductance-based DCM.


Abstract:
  Direct simulation of physical processes on a kinetic level is prohibitively
expensive in aerospace applications due to the extremely high dimension of the
solution spaces. In this paper, we consider the moment system of the Boltzmann
equation, which projects the kinetic physics onto the hydrodynamic scale. The
unclosed moment system can be solved in conjunction with the entropy closure
strategy. Using an entropy closure provides structural benefits to the physical
system of partial differential equations. Usually computing such closure of the
system spends the majority of the total computational cost, since one needs to
solve an ill-conditioned constrained optimization problem. Therefore, we build
a neural network surrogate model to close the moment system, which preserves
the structural properties of the system by design, but reduces the
computational cost significantly. Numerical experiments are conducted to
illustrate the performance of the current method in comparison to the
traditional closure.


Abstract:
  Document grounded generation is the task of using the information provided in
a document to improve text generation. This work focuses on two different
document grounded generation tasks: Wikipedia Update Generation task and
Dialogue response generation. Our work introduces two novel adaptations of
large scale pre-trained encoder-decoder models focusing on building context
driven representation of the document and enabling specific attention to the
information in the document. Additionally, we provide a stronger BART baseline
for these tasks. Our proposed techniques outperform existing methods on both
automated (at least 48% increase in BLEU-4 points) and human evaluation for
closeness to reference and relevance to the document. Furthermore, we perform
comprehensive manual inspection of the generated output and categorize errors
to provide insights into future directions in modeling these tasks.


Abstract:
  Physics-Informed Machine Learning (PIML) has gained momentum in the last 5
years with scientists and researchers aiming to utilize the benefits afforded
by advances in machine learning, particularly in deep learning. With large
scientific data sets with rich spatio-temporal data and high-performance
computing providing large amounts of data to be inferred and interpreted, the
task of PIML is to ensure that these predictions, categorizations, and
inferences are enforced by, and conform to the limits imposed by physical laws.
In this work a new approach to utilizing PIML is discussed that deals with the
use of physics-based loss functions. While typical usage of physical equations
in the loss function requires complex layers of derivatives and other functions
to ensure that the known governing equation is satisfied, here we show that a
similar level of enforcement can be found by implementing more simpler loss
functions on specific kinds of output data. The generalizability that this
approach affords is shown using examples of simple mechanical models that can
be thought of as sufficiently simplified surrogate models for a wide class of
problems.


Abstract:
  We discover a new minimality property of the absolute minimisers of supremal
functionals (also known as $L^\infty$ Calculus of Variations problems).


Abstract:
  We present a method to tune the resonantly enhanced harmonic emission from
engineered potentials, which would be experimentally feasible in the purview of
the recent advances in atomic and condensed matter physics. The recombination
of the electron from the potential dependent excited state to the ground state
causes the emission of photons with a specific energy. The energy of the
emitted photons can be controlled by appropriately tweaking the potential
parameters. The resonant enhancement in high-harmonic generation enables the
emission of very intense extreme ultra-violet or soft x-ray radiations. The
scaling law of the resonant harmonic emission with the model parameter of the
potential is also obtained by numerically solving the time-dependent
Schr\"odinger equation in two dimensions.


Abstract:
  We prove equidistribution theorems for a family of holomorphic Siegel cusp
forms of general degree in the level aspect. Our main contribution is to
estimate unipotent contributions for general degree in the geometric side of
Arthur's invariant trace formula in terms of Shintani zeta functions. Several
applications including the vertical Sato-Tate theorem and low-lying zeros for
standard $L$-functions of holomorphic Siegel cusp forms are discussed. We also
show that the ``non-genuine forms" which come from non-trivial endoscopic
contributions by Langlands functoriality classified by Arthur are negligible.


Abstract:
  Image decomposition is a crucial subject in the field of image processing. It
can extract salient features from the source image. We propose a new image
decomposition method based on convolutional neural network. This method can be
applied to many image processing tasks. In this paper, we apply the image
decomposition network to the image fusion task. We input infrared image and
visible light image and decompose them into three high-frequency feature images
and a low-frequency feature image respectively. The two sets of feature images
are fused using a specific fusion strategy to obtain fusion feature images.
Finally, the feature images are reconstructed to obtain the fused image.
Compared with the state-of-the-art fusion methods, this method has achieved
better performance in both subjective and objective evaluation.


Abstract:
  Exascale computing systems will exhibit high degrees of hierarchical
parallelism, with thousands of computing nodes and hundreds of cores per node.
  Efficiently exploiting hierarchical parallelism is challenging due to load
imbalance that arises at multiple levels.
  OpenMP is the most widely-used standard for expressing and exploiting the
ever-increasing node-level parallelism.
  The scheduling options in OpenMP are insufficient to address the load
imbalance that arises during the execution of multithreaded applications.
  The limited scheduling options in OpenMP hinder research on novel scheduling
techniques which require comparison with others from the literature.
  This work introduces LB4OMP, an open-source dynamic load balancing library
that implements successful scheduling algorithms from the literature.
  LB4OMP is a research infrastructure designed to spur and support present and
future scheduling research, for the benefit of multithreaded applications
performance.
  Through an extensive performance analysis campaign, we assess the
effectiveness and demystify the performance of all loop scheduling techniques
in the library.
  We show that, for numerous applications-systems pairs, the scheduling
techniques in LB4OMP outperform the scheduling options in OpenMP.
  Node-level load balancing using LB4OMP leads to reduced cross-node load
imbalance and to improved MPI+OpenMP applications performance, which is
critical for Exascale computing.


Abstract:
  We study the steady-state patterns of population of the coupled oscillators
that sync and swarm, where the interaction distances among oscillators have
finite-cutoff in interaction distance. We examine how the static patterns known
in the infinite-cutoff are reproduced or deformed, and explore a new static
pattern that does not appear until a finite-cutoff is considered. All
steady-state patterns of the infinite-cutoff, static sync, static async, and
static phase wave are respectively repeated in space for proper finite-cutoff
ranges. Their deformation in shape and density takes place for the other
finite-cutoff ranges. Bar-like phase wave states are observed, which has not
been the case for the infinite-cutoff. All the patterns are investigated via
numerical and theoretical analysis.


Abstract:
  We consider linear parameter-dependent systems $A(\mu) x(\mu) = b$ for many
different $\mu$, where $A$ is large and sparse, and depends nonlinearly on
$\mu$. Solving such systems individually for each $\mu$ would require great
computational effort. In this work we propose to compute a partial
parameterization $\tilde{x} \approx x(\mu)$ where $\tilde{x}(\mu)$ is cheap to
compute for many different $\mu$. Our methods are based on the observation that
a companion linearization can be formed where the dependence on $\mu$ is only
linear. In particular, we develop methods which combine the well-established
Krylov subspace method for linear systems, GMRES, with algorithms for nonlinear
eigenvalue problems (NEPs) to generate a basis for the Krylov subspace. Within
this new approach, the basis matrix is constructed in three different ways,
using a tensor structure and exploiting that certain problems have low-rank
properties. We show convergence factor bounds obtained similarly to those for
the method GMRES for linear systems. More specifically, a bound is obtained
based on the magnitude of the parameter $\mu$ and the spectrum of the linear
companion matrix, which corresponds to the reciprocal solutions to the
corresponding NEP. Numerical experiments illustrate the competitiveness of our
methods for large-scale problems. The simulations are reproducible and publicly
available online.


Abstract:
  We prove a folklore conjecture concerning the sum-of-digits functions in
bases two and three: there are infinitely many positive integers $n$ such that
the binary sum of digits of $n$ equals its ternary sum of digits.


Abstract:
  Context. Radiation-driven mass loss is key to our understanding of
massive-star evolution. However, for low-luminosity O-type stars there are big
discrepancies between theoretically predicted and empirically derived mass-loss
rates (called the weak-wind problem). Aims. We compute radiation-line-driven
wind models of a typical weak-wind star to determine its temperature structure
and the corresponding impact on ultra-violet (UV) line formation. Methods. We
carried out hydrodynamic simulations of the line-deshadowing instability (LDI)
for a weak-wind star in the Galaxy. Subsequently, we used this LDI model as
input in a short-characteristics radiative transfer code to compute synthetic
UV line profiles. Results. We find that the line-driven weak wind is
significantly shock heated to high temperatures and is unable to cool down
effciently. This results in a complex temperature structure where more than
half of the wind volume has temperatures significantly higher than the stellar
effective temperature. Therefore, a substantial portion of the weak wind will
be more ionised, resulting in a reduction of the UV line opacity and therefore
in weaker line profiles for a given mass-loss rate. Quantifying this, we find
that weak-wind mass-loss rates derived from unsaturated UV lines could be
underestimated by a factor of between 10 and 100 if the high-temperature gas is
not properly taken into account in the spectroscopic analysis. This offers a
tentative basic explanation for the weak-wind problem: line-driven weak winds
are not really weaker than theoretically expected, but rather a large portion
of their wind volume is much hotter than the stellar effective temperature.


Abstract:
  In end-to-end optimized learned image compression, it is standard practice to
use a convolutional variational autoencoder with generalized divisive
normalization (GDN) to transform images into a latent space. Recently,
Operational Neural Networks (ONNs) that learn the best non-linearity from a set
of alternatives, and their self-organized variants, Self-ONNs, that approximate
any non-linearity via Taylor series have been proposed to address the
limitations of convolutional layers and a fixed nonlinear activation. In this
paper, we propose to replace the convolutional and GDN layers in the
variational autoencoder with self-organized operational layers, and propose a
novel self-organized variational autoencoder (Self-VAE) architecture that
benefits from stronger non-linearity. The experimental results demonstrate that
the proposed Self-VAE yields improvements in both rate-distortion performance
and perceptual image quality.


Abstract:
  In wireless sensor networks (WSNs), designing a stable, low-power routing
protocol is a major challenge because successive changes in links or breakdowns
destabilize the network topology. Therefore, choosing the right route in this
type of network due to resource constraints and their operating environment is
one of the most important challenges in these networks. Therefore, the main
purpose of these networks is to collect appropriate routing information about
the environment around the network sensors while observing the energy
consumption of the sensors. One of the important approaches to reduce energy
consumption in sensor networks is the use of the clustering technique, but in
most clustering methods, only the criterion of the amount of energy of the
cluster or the distance of members to the cluster has been considered.
Therefore, in this paper, a method is presented using the firefly algorithm and
using the four criteria of residual energy, noise rate, number of hops, and
distance. The proposed method called EM-FIREFLY is introduced which selects the
best cluster head with high attractiveness and based on the fitness function
and transfers the data packets through these cluster head to the sink. The
proposed method is evaluated with NS-2 simulator and compared with the
algorithm-PSO and optimal clustering methods. The evaluation results show the
efficiency of the EM-FIREFLY method in maximum relative load and network
lifetime criteria compared to other methods discussed in this article.


Abstract:
  Employing the time-dependent variational principle combined with the multiple
Davydov $\mathrm{D}_2$ Ansatz, we investigate Landau-Zener (LZ) transitions in
a qubit coupled to a photon mode with various initial photon states at zero
temperature. Thanks to the multiple Davydov trial states, exact photonic
dynamics taking place in the course of the LZ transition is also studied
efficiently. With the qubit driven by a linear external field and the photon
mode initialized with Schr\"odinger-cat states, asymptotic behavior of the
transition probability beyond the rotating-wave approximation is uncovered for
a variety of initial states. Using a sinusoidal external driving field, we also
explore the photon-assisted dynamics of Landau-Zener-St\"{u}ckelberg-Majorana
interferometry. Transition pathways involving multiple energy levels are
unveiled by analyzing the photon dynamics.


Abstract:
  In this paper, we propose a novel framework for multi-target multi-camera
tracking (MTMCT) of vehicles based on metadata-aided re-identification
(MA-ReID) and the trajectory-based camera link model (TCLM). Given a video
sequence and the corresponding frame-by-frame vehicle detections, we first
address the isolated tracklets issue from single camera tracking (SCT) by the
proposed traffic-aware single-camera tracking (TSCT). Then, after automatically
constructing the TCLM, we solve MTMCT by the MA-ReID. The TCLM is generated
from camera topological configuration to obtain the spatial and temporal
information to improve the performance of MTMCT by reducing the candidate
search of ReID. We also use the temporal attention model to create more
discriminative embeddings of trajectories from each camera to achieve robust
distance measures for vehicle ReID. Moreover, we train a metadata classifier
for MTMCT to obtain the metadata feature, which is concatenated with the
temporal attention based embeddings. Finally, the TCLM and hierarchical
clustering are jointly applied for global ID assignment. The proposed method is
evaluated on the CityFlow dataset, achieving IDF1 76.77%, which outperforms the
state-of-the-art MTMCT methods.


Abstract:
  We investigate latent-space scalability for multi-task collaborative
intelligence, where one of the tasks is object detection and the other is input
reconstruction. In our proposed approach, part of the latent space can be
selectively decoded to support object detection while the remainder can be
decoded when input reconstruction is needed. Such an approach allows reduced
computational resources when only object detection is required, and this can be
achieved without reconstructing input pixels. By varying the scaling factors of
various terms in the training loss function, the system can be trained to
achieve various trade-offs between object detection accuracy and input
reconstruction quality. Experiments are conducted to demonstrate the adjustable
system performance on the two tasks compared to the relevant benchmarks.


Abstract:
  Observatory publications comprise the work of local astronomers from
observatories around the world and are traditionally exchanged between
observatories through libraries. However, large collections of observatory
publications seem to be rare; or at the least rarely digitally described or
accessible on the Internet. Notable examples to the contrary are the Woodman
Astronomical Library at Wisconsin-Madison and the Dudley Observatory in
Loudonville, New York both in the US. Due to the irregularities in receiving
material, the collections are generally often incomplete both with respect to
the observatories included as well as volumes. In order to assess the unique
properties of the collections, we summarize and compare observatories present
in our own as well as the collections from the Woodman Library and the Dudley
Observatory.


Abstract:
  We present a detailed comparison of several recent and new approaches to
multigrid solver algorithms suitable for the solution of 5d chiral fermion
actions such as Domain Wall fermions in the Shamir formulation, and also for
the Partial Fraction and Continued Fraction overlap. Our focus is on the
acceleration of gauge configuration sampling, and a compact nearest neighbour
stencil is required to limit the calculational cost of obtaining a coarse
operator. This necessitates the coarsening of a nearest neighbour operator to
preserve sparsity in coarsened grids, unlike HDCG. We compare the approaches of
HDCR and the Multigrid algorithm and also several new hybrid schemes. In this
work we introduce a new recursive Chebyshev polynomial based setup scheme. We
find that the HDCR approach, can both setup, and solve standard Shamir Domain
Wall Fermions faster than a single solve with red-black preconditioned
Conjugate Gradients on large volumes and for modern GPU systems such as the
Summit supercomputer. This is promising for the acceleration of HMC,
particularly if setup costs are shared across multiple Hasenbusch determinant
factors. The setup scheme is likely generally applicable to other Fermion
actions.


Abstract:
  Let $P,Q$ be longest paths in a simple graph. We analyze the possible
connections between the components of $P\cup Q\setminus (V(P)\cap V(Q))$ and
introduce the notion of a bi-traceable graph. We use the results for all the
possible configurations of the intersection points when $\#V(P)\cap V(Q)\le 5$
in order to prove that if the intersection of three longest paths $P,Q,R$ is
empty, then $\#(V(P)\cap V(Q))\ge 6$. We also prove Hippchen's conjecture for
$k\le 6$: If a graph $G$ is $k$-connected for $k\le 6$, and $P$ and $Q$ are
longest paths in $G$, then $\#(V(P)\cap V(Q))\ge 6$.


Abstract:
  This paper presents a framework for the design and analysis of an
$\mathcal{L}_1$ adaptive controller with a switching reference system. The use
of a switching reference system allows the desired behavior to be scheduled
across the operating envelope, which is often required in aerospace
applications. The analysis uses a switched reference system that assumes
perfect knowledge of uncertainties and uses a corresponding non-adaptive
controller. Provided that this switched reference system is stable, it is shown
that the closed-loop system with unknown parameters and disturbances and the
$\mathcal{L}_1$ adaptive controller can behave arbitrarily close to this
reference system. Simulations of the short period dynamics of a transport class
aircraft during the approach phase illustrate the theoretical results.


Abstract:
  Music streaming services heavily rely on recommender systems to improve their
users' experience, by helping them navigate through a large musical catalog and
discover new songs, albums or artists. However, recommending relevant and
personalized content to new users, with few to no interactions with the
catalog, is challenging. This is commonly referred to as the user cold start
problem. In this applied paper, we present the system recently deployed on the
music streaming service Deezer to address this problem. The solution leverages
a semi-personalized recommendation strategy, based on a deep neural network
architecture and on a clustering of users from heterogeneous sources of
information. We extensively show the practical impact of this system and its
effectiveness at predicting the future musical preferences of cold start users
on Deezer, through both offline and online large-scale experiments. Besides, we
publicly release our code as well as anonymized usage data from our
experiments. We hope that this release of industrial resources will benefit
future research on user cold start recommendation.


Abstract:
  Government agencies always need to carefully consider potential risks of
disclosure whenever they publish statistics based on their data or give
external researchers access to the collected data. For this reason, research on
disclosure avoiding techniques has a long tradition at statistical agencies. In
this context, the promise of formal privacy guarantees offered by concepts such
as differential privacy seem to be the panacea enabling the agencies to exactly
quantify and control the privacy loss incurred by any data release. Still,
despite the excitement in academia and industry, most agencies-with the
prominent exception of the U.S. Census Bureau-have been reluctant to even
consider the concept for their data release strategy.
  This paper aims to shed some light on potential reasons for this. We argue
that the requirements when implementing differential privacy approaches at
government agencies are often fundamentally different from the requirements in
industry. This raises many challenging problems and open questions that still
need to be addressed before the concept might be used as an overarching
principle when sharing data with the public. The paper will not offer any
solutions to these challenges. Instead, we hope to stimulate some collaborative
research efforts, as we believe that many of the problems can only be addressed
by inter-disciplinary collaborations.


Abstract:
  To characterize entanglement of tripartite
$\mathbb{C}^d\otimes\mathbb{C}^d\otimes\mathbb{C}^d$ systems, we employ
algebraic-geometric tools that are invariants under Stochastic Local Operation
and Classical Communication (SLOCC), namely $k$-secant varieties and
one-multilinear ranks. Indeed, by means of them, we present a classification of
tripartite pure states in terms of a finite number of families and subfamilies.
At the core of it stands out a fine-structure grouping of three-qutrit
entanglement.


Abstract:
  Electric currents carrying a net spin polarization are widely used in
spintronics, whereas globally spin-neutral currents are expected to play no
role in spin-dependent phenomena. Here we show that, in contrast to this common
expectation, spin-independent conductance in compensated antiferromagnets and
normal metals can be efficiently exploited in spintronics, provided their
magnetic space group symmetry supports a non-spin-degenerate Fermi surface. Due
to their momentum-dependent spin polarization, such antiferromagnets can be
used as active elements in antiferromagnetic tunnel junctions (AFMTJs) and
produce a giant tunneling magnetoresistance (TMR) effect. Using RuO$_{2}$ as a
representative compensated antiferromagnet exhibiting spin-independent
conductance along the [001] direction but a non-spin-degenerate Fermi surface,
we design a RuO$_{2}$/TiO$_{2}$/RuO$_{2}$ (001) AFMTJ, where a globally
spin-neutral charge current is controlled by the relative orientation of the
N\'eel vectors of the two RuO$_{2}$ electrodes, resulting in the TMR effect as
large as ~500%. These results are expanded to normal metals which can be used
as a counter electrode in AFMTJs with a single antiferromagnetic layer or other
elements in spintronic devices. Our work uncovers an unexplored potential of
the materials with no global spin polarization for utilizing them in
spintronics.


Abstract:
  Predictive uncertainty estimation is an essential next step for the reliable
deployment of deep object detectors in safety-critical tasks. In this work, we
focus on estimating predictive distributions for bounding box regression output
with variance networks. We show that in the context of object detection,
training variance networks with negative log likelihood (NLL) can lead to high
entropy predictive distributions regardless of the correctness of the output
mean. We propose to use the energy score as a non-local proper scoring rule and
find that when used for training, the energy score leads to better calibrated
and lower entropy predictive distributions than NLL. We also address the
widespread use of non-proper scoring metrics for evaluating predictive
distributions from deep object detectors by proposing an alternate evaluation
approach founded on proper scoring rules. Using the proposed evaluation tools,
we show that although variance networks can be used to produce high quality
predictive distributions, ad-hoc approaches used by seminal object detectors
for choosing regression targets during training do not provide wide enough data
support for reliable variance learning. We hope that our work helps shift
evaluation in probabilistic object detection to better align with predictive
uncertainty evaluation in other machine learning domains. Code for all models,
evaluation, and datasets is available at:
https://github.com/asharakeh/probdet.git.


Abstract:
  Numerous missions planned for the next decade are likely to target a handful
of smal sites of interest on the Moon's surface, creating risks of crowding and
interference at these locations. The Moon presents finite and scarce areas with
rare topography or concentrations of resources of special value. Locations of
interest to science, notably for astronomy, include the Peaks of Eternal Light,
the coldest of the cold traps and smooth areas on the far side. Regions richest
in physical resources could also be uniquely suited to settlement and commerce.
Such sites of interest are both few and small. Typically, there are fewer than
ten key sites of each type, each site spanning a few kilometres across. We
survey the implications for different kins of mission and find that the diverse
actors pursuing incomptible ends at these sites could soon crowd and interfere
with each other, leaving almost all actors worse off. Without proactive
measures to prevent these outcomes, lunar actors are likely to experience
significant losses of opportunity. We highlight the legal, policy, and ethical
ramifications. Insights from research on comparable sites on Earth present a
path toward managing lunar crowding and interference grounded in ethical and
practical near-term considerations. This article is part of a discussion
meeting issue 'Astronomy from the Moon: the next decades'.


Abstract:
  To improve online search results, clarification questions can be used to
elucidate the information need of the user. This research aims to predict the
user engagement with the clarification pane as an indicator of relevance based
on the lexical information: query, question, and answers. Subsequently, the
predicted user engagement can be used as a feature to rank the clarification
panes. Regression and classification are applied for predicting user engagement
and compared to naive heuristic baselines (e.g. mean) on the new MIMICS dataset
[20]. An ablation study is carried out using a RankNet model to determine
whether the predicted user engagement improves clarification pane ranking
performance. The prediction models were able to improve significantly upon the
naive baselines, and the predicted user engagement feature significantly
improved the RankNet results in terms of NDCG and MRR. This research
demonstrates the potential for ranking clarification panes based on lexical
information only and can serve as a first neural baseline for future research
to improve on. The code is available online.


Abstract:
  The NLP community has witnessed steep progress in a variety of tasks across
the realms of monolingual and multilingual language processing recently. These
successes, in conjunction with the proliferating mixed language interactions on
social media have boosted interest in modeling code-mixed texts. In this work,
we present CodemixedNLP, an open-source library with the goals of bringing
together the advances in code-mixed NLP and opening it up to a wider machine
learning community. The library consists of tools to develop and benchmark
versatile model architectures that are tailored for mixed texts, methods to
expand training sets, techniques to quantify mixing styles, and fine-tuned
state-of-the-art models for 7 tasks in Hinglish. We believe this work has a
potential to foster a distributed yet collaborative and sustainable ecosystem
in an otherwise dispersed space of code-mixing research. The toolkit is
designed to be simple, easily extensible, and resourceful to both researchers
as well as practitioners.


Abstract:
  In the work we use integral formulas for calculating the monodromy data for
the Painlev\'e-2 equation. The perturbation theory for the auxiliary linear
system is constructed and formulas for the variation of the monodromy data are
obtained. We also derive a formula for solving the linearized Painlev\'e-2
equation based on the Fourier-type integral of the squared solutions of the
auxiliary linear system of equations.


Abstract:
  A liquid droplet impacting on a solvophobic surface normally rebounds. The
rebound is suppressed by a small amount of dissolved polymer. In this work,
using multi-body dissipative particle dynamics simulations, two anti-rebound
mechanisms, the slow-retraction and the slow-hopping mechanisms, are
identified. Which of them dominates depends on the polymer-surface attraction
strength. However, these two mechanisms are not excluding each other but may
coexist. During the droplet rebound, the surface-adsorbed polymer acts in two
ways: the adsorbed beads mediate solvent-surface interactions, and highly
stretching unadsorbed polymer segment exerts a retraction force on the liquid.
Both actions increase the friction against retraction and the resistance
against hopping. We also investigate the effects of the molecular weight and
the concentration of the polymer additive, the droplet size, and the impact
velocity on the rebound tendency. As the first work to provide a microscopic
explanation of the anti-rebound mechanism by polymer additives, this study
allows better understanding of wetting behavior by polymer-solution droplets.


Abstract:
  The successful deployment of artificial intelligence (AI) in many domains
from healthcare to hiring requires their responsible use, particularly in model
explanations and privacy. Explainable artificial intelligence (XAI) provides
more information to help users to understand model decisions, yet this
additional knowledge exposes additional risks for privacy attacks. Hence,
providing explanation harms privacy. We study this risk for image-based model
inversion attacks and identified several attack architectures with increasing
performance to reconstruct private image data from model explanations. We have
developed several multi-modal transposed CNN architectures that achieve
significantly higher inversion performance than using the target model
prediction only. These XAI-aware inversion models were designed to exploit the
spatial knowledge in image explanations. To understand which explanations have
higher privacy risk, we analyzed how various explanation types and factors
influence inversion performance. In spite of some models not providing
explanations, we further demonstrate increased inversion performance even for
non-explainable target models by exploiting explanations of surrogate models
through attention transfer. This method first inverts an explanation from the
target prediction, then reconstructs the target image. These threats highlight
the urgent and significant privacy risks of explanations and calls attention
for new privacy preservation techniques that balance the dual-requirement for
AI explainability and privacy.


Abstract:
  This paper explores supply chain viability through empirical network-level
analysis of supplier reachability under various scenarios. Specifically, this
study investigates the effect of multi-tier random failures across different
scales, as well as intelligent attacks on the global supply chain of medical
equipment, an industry whose supply chain's viability was put under a crucial
test during the COVID-19 pandemic. The global supply chain data was mined and
analyzed from about 45,000 firms with about 115,000 intertwined relationships
spanning across 10 tiers of the backward supply chain of medical equipment.
This complex supply chain network was analyzed at four scales, namely: firm,
country-industry, industry, and country. A notable contribution of this study
is the application of a supply chain tier optimization tool to identify the
lowest tier of the supply chain that can provide adequate resolution for the
study of the supply chain pattern in the medical equipment sector. We also
developed data-driven-tools to identify the thresholds for the breakdown and
fragmentation of the medical equipment supply chain when faced with random
failures, or different intelligent attack scenarios. The novel network analysis
tools utilized in the study can be applied to the study of supply chain
reachability and viability in other industries.


Abstract:
  We revisit the dynamic relationship between stock market and domestic
economic policy uncertainty (EPU) with the symmetric thermal optimal path
(TOPS) method. We observe totally different interaction pattern in emerging and
developed markets. Economic policy uncertainty can drive stock market in China,
while stock market plays a leading role in the UK and the US. Meanwhile, the
lead-lag relationship of the three countries react significantly when extreme
events happen. Our findings have important implications for investors and
policy makers.


Abstract:
  We provide necessary and sufficient conditions for generic n-qubit states to
be equivalent under Stochastic Local Operations with Classical Communication
(SLOCC) using a single polynomial entanglement measure. SLOCC operations may be
represented geometrically by M\"obius transformations on the roots of the
entanglement measure on the Bloch sphere. Moreover, we show how the roots of
the 3-tangle measure classify 4-qubit generic states and propose a method to
obtain the normal form of a 4-qubit state which bypasses the possibly infinite
iterative procedure.


Abstract:
  One of the leading single-channel speech separation (SS) models is based on a
TasNet with a dual-path segmentation technique, where the size of each segment
remains unchanged throughout all layers. In contrast, our key finding is that
multi-granularity features are essential for enhancing contextual modeling and
computational efficiency. We introduce a self-attentive network with a novel
sandglass-shape, namely Sandglasset, which advances the state-of-the-art (SOTA)
SS performance at significantly smaller model size and computational cost.
Forward along each block inside Sandglasset, the temporal granularity of the
features gradually becomes coarser until reaching half of the network blocks,
and then successively turns finer towards the raw signal level. We also unfold
that residual connections between features with the same granularity are
critical for preserving information after passing through the bottleneck layer.
Experiments show our Sandglasset with only 2.3M parameters has achieved the
best results on two benchmark SS datasets -- WSJ0-2mix and WSJ0-3mix, where the
SI-SNRi scores have been improved by absolute 0.8 dB and 2.4 dB, respectively,
comparing to the prior SOTA results.


Abstract:
  The anomalous magnetic and electric dipole moments in spin motion equation
acquire pseudoscalar corrections if the $T(CP)$-noninvariance is admitted. It
allows to explain the discrepancy between experimental and theoretical values
of muon $(g-2)$ factor under assumption that the pseudoscalar correction is the
dominant source of this discrepancy.


Abstract:
  To be able to predict a molecular graph structure ($W$) given a 2D image of a
chemical compound ($U$) is a challenging problem in machine learning. We are
interested to learn $f: U \rightarrow W$ where we have a fully mediating
representation $V$ such that $f$ factors into $U \rightarrow V \rightarrow W$.
However, observing V requires detailed and expensive labels. We propose graph
aligning approach that generates rich or detailed labels given normal labels
$W$. In this paper we investigate the scenario of domain adaptation from the
source domain where we have access to the expensive labels $V$ to the target
domain where only normal labels W are available. Focusing on the problem of
predicting chemical compound graphs from 2D images the fully mediating layer is
represented using the planar embedding of the chemical graph structure we are
predicting. The use of a fully mediating layer implies some assumptions on the
mechanism of the underlying process. However if the assumptions are correct it
should allow the machine learning model to be more interpretable, generalize
better and be more data efficient at training time. The empirical results show
that, using only 4000 data points, we obtain up to 4x improvement of
performance after domain adaptation to target domain compared to pretrained
model only on the source domain. After domain adaptation, the model is even
able to detect atom types that were never seen in the original source domain.
Finally, on the Maybridge data set the proposed self-labeling approach reached
higher performance than the current state of the art.


Abstract: The paper is an introduction to intuitionistic mathematics.


Abstract:
  As machine learning black boxes are increasingly being deployed in critical
domains such as healthcare and criminal justice, there has been a growing
emphasis on developing techniques for explaining these black boxes in a post
hoc manner. In this work, we analyze two popular post hoc interpretation
techniques: SmoothGrad which is a gradient based method, and a variant of LIME
which is a perturbation based method. More specifically, we derive explicit
closed form expressions for the explanations output by these two methods and
show that they both converge to the same explanation in expectation, i.e., when
the number of perturbed samples used by these methods is large. We then
leverage this connection to establish other desirable properties, such as
robustness, for these techniques. We also derive finite sample complexity
bounds for the number of perturbations required for these methods to converge
to their expected explanation. Finally, we empirically validate our theory
using extensive experimentation on both synthetic and real world datasets.


Abstract:
  The purpose of this note is twofold: firstly to characterize all the
sequences of orthogonal polynomials $(P_n)_{n\geq 0}$ such that $$
\frac{\triangle}{{\bf \triangle} x(s-1/2)}P_{n+1}(x(s-1/2))=c_n(\triangle
+2\,\mathrm{I})P_n(x(s-1/2)), $$ where $\mathrm{I}$ is the identity operator,
$x$ defines a class of lattices with, generally, nonuniform step-size, and
$\triangle f(s)=f(s+1)-f(s)$; and secondly to present, in a friendly way, a
method to deal with these kind of problems.


Abstract:
  The addition of an external starshade to the {\it Nancy Grace Roman Space
Telescope} will enable the direct imaging of Earth-radius planets orbiting at
$\sim$1 AU. Classification of any detected planets as Earth-like requires both
spectroscopy to characterize their atmospheres and multi-epoch imaging to trace
their orbits. We consider here the ability of the Starshade Rendezvous Probe to
constrain the orbits of directly imaged Earth-like planets. The target list for
this proposed mission consists of the 16 nearby stars best suited for direct
imaging. The field of regard for a starshade mission is constrained by solar
exclusion angles, resulting in four observing windows during a two-year
mission. We find that for habitable-zone planetary orbits that are detected at
least three times during the four viewing opportunities, their semi-major axes
are measured with a median precision of 7 mas, or a median fractional precision
of 3\%. Habitable-zone planets can be correctly identified as such 96.7\% of
the time, with a false positive rate of 2.8\%. If a more conservative criteria
is used for habitable-zone classification (95\% probability), the false
positive rate drops close to zero, but with only 81\% of the truly Earth-like
planets correctly classified as residing in the habitable zone.


Abstract:
  We consider the incompressible Euler equations in $R^2$ when the initial
vorticity is bounded, radially symmetric and non-increasing in the radial
direction. Such a radial distribution is stationary, and we show that the
monotonicity produces stability in some weighted norm related to the angular
impulse. For instance, it covers the cases of circular vortex patches and
Gaussian distributions. Our stability does not depend on $L^\infty$-bound or
support size of perturbations. The proof is based on the fact that such a
radial monotone distribution minimizes the impulse of functions having the same
level set measure.


Abstract:
  This paper presents a novel method for clustering surfaces. The proposal
involves first using basis functions in a tensor product to smooth the data and
thus reduce the dimension to a finite number of coefficients, and then using
these estimated coefficients to cluster the surfaces via the k-means algorithm.
An extension of the algorithm to clustering tensors is also discussed. We show
that the proposed algorithm exhibits the property of strong consistency, with
or without measurement errors, in correctly clustering the data as the sample
size increases. Simulation studies suggest that the proposed method outperforms
the benchmark k-means algorithm which uses the original vectorized data. In
addition, an EGG real data example is considered to illustrate the practical
application of the proposal.


Abstract:
  Acoustic transparency is the capability of a medium to transmit mechanical
waves to adjacent media, without scattering. This characteristic can be
achieved by carefully engineering the acoustic impedance of the medium -- a
combination of wave speed and density, to match that of the surroundings. Owing
to the strong correlation between acoustic wave speed and static stiffness, it
is challenging to design acoustically transparent materials in a fluid, while
maintaining their high structural rigidity. In this work, we propose a method
to design architected lattices with independent control of the elastic wave
speed at a chosen frequency, the mass density, and the static stiffness, along
a chosen loading direction. We provide a sensitivity analysis to optimize these
properties with respect to design parameters of the structure, that include
localized masses at specific positions. We demonstrate the method on five
different periodic, three dimensional lattices, to calculate bounds on the
longitudinal wave speed as a function of their density and stiffness. We then
perform experiments on 3-D printed structures, to validate our numerical
simulations. The tools developed in this work can be used to design lightweight
and stiff materials with optimized acoustic impedance for a plethora of
applications, including ultrasound imaging, wave filtering and waveguiding.


Abstract:
  We present a short review of possible applications of the Wheeler-De Witt
equation to cosmological models based on the low-energy string effective
action, and characterised by an initial regime of asymptotically flat, low
energy, weak coupling evolution. Considering in particular a class of
duality-related (but classically disconnected) background solutions, we shall
discuss the possibility of quantum transitions between the phases of pre-big
bang and post-big bang evolution. We will show that it is possible, in such a
context, to represent the birth of our Universe as a quantum process of
tunneling or "anti-tunneling" from an initial state asymptotically approaching
the string perturbative vacuum.


Abstract:
  Finite-difference (FD) modeling of seismic waves in the vicinity of dipping
interfaces gives rise to artifacts. Examples are phase and amplitude errors, as
well as staircase diffractions. Such errors can be reduced in two general ways.
In the first approach, the interface can be anti-aliased (i.e., with an
anti-aliased step-function, or a lowpass filter). Alternatively, the interface
may be replaced with an equivalent medium (i.e., using Schoenberg \& Muir (SM)
calculus or orthorhombic averaging). We test these strategies in acoustic,
elastic isotropic, and elastic anisotropic settings. Computed FD solutions are
compared to analytical solutions. We find that in acoustic media, anti-aliasing
methods lead to the smallest errors. Conversely, in elastic media, the SM
calculus provides the best accuracy. The downside of the SM calculus is that it
requires an anisotropic FD solver even to model an interface between two
isotropic materials. As a result, the computational cost increases compared to
when using isotropic FD solvers. However, since coarser grid spacings can be
used to represent the dipping interfaces, the two effects (an expensive FD
solver on a coarser FD grid) equal out. Hence, the SM calculus can provide an
efficient means to reduce errors, also in elastic isotropic media.


Abstract:
  The singlemode condition is one of the most important design rules for
optical waveguides in guided-wave optics. The reason following the singlemode
condition is that higher-order modes might be excited and thus introduce some
undesired mode-mismatching loss as well as inter-mode crosstalk when light
propagates along an optical waveguide beyond the singlemode regime. As a
result, multimode photonic waveguides are usually not allowed. In this paper,
we propose the concept of silicon photonics beyond the singlemode regime,
developed with low-loss and low-crosstalk light propagation in multimode
photonic waveguides with broadened silicon cores. In particular, silicon
photonic waveguides with a broadened core region have shown an ultra-low-loss
of ~0.1 dB/cm for the fundamental mode even without any special fabrication
process. A micro-racetrack resonator fabricated with standard 220-nm-SOI
MPW-foundry processes shows a record intrinsic Q-factor as high as 1.02*107 for
the first time, corresponding to ultra-low waveguide propagation loss of only
0.065 dB/cm. A high-performance microwave photonic filter on silicon is then
realized with an ultra-narrow 3-dB bandwidth of 20.6 MHz as well as a tuning
range of ~20 GHz for the first time. An on-chip 100-cm-long delayline is also
demonstrated by using the present broadened SOI photonic waveguides with
compact Euler-curve bends, the measured propagation loss is ~0.14 dB/cm. The
proposed concept of silicon photonics beyond the singlemode regime helps solve
the issue of high propagation loss and also significantly reduces the random
phase errors of light due to the random variations of waveguide dimensions. In
particularity it enables silicon photonic devices with enhanced performances,
which paves the way for new-generation silicon photonics realizing the
large-scale photonic integration.


Abstract:
  Useful materials must satisfy multiple objectives, where the optimization of
one objective is often at the expense of another. The Pareto front reports the
optimal trade-offs between competing objectives. Here we report a self-driving
laboratory, "Ada", that defines the Pareto front of conductivities and
processing temperatures for palladium films formed by combustion synthesis. Ada
identified previously untested combustion synthesis conditions that resulted in
the discovery of lower processing temperatures (below 200 {\deg}C) relative to
the prior art for this technique (250 {\deg}C), a temperature difference that
makes the coating of different commodity plastic materials possible (e.g.,
Nafion, polyethersulfone). These conditions enabled us to use combustion
synthesis to spray coat uniform palladium films with moderate conductivity (1.1
$\times$ 10$^5$ S m$^{-1}$) at 191 {\deg}C. Spray coating at 226 {\deg}C
yielded films with conductivities (2.0 $\times$ 10$^6$ S m$^{-1}$) comparable
to those of sputtered films (2.0 to 5.8 $\times$ 10$^6$ S m$^{-1}$). This work
shows how self-driving laboratories can discover materials satisfying multiple
objectives.


Abstract:
  Observations suggest that satellite quenching plays a major role in the
build-up of passive, low-mass galaxies at late cosmic times. Studies of
low-mass satellites, however, are limited by the ability to robustly
characterize the local environment and star-formation activity of faint
systems. In an effort to overcome the limitations of existing data sets, we
utilize deep photometry in Stripe 82 of the Sloan Digital Sky Survey, in
conjunction with a neural network classification scheme, to study the
suppression of star formation in low-mass satellite galaxies in the local
Universe. Using a statistically-driven approach, we are able to push beyond the
limits of existing spectroscopic data sets, measuring the satellite quenched
fraction down to satellite stellar masses of ${\sim}10^7~{\rm M}_{\odot}$ in
group environments (${M}_{\rm{halo}} = 10^{13-14}~h^{-1}~{\rm M}_{\odot}$). At
high satellite stellar masses ($\gtrsim 10^{10}~{\rm M}_{\odot}$), our analysis
successfully reproduces existing measurements of the quenched fraction based on
spectroscopic samples. Pushing to lower masses, we find that the fraction of
passive satellites increases, potentially signaling a change in the dominant
quenching mechanism at ${M}_{\star} \sim 10^{9}~{\rm M}_{\odot}$. Similar to
the results of previous studies of the Local Group, this increase in the
quenched fraction at low satellite masses may correspond to an increase in the
efficacy of ram-pressure stripping as a quenching mechanism in groups.


Abstract:
  We are concerned with the global bifurcation analysis of positive solutions
to free boundary problems arising in plasma physics. We show that in general,
in the sense of domain variations, the following alternative holds: either the
shape of the branch of solutions resembles the monotone one of the model case
of the two-dimensional disk, or it is a continuous simple curve without
bifurcation points which ends up at a point where the boundary density
vanishes. On the other hand, we deduce a general criterion ensuring the
existence of a free boundary in the interior of the domain. Application to a
classic nonlinear eigenvalue problem is also discussed.


Abstract:
  Flexible optical network is a promising technology to accommodate
high-capacity demands in next-generation networks. To ensure uninterrupted
communication, existing lightpath provisioning schemes are mainly done with the
assumption of worst-case resource under-provisioning and fixed channel spacing,
which preserves an excessive signal-to-noise ratio (SNR) margin. However, under
a resource over-provisioning scenario, the excessive SNR margin restricts the
transmission bit-rate or transmission reach, leading to physical layer resource
waste and stranded transmission capacity. To tackle this challenging problem,
we leverage an iterative feedback tuning algorithm to provide a just-enough SNR
margin, so as to maximize the network throughput. Specifically, the proposed
algorithm is implemented in three steps. First, starting from the high SNR
margin setup, we establish an integer linear programming model as well as a
heuristic algorithm to maximize the network throughput by solving the problem
of routing, modulation format, forward error correction, baud-rate selection,
and spectrum assignment. Second, we optimize the channel spacing of the
lightpaths obtained from the previous step, thereby increasing the available
physical layer resources. Finally, we iteratively reduce the SNR margin of each
lightpath until the network throughput cannot be increased. Through numerical
simulations, we confirm the throughput improvement in different networks and
with different baud-rates. In particular, we find that our algorithm enables
over 20\% relative gain when network resource is over-provisioned, compared to
the traditional method preserving an excessive SNR margin.


Abstract:
  We prove limit equalities between the sharp constants in weighted
Nikolskii-type inequalities for multivariate polynomials on an $m$-dimensional
cube and ball and the corresponding constants for entire functions of
exponential type.


Abstract:
  Developing the flocking behavior for a dynamic squad of fixed-wing UAVs is
still a challenge due to kinematic complexity and environmental uncertainty. In
this paper, we deal with the decentralized flocking and collision avoidance
problem through deep reinforcement learning (DRL). Specifically, we formulate a
decentralized DRL-based decision making framework from the perspective of every
follower, where a collision avoidance mechanism is integrated into the flocking
controller. Then, we propose a novel reinforcement learning algorithm PS-CACER
for training a shared control policy for all the followers. Besides, we design
a plug-n-play embedding module based on convolutional neural networks and the
attention mechanism. As a result, the variable-length system state can be
encoded into a fixed-length embedding vector, which makes the learned DRL
policy independent with the number and the order of followers. Finally,
numerical simulation results demonstrate the effectiveness of the proposed
method, and the learned policies can be directly transferred to semi-physical
simulation without any parameter finetuning.


Abstract:
  Motivated by the recent advances in the categorification of the cluster
structure on the coordinate rings of Grassmannians of $k$-subspaces in
$n$-space, we investigate a particular construction of root systems of type
$\mathsf{T}_{2,p,q}$, including the type $\mathsf{E}_n$. This construction
generalizes Manin's ``hyperbolic construction'' of $\mathsf{E}_8$ and reveals a
lot of otherwise hidden regularities in this family of root systems.


Abstract:
  We study the evolutionary dynamics of a phenotypically structured population
in a changing environment , where the environmental conditions vary with a
linear trend but in an oscillatory manner. Such phenomena can be described by
parabolic Lotka-Volterra type equations with non-local competition and a time
dependent growth rate. We first study the long time behavior of the solution to
this problem. Next, using an approach based on Hamilton-Jacobi equations we
study asymptotically such long time solutions when the effects of the mutations
are small. We prove that, as the effect of the mutations vanishes, the
phenotypic density of the population concentrates on a single trait which
varies linearly with time, while the size of the population oscillates
periodically. In contrast with the case of an environment without linear shift,
such dominant trait does not have the maximal growth rate in the averaged
environment and there is a cost on the growth rate due to the climate shift. We
also provide an asymptotic expansion for the average size of the population and
for the critical speed above which the population goes extinct, which is
closely related to the derivation of an asymptotic expansion for the Floquet
eigenvalue in terms of the diffusion rate. By mean of a biological example,
this expansion allows to show that the fluctuations on the environment may help
the population to follow the climatic shift in a better way.


Abstract:
  The paper was suggested by a brief note of the second author about the
application of the Hubbert curve to predict decay of resource exploitation. A
further suggestion came from the interpretation of the Hubbert curve in terms
of a specific Lotka Volterra (LV) equation. The link with population dynamics
was obvious as logistic function and LV equation were proposed within the
demography science field. Mathematical population dynamics has a history of
about two centuries. The first principle and model of population dynamics can
be regarded the exponential law of Malthus. In the XIX century, the Malthusian
demographic model was first refined to include mortality rate by Gompertz. In
the early XIX century the model was further refined by Verhulst by introducing
the standard logistic function. The previous models only concern the population
of a single species. In the early XX century, the American demographer Lotka
and the Italian mathematician Volterra proposed a pair of state equations which
describe the population dynamics of two competing species, the predator and the
prey. This paper is concerned with the single and two-species fundamental
equations: the logistic and LV equation. The paper starts with the generalized
logistic equation whose free response is derived together with equilibrium
points and stability properties. The parameter estimation of the logistic
function is applied to the raw data of the US crude oil production. The paper
proceeds with the Lotka Volterra equation of the competition between two
species, with the goal of applying it to resource exploitation. At the end, a
limiting version of the LV equation is studied since it describes a competition
model between the production rate of exploited resources and the relevant
capital stock employed in the exploitation.


Abstract:
  We conduct spectral observations of 138 superthin galaxies (STGs) with high
radial-to-vertical stellar disk scales ratio with the Dual Imaging Spectrograph
(DIS) on the 3.5m telescope at the Apache Point Observatory (APO) to obtain the
ionized gas rotation curves with R ~ 5000 resolution. We also performed near
infrared (NIR) H and Ks photometry for 18 galaxies with the NICFPS camera on
the 3.5m telescope. The spectra, the NIR photometry and published optical and
NIR photometry are used for modeling that utilizes the thickness of the stellar
disk and rotation curves simultaneously. The projection and dust extinction
effects are taken into account. We evaluate eight models that differ by their
free parameters and constraints. As a result, we estimated masses and scale
lengths of the galactic dark halos. We find systematic differences between the
properties of our red and blue STGs. The blue STGs have a large fraction of
dynamically under-evolved galaxies whose vertical velocity dispersion is low in
both gas and stellar disks. The dark halo-to-disk scale ratio is shorter in the
red STGs than in the blue ones, but in a majority of all STGs this ratio is
under 2. The optical color $(r-i)$ of the superthin galaxies correlates with
their rotation curve maximum, vertical velocity dispersion in stellar disks,
and mass of the dark halo. We conclude that there is a threshold central
surface density of 50 $M_{\odot}$\,pc$^{-2}$ below which we do not observe very
thin, rotationally supported galactic disks.


Abstract:
  UV radiation has been used as a disinfection strategy to deactivate a wide
range of pathogens, but existing irradiation strategies do not ensure
sufficient exposure of all environmental surfaces and/or require long
disinfection times. We present a near-optimal coverage planner for mobile UV
disinfection robots. The formulation optimizes the irradiation time efficiency,
while ensuring that a sufficient dosage of radiation is received by each
surface. The trajectory and dosage plan are optimized taking collision and
light occlusion constraints into account. We propose a two-stage scheme to
approximate the solution of the induced NP-hard optimization, and, for
efficiency, perform key irradiance and occlusion calculations on a GPU.
Empirical results show that our technique achieves more coverage for the same
exposure time as strategies for existing UV robots, can be used to compare UV
robot designs, and produces near-optimal plans. This is an extended version of
the paper originally contributed to ICRA2021.


Abstract:
  A good joint training framework is very helpful to improve the performances
of weakly supervised audio tagging (AT) and acoustic event detection (AED)
simultaneously. In this study, we propose three methods to improve the best
teacher-student framework of DCASE2019 Task 4 for both AT and AED tasks. A
frame-level target-events based deep feature distillation is first proposed, it
aims to leverage the potential of limited strong-labeled data in weakly
supervised framework to learn better intermediate feature maps. Then we propose
an adaptive focal loss and two-stage training strategy to enable an effective
and more accurate model training, in which the contribution of
difficult-to-classify and easy-to-classify acoustic events to the total cost
function can be automatically adjusted. Furthermore, an event-specific post
processing is designed to improve the prediction of target event time-stamps.
Our experiments are performed on the public DCASE2019 Task4 dataset, and
results show that our approach achieves competitive performances in both AT
(49.8% F1-score) and AED (81.2% F1-score) tasks.


Abstract:
  We present a novel large-scale dataset and accompanying machine learning
models aimed at providing a detailed understanding of the interplay between
visual content, its emotional effect, and explanations for the latter in
language. In contrast to most existing annotation datasets in computer vision,
we focus on the affective experience triggered by visual artworks and ask the
annotators to indicate the dominant emotion they feel for a given image and,
crucially, to also provide a grounded verbal explanation for their emotion
choice. As we demonstrate below, this leads to a rich set of signals for both
the objective content and the affective impact of an image, creating
associations with abstract concepts (e.g., "freedom" or "love"), or references
that go beyond what is directly visible, including visual similes and
metaphors, or subjective references to personal experiences. We focus on visual
art (e.g., paintings, artistic photographs) as it is a prime example of imagery
created to elicit emotional responses from its viewers. Our dataset, termed
ArtEmis, contains 439K emotion attributions and explanations from humans, on
81K artworks from WikiArt. Building on this data, we train and demonstrate a
series of captioning systems capable of expressing and explaining emotions from
visual stimuli. Remarkably, the captions produced by these systems often
succeed in reflecting the semantic and abstract content of the image, going
well beyond systems trained on existing datasets. The collected dataset and
developed methods are available at https://artemisdataset.org.


Abstract:
  Finding the mean square averages of the Dirichlet $L$-functions over
Dirichlet characters $\chi$ of same parity is an active problem in number
theory. Here we explicitly evaluate such averages of $L(3,\chi)$ and
$L(4,\chi)$ using certain trigonometric sums and Bernoulli polynomials and
express them in terms of the Euler totient function $\phi$ and the Jordan
totient function $J_s$.


Abstract:
  Direct measurements of three-dimensional magnetic fields in the interstellar
medium (ISM) are not achievable. However, the anisotropic nature of
magnetohydrodynamic (MHD) turbulence provides a novel way of tracing the
magnetic fields. Guided by the advanced understanding of turbulence's
anisotropy in the Position-Position-Velocity (PPV) space, we extend the
Structure-Function Analysis (SFA) to measure both the three-dimensional
magnetic field orientation and Alfven Mach number $M_A$, which provides the
information on magnetic field strength. Following the theoretical framework
developed in Kandel et al. (2016), we find that the anisotropy in a given
velocity channel is affected by the inclination angle between the 3D magnetic
field direction and the line-of-sight as well as media magnetization. We
analyze the synthetic PPV cubes generated by incompressible and compressible
MHD simulations. We confirm that the PPV channel's intensity fluctuations
measured in various position angles reveal plane-of-the-sky magnetic field
orientation. We show that by varying the channel width, the anisotropies of the
intensity fluctuations in PPV space can be used to simultaneously estimate both
magnetic field inclination angle and strength of total magnetic fields.


Abstract:
  We develop a general framework to significantly reduce the degree of
sum-of-squares proofs by introducing new variables. To illustrate the power of
this framework, we use it to speed up previous algorithms based on
sum-of-squares for two important estimation problems, clustering and robust
moment estimation. The resulting algorithms offer the same statistical
guarantees as the previous best algorithms but have significantly faster
running times. Roughly speaking, given a sample of $n$ points in dimension $d$,
our algorithms can exploit order-$\ell$ moments in time $d^{O(\ell)}\cdot
n^{O(1)}$, whereas a naive implementation requires time $(d\cdot n)^{O(\ell)}$.
Since for the aforementioned applications, the typical sample size is
$d^{\Theta(\ell)}$, our framework improves running times from $d^{O(\ell^2)}$
to $d^{O(\ell)}$.


Abstract:
  Let $\mathbf{P}$ be a parabolic subgroup with Levi $\mathbf{M}$ of a
connected reductive group defined over a locally compact non-archimedean field
$F$. Given a certain compact open subgroup $\Gamma$ of $\mathbf{P}(F)$, this
note proves that the Hecke algebra $\mathcal{H}(\mathbf{M}(F))$ of
$\mathbf{M}(F)$ with respect to $\Gamma\cap \mathbf{M}(F)$ is a left ring of
fractions of the Hecke algebra $\mathcal{H}(\mathbf{P}(F))$ of $\mathbf{P}(F)$
with respect to $\Gamma$. This leads to a characterization of
$\mathcal{H}(\mathbf{P}(F))$-modules that come from
$\mathcal{H}(\mathbf{M}(F))$-modules.


Abstract:
  The presence of stars on retrograde orbits in disc galaxies is usually
attributed to accretion events, both via direct accretion, as well as through
the heating of the disc stars. Recent studies have shown that retrograde orbits
can also be produced via scattering by dense clumps, which are often present in
the early stages of a galaxy's evolution. However, so far it has been unclear
whether other internally-driven mechanisms, such as bars, are also capable of
driving retrograde motion. Therefore, in this paper, we investigate the
efficiencies with which bars and clumps produce retrograde orbits in disc
galaxies. We do this by comparing the retrograde fractions and the spatial
distributions of the retrograde populations in four $N$-body$+$smooth particle
hydrodynamics (SPH) simulations of isolated disc galaxies spanning a range of
evolutionary behaviours. We find that both bars and clumps are capable of
generating significant retrograde populations of order $\sim 10\%$ of all
stars. We also find that while clump-driven retrograde stars may be found at
large galactocentric radii, bar-driven retrograde stars remain in the vicinity
of the bar, even if the bar dissolves. Consequently, we find that retrograde
stars in the Solar Neighbourhood in the clumpy models are exclusively
clump-driven, but this is a trace population, constituting $0.01-0.04\%$ of the
total stellar population in this region. Finally, we find that neither bars
(including dissolving ones) nor clumps in the models are able to produce
rotationally supported counter-rotating discs.


Abstract:
  A reinforcement learning (RL) policy trained in a nominal environment could
fail in a new/perturbed environment due to the existence of dynamic variations.
Existing robust methods try to obtain a fixed policy for all envisioned dynamic
variation scenarios through robust or adversarial training. These methods could
lead to conservative performance due to emphasis on the worst case, and often
involve tedious modifications to the training environment. We propose an
approach to robustifying a pre-trained non-robust RL policy with
$\mathcal{L}_1$ adaptive control. Leveraging the capability of an
$\mathcal{L}_1$ control law in the fast estimation of and active compensation
for dynamic variations, our approach can significantly improve the robustness
of an RL policy trained in a standard (i.e., non-robust) way, either in a
simulator or in the real world. Numerical experiments are provided to validate
the efficacy of the proposed approach.