Abstract:
  Motivation: SBML is the most widespread language for the definition of
biochemical models. Although dozens of SBML simulators are available, there is
a general lack of support to the integration of SBML models within
open-standard general-purpose simulation ecosystems. This hinders co-simulation
and integration of SBML models within larger model networks, in order to, e.g.
enable in silico clinical trials of drugs, pharmacological protocols, or
engineering artefacts such as biomedical devices against Virtual Physiological
Human models. Modelica is one of the most popular existing open-standard
general-purpose simulation languages, supported by many simulators. Modelica
models are especially suited for the definition of complex networks of
heterogeneous models from virtually all application domains. Models written in
Modelica (and in 100+ other languages) can be readily exported into black-box
Functional Mock-Up Units (FMUs), and seamlessly co-simulated and integrated
into larger model networks within open-standard language-independent simulation
ecosystems.
  Results: In order to enable SBML model integration within heterogeneous model
networks, we present SBML2Modelica, a software system translating SBML models
into well-structured, user-intelligible, easily modifiable Modelica models.
SBML2Modelica is SBML Level 3 Version 2-compliant and succeeds on 96.47% of the
SBML Test Suite Core (with a few rare, intricate and easily avoidable
combinations of constructs unsupported and cleanly signalled to the user). Our
experimental campaign on 613 models from the BioModels database (with up to
5438 variables) shows that the major open-source (general-purpose) Modelica and
FMU simulators achieve performance comparable to state-of-the-art specialized
SBML simulators.
  Availability and implementation: https://bitbucket.org/mclab/sbml2modelica


Abstract:
  Ensemble clustering is a fundamental problem in the machine learning field,
combining multiple base clusterings into a better clustering result. However,
most of the existing methods are unsuitable for large-scale ensemble clustering
tasks due to the efficiency bottleneck. In this paper, we propose a large-scale
spectral ensemble clustering (LSEC) method to strike a good balance between
efficiency and effectiveness. In LSEC, a large-scale spectral clustering based
efficient ensemble generation framework is designed to generate various base
clusterings within a low computational complexity. Then all based clustering
are combined through a bipartite graph partition based consensus function into
a better consensus clustering result. The LSEC method achieves a lower
computational complexity than most existing ensemble clustering methods.
Experiments conducted on ten large-scale datasets show the efficiency and
effectiveness of the LSEC method. The MATLAB code of the proposed method and
experimental datasets are available at https://github.com/Li-
Hongmin/MyPaperWithCode.


Abstract:
  The duality symmetry between electricity and magnetism hidden in classical
Maxwell equations suggests the existence of dual charges, which have usually
been interpreted as magnetic charges and have not been observed in experiments.
In quantum electrodynamics (QED), both the electric and magnetic fields have
been unified into one gauge field $A_{\mu}$, which makes this symmetry
inconspicuous. Here, we recheck the duality symmetry of QED by introducing a
dual gauge field. Within the framework of gauge-field theory, we show that the
electric-magnetic duality symmetry cannot give any new conservation law. By
checking charge-charge interaction and specifically the quantum Lorentz force
equation, we find that the dual charges are electric charges, not magnetic
charges. More importantly, we show that true magnetic charges are not
compatible with the gauge-field theory of QED, because the interaction between
a magnetic charge and an electric charge can not be mediated by gauge photons.


Abstract:
  We study the dynamics of an initially flat interface between two immiscible
fluids, with a vortex situated on it. We show how surface tension causes
vorticity generation at a general curved interface. This creates a velocity
jump across the interface which increases quadratically in time, and causes the
Kelvin-Helmholtz instability. Surface tension thus acts as a destabiliser by
vorticity creation, winning over its own tendency to stabilize by smoothing out
interfacial perturbations to reduce surface energy. We further show that this
instability is manifested within the vortex core at times larger than $\sim (k
We)^{1/4}$ for a Weber number $We$ and perturbation wavenumber $k$, destroying
the flow structure. The vorticity peels off into small-scale structures away
from the interface. Using energy balance we provide the growth with time in
total interface length. A density difference between the fluids produces
additional instabilities outside the vortex core due to centrifugal effects. We
demonstrate the importance of this mechanism in two-dimensional turbulence
simulations with a prescribed initial interface. We show an increase in kinetic
energy at large wave numbers due to the creation of small-scale vortices by the
action of surface tension.


Abstract:
  Linear mixed-effects models are commonly used to analyze clustered data
structures. There are numerous packages to fit these models in R and conduct
likelihood-based inference. The implementation of resampling-based procedures
for inference are more limited. In this paper, we introduce the lmeresampler
package for bootstrapping nested linear mixed-effects models fit via lme4 or
nlme. Bootstrap estimation allows for bias correction, adjusted standard errors
and confidence intervals for small samples sizes and when distributional
assumptions break down. We will also illustrate how bootstrap resampling can be
used to diagnose this model class. In addition, lmeresampler makes it easy to
construct interval estimates of functions of model parameters.


Abstract:
  In blind source separation of speech signals, the inherent imbalance in the
source spectrum poses a challenge for methods that rely on single-source
dominance for the estimation of the mixing matrix. We propose an algorithm
based on the directional sparse filtering (DSF) framework that utilizes the
Lehmer mean with learnable weights to adaptively account for source imbalance.
Performance evaluation in multiple real acoustic environments show improvements
in source separation compared to the baseline methods.


Abstract:
  Three-dimensional metallic microstructures find applications as stents in
medicine, as ultrabroadband antennas in communications, in micromechanical
parts or as structures of more fundamental interest in photonics like
metamaterials. Direct metal printing of such structures using three-dimensional
laser lithography is a promising approach, which is not extensively applied
yet, as fabrication speed, surface quality, and stability of the resulting
structures are limited so far. In order to identify the limiting factors, we
investigate the influence of light-particle interactions and varying scan speed
on heat generation and particle deposition in direct laser writing of silver.
We introduce a theoretical model which captures diffusion of particles and heat
as well as the fluid dynamics of the photo-resist. Chemical reactions are
excluded from the model but particle production is calibrated using
experimental data. We find that optical forces generally surmount those due to
convection of the photo-resist. Simulations predict overheating of the
photo-resist at laser powers similar to those found in experiments. The thermal
sensitivity of the system is essentially determined by the largest particles
present in the laser focus. Our results suggest that to improve particle
deposition and to achieve higher writing speeds in metal direct laser writing,
strong optical trapping of the emerging particles is desirable. Furthermore,
precise control of the particle size reduces the risk of spontaneous
overheating.


Abstract:
  We present the complete two-loop corrections in massless QCD for the
production of two photons and a jet, taking into account all color structures.
In particular, we analytically compute all two-loop helicity amplitudes for the
quark-antiquark, quark-gluon, and antiquark-gluon channel, and check them with
an independent calculation of the polarization-summed interference with the
tree amplitude. This is the first time that two-loop QCD corrections to a
five-point scattering process have been computed beyond the leading-color
approximation for all helicity configurations.


Abstract:
  Aspect sentiment triplet extraction (ASTE), which aims to identify aspects
from review sentences along with their corresponding opinion expressions and
sentiments, is an emerging task in fine-grained opinion mining. Since ASTE
consists of multiple subtasks, including opinion entity extraction, relation
detection, and sentiment classification, it is critical and challenging to
appropriately capture and utilize the associations among them. In this paper,
we transform ASTE task into a multi-turn machine reading comprehension (MTMRC)
task and propose a bidirectional MRC (BMRC) framework to address this
challenge. Specifically, we devise three types of queries, including
non-restrictive extraction queries, restrictive extraction queries and
sentiment classification queries, to build the associations among different
subtasks. Furthermore, considering that an aspect sentiment triplet can derive
from either an aspect or an opinion expression, we design a bidirectional MRC
structure. One direction sequentially recognizes aspects, opinion expressions,
and sentiments to obtain triplets, while the other direction identifies opinion
expressions first, then aspects, and at last sentiments. By making the two
directions complement each other, our framework can identify triplets more
comprehensively. To verify the effectiveness of our approach, we conduct
extensive experiments on four benchmark datasets. The experimental results
demonstrate that BMRC achieves state-of-the-art performances.


Abstract:
  In this paper, we find the existence of critical features hidden in Deep
NeuralNetworks (DNNs), which are imperceptible but can actually dominate the
outputof DNNs. We call these features dominant patterns. As the name suggests,
for a natural image, if we add the dominant pattern of a DNN to it, the output
of this DNN is determined by the dominant pattern instead of the original
image, i.e., DNN's prediction is the same with the dominant pattern's. We
design an algorithm to find such patterns by pursuing the insensitivity in the
feature space. A direct application of the dominant patterns is the Universal
Adversarial Perturbations(UAPs). Numerical experiments show that the found
dominant patterns defeat state-of-the-art UAP methods, especially in label-free
settings. In addition, dominant patterns are proved to have the potential to
attack downstream tasks in which DNNs share the same backbone. We claim that
DNN-specific dominant patterns reveal some essential properties of a DNN and
are of great importance for its feature analysis and robustness enhancement.


Abstract:
  Recently many first and second order variants of SGD have been proposed to
facilitate training of Deep Neural Networks (DNNs). A common limitation of
these works stem from the fact that they use the same learning rate across all
instances present in the dataset. This setting is widely adopted under the
assumption that loss functions for each instance are similar in nature, and
hence, a common learning rate can be used. In this work, we relax this
assumption and propose an optimization framework which accounts for difference
in loss function characteristics across instances. More specifically, our
optimizer learns a dynamic learning rate for each instance present in the
dataset. Learning a dynamic learning rate for each instance allows our
optimization framework to focus on different modes of training data during
optimization. When applied to an image classification task, across different
CNN architectures, learning dynamic learning rates leads to consistent gains
over standard optimizers. When applied to a dataset containing corrupt
instances, our framework reduces the learning rates on noisy instances, and
improves over the state-of-the-art. Finally, we show that our optimization
framework can be used for personalization of a machine learning model towards a
known targeted data distribution.


Abstract:
  Understanding the attitudes expressed in texts, also known as stance
detection, plays an important role in systems aiming to detect false
information online, be it misinformation (unintentionally false) or
disinformation (intentionally false, spread deliberately with malicious
intent). Stance detection has been framed in different ways in the literature,
including (a) as a component of fact-checking, rumour detection, and detecting
previously fact-checked claims, or (b) as a task in its own right; and here we
look at both. While there have been prior efforts to contrast stance detection
with other related tasks such as argumentation mining and sentiment analysis,
there has been no survey examining the relationship between stance detection
and mis- and disinformation detection. Here we aim to bridge this gap. In
particular, we review and analyse existing work in this area, with mis- and
disinformation in focus, and then we discuss lessons learnt and future
challenges.


Abstract:
  Radio observations of tidal disruption events (TDEs) - when a star is tidally
disrupted by a supermassive black hole (SMBH) - provide a unique laboratory for
studying outflows in the vicinity of SMBHs and their connection to accretion
onto the SMBH. Radio emission has been detected in only a handful of TDEs so
far. Here, we report the detection of delayed radio flares from an
optically-discovered TDE. Our prompt radio observations of the TDE ASASSN-15oi
showed no radio emission until the detection of a flare six months later,
followed by a second and brighter flare, years later. We find that the standard
scenario, in which an outflow is launched briefly after the stellar disruption,
is unable to explain the combined temporal and spectral properties of the
delayed flare. We suggest that the flare is due to the delayed ejection of an
outflow, perhaps following a transition in accretion states. Our discovery
motivates observations of TDEs at various timescales and highlights a need for
new models.


Abstract:
  An instrument and software algorithm is described for the purpose of
characterization of large single crystals at the Alignment Facility (ALF) of
the ISIS spallation neutron source. We describe a method for both
characterizing the quality of the sample and also aligning it in a particular
scattering plane. We present a software package written for this instrument and
demonstrate its utility by way of an example of the structural characterization
of large singles crystals of Pb(Mg$_{1/3}$Nb$_{2/3}$)O$_{3}$. We suggest
extensions and modifications of characterization instruments for future
improved beamlines. It is hoped that this software will be used by the neutron
user community for pre characterizing large single crystals for spectroscopy
experiments and that future facilities will include such a facility as part of
the spectroscopy suite at spallation neutron sources.


Abstract:
  If quantum mechanics is taken for granted the randomness derived from it may
be vacuous or even delusional, yet sufficient for many practical purposes.
"Random" quantum events are intimately related to the emergence of both
space-time as well as the identification of physical properties through which
so-called objects are aggregated. We also present a brief review of the
metaphysics of indeterminism.


Abstract:
  We establish a connection between two areas of independent interest in
representation theory, namely Koszul duality and higher homological algebra.
This is done through a generalization of the notion of $T$-Koszul algebras, for
which we obtain a higher version of classical Koszul duality. Our approach is
motivated by and has applications for $n$-hereditary algebras. In particular,
we characterize an important class of $n$-$T$-Koszul algebras of highest degree
$a$ in terms of $(na-1)$-representation infinite algebras. As a consequence, we
see that an algebra is $n$-representation infinite if and only if its trivial
extension is $(n+1)$-Koszul with respect to its degree $0$ part. Furthermore,
we show that when an $n$-representation infinite algebra is $n$-representation
tame, then the bounded derived categories of graded modules over the trivial
extension and over the associated $(n+1)$-preprojective algebra are equivalent.
In the $n$-representation finite case, we introduce the notion of almost
$n$-$T$-Koszul algebras and obtain similar results.


Abstract:
  In this work, we develop the theory of quasi-exact fault-tolerant quantum
(QEQ) computation, which uses qubits encoded into quasi-exact quantum
error-correction codes ("quasi codes"). By definition, a quasi code is a
parametric approximate code that can become exact by tuning its parameters. The
model of QEQ computation lies in between the two well-known ones: the usual
noisy quantum computation without error correction and the usual fault-tolerant
quantum computation, but closer to the later. Many notions of exact quantum
codes need to be adjusted for the quasi setting. Here we develop quasi
error-correction theory using quantum instrument, the notions of quasi
universality, quasi code distances, and quasi thresholds, etc. We find a wide
class of quasi codes which are called valence-bond-solid codes, and we use them
as concrete examples to demonstrate QEQ computation.


Abstract:
  This work is an update of a previous paper on the same topic published a few
years ago. With the dramatic progress in generative modeling, a suite of new
quantitative and qualitative techniques to evaluate models has emerged.
Although some measures such as Inception Score, Frechet Inception Distance,
Precision-Recall, and Perceptual Path Length are relatively more popular, GAN
evaluation is not a settled issue and there is still room for improvement.
Here, I describe new dimensions that are becoming important in assessing models
(e.g. bias and fairness) and discuss the connection between GAN evaluation and
deepfakes. These are important areas of concern in the machine learning
community today and progress in GAN evaluation can help mitigate them.


Abstract:
  Hardware faults on the regular 2-D computing array of a typical deep learning
accelerator (DLA) can lead to dramatic prediction accuracy loss. Prior
redundancy design approaches typically have each homogeneous redundant
processing element (PE) to mitigate faulty PEs for a limited region of the 2-D
computing array rather than the entire computing array to avoid the excessive
hardware overhead. However, they fail to recover the computing array when the
number of faulty PEs in any region exceeds the number of redundant PEs in the
same region. The mismatch problem deteriorates when the fault injection rate
rises and the faults are unevenly distributed. To address the problem, we
propose a hybrid computing architecture (HyCA) for fault-tolerant DLAs. It has
a set of dot-production processing units (DPPUs) to recompute all the
operations that are mapped to the faulty PEs despite the faulty PE locations.
According to our experiments, HyCA shows significantly higher reliability,
scalability, and performance with less chip area penalty when compared to the
conventional redundancy approaches. Moreover, by taking advantage of the
flexible recomputing, HyCA can also be utilized to scan the entire 2-D
computing array and detect the faulty PEs effectively at runtime.


Abstract:
  What we expect from radiology AI algorithms will shape the selection and
implementation of AI in the radiologic practice. In this paper I consider
prevailing expectations of AI and compare them to expectations that we have of
human readers. I observe that the expectations from AI and radiologists are
fundamentally different. The expectations of AI are based on a strong and
justified mistrust about the way that AI makes decisions. Because AI decisions
are not well understood, it is difficult to know how the algorithms will behave
in new, unexpected situations. However, this mistrust is not mirrored in our
expectations of human readers. Despite well-proven idiosyncrasies and biases in
human decision making, we take comfort from the assumption that others make
decisions in a way as we do, and we trust our own decision making. Despite poor
ability to explain decision making processes in humans, we accept explanations
of decisions given by other humans. Because the goal of radiology is the most
accurate radiologic interpretation, our expectations of radiologists and AI
should be similar, and both should reflect a healthy mistrust of complicated
and partially opaque decision processes undergoing in computer algorithms and
human brains. This is generally not the case now.


Abstract:
  Quantum error mitigation is essential for computing on the noisy quantum
computer with a limited number of qubits. In this paper, we propose a practical
protocol of error mitigation by virtually purifying the quantum state without
qubit overhead or requiring only one ancillary qubit. In dual-state
purification, we effectively generate a purified state with increased fidelity
using the erroneous state and its dual state, respectively, prepared with the
noisy quantum circuit and the dual map of its inverse circuit. Combined with
tomography purification, we can make sure that the final estimate of an
observable is obtained from a pure state. The numerical result suggests that
our protocol reduces the error by a rescaling factor decreasing with the qubit
number and circuit depth, i.e. the performance of purification is better for
larger circuits. On a cloud quantum computer, we successfully demonstrate the
reduced error with a quantum variational eigensolver circuit.


Abstract:
  One important challenge of applying deep learning to electronic health
records (EHR) is the complexity of their multimodal structure. EHR usually
contains a mixture of structured (codes) and unstructured (free-text) data with
sparse and irregular longitudinal features -- all of which doctors utilize when
making decisions. In the deep learning regime, determining how different
modality representations should be fused together is a difficult problem, which
is often addressed by handcrafted modeling and intuition. In this work, we
extend state-of-the-art neural architecture search (NAS) methods and propose
MUltimodal Fusion Architecture SeArch (MUFASA) to simultaneously search across
multimodal fusion strategies and modality-specific architectures for the first
time. We demonstrate empirically that our MUFASA method outperforms established
unimodal NAS on public EHR data with comparable computation costs. In addition,
MUFASA produces architectures that outperform Transformer and Evolved
Transformer. Compared with these baselines on CCS diagnosis code prediction,
our discovered models improve top-5 recall from 0.88 to 0.91 and demonstrate
the ability to generalize to other EHR tasks. Studying our top architecture in
depth, we provide empirical evidence that MUFASA's improvements are derived
from its ability to both customize modeling for each data modality and find
effective fusion strategies.


Abstract:
  This paper proposes a method to estimate the pose of a sensor mounted on a
vehicle as the vehicle moves through the world, an important topic for
autonomous driving systems. Based on a set of commonly deployed vehicular
odometric sensors, with outputs available on automotive communication buses
(e.g. CAN or FlexRay), we describe a set of steps to combine a planar odometry
based on wheel sensors with a suspension model based on linear suspension
sensors. The aim is to determine a more accurate estimate of the camera pose.
We outline its usage for applications in both visualisation and computer
vision.


Abstract:
  A formal cyber reasoning framework for automating the threat hunting process
is described. The new cyber reasoning methodology introduces an operational
semantics that operates over three subspaces -- knowledge, hypothesis, and
action -- to enable human-machine co-creation of threat hypotheses and
protective recommendations. An implementation of this framework shows that the
approach is practical and can be used to generalize evidence-based
multi-criteria threat investigations.


Abstract:
  In this paper, we investigate projection-based intrusive and data-driven
non-intrusive model order reduction methods in numerical simulation of rotating
thermal shallow water equation (RTSWE) in parametric and non-parametric form.
Discretization of the RTSWE in space with centered finite differences leads to
Hamiltonian system of ordinary differential equations with linear and quadratic
terms. The full-order model (FOM) is obtained by applying linearly implicit
Kahan's method in time. Applying proper orthogonal decomposition with Galerkin
projection (POD-G), we construct the intrusive reduced-order model (ROM). We
apply operator inference (OpInf) with re-projection for non-intrusive
reduced-order modeling. In the parametric case, we make use of the parameter
dependency at the level of the PDE without interpolating between the reduced
operators. The least-squares problem of the OpInf is regularized with the
minimum norm solution. Both ROMs behave similar and are able to accurately
predict the test and training data and capture system behavior in the
prediction phase with several orders of computational speedup over the FOM. The
preservation of system physics such as the conserved quantities of the RTSWE by
both ROMs enables that the models fit better to data and stable solutions are
obtained in long-term predictions, which are robust to parameter changes.


Abstract:
  Spontaneous collapse models and Bohmian mechanics are two different solutions
to the measurement problem plaguing orthodox quantum mechanics. They have, a
priori nothing in common. At a formal level, collapse models add a non-linear
noise term to the Schr\"odinger equation, and extract definite measurement
outcomes either from the wave function (e.g. mass density ontology) or the
noise itself (flash ontology). Bohmian mechanics keeps the Schr\"odinger
equation intact but uses the wave function to guide particles (or fields),
which comprise the primitive ontology. Collapse models modify the predictions
of orthodox quantum mechanics, whilst Bohmian mechanics can be argued to
reproduce them. However, it turns out that collapse models and their primitive
ontology can be exactly recast as Bohmian theories. More precisely, considering
(i) a system described by a non-Markovian collapse model, and (ii) an extended
system where a carefully tailored bath is added and described by Bohmian
mechanics, the stochastic wave-function of the collapse model is exactly the
wave-function of the original system conditioned on the Bohmian hidden
variables of the bath. Further, the noise driving the collapse model is a
linear functional of the Bohmian variables. The randomness that seems
progressively revealed in the collapse models lies entirely in the initial
conditions in the Bohmian-like theory. Our construction of the appropriate bath
is not trivial and exploits an old result from the theory of open quantum
systems. This reformulation of collapse models as Bohmian theories brings to
the fore the question of whether there exists `unromantic' realist
interpretations of quantum theory that cannot ultimately be rewritten this way,
with some guiding law. It also points to important foundational differences
between `true' (Markovian) collapse models and non-Markovian models.


Abstract:
  An increasing number of machine learning models have been deployed in domains
with high stakes such as finance and healthcare. Despite their superior
performances, many models are black boxes in nature which are hard to explain.
There are growing efforts for researchers to develop methods to interpret these
black-box models. Post hoc explanations based on perturbations, such as LIME,
are widely used approaches to interpret a machine learning model after it has
been built. This class of methods has been shown to exhibit large instability,
posing serious challenges to the effectiveness of the method itself and harming
user trust. In this paper, we propose S-LIME, which utilizes a hypothesis
testing framework based on central limit theorem for determining the number of
perturbation points needed to guarantee stability of the resulting explanation.
Experiments on both simulated and real world data sets are provided to
demonstrate the effectiveness of our method.


Abstract:
  We present a novel verification technique to prove interesting properties of
a class of array programs with a symbolic parameter N denoting the size of
arrays. The technique relies on constructing two slightly different versions of
the same program. It infers difference relations between the corresponding
variables at key control points of the joint control-flow graph of the two
program versions. The desired post-condition is then proved by inducting on the
program parameter $N$, wherein the difference invariants are crucially used in
the inductive step. This contrasts with classical techniques that rely on
finding potentially complex loop invaraints for each loop in the program. Our
synergistic combination of inductive reasoning and finding simple difference
invariants helps prove properties of programs that cannot be proved even by the
winner of Arrays sub-category from SV-COMP 2021. We have implemented a
prototype tool called diffy to demonstrate these ideas. We present results
comparing the performance of diffy with that of state-of-the-art tools.


Abstract:
  Descriptive code comments are essential for supporting code comprehension and
maintenance. We propose the task of automatically generating comments for
overriding methods. We formulate a novel framework which accommodates the
unique contextual and linguistic reasoning that is required for performing this
task. Our approach features: (1) incorporating context from the class
hierarchy; (2) conditioning on learned, latent representations of specificity
to generate comments that capture the more specialized behavior of the
overriding method; and (3) unlikelihood training to discourage predictions
which do not conform to invariant characteristics of the comment corresponding
to the overridden method. Our experiments show that the proposed approach is
able to generate comments for overriding methods of higher quality compared to
prevailing comment generation techniques.


Abstract:
  We studied the COVID-19 pandemic evolution in selected African countries. For
each country considered, we modeled simultaneously the data of the active,
recovered and death cases. In this study, we used a year of data since the
first cases were reported. We estimated the time-dependent basic reproduction
numbers, $R_0$, and the fractions of infected but unaffected populations, to
offer insights into containment and vaccine strategies in African countries. We
found that $R_0\leq 4$ at the start of the pandemic but has since fallen to
$R_0 \sim 1$. The unaffected fractions of the populations studied vary between
$1-10$\% of the recovered cases.


Abstract:
  Quantum embedding methods have become a powerful tool to overcome
deficiencies of traditional quantum modelling in materials science. However
while these are systematically improvable in principle, in practice it is
rarely possible to achieve rigorous convergence and often necessary to employ
empirical parameters. Here, we formulate a quantum embedding, building on the
methods of density-matrix embedding theory combined with local correlation
approaches of quantum chemistry, to ensure the ability to systematically
converge properties of real materials with accurate correlated wave~function
methods, controlled by a single, rapidly convergent parameter. By expanding
supercell size, basis set, and the resolution of the fluctuation space of an
embedded fragment, we show that the systematic improvability of the approach
yields accurate structural and electronic properties of realistic solids
without empirical parameters, even across changes in geometry. Results are
presented in insulating, semi-metallic, and more strongly correlated regimes,
finding state of the art agreement to experimental data.


Abstract:
  The discovery of infinite-layer nickelate superconductors has spurred
enormous interest. While the Ni$^{1+}$ cations possess nominally the same
3d$^9$ configuration as Cu$^{2+}$ in high-$T_C$ cuprates, the electronic
structure consistencies and variances remain elusive, due to the lack of direct
experimental probes. Here, we present a soft x-ray photoemission spectroscopy
study on both parent and doped infinite-layer Pr-nickelate thin films with a
doped perovskite reference. By identifying the Ni character with resonant
photoemission and comparison to density function theory + U calculations, we
estimate U ~ 5 eV, smaller than the charge transfer energy $\Delta$ ~ 8 eV, in
contrast to the cuprates being charge transfer insulators. Near the Fermi level
(EF), we observe a signature of rare-earth spectral intensity in the parent
compound, which is depleted upon doping. The parent compound, "self-doped" from
rare-earth electrons, exhibits higher density of states at EF but manifests
weaker superconducting instability than the Sr-doped case, demonstrating a
complex interplay between the strongly-correlated Ni 3d and the
weakly-interacting rare-earth 5d states in these "oxide-intermetallic"
nickelates.


Abstract:
  The density matrix renormalization group (DMRG) of White 1992 remains to this
day an integral component of many state-of-the-art methods for efficiently
simulating strongly correlated quantum systems. In quantum chemistry, QC-DMRG
became a powerful tool for ab initio calculations with the non-relativistic
Schr\"odinger equation. An important issue in QC-DMRG is the so-called ordering
problem -- the optimal ordering of DMRG sites corresponding to electronic
orbitals that produces the most accurate results. To this end, a commonly used
heuristic is the grouping of strongly correlated orbitals as measured via
quantum mutual information. In this work, we show how such heuristics can be
directly related to minimizing the entanglement entropy of matrix product
states and, consequently, to the truncation error of a fixed bond dimension
approximation. Key to establishing this link is the strong subadditivity of
entropy. This provides rigorous theoretical justification for the orbital
ordering methods and suggests alternate ordering criteria.


Abstract:
  We study the structure of string theory flux compactification for a general
family of elliptic CY 3-folds. We investigate the locus of the attractor points
of the flux compactification in type IIB string theory on the boundary
components of period domains. Specifically we give equations describing this
locus through the asymptotic of nilpotent orbits on period domains. our
approach is a mixture of techniques of asymptotic Hodge theory and the
numerical period vectors used in physics.


Abstract:
  Marginalising over families of Gaussian Process kernels produces flexible
model classes with well-calibrated uncertainty estimates. Existing approaches
require likelihood evaluations of many kernels, rendering them prohibitively
expensive for larger datasets. We propose a Bayesian Quadrature scheme to make
this marginalisation more efficient and thereby more practical. Through use of
the maximum mean discrepancies between distributions, we define a kernel over
kernels that captures invariances between Spectral Mixture (SM) Kernels. Kernel
samples are selected by generalising an information-theoretic acquisition
function for warped Bayesian Quadrature. We show that our framework achieves
more accurate predictions with better calibrated uncertainty than
state-of-the-art baselines, especially when given limited (wall-clock) time
budgets.


Abstract:
  In many areas of interest, modern risk assessment requires estimation of the
extremal behaviour of sums of random variables. We derive the first order
upper-tail behaviour of the weighted sum of bivariate random variables under
weak assumptions on their marginal distributions and their copula. The extremal
behaviour of the marginal variables is characterised by the generalised Pareto
distribution and their extremal dependence through subclasses of the limiting
representations of Ledford and Tawn (1997) and Heffernan and Tawn (2004). We
find that the upper tail behaviour of the aggregate is driven by different
factors dependent on the signs of the marginal shape parameters; if they are
both negative, the extremal behaviour of the aggregate is determined by both
marginal shape parameters and the coefficient of asymptotic independence
(Ledford and Tawn, 1996); if they are both positive or have different signs,
the upper-tail behaviour of the aggregate is given solely by the largest
marginal shape. We also derive the aggregate upper-tail behaviour for some well
known copulae which reveals further insight into the tail structure when the
copula falls outside the conditions for the subclasses of the limiting
dependence representations.


Abstract:
  We study the defect groups of $D_p^b(G)$ theories using geometric engineering
and BPS quivers. In the simple case when $b=h^\vee (G)$, we use the BPS quivers
of the theory to see that the defect group is compatible with a known
Maruyoshi-Song flow. To extend to the case where $b\neq h^\vee (G)$, we use a
similar Maruyoshi-Song flow to conjecture that the defect groups of $D_p^b(G)$
theories are given by those of $G^{(b)}[k]$ theories. In the cases of $G=A_n,
\;E_6, \;E_8$ we cross check our result by calculating the BPS quivers of the
$G^{(b)}[k]$ theories and looking at the cokernel of their intersection matrix.


Abstract:
  Despite tremendous investigations, a quantum spin liquid state realized in
spin-1/2 kagome Heisenberg antiferromagnet remains largely elusive. In
herbertsmithite ZnCu$_3$(OH)$_6$Cl$_2$, a quantum spin liquid candidate on the
perfect kagome lattice, precisely characterizing the intrinsic physics of the
kagome layers is extremely challenging due to the presence of interlayer Cu/Zn
antisite disorder within its crystal structure. Here we measured the specific
heat and thermal conductivity of single crystal herbertsmithite in magnetic
fields with high resolution. Our results are highlighted by the excellent
scaling collapse of the intrinsic magnetic specific heat contribution arising
from the kagome layers as a function of $T/H$ (temperature/magnetic field). In
addition, no residual linear term in the thermal conductivity
$\kappa/T(T\rightarrow 0)$ is observed in zero and applied magnetic fields,
indicating the absence of itinerant gapless excitations. These results suggest
a new picture for a quantum spin liquid state of the kagome layers of
herbertsmithite, wherein localized orphan spins arise and interact with random
exchanges in conjunction with a non-itinerant quantum spin liquid.


Abstract:
  Aromatic organic compounds, because of their small excitation energies ~
O(few eV) and scintillating properties, are promising targets for detecting
dark matter of mass ~ O(few MeV). Additionally, their planar molecular
structures lead to large anisotropies in the electronic wavefunctions, yielding
a significant daily modulation in the event rate expected to be observed in
crystals of these molecules. We characterize the daily modulation rate of dark
matter interacting with an anisotropic scintillating organic crystal such as
trans-stilbene, and show that daily modulation is an ~ O(1) fraction of the
total rate for small DM masses and comparable to, or larger than, the ~ 10%
annual modulation fraction at large DM masses. As we discuss in detail, this
modulation provides significant leverage for detecting or excluding dark matter
scattering, even in the presence of a non-negligible background rate. Assuming
a non-modulating background rate of 1/min/kg that scales with total exposure,
we find that a 100 kg yr experiment is sensitive to the cross section
corresponding to the correct relic density for dark matter masses between
1.3-14 MeV (1.5-1000 MeV) if dark matter interacts via a heavy (light)
mediator. This modulation can be understood using an effective velocity scale
v* = Delta E/q*, where Delta E is the electronic transition energy and q* is a
characteristic momentum scale of the electronic orbitals. We also characterize
promising future directions for development of scintillating organic crystals
as dark matter detectors.


Abstract:
  In this work, we analyze the performance of a simple majority-rule protocol
solving a fundamental coordination problem in distributed systems -
\emph{binary majority consensus}, in the presence of probabilistic message
loss. Using probabilistic analysis for a large scale, fully-connected, network
of $2n$ agents, we prove that the Simple Majority Protocol (SMP) reaches
consensus in only three communication rounds with probability approaching $1$
as $n$ grows to infinity. Moreover, if the difference between the numbers of
agents that hold different opinions grows at a rate of $\sqrt{n}$, then the SMP
with only two communication rounds attains consensus on the majority opinion of
the network, and if this difference grows faster than $\sqrt{n}$, then the SMP
reaches consensus on the majority opinion of the network in a single round,
with probability converging to $1$ exponentially fast as $n \rightarrow
\infty$. We also provide some converse results, showing that these requirements
are not only sufficient, but also necessary.


Abstract:
  Sexism has become an increasingly major problem on social networks during the
last years. The first shared task on sEXism Identification in Social neTworks
(EXIST) at IberLEF 2021 is an international competition in the field of Natural
Language Processing (NLP) with the aim to automatically identify sexism in
social media content by applying machine learning methods. Thereby sexism
detection is formulated as a coarse (binary) classification problem and a
fine-grained classification task that distinguishes multiple types of sexist
content (e.g., dominance, stereotyping, and objectification). This paper
presents the contribution of the AIT_FHSTP team at the EXIST2021 benchmark for
both tasks. To solve the tasks we applied two multilingual transformer models,
one based on multilingual BERT and one based on XLM-R. Our approach uses two
different strategies to adapt the transformers to the detection of sexist
content: first, unsupervised pre-training with additional data and second,
supervised fine-tuning with additional and augmented data. For both tasks our
best model is XLM-R with unsupervised pre-training on the EXIST data and
additional datasets and fine-tuning on the provided dataset. The best run for
the binary classification (task 1) achieves a macro F1-score of 0.7752 and
scores 5th rank in the benchmark; for the multiclass classification (task 2)
our best submission scores 6th rank with a macro F1-score of 0.5589.


Abstract:
  The Advanced LIGO and Virgo gravitational wave observatories have opened a
new window with which to study the inspiral and mergers of binary compact
objects. These observations are most powerful when coordinated with
multi-messenger observations. This was underlined by the first observation of a
binary neutron star merger GW170817, coincident with a short Gamma-ray burst,
GRB170817A, and the identification of the host galaxy NGC~4993 from the optical
counterpart AT~2017gfo. Finding the fast-fading optical counterpart critically
depends on the rapid production of a sky-map based on LIGO/Virgo data.
Currently, a rapid initial sky map is produced followed by a more accurate,
high-latency, $\gtrsim{12}{\rm{hr}}$ sky map. We study optimization choices of
the Bayesian prior and signal model which can be used alongside other
approaches such as reduced order quadrature. We find these yield up to a $60\%$
reduction in the time required to produce the high-latency localisation for
binary neutron star mergers.


Abstract:
  We characterize the optimal control for a class of singular stochastic
control problems as the unique solution to a related Skorokhod reflection
problem. The considered optimization problems concern the minimization of a
discounted cost functional over an infinite time-horizon through a process of
bounded variation affecting an It\^o-diffusion. The setting is
multidimensional, the drift of the state equation and the costs are convex, the
volatility matrix can be constant or linear in the state. We prove that the
optimal control acts only when the underlying diffusion attempts to exit the
so-called waiting region, and that the direction of this action is prescribed
by the derivative of the value function. Our approach is based on the study of
a suitable monotonicity property of the derivative of the value function
through its interpretation as the value of an optimal stopping game. Such a
monotonicity allows to construct nearly optimal policies which reflect the
underlying diffusion at the boundary of approximating waiting regions. The
limit of this approximation scheme then provides the desired characterization.
Our result applies to a relevant class of linear-quadratic models, among
others. Furthermore, it allows to construct the optimal control in degenerate
and non degenerate settings considered in the literature, where this important
aspect was only partially addressed.


Abstract:
  The curse of dimensionality is a recognized challenge in nonparametric
estimation. This paper develops a new L0-norm regularization approach to the
convex quantile and expectile regressions for subset variable selection. We
show how to use mixed integer programming to solve the proposed L0-norm
regularization approach in practice and build a link to the commonly used
L1-norm regularization approach. A Monte Carlo study is performed to compare
the finite sample performances of the proposed L0-penalized convex quantile and
expectile regression approaches with the L1-norm regularization approaches. The
proposed approach is further applied to benchmark the sustainable development
performance of the OECD countries and empirically analyze the accuracy in the
dimensionality reduction of variables. The results from the simulation and
application illustrate that the proposed L0-norm regularization approach can
more effectively address the curse of dimensionality than the L1-norm
regularization approach in multidimensional spaces.


Abstract:
  In recent work, we proposed a distributed Picard iteration (DPI) that allows
a set of agents, linked by a communication network, to find a fixed point of a
locally contractive (LC) map that is the average of individual maps held by
said agents. In this work, we build upon the DPI and its local linear
convergence (LLC) guarantees to make several contributions. We show that
Sanger's algorithm for principal component analysis (PCA) corresponds to the
iteration of an LC map that can be written as the average of local maps, each
map known to each agent holding a subset of the data. Similarly, we show that a
variant of the expectation-maximization (EM) algorithm for parameter estimation
from noisy and faulty measurements in a sensor network can be written as the
iteration of an LC map that is the average of local maps, each available at
just one node. Consequently, via the DPI, we derive two distributed algorithms
- distributed EM and distributed PCA - whose LLC guarantees follow from those
that we proved for the DPI. The verification of the LC condition for EM is
challenging, as the underlying operator depends on random samples, thus the LC
condition is of probabilistic nature.


Abstract:
  Diffusion MRI measures of the human brain provide key insight into
microstructural variations across individuals and into the impact of central
nervous system diseases and disorders. One approach to extract information from
diffusion signals has been to use biologically relevant analytical models to
link millimetre scale diffusion MRI measures with microscale influences. The
other approach has been to represent diffusion as an anomalous process and
infer information from the different anomalous diffusion equation parameters.
How parameters of various anomalous diffusion models vary with age in the human
brain white matter has not been investigated. We perform the tests within a
general continuous time random walk framework, in which temporal jump lengths
and structural barriers to diffusion are considered. We outline the
formulations for the super-diffusion (i.e., stretched exponential),
sub-diffusion, quasi-diffusion and fractional Bloch-Torrey models with respect
to the continuous time random walk expression. We also provide a direct link
between diffusion kurtosis and the sub-diffusion model and demonstrate
mathematical equivalence between parameters. 7T diffusion weighted MRI data in
the age range 19-67 was used in this study, particularly focusing on the corpus
callosum. Anomalous diffusion model parameters were found to show trends with
aging, and interestingly, our investigations led to a new robust technique for
generating maps of diffusion kurtosis.


Abstract:
  The evidence-based approach has increasingly been employed to synthesize
empirical findings from the primary research in software engineering.
Nevertheless, the reproducibility of evidence-based software engineering (EBSE)
studies seems to be underemphasized. In our investigation into the automatic
search of 311 sample studies, more than 50% of the search strings are not
reusable; about 87.5% of the search activities (e.g., search field settings)
are unrepeatable; and more than 95% of the whole automatic search
implementations are unreproducible. Considering that searching is a cornerstone
of an EBSE study, we are afraid that the reproducibility of the current
secondary research could be worse than we can imagine. By analyzing and
reporting the root causes of the aforementioned observations, we urge
collaboration and cooperation among all the stakeholders in our community to
improve the research reproducibility in EBSE.


Abstract:
  For which graphs $F$ is there a sparse $F$-counting lemma in $C_4$-free
graphs? We are interested in identifying graphs $F$ with the property that,
roughly speaking, if $G$ is an $n$-vertex $C_4$-free graph with on the order of
$n^{3/2}$ edges, then the density of $F$ in $G$, after a suitable
normalization, is approximately at least the density of $F$ in an
$\epsilon$-regular approximation of $G$. In recent work, motivated by
applications in extremal and additive combinatorics, we showed that $C_5$ has
this property. Here we construct a family of graphs with the property.


Abstract:
  We consider a one-dimensional stationary stochastic process $x(\tau)$ of
duration $T$. We study the probability density function (PDF) $P(t_{\rm m}|T)$
of the time $t_{\rm m}$ at which $x(\tau)$ reaches its global maximum. By using
a path integral method, we compute $P(t_{\rm m}|T)$ for a number of equilibrium
and nonequilibrium stationary processes, including the Ornstein-Uhlenbeck
process, Brownian motion with stochastic resetting and a single confined
run-and-tumble particle. For a large class of equilibrium stationary processes
that correspond to diffusion in a confining potential, we show that the scaled
distribution $P(t_{\rm m}|T)$, for large $T$, has a universal form (independent
of the details of the potential). This universal distribution is uniform in the
``bulk'', i.e., for $0 \ll t_{\rm m} \ll T$ and has a nontrivial edge scaling
behavior for $t_{\rm m} \to 0$ (and when $t_{\rm m} \to T$), that we compute
exactly. Moreover, we show that for any equilibrium process the PDF $P(t_{\rm
m}|T)$ is symmetric around $t_{\rm m}=T/2$, i.e., $P(t_{\rm m}|T)=P(T-t_{\rm
m}|T)$. This symmetry provides a simple method to decide whether a given
stationary time series $x(\tau)$ is at equilibrium or not.


Abstract:
  The article examines Nikolskii and Besov spaces with norms defined using
"$L_p$-averaged" mixed moduli of continuity of functions of appropriate orders,
instead of mixed moduli of continuity of known orders for certain mixed
derivative functions. For such spaces of functions defined in bounded domains
of $m$-kind (in a broad sense), the author builds continuous linear mappings of
those spaces to ordinary Nikolskii and Besov spaces of mixed smoothness in
$\mathbb{R}^d$, that are function extension operators, thus incurring
coincidence of both categories of spaces defined in the said domains.


Abstract:
  We consider the downlink of a cell-free massive multiple-input
multiple-output (MIMO) system with \textcolor{red}{single}-antenna access
points (APs) and single-antenna users. An iterative robust minimum mean-square
error (RMMSE) precoder based on generalized loading is developed to mitigate
interference in the presence of imperfect channel state information (CSI). An
achievable rate analysis is carried out and optimal and uniform power
allocation schemes are developed based on the signal-to-interference-plus-noise
ratio. An analysis of the computational costs of the proposed RMMSE and
existing schemes is also presented. Numerical results show the improvement
provided by the proposed RMMSE precoder against linear minimum mean-square
error, zero-forcing and conjugate beamforming precoders in the presence of
imperfect CSI.


Abstract:
  PQ-trees and PC-trees are data structures that represent sets of linear and
circular orders, respectively, subject to constraints that specific subsets of
elements have to be consecutive. While equivalent to each other, PC-trees are
conceptually much simpler than PQ-trees; updating a PC-trees so that a set of
elements becomes consecutive requires only a single operation, whereas PQ-trees
use an update procedure that is described in terms of nine transformation
templates that have to be recursively matched and applied.
  Despite these theoretical advantages, to date no practical PC-tree
implementation is available. This might be due to the original description by
Hsu and McConnell in some places only sketching the details of the
implementation. In this paper, we describe two alternative implementations of
PC-trees. For the first one, we follow the approach by Hsu and McConnell,
filling in the necessary details and also proposing improvements on the
original algorithm. For the second one, we use a different technique for
efficiently representing the tree using a Union-Find data structure. In an
extensive experimental evaluation we compare our implementations to a variety
of other implementations of PQ-trees that are available on the web as part of
academic and other software libraries. Our results show that both PC-tree
implementations beat their closest fully correct competitor, the PQ-tree
implementation from the OGDF library, by a factor of 2 to 4, showing that
PC-trees are not only conceptually simpler but also fast in practice. Moreover,
we find the Union-Find-based implementation, while having a slightly worse
asymptotic runtime, to be twice as fast as the one based on the description by
Hsu and McConnell.


Abstract:
  We consider a multi-dimensional continuum Schr\"odinger operator $H$ which is
given by a perturbation of the negative Laplacian by a compactly supported
bounded potential. We show that, for a fairly large class of test functions,
the second-order Szeg\H{o}-type asymptotics for the spatially truncated Fermi
projection of $H$ is independent of the potential and, thus, identical to the
known asymptotics of the Laplacian.


Abstract:
  The Bogoliubov energy functional proposed recently by Napi\'orkowski, Reuvers
and Solovej is revisited. We offer a direct proof of the existence of
minimizers at zero temperature, which covers a significantly larger class of
interaction potentials. The ideas used in this proof also imply that in any
ground state, more than half of the particles are inside the Bose-Einstein
condensate.


Abstract:
  Deep learning has become the gold standard for image processing over the past
decade. Simultaneously, we have seen growing interest in orbital activities
such as satellite servicing and debris removal that depend on proximity
operations between spacecraft. However, two key challenges currently pose a
major barrier to the use of deep learning for vision-based on-orbit proximity
operations. Firstly, efficient implementation of these techniques relies on an
effective system for model development that streamlines data curation,
training, and evaluation. Secondly, a scarcity of labeled training data (images
of a target spacecraft) hinders creation of robust deep learning models. This
paper presents an open-source deep learning pipeline, developed specifically
for on-orbit visual navigation applications, that addresses these challenges.
The core of our work consists of two custom software tools built on top of a
cloud architecture that interconnects all stages of the model development
process. The first tool leverages Blender, an open-source 3D graphics toolset,
to generate labeled synthetic training data with configurable model poses
(positions and orientations), lighting conditions, backgrounds, and commonly
observed in-space image aberrations. The second tool is a plugin-based
framework for effective dataset curation and model training; it provides common
functionality like metadata generation and remote storage access to all
projects while giving complete independence to project-specific code.
Time-consuming, graphics-intensive processes such as synthetic image generation
and model training run on cloud-based computational resources which scale to
any scope and budget and allow development of even the largest datasets and
models from any machine. The presented system has been used in the Texas
Spacecraft Laboratory with marked benefits in development speed and quality.


Abstract:
  The coherent population trapping (CPT) atomic clock is very promising for use
in next-generation spaceborne applications owing to its compactness and high
performance. In this paper, we propose and implement a CPT atomic clock based
on the direct modulation of a large-modulation-bandwidth and narrow-linewidth
distributed Bragg reflector laser, which replaces the usually used external
bulk modulator in the high-performance CPT clock. Our method retains the high
performance while significantly reducing the size. Using this highly compact
bichromatic light source and simplest CPT configuration, in which a circularly
polarized bichromatic laser interrogates the ^{87}Rb atom system, a CPT signal
of clock transition with a narrow linewidth and high contrast is observed. We
then lock the local oscillator frequency to the CPT error signal and
demonstrate a short-term frequency stability of 3.6 \times 10^{-13}
{\tau}^{-1/2} (4 s \le {\tau} \le 200 s). We attribute it to the ultralow laser
frequency and intensity noise as well as to the high-quality-factor CPT signal.
This study can pave the way for the development of compact high-performance CPT
clocks based on our scheme.


Abstract:
  We consider capacity (fuzzy measure, non-additive probability) on a compactum
as a monotone cooperative normed game. Then it is naturally to consider
probability measures as elements of core of such game. We prove an analogue of
Bondareva-Shapley theorem that non-emptiness of the core is equivalent to
balancedness of the capacity. We investigate categorical properties of balanced
capacities and give characterizations of some fuzzy integrals of balanced
capacities.


Abstract:
  Graph Neural Networks (GNNs) have garnered a lot of recent interest because
of their success in learning representations from graph-structured data across
several critical applications in cloud and HPC. Owing to their unique compute
and memory characteristics that come from an interplay between dense and sparse
phases of computations, the emergence of reconfigurable dataflow (aka spatial)
accelerators offers promise for acceleration by mapping optimized dataflows
(i.e., computation order and parallelism) for both phases. The goal of this
work is to characterize and understand the design-space of dataflow choices for
running GNNs on spatial accelerators in order for the compilers to optimize the
dataflow based on the workload. Specifically, we propose a taxonomy to describe
all possible choices for mapping the dense and sparse phases of GNNs spatially
and temporally over a spatial accelerator, capturing both the intra-phase
dataflow and the inter-phase (pipelined) dataflow. Using this taxonomy, we do
deep-dives into the cost and benefits of several dataflows and perform case
studies on implications of hardware parameters for dataflows and value of
flexibility to support pipelined execution.


Abstract:
  The interaction between a central qubit spin and a surrounding bath of spins
is critical to spin-based solid state quantum sensing and quantum information
processing. Spin-bath interactions are typically strongly anisotropic, and
rapid physical rotation has long been used in solid-state nuclear magnetic
resonance to simulate motional averaging of anisotropic interactions, such as
dipolar coupling between nuclear spins. Here, we show that the interaction
between electron spins of nitrogen-vacancy centers and a bath of $^{13}$C
nuclear spins in a diamond rotated at up to 300,000rpm introduces decoherence
into the system via frequency-modulation of the nuclear spin Larmor precession.
The presence of an off-axis magnetic field necessary for averaging of the
dipolar coupling leads to a rotational dependence of the electron-nuclear
hyperfine interaction, which cannot be averaged out with experimentally
achievable rotation speeds. Our findings offer new insights into the use of
physical rotation for quantum control with implications for quantum systems
having motional and rotational degrees of freedom that are not fixed.


Abstract:
  Bayesian inference is used to extract unknown parameters from gravitational
wave signals. Detector noise is typically modelled as stationary, although data
from the LIGO and Virgo detectors is not stationary. We demonstrate that the
posterior of estimated waveform parameters is no longer valid under the
assumption of stationarity. We show that while the posterior is unbiased, the
errors will be under- or overestimated compared to the true posterior. A
formalism was developed to measure the effect of the mismodelling, and found
the effect of any form of non-stationarity has an effect on the results, but
are not significant in certain circumstances. We demonstrate the effect of
short-duration Gaussian noise bursts and persistent oscillatory modulation of
the noise on binary-black-hole-like signals. In the case of short signals,
non-stationarity in the data does not have a large effect on the parameter
estimation, but the errors from non-stationary data containing signals lasting
tens of seconds or longer will be several times worse than if the noise was
stationary. Accounting for this limiting factor in parameter sensitivity could
be very important for achieving accurate astronomical results, including an
estimation of the Hubble parameter. This methodology for handling the
non-stationarity will also be invaluable for analysis of waveforms that last
minutes or longer, such as those we expect to see with the Einstein Telescope.


Abstract:
  Music is a fundamental human construct, and harmony provides the building
blocks of musical language. Using the Kunstderfuge corpus of classical music,
we analyze the historical evolution of the richness of harmonic vocabulary of
76 classical composers, covering almost 6 centuries. Such corpus comprises
about 9500 pieces, resulting in more than 5 million tokens of music codewords.
The fulfilment of Heaps' law for the relation between the size of the harmonic
vocabulary of a composer (in codeword types) and the total length of his works
(in codeword tokens), with an exponent around 0.35, allows us to define a
relative measure of vocabulary richness that has a transparent interpretation.
When coupled with the considered corpus, this measure allows us to quantify
harmony richness across centuries, unveiling a clear increasing linear trend.
In this way, we are able to rank the composers in terms of richness of
vocabulary, in the same way as for other related metrics, such as entropy. We
find that the latter is particularly highly correlated with our measure of
richness. Our approach is not specific for music and can be applied to other
systems built by tokens of different types, as for instance natural language.


Abstract:
  In this work, we present, analyze, and implement a class of Multi-Level
Markov chain Monte Carlo (ML-MCMC) algorithms based on independent
Metropolis-Hastings proposals for Bayesian inverse problems. In this context,
the likelihood function involves solving a complex differential model, which is
then approximated on a sequence of increasingly accurate discretizations. The
key point of this algorithm is to construct highly coupled Markov chains
together with the standard Multi-level Monte Carlo argument to obtain a better
cost-tolerance complexity than a single-level MCMC algorithm. Our method
extends the ideas of Dodwell, et al. "A hierarchical multilevel Markov chain
Monte Carlo algorithm with applications to uncertainty quantification in
subsurface flow," \textit{SIAM/ASA Journal on Uncertainty Quantification 3.1
(2015): 1075-1108,} to a wider range of proposal distributions. We present a
thorough convergence analysis of the ML-MCMC method proposed, and show, in
particular, that (i) under some mild conditions on the (independent) proposals
and the family of posteriors, there exists a unique invariant probability
measure for the coupled chains generated by our method, and (ii) that such
coupled chains are uniformly ergodic. We also generalize the cost-tolerance
theorem of Dodwell et al., to our wider class of ML-MCMC algorithms. Finally,
we propose a self-tuning continuation-type ML-MCMC algorithm (C-ML-MCMC). The
presented method is tested on an array of academic examples, where some of our
theoretical results are numerically verified. These numerical experiments
evidence how our extended ML-MCMC method is robust when targeting some
\emph{pathological} posteriors, for which some of the previously proposed
ML-MCMC algorithms fail.


Abstract:
  A method to design gratings in integrated photonics, is presented. The method
is based on a transfer matrix formalism enhanced by Finite Element Method (FEM)
parameter calculations. The main advantages of the proposed technique are the
easy of use, the fast optimization time and the versatility of the approach.
Few examples of optimized gratings to obtain various scattered light field
profiles for different applications are presented: a double-Gaussian profile, a
flat top square profile, a spot profile on a chip surface, profiles suited to
get efficient and selective coupling to single mode and multimode fibers. A
discussion of the limits of the method and some insights on how to improve it
are also discussed.


Abstract:
  We find that spinning particles with suitable couplings propagating in
certain supersymmetric backgrounds of $D=4$, $N=2$ and $D=5$, $N=1$ minimal
supergravities are invariant under symmetries generated by the twisted
covariant form hierarchies of these theories. We also compare our results with
the symmetries of spinning particles generated by Killing-Yano forms which are
responsible for the separability properties of some gravitational backgrounds.


Abstract:
  The slope gap distribution of a translation surface is a measure of how
random the directions of the saddle connections on the surface are. It is known
that Veech surfaces, a highly symmetric type of translation surface, have gap
distributions that are piecewise real analytic. Beyond that, however, very
little is currently known about the general behavior of the slope gap
distribution, including the number of points of non-analyticity or the tail.
  We show that the limiting gap distribution of slopes of saddle connections on
a Veech translation surface is always piecewise real-analytic with
\emph{finitely} many points of non-analyticity. We do so by taking an explicit
parameterization of a Poincar\'{e} section to the horocycle flow on
$\text{SL}(2,\mathbb{R})/\text{SL}(X,\omega)$ associated to an arbitrary Veech
surface $\text{SL}(X,\omega)$ and establishing a key finiteness result for the
first return map under this flow. We use the finiteness result to show that the
tail of the slope gap distribution of Veech surfaces always has quadratic
decay.


Abstract:
  Early detection of skin cancers like melanoma is crucial to ensure high
chances of survival for patients. Clinical application of Deep Learning
(DL)-based Decision Support Systems (DSS) for skin cancer screening has the
potential to improve the quality of patient care. The majority of work in the
medical AI community focuses on a diagnosis setting that is mainly relevant for
autonomous operation. Practical decision support should, however, go beyond
plain diagnosis and provide explanations. This paper provides an overview of
works towards explainable, DL-based decision support in medical applications
with the example of skin cancer diagnosis from clinical, dermoscopic and
histopathologic images. Analysis reveals that comparably little attention is
payed to the explanation of histopathologic skin images and that current work
is dominated by visual relevance maps as well as dermoscopic feature
identification. We conclude that future work should focus on meeting the
stakeholder's cognitive concepts, providing exhaustive explanations that
combine global and local approaches and leverage diverse modalities. Moreover,
the possibility to intervene and guide models in case of misbehaviour is
identified as a major step towards successful deployment of AI as DL-based DSS
and beyond.


Abstract:
  We forecast the ability of future-generation experiments to detect the
fine-structure lines of the carbon and oxygen ions, [CII] and [OIII] in
intensity mapping (IM) from the Epoch of Reionization ($z \sim 6-8$). Combining
the latest empirically derived constraints relating the luminosity of the
[OIII] line to the ambient star-formation rate, and using them in conjunction
with previously derived estimates for the abundance of [CII] in haloes, we
predict the expected auto-correlation IM signal to be observed using
next-generation facilities based on the Fred Young Submillimetre Telescope
(FYST) and the balloon-borne facility, Experiment for Cryogenic Large-Aperture
Intensity Mapping (EXCLAIM) over $z \sim 5.3 - 7$. We describe how improvements
to both the ground-based and balloon-based surveys in the future will enable a
cross-correlation signal to be detected at $\sim$ 10-40 $\sigma$ over $z \sim
5.3 - 7$. Finally, we propose a space-based mission targeting the [OIII] 88 and
52 $\mu$m lines along with the [CII] 158 $\mu$m line, configured to enhance the
signal-to-noise ratio of cross-correlation measurements. We find that such a
configuration can achieve a high-significance detection (hundreds to thousands
of $\sigma$) in both auto- and cross-correlation modes.


Abstract:
  This paper gives a new approach for the maximum likelihood estimation of the
joint of the location and scale of the Cauchy distribution. We regard the joint
as a single complex parameter and derive a new form of the likelihood equation
of a complex variable. Based on the equation, we provide a new iterative scheme
approximating the maximum likelihood estimate. We also handle the equation in
an algebraic manner and derive a polynomial containing the maximum likelihood
estimate as a root. This algebraic approach provides another scheme
approximating the maximum likelihood estimate by root-finding algorithms for
polynomials, and furthermore, gives non-existence of closed-form formulae for
the case that the sample size is five. We finally provide some numerical
examples to show our method is effective.


Abstract:
  Freeform optical components offer significant compactization of multi-lens
systems, as well as advanced manipulation of light that is not possible with
traditional systems. However, their fabrication relies on machining processes
that are complex, time-consuming, and incompatible with rapid prototyping. This
work presents the ability to shape liquid volumes and solidify them into
desired freeform components, enabling rapid freeform prototyping with high
surface quality. The method is based on controlling the minimum energy state of
the interface between a curable optical liquid and an immersion liquid, by
dictating a geometrical boundary constraint. The boundary shape is modeled as a
cylinder whose arbitrary height is expressed as a Fourier series, allowing for
an analytical solution of the resulting freeform surface as a sum of
Fourier-Bessel functions. Each of these functions represents a different basic
mode, whose superposition creates complex topographies. This solution allows
deterministic design of freeform surfaces by controlling three key parameters -
the volume of the optical liquid, the density of the immersion liquid, and the
shape of the bounding frame. The paper describes a complete workflow for rapid
prototyping of such components, and demonstrates the fabrication of a 35 mm
diameter freeform component with sub-nanometer surface roughness within
minutes.


Abstract:
  We study fixed loci of antisymplectic involutions on projective hyperk\"ahler
manifolds. When the involution is induced by an ample class of square 2 in the
Beauville-Bogomolov-Fujiki lattice, we show that the number of connected
components of the fixed locus is equal to the divisibility of the class, which
is either 1 or 2.


Abstract:
  We study the function series $\sum_{n=1}^\infty \phi^{2m+2}
\text{cosch}^{2m+2}(n\phi/2)$, and similar series, for integers $m$ and complex
$\phi$. This hyperbolic series is linearly related to the Lambert series. The
Lambert series is known to satisfy a functional equation which defines the
Ramanujan polynomials. By using residue theorem (summation theorem) we find the
functional equation satisfied by this hyperbolic series. The functional
equation identifies a class of polynomials which can be seen as a
generalization of the Ramanujan polynomials. These polynomials coincide with
the asymptotic expansion of the hyperbolic series at the origin and they all
vanish for $\phi=\pm 2\pi i$. We furthermore derive several identities between
Harmonic numbers and ordinary and generalized Bernoulli polynomials.


Abstract:
  The multimessenger event GW170817/GRB 170817A confirmed that binary neutron
star (BNS) mergers can produce short gamma-ray burst (SGRB) jets. This evidence
promoted new investigations on the mechanisms through which a BNS merger
remnant can launch such a powerful relativistic outflow and on the propagation
of the latter across the surrounding post-merger environment. In particular,
great strides have been made in jet propagation models, establishing
connections between the initial jet launching conditions, including the
incipient jet launching time (with respect to merger) and the injection
parameters, and the observable SGRB prompt and afterglow emission. However,
present semi-analytical models and numerical simulations (with one notable
exception) adopt simple hand-made prescriptions to account for the post-merger
environment, lacking a direct association with any specific merging BNS system.
Here, we present the first three-dimensional relativistic hydrodynamics
simulations of incipient SGRB jets propagating through a post-merger
environment that is directly imported from the outcome of a previous general
relativistic BNS merger simulation. Our results show that the evolution and
final properties of the jet can be largely affected by the anisotropies and the
deviations from axisymmetry and homologous expansion characterizing more
realistic BNS merger environments. In addition, we find that the inclusion of
the gravitational pull from the central compact object, often overlooked, can
have a major impact. Finally, we consider different jet launching times
referred to the same BNS merger model and discuss the consequences for the
ultimate jet properties.


Abstract:
  Experimental results are presented on the efficiency limits for a quantum
interface between a matter-based qubit and a photonic qubit. Using a trapped
ion in an optical cavity, we obtain a single ion-entangled photon at the cavity
output with a probability of 0.69(3). The performance of our system is shown to
saturate the upper limit to photon-collection probability from a quantum
emitter in a cavity, set by the emitter's electronic structure and by the
cavity parameters. The probability for generating and detecting the
ion-entangled fiber-coupled photon is 0.462(3), a five-fold increase over the
previous best performance. Finally, the generation and detection of up to 15
sequential polarised photons demonstrates the ability of a trapped ion to serve
as a multi-photon source. The comparison between measured probabilities and
predicted bounds is relevant for quantum emitters beyond trapped ions, in
particular, for the design of future systems optimising photon collection from,
and absorption in, quantum matter.


Abstract:
  In this article, we explore the relationship between cellular phone data and
housing prices in Budapest, Hungary. We determine mobility indicators from one
months of Call Detail Records (CDR) data, while the property price data are
used to characterize the socioeconomic status at the Capital of Hungary. First,
we validated the proposed methodology by comparing the Home and Work locations
estimation and the commuting patterns derived from the cellular network dataset
with reports of the national mini census. We investigated the statistical
relationships between mobile phone indicators, such as Radius of Gyration, the
distance between Home and Work locations or the Entropy of visited cells, and
measures of economic status based on housing prices. Our findings show that the
mobility correlates significantly with the socioeconomic status. We performed
Principal Component Analysis (PCA) on combined vectors of mobility indicators
in order to characterize the dependence of mobility habits on socioeconomic
status. The results of the PCA investigation showed remarkable correlation of
housing prices and mobility customs.


Abstract:
  The independence polynomial originates in statistical physics as the
partition function of the hard-core model. The location of the complex zeros of
the polynomial is related to phase transitions, and plays an important role in
the design of efficient algorithms to approximately compute evaluations of the
polynomial.
  In this paper we directly relate the location of the complex zeros of the
independence polynomial to computational hardness of approximating evaluations
of the independence polynomial. We do this by moreover relating the location of
zeros to chaotic behaviour of a naturally associated family of rational
functions; the occupation ratios.


Abstract:
  We develop a new equation of state (EoS) table involving thermal (anti)kaons,
Bose-Einstein condensate of $K^{-}$ mesons and $\Lambda$-hyperons for
core-collapse supernova and neutron star merger simulations. This EoS table is
based on a finite temperature density-dependent relativistic hadron field
theory where baryon-baryon interaction is mediated by scalar $\sigma$, vector
$\omega$ and $\rho$ mesons, using the parameter set DD2 for nucleons. The
repulsive hyperon-hyperon interaction is mediated by an additional strange
$\phi$ meson. The EoS for the $K^-$ condensed matter is also calculated within
the framework of relativistic mean field model, whereas the low-density,
inhomogeneous matter is calculated in the extended Nuclear Statistical
Equilibrium model (NSE). The EoS table is generated for a wide range of values
of three parameters - baryon density ($10^{-12}$ to $\sim$ 1 fm$^{-3}$),
positive charge fraction(0.01 to 0.60) and temperature(0.1 to 158.48 MeV).


Abstract:
  In this paper, we propose a pseudo polynomial size LP formulation for finding
a payoff vector in the least core of a weighted voting game. The numbers of
variables and constraints in our formulation are both bounded by $\mbox{O}(n
W_+)$, where $n$ is the number of players and $W_+$ is the total sum of
(integer) voting weights. When we employ our formulation, a commercial LP
solver calculates a payoff vector in the least core of practical weighted
voting games in a few seconds. We also extend our approach to vector weighted
voting games.


Abstract:
  The motion of a vertically positioned bicycle is considered when a horizontal
control force, which maybe both internal and external in relation to the
bicycle, is applied to its pedal. Tangential forces of dryfriction obeying the
Euler { Coulomb law act at points of contact of the wheels with the horizontal
supportplane. The constraint at the points of contact of the wheels with the
support is assumed to be unilateral.The problem of determining the acceleration
of the centre of mass of the bicycle and the realized motionsof its wheels
(with or without slip, and with or without detachment) with dierent values of
the design parameters and control force is solved. This approach is usefull for
control of vehicle and optimization of its dynamical parameters. Cases of
non-uniqueness of motion the Painleve paradox are found.


Abstract:
  Attention is an effective mechanism to improve the deep model capability.
Squeeze-and-Excite (SE) introduces a light-weight attention branch to enhance
the network's representational power. The attention branch is gated using the
Sigmoid function and multiplied by the feature map's trunk branch. It is too
sensitive to coordinate and balance the trunk and attention branches'
contributions. To control the attention branch's influence, we propose a new
attention method, called Shift-and-Balance (SB). Different from
Squeeze-and-Excite, the attention branch is regulated by the learned control
factor to control the balance, then added into the feature map's trunk branch.
Experiments show that Shift-and-Balance attention significantly improves the
accuracy compared to Squeeze-and-Excite when applied in more layers, increasing
more size and capacity of a network. Moreover, Shift-and-Balance attention
achieves better or close accuracy compared to the state-of-art Dynamic
Convolution.


Abstract:
  Memory-efficient continuous Sign Language Translation is a significant
challenge for the development of assisted technologies with real-time
applicability for the deaf. In this work, we introduce a paradigm of designing
recurrent deep networks whereby the output of the recurrent layer is derived
from appropriate arguments from nonparametric statistics. A novel variational
Bayesian sequence-to-sequence network architecture is proposed that consists of
a) a full Gaussian posterior distribution for data-driven memory compression
and b) a nonparametric Indian Buffet Process prior for regularization applied
on the Gated Recurrent Unit non-gate weights. We dub our approach
Stick-Breaking Recurrent network and show that it can achieve a substantial
weight compression without diminishing modeling performance.


Abstract:
  The last 15 years have witnessed important progresses in our understanding of
the mechanism of superconductivity in the high-$T_{c}$ cuprates. There is now
strong evidence that the strange metal behavior is induced by the quantum
critical fluctuation at the pseudogap end point, where the Fermi surface
changes its topology from hole-like to electron-like. However, experiments show
that the quantum critical behavior in the high-$T_{c}$ cuprates is
qualitatively different from that observed in the heavy Fermion systems and the
iron-based superconductors, in both of which the quantum critical behavior can
be attributed to the quantum phase transition toward a symmetry breaking phase.
The fact that the pseudogap exists as a spectral gap without a corresponding
symmetry breaking order, together with the fact that the strange metal behavior
occurs as a quantum critical behavior without a corresponding symmetry breaking
phase transition, exposes the central difficulty of the field: the lack of a
universal low energy effective theory description of the high-$T_{c}$
phenomenology beyond the Landau paradigm. Recent experiments imply that the
dualism between the local moment and the itinerant quasiparticle character of
the electron in the high-$T_{c}$ cuprates may serve as an organizing principle
to go beyond the Landau paradigm and may hold the key to the mystery of the
pseudogap phenomena and the strange metal behavior.


Abstract:
  We ask the question of how small a self-assembling set of tiles can be yet
have interesting computational behaviour. We study this question in a model
where supporting walls are provided as an input structure for tiles to grow
along: we call it the Maze-Walking Tile Assembly Model. The model has a number
of implementation prospects, one being DNA strands that attach to a DNA origami
substrate. Intuitively, the model suggests a separation of signal routing and
computation: the input structure (maze) supplies a routing diagram, and the
programmer's tile set provides the computational ability. We ask how simple the
computational part can be.
  We give two tiny tile sets that are computationally universal in the
Maze-Walking Tile Assembly Model. The first has four tiles and simulates
Boolean circuits by directly implementing NAND, NXOR and NOT gates. Our second
tile set has 6 tiles and is called the Collatz tile set as it produces patterns
found in binary/ternary representations of iterations of the Collatz function.
Using computer search we find that the Collatz tile set is expressive enough to
encode Boolean circuits using blocks of these patterns. These two tile sets
give two different methods to find simple universal tile sets, and provide
motivation for using pre-assembled maze structures as circuit wiring diagrams
in molecular self-assembly based computing.


Abstract:
  In this paper, we develop numerical methods based on the weighted Birkhoff
average for studying two-dimensional invariant tori for volume-preserving maps.
The methods do not rely on symmetries, such as time-reversal symmetry, nor on
approximating tori by periodic orbits. The rate of convergence of the average
gives a sharp distinction between chaotic and regular dynamics and allows
accurate computation of rotation vectors for regular orbits. Resonant and
rotational tori are distinguished by computing the resonance order of the
rotation vector to a given precision. Critical parameter values, where tori are
destroyed, are computed by a sharp decrease in convergence rate of the Birkhoff
average. We apply these methods for a three-dimensional generalization of
Chirikov's standard map: an angle-action map with two angle variables.
Computations on grids in frequency and perturbation amplitude allow estimates
of the critical set. We also use continuation to follow tori with fixed
rotation vectors. We test three conjectures for cubic fields that have been
proposed to give locally robust invariant tori.


Abstract:
  Statutory reasoning is the task of determining whether a legal statute,
stated in natural language, applies to the text description of a case. Prior
work introduced a resource that approached statutory reasoning as a monolithic
textual entailment problem, with neural baselines performing nearly at-chance.
To address this challenge, we decompose statutory reasoning into four types of
language-understanding challenge problems, through the introduction of concepts
and structure found in Prolog programs. Augmenting an existing benchmark, we
provide annotations for the four tasks, and baselines for three of them. Models
for statutory reasoning are shown to benefit from the additional structure,
improving on prior baselines. Further, the decomposition into subtasks
facilitates finer-grained model diagnostics and clearer incremental progress.


Abstract:
  In the past few years, mobile deep-learning deployment progressed by leaps
and bounds, but solutions still struggle to accommodate its severe and
fluctuating operational restrictions, which include bandwidth, latency,
computation, and energy. In this work, we help to bridge that gap, introducing
the first configurable solution for object detection that manages the triple
communication-computation-accuracy trade-off with a single set of weights. Our
solution shows state-of-the-art results on COCO-2017, adding only a minor
penalty on the base EfficientDet-D2 architecture. Our design is robust to the
choice of base architecture and compressor and should adapt well for future
architectures.


Abstract:
  We construct algebra homomorphisms from affine Yangians to the current
algebras of rectangular $W$-algebras both in type A. The construction is given
via the coproduct and the evaluation map for the affine Yangians. As a
consequence, we show that parabolic inductions for representations of the
rectangular $W$-algebras can be regarded as tensor product representations of
the affine Yangians under the homomorphisms. The same method is applicable also
to the super setting.


Abstract:
  Energy harvesting offers an attractive and promising mechanism to power
low-energy devices. However, it alone is insufficient to enable an
energy-neutral operation, which can eliminate tedious battery charging and
replacement requirements. Achieving an energy-neutral operation is challenging
since the uncertainties in harvested energy undermine the quality of service
requirements. To address this challenge, we present a runtime energy-allocation
framework that optimizes the utility of the target device under energy
constraints using a rollout algorithm, which is a sequential approach to solve
dynamic optimization problems. The proposed framework uses an efficient
iterative algorithm to compute initial energy allocations at the beginning of a
day. The initial allocations are then corrected at every interval to compensate
for the deviations from the expected energy harvesting pattern. We evaluate
this framework using solar and motion energy harvesting modalities and American
Time Use Survey data from 4772 different users. Compared to prior techniques,
the proposed framework achieves up to 35% higher utility even under
energy-limited scenarios. Moreover, measurements on a wearable device prototype
show that the proposed framework has 1000x smaller energy overhead than
iterative approaches with a negligible loss in utility.


Abstract:
  We introduce the Software Heritage filesystem (SwhFS), a user-space
filesystem that integrates large-scale open source software archival with
development workflows. SwhFS provides a POSIX filesystem view of Software
Heritage, the largest public archive of software source code and version
control system (VCS) development history.Using SwhFS, developers can quickly
"checkout" any of the 2 billion commits archived by Software Heritage, even
after they disappear from their previous known location and without incurring
the performance cost of repository cloning. SwhFS works across unrelated
repositories and different VCS technologies. Other source code artifacts
archived by Software Heritage-individual source code files and trees, releases,
and branches-can also be accessed using common programming tools and custom
scripts, as if they were locally available.A screencast of SwhFS is available
online at dx.doi.org/10.5281/zenodo.4531411.


Abstract:
  Developing human-like conversational agents is a prime area in HCI research
and subsumes many tasks. Predicting listener backchannels is one such
actively-researched task. While many studies have used different approaches for
backchannel prediction, they all have depended on manual annotations for a
large dataset. This is a bottleneck impacting the scalability of development.
To this end, we propose using semi-supervised techniques to automate the
process of identifying backchannels, thereby easing the annotation process. To
analyze our identification module's feasibility, we compared the backchannel
prediction models trained on (a) manually-annotated and (b) semi-supervised
labels. Quantitative analysis revealed that the proposed semi-supervised
approach could attain 95% of the former's performance. Our user-study findings
revealed that almost 60% of the participants found the backchannel responses
predicted by the proposed model more natural. Finally, we also analyzed the
impact of personality on the type of backchannel signals and validated our
findings in the user-study.


Abstract:
  We study the non-equilibrium dissipative dynamics of the center of mass of a
particle coupled to a field via its internal degrees of freedom. We model the
internal and external degrees of freedom of the particle as quantum harmonic
oscillators in 1+1 D, with the internal oscillator coupled to a scalar quantum
field at the center of mass position. Such a coupling results in a nonlinear
interaction between the three pertinent degrees of freedom -- the center of
mass, internal degree of freedom, and the field. It is typically assumed that
the internal dynamics is decoupled from that of the center of mass owing to
their disparate characteristic time scales. Here we use an influence functional
approach that allows one to account for the self-consistent backaction of the
different degrees of freedom on each other, including the coupled
non-equilibrium dynamics of the internal degree of freedom and the field, and
their influence on the dissipation and noise of the center of mass. Considering
a weak nonlinear interaction term, we employ a perturbative generating
functional approach to derive a second order effective action and a
corresponding quantum Langevin equation describing the non-equilibrium dynamics
of the center of mass. We analyze the resulting dissipation and noise arising
from the field and the internal degree of freedom as a composite environment.
Furthermore, we establish a generalized fluctuation-dissipation relation for
the late-time dissipation and noise kernels. Our results are pertinent to open
quantum systems that possess intermediary degrees of freedom between system and
environment, such as in the case of optomechanical interactions.


Abstract:
  Multilayer-perceptrons (MLP) are known to struggle with learning functions of
high-frequencies, and in particular cases with wide frequency bands. We present
a spatially adaptive progressive encoding (SAPE) scheme for input signals of
MLP networks, which enables them to better fit a wide range of frequencies
without sacrificing training stability or requiring any domain specific
preprocessing. SAPE gradually unmasks signal components with increasing
frequencies as a function of time and space. The progressive exposure of
frequencies is monitored by a feedback loop throughout the neural optimization
process, allowing changes to propagate at different rates among local spatial
portions of the signal space. We demonstrate the advantage of SAPE on a variety
of domains and applications, including regression of low dimensional signals
and images, representation learning of occupancy networks, and a geometric task
of mesh transfer between 3D shapes.


Abstract:
  The short period variable star MG1-688432 has been discovered to exhibit
occasional extremely high energy optical outbursts as high as 10^31 joules.
Outbursts are typically of several hours duration. These events are often
highly structured, resembling sequential associated releases of energy. Twenty
years of time sequence photometry is presented, indicating a basic sinusoidal
light curve of mean period 6.65d, with some phase shifting and long-term
temporal trends in amplitude and mean brightness. Spectroscopy reveals a
peculiar star, best resembling a K3 subgiant that has evolved off the main
sequence moderately red-ward of the giant branch. Spectroscopic and radial
velocity analyses indicate a binary system orbiting its barycenter with an
unseen companion to the K3IV primary. This is not an eclipsing system with the
inclination of the orbit precluding eclipse by the secondary. The system is at
a distance of 1.5kpc and analysis of GAIA observations leads to the conclusion
that the HR diagram position of MG1-688432 is established by an intrinsic
feature of the system, most likely either the stellar evolutionary state of the
observed star or the presence of small (non-gray) dust within the system. Two
mechanisms that might give rise to the system are 1) impacts with tidally
disrupted planetary debris, and 2) magnetically induced chromospheric activity.
An intriguing idea that requires further investigation suggests that the unseen
companion is perhaps a white dwarf star which has encountered a planet and
tidally shredded it to produce a debris and dust veil that modulates the
brightness of the primary.


Abstract:
  Motivated by the Ax-Kochen/Ershov principle, a large number of questions
about henselian valued fields have been shown to reduce to analogous questions
about the value group and residue field. In this paper, we investigate the
burden of henselian valued fields in the three-sorted Denef-Pas language. If
$T$ is a theory of henselian valued fields admitting relative quantifier
elimination (in any characteristic), we show that the burden of $T$ is equal to
the sum of the burdens of its value group and residue field. As a consequence,
$T$ is NTP${}_2$ if and only if its residue field and value group are; the same
is true for the statements "$T$ is strong" and "$T$ has finite burden."


Abstract:
  The discipline of process mining aims to study processes in a data-driven
manner by analyzing historical process executions, often employing Petri nets.
Event data, extracted from information systems (e.g. SAP), serve as the
starting point for process mining. Recently, novel types of event data have
gathered interest among the process mining community, including uncertain event
data. Uncertain events, process traces and logs contain attributes that are
characterized by quantified imprecisions, e.g., a set of possible attribute
values. The PROVED tool helps to explore, navigate and analyze such uncertain
event data by abstracting the uncertain information using behavior graphs and
nets, which have Petri nets semantics. Based on these constructs, the tool
enables discovery and conformance checking.