Abstract:
  Agile software teams are expected to follow a number of specific Team
Practices (TPs) during each iteration, such as estimating the effort ("points")
required to complete user stories and coordinating the management of the
codebase with the delivery of features. For software engineering instructors
trying to teach such TPs to student teams, manually auditing teams if teams are
following the TPs and improving over time is tedious, time-consuming and
error-prone. It is even more difficult when those TPs involve two or more
tools. For example, starting work on a feature in a project-management tool
such as Pivotal Tracker should usually be followed relatively quickly by the
creation of a feature branch on GitHub. Merging a feature branch on GitHub
should usually be followed relatively quickly by deploying the new feature to a
staging server for customer feedback. Few systems are designed specifically to
audit such TPs, and existing ones, as far as we know, are limited to a single
specific tool.
  We present Bluejay, an open-source extensible platform that uses the APIs of
multiple tools to collect raw data, synthesize it into TP measurements, and
present dashboards to audit the TPs. A key insight in Bluejay's design is that
TPs can be expressed in terminology similar to that used for modeling and
auditing Service Level Agreement (SLA) compliance. Bluejay therefore builds on
mature tools used in that ecosystem and adapts them for describing, auditing,
and reporting on TPs. Bluejay currently consumes data from five different
widely-used development tools, and can be customized by connecting it to any
service with a REST API. Video showcase available at
governify.io/showcase/bluejay


Abstract:
  Recently, there is an effort to extend fine-grained entity typing by using a
richer and ultra-fine set of types, and labeling noun phrases including
pronouns and nominal nouns instead of just named entity mentions. A key
challenge for this ultra-fine entity typing task is that human annotated data
are extremely scarce, and the annotation ability of existing distant or weak
supervision approaches is very limited. To remedy this problem, in this paper,
we propose to obtain training data for ultra-fine entity typing by using a BERT
Masked Language Model (MLM). Given a mention in a sentence, our approach
constructs an input for the BERT MLM so that it predicts context dependent
hypernyms of the mention, which can be used as type labels. Experimental
results demonstrate that, with the help of these automatically generated
labels, the performance of an ultra-fine entity typing model can be improved
substantially. We also show that our approach can be applied to improve
traditional fine-grained entity typing after performing simple type mapping.


Abstract:
  In this paper, we prove that Crouzeix-Raviart finite elements of polynomial
order $p\geq5$, $p$ odd, are inf-sup stable for the Stokes problem on
triangulations. For $p\geq4$, $p$ even, the stability was proved by \'{A}.
Baran and G. Stoyan in 2007 by using the \textit{macroelement technique,} a
\textit{dimension formula}, the concept of \textit{critical points} in a
triangulation and a representation of the corresponding \textit{critical
functions}. Baran and Stoyan proved that these critical functions belong to the
range of the divergence operator applied to Crouzeix-Raviart velocity functions
and the macroelement technique implies the inf-sup stability.
  The generalization of this theory to cover odd polynomial orders $p\geq5$ is
involved; one reason is that the macroelement classes, which have been used for
even $p$, are unsuitable for odd $p$. In this paper, we introduce a new and
simple representation of non-conforming Crouzeix-Raviart basis functions of odd
degree. We employ only one type of macroelement and derive representations of
all possible critical functions. Finally, we show that they are in the range of
the divergence operator applied to Crouzeix-Raviart velocities from which the
stability of the discretization follows.


Abstract:
  In this work we study the coexistence in the same Radio Access Network (RAN)
of two generic services present in the Fifth Generation (5G) of wireless
communication systems: enhanced Mobile BroadBand (eMBB) and massive
Machine-Type Communications (mMTC). eMBB services are requested for
applications that demand extremely high data rates and moderate requirements on
latency and reliability, whereas mMTC enables applications for connecting a
massive number of low-power and low-complexity devices. The coexistence of both
services is enabled by means of network slicing and Non-Orthogonal Multiple
Access (NOMA) with Successive Interference Cancellation (SIC) decoding. Under
the orthogonal slicing, the radio resources are exclusively allocated to each
service, while in the non-orthogonal slicing the traffics from both services
overlap in the same radio resources. We evaluate the uplink performance of both
services in a scenario with a multi-antenna Base Station (BS). Our simulation
results show that the performance gains obtained through multiple receive
antennas are more accentuated for the non-orthogonal slicing than for the
orthogonal allocation of resources, such that the non-orthogonal slicing
outperforms its orthogonal counterpart in terms of achievable data rates or
number of connected devices as the number of receive antennas increases.


Abstract:
  Resting-state fMRI has been shown to provide surrogate biomarkers for the
analysis of various diseases. In addition, fMRI data helps in understanding the
brain's functional working during resting state and task-induced activity. To
improve the statistical power of biomarkers and the understanding mechanism of
the brain, pooling of multi-center studies has become increasingly popular. But
pooling the data from multiple sites introduces variations due to hardware,
software, and environment. In this paper, we look at the estimation problem of
hierarchical Sparsity Connectivity Patterns (hSCPs) in fMRI data acquired on
multiple sites. We introduce a simple yet effective matrix factorization based
formulation to reduce site-related effects while preserving biologically
relevant variations. We leverage adversarial learning in the unsupervised
regime to improve the reproducibility of the components. Experiments on
simulated datasets display that the proposed method can estimate components
with improved accuracy and reproducibility. We also demonstrate the improved
reproducibility of the components while preserving age-related variation on a
real dataset compiled from multiple sites.


Abstract:
  We propose an end-to-end deep learning framework that comprehensively solves
the inverse wave scattering problem across all length scales. Our framework
consists of the newly introduced wide-band butterfly network coupled with a
simple training procedure that dynamically injects noise during training. While
our trained network provides competitive results in classical imaging regimes,
most notably it also succeeds in the super-resolution regime where other
comparable methods fail. This encompasses both (i) reconstruction of scatterers
with sub-wavelength geometric features, and (ii) accurate imaging when two or
more scatterers are separated by less than the classical diffraction limit. We
demonstrate these properties are retained even in the presence of strong noise
and extend to scatterers not previously seen in the training set. In addition,
our network is straightforward to train requiring no restarts and has an online
runtime that is an order of magnitude faster than optimization-based
algorithms. We perform experiments with a variety of wave scattering mediums
and we demonstrate that our proposed framework outperforms both classical
inversion and competing network architectures that specialize in oscillatory
wave scattering data.


Abstract:
  Biologically-inspired robots are a very interesting and difficult branch of
robotics dues to its very rich dynamical and morphological complexities. Among
them, flying animals, such as bats, have been among the most difficult to take
inspiration from as they exhibit complex wing articulation. We attempt to
capture several of the key degrees-of-freedom that are present in the natural
flapping gait of a bat. In this work, we present the mechanical design and
analysis of our flapping wing robot, the Aerobat, where we capture the plunging
and flexion-extension in the bat's flapping modes. This robot utilizes gears,
cranks, and four-bar linkage mechanisms to actuate the arm-wing structure
composed of rigid and flexible components monolithically fabricated using
PolyJet 3D printing. The resulting robot exhibits wing expansion and retraction
during the downstroke and upstroke respectively which minimizes the negative
lift and results in a more efficient flapping gait.


Abstract:
  Many research papers that propose models to predict the course of the
COVID-19 pandemic either use handcrafted statistical models or large neural
networks. Even though large neural networks are more powerful than simpler
statistical models, they are especially hard to train on small datasets. This
paper not only presents a model with grater flexibility than the other proposed
neural networks, but also presents a model that is effective on smaller
datasets. To improve performance on small data, six regularisation methods were
tested. The results show that the GRU combined with 20% Dropout achieved the
lowest RMSE scores. The main finding was that models with less access to data
relied more on the regulariser. Applying Dropout to a GRU model trained on only
28 days of data reduced the RMSE by 23%.


Abstract:
  Not all experiments publish their results with a description of the
correlations between the data points. This makes it difficult to do hypothesis
tests or model fits with that data, since just assuming no correlation can lead
to an over- or underestimation of the resulting uncertainties. This work
presents robust test statistics that can be used with data sets with missing
correlation information. They are exact in the case of no correlation and
either guaranteed to be conservative -- i.e. the uncertainty is never
underestimated -- in the presence of correlations, or they are also exact in
the degenerate case of perfect correlation between the data points.


Abstract:
  We describe a single beam compact Spin Exchange Relaxation Free(SERF)
magnetometer whose configuration is compatible with the silicon-glass bonding
micro-machining method. A cylindrical vapor cell with 3mm diameter and 3mm in
length is utilized in the magnetometer. In order to reduce the wall relaxation
which could not be neglected in micro-machined SERF magnetometer, 3
Amagats(1Amagat=2.69$\times$ 10$^{19}$/cm$^3$) neon buffer gas is filled in the
vapor cell and this is the first demonstration of a Cs-Ne SERF magnetometer. We
also did a simulation to show that neon is a better buffer gas than nitrogen
and helium which is typical utilized in vapor cells. In order to reduce the
laser amplitude noise and the large background detection offset which is
reported to be the main noise source of a single beam absorption SERF
magnetometer, we developed a laser power differential method and a factor of 2
improvement of the power noise suppression has been demonstrated. Finally, we
did an optimization of the magnetometer and sensitivity of 40$fT/Hz^{1/2}$@30Hz
has been achieved.


Abstract:
  Current network access infrastructures are characterized by heterogeneity,
low latency, high throughput, and high computational capability, enabling
massive concurrent connections and various services. Unfortunately, this design
does not pay significant attention to mobile services in underserved areas. In
this context, the use of aerial radio access networks (ARANs) is a promising
strategy to complement existing terrestrial communication systems. Involving
airborne components such as unmanned aerial vehicles, drones, and satellites,
ARANs can quickly establish a flexible access infrastructure on demand. ARANs
are expected to support the development of seamless mobile communication
systems toward a comprehensive sixth-generation (6G) global access
infrastructure. This paper provides an overview of recent studies regarding
ARANs in the literature. First, we investigate related work to identify areas
for further exploration in terms of recent knowledge advancements and analyses.
Second, we define the scope and methodology of this study. Then, we describe
ARAN architecture and its fundamental features for the development of 6G
networks. In particular, we analyze the system model from several perspectives,
including transmission propagation, energy consumption, communication latency,
and network mobility. Furthermore, we introduce technologies that enable the
success of ARAN implementations in terms of energy replenishment, operational
management, and data delivery. Subsequently, we discuss application scenarios
envisioned for these technologies. Finally, we highlight ongoing research
efforts and trends toward 6G ARANs.


Abstract:
  The analysis of government proposal during elections from political parties
is vital to choose the next authorities in any city or country. In this paper,
we use a text mining approach to analyze the documents and provide an easy
visualization to support an easy analysis. Besides, a comparison with a
national plan based on sustainable development objectives of UN(United Nations)
from 2030 Agenda is perfomed using Natural Language techniques.


Abstract:
  We analyse the use of wavelet transform in quantum field theory models
written in light-front coordinates. In a recent paper [W.N Polyzou, Phys. Rev.
D 101(2020) 096004] W.N.Polyzou used $x^+$ variable as 'time', and applied
wavelet transform to the 'spatial' coordinates only. This makes the theory
asymmetric with respect to space and time coordinates. In present paper we
generalise the concept of continuous causal path, which is the basis of path
integration, to the sequences of causally ordered spacetime regions, and
present evaluation rules for Feynman path integrals over such sequences in
terms of wavelet transform. Both the path integrals and the wavelet transform
in our model are symmetric with respect to the light-front variables
($x^+,x^-$). The definition of a spacetime event in our generalization is very
much like the definition of event in probability theory.


Abstract:
  Machine learning has achieved tremendous success in a variety of domains in
recent years. However, a lot of these success stories have been in places where
the training and the testing distributions are extremely similar to each other.
In everyday situations when models are tested in slightly different data than
they were trained on, ML algorithms can fail spectacularly. This research
attempts to formally define this problem, what sets of assumptions are
reasonable to make in our data and what kind of guarantees we hope to obtain
from them. Then, we focus on a certain class of out of distribution problems,
their assumptions, and introduce simple algorithms that follow from these
assumptions that are able to provide more reliable generalization. A central
topic in the thesis is the strong link between discovering the causal structure
of the data, finding features that are reliable (when using them to predict)
regardless of their context, and out of distribution generalization.


Abstract:
  Deep learning models with large learning capacities often overfit to medical
imaging datasets. This is because training sets are often relatively small due
to the significant time and financial costs incurred in medical data
acquisition and labelling. Data augmentation is therefore often used to expand
the availability of training data and to increase generalization. However,
augmentation strategies are often chosen on an ad-hoc basis without
justification. In this paper, we present an augmentation policy search method
with the goal of improving model classification performance. We include in the
augmentation policy search additional transformations that are often used in
medical image analysis and evaluate their performance. In addition, we extend
the augmentation policy search to include non-linear mixed-example data
augmentation strategies. Using these learned policies, we show that principled
data augmentation for medical image model training can lead to significant
improvements in ultrasound standard plane detection, with an an average
F1-score improvement of 7.0% overall over naive data augmentation strategies in
ultrasound fetal standard plane classification. We find that the learned
representations of ultrasound images are better clustered and defined with
optimized data augmentation.


Abstract:
  By using extensive tensor network calculations, we map out the phase diagram
of the frustrated $J_1$-$J_2$ Ising model on the square lattice. In particular,
we focus on the cases with controversy in the phase diagram, especially the
stripe transition in the regime $g = |J_2/J_1|>\frac{1}{2}$, $(J_2>0,J_1<0)$.
While recent studies claimed that the phase transition is of first order when
$\frac{1}{2}<g<g^*$ (with the smallest $g^*$ being $0.67$), our simulations
suggest that if there is such a first-order region, it is smaller than those
found in earlier studies by other methods. Combining with the analysis of
critical properties, we provide evidence that the classical $J_1$-$J_2$ model
evolves continuously from two decoupled Ising models ($g\to\infty$ with central
charge $c = 1$) to a point belonging to the tricritical Ising universality
class (with $c = 0.7$) as $g$ decreases to $g^*\simeq 0.54$.


Abstract:
  Plutonium-based compounds establish an ideal platform for exploring the
interplay between long-standing itinerant-localized 5$f$ states and strongly
correlated electronic states. In this paper, we exhaustively investigate the
correlated 5$f$ electronic states of PuSn$_3$ dependence on temperature by
means of a combination of the density functional theory and the embedded
dynamical mean-field theory. It is found that the spectral weight of narrow
5$f$ band grows significantly and remarkable quasiparticle multiplets appear
around the Fermi level at low temperature. A striking $c-f$ hybridization and
prominent valence state fluctuations indicate the advent of coherence and
itinerancy of 5$f$ states. It is predicted that a 5$f$ localized to itinerant
crossover is induced by temperature accompanied by the change in Fermi surface
topology. Therefore itinerant 5$f$ states are inclined to take in active
chemical bonding, suppressing the formation of local magnetic moment of Pu
atoms, which partly elucidates the intrinsic feature of paramagnetic ground
state of PuSn$_3$. Furthermore, the 5$f$ electronic correlations are orbital
selective manifested themselves in differentiated band renormalizations and
electron effective masses. Consequently, the convincing results remain crucial
to our understanding of plutonium-based compounds and promote ongoing research.


Abstract:
  Semi-supervised learning on class-imbalanced data, although a realistic
problem, has been under studied. While existing semi-supervised learning (SSL)
methods are known to perform poorly on minority classes, we find that they
still generate high precision pseudo-labels on minority classes. By exploiting
this property, in this work, we propose Class-Rebalancing Self-Training
(CReST), a simple yet effective framework to improve existing SSL methods on
class-imbalanced data. CReST iteratively retrains a baseline SSL model with a
labeled set expanded by adding pseudo-labeled samples from an unlabeled set,
where pseudo-labeled samples from minority classes are selected more frequently
according to an estimated class distribution. We also propose a progressive
distribution alignment to adaptively adjust the rebalancing strength dubbed
CReST+. We show that CReST and CReST+ improve state-of-the-art SSL algorithms
on various class-imbalanced datasets and consistently outperform other popular
rebalancing methods. Code has been made available at
https://github.com/google-research/crest.


Abstract:
  We address in this work the instrumental systematic errors that can
potentially affect the forthcoming and future Cosmic Microwave Background
experiments aimed at observing its polarized emission. In particular, we focus
on the systematics induced by the beam and calibration, which are considered
the major sources of leakage from total intensity measurements to polarization.
We simulated synthetic data sets with Time-Ordered Astrophysics Scalable Tools,
a publicly available simulation and data analysis package. We also propose a
mitigation technique aiming at reducing the leakage by means of a template
fitting approach. This technique has shown promising results reducing the
leakage by 2 orders of magnitude at the power spectrum level when applied to a
realistic simulated data set of the LiteBIRD satellite mission.


Abstract:
  Aluminum nitride is piezoelectric and exhibits spontaneous polarization along
the $c$-axis, but the polarization cannot be switched by applying an electric
field. Adding Sc to AlN enhances the piezoelectric properties, and can make the
alloy ferroelectric. We perform a detailed first-principles analysis of
spontaneous and piezoelectric polarization. Comparisons between explicit
supercell calculations show that the virtual crystal approximation produces
accurate results for polarization, but falls short in describing the phase
stability of the alloy. We relate the behavior of the piezoelectric constant
$e_{33}$ to the microscopic behavior of the internal displacement parameter
$u$, finding that the internal strain contribution dominates in the Sc-induced
enhancement. The value of $u$ increases with scandium concentration, bringing
the alloy locally closer to a layered hexagonal structure. Our approach allows
us to calculate the ferroelectric switching barrier, which we analyze as a
function of Sc concentration and temperature based on Ginzburg-Landau theory.


Abstract:
  We investigate under which assumptions the flow associated to autonomous
planar vector fields inherits the Sobolev or BV regularity of the vector field.
We consider nearly incompressible and divergence-free vector fields, taking
advantage in both cases of the underlying Hamiltonian structure. Finally we
provide an example of an autonomous planar Sobolev divergence-free vector
field, such that the corresponding regular Lagrangian flow has no bounded
variation.


Abstract:
  We study the competition between two different topological orders in three
dimensions by considering the X-cube model and the three-dimensional toric
code. The corresponding Hamiltonian can be decomposed into two commuting parts,
one of which displaying a self-dual spectrum. To determine the phase diagram,
we compute the high-order series expansions of the ground-state energy in all
limiting cases. Apart from the topological order related to the toric code and
the fractonic order related to the X-cube model, we found two new phases which
are adiabatically connected to classical limits with nontrivial sub-extensive
degeneracies. All phase transitions are found to be first order.


Abstract:
  An algebraic group is called semi-reductive if it is a semi-direct product of
a reductive subgroup and the unipotent radical. Such a semi-reductive algebraic
group naturally arises and also plays a key role in the study of modular
representations of non-classical finite-dimensional simple Lie algebras in
positive characteristic, and some other cases. Let $G$ ba a connected
semi-reductive algebraic group over an algebraically closed field $\mathbb{F}$
and $\mathfrak{g}=Lie(G)$. It turns out that $G$ has many same properties as
reductive groups, such as the Bruhat decomposition. In this note, we obtain an
analogue of classical Chevalley restriction theorem for $\mathfrak{g}$, which
says that the $G$-invariant ring $\mathbb{F}[\mathfrak{g}]^G$ is a polynomial
ring if $\mathfrak{g}$ satisfies a certain "posivity" condition suited for lots
of cases we are interested in. As applications, we further investigate the
nilpotent cones and resolutions of singularities for semi-reductive Lie
algebras.


Abstract:
  We prove that many seemingly simple theories have Borel complete reducts.
Specifically, if a countable theory has uncountably many complete 1-types, then
it has a Borel complete reduct. Similarly, if $Th(M)$ is not small, then
$M^{eq}$ has a Borel complete reduct, and if a theory $T$ is not
$\omega$-stable, then the elementary diagram of some countable model of $T$ has
a Borel complete reduct.


Abstract:
  We provide a method to calculate the rate of false vacuum decay induced by a
black hole. The method uses complex tunneling solutions and consistently takes
into account the structure of different quantum vacua in the black hole metric
via boundary conditions. The latter are connected to the asymptotic behavior of
the time-ordered Green's function in the corresponding vacua. We illustrate the
technique on a two-dimensional toy model of a scalar field with inverted
Liouville potential in an external background of a dilaton black hole. We
analytically derive the exponential suppression of tunneling from the Boulware,
Hartle-Hawking and Unruh vacua and show that they are parametrically different.
The Unruh vacuum decay rate is exponentially smaller than the decay rate of the
Hartle-Hawking state, though both rates become unsuppressed at high enough
black hole temperature. We interpret the vanishing suppression of the Unruh
vacuum decay at high temperature as an artifact of the two-dimensional model
and discuss why this result can be modified in the realistic case of black
holes in four dimensions.


Abstract:
  Conversational recommender systems (CRS) enable the traditional recommender
systems to explicitly acquire user preferences towards items and attributes
through interactive conversations. Reinforcement learning (RL) is widely
adopted to learn conversational recommendation policies to decide what
attributes to ask, which items to recommend, and when to ask or recommend, at
each conversation turn. However, existing methods mainly target at solving one
or two of these three decision-making problems in CRS with separated
conversation and recommendation components, which restrict the scalability and
generality of CRS and fall short of preserving a stable training procedure. In
the light of these challenges, we propose to formulate these three
decision-making problems in CRS as a unified policy learning task. In order to
systematically integrate conversation and recommendation components, we develop
a dynamic weighted graph based RL method to learn a policy to select the action
at each conversation turn, either asking an attribute or recommending items.
Further, to deal with the sample efficiency issue, we propose two action
selection strategies for reducing the candidate action space according to the
preference and entropy information. Experimental results on two benchmark CRS
datasets and a real-world E-Commerce application show that the proposed method
not only significantly outperforms state-of-the-art methods but also enhances
the scalability and stability of CRS.


Abstract:
  Transport services (TAPS) is a working group of the Internet's
standardization body, the Internet Engineering Task Force (IETF). TAPS defines
a new recommended API for the Internet's transport layer. This API gives access
to a wide variety of services from various protocols, and it is
protocol-independent: the transport layer becomes adaptive, and applications
are no longer statically bound to a particular protocol and/or network
interface. We give an overview of the TAPS API, and we demonstrate its
flexibility and ease of use with an example using a Python-based open-source
implementation.


Abstract:
  Recently, the inverse electrodynamics model (IEM) was introduced and applied
to fined Reissner-Nordstr\"{o}m black holes in the context of the general
relativity coupled minimally with the nonlinear electrodynamics \cite{1}. The
solution consists of both electric and magnetic fields as of the dyonic
solutions. Here in this note, we show that the IEM model belongs to a more
general class of nonlinear electrodynamics with $T=T_{\mu }^{\mu }=0$. Here
$T_{\mu }^{\nu }$ is the energy-momentum tensor of the nonlinear electrodynamic
Lagrangian. Naturally, such a dyonic RN black hole solution is the solution for
this general class.


Abstract:
  Supermassive stars (SMSs) collapsing via the general-relativistic (GR)
instability are invoked as the possible progenitors of supermassive black
holes. Their mass and angular momentum at the onset of the instability are key
in many respects, in particular regarding the possibility for observational
signatures of direct collapse. Here, we study the stability of rotating,
rapidly accreting SMSs against GR and derive the properties of these stars at
death. On the basis of hylotropic structures, relevant for rapidly accreting
SMSs, we define rotation profiles under the assumption of local angular
momentum conservation in radiative regions, which allows for differential
rotation. We find that rotation favours the stability of rapidly accreting SMSs
as soon as the accreted angular momentum represents a fraction f > 0.1% of the
Keplerian angular momentum. For f = 0.3%-0.5% the maximum masses consistent
with GR stability are increased by an order of magnitude compared to the
non-rotating case. For f = 1%, the GR instability cannot be reached if the
stellar mass does not exceed 10^7-10^8 Msun. These results imply that, like in
the non-rotating case, the final masses of the progenitors of direct collapse
black holes range in distinct intervals depending on the scenario considered:
10^5 Msun < M < 10^6 Msun for primordial atomically cooled haloes; 10^6 Msun <
M < 10^9 Msun for metal-rich galaxy mergers. The models suggest that the
centrifugal barrier is inefficient to prevent the direct formation of a
supermassive black hole at the collapse of a SMS. Moreover, the conditions of
galaxy mergers appear as more favorable than those of atomically cooled haloes
for detectable gravitational wave emission and ultra-long gamma-ray bursts at
black hole formation.


Abstract:
  In this paper, we study the Joule-Thomson expansion for RN-AdS black holes
immersed in perfect fluid dark matter. Firstly, the negative cosmological
constant could be interpreted as thermodynamic pressure and its conjugate
quantity with the volume gave us more physical insights into the black hole. We
derive the thermodynamic definitions and study the critical behaviour of this
black hole. Secondly, the explicit expression of Joule-Thomson coefficient is
obtained from the basic formulas of enthalpy and temperature. Then, we obtain
the isenthalpic curve in $T-P$ graph and demonstrate the cooling-heating region
by the inversion curve. At last, we derive the ratio of minimum inversion
temperature to critical temperature and the inversion curves in terms of charge
$Q$ and parameter $\lambda$.


Abstract:
  Although topological artificial systems, like acoustic/photonic crystals and
cold atoms in optical lattices were initially motivated by simulating
topological phases of electronic systems, they have their own unique features
such as the spinless time-reversal symmetry and tunable $\mathbb{Z}_2$ gauge
fields. Hence, it is fundamentally important to explore new topological phases
based on their unique features. Here, we point out that the $\mathbb{Z}_2$
gauge field leads to two fundamental modifications of the conventional $k\cdot
p$ method: (i) The little co-group must include the translations with
nontrivial algebraic relations; (ii) The algebraic relations of the little
co-group are projectively represented. These give rise to higher-dimensional
irreducible representations and therefore highly degenerate Fermi points.
Breaking the primitive translations can transform the Fermi points to
interesting topological phases. We demonstrate our theory by two models: a
rectangular $\pi$-flux model exhibiting graphene-like semimetal phases, and a
graphite model with interlayer $\pi$ flux that realizes the real second-order
nodal-line semimetal phase with hinge helical modes. Their physical
realizations with a general bright-dark mechanism are discussed. Our finding
opens a new direction to explore novel topological phases unique to artificial
systems and establishes the approach to analyze these phases.


Abstract:
  In a recent paper, Iba\~nez-Azpiroz et al. [Phys. Rev. B 97, 245143 (2018)]
derive a band-truncation-error-free formula for calculating the generalized
derivative of the interband dipole matrix using Wannier interpolation. In
practice, the denominators involving intermediate states are regularized by
introducing a finite broadening parameter. In this Comment, I show that when a
finite broadening parameter is used, a correction term must be added to the
generalized derivative to obtain results that are independent of the phase
convention for the Bloch sums.


Abstract:
  The gauge function, closely related to the atomic norm, measures the
complexity of a statistical model, and has found broad applications in machine
learning and statistical signal processing. In a high-dimensional learning
problem, the gauge function attempts to safeguard against overfitting by
promoting a sparse (concise) representation within the learning alphabet.
  In this work, within the context of linear inverse problems, we pinpoint the
source of its success, but also argue that the applicability of the gauge
function is inherently limited by its convexity, and showcase several learning
problems where the classical gauge function theory fails. We then introduce a
new notion of statistical complexity, gauge$_p$ function, which overcomes the
limitations of the gauge function. The gauge$_p$ function is a simple
generalization of the gauge function that can tightly control the sparsity of a
statistical model within the learning alphabet and, perhaps surprisingly, draws
further inspiration from the Burer-Monteiro factorization in computational
mathematics.
  We also propose a new learning machine, with the building block of gauge$_p$
function, and arm this machine with a number of statistical guarantees. The
potential of the proposed gauge$_p$ function theory is then studied for two
stylized applications. Finally, we discuss the computational aspects and, in
particular, suggest a tractable numerical algorithm for implementing the new
learning machine.


Abstract:
  We have investigated the valence transition in the extended periodic Anderson
model (EPAM) within the framework of dynamical mean field theory. Previous
theoretical studies have indicated the presence of this transition in parameter
ranges that are experimentally inaccessible. In this work, we show that,
inclusion of dynamics beyond static mean-field brings the valence transition in
physically relevant regimes. We show the strong dependence of the
quasiparticle-weight scale across the sharp valence crossover. The density of
states also exhibit concomitant large scale spectral weight transfers, which
can be observed in experiments.


Abstract:
  Previously, the Einstein equation has been described as an equation of state,
general relativity as the equilibrium state of gravity, and $f({\cal R})$
gravity as a non-equilibrium one. We apply Eckart's first order thermodynamics
to the effective dissipative fluid describing scalar-tensor gravity.
Surprisingly, we obtain simple expressions for the effective heat flux,
"temperature of gravity", shear and bulk viscosity, and entropy density, plus a
generalized Fourier law in a consistent Eckart thermodynamical picture.
Well-defined notions of temperature and approach to equilibrium, missing in the
current thermodynamics of spacetime scenarios, naturally emerge.


Abstract:
  Recent studies in deep learning have shown significant progress in named
entity recognition (NER). Most existing works assume clean data annotation, yet
a fundamental challenge in real-world scenarios is the large amount of noise
from a variety of sources (e.g., pseudo, weak, or distant annotations). This
work studies NER under a noisy labeled setting with calibrated confidence
estimation. Based on empirical observations of different training dynamics of
noisy and clean labels, we propose strategies for estimating confidence scores
based on local and global independence assumptions. We partially marginalize
out labels of low confidence with a CRF model. We further propose a calibration
method for confidence scores based on the structure of entity labels. We
integrate our approach into a self-training framework for boosting performance.
Experiments in general noisy settings with four languages and distantly labeled
settings demonstrate the effectiveness of our method. Our code can be found at
https://github.com/liukun95/Noisy-NER-Confidence-Estimation


Abstract:
  We study general properties of the mapping between 5$d$ and 4$d$
superconformal field theories (SCFTs) under both twisted circle
compactification and tuning of local relevant deformation and CB moduli. After
elucidating in generality when a 5$d$ SCFT reduces to a 4$d$ one, we identify
nearly all $\mathcal{N}=1$ 5$d$ SCFT parents of rank-2 4$d$ $\mathcal{N}=2$
SCFTs. We then use this result to map out the mass deformation trajectories
among the rank-2 theories in 4$d$. This can be done by first understanding the
mass deformations of the 5$d$ $\mathcal{N}=1$ SCFTs and then map them to 4$d$.
The former task can be easily achieved by exploiting the fact that the 5$d$
parent theories can be obtained as the strong coupling limit of Lagrangian
theories, and the latter by understanding the behavior under compactification.
Finally we identify a set of general criteria that 4$d$ moduli spaces of vacua
have to satisfy when the corresponding SCFTs are related by mass deformations
and check that all our RG-flows satisfy them. Many of the mass deformations we
find are not visible from the corresponding complex integrable systems.


Abstract:
  The scalability of photonic implementations of fault-tolerant quantum
computing based on Gottesman-Kitaev-Preskill (GKP) qubits is injured by the
requirements of inline squeezing and reconfigurability of the linear optical
network. In this work we propose a topologically error-corrected architecture
that does away with these elements at no cost - in fact, at an advantage - to
state preparation overheads. Our computer consists of three modules: a 2D array
of probabilistic sources of GKP states; a depth-four circuit of static
beamsplitters, phase shifters, and single-time-step delay lines; and a 2D array
of homodyne detectors. The symmetry of our proposed circuit allows us to
combine the effects of finite squeezing and uniform photon loss within the
noise model, resulting in more comprehensive threshold estimates. These jumps
over both architectural and analytical hurdles considerably expedite the
construction of a photonic quantum computer.


Abstract:
  The microring resonators (MRR) weight bank, a ubiquitous element in silicon
photonics providing weight addition, yet, has long suffered from the weakness
of low accuracy, so that harming its potential in various promising
applications. Here, we demonstrated a novel dithering method for MRR weight
control, reaching an accuracy of up to 8.5 bits and a precision of up to 9.0
bits, which rivals that of the analog processors. Also, this method maintains
good scalability to control multi-MRRs with little additional increase in
searching time and system complexity, and has efficacious suppression to the
inter-channel crosstalk. Given these features, this method could widely benefit
the applications of MRR weight banks and facilitate the prevalence of the MRR
in silicon photonics.


Abstract:
  Smooth minimax games often proceed by simultaneous or alternating gradient
updates. Although algorithms with alternating updates are commonly used in
practice for many applications (e.g., GAN training), the majority of existing
theoretical analyses focus on simultaneous algorithms for convenience of
analysis. In this paper, we study alternating gradient descent-ascent (Alt-GDA)
in minimax games and show that Alt-GDA is superior to its simultaneous
counterpart (Sim-GDA) in many settings. In particular, we prove that Alt-GDA
achieves a near-optimal local convergence rate for strongly convex-strongly
concave (SCSC) problems while Sim-GDA converges at a much slower rate. To our
knowledge, this is the \emph{first} result of any setting showing that Alt-GDA
converges faster than Sim-GDA by more than a constant. We further prove that
the acceleration effect of alternating updates remains when the minimax problem
has only strong concavity in the dual variables. Lastly, we adapt the theory of
integral quadratic constraints and show that Alt-GDA attains the same rate
\emph{globally} for a class of SCSC minimax problems. Numerical experiments on
quadratic minimax games validate our claims. Empirically, we demonstrate that
alternating updates speed up GAN training significantly and the use of optimism
only helps for simultaneous algorithms.


Abstract:
  Quantum algorithms designed for noisy intermediate-scale quantum devices
usually require repeatedly perform a large number of quantum measurements in
estimating observable expectation values of a many-qubit quantum state.
Exploiting the ideas of importance sampling, observable compatibility, and
classical shadows of quantum states, different advanced quantum measurement
schemes have been proposed to greatly reduce the large measurement cost. Yet,
the underline cost reduction mechanisms seem distinct to each other, and how to
systematically find the optimal scheme remains a critical theoretical
challenge. Here, we address this challenge by firstly proposing a unified
framework of quantum measurements, incorporating the advanced measurement
methods as special cases. Our framework further allows us to introduce a
general scheme -- overlapped grouping measurement, which simultaneously
exploits the advantages of the existing methods. We show that an optimal
measurement scheme corresponds to partitioning the observables into overlapped
groups with each group consisting of compatible ones. We provide explicit
grouping strategies and numerically verify its performance for different
molecular Hamiltonians. Our numerical results show great improvements to the
overall existing measurement schemes. Our work paves the way for efficient
quantum measurement with near-term quantum devices.


Abstract:
  Magnetic skyrmions are the self-organized topological spin textures behaving
like particles. Because of their fast creation and typically long lifetime,
experimental verification of skyrmion's creation/annihilation processes has
been challenging. Here we successfully track skyrmions dynamics in
defect-introduced Co9Zn9Mn2, by using pump-probe Lorentz transmission electron
microscope. Following the nanosecond-photothermal excitation, we resolve
160-nm-skyrmion's proliferation at <1 ns, contraction at 5 ns, drift from 10 ns
to 4 microsecond and coalescence at 5 microsecond. These motions relay the
multiscale arrangement and relaxation of skyrmion clusters in a repeatable
cycle of 20 kHz. Such repeatable dynamics of skyrmions, arising from the
weakened but still persistent topological protection around defects, enables us
to visualize the whole life of the skyrmions, as well as demonstrating the
possible high-frequency manipulations of topological charges brought by
skyrmions.


Abstract:
  We introduce a definition of thickness in $\mathbb{R}^d$ and obtain a lower
bound for the Hausdorff dimension of the intersection of finitely or countably
many thick compact sets using a variant of Schmidt's game. As an application we
prove that given any compact set in $\mathbb{R}^d$ with thickness $\tau$, there
is a number $N(\tau)$ such that the set contains a translate of all
sufficiently small similar copies of every set in $\mathbb{R}^d$ with at most
$N(\tau)$ elements; indeed the set of such translations has positive Hausdorff
dimension. We also prove a gap lemma and bounds relating Hausdorff dimension
and thickness.


Abstract:
  Many active matter systems are known to perform L\'{e}vy walks during
migration or foraging. Such superdiffusive transport indicates long-range
correlated dynamics. These behavior patterns have been observed for
microswimmers such as bacteria in microfluidic experiments, where Gaussian
noise assumptions are insufficient to explain the data. We introduce
\textit{active L\'evy swimmers} to model such behavior. The focus is on ideal
swimmers that only interact with the walls but not with each other, which
reduces to the classical L\'evy walk model but now under confinement. We study
the density distribution in the channel and force exerted on the walls by the
L\'evy swimmers, where the boundaries require proper explicit treatment. We
analyze stronger confinement via a set of coupled kinetics equations and the
swimmers' stochastic trajectories. Previous literature demonstrated that
power-law scaling in a multiscale analysis in free space results in a
fractional diffusion equation. We show that in a channel, in the weak
confinement limit active L\'evy swimmers are governed by a modified Riesz
fractional derivative. Leveraging recent results on fractional fluxes, we
derive steady state solutions for the bulk density distribution of active
L\'evy swimmers in a channel, and demonstrate that these solutions agree well
with particle simulations. The profiles are non-uniform over the entire domain,
in contrast to constant-in-the-bulk profiles of active Brownian and
run-and-tumble particles. Our theory provides a mathematical framework for
L\'evy walks under confinement with sliding no-flux boundary conditions and
provides a foundation for studies of interacting active L\'evy swimmers.


Abstract:
  Several European countries have suspended the inoculation of the AstraZeneca
vaccine out of suspicion of causing deep vein thrombosis. In this letter we
report some Fermi estimates performed using a stochastic model aimed at making
a risk-benefit analysis of the interruption of the delivery of the AstraZeneca
vaccine in France and Italy. Our results clearly show that excess deaths due to
the interruption of the vaccination campaign injections largely overrun those
due to thrombosis even in worst case scenarios of frequency and gravity of the
vaccine side effects.


Abstract:
  It was recently argued that one-dimensional systems of several strongly
interacting fermions of different mass undergo critical transitions between
different spatial orderings when the external confinement adiabatically changes
its shape. In this work, we explore their dynamical properties when finite-time
drivings are considered. By detailed analysis of many-body spectra, we show
that the dynamics is typically guided only by the lowest eigenstates and may be
well-understood in the language of the generalized Landau-Zener mechanism. In
this way, we can capture precisely the dynamical response of the system to the
external driving. As consequence, we show that by appropriate tailoring
parameters of the driving one can target desired many-body state in a
non-infinite time. Our theoretical predictions can be straightforwardly
utilized in upcoming state-of-the-art experiments with ultracold atoms.


Abstract:
  These days, cyber-criminals target humans rather than machines since they try
to accomplish their malicious intentions by exploiting the weaknesses of end
users. Thus, human vulnerabilities pose a serious threat to the security and
integrity of computer systems and data. The human tendency to trust and help
others, as well as personal, social, and cultural characteristics, are
indicative of the level of susceptibility that one may exhibit towards certain
attack types and deception strategies. This work aims to investigate the
factors that affect human susceptibility by studying the existing literature
related to this subject. The objective is also to explore and describe state of
the art human vulnerability assessment models, current prevention, and
mitigation approaches regarding user susceptibility, as well as educational and
awareness raising training strategies. Following the review of the literature,
several conclusions are reached. Among them, Human Vulnerability Assessment has
been included in various frameworks aiming to assess the cyber security
capacity of organizations, but it concerns a one time assessment rather than a
continuous practice. Moreover, human maliciousness is still neglected from
current Human Vulnerability Assessment frameworks; thus, insider threat actors
evade identification, which may lead to an increased cyber security risk.
Finally, this work proposes a user susceptibility profile according to the
factors stemming from our research.


Abstract:
  News recommendation calls for deep insights of news articles' underlying
semantics. Therefore, pretrained language models (PLMs), like BERT and RoBERTa,
may substantially contribute to the recommendation quality. However, it's
extremely challenging to have news recommenders trained together with such big
models: the learning of news recommenders requires intensive news encoding
operations, whose cost is prohibitive if PLMs are used as the news encoder. In
this paper, we propose a novel framework, {SpeedyFeed}, which efficiently
trains PLMs-based news recommenders of superior quality. SpeedyFeed is
highlighted for its light-weighted encoding pipeline, which gives rise to three
major advantages. Firstly, it makes the intermedia results fully reusable for
the training workflow, which removes most of the repetitive but redundant
encoding operations. Secondly, it improves the data efficiency of the training
workflow, where non-informative data can be eliminated from encoding. Thirdly,
it further saves the cost by leveraging simplified news encoding and compact
news representation. Extensive experiments show that SpeedyFeed leads to more
than 100$\times$ acceleration of the training process, which enables big models
to be trained efficiently and effectively over massive user data. The
well-trained PLMs-based model from SpeedyFeed demonstrates highly competitive
performance, where it outperforms the state-of-the-art news recommenders with
significant margins. SpeedyFeed is also a model-agnostic framework, which is
potentially applicable to a wide spectrum of content-based recommender systems;
therefore, the whole framework is open-sourced to facilitate the progress in
related areas.


Abstract:
  We prove new complexity results for Feedback Vertex Set and Even Cycle
Transversal on $H$-free graphs, that is, graphs that do not contain some fixed
graph $H$ as an induced subgraph. In particular, we prove that for every $s\geq
1$, both problems are polynomial-time solvable for $sP_3$-free graphs and
$(sP_1+P_5)$-free graphs; here, the graph $sP_3$ denotes the disjoint union of
$s$ paths on three vertices and the graph $sP_1+P_5$ denotes the disjoint union
of $s$ isolated vertices and a path on five vertices. Our new results for
Feedback Vertex Set extend all known polynomial-time results for Feedback
Vertex Set on $H$-free graphs, namely for $sP_2$-free graphs [Chiarelli et al.,
TCS 2018], $(sP_1+P_3)$-free graphs [Dabrowski et al., Algorithmica 2020] and
$P_5$-free graphs [Abrishami et al., SODA 2021]. Together, the new results also
show that both problems exhibit the same behaviour on $H$-free graphs (subject
to some open cases). This is in part due to a new general algorithm we design
for finding in a ($sP_3)$-free or $(sP_1+P_5)$-free graph $G$ a largest induced
subgraph whose blocks belong to some finite class ${\cal C}$ of graphs. We also
compare our results with the state-of-the-art results for the Odd Cycle
Transversal problem, which is known to behave differently on $H$-free graphs.


Abstract:
  We consider four-point integrals arising in the planar limit of the conformal
"fishnet" theory in four dimensions. They define a two-parameter family of
higher-loop Feynman integrals, which extend the series of ladder integrals and
were argued, based on integrability and analyticity, to admit matrix-model-like
integral and determinantal representations. In this paper, we prove the
equivalence of all these representations using exact summation and integration
techniques. We then analyze the large-order behaviour, corresponding to the
thermodynamic limit of a large fishnet graph. The saddle-point equations are
found to match known two-cut singular equations arising in matrix models,
enabling us to obtain a concise parametric expression for the free-energy
density in terms of complete elliptic integrals. Interestingly, the latter
depends non-trivially on the fishnet aspect ratio and differs from a scaling
formula due to Zamolodchikov for large periodic fishnets, suggesting a strong
sensitivity to the boundary conditions. We also find an intriguing connection
between the saddle-point equation and the equation describing the
Frolov-Tseytlin spinning string in $AdS_{3}\times S^{1}$, in a generalized
scaling combining the thermodynamic and short-distance limits.


Abstract:
  This work introduces a new concept of functional areas called Mobility
Functional Areas (MFAs), i.e., the geographic zones highly interconnected
according to the analysis of mobile positioning data. The MFAs do not coincide
necessarily with administrative borders as they are built observing natural
human mobility and, therefore, they can be used to inform, in a bottom-up
approach, local transportation, spatial planning, health and economic policies.
After presenting the methodology behind the MFAs, this study focuses on the
link between the COVID-19 pandemic and the MFAs in Austria. It emerges that the
MFAs registered an average number of infections statistically larger than the
areas in the rest of the country, suggesting the usefulness of the MFAs in the
context of targeted re-escalation policy responses to this health crisis. The
MFAs dataset is openly available to other scholars for further analyses.


Abstract:
  Multi-armed bandit algorithms have been argued for decades as useful for
adaptively randomized experiments. In such experiments, an algorithm varies
which arms (e.g. alternative interventions to help students learn) are assigned
to participants, with the goal of assigning higher-reward arms to as many
participants as possible. We applied the bandit algorithm Thompson Sampling
(TS) to run adaptive experiments in three university classes. Instructors saw
great value in trying to rapidly use data to give their students in the
experiments better arms (e.g. better explanations of a concept). Our
deployment, however, illustrated a major barrier for scientists and
practitioners to use such adaptive experiments: a lack of quantifiable insight
into how much statistical analysis of specific real-world experiments is
impacted (Pallmann et al, 2018; FDA, 2019), compared to traditional uniform
random assignment. We therefore use our case study of the ubiquitous two-arm
binary reward setting to empirically investigate the impact of using Thompson
Sampling instead of uniform random assignment. In this setting, using common
statistical hypothesis tests, we show that collecting data with TS can as much
as double the False Positive Rate (FPR; incorrectly reporting differences when
none exist) and the False Negative Rate (FNR; failing to report differences
when they exist)...


Abstract:
  Though convolutional neural networks (CNNs) have demonstrated remarkable
ability in learning discriminative features, they often generalize poorly to
unseen domains. Domain generalization aims to address this problem by learning
from a set of source domains a model that is generalizable to any unseen
domain. In this paper, a novel approach is proposed based on probabilistically
mixing instance-level feature statistics of training samples across source
domains. Our method, termed MixStyle, is motivated by the observation that
visual domain is closely related to image style (e.g., photo vs.~sketch
images). Such style information is captured by the bottom layers of a CNN where
our proposed style-mixing takes place. Mixing styles of training instances
results in novel domains being synthesized implicitly, which increase the
domain diversity of the source domains, and hence the generalizability of the
trained model. MixStyle fits into mini-batch training perfectly and is
extremely easy to implement. The effectiveness of MixStyle is demonstrated on a
wide range of tasks including category classification, instance retrieval and
reinforcement learning.


Abstract:
  The Department of Homeland Security in the United States estimates that 90%
of software vulnerabilities can be traced back to defects in design and
software coding. The financial impact of these vulnerabilities has been shown
to exceed 380 million USD in industrial control systems alone. Since software
developers write software, they also introduce these vulnerabilities into the
source code. However, secure coding guidelines exist to prevent software
developers from writing vulnerable code. This study focuses on the human
factor, the software developer, and secure coding, in particular secure coding
guidelines. We want to understand the software developers' awareness and
compliance to secure coding guidelines and why, if at all, they aren't
compliant or aware. We base our results on a large-scale survey on secure
coding guidelines, with more than 190 industrial software developers. Our
work's main contribution motivates the need to educate industrial software
developers on secure coding guidelines, and it gives a list of fifteen
actionable items to be used by practitioners in the industry. We also make our
raw data openly available for further research.


Abstract:
  Interaction in quantum systems can spread initially localized quantum
information into the many degrees of freedom of the entire system.
Understanding this process, known as quantum scrambling, is the key to
resolving various conundrums in physics. Here, by measuring the time-dependent
evolution and fluctuation of out-of-time-order correlators, we experimentally
investigate the dynamics of quantum scrambling on a 53-qubit quantum processor.
We engineer quantum circuits that distinguish the two mechanisms associated
with quantum scrambling, operator spreading and operator entanglement, and
experimentally observe their respective signatures. We show that while operator
spreading is captured by an efficient classical model, operator entanglement
requires exponentially scaled computational resources to simulate. These
results open the path to studying complex and practically relevant physical
observables with near-term quantum processors.


Abstract:
  Entity embeddings, which represent different aspects of each entity with a
single vector like word embeddings, are a key component of neural entity
linking models. Existing entity embeddings are learned from canonical Wikipedia
articles and local contexts surrounding target entities. Such entity embeddings
are effective, but too distinctive for linking models to learn contextual
commonality. We propose a simple yet effective method, FGS2EE, to inject
fine-grained semantic information into entity embeddings to reduce the
distinctiveness and facilitate the learning of contextual commonality. FGS2EE
first uses the embeddings of semantic type words to generate semantic
embeddings, and then combines them with existing entity embeddings through
linear aggregation. Extensive experiments show the effectiveness of such
embeddings. Based on our entity embeddings, we achieved new sate-of-the-art
performance on entity linking.


Abstract:
  Adversarial examples contain carefully crafted perturbations that can fool
deep neural networks (DNNs) into making wrong predictions. Enhancing the
adversarial robustness of DNNs has gained considerable interest in recent
years. Although image transformation-based defenses were widely considered at
an earlier time, most of them have been defeated by adaptive attacks. In this
paper, we propose a new image transformation defense based on error diffusion
halftoning, and combine it with adversarial training to defend against
adversarial examples. Error diffusion halftoning projects an image into a 1-bit
space and diffuses quantization error to neighboring pixels. This process can
remove adversarial perturbations from a given image while maintaining
acceptable image quality in the meantime in favor of recognition. Experimental
results demonstrate that the proposed method is able to improve adversarial
robustness even under advanced adaptive attacks, while most of the other image
transformation-based defenses do not. We show that a proper image
transformation can still be an effective defense approach. Code:
https://github.com/shaoyuanlo/Halftoning-Defense


Abstract:
  2-local derivation is a generalized derivation for a Lie algebra, which plays
an important role to the study of local properties of the structure of the Lie
algebra. In this paper, we prove that every 2-local derivation on the conformal
Galilei algebra is a derivation.


Abstract:
  Pseudo-goldstinos appear in the scenario of multi-sector SUSY breaking.
Unlike the true goldstino which is massless and absorbed by the gravitino,
pseudo-goldstinos could obtain mass from radiative effects. In this note,
working in the scenario of two-sector SUSY breaking with gauge mediation, we
explicitly calculate the pseudo-goldstino mass at the leading three-loop level
and provide the analytical results. In our calculation we consider the general
case of messenger masses (not necessarily equal) and include the higher order
terms of SUSY breaking scales. Our results can reproduce the numerical value
estimated previously at the leading order of SUSY breaking scales with the
assumption of equal messenger masses. It turns out that the results are very
sensitive to the ratio of messenger masses, while the higher order terms of
SUSY breaking scales are rather small in magnitude. Depending on the ratio of
messenger masses, the pseudo-goldstino mass can be as low as ${{\cal O} (0.1)}$
GeV.


Abstract:
  In the field of biometrics, fingerprint recognition systems are vulnerable to
presentation attacks made by artificially generated spoof fingerprints.
Therefore, it is essential to perform liveness detection of a fingerprint
before authenticating it. Fingerprint liveness detection mechanisms perform
well under the within-dataset environment but fail miserably under cross-sensor
(when tested on a fingerprint acquired by a new sensor) and cross-dataset (when
trained on one dataset and tested on another) settings. To enhance the
generalization abilities, robustness and the interoperability of the
fingerprint spoof detectors, the learning models need to be adaptive towards
the data. We propose a generic model, EaZy learning which can be considered as
an adaptive midway between eager and lazy learning. We show the usefulness of
this adaptivity under cross-sensor and cross-dataset environments. EaZy
learning examines the properties intrinsic to the dataset while generating a
pool of hypotheses. EaZy learning is similar to ensemble learning as it
generates an ensemble of base classifiers and integrates them to make a
prediction. Still, it differs in the way it generates the base classifiers.
EaZy learning develops an ensemble of entirely disjoint base classifiers which
has a beneficial influence on the diversity of the underlying ensemble. Also,
it integrates the predictions made by these base classifiers based on their
performance on the validation data. Experiments conducted on the standard high
dimensional datasets LivDet 2011, LivDet 2013 and LivDet 2015 prove the
efficacy of the model under cross-dataset and cross-sensor environments.


Abstract:
  With autonomous vehicles now sharing roads with human drivers, the era of
mixed autonomy brings new challenges in dealing with congestion. One cause of
congestion is when vehicle users choose their routes selfishly to minimize
their personal travel delay rather than a global travel delay, and prior works
address this phenomenon using tolling to influence routing choices, but do not
address the setting of mixed autonomy. Tolls may be differentiated, meaning
different users of a road experience different tolls, or they may be anonymous;
the latter is desirable to allay concerns of fairness and privacy, as well as
logistical challenges. In this work we examine the role of differentiation in
traffic networks with mixed autonomy. Specifically, we first establish
differentiated tolls which completely eliminate inefficiency due to selfish
routing. We then show the fundamental limitations of anonymous tolls in our
setting, and we provide anonymous tolls with mild performance guarantees. We
show that in parallel networks, an infinitesimal differentiation in tolls is
enough to guarantee optimality, and finally we establish a lower bound on the
inefficiency of variable marginal cost tolling in the mixed autonomy setting.


Abstract:
  The present paper deals with integral classes $\xi_p\in
H_{2p+1}(L_1^{2p+1}\times L_2^{2p+1})$ which are counterexamples for the
Steenrod realization problem, where $L_1^{2p+1}$ is the $(2p+1)$-Lens space and
$p\geq 3$ is a prime number. For $p=3$, this is the Thom's famous
counterexample. We give a geometric description of this class using the theory
of stratifolds. As a consequence, we obtain a geometric interpretation of the
obstruction to realizability, in terms of the Atiyah-Hirzebruch spectral
sequence.


Abstract:
  Opportunities such as higher education can promote intergenerational
mobility, leading individuals to achieve levels of socioeconomic status above
that of their parents. We develop a dynamic model for allocating such
opportunities in a society that exhibits bottlenecks in mobility; the problem
of optimal allocation reflects a trade-off between the benefits conferred by
the opportunities in the current generation and the potential to elevate the
socioeconomic status of recipients, shaping the composition of future
generations in ways that can benefit further from the opportunities. We show
how optimal allocations in our model arise as solutions to continuous
optimization problems over multiple generations, and we find in general that
these optimal solutions can favor recipients of low socioeconomic status over
slightly higher-performing individuals of high socioeconomic status -- a form
of socioeconomic affirmative action that the society in our model discovers in
the pursuit of purely payoff-maximizing goals. We characterize how the
structure of the model can lead to either temporary or persistent affirmative
action, and we consider extensions of the model with more complex processes
modulating the movement between different levels of socioeconomic status.


Abstract:
  We present the serendipitous detection of the two main OH maser lines at 1667
and 1665 MHz associated with IRAS 10597+5926 at z = 0.19612 in the untargeted
Apertif Wide-area Extragalactic Survey (AWES), and the subsequent measurement
of the OH 1612 MHz satellite line in the same source. With a total OH
luminosity of log(L/L_Sun) = 3.90 +/- 0.03, IRAS 10597+5926 is the fourth
brightest OH megamaser (OHM) known. We measure a lower limit for the 1667/1612
ratio of R_1612 > 45.9 which is the highest limiting ratio measured for the
1612 MHz OH satellite line to date. OH satellite line measurements provide a
potentially valuable constraint by which to compare detailed models of OH maser
pumping mechanisms. Optical imaging shows the galaxy is likely a late-stage
merger. Based on published infrared and far ultraviolet fluxes, we find that
the galaxy is an ultra luminous infrared galaxy (ULIRG) with log(L_TIR/L_Sun) =
12.24, undergoing a star burst with an estimated star formation rate of 179 +/-
40 M_Sun/yr. These host galaxy properties are consistent with the physical
conditions responsible for very bright OHM emission. Finally, we provide an
update on the predicted number of OH masers that may be found in AWES, and
estimate the total number of OH masers that will be detected in each of the
individual main and satellite OH 18 cm lines.


Abstract:
  We investigate two different models. In one of them massive fermions interact
with a massive scalar field and in the other the fermion field is in an
electrical field (QED2). The chiral condensates are calculated in one-loop
approximation. We found that the chiral condensate in the case of the Yukawa
interaction the fermions and scalar field does not vanish if the mass of the
fermion field tends to zero. The chiral condensate disappears in QED2, if the
fermion mass is zero.


Abstract:
  In the family of Intelligent Transportation Systems (ITS), Multimodal
Transport Systems (MMTS) have placed themselves as a mainstream transportation
mean of our time as a feasible integrative transportation process. The Global
Economy progressed with the help of transportation. The volume of goods and
distances covered have doubled in the last ten years, so there is a high demand
of an optimized transportation, fast but with low costs, saving resources but
also safe, with low or zero emissions. Thus, it is important to have an
overview of existing research in this field, to know what was already done and
what is to be studied next. The main objective is to explore a beneficent
selection of the existing research, methods and information in the field of
multimodal transportation research, to identify industry needs and gaps in
research and provide context for future research. The selective survey covers
multimodal transport design and optimization in terms of: cost, time, and
network topology. The multimodal transport theoretical aspects, context and
resources are also covering various aspects. The survey's selection includes
nowadays best methods and solvers for Intelligent Transportation Systems (ITS).
The gap between theory and real-world applications should be further solved in
order to optimize the global multimodal transportation system.


Abstract:
  In this paper, we show that any compact quasi $k$-Yamabe gradient solitons
must have constant $\sigma_{k}$-curvature. Moreover, we provide a certain
condition for a compact quasi $k$-Yamabe soliton to be gradient, and for
noncompact solitons, we present a geometric rigidity under a decaiment
condition on the norm of the soliton field.


Abstract:
  Physical and mental well-being during the COVID-19 pandemic is typically
assessed via surveys, which might make it difficult to conduct longitudinal
studies and might lead to data suffering from recall bias. Ecological momentary
assessment (EMA) driven smartphone apps can help alleviate such issues,
allowing for in situ recordings. Implementing such an app is not trivial,
necessitates strict regulatory and legal requirements, and requires short
development cycles to appropriately react to abrupt changes in the pandemic.
Based on an existing app framework, we developed Corona Health, an app that
serves as a platform for deploying questionnaire-based studies in combination
with recordings of mobile sensors. In this paper, we present the technical
details of Corona Health and provide first insights into the collected data.
Through collaborative efforts from experts from public health, medicine,
psychology, and computer science, we released Corona Health publicly on Google
Play and the Apple App Store (in July, 2020) in 8 languages and attracted 7,290
installations so far. Currently, five studies related to physical and mental
well-being are deployed and 17,241 questionnaires have been filled out. Corona
Health proves to be a viable tool for conducting research related to the
COVID-19 pandemic and can serve as a blueprint for future EMA-based studies.
The data we collected will substantially improve our knowledge on mental and
physical health states, traits and trajectories as well as its risk and
protective factors over the course of the COVID-19 pandemic and its diverse
prevention measures.


Abstract:
  There is a remarkable connection between the boundary structure of the
positive kinematic region and branch points of integrated amplitudes in planar
$\mathcal{N}=4$ SYM. A long standing question has been precisely how algebraic
branch points emerge from this picture. We use wall crossing and scattering
diagrams to systematically study the boundary structure of the positive
kinematic regions associated with MHV amplitudes. The notion of asymptotic
chambers in the scattering diagram naturally explains the appearance of
algebraic branch points. Furthermore, the scattering diagram construction also
motivates a new coordinate system for kinematic space that rationalizes the
relations between algebraic letters in the symbol alphabet. As a direct
application, we conjecture a complete list of all algebraic letters that could
appear in the symbol alphabet of the 8-point MHV amplitude.


Abstract:
  We describe a systematic procedure to calculate the resolvent operator for a
linear pencil on Banach space and thereby simplify, unify and extend known
methods for resolution and representation of marginally stable time series. We
pay particular attention to those time series commonly known as unit root
processes. The new method uses infinite-length Jordan chains to find the key
spectral separation projections which enable separation and solution of the
fundamental equations for the Laurent series coefficients of the resolvent. It
is then possible to define the desired Granger-Johansen representation for the
time series. The method remains valid when the resolvent has an isolated
essential singularity at unity.


Abstract:
  The Faddeev equations for the $\Xi NN$ bound-state problem are solved where
the three $S$=$-2$ baryon-baryon interactions of J\"ulich-Bonn-M\"unchen chiral
EFT, HAL QCD and Nijmegen ESC08c are used. The $T$-matrix $T_{\Xi N, \Xi N}$
obtained within the original $\Lambda\Lambda$-$\Xi N$-$\Sigma\Sigma$ $/$ $\Xi
N$-$\Lambda \Sigma$-$\Sigma\Sigma$ coupled-channel framework is employed as an
input to the equations. We found no bound state for J\"ulich-Bonn-M\"unchen
chiral EFT and HAL QCD but ESC08c generates a bound state with the total
isospin and spin-parity $(T,J^{\pi})=(1/2, 3/2^+)$ where the decays into
$\Lambda\Lambda N$ are suppressed.


Abstract:
  It is shown that for deep neural networks, a single wide layer of width $N+1$
($N$ being the number of training samples) suffices to prove the connectivity
of sublevel sets of the training loss function. In the two-layer setting, the
same property may not hold even if one has just one neuron less (i.e. width $N$
can lead to disconnected sublevel sets).


Abstract:
  We present a Markov Chain Monte Carlo (MCMC)-based parameter estimation
package, CosmoReionMC, to jointly constrain cosmological parameters of the
$\Lambda$CDM model and the astrophysical parameters related to hydrogen
reionization. The package is based on a previously developed physically
motivated semi-analytical model for reionization, a similar semi-analytical
model for computing the global 21~cm signal during the cosmic dawn and using an
appropriately modified version of the publicly available CAMB for computing the
CMB anisotropies. These calculations are then coupled to an MCMC ensemble
sampler \texttt{emcee} to compute the posterior distributions of the model
parameter. The model has twelve free parameters in total: five cosmological and
seven related to the stellar populations. We constrain the parameters by
matching the theoretical predictions with CMB data from Planck, observations
related to the quasar absorption spectra and, for the first time, the global
21~cm signal from EDGES. We find that incorporating the quasar spectra data in
the analysis tightens the bounds on the electron scattering optical depth
$\tau$ and consequently the normalization $A_s$ of the primordial matter power
spectrum (or equivalently $\sigma_8$). Furthermore, when we include the EDGES
data in the analysis, we find that an early population of metal-free stars with
efficient radio emission is necessary to match the absorption amplitude. The
CosmoReionMC package should have interesting future applications, e.g., probing
non-standard extensions to the $\Lambda$CDM model.


Abstract:
  Rehearsal is a critical component for class-incremental continual learning,
yet it requires a substantial memory budget. Our work investigates whether we
can significantly reduce this memory budget by leveraging unlabeled data from
an agent's environment in a realistic and challenging continual learning
paradigm. Specifically, we explore and formalize a novel semi-supervised
continual learning (SSCL) setting, where labeled data is scarce yet non-i.i.d.
unlabeled data from the agent's environment is plentiful. Importantly, data
distributions in the SSCL setting are realistic and therefore reflect object
class correlations between, and among, the labeled and unlabeled data
distributions. We show that a strategy built on pseudo-labeling, consistency
regularization, Out-of-Distribution (OoD) detection, and knowledge distillation
reduces forgetting in this setting. Our approach, DistillMatch, increases
performance over the state-of-the-art by no less than 8.7% average task
accuracy and up to 54.5% average task accuracy in SSCL CIFAR-100 experiments.
Moreover, we demonstrate that DistillMatch can save up to 0.23 stored images
per processed unlabeled image compared to the next best method which only saves
0.08. Our results suggest that focusing on realistic correlated distributions
is a significantly new perspective, which accentuates the importance of
leveraging the world's structure as a continual learning strategy.


Abstract:
  Given restrictions on the availability of data, active learning is the
process of training a model with limited labeled data by selecting a core
subset of an unlabeled data pool to label. Although selecting the most useful
points for training is an optimization problem, the scale of deep learning data
sets forces most selection strategies to employ efficient heuristics. Instead,
we propose a new integer optimization problem for selecting a core set that
minimizes the discrete Wasserstein distance from the unlabeled pool. We
demonstrate that this problem can be tractably solved with a Generalized
Benders Decomposition algorithm. Our strategy requires high-quality latent
features which we obtain by unsupervised learning on the unlabeled pool.
Numerical results on several data sets show that our optimization approach is
competitive with baselines and particularly outperforms them in the low budget
regime where less than one percent of the data set is labeled.


Abstract:
  Aberrations limit scanning fluorescence microscopy when imaging in scattering
materials such as biological tissue. Model-based approaches for adaptive optics
take advantage of a computational model of the optical setup. Such models can
be combined with the optimization techniques of machine learning frameworks to
find aberration corrections, as was demonstrated for focusing a laser beam
through aberrations onto a camera [arXiv:2007.13400]. Here, we extend this
approach to two-photon scanning microscopy. The developed sensorless technique
finds corrections for aberrations in scattering samples and will be useful for
a range of imaging application, for example in brain tissue.


Abstract:
  Parallels between the signal processing tasks and biological neurons lead to
an understanding of the principles of self-organized optimization of input
signal recognition. In the present paper, we discuss such similarities among
biological and technical systems. We propose adding the well-known STDP
synaptic plasticity rule to direct the weight modification towards the state
associated with the maximal difference between background noise and correlated
signals. We use the principle of physically constrained weight growth as a
basis for such weights' modification control. It is proposed that the existence
and production of bio-chemical 'substances' needed for plasticity development
restrict a biological synaptic straight modification. In this paper, the
information about the noise-to-signal ratio controls such a substances'
production and storage and drives the neuron's synaptic pressures towards the
state with the best signal-to-noise ratio. We consider several experiments with
different input signal regimes to understand the functioning of the proposed
approach.


Abstract:
  Modulations of interfacial Dzyaloshinskii-Moriya interaction (iDMI) on
current-driven dynamics of 180$^{\circ}$ domain walls (180DWs) in long and
narrow spin valves (LNSVs) with heavy-metal caplayers are systematically
investigated. We focus on LNSVs with in-plane magnetic anisotropy in their free
layers. For planar-transverse polarizers (pinned layers of LNSVs), the Walker
breakdown can be postponed considerably (practical infinity) by iDMI. More
interestingly, the originally unstable traveling mode is also stabilized by
iDMI with high saturation velocity thus serves as fast carrier of information.
For parallel polarizers, the Walker limit is increased and the corresponding
modifications of iDMI to wall velocity in both steady and precessional flows of
180DWs are provided. For perpendicular polarizers, precessional flow of 180DWs
is absent due to the stability of stationary mode beyond the modified Walker
limit. For LNSVs with perpendicular magnetic anisotropy in their free layers,
similar results are obtained. Our findings open new possibilities for
developing magnetic nanodevices based on 180DW propagation with low energy
consumption and high robustness.


Abstract:
  In a Type II superconductor, the vortex core behaves like a normal metal.
Consequently, the single-particle density of states in the vortex core of a
conventional Type II superconductor remains either flat or (for very clean
single crystals) exhibits a peak at zero bias due to the formation of Caroli-de
Gennes-Matricon bound state inside the core. Here we report an unusual
observation from scanning tunneling spectroscopy measurements in a weakly
pinned thin film of the conventional s-wave superconductor a-MoGe, namely, that
a soft gap in the local density of states continues to exist even at the center
of the vortex core. We ascribe this observation to rapid fluctuation of
vortices about their mean position that blurs the boundary between the gapless
normal core and the gapped superconducting region outside. Analyzing the data
as a function of magnetic field we show that the variation of fluctuation
amplitude as a function of magnetic field is consistent with quantum zero-point
motion of vortices.


Abstract:
  Recent studies on computer vision mainly focus on natural images that express
real-world scenes. They achieve outstanding performance on diverse tasks such
as visual question answering. Diagram is a special form of visual expression
that frequently appears in the education field and is of great significance for
learners to understand multimodal knowledge. Current research on diagrams
preliminarily focuses on natural disciplines such as Biology and Geography,
whose expressions are still similar to natural images. Another type of diagrams
such as from Computer Science is composed of graphics containing complex
topologies and relations, and research on this type of diagrams is still blank.
The main challenges of graphic diagrams understanding are the rarity of data
and the confusion of semantics, which are mainly reflected in the diversity of
expressions. In this paper, we construct a novel dataset of graphic diagrams
named Computer Science Diagrams (CSDia). It contains more than 1,200 diagrams
and exhaustive annotations of objects and relations. Considering the visual
noises caused by the various expressions in diagrams, we introduce the topology
of diagrams to parse topological structure. After that, we propose Diagram
Parsing Net (DPN) to represent the diagram from three branches: topology,
visual feature, and text, and apply the model to the diagram classification
task to evaluate the ability of diagrams understanding. The results show the
effectiveness of the proposed DPN on diagrams understanding.


Abstract:
  The concept of literary genre is a highly complex one: not only are different
genres frequently defined on several, but not necessarily the same levels of
description, but consideration of genres as cognitive, social, or scholarly
constructs with a rich history further complicate the matter. This contribution
focuses on thematic aspects of genre with a quantitative approach, namely Topic
Modeling. Topic Modeling has proven to be useful to discover thematic patterns
and trends in large collections of texts, with a view to class or browse them
on the basis of their dominant themes. It has rarely if ever, however, been
applied to collections of dramatic texts.
  In this contribution, Topic Modeling is used to analyze a collection of
French Drama of the Classical Age and the Enlightenment. The general aim of
this contribution is to discover what semantic types of topics are found in
this collection, whether different dramatic subgenres have distinctive dominant
topics and plot-related topic patterns, and inversely, to what extent
clustering methods based on topic scores per play produce groupings of texts
which agree with more conventional genre distinctions. This contribution shows
that interesting topic patterns can be detected which provide new insights into
the thematic, subgenre-related structure of French drama as well as into the
history of French drama of the Classical Age and the Enlightenment.


Abstract:
  Dark patterns are interface designs that nudge users towards behavior that is
against their best interests. Since humans are often not even aware that they
are influenced by these malicious patterns, research has to identify ways to
protect web users against them. One approach to this is the automatic detection
of dark patterns which enables the development of tools that are able to
protect users by proactively warning them in cases where they face a dark
pattern. In this paper, we present ongoing work in the direction of automatic
detection of dark patterns, and outline an example to detect malicious patterns
within the domain of cookie banners.


Abstract:
  In this work, we present a new semantic segmentation model for historical
city maps that surpasses the state of the art in terms of flexibility and
performance. Research in automatic map processing is largely focused on
homogeneous corpora or even individual maps, leading to inflexible algorithms.
Recently, convolutional neural networks have opened new perspectives for the
development of more generic tools. Based on two new maps corpora, the first one
centered on Paris and the second one gathering cities from all over the world,
we propose a method for operationalizing the figuration based on traditional
computer vision algorithms that allows large-scale quantitative analysis. In a
second step, we propose a semantic segmentation model based on neural networks
and implement several improvements. Finally, we analyze the impact of map
figuration on segmentation performance and evaluate future ways to improve the
representational flexibility of neural networks. To conclude, we show that
these networks are able to semantically segment map data of a very large
figurative diversity with efficiency.


Abstract:
  Within this article, we develop a residual type a posteriori error estimator
for a time discrete quasi-static phase-field fracture model. Particular
emphasize is given to the robustness of the error estimator for the variational
inequality governing the phase-field evolution with respect to the phase-field
regularization parameter $\epsilon$. The article concludes with numerical
examples highlighting the performance of the proposed a posteriori error
estimators on three standard test cases; the single edge notched tension and
shear test as well as the L-shaped panel test.


Abstract:
  We measure the Shubnikov-de Haas effect in thin-film Sr$_2$RuO$_4$ grown on
an (LaAlO$_3$)$_{0.29}$-(SrAl$_{1/2}$Ta$_{1/2}$O$_3$)$_{0.71}$ (LSAT)
substrate. We detect all three known Fermi surfaces and extract the Fermi
surface volumes, cyclotron effective masses, and quantum lifetimes. We show
that the electronic structure is nearly identical to that of single-crystal
Sr$_2$RuO$_4$, and that the quasiparticle lifetime is consistent with the Tc of
comparably clean, single-crystal Sr$_2$RuO$_4$. Unlike single-crystal
Sr$_2$RuO$_4$, where the quantum and transport lifetimes are roughly equal, we
find that the transport lifetime is $1.3\pm0.1$ times longer than the quantum
lifetime. This suggests that extended (rather than point) defects may be the
dominant source of quasiparticle scattering in these films. To test this idea,
we perform cross-sectional STEM and find that out-of-phase boundaries extending
the entire thickness of the film occur with a density that is consistent with
the quantum mean free path. The long quasiparticle lifetimes make these films
ideal for studying the unconventional superconducting state in Sr$_2$RuO$_4$
through the fabrication of devices -- such as planar tunnel junctions and
SQUIDs.


Abstract:
  We present the supernova constraints on an axion-photon-dark photon coupling,
which can be the leading coupling to dark sector models and can also lead to
dramatic changes to axion cosmology. We show that the supernova bound on this
coupling has two unusual features. One occurs because the scattering that leads
to the trapping regime converts axions and dark photons into each other. Thus,
if one of the two new particles is sufficiently massive, both production and
scattering become suppressed and the bounds from bulk emission and trapped
(area) emission both weaken exponentially and do not intersect. The other
unusual feature occurs because for light dark photons, longitudinal modes
couple more weakly than transverse modes do. Since the longitudinal mode is
more weakly coupled, it can still cause excessive cooling even if the
transverse mode is trapped. Thus, the supernova constraints for massive dark
photons look like two independent supernova bounds super-imposed on top of each
other.


Abstract:
  The Wiener index of a connected graph is the sum of the distances between all
unordered pairs of vertices. A connected graph is Eulerian if its vertex
degrees are all even. In [Gutman, Cruz, Rada, Wiener index of Eulerian Graphs,
Discrete Applied Mathematics 132 (2014), 247-250] the authors proved that the
cycle is the unique graph maximising the Wiener index among all Eulerian graphs
of given order. They also conjectured that for Eulerian graphs of order $n \geq
26$ the graph consisting of a cycle on $n-2$ vertices and a triangle that share
a vertex is the unique Eulerian graph with second largest Wiener index. The
conjecture is known to hold for all $n\leq 25$ with exception of six values. In
this paper we prove the conjecture.


Abstract:
  The helical edge states of time-reversal invariant two-dimensional
topological insulators are protected against backscattering in idealized
models. In more realistic scenarios with a shallow confining potential at the
sample boundary, additional strongly interacting edge states may arise, that
could interfere with the topological protection of edge conduction. We find
that interaction effects within the reconstructed edges are well described by
the Luttinger liquid model. While interactions between this Luttinger liquid
and the helical edge states can in principle give rise to dynamical spin
polarization and the breaking of time-reversal symmetry, we demonstrate that
random spin-orbit coupling strongly suppresses such dynamical spin
polarization, resulting in the persistence of quantized edge conduction.


Abstract:
  Empirical natural language processing (NLP) systems in application domains
(e.g., healthcare, finance, education) involve interoperation among multiple
components, ranging from data ingestion, human annotation, to text retrieval,
analysis, generation, and visualization. We establish a unified open-source
framework to support fast development of such sophisticated NLP workflows in a
composable manner. The framework introduces a uniform data representation to
encode heterogeneous results by a wide range of NLP tasks. It offers a large
repository of processors for NLP tasks, visualization, and annotation, which
can be easily assembled with full interoperability under the unified
representation. The highly extensible framework allows plugging in custom
processors from external off-the-shelf NLP and deep learning libraries. The
whole framework is delivered through two modularized yet integratable
open-source projects, namely Forte (for workflow infrastructure and NLP
function processors) and Stave (for user interaction, visualization, and
annotation).


Abstract:
  We propose in this paper a motion planning method for legged robot locomotion
based on Geometric Heat Flow framework. The motion planning task is challenging
due to the hybrid nature of dynamics and contact constraints. We encode the
hybrid dynamics and constraints into Riemannian inner product, and this inner
product is defined so that short curves correspond to admissible motions for
the system. We rely on the affine geometric heat flow to deform an arbitrary
path connecting the desired initial and final states to this admissible motion.
The method is able to automatically find the trajectory of robot's center of
mass, feet contact positions and forces on uneven terrain.


Abstract:
  Event generators in high-energy nuclear and particle physics play an
important role in facilitating studies of particle reactions. We survey the
state-of-the-art of machine learning (ML) efforts at building physics event
generators. We review ML generative models used in ML-based event generators
and their specific challenges, and discuss various approaches of incorporating
physics into the ML model designs to overcome these challenges. Finally, we
explore some open questions related to super-resolution, fidelity, and
extrapolation for physics event generation based on ML technology.


Abstract:
  We consider an epidemic model of SIR type set on a homogeneous tree and
investigate the spreading properties of the epidemic as a function of the
degree of the tree, the intrinsic basic reproduction number and the strength of
the interactions within the population of infected individuals. When the degree
is one, the homogeneous tree is nothing but the standard lattice on the
integers and our model reduces to a SIR model with discrete diffusion for which
the spreading properties are very similar to the continuous case. On the other
hand, when the degree is larger than two, we observe some new features in the
spreading properties. Most notably, there exists a critical value of the
strength of interactions above which spreading of the epidemic in the tree is
no longer possible.


Abstract:
  We present here an explicit form of the random spectral measure element, what
allows us to express a stationary random field as a stochastic integral
explicitly depending on its power spectrum and a spectral tensor if the field
is a vector one. It has been shown here that convergence mechanism of such
integral is significantly different from the one of the Fourier transform and
that the traditional formalism is a partial limiting case of the one presented
here. The fact that there is an explicit expression of a random field makes
calculation of higher order statistics of it much more straightforward (see for
example Chepurnov et al. 2020). For a vector field such expression contains a
projection of an isotropically distributed random vector by a spectral tensor,
what makes geometrical interpretation of harmonics behavior possible,
simplifying its analysis (see Sect. 2). This spectral representation also makes
straightforward numerical generation of a random field, what is extensively
used by Chepurnov et al. 2020. We also present here some practical applications
of this formalism.


Abstract:
  A fresh approach to the full wave analysis of time evolution of the
polarization induced in the electromagnetic scattering from dispersive non
magnetic particles is presented. It is grounded on the combination of the
Hopfield model for the polarization field, the expansion of the polarization
field in terms of static longitudinal and transverse modes of the particle, the
expansion of the radiation field in terms of transverse wave modes of free
space, and the principle of least action. The polarization field is linearly
coupled to the electromagnetic field. The losses of the matter are provided
through a linear coupling of the polarization field to a bath of harmonic
oscillators with a continuous range of natural frequencies. The set of linear
ordinary differential integral equations of convolution type of the overall
system is reduced by eliminating both the radiation degrees of freedoms and the
bath degrees of freedom, and the reduced system of equations is studied. The
role played by the radiation field in the coupling between the longitudinal and
transverse mode amplitudes of the polarization is described. The principal
characteristics of the temporal evolution of the mode amplitudes are found as
the particle size varies, including the impulse response. Results are presented
for the analytically solvable spherical particle. The proposed approach leads
to a general method for the analysis of the temporal evolution of the
polarization field induced in dispersive particles of any shape, as well as for
the computation of transients and steady states.