Abstract:
  Change detection from synthetic aperture radar (SAR) imagery is a critical
yet challenging task. Existing methods mainly focus on feature extraction in
spatial domain, and little attention has been paid to frequency domain.
Furthermore, in patch-wise feature analysis, some noisy features in the
marginal region may be introduced. To tackle the above two challenges, we
propose a Dual-Domain Network. Specifically, we take features from the discrete
cosine transform domain into consideration and the reshaped DCT coefficients
are integrated into the proposed model as the frequency domain branch. Feature
representations from both frequency and spatial domain are exploited to
alleviate the speckle noise. In addition, we further propose a multi-region
convolution module, which emphasizes the central region of each patch. The
contextual information and central region features are modeled adaptively. The
experimental results on three SAR datasets demonstrate the effectiveness of the
proposed model. Our codes are available at
https://github.com/summitgao/SAR_CD_DDNet.


Abstract:
  Domain mismatch is a noteworthy issue in acoustic event detection tasks, as
the target domain data is difficult to access in most real applications. In
this study, we propose a novel CNN-based discriminative training framework as a
domain compensation method to handle this issue. It uses a parallel CNN-based
discriminator to learn a pair of high-level intermediate acoustic
representations. Together with a binary discriminative loss, the discriminators
are forced to maximally exploit the discrimination of heterogeneous acoustic
information in each audio clip with target events, which results in a robust
paired representations that can well discriminate the target events and
background/domain variations separately. Moreover, to better learn the
transient characteristics of target events, a frame-wise classifier is designed
to perform the final classification. In addition, a two-stage training with the
CNN-based discriminator initialization is further proposed to enhance the
system training. All experiments are performed on the DCASE 2018 Task3
datasets. Results show that our proposal significantly outperforms the official
baseline on cross-domain conditions in AUC by relative $1.8-12.1$% without any
performance degradation on in-domain evaluation conditions.


Abstract:
  We introduce a novel inferential framework for marked point processes that
enjoys both scalability and interpretability. The framework is based on
variational inference and it aims to speed up inference for a flexible family
of marked point processes where the joint distribution of times and marks can
be specified in terms of the conditional distribution of times given the
process filtration, and of the conditional distribution of marks given the
process filtration and the current time. We assess the predictive ability of
our proposed method over four real-world datasets where results show its
competitive performance against other baselines. The attractiveness of our
framework for the modelling of marked point processes is illustrated through a
case study of association football data where scalability and interpretability
are exploited for extracting useful informative patterns.


Abstract:
  Decision-based attacks (DBA), wherein attackers perturb inputs to spoof
learning algorithms by observing solely the output labels, are a type of severe
adversarial attacks against Deep Neural Networks (DNNs) requiring minimal
knowledge of attackers. State-of-the-art DBA attacks relying on zeroth-order
gradient estimation require an excessive number of queries. Recently, Bayesian
optimization (BO) has shown promising in reducing the number of queries in
score-based attacks (SBA), in which attackers need to observe real-valued
probability scores as outputs. However, extending BO to the setting of DBA is
nontrivial because in DBA only output labels instead of real-valued scores, as
needed by BO, are available to attackers. In this paper, we close this gap by
proposing an efficient DBA attack, namely BO-DBA. Different from existing
approaches, BO-DBA generates adversarial examples by searching so-called
\emph{directions of perturbations}. It then formulates the problem as a BO
problem that minimizes the real-valued distortion of perturbations. With the
optimized perturbation generation process, BO-DBA converges much faster than
the state-of-the-art DBA techniques. Experimental results on pre-trained
ImageNet classifiers show that BO-DBA converges within 200 queries while the
state-of-the-art DBA techniques need over 15,000 queries to achieve the same
level of perturbation distortion. BO-DBA also shows similar attack success
rates even as compared to BO-based SBA attacks but with less distortion.


Abstract:
  In this chapter we give an overview of the consensus-based global
optimization algorithm and its recent variants. We recall the formulation and
analytical results of the original model, then we discuss variants using
component-wise independent or common noise. In combination with mini-batch
approaches those variants were tailored for machine learning applications.
Moreover, it turns out that the analytical estimates are dimension independent,
which is useful for high-dimensional problems. We discuss the relationship of
consensus-based optimization with particle swarm optimization, a method widely
used in the engineering community. Then we survey a variant of consensus-based
optimization that is proposed for global optimization problems constrained to
hyper-surfaces. We conclude the chapter with remarks on applications, preprints
and open problems.


Abstract:
  With the growth of the open-source data science community, both the number of
data science libraries and the number of versions for the same library are
increasing rapidly. To match the evolving APIs from those libraries,
open-source organizations often have to exert manual effort to refactor the
APIs used in the code base. Moreover, due to the abundance of similar
open-source libraries, data scientists working on a certain application may
have an abundance of libraries to choose, maintain and migrate between. The
manual refactoring between APIs is a tedious and error-prone task. Although
recent research efforts were made on performing automatic API refactoring
between different languages, previous work relies on statistical learning with
collected pairwise training data for the API matching and migration. Using
large statistical data for refactoring is not ideal because such training data
will not be available for a new library or a new version of the same library.
We introduce Synthesis for Open-Source API Refactoring (SOAR), a novel
technique that requires no training data to achieve API migration and
refactoring. SOAR relies only on the documentation that is readily available at
the release of the library to learn API representations and mapping between
libraries. Using program synthesis, SOAR automatically computes the correct
configuration of arguments to the APIs and any glue code required to invoke
those APIs. SOAR also uses the interpreter's error messages when running
refactored code to generate logical constraints that can be used to prune the
search space. Our empirical evaluation shows that SOAR can successfully
refactor 80% of our benchmarks corresponding to deep learning models with up to
44 layers with an average run time of 97.23 seconds, and 90% of the data
wrangling benchmarks with an average run time of 17.31 seconds.


Abstract:
  This paper considers (partial) identification of a variety of counterfactual
parameters in binary response models with possibly endogenous regressors. Our
framework allows for nonseparable index functions with multi-dimensional latent
variables, and does not require parametric distributional assumptions. We
leverage results on hyperplane arrangements and cell enumeration from the
literature on computational geometry in order to provide a tractable means of
computing the identified set. We demonstrate how various functional form,
independence, and monotonicity assumptions can be imposed as constraints in our
optimization procedure to tighten the identified set, and we show how these
assumptions can be assigned meaningful interpretations in terms of restrictions
on latent response types. Finally, we apply our method to study the effects of
health insurance on the decision to seek medical treatment.


Abstract:
  Scalar metric fluctuations generically source a spectrum of gravitational
waves at second order in perturbation theory, poising gravitational wave
experiments as potentially powerful probes of the small-scale curvature power
spectrum. We perform a detailed study of the imprint of primordial
non-Gaussianity on these induced gravitational waves, emphasizing the role of
both the disconnected and connected components of the primoridal trispectrum.
Specializing to local-type non-Gaussianity, we numerically compute all
contributions and present results for a variety of enhanced primordial
curvature power spectra.


Abstract:
  The disruption of asteroids and comets produces cm-sized meteoroids that end
up impacting the Earth's atmosphere and producing bright fireballs that might
have associated shock waves or, in geometrically-favorable occasions excavate
craters that put them into unexpected hazardous scenarios. The astrometric
reduction of meteors and fireballs to infer their atmospheric trajectories and
heliocentric orbits involves a complex and tedious process that generally
requires many manual tasks. To streamline the process, we present a software
package called SPMN 3D Fireball Trajectory and Orbit Calculator (3D-FireTOC),
an automatic Python code for detection, trajectory reconstruction of meteors,
and heliocentric orbit computation from video recordings. The automatic
3D-FireTOC package comprises of a user interface and a graphic engine that
generates a realistic 3D representation model, which allows users to easily
check the geometric consistency of the results and facilitates scientific
content production for dissemination. The software automatically detects
meteors from digital systems, completes the astrometric measurements, performs
photometry, computes the meteor atmospheric trajectory, calculates the velocity
curve, and obtains the radiant and the heliocentric orbit, all in all
quantifying the error measurements in each step. The software applies
corrections such as light aberration, refraction, zenith attraction, diurnal
aberration and atmospheric extinction. It also characterizes the atmospheric
flight and consequently determines fireball fates by using the $\alpha - \beta$
criterion that analyses the ability of a fireball to penetrate deep into the
atmosphere and produce meteorites. We demonstrate the performance of the
software by analyzing two bright fireballs recorded by the Spanish Fireball and
Meteorite Network (SPMN).


Abstract:
  The fermionic quantum emulator (FQE) is a collection of protocols for
emulating quantum dynamics of fermions efficiently taking advantage of common
symmetries present in chemical, materials, and condensed-matter systems. The
library is fully integrated with the OpenFermion software package and serves as
the simulation backend. The FQE reduces memory footprint by exploiting number
and spin symmetry along with custom evolution routines for sparse and dense
Hamiltonians, allowing us to study significantly larger quantum circuits at
modest computational cost when compared against qubit state vector simulators.
This release paper outlines the technical details of the simulation methods and
key advantages.


Abstract:
  Musculoskeletal models have the potential to improve diagnosis and optimize
clinical treatment by predicting accurate outcomes on an individual basis.
However, the subject-specific modeling of spinal alignment is often strongly
simplified or is based on radiographic assessments, exposing subjects to
unnecessary radiation. We therefore developed a novel skin marker-based
approach for modeling subject-specific spinal alignment and evaluated its
feasibility by comparing the predicted with the actual intervertebral joint
(IVJ) locations/orientations (ground truth) using lateral-view radiographic
images. Moreover, the predictive performance of the subject-specific models was
evaluated by comparing the predicted L1/L2 spinal loads during various
functional activities with in vivo measured data obtained from the OrthoLoad
database. IVJ locations/orientations were predicted closer to ground truth as
opposed to standard model scaling, with average location prediction errors of
0.99+/-0.68 cm on the frontal and 1.21+/-0.97 cm on the transverse axis as well
as an average orientation prediction error of 4.74{\deg}+/-2.80{\deg}.
Simulated spinal loads showed similar curve patterns but considerably larger
values as compared to in vivo measured data. Differences in spinal loads
between generic and subject-specific models become only apparent on an
individual subject level. These results underline the feasibility of the
proposed method and associated workflow for inter- and intra-subject
investigations using musculoskeletal simulations. When implemented into
standard model scaling workflows, it is expected to improve the accuracy of
muscle activity and joint loading simulations, which is crucial for
investigations of treatment effects or pathology-dependent deviations.


Abstract:
  We identify Whittaker vectors for $\mathcal{W}_k(\mathfrak{g})$-modules with
partition functions of higher Airy structures. This implies that Gaiotto
vectors, describing the fundamental class in the equivariant cohomology of a
suitable compactification of the moduli space of $G$-bundles over
$\mathbb{P}^2$ for $G$ a complex simple Lie group, can be computed by a
non-commutative version of the Chekhov-Eynard-Orantin topological recursion. We
formulate the connection to higher Airy structures for Gaiotto vectors of type
A, B, C, and D, and explicitly construct the topological recursion for type A
(at arbitrary level) and type B (at self-dual level). On the physics side, it
means that the Nekrasov partition function for pure $\mathcal{N} = 2$
four-dimensional supersymmetric gauge theories can be accessed by topological
recursion methods.


Abstract:
  I present a strategy for unsupervised manifold learning on local atomic
environments in molecular simulations based on simple rotation- and
permutation-invariant three-body features. These features are highly
descriptive, generalize to multiple chemical species, and are
human-interpretable. The low-dimensional embeddings of each atomic environment
can be used to understand and quantify messy crystal structures such as those
near interfaces and defects or well-ordered crystal lattices such as in bulk
materials without modification. The same method can also yield collective
variables describing collections of particles such as for an entire simulation
domain. I demonstrate the method on colloidal crystallization, ice crystals,
and binary mesophases to illustrate its broad applicability. In each case, the
learned latent space yields insights into the details of the observed
microstructures. For ices and mesophases, supervised classifiers are trained
based on the learned manifolds and directly compared against a recent
neural-network-based approach. Notably, while this method provides comparable
classification performance, it can also be deployed on even a handful of
observed environments without labels or \textit{a priori} knowledge. Thus, the
current approach provides an incredibly versatile strategy to characterize and
classify local atomic environments, and may unlock insights in a wide variety
of molecular simulation contexts.


Abstract:
  The Weisfeiler-Leman (WL) algorithm is a well-known combinatorial procedure
for detecting symmetries in graphs and it is widely used in graph-isomorphism
tests. It proceeds by iteratively refining a colouring of vertex tuples. The
number of iterations needed to obtain the final output is crucial for the
parallelisability of the algorithm.
  We show that there is a constant k such that every planar graph can be
identified (that is, distinguished from every non-isomorphic graph) by the
k-dimensional WL algorithm within a logarithmic number of iterations. This
generalises a result due to Verbitsky (STACS 2007), who proved the same for
3-connected planar graphs.
  The number of iterations needed by the k-dimensional WL algorithm to identify
a graph corresponds to the quantifier depth of a sentence that defines the
graph in the (k+1)-variable fragment C^{k+1} of first-order logic with counting
quantifiers. Thus, our result implies that every planar graph is definable with
a C^{k+1}-sentence of logarithmic quantifier depth.


Abstract:
  We present a methodical study of the thermal and nuclear properties for the
hot nuclear matter using relativistic-mean field theory. We examine the effects
of temperature on the binding energy, pressure, thermal index, symmetry energy,
and its derivative for the symmetric nuclear matter using temperature-dependent
relativistic mean-field formalism for the well-known G2$^{*}$ and recently
developed IOPB-I parameter sets. The critical temperature for the liquid-gas
phase transition in an asymmetric nuclear matter system has also been
calculated and collated with the experimentally available data. We investigate
the approach of the thermal index as a function of nucleon density in the wake
of relativistic and non-relativistic formalism. The computation of neutrino
emissivity through the direct Urca process for the supernovae remnants has also
been performed, which manifests some exciting results about the thermal
stabilization and evolution of the newly born proto-neutron star. The central
temperature and the maximum mass of the proto-neutron star have also been
calculated for different entropy values.


Abstract:
  Let $E/\mathbb{Q}$ be an elliptic curve having multiplicative reduction at a
prime $p$. Let $(g,h)$ be a pair of eigenforms of weight $1$ arising as the
theta series of an imaginary quadratic field $K$, and assume that the
triple-product $L$-function $L(f,g,h,s)$ is self-dual and does not vanish at
the central critical point $s=1$. The main result of this article is a formula
expressing the $p$-adic iterated integrals introduced in [DLR] to the Kolyvagin
classes associated by Bertolini and Darmon to a system of Heegner points on
$E$.


Abstract:
  This paper is the first paper of a series that will present the derivation of
the modal mineralogy of Mars (M3 project) at a global scale from the
near-infrared dataset acquired by the imaging spectrometer OMEGA (Observatoire
pour la Min\'eralogie, l'Eau, les Glaces et l'Activit\'e) on board ESA/Mars
Express. The objective is to create and provide a global 3-D image-cube of Mars
at 32px/{\deg} covering most of Mars surface. This product has several
advantages. First, it can be used to instantaneously extract atmospheric- and
aerosol-corrected near-infrared (NIR) spectra from any location on Mars.
Second, several new data maps can be built as discussed here. That includes new
global mineral distributions, quantitative mineral abundance distributions and
maps of Martian surface chemistry (wt % oxide) detailed in a companion paper
(Riu et al., submitted). Here we present the method to derive the global
hyperspectral cube from several hundred millions of spectra. Global maps of
some mafic minerals are then shown, and compared to previous works.


Abstract:
  Learning from demonstration (LfD) is commonly considered to be a natural and
intuitive way to allow novice users to teach motor skills to robots. However,
it is important to acknowledge that the effectiveness of LfD is heavily
dependent on the quality of teaching, something that may not be assured with
novices. It remains an open question as to the most effective way of guiding
demonstrators to produce informative demonstrations beyond ad hoc advice for
specific teaching tasks. To this end, this paper investigates the use of
machine teaching to derive an index for determining the quality of
demonstrations and evaluates its use in guiding and training novices to become
better teachers. Experiments with a simple learner robot suggest that guidance
and training of teachers through the proposed approach can lead to up to 66.5%
decrease in error in the learnt skill.


Abstract:
  Financial markets are difficult to predict due to its complex systems
dynamics. Although there have been some recent studies that use machine
learning techniques for financial markets prediction, they do not offer
satisfactory performance on financial returns. We propose a novel
one-dimensional convolutional neural networks (CNN) model to predict financial
market movement. The customized one-dimensional convolutional layers scan
financial trading data through time, while different types of data, such as
prices and volume, share parameters (kernels) with each other. Our model
automatically extracts features instead of using traditional technical
indicators and thus can avoid biases caused by selection of technical
indicators and pre-defined coefficients in technical indicators. We evaluate
the performance of our prediction model with strictly backtesting on historical
trading data of six futures from January 2010 to October 2017. The experiment
results show that our CNN model can effectively extract more generalized and
informative features than traditional technical indicators, and achieves more
robust and profitable financial performance than previous machine learning
approaches.


Abstract:
  The natural world is long-tailed: rare classes are observed orders of
magnitudes less frequently than common ones, leading to highly-imbalanced data
where rare classes can have only handfuls of examples. Learning from few
examples is a known challenge for deep learning based classification
algorithms, and is the focus of the field of low-shot learning. One potential
approach to increase the training data for these rare classes is to augment the
limited real data with synthetic samples. This has been shown to help, but the
domain shift between real and synthetic hinders the approaches' efficacy when
tested on real data.
  We explore the use of image-to-image translation methods to close the domain
gap between synthetic and real imagery for animal species classification in
data collected from camera traps: motion-activated static cameras used to
monitor wildlife. We use low-level feature alignment between source and target
domains to make synthetic data for a rare species generated using a graphics
engine more "realistic". Compared against a system augmented with unaligned
synthetic data, our experiments show a considerable decrease in classification
error rates on a rare species.


Abstract:
  Efficient discovery of a speaker's emotional states in a multi-party
conversation is significant to design human-like conversational agents. During
a conversation, the cognitive state of a speaker often alters due to certain
past utterances, which may lead to a flip in their emotional state. Therefore,
discovering the reasons (triggers) behind the speaker's emotion-flip during a
conversation is essential to explain the emotion labels of individual
utterances. In this paper, along with addressing the task of emotion
recognition in conversations (ERC), we introduce a novel task - Emotion-Flip
Reasoning (EFR), that aims to identify past utterances which have triggered
one's emotional state to flip at a certain time. We propose a masked memory
network to address the former and a Transformer-based network for the latter
task. To this end, we consider MELD, a benchmark emotion recognition dataset in
multi-party conversations for the task of ERC, and augment it with new
ground-truth labels for EFR. An extensive comparison with five state-of-the-art
models suggests improved performances of our models for both tasks. We further
present anecdotal evidence and both qualitative and quantitative error analyses
to support the superiority of our models compared to the baselines.


Abstract:
  Neural keyphrase generation models have recently attracted much interest due
to their ability to output absent keyphrases, that is, keyphrases that do not
appear in the source text. In this paper, we discuss the usefulness of absent
keyphrases from an Information Retrieval (IR) perspective, and show that the
commonly drawn distinction between present and absent keyphrases is not made
explicit enough. We introduce a finer-grained categorization scheme that sheds
more light on the impact of absent keyphrases on scientific document retrieval.
Under this scheme, we find that only a fraction (around 20%) of the words that
make up keyphrases actually serves as document expansion, but that this small
fraction of words is behind much of the gains observed in retrieval
effectiveness. We also discuss how the proposed scheme can offer a new angle to
evaluate the output of neural keyphrase generation models.


Abstract:
  The radio emission in many pulsars show sudden changes, usually within a
period, that cannot be related to the steady state processes within the inner
acceleration region (IAR) above the polar cap. These changes are often
quasi-periodic in nature, where regular transitions between two or more stable
emission states are seen. The durations of these states show a wide variety
ranging from several seconds to hours at a time. There are strong, small scale
magnetic field structures and huge temperature gradients present at the polar
cap surface. We have considered several processes that can cause temporal
modifications of the local magnetic field structure and strength at the surface
of the polar cap. Using different magnetic field strengths and scales, and also
assuming realistic scales of the temperature gradients, the evolutionary
timescales of different phenomena affecting the surface magnetic field was
estimated. We find that the Hall drift results in faster changes in comparison
to both Ohmic decay and thermoelectric effects. A mechanism based on the
Partially Screened Gap (PSG) model of the IAR has been proposed, where the Hall
and thermoelectric oscillations perturb the polar cap magnetic field to alter
the sparking process in the PSG. This is likely to affect the observed radio
emission resulting in the observed state changes.


Abstract:
  Robots are used for collecting samples from natural environments to create
models of, for example, temperature or algae fields in the ocean. Adaptive
informative sampling is a proven technique for this kind of spatial field
modeling. This paper compares the performance of humans versus adaptive
informative sampling algorithms for selecting informative waypoints. The humans
and simulated robot are given the same information for selecting waypoints, and
both are evaluated on the accuracy of the resulting model. We developed a
graphical user interface for selecting waypoints and visualizing samples.
Eleven participants iteratively picked waypoints for twelve scenarios. Our
simulated robot used Gaussian Process regression with two entropy-based
optimization criteria to iteratively choose waypoints. Our results show that
the robot can on average perform better than the average human, and
approximately as good as the best human, when the model assumptions correspond
to the actual field. However, when the model assumptions do not correspond as
well to the characteristics of the field, both human and robot performance are
no better than random sampling.


Abstract:
  We present a two-dimensional continuum model of tumor growth, which treats
the tissue as a composition of six distinct fluid phases; their dynamics are
governed by the equations of mass and momentum conservation. Our model divides
the cancer cells phase into two sub-phases depending on their maturity state.
The same approach is also applied for the vasculature phase, which is divided
into young sprouts (products of angiogenesis), and fully formed-mature vessels.
The remaining two phases correspond to healthy cells and extracellular material
(ECM). Furthermore, the model foresees the existence of nutrient chemical
species, which are transferred within the tissue through diffusion or supplied
by the vasculature (blood vessels). The model is numerically solved with the
Finite Elements Method and computations are performed with the commercial
software Comsol Multiphysics. The numerical simulations predict that mature
cancer cells are well separated from young cancer cells, which form a
protective shield for the growing tumor. We study the effect of different
mitosis and death rates for mature and young cancer cells on the tumor growth
rate, and predict accelerated rates when the mitosis rate of young cancer cells
is higher compared to mature cancer cells.


Abstract:
  This paper presents the equivariant systems theory and observer design for
second order kinematic systems on matrix Lie groups. The state of a second
order kinematic system on a matrix Lie group is naturally posed on the tangent
bundle of the group with the inputs lying in the tangent of the tangent bundle
known as the double tangent bundle. We provide a simple parameterization of
both the tangent bundle state space and the input space (the fiber space of the
double tangent bundle) and then introduce a semi-direct product group and group
actions onto both the state and input spaces. We show that with the proposed
group actions the second order kinematics are equivariant. An equivariant lift
of the kinematics onto the symmetry group is defined and used to design a
nonlinear observer on the lifted state space using nonlinear constructive
design techniques. A simple hovercraft simulation verifies the performance of
our observer.


Abstract:
  This paper investigates the problem of co-synthesis of edit function and
supervisor for opacity enforcement in the supervisory control of discrete-event
systems (DES), assuming the presence of an external (passive) intruder, where
the following goals need to be achieved: 1) the external intruder should never
infer the system secret, i.e., the system is opaque, and never be sure about
the existence of the edit function, i.e., the edit function remains covert; 2)
the controlled plant behaviors should satisfy some safety and nonblockingness
requirements, in the presence of the edit function. We focus on the class of
edit functions that satisfy the following properties: 1) the observation
capability of the edit function in general can be different from those of the
supervisor and the intruder; 2) the edit function can implement insertion,
deletion, and replacement operations; 3) the edit function performs bounded
edit operations, i.e., the length of each string output of the edit function is
upper bounded by a given constant. We propose an approach to solve this
co-synthesis problem by modeling it as a distributed supervisor synthesis
problem in the Ramadge-Wonham supervisory control framework. By taking the
special structure of this distributed supervisor synthesis problem into
consideration and to improve the possibility of finding a non-empty distributed
supervisor, we propose two novel synthesis heuristics that incrementally
synthesize the supervisor and the edit function. The effectiveness of our
approach is illustrated on an example in the enforcement of the location
privacy.


Abstract:
  In this work we shall study the implications of a subclass of $E$-models
cosmological attractors, namely of $a$-attractors, on hydrodynamically stable
slowly rotating neutron stars. Specifically, we shall present the Jordan frame
theory of the $a$-attractors, and by using a conformal transformation we shall
derive the Einstein frame theory. We discuss the inflationary context of
$a$-attractors in order to specify the allowed range of values for the free
parameters of the model based on the latest cosmic-microwave-background-based
Planck 2018 data. Accordingly, using the notation and physical units frequently
used in theoretical astrophysics contexts, we shall derive the
Tolman-Oppenheimer-Volkoff equations in the Einstein frame. Assuming a
piecewise polytropic equation of state, the lowest density part of which shall
be chosen to be the WFF1, or APR or the SLy EoS, we numerically solve the
Tolman-Oppenheimer-Volkoff equations using a double shooting python-based
"LSODA" numerical code. The resulting picture depends on the value of the
parameter $a$ characterizing the $a$-attractors. As we show, for large values
of $a$, which do not produce a viable inflationary era, the $M-R$ graphs are
nearly identical to the general relativistic result, and these two are
discriminated at large central densities values. Also, for large $a$-values,
the WFF1 equation of state is excluded, due to the GW170817 constraints. In
addition, the small $a$ cases produce larger masses and radii compared to the
general relativistic case and are compatible with the GW170817 constraints on
the radii of neutron stars. Our results indicate deep and not yet completely
understood connections between non-minimal inflationary attractors and neutron
stars phenomenology in scalar-tensor theory.


Abstract:
  The observation of Majorana fermions as collective excitations in
condensed-matter systems is an ongoing quest, and several state-of-the-art
experiments have been performed in the last decade. As a potential avenue in
this direction, we simulate the high-harmonic spectrum of Kitaev's
superconducting chain model that hosts Majorana edge modes in its topological
phase, and find their fingerprints in the spectral profiles. It is well-known
that this system exhibits a topological--trivial superconducting phase
transition. We demonstrate that high-harmonic spectroscopy is sensitive to the
phase transition in presence of open boundary conditions due to the presence or
absence of these edge modes. The population dynamics of the Majorana edge modes
are different from the bulk modes, which is the underlying reason for the
distinct harmonic profile of both the phases. On the contrary, in presence of
periodic boundary conditions with only bulk modes, high-harmonic spectroscopy
becomes insensitive to the phase transition with similar harmonic profiles in
both phases.


Abstract:
  Skyrmions are important in topological quantum field theory for being soliton
solutions of a nonlinear sigma model and in information technology for their
attractive applications. Skyrmions are believed to be circular and stripy spin
textures appeared in the vicinity of skyrmion crystals are termed spiral,
helical, and cycloid spin orders, but not skyrmions. Here we present convincing
evidences showing that those stripy spin textures are skyrmions, "siblings" of
circular skyrmions in skyrmion crystals and "cousins" of isolated circular
skyrmions. Specifically, isolated skyrmions are excitations when skyrmion
formation energy is positive. The skyrmion morphologies are various stripy
structures when the ground states of chiral magnetic films are skyrmions. The
density of skyrmion number determines the morphology of condensed skyrmion
states. At the extreme of one skyrmion in the whole sample, the skyrmion is a
ramified stripe. As the skyrmion number density increases, individual skyrmion
shapes gradually change from ramified stripes to rectangular stripes, and
eventually to disk-like objects. At a low skyrmion number density, the natural
width of stripes is proportional to the ratio between the exchange stiffness
constant and Dzyaloshinskii-Moriya interaction coefficient. At a high skyrmion
number density, skyrmion crystals are the preferred states. Our findings reveal
the nature and properties of stripy spin texture, and open a new avenue for
manipulating skyrmions, especially condensed skyrmions such as skyrmion
crystals.


Abstract:
  Theoretical understanding of evolutionary dynamics in spatially structured
populations often relies on non-spatial models. Biofilms are among such
populations where a more accurate understanding is of theoretical interest and
can reveal new solutions to existing challenges. Here, we studied how the
geometry of the environment affects the evolutionary dynamics of expanding
populations, using the Eden model. Our results show that fluctuations of
sub-populations during range expansion in 2D and 3D environments are not
Brownian. Furthermore, we found that the substrate's geometry interferes with
the evolutionary dynamics of populations that grow upon it. Inspired by these
findings, we propose a periodically wedged pattern on surfaces prone to develop
biofilms. On such patterned surfaces, natural selection becomes less effective
and beneficial mutants would have a harder time establishing. Additionally,
this modification accelerates genetic drift and leads to less diverse biofilms.
Both interventions are highly desired for biofilms.


Abstract:
  Transformer-based language model approaches to automated story generation
currently provide state-of-the-art results. However, they still suffer from
plot incoherence when generating narratives over time, and critically lack
basic commonsense reasoning. Furthermore, existing methods generally focus only
on single-character stories, or fail to track characters at all. To improve the
coherence of generated narratives and to expand the scope of character-centric
narrative generation, we introduce Commonsense-inference Augmented neural
StoryTelling (CAST), a framework for introducing commonsense reasoning into the
generation process while modeling the interaction between multiple characters.
We find that our CAST method produces significantly more coherent and on-topic
two-character stories, outperforming baselines in dimensions including plot
plausibility and staying on topic. We also show how the CAST method can be used
to further train language models that generate more coherent stories and reduce
computation cost.


Abstract:
  Prolonged power outages debilitate the economy and threaten public health.
Existing research is generally limited in its scope to a single event, an
outage cause, or a region. Here, we provide one of the most comprehensive
analyses of U.S. power outages for 2002--2019. We categorized all outage data
collected under U.S. federal mandates into four outage causes and computed
industry-standard reliability metrics. Our spatiotemporal analysis reveals six
of the most resilient U.S. states since 2010, improvement of power resilience
against natural hazards in the south and northeast regions, and a
disproportionately large number of human attacks for its population in the
Western Electricity Coordinating Council region. Our regression analysis
identifies several statistically significant predictors and hypotheses for
power resilience. Furthermore, we propose a novel framework for analyzing
outage data using differential weighting and influential points to better
understand power resilience. We share curated data and code as Supplementary
Materials.


Abstract:
  The cross-correlation sensitivity of two identical balanced photodiode
heterodyne receivers is characterized. Both balanced photodiodes receive the
same weak signal split up equally, a situation equivalent to an astronomical
spatial interferometer. A common local oscillator (LO) is also split up equally
and its phase difference between both receivers is stabilized. We show by
semi-classical photon deletion theory that the post-detection laser shot noise
contributions on both receivers must be completely uncorrelated in this case of
passing three power splitters. We measured the auto- and cross-correlation
outputs as a function of weak signal power (system noise temperature
measurement), and obtain a cross-correlation system noise temperature up to 20
times lower than for the auto-correlation system noise temperature of each
receiver separately. This is supported by Allan plot measurements showing
cross-correlation standard deviations 30 times lower than in auto-correlation.
Careful calibration of the source power shows that the auto-correlation
(regular) noise temperature of the single balanced receivers is already very
near to the quantum limit as expected, which suggests a cross-correlation
system noise temperature below the quantum limit. If validated further, this
experimentally clear finding will not only be relevant for astronomical
instrumentation but also for other fields like telecommunications and medical
imaging.


Abstract:
  We present a systematic analysis of our ability to tune chiral
Dzyaloshinskii-Moriya Interactions (DMI) in compensated ferrimagnetic
Pt/GdCo/Pt1-xWx trilayers by cap layer composition. Using first principles
calculations, we show that the DMI increases rapidly for only ~ 10% W and
saturates thereafter, in agreement with experiments. The calculated DMI shows a
spread in values around the experimental mean, depending on the atomic
configuration of the cap layer interface. The saturation is attributed to the
vanishing of spin orbit coupling energy at the cap layer and the simultaneous
constancy at the bottom interface. Additionally, we predict the DMI in
Pt/GdCo/X (X=Ta, W, Ir) and find that W in the cap layer favors a higher DMI
than Ta and Ir that can be attributed to the difference in d-band alignment
around the Fermi level. Our results open up exciting combinatorial
possibilities for controlling the DMI in ferrimagnets towards nucleating and
manipulating ultrasmall high-speed skyrmions.


Abstract:
  Many speech processing methods based on deep learning require an automatic
and differentiable audio metric for the loss function. The DPAM approach of
Manocha et al. learns a full-reference metric trained directly on human
judgments, and thus correlates well with human perception. However, it requires
a large number of human annotations and does not generalize well outside the
range of perturbations on which it was trained. This paper introduces CDPAM, a
metric that builds on and advances DPAM. The primary improvement is to combine
contrastive learning and multi-dimensional representations to build robust
models from limited data. In addition, we collect human judgments on triplet
comparisons to improve generalization to a broader range of audio
perturbations. CDPAM correlates well with human responses across nine varied
datasets. We also show that adding this metric to existing speech synthesis and
enhancement methods yields significant improvement, as measured by objective
and subjective tests.


Abstract:
  In upcoming years, the number of Internet-of-Things (IoT) devices is expected
to surge up to tens of billions of physical objects. However, while the IoT is
often presented as a promising solution to tackle environmental challenges, the
direct environmental impacts generated over the life cycle of the physical
devices are usually overlooked. It is implicitly assumed that their
environmental burden is negligible compared to the positive impacts they can
generate. In this paper, we present a parametric framework based on hardware
profiles to evaluate the cradle-to-gate carbon footprint of IoT edge devices.
We exploit our framework in three ways. First, we apply it on four use cases to
evaluate their respective production carbon footprint. Then, we show that the
heterogeneity inherent to IoT edge devices must be considered as the production
carbon footprint between simple and complex devices can vary by a factor of
more than 150x. Finally, we estimate the absolute carbon footprint induced by
the worldwide production of IoT edge devices through a macroscopic analysis
over a 10-year period. Results range from 22 to 562 MtCO2-eq/year in 2027
depending on the deployment scenarios. However, the truncation error
acknowledged for LCA bottom-up approaches usually lead to an undershoot of the
environmental impacts. We compared the results of our use cases with the few
reports available from Google and Apple, which suggest that our estimates could
be revised upwards by a factor around 2x to compensate for the truncation
error. Worst-case scenarios in 2027 would therefore reach more than 1000
MtCO2-eq/year. This truly stresses the necessity to consider environmental
constraints when designing and deploying IoT edge devices.


Abstract:
  Visual search, recommendation, and contrastive similarity learning power a
wide breadth of technologies that impact billions of users across the world.
The best-performing approaches are often complex and difficult to interpret,
and there are several competing techniques one can use to explain a search
engine's behavior. We show that the theory of fair credit assignment provides a
unique axiomatic solution that generalizes several existing recommendation- and
metric-explainability techniques in the literature. Using this formalism, we
are able to determine in what regimes existing approaches fall short of
fairness and provide variations that are fair in more situations and handle
counterfactual information. More specifically, we show existing approaches
implicitly approximate second-order Shapley-Taylor indices and use this
perspective to extend CAM, GradCAM, LIME, SHAP, SBSM, and other methods to
search engines. These extensions can extract pairwise correspondences between
images from trained black-box models. We also introduce a fast kernel-based
method for estimating Shapley-Taylor indices that require orders of magnitude
fewer function evaluations to converge. Finally, we evaluate these methods and
show that these game-theoretic measures yield more consistent explanations for
image similarity architectures.


Abstract:
  Recently discovered simple quantitative relations, known as bacterial growth
laws, hint on the existence of simple underlying principles at the heart of
bacterial growth. In this work, we provide a unifying picture on how these
known relations, as well as new relations that we derive, stems from a
universal autocatalytic network common to all bacteria, facilitating balanced
exponential growth of individual cells. We show that the core of the cellular
autocatalytic network is the transcription -- translation machinery -- in
itself an autocatalytic network comprising several coupled autocatalytic
cycles, including the ribosome, RNA polymerase, and tRNA charging cycles. We
derive two types of growth laws per autocatalytic cycle, one relating growth
rate to the relative fraction of the catalyst and its catalysis rate, and the
other relating growth rate to all the time scales in the cycle. The structure
of the autocatalytic network generates numerous regimes in state space,
determined by the limiting components, while the number of growth laws can be
much smaller. We also derive a growth law that accounts for the RNA polymerase
autocatalytic cycle, which we use to explain how growth rate depends on the
inducible expression of the rpoB and rpoC genes, which code for the RpoB and C
protein subunits of RNA polymerase, and how the concentration of rifampicin,
which targets RNA polymerase, affects growth rate without changing the
RNA-to-protein ratio. We derive growth laws for tRNA synthesis and charging,
and predict how growth rate depends on temperature, perturbation to ribosome
assembly, and membrane synthesis.


Abstract:
  Image classification is a common step in image recognition for machine
learning in overhead applications. When applying popular model architectures
like MobileNetV2, known vulnerabilities expose the model to counter-attacks,
either mislabeling a known class or altering box location. This work proposes
an automated approach to defend these models. We evaluate the use of
multi-spectral image arrays and ensemble learners to combat adversarial
attacks. The original contribution demonstrates the attack, proposes a remedy,
and automates some key outcomes for protecting the model's predictions against
adversaries. In rough analogy to defending cyber-networks, we combine
techniques from both offensive ("red team") and defensive ("blue team")
approaches, thus generating a hybrid protective outcome ("green team"). For
machine learning, we demonstrate these methods with 3-color channels plus
infrared for vehicles. The outcome uncovers vulnerabilities and corrects them
with supplemental data inputs commonly found in overhead cases particularly.


Abstract:
  While the particle-in-cell (PIC) method is quite mature, verification and
validation of both newly developed methods and individual codes has largely
focused on an idiosyncratic choice of a few test cases. Many of these test
cases involve either one- or two-dimensional simulations. This is either due to
availability of (quasi) analytic solutions or historical reasons. Additionally,
tests often focus on investigation of particular physics problems, such as
particle emission or collisions, and do not necessarily study the combined
impact of the suite of algorithms necessary for a full featured PIC code. As
three dimensional (3D) codes become the norm, there is a lack of benchmarks
test that can establish the validity of these codes; existing papers either do
not delve into the details of the numerical experiment or provide other
measurable numeric metrics (such as noise) that are outcomes of the simulation.
This paper seeks to provide several test cases that can be used for validation
and bench-marking of particle in cell codes in 3D. We focus on examples that
are collisionless, and can be run with a reasonable amount of computational
power. Four test cases are presented in significant detail; these include,
basic particle motion, beam expansion, adiabatic expansion of plasma, and two
stream instability. All presented cases are compared either against existing
analytical data or other codes. We anticipate that these cases should help fill
the void of bench-marking and validation problems and help the development of
new particle in cell codes.


Abstract:
  The unscented transform uses a weighted set of samples called sigma points to
propagate the means and covariances of nonlinear transformations of random
variables. However, unscented transforms developed using either the Gaussian
assumption or a minimum set of sigma points typically fall short when the
random variable is not Gaussian distributed and the nonlinearities are
substantial. In this paper, we develop the generalized unscented transform
(GenUT), which uses 2n+1 sigma points to accurately capture up to the diagonal
components of the skewness and kurtosis tensors of most probability
distributions. Constraints can be analytically enforced on the sigma points
while guaranteeing at least second-order accuracy. The GenUT uses the same
number of sigma points as the original unscented transform while also being
applicable to non-Gaussian distributions, including the assimilation of
observations in the modeling of infectious diseases such as coronavirus
(SARS-CoV-2) causing COVID-19.


Abstract:
  The hard-sphere model is one of the most extensively studied models in
statistical physics. It describes the continuous distribution of spherical
particles, governed by hard-core interactions. An important quantity of this
model is the normalizing factor of this distribution, called the partition
function. We propose a Markov chain Monte Carlo algorithm for approximating the
grand-canonical partition function of the hard-sphere model in $d$ dimensions.
Up to a fugacity of $\lambda < \text{e}/2^d$, the runtime of our algorithm is
polynomial in the volume of the system. This covers the entire known
real-valued regime for the uniqueness of the Gibbs measure.
  Key to our approach is to define a discretization that closely approximates
the partition function of the continuous model. This results in a discrete
hard-core instance that is exponential in the size of the initial hard-sphere
model. Our approximation bound follows directly from the correlation decay
threshold of an infinite regular tree with degree equal to the maximum degree
of our discretization. To cope with the exponential blow-up of the discrete
instance we use clique dynamics, a Markov chain that was recently introduced in
the setting of abstract polymer models. We prove rapid mixing of clique
dynamics up to the tree threshold of the univariate hard-core model. This is
achieved by relating clique dynamics to block dynamics and adapting the
spectral expansion method, which was recently used to bound the mixing time of
Glauber dynamics within the same parameter regime.


Abstract:
  Novel emergent phenomena are expected to occur under conditions exceeding the
QED critical electric field, where the vacuum becomes unstable to
electron-positron pair production. The required intensity to reach this regime,
$\sim10^{29}\,\mathrm{Wcm^{-2}}$, cannot be achieved even with the most intense
lasers now being planned/constructed without a sizeable Lorentz boost provided
by interactions with ultrarelativistic particles. Seeded laser-laser collisions
may access this strong-field QED regime at laser intensities as low as
$\sim10^{24}\,\mathrm{Wcm^{-2}}$. Counterpropagating e-beam--laser interactions
exceed the QED critical field at still lower intensities
($\sim10^{20}\,\mathrm{Wcm^{-2}}$ at $\sim10\,\mathrm{GeV}$). Novel emergent
phenomena are predicted to occur in the "QED plasma regime", where strong-field
quantum and collective plasma effects play off one another. Here the electron
beam density becomes a decisive factor. Thus, the challenge is not just to
exceed the QED critical field, but to do so with high quality, approaching
solid-density electron beams. Even though laser wakefield accelerators (LWFA)
represent a very promising research field, conventional accelerators still
provide orders of magnitude higher charge densities at energies
$\gtrsim10\,\mathrm{GeV}$. Co-location of extremely dense and highly energetic
electron beams with a multi-petawatt laser system would therefore enable
seminal research opportunities in high-field physics and laboratory
astrophysics. This white paper elucidates the potential scientific impact of
multi-beam capabilities that combine a multi-PW optical laser,
high-energy/density electron beam, and high-intensity x rays and outlines how
to achieve such capabilities by co-locating a 3-10 PW laser with a
state-of-the-art linear accelerator.


Abstract:
  The key feature of nonlocal kinetic energy functionals is their ability to
reduce to the Thomas-Fermi functional in the regions of high density and to the
von Weizs\"acker functional in the region of low density/high density gradient.
This behavior is crucial when these functionals are employed in subsystem DFT
simulations to approximate the nonadditive kinetic energy. We propose a GGA
nonadditive kinetic energy functional which mimics the good behavior of
nonlocal functionals retaining the computational complexity of typical
semilocal functionals. The new functional reproduces Kohn-Sham DFT and
benchmark CCSD(T) interaction energies of weakly interacting dimers in the
S22-5 and S66 test sets with a mean absolute deviation well below 1 kcal/mol.


Abstract:
  Federated Bayesian learning offers a principled framework for the definition
of collaborative training algorithms that are able to quantify epistemic
uncertainty and to produce trustworthy decisions. Upon the completion of
collaborative training, an agent may decide to exercise her legal "right to be
forgotten", which calls for her contribution to the jointly trained model to be
deleted and discarded. This paper studies federated learning and unlearning in
a decentralized network within a Bayesian framework. It specifically develops
federated variational inference (VI) solutions based on the decentralized
solution of local free energy minimization problems within exponential-family
models and on local gossip-driven communication. The proposed protocols are
demonstrated to yield efficient unlearning mechanisms.


Abstract:
  We deploy and demonstrate the CoinTossX low-latency, high-throughput,
open-source matching engine with orders sent using the Julia and Python
languages. We show how this can be deployed for small-scale local desk-top
testing and discuss a larger scale, but local hosting, with multiple traded
instruments managed concurrently and managed by multiple clients. We then
demonstrate a cloud based deployment using Microsoft Azure, with large-scale
industrial and simulation research use cases in mind. The system is exposed and
interacted with via sockets using UDP SBE message protocols and can be
monitored using a simple web browser interface using HTTP. We give examples
showing how orders can be be sent to the system and market data feeds monitored
using the Julia and Python languages. The system is developed in Java with
orders submitted as binary encodings (SBE) via UDP protocols using the Aeron
Media Driver as the low-latency, high throughput message transport. The system
separates the order-generation and simulation environments e.g. agent-based
model simulation, from the matching of orders, data-feeds and various
modularised components of the order-book system. This ensures a more natural
and realistic asynchronicity between events generating orders, and the events
associated with order-book dynamics and market data-feeds. We promote the use
of Julia as the preferred order submission and simulation environment.


Abstract:
  Training GANs on videos is even more sophisticated than on images because
videos have a distinguished dimension: time. While recent methods designed a
dedicated architecture considering time, generated videos are still far from
indistinguishable from real videos. In this paper, we introduce ArrowGAN
framework, where the discriminators learns to classify arrow of time as an
auxiliary task and the generators tries to synthesize forward-running videos.
We argue that the auxiliary task should be carefully chosen regarding the
target domain. In addition, we explore categorical ArrowGAN with recent
techniques in conditional image generation upon ArrowGAN framework, achieving
the state-of-the-art performance on categorical video generation. Our extensive
experiments validate the effectiveness of arrow of time as a self-supervisory
task, and demonstrate that all our components of categorical ArrowGAN lead to
the improvement regarding video inception score and Frechet video distance on
three datasets: Weizmann, UCFsports, and UCF-101.


Abstract:
  Understanding the 3D world from 2D projected natural images is a fundamental
challenge in computer vision and graphics. Recently, an unsupervised learning
approach has garnered considerable attention owing to its advantages in data
collection. However, to mitigate training limitations, typical methods need to
impose assumptions for viewpoint distribution (e.g., a dataset containing
various viewpoint images) or object shape (e.g., symmetric objects). These
assumptions often restrict applications; for instance, the application to
non-rigid objects or images captured from similar viewpoints (e.g., flower or
bird images) remains a challenge. To complement these approaches, we propose
aperture rendering generative adversarial networks (AR-GANs), which equip
aperture rendering on top of GANs, and adopt focus cues to learn the depth and
depth-of-field (DoF) effect of unlabeled natural images. To address the
ambiguities triggered by unsupervised setting (i.e., ambiguities between smooth
texture and out-of-focus blurs, and between foreground and background blurs),
we develop DoF mixture learning, which enables the generator to learn real
image distribution while generating diverse DoF images. In addition, we devise
a center focus prior to guiding the learning direction. In the experiments, we
demonstrate the effectiveness of AR-GANs in various datasets, such as flower,
bird, and face images, demonstrate their portability by incorporating them into
other 3D representation learning GANs, and validate their applicability in
shallow DoF rendering.


Abstract:
  Near-field imaging experiments exist both in optics and microwaves with often
different methods and theoretical supports. For millimeter waves or THz waves,
techniques from both fields can be merged to identify materials at the micron
scale on the surface or in near-surface volumes. The principle of such
near-field vector imaging at the frequency of 60 GHz is discussed in detail
here. We develop techniques for extracting vector voltages and methods for
extracting the normalized near-field vector reflection on the sample. In
particular, the subharmonic IQ mixer imbalance, which produced corrupted
outputs either due to amplitude or phase differences, must be taken into
account and compensated for to avoid any systematic errors. We provide a method
to fully characterize these imperfections and to isolate the only contribution
of the near-field interaction between the probe and the sample. The effects of
the mechanical modulation waveform and harmonic rank used for signal
acquisition are also discussed.


Abstract:
  The characteristic electron densities, temperatures, and thermal
distributions of 1MK active region loops are now fairly well established, but
their coronal magnetic field strengths remain undetermined. Here we present
measurements from a sample of coronal loops observed by the Extreme-ultraviolet
Imaging Spectrometer (EIS) on Hinode. We use a recently developed diagnostic
technique that involves atomic radiation modeling of the contribution of a
magnetically induced transition (MIT) to the Fe X 257.262A spectral line
intensity. We find coronal magnetic field strengths in the range of 60--150G.
We discuss some aspects of these new results in the context of previous
measurements using different spectropolarimetric techniques, and their
influence on the derived Alfv\'{e}n speeds and plasma $\beta$ in coronal loops.


Abstract:
  In this paper, we study the existence of traveling waves for a fourth order
Schr\" odinger equations with mixed dispersion, that is, solutions to
$$\Delta^2 u +\beta \Delta u +i V \nabla u +\alpha u =|u|^{p-2} u,\ in\ \R^N ,\
N\geq 2.$$ We consider this equation in the Helmholtz regime, when the Fourier
symbol $P$ of our operator is strictly negative at some point. Under suitable
assumptions, we prove the existence of solution using the dual method of
Evequoz and Weth provided that $p\in (p_1 , 2N/(N-4)_+)$. The real number $p_1$
depends on the number of principal curvature of $M$ staying bounded away from
$0$, where $M$ is the hypersurface defined by the roots of $P$. We also
obtained estimates on the Green function of our operator and a $L^p - L^q$
resolvent estimate which can be of independent interest and can be applied to
other operators.


Abstract:
  Polyphenols are natural molecules of crucial importance in many applications,
of which tannic acid (TA) is one of the most abundant and established. Most
high-value applications require precise control of TA interactions with the
system of interest. However, the molecular structure of TA is still not
comprehended at the atomic level, of which all electronic and reactivity
properties depend. Here, we combine an enhanced sampling global optimization
method with density functional theory (DFT)-based calculations to explore the
conformational space of TA assisted by unsupervised machine learning
visualization, and then investigate its lowest energy conformers. We study the
external environment's effect on the TA structure and properties. We find that
vacuum favors compact structures by stabilizing peripheral atoms' weak
interactions, while in water, the molecule adopts more open conformations. The
frontier molecular orbitals of the conformers with lowest harmonic vibrational
free energy have a HOMO-LUMO energy gap of 2.21 (3.27) eV, increasing to 2.82
(3.88) eV in water, at the DFT generalized gradient approximation (and hybrid)
level of theory. Structural differences also change the distribution of
potential reactive sites. We establish the fundamental importance of accurate
structural consideration in determining TA and related polyphenols interactions
in relevant technological applications.


Abstract:
  In this paper we state two quantitative Sylvester-Gallai results for high
degree curves. Moreover we give two constructions which show that these results
are not trivial.


Abstract:
  The main goal of this paper is to develop the MRA theory along with wavelet
theory in L2(Qp). Generalized scaling sets are important in wavelet theory
because it determine multiwavelet sets. Although the theory of scaling set and
generalized scaling set on R and many other local field of positive
characteristic are available but not on Qp. This article contains discussion of
some necessary conditions of scaling set and characterize generalized scaling
set with examples.


Abstract:
  Lattice QCD calculations of scattering phaseshifts and resonance parameters
in the two-body sector are becoming precision studies. Early calculations
employed L\"uscher's formula for extracting these quantities at lowest order.
As the calculations become more ambitious, higher-order relations are required.
In this study we present a way to validate the higher-order quantization
conditions. This is an important step given the involved derivations of these
formulae. We derive and validate quantization conditions up to $\ell=5$ partial
waves in both cubic and elongated geometries, and for states zero and non-zero
total momentum. For all 45 quantization conditions we considered (22 in cubic
box, 23 in elongated box) we find perfect agreement.


Abstract:
  When analysing multiple time series that may be subject to changepoints, it
is sometimes possible to specify a priori, by means of a graph G, which pairs
of time series are likely to be impacted by simultaneous changepoints. This
article proposes a novel Bayesian changepoint model for multiple time series
that borrows strength across clusters of connected time series in G to detect
weak signals for synchronous changepoints. The graphical changepoint model is
further extended to allow dependence between nearby but not necessarily
synchronous changepoints across neighbour time series in G. A novel reversible
jump MCMC algorithm making use of auxiliary variables is proposed to sample
from the graphical changepoint model. The merit of the proposed model is
demonstrated via a changepoint analysis of real network authentication data
from Los Alamos National Laboratory (LANL), with some success at detecting weak
signals for network intrusions across users that are linked by network
connectivity, whilst limiting the number of false alerts.


Abstract:
  We use Volterra-Hamilton systems theory and their associated cost functional
to study the population dynamics and productive processes of coral reefs in
recovery from bleaching and show that the cost of production remains the same
after the process. The KCC-theory geometrical invariants are determined for the
model proposed to describe the renewed symbiotic interaction between coral and
algae.


Abstract:
  In this work we consider a multidimensional KdV type equation, the
Zakharov-Kuznetsov (ZK) equation. We derive the 3-wave kinetic equation from
both the stochastic ZK equation and the deterministic ZK equation with random
initial condition. The equation is given on a hypercubic lattice of size $L$.
In the case of the stochastic ZK equation, we show that the two point
correlation function can be asymptotically expressed as the solution of the
3-wave kinetic equation at the kinetic limit under very general assumptions, in
which the initial condition is out of equilibrium and the size $L$ of the
domain is fixed. In the case of the deterministic ZK equation with random
initial condition, the kinetic equation can also be derived at the kinetic
limit, but under more restrictive assumptions.


Abstract:
  The commercialization of Virtual Reality (VR) headsets has made immersive and
360-degree video streaming the subject of intense interest in the industry and
research communities. While the basic principles of video streaming are the
same, immersive video presents a set of specific challenges that need to be
addressed. In this survey, we present the latest developments in the relevant
literature on four of the most important ones: (i) omnidirectional video coding
and compression, (ii) subjective and objective Quality of Experience (QoE) and
the factors that can affect it, (iii) saliency measurement and Field of View
(FoV) prediction, and (iv) the adaptive streaming of immersive 360-degree
videos. The final objective of the survey is to provide an overview of the
research on all the elements of an immersive video streaming system, giving the
reader an understanding of their interplay and performance.


Abstract:
  We introduce twisted arrow categories of operads and of algebras over
operads. Up to equivalence of categories, the simplex category $\Delta$,
Segal's category $\Gamma$, Connes cyclic category $\Lambda$, Moerdijk--Weiss
dendroidal category $\Omega$, and categories similar to graphical categories of
Hackney--Robertson--Yau are twisted arrow categories of symmetric or cyclic
operads. Twisted arrow categories of operads admit Segal presheaves and 2-Segal
presheaves, or decomposition spaces. Twisted arrow category of an operad $P$ is
the $(\infty, 1)$-localization of the corresponding category $\Omega/P$ by the
boundary preserving morphisms. Under mild assumptions, twisted arrow categories
of operads, and closely related universal enveloping categories, are
generalized Reedy. We also introduce twisted arrow operads, which are related
to Baez--Dolan plus construction.


Abstract:
  In this work we explore the new catalog of galactic open clusters that became
available recently, containing 1750 clusters that have been re-analysed using
the Gaia DR2 catalog to determine the stellar memberships. We used the young
open clusters as tracers of spiral arms and determined the spiral pattern
rotation speed of the Galaxy and the corotation radius, the strongest Galactic
resonance. The sample of open clusters used here increases the last one from
Dias et al. (2019) used in the previous determination of the pattern speed by
dozens objects. In addition, the distances and ages values are better
determined, using improvements to isochrone fitting and including an updated
extinction polynomial for the Gaia DR2 photometric band-passes, and the
Galactic abundance gradient as a prior for metallicity. In addition to the
better age determinations, the catalog contains better positions in the
Galactic plane and better proper motions. This allow us to discuss not only the
present space distribution of the clusters, but also the space distribution of
the clusters's birthplaces, obtained by integration of the orbits for a time
equal to their age. The value of the rotation velocity of the arms ($28.5 \pm
1.0$ km s$^{-1}$ kpc$^{-1}$) implies that the corotation radius ($R_c$) is
close to the solar Galactic orbit ($R_c/R_0 = 1.01\pm0.08$), which is supported
by other observational evidence discussed in this text. A simulation is
presented, illustrating the motion of the clusters in the reference frame of
corotation. We also present general statistics of the catalog of clusters, like
spatial distribution, distribution relative to height from the Galactic plane,
and distribution of ages and metallicity. An important feature of the space
distribution, the corotation gap in the gas distribution and its consequences
for the young clusters, is discussed.


Abstract:
  We numerically investigate the dynamical properties of $\kappa$-type
molecular conductors in their antiferromagnetic Mott insulating state. By
treating the extended Hubbard model on the two-dimensional $\kappa$-type
lattice within the Lanzcos exact diagonalization method, we calculate the
one-particle spectral function $A(\boldsymbol{k}, \omega)$ and the optical
absorption spectra taking advantage of twisted boundary conditions. We find
spin splitting in $A(\boldsymbol{k}, \omega)$ predicted by a previous
Hartree-Fock study [M. Naka et al., Nat. Commun. 10, 4305 (2019)]; namely,
their up- and down-spin components become different in the general
$\boldsymbol{k}$-points of the Brillouin zone, even without the spin-orbit
coupling. Furthermore, we demonstrate the variation in the optical properties
near the Mott gap by the presence or absence of the antiferromagnetic order,
tuned by a small staggered magnetic field.


Abstract:
  The leap eccentric connectivity index of $G$ is defined as
$$L\xi^{C}(G)=\sum_{v\in V(G)}d_{2}(v|G)e(v|G)$$ where $d_{2}(v|G) $ be the
second degree of the vertex $v$ and $e(v|G)$ be the eccentricity of the vertex
$v$ in $G$. In this paper, we first give a counterexample for that if $G$ be a
graph and $S(G)$ be its the subdivision graph, then each vertex $v\in V(G)$,
$e(v|S(G))=2e(v|G)$ by Yarahmadi in \cite{yar14} in Theorem 3.1. And we
describe the upper and lower bounds of the leap eccentric connectivity index of
four graphs based on subdivision edges, and then give the expressions of the
leap eccentric connectivity index of join graph based on subdivision, finally,
give the bounds of the leap eccentric connectivity index of four variants of
the corona graph.


Abstract:
  Radially self-accelerating light exhibits an intensity pattern that describes
a spiraling trajectory around the optical axis as the beam propagates. In this
article, we show in simulation and experiment how such beams can be used to
perform a high-accuracy distance measurement with respect to a reference using
simple off-axis intensity detection. We demonstrate that generating beams whose
intensity pattern simultaneously spirals with fast and slow rotation components
enables a distance measurement with high accuracy over a broad range, using the
high and low rotation frequency, respectively. In our experiment, we achieve an
accuracy of around 2~$\mu$m over a longitudinal range of more than 2~mm using a
single beam and only two quadrant detectors. As our method relies on
single-beam interference and only requires a static generation and simple
intensity measurements, it is intrinsically stable and might find applications
in high-speed measurements of longitudinal position.


Abstract:
  The parallel strong-scaling of Krylov iterative methods is largely determined
by the number of global reductions required at each iteration. The GMRES and
Krylov-Schur algorithms employ the Arnoldi algorithm for nonsymmetric matrices.
The underlying orthogonalization scheme is left-looking and processes one
column at a time. Thus, at least one global reduction is required per
iteration. The traditional algorithm for generating the orthogonal Krylov basis
vectors for the Krylov-Schur algorithm is classical Gram Schmidt applied twice
with reorthogonalization (CGS2), requiring three global reductions per step. A
new variant of CGS2 that requires only one reduction per iteration is applied
to the Arnoldi-QR iteration. Strong-scaling results are presented for finding
eigenvalue-pairs of nonsymmetric matrices. A preliminary attempt to derive a
similar algorithm (one reduction per Arnoldi iteration with a robust
orthogonalization scheme) was presented by Hernandez et al.(2007). Unlike our
approach, their method is not forward stable for eigenvalues.


Abstract:
  Recently, the magnetic topological insulator MnBi$_2$Te$_4$ emerged as a
competitive platform to realize quantum anomalous Hall (QAH) states. We report
a Berry-curvature splitting mechanism to realize the QAH effect in the
disordered magnetic TI multilayers when switching from an antiferromagnetic
order to a ferromagnetic order. We reveal that the splitting of spin-resolved
Berry curvature, originating from the separation of the mobility edge during
the magnetic switching, can give rise to a QAH insulator even \emph{without}
closing the band gap. We present a global phase diagram, and also provide a
phenomenological picture to elucidate the Berry curvature splitting mechanism
by the evolution of topological charges. At last, we predict that the Berry
curvature splitting mechanism will lead to a reentrant QAH effect, which can be
detected by tuning gate voltage. Our theory will be instructive for the studies
of the QAH effect in MnBi$_2$Te$_4$ in future experiments.


Abstract:
  We study the effect of Dzyaloshinskii-Moriya (DM) interaction on the
triangular lattice $U(1)$ quantum spin liquid (QSL) which is stabilized by
ring-exchange interactions. A weak DM interaction introduces a staggered flux
to the $U(1)$ QSL state and changes the density of states at the spinon fermi
surface. If the DM vector contains in-plane components, then the spinons gain
nonzero Berry phase. The resultant thermal conductances $\kappa_{xx}$ and
$\kappa_{xy}$ qualitatively agree with the experimental results on the material
EtMe$_3$Sb[Pd(dmit)$_2]_2$. Furthermore, owing to perfect nesting of the fermi
surface, a spin density wave state is triggered by larger DM interactions. On
the other hand, when the ring-exchange interaction decreases, another
anti-ferromagnetic (AFM) phase with $120^\circ$ order shows up which is
proximate to a $U(1)$ Dirac QSL. We discuss the difference of the two AFM
phases from their static structure factors and excitation spectra.


Abstract:
  We propose a principled method for projecting an arbitrary square matrix to
the non-convex set of asymptotically stable matrices. Leveraging ideas from
large deviations theory, we show that this projection is optimal in an
information-theoretic sense and that it simply amounts to shifting the initial
matrix by an optimal linear quadratic feedback gain, which can be computed
exactly and highly efficiently by solving a standard linear quadratic regulator
problem. The proposed approach allows us to learn the system matrix of a stable
linear dynamical system from a single trajectory of correlated state
observations. The resulting estimator is guaranteed to be stable and offers
explicit statistical bounds on the estimation error.


Abstract:
  In this paper we shed light on the impact of fine-tuning over social media
data in the internal representations of neural language models. We focus on bot
detection in Twitter, a key task to mitigate and counteract the automatic
spreading of disinformation and bias in social media. We investigate the use of
pre-trained language models to tackle the detection of tweets generated by a
bot or a human account based exclusively on its content. Unlike the general
trend in benchmarks like GLUE, where BERT generally outperforms generative
transformers like GPT and GPT-2 for most classification tasks on regular text,
we observe that fine-tuning generative transformers on a bot detection task
produces higher accuracies. We analyze the architectural components of each
transformer and study the effect of fine-tuning on their hidden states and
output representations. Among our findings, we show that part of the
syntactical information and distributional properties captured by BERT during
pre-training is lost upon fine-tuning while the generative pre-training
approach manage to preserve these properties.


Abstract:
  An Unmanned Aerial Vehicle (UAV) is a promising technology for providing
wireless coverage to ground user devices. For all the infrastructure
communication networks destroyed in disasters, UAVs battery life is challenging
during service delivery in a post-disaster scenario. Therefore, selecting
cluster heads among user devices plays a vital role in detecting UAV signals
and processing data for improving UAV energy efficacy and reliable
Connectivity. This paper focuses on the performance evaluation of the
clustering approach performance in detecting wireless coverage services with
improving energy efficiency. The evaluation performance is a realistic
simulation for the ground to air channel Line of Sight (LoS). The results show
that the cluster head can effectively link the UAVs and cluster members at
minimal energy expenditure. The UAVs altitudes and path loss exponent affected
user devices for detecting wireless coverage. Moreover, the bit error rate in
the cluster heads is considered for reliable Connectivity in post disaster.
Clustering stabilizes the clusters linking the uncovered nodes to the UAV, and
its effectiveness in doing so resulted in its ubiquity in emergency
communication systems.


Abstract:
  We construct regular, asymptotically flat black holes of higher order scalar
tensor (DHOST) theories, which are obtained by making use of a generalized
Kerr-Schild solution generating method. The solutions depend on a mass
integration constant, admit a smooth core of chosen regularity, and generically
have an inner and outer event horizon. In particular, below a certain mass
threshold, we find massive, horizonless, particle-like solutions. We scan
through possible observational signatures ranging from weak to strong gravity
and study the thermodynamics of our regular solutions, comparing them, when
possible, to General Relativity black holes and their thermodynamic laws.


Abstract:
  We calculate the superconformal indices of a class of six-dimensional ${\cal
N}=(1,0)$ superconformal field theories realized on M5-branes at
$\mathbb{C}^2/\mathbb{Z}_k$ singularity by using the method developed in
previous works of the authors and collaborators. We use the AdS/CFT
correspondence, and finite $N$ corrections are included as the contribution of
M2-branes wrapped on two-cycles in $S^4/\mathbb{Z}_k$. We confirm that the
indices are consistent with the expected flavor symmetries.


Abstract:
  There is a growing desire to create computer systems that can communicate
effectively to collaborate with humans on complex, open-ended activities.
Assessing these systems presents significant challenges. We describe a
framework for evaluating systems engaged in open-ended complex scenarios where
evaluators do not have the luxury of comparing performance to a single right
answer. This framework has been used to evaluate human-machine creative
collaborations across story and music generation, interactive block building,
and exploration of molecular mechanisms in cancer. These activities are
fundamentally different from the more constrained tasks performed by most
contemporary personal assistants as they are generally open-ended, with no
single correct solution, and often no obvious completion criteria.
  We identified the Key Properties that must be exhibited by successful
systems. From there we identified "Hallmarks" of success -- capabilities and
features that evaluators can observe that would be indicative of progress
toward achieving a Key Property. In addition to being a framework for
assessment, the Key Properties and Hallmarks are intended to serve as goals in
guiding research direction.


Abstract:
  We consider the problem of efficient ultra-massive multiple-input
multiple-output (UM-MIMO) data detection in terahertz (THz)-band non-orthogonal
multiple access (NOMA) systems. We argue that the most common THz NOMA
configuration is power-domain superposition coding over quasi-optical
doubly-massive MIMO channels. We propose spatial tuning techniques that modify
antenna subarray arrangements to enhance channel conditions. Towards recovering
the superposed data at the receiver side, we propose a family of data detectors
based on low-complexity channel matrix puncturing, in which higher-order
detectors are dynamically formed from lower-order component detectors. We first
detail the proposed solutions for the case of superposition coding of multiple
streams in point-to-point THz MIMO links. We then extend the study to
multi-user NOMA, in which randomly distributed users get grouped into narrow
cell sectors and are allocated different power levels depending on their
proximity to the base station. We show that successive interference
cancellation is carried with minimal performance and complexity costs under
spatial tuning. We derive approximate bit error rate (BER) equations, and we
propose an architectural design to illustrate complexity reductions. Under
typical THz conditions, channel puncturing introduces more than an order of
magnitude reduction in BER at high signal-to-noise ratios while reducing
complexity by approximately 90%.


Abstract:
  We analyze the correlation coefficient T(E_e), which was introduced by Ebel
and Feldman (Nucl. Phys. 4, 213 (1957)). The correlation coefficient T(E_e) is
induced by the correlations of the neutron spin with the antineutrino
3-momentum and the electron spin with the electron 3-momentum. Such a
correlation structure is invariant under discrete P, C and T symmetries. The
correlation coefficient T(E_e), calculated to leading order in the large
nucleon mass m_N expansion, is equal to T(E_e) = - 2 g_A(1 + g_A)/(1 + 3 g^2_A)
= - B_0, i.e. of order |T(E_e)| ~ 1, where $g_A$ is the axial coupling
constant. Within the Standard Model (SM) we describe the correlation
coefficient $T(E_e)$ at the level of 10^{-3} by taking into the radiative
corrections of order O(\alpha/\pi) or the outer model-independent radiative
corrections, where \alpha is the fine-structure constant, and the corrections
of order O(E_e/m_N), caused by weak magnetism and proton recoil. We calculate
also the contributions of interactions beyond the SM, including the
contributions of the second class currents.


Abstract:
  Interactions between the intra- and inter-domain routing protocols received
little attention despite playing an important role in forwarding transit
traffic. More precisely, by default, IGP distances are taken into account by
BGP to select the closest exit gateway for the transit traffic (hot-potato
routing). Upon an IGP update, the new best gateway may change and should be
updated through the (full) re-convergence of BGP, causing superfluous BGP
processing and updates in many cases. We propose OPTIC (Optimal Protection
Technique for Inter-intra domain Convergence), an efficient way to assemble
both protocols without losing the hot-potato property. OPTIC pre-computes sets
of gateways (BGP next-hops) shared by groups of prefixes. Such sets are
guaranteed to contain the post-convergence gateway after any single IGP event
for the grouped prefixes. The new optimal exits can be found through a single
walk-through of each set, allowing the transit traffic to benefit from optimal
BGP routes almost as soon as the IGP converges. Compared to vanilla BGP,
OPTIC's structures allow it to consider a reduced number of entries: this
number can be reduced by 99\% for stub networks. The update of OPTIC's
structures, which is not required as long as border routers remain at least
bi-connected, scales linearly in time with its number of groups.


Abstract:
  Immigration to the United States is certainly not a new phenomenon, and it is
therefore natural for immigration, culture and identity to be given due
attention by the public and policy makers. However, current discussion of
immigration, legal and illegal, and the philosophical underpinnings is lost in
translation, not necessarily on ideological lines, but on political
orientation. In this paper we reexamine the philosophical underpinnings of the
melting pot versus multiculturalism as antecedents and precedents of current
immigration debate and how the core issues are lost in translation. We take a
brief look at immigrants and the economy to situate the current immigration
debate. We then discuss the two philosophical approaches to immigration and how
the understanding of the philosophical foundations can help streamline the
current immigration debate.


Abstract:
  How the solar electromagnetic energy entering the Earth's atmosphere varied
since pre-industrial times is an important consideration in the climate change
debate. Detrimental to this debate, estimates of the change in total solar
irradiance (TSI) since the Maunder minimum, an extended period of weak solar
activity preceding the industrial revolution, differ markedly, ranging from a
drop of 0.75 Wm-2 to a rise of 6.3 Wm-2. Consequently, the exact contribution
by solar forcing to the rise in global temperatures over the past centuries
remains inconclusive. Adopting a novel approach based on state-of-the-art solar
imagery and numerical simulations, we establish the TSI level of the Sun when
it is in its least-active state to be 2.0 +/- 0.7 Wm-2 below the 2019 level.
This means TSI could not have risen since the Maunder minimum by more than this
amount, thus restricting the possible role of solar forcing in global warming.


Abstract:
  We calculate the lifetime of the deuteron from dimension-six quark operators
that violate baryon number by one unit. We construct an effective field theory
for $|\Delta B|=1$ interactions that give rise to nucleon and $\Delta B=1$
deuteron decay in a systematic expansion. We show that up to and including
next-to-leading order the deuteron decay rate is given by the sum of the decay
rates of the free proton and neutron. The first nuclear correction is expected
to contribute at the few-percent level and comes with an undetermined
low-energy constant. We discuss its relation to earlier potential-model
calculations.


Abstract:
  Quantum resources and protocols are known to outperform their classical
counterparts in variety of communication and information processing tasks.
Random Access Codes (RACs) are one such cryptographically significant family of
bipartite communication tasks, wherein, the sender encodes a data set
(typically a string of input bits) onto a physical system of bounded dimension
and transmits it to the receiver, who then attempts to guess a randomly chosen
part of the sender's data set (typically one of the sender's input bits). In
this work, we introduce a generalization of this task wherein the receiver, in
addition to the individual input bits, aims to retrieve randomly chosen
functions of sender's input string. Specifically, we employ sets of mutually
unbiased balanced functions (MUBS), such that perfect knowledge of any one of
the constituent functions yields no knowledge about the others. We investigate
and bound the performance of (i.) classical, (ii.) quantum prepare and measure,
and (iii.) entanglement assisted classical communication (EACC) protocols for
the resultant generalized RACs (GRACs). Finally, we detail the case of GRACs
with three input bits, find maximal success probabilities for classical,
quantum and EACC protocols, along with the effect of noisy quantum channels on
the performance of quantum protocols. Moreover, with the help of this case
study, we reveal several characteristic properties of the GRACs which deviate
from the standard RACs.


Abstract:
  Machine translation models have discrete vocabularies and commonly use
subword segmentation techniques to achieve an 'open vocabulary.' This approach
relies on consistent and correct underlying unicode sequences, and makes models
susceptible to degradation from common types of noise and variation. Motivated
by the robustness of human language processing, we propose the use of visual
text representations, which dispense with a finite set of text embeddings in
favor of continuous vocabularies created by processing visually rendered text
with sliding windows. We show that models using visual text representations
approach or match performance of traditional text models on small and larger
datasets. More importantly, models with visual embeddings demonstrate
significant robustness to varied types of noise, achieving e.g., 25.9 BLEU on a
character permuted German-English task where subword models degrade to 1.9.


Abstract:
  Spine-related diseases have high morbidity and cause a huge burden of social
cost. Spine imaging is an essential tool for noninvasively visualizing and
assessing spinal pathology. Segmenting vertebrae in computed tomography (CT)
images is the basis of quantitative medical image analysis for clinical
diagnosis and surgery planning of spine diseases. Current publicly available
annotated datasets on spinal vertebrae are small in size. Due to the lack of a
large-scale annotated spine image dataset, the mainstream deep learning-based
segmentation methods, which are data-driven, are heavily restricted. In this
paper, we introduce a large-scale spine CT dataset, called CTSpine1K, curated
from multiple sources for vertebra segmentation, which contains 1,005 CT
volumes with over 11,100 labeled vertebrae belonging to different spinal
conditions. Based on this dataset, we conduct several spinal vertebrae
segmentation experiments to set the first benchmark. We believe that this
large-scale dataset will facilitate further research in many spine-related
image analysis tasks, including but not limited to vertebrae segmentation,
labeling, 3D spine reconstruction from biplanar radiographs, image
super-resolution, and enhancement.


Abstract:
  A classical branch of graph algorithms is graph transversals, where one seeks
a minimum-weight subset of nodes in a node-weighted graph $G$ which intersects
all copies of subgraphs~$F$ from a fixed family $\mathcal F$. Many such graph
transversal problems have been shown to admit polynomial-time approximation
schemes (PTAS) for planar input graphs $G$, using a variety of techniques like
the shifting technique (Baker, J. ACM 1994), bidimensionality (Fomin et al.,
SODA 2011), or connectivity domination (Cohen-Addad et al., STOC 2016). These
techniques do not seem to apply to graph transversals with parity constraints,
which have recently received significant attention, but for which no PTASs are
known. In the even-cycle transversal (\ECT) problem, the goal is to find a
minimum-weight hitting set for the set of even cycles in an undirected graph.
  For ECT, Fiorini et al. (IPCO 2010) showed that the integrality gap of the
standard covering LP relaxation is $\Theta(\log n)$, and that adding sparsity
inequalities reduces the integrality gap to~10. Our main result is a
primal-dual algorithm that yields a $47/7\approx6.71$-approximation for ECT on
node-weighted planar graphs, and an integrality gap of the same value for the
standard LP relaxation on node-weighted planar graphs.


Abstract:
  Geo-indistinguishability and expected inference error are two complementary
notions for location privacy. The joint guarantee of differential privacy
(indistinguishability) and distortion privacy (inference error) limits the
information leakage. In this paper, we analyze the differential privacy of PIVE
dynamic location obfuscation mechanism proposed by Yu, Liu and Pu (ISOC Network
and Distributed System Security Symposium, 2017) and show that PIVE fails to
offer differential privacy guarantees on adaptive protection location set as
claimed. Specifically, we demonstrate that different protection location sets
could intersect with one another due to the defined search algorithm and then
different locations in the same protection location set could have different
protection diameters. As a result, we can show that the proof of differential
privacy for PIVE is incorrect. We also make some detailed discussions on
feasible privacy frameworks with achieving personalized error bounds.


Abstract:
  We introduced a generalized Wilson line gauge link that reproduces both
staple and near straight links in different limits. We then studied the
gauge-invariant bi-local orbital angular momentum operator with such a general
gauge link, in the framework of Chen et. al. decomposition of gauge fields. At
the appropriate combination of limits, the operator reproduces both
Jaffe-Manohar and Ji's operator structure and offers a continuous analytical
interpolation between the two in the small-$x$ limit. We also studied the
potential OAM which is defined as the difference between the two, and how it
depends on the geometry or orientation of the gauge links.


Abstract:
  Graphs naturally lend themselves to model the complexities of Hyperspectral
Image (HSI) data as well as to serve as semi-supervised classifiers by
propagating given labels among nearest neighbours. In this work, we present a
novel framework for the classification of HSI data in light of a very limited
amount of labelled data, inspired by multi-view graph learning and graph signal
processing. Given an a priori superpixel-segmented hyperspectral image, we seek
a robust and efficient graph construction and label propagation method to
conduct semi-supervised learning (SSL). Since the graph is paramount to the
success of the subsequent classification task, particularly in light of the
intrinsic complexity of HSI data, we consider the problem of finding the
optimal graph to model such data. Our contribution is two-fold: firstly, we
propose a multi-stage edge-efficient semi-supervised graph learning framework
for HSI data which exploits given label information through pseudo-label
features embedded in the graph construction. Secondly, we examine and enhance
the contribution of multiple superpixel features embedded in the graph on the
basis of pseudo-labels in an extension of the previous framework, which is less
reliant on excessive parameter tuning. Ultimately, we demonstrate the
superiority of our approaches in comparison with state-of-the-art methods
through extensive numerical experiments.


Abstract:
  The quasiparticle spectra of atomically thin semiconducting transition metal
dichalcogenides (TMDCs) and their response to an ultrafast optical excitation
critically depend on interactions with the underlying substrate. Here, we
present a comparative time- and angle-resolved photoemission spectroscopy
(TR-ARPES) study of the transient electronic structure and ultrafast carrier
dynamics in the single- and bilayer TMDCs MoS$_2$ and WS$_2$ on three different
substrates: Au(111), Ag(111) and graphene/SiC. The photoexcited quasiparticle
bandgaps are observed to vary over the range of 1.9-2.3 eV between our systems.
The transient conduction band signals decay on a sub-100 fs timescale on the
metals, signifying an efficient removal of photoinduced carriers into the bulk
metallic states. On graphene, we instead observe two timescales on the order of
200 fs and 50 ps, respectively, for the conduction band decay in MoS$_2$. These
multiple timescales are explained by Auger recombination involving MoS$_2$ and
in-gap defect states. In bilayer TMDCs on metals we observe a complex
redistribution of excited holes along the valence band that is substantially
affected by interactions with the continuum of bulk metallic states.


Abstract:
  In a domain $\Omega\subseteq \mathbb{R}^\mathbf{N}$ we consider compact,
Birman-Schwinger type, operators of the form
$\mathbf{T}_{P,\mathfrak{A}}=\mathfrak{A}^*P\mathfrak{A}$; here $P$ is a
singular Borel measure in $\Omega$ and $\mathfrak{A}$ is a noncritical order
$-l\ne -\mathbf{N}/2$ pseudodifferential operator. For a class of such
operators, we obtain estimates and a proper version of H.Weyl's asymptotic law
for eigenvalues, with order depending on dimensional characteristics of the
measure. A version of the CLR estimate for singular measures is proved. For
non-selfadjoint operators of the form $P_2 \mathfrak{A} P_1$ and
$\mathfrak{A}_2 P \mathfrak{A}_1$ with singular measures $P,P_1,P_2$ and
negative order pseudodifferential operators
$\mathfrak{A},\mathfrak{A}_1,\mathfrak{A}_2$ we obtain estimates for singular
numbers.