Abstract:
  The glissadic overshoot is characterized by an unwanted type of movement
known as glissades. The glissades are a short ocular movement that describe the
failure of the neural programming of saccades to move the eyes in order to
reach a specific target. In this paper we develop a procedure to determine if a
specific saccade have a glissade appended to the end of it. The use of the
third partial sum of the Gauss series as mathematical model, a comparison
between some specific parameters and the RMSE error are the steps made to reach
this goal. Finally a machine learning algorithm is trained, returning expected
responses of the presence or not of this kind of ocular movement.


Abstract:
  In this article, we present a $T$-matrix method for numerical computation of
second-harmonic generation from clusters of arbitrarily distributed spherical
particles made of centrosymmetric optical materials. The electromagnetic fields
at the fundamental and second-harmonic (SH) frequencies are expanded in series
of vector spherical wave functions, and the single sphere $T$-matrix entries
are computed by imposing field boundary conditions at the surface of the
particles. Different from previous approaches, we compute the SH fields by
taking into account both local surface and nonlocal bulk polarization sources,
which allows one to accurately describe the generation of SH in arbitrary
clusters of spherical particles. Our numerical method can be used to
efficiently analyze clusters of spherical particles made of various optical
materials, including metallic, dielectric, semiconductor, and polaritonic
materials.


Abstract:
  Gross and Siebert identified a class of singular Lagrangian torus fibrations
which arise when smoothing toroidal degenerations, and which come in pairs that
are related by mirror symmetry. We identify an immersed Lagrangian in each of
these local models which supports a moduli space of objects that is isomorphic
to the mirror space, and prove a homological mirror statement along the way.


Abstract:
  In their seminal paper Artemov and Protopopescu provide Hilbert formal
systems, Brower-Heyting-Kolmogorov and Kripke semantics for the logics of
intuitionistic belief and knowledge. Subsequently Krupski has proved that the
logic of intuitionistic knowledge is PSPACE-complete and Su and Sano have
provided calculi enjoying the subformula property. This paper continues the
investigations around to sequent calculi for Intuitionistic Epistemic Logics by
providing sequent calculi that have the subformula property and that are
terminating in linear depth. Our calculi allow us to design a procedure that
for invalid formulas returns a Kripke model of minimal depth. Finally we also
discuss refutational sequent calculi, that is sequent calculi to prove the
invalidity.


Abstract:
  We present the results of an all-sky search for continuous gravitational wave
signals with frequencies in the 20-500 Hz range from neutron stars with
ellipticity of 1e-8. This frequency region is particularly hard to probe
because of the quadratic dependence of signal strength on frequency. The search
employs the Falcon analysis pipeline on LIGO O2 public data. Compared to
previous Falcon analyses the coherence length has been quadrupled, with a
corresponding increase in sensitivity. This enables us to search for small
ellipticity neutron stars in this low frequency region up to 44 pc away. The
frequency derivative range is up to 3e-13 Hz/s easily accommodating sources
with ellipticities of 1e-7 and a corresponding factor of 10 increase in reach.
New outliers are found, many of which we are unable to associate with any
instrumental cause.


Abstract:
  This paper investigates the design and performance of delayed bit-interleaved
coded modulation (DBICM) with low-density parity-check (LDPC) codes. For Gray
labeled square $M$-ary quadrature amplitude modulation (QAM) constellations, we
investigate the optimal delay scheme with the largest spectrum efficiency of
DBICM for a fixed maximum number of delayed time slots and a given
signal-to-noise ratio. When analyzing the capacity of DBICM, we find two
important properties: the capacity improvement due to delayed coded bits being
mapped to the real and imaginary parts of the transmitted symbols are
independent of each other; a pair of delay schemes with delayed coded bits
having identical bit-channel capacity lead to equivalent DBICM capacity. Using
these two properties, we efficiently optimize the delay scheme for any uniform
Gray-QAM systems. Furthermore, these two properties enable efficient LDPC code
designs regarding unequal error protection via bit-channel type
classifications. Moreover, we use protograph-based extrinsic information
transfer charts to jointly optimize degree distributions and channel
assignments of LDPC codes and propose a constrained progressive edge growth
like algorithm to jointly construct LDPC codes and bit-interleavers for DBICM,
taking distinctive bit-channel's capacity into account. Simulation results
demonstrate that the designed LDPC coded DBICM systems significantly outperform
LDPC coded BICM systems.


Abstract:
  In recent years the number of CubeSats (U-class spacecrafts) launched into
space has increased exponentially marking the dawn of the nanosatellite
technology. In general these satellites have a much smaller mass budget
compared to conventional scientific satellites which limits shielding of
scientific instruments against direct and indirect radiation in space. In this
paper we present a simulation framework to quantify the signal in large
field-of-view gamma-ray scintillation detectors of satellites induced by
X-ray/gamma-ray transients, by taking into account the response of the
detector. Furthermore, we quantify the signal induced by X-ray and particle
background sources at a Low-Earth Orbit outside South Atlantic Anomaly and
polar regions. Finally, we calculate the signal-to-noise ratio taking into
account different energy threshold levels. Our simulation can be used to
optimize material composition and predict detectability of various
astrophysical sources by CubeSats. We apply the developed simulation to a
satellite belonging to a planned CAMELOT CubeSat constellation. This project
mainly aims to detect short and long gamma-ray bursts (GRBs) and as a secondary
science objective, to detect soft gamma-ray repeaters (SGRs) and terrestrial
gamma-ray flashes (TGFs). The simulation includes a detailed computer-aided
design (CAD) model of the satellite to take into account the interaction of
particles with the material of the satellite as accurately as possible. Results
of our simulations predict that CubeSats can complement the large space
observatories in high-energy astrophysics for observations of GRBs, SGRs and
TGFs. For the detectors planned to be on board of the CAMELOT CubeSats the
simulations show that detections with signal-to-noise ratio of at least 9 for
median GRB and SGR fluxes are achievable.


Abstract:
  We study real-time routing policies in smart transit systems, where the
platform has a combination of cars and high-capacity vehicles (e.g., buses or
shuttles) and seeks to serve a set of incoming trip requests. The platform can
use its fleet of cars as a feeder to connect passengers to its high-capacity
fleet, which operates on fixed routes. Our goal is to find the optimal set of
(bus) routes and corresponding frequencies to maximize the social welfare of
the system in a given time window. This generalizes the Line Planning Problem,
a widely studied topic in the transportation literature, for which existing
solutions are either heuristic (with no performance guarantees), or require
extensive computation time (and hence are impractical for real-time use). To
this end, we develop a $1-\frac{1}{e}-\varepsilon$ approximation algorithm for
the Real-Time Line Planning Problem, using ideas from randomized rounding and
the Generalized Assignment Problem. Our guarantee holds under two assumptions:
$(i)$ no inter-bus transfers and $(ii)$ access to a pre-specified set of
feasible bus lines. We moreover show that these two assumptions are crucial by
proving that, if either assumption is relaxed, the Real-Time Line Planning
Problem does not admit any constant-factor approximation. Finally, we
demonstrate the practicality of our algorithm via numerical experiments on
real-world and synthetic datasets, in which we show that, given a fixed time
budget, our algorithm outperforms Integer Linear Programming-based exact
methods.


Abstract:
  Neural networks have proved to be very robust at processing unstructured data
like images, text, videos, and audio. However, it has been observed that their
performance is not up to the mark in tabular data; hence tree-based models are
preferred in such scenarios. A popular model for tabular data is boosted trees,
a highly efficacious and extensively used machine learning method, and it also
provides good interpretability compared to neural networks. In this paper, we
describe a novel architecture XBNet, which tries to combine tree-based models
with that of neural networks to create a robust architecture trained by using a
novel optimization technique, Boosted Gradient Descent for Tabular Data which
increases its interpretability and performance.


Abstract:
  This article summarizes some of the open questions in the field of active
matter that have emerged during Active20, a nine-week program held at the Kavli
Institute for Theoretical Physics (KITP) in Spring 2020. The article does not
provide a review of the field, but rather a personal view of the authors,
informed by contributions of all participants, on new directions in active
matter research. The topics highlighted include: the ubiquitous occurrence of
spontaneous flows and active turbulence and the theoretical and experimental
challenges associated with controlling and harnessing such flows; the role of
motile topological defects in ordered states of active matter and their
possible biological relevance; the emergence of non-reciprocal effective
interactions and the role of chirality in active systems and their intriguing
connections to non-Hermitian quantum mechanics; the progress towards a
formulation of the thermodynamics of active systems thanks to the feedback
between theory and experiments; the impact of the active matter framework on
our understanding of the emergent mechanics of biological tissue. These
seemingly diverse phenomena all stem from the defining property of active
matter - assemblies of self-driven entities that individually break
time-reversal symmetry and collectively organize in a rich variety of
nonequilibrium states.


Abstract:
  This paper is devoted to investigating the Freidlin-Wentzell's large
deviation principle for a class of McKean-Vlasov quasilinear SPDEs perturbed by
small multiplicative noise. We adopt the variational framework and the modified
weak convergence criteria to prove the Laplace principle for McKean-Vlasov type
SPDEs, which is equivalent to the large deviation principle. Moreover, we do
not assume any compactness condition of embedding in the Gelfand triple to
handle both the cases of bounded and unbounded domains in applications. The
main results can be applied to various McKean-Vlasov type SPDEs such as
distribution dependent stochastic porous media type equations and stochastic
p-Laplace type equations.


Abstract:
  Students in quantum mechanics class are taught that the wave function
contains all knowable information about an isolated system. Later in the
course, this view seems to be contradicted by the mysterious density matrix,
which introduces a new set of probabilities in addition to those that are built
into the wave function. This paper brings attention to the fact that the
density matrix can be reconciled with the underlying quantum-mechanical
description using a two-particle entangled state with a one-particle subsystem
as the simplest illustration of the basic principle. The extra-quantum
probabilities are traced to the coefficients of superposition of the quantum
state vector and the seemingly irreversible exponential population decay is
shown to be compatible with the unitary time evolution of a pure state when the
two particles interact. The two-particle universe thus provides the student
with a tool for understanding how the density operator, with all its richness,
emerges from quantum mechanics.


Abstract:
  We study the electronic properties of a position-dependent effective mass
electron on a bilayer graphene catenoid bridge. We propose a position-dependent
mass (PDM) as a function of both gaussian and mean curvature. The hamiltonian
exhibits parity and time-reversal steaming from the bridge symmetry. The
effective potential contains the da Costa, centrifugal and PDM terms which are
concentrated around the catenoid bridge. For zero angular momentum states, the
PDM term provides a transition between a reflectionless to a double-well
potential. As a result, the bound states undergo a transition from a single
state around the bridge throat into two states each one located at rings around
the bridge. Above some critical value of the PDM coupling constant, the
degeneracy is restored due to double-well tunneling resonance.


Abstract:
  Realizing optical manipulation of microscopic objects is crucial in the
research fields of life science, condensed matter physics and physical
chemistry. In non-liquid environments, this task is commonly regarded as
difficult due to strong adhesive surface force ($\sim\mu\rm N$) between solid
interfaces that makes tiny optical driven force ($\sim\rm pN$) insignificant.
Here, by recognizing the microscopic interaction mechanism between friction
force -- the parallel component of surface force on the contact surface -- and
thermoelastic waves induced by pulsed optical absorption, we establish a
general principle enabling the actuation of micro-objects on dry frictional
surfaces based on the opto-thermo-mechanical effects. Theoretically, we predict
that nanosecond pulsed optical absorption with mW-scale peak power is
sufficient to tame $\mu\rm N$-scale friction force. Experimentally, we
demonstrate that two-dimensional spiral motion of gold plates on micro-fibers
driven by a nanosecond pulsed laser, and reveal the specific rules of motion
control. Our results pave the way for future development of micro-scale
actuators in nonliquid environments.


Abstract:
  We consider the Cauchy problem for the heat diffusion equation in the whole
Euclidean space consisting of two media with different constant conductivities.
The large time behavior of temperature, the solution of the problem, is studied
when initially temperature is assigned to be 0 on one medium and $1$ on the
other. We show that under a certain geometric condition of the configuration of
the media, temperature is stabilized to a constant as time tends to infinity.
We also show by examples that temperature in general oscillates and is not
stabilized.


Abstract:
  We report the discovery of one super-Earth- (TOI-1749b) and two
sub-Neptune-sized planets (TOI-1749c and TOI-1749d) transiting an early M dwarf
at a distance of 100~pc, which were first identified as planetary candidates
using data from the TESS photometric survey. We have followed up this system
from the ground by means of multiband transit photometry, adaptive-optics
imaging, and low-resolution spectroscopy, from which we have validated the
planetary nature of the candidates. We find that TOI-1749b, c, and d have
orbital periods of 2.39, 4.49, and 9.05 days, and radii of 1.4, 2.1, and 2.5
$R_\oplus$, respectively. We also place 95\% confidence upper limits on the
masses of 57, 14, and 15 $M_\oplus$ for TOI-1749b, c, and d, respectively, from
transit timing variations. The periods, sizes, and tentative masses of these
planets are in line with a scenario in which all three planets initially had a
hydrogen envelope on top of a rocky core, and only the envelope of the
innermost planet has been stripped away by photoevaporation and/or core-powered
mass loss mechanisms. These planets are similar to other planetary trios found
around M dwarfs, such as TOI-175b,c,d and TOI-270b,c,d, in the sense that the
outer pair has a period ratio within 1\% of 2. Such a characteristic orbital
configuration, in which an additional planet is located interior to a near 2:1
period-ratio pair, is relatively rare around FGK dwarfs.


Abstract:
  In order to investigate the tomographical structure of light pseudoscalar
mesons, particularly, pion and kaon, we study the valence quark distribution
function (PDF) and the generalized parton distributions (GPDs) using light-cone
quark model (LCQM).


Abstract:
  Estimation of population size using incomplete lists (also called the
capture-recapture problem) has a long history across many biological and social
sciences. For example, human rights and other groups often construct partial
and overlapping lists of victims of armed conflicts, with the hope of using
this information to estimate the total number of victims. Earlier statistical
methods for this setup either use potentially restrictive parametric
assumptions, or else rely on typically suboptimal plug-in-type nonparametric
estimators; however, both approaches can lead to substantial bias, the former
via model misspecification and the latter via smoothing. Under an identifying
assumption that two lists are conditionally independent given measured
covariate information, we make several contributions. First, we derive the
nonparametric efficiency bound for estimating the capture probability, which
indicates the best possible performance of any estimator, and sheds light on
the statistical limits of capture-recapture methods. Then we present a new
estimator, and study its finite-sample properties, showing that it has a double
robustness property new to capture-recapture, and that it is near-optimal in a
non-asymptotic sense, under relatively mild nonparametric conditions. Next, we
give a method for constructing confidence intervals for total population size
from generic capture probability estimators, and prove non-asymptotic
near-validity. Finally, we study our methods in simulations, and apply them to
estimate the number of killings and disappearances attributable to different
groups in Peru during its internal armed conflict between 1980 and 2000.


Abstract:
  With the increasing popularity of cloud based machine learning (ML)
techniques there comes a need for privacy and integrity guarantees for ML data.
In addition, the significant scalability challenges faced by DRAM coupled with
the high access-times of secondary storage represent a huge performance
bottleneck for ML systems. While solutions exist to tackle the security aspect,
performance remains an issue. Persistent memory (PM) is resilient to power loss
(unlike DRAM), provides fast and fine-granular access to memory (unlike disk
storage) and has latency and bandwidth close to DRAM (in the order of ns and
GB/s, respectively). We present PLINIUS, a ML framework using Intel SGX
enclaves for secure training of ML models and PM for fault tolerance
guarantees. P LINIUS uses a novel mirroring mechanism to create and maintain
(i) encrypted mirror copies of ML models on PM, and (ii) encrypted training
data in byte-addressable PM, for near-instantaneous data recovery after a
system failure. Compared to disk-based checkpointing systems,PLINIUS is 3.2x
and 3.7x faster respectively for saving and restoring models on real PM
hardware, achieving robust and secure ML model training in SGX enclaves.


Abstract:
  Non-interacting spinless electrons in one-dimensional quasicrystals,
described by the Aubry-Andr\'{e}-Harper (AAH) Hamiltonian with nearest
neighbour hopping, undergoes metal to insulator transition (MIT) at a critical
strength of the quasi-periodic potential. This transition is related to the
self-duality of the AAH Hamiltonian. Interestingly, at the critical point,
which is also known as the self-dual point, all the single particle wave
functions are multifractal or non-ergodic in nature, while they are ergodic and
delocalized (localized) below (above) the critical point. In this work, we have
studied the one dimensional quasi-periodic AAH Hamiltonian in the presence of
spin-orbit (SO) coupling of Rashba type, which introduces an additional spin
conserving complex hopping and a spin-flip hopping. We have found that,
although the self-dual nature of AAH Hamiltonian remains unaltered, the
self-dual point gets affected significantly. Moreover, the effect of the
complex and spin-flip hoppings are identical in nature. We have extended the
idea of Kohn's localization tensor calculations for quasi-particles and
detected the critical point very accurately. These calculations are followed by
detailed multifractal analysis along with the computation of inverse
participation ratio and von Neumann entropy, which clearly demonstrate that the
quasi-particle eigenstates are indeed multifractal and non-ergodic at the
critical point. Finally, we mapped out the phase diagram in the parameter space
of quasi-periodic potential and SO coupling strength.


Abstract:
  This paper presents a novel deep learning-based approach for automatically
vectorizing and synthesizing the clipart of man-made objects. Given a raster
clipart image and its corresponding object category (e.g., airplanes), the
proposed method sequentially generates new layers, each of which is composed of
a new closed path filled with a single color. The final result is obtained by
compositing all layers together into a vector clipart image that falls into the
target category. The proposed approach is based on an iterative generative
model that (i) decides whether to continue synthesizing a new layer and (ii)
determines the geometry and appearance of the new layer. We formulated a joint
loss function for training our generative model, including the shape
similarity, symmetry, and local curve smoothness losses, as well as vector
graphics rendering accuracy loss for synthesizing clipart recognizable by
humans. We also introduced a collection of man-made object clipart, ClipNet,
which is composed of closed-path layers, and two designed preprocessing tasks
to clean up and enrich the original raw clipart. To validate the proposed
approach, we conducted several experiments and demonstrated its ability to
vectorize and synthesize various clipart categories. We envision that our
generative model can facilitate efficient and intuitive clipart designs for
novice users and graphic designers.


Abstract:
  Recently, a new measure of information called extropy has been introduced by
Lad, Sanfilippo and Agr\`o as the dual version of Shannon entropy. In the
literature, Tsallis introduced a measure for a discrete random variable, named
Tsallis entropy, as a generalization of Boltzmann-Gibbs statistics. In this
work, a new measure of discrimination, called Tsallis extropy, is introduced
and some of its properties are then discussed. The relation between Tsallis
extropy and entropy is given and some bounds are also presented. Finally, an
application of this extropy to pattern recognition is demonstrated.


Abstract:
  The near threshold photo or electroproduction of heavy vector quarkonium off
the proton is studied in quantum chromodynamics. Similar to the high-energy
limit, the production amplitude can be factorized in terms of gluonic
Generalized Parton Distributions and the quarkonium distribution amplitude. At
the threshold, the threshold kinematics has a large skewness parameter $\xi$,
leading to the dominance of the spin-2 contribution over higher-spin twist-2
operators. Thus threshold production data are useful to extract the gluonic
gravitational form factors, allowing studying the gluonic contributions to the
quantum anomalous energy, mass radius, spin and mechanical pressure in the
proton. We use the recent GlueX data on the $J/\psi$ photoproduction to
illustrate the potential physics impact from the high-precision data from
future JLab 12 GeV and EIC physics program.


Abstract:
  Electron microscopy (EM) enables the reconstruction of neural circuits at the
level of individual synapses, which has been transformative for scientific
discoveries. However, due to the complex morphology, an accurate reconstruction
of cortical axons has become a major challenge. Worse still, there is no
publicly available large-scale EM dataset from the cortex that provides dense
ground truth segmentation for axons, making it difficult to develop and
evaluate large-scale axon reconstruction methods. To address this, we introduce
the AxonEM dataset, which consists of two 30x30x30 um^3 EM image volumes from
the human and mouse cortex, respectively. We thoroughly proofread over 18,000
axon instances to provide dense 3D axon instance segmentation, enabling
large-scale evaluation of axon reconstruction methods. In addition, we densely
annotate nine ground truth subvolumes for training, per each data volume. With
this, we reproduce two published state-of-the-art methods and provide their
evaluation results as a baseline. We publicly release our code and data at
https://connectomics-bazaar.github.io/proj/AxonEM/index.html to foster the
development of advanced methods.


Abstract:
  The negative sign of the anomalous Nernst thermopower ($S_\text{ANE}$)
observed in Mn-Ga ordered alloys is an attractive property for thermoelectric
applications exploiting the anomalous Nernst effect (ANE); however, its origin
has not been clarified. In this study, to gain insight into the negative
$S_\text{ANE}$, we prepared epitaxial thin films of Mn$_{x}$Ga$_{100-x}$ with
$x$ ranging from 56.2 to 71.7, and systematically investigated the structural,
magnetic, and transport properties including the anomalous Hall effect (AHE)
and the ANE. The measured $S_\text{ANE}$ is negative for all samples and shows
close to one order of magnitude difference among different compositions.
Together with the measured transport properties, we were able to separate the
two different contributions of the ANE, i.e., one originating from the
transverse thermoelectric coefficient ($\alpha_{xy}$), and the other one
originating from the AHE acting on the longitudinal carrier flow induced by the
Seebeck effect. Both contributions are found to be negative for all samples,
while the experimentally obtained negative $\alpha_{xy}$ exhibits a monotonic
increase towards zero with increasing $x$, which is consistent with the
tendency indicated by first-principles calculations. Our results show that the
large difference in the negative $S_\text{ANE}$ is mostly attributed to
$\alpha_{xy}$, and thus shed light on further enhancement of the ANE in
Mn-based ordered alloys.


Abstract:
  The goal of this paper is to present a theoretical and practical introduction
to generalized eigendecomposition (GED), which is a robust and flexible
framework used for dimension reduction and source separation in multichannel
signal processing. In cognitive electrophysiology, GED is used to create
spatial filters that maximize a researcher-specified contrast, such as relative
spectral power or experiment condition differences. GED is fast and easy to
compute, performs well in simulated and real data, and is easily adaptable to a
variety of specific research goals. This paper introduces GED in a way that
ties together myriad individual publications and applications of GED in
electrophysiology, and provides sample MATLAB and Python code that can be
tested and adapted. Practical considerations and issues that often arise in
applications are discussed.


Abstract:
  Thin-film optical filters can nowadays be integrated onto pixels of
commercial image sensors used for spectral imaging. A drawback of having more
filters on an image sensor is a loss in spatial resolution which could be
regained by using smaller pixels. However, this work shows that miniaturization
causes a reduction in transmittance and requires a trade-off between pixel size
and filter bandwidth. This is caused by diffraction and the reduced number of
reflections in the filter. Analytical wave and ray optics models are developed
and used for fast transmittance prediction and this is validated using a
commercial spectral sensor and numerical simulations. The identified trade-off
is an intrinsic limitation of pixel-integrated thin-film filter technology and
the developed models will be useful tools for exploring new hardware and
computational solutions.


Abstract:
  Topological insulator lasers (TILs) are a recently introduced family of
lasing arrays in which phase locking is achieved through synthetic gauge
fields. These single frequency light source arrays operate in the spatially
extended edge modes of topologically non-trivial optical lattices. Because of
the inherent robustness of topological modes against perturbations and defects,
such topological insulator lasers tend to demonstrate higher slope efficiencies
as compared to their topologically trivial counterparts. So far, magnetic and
non-magnetic optically pumped topological laser arrays as well as electrically
pumped TILs that are operating at cryogenic temperatures have been
demonstrated. Here we present the first room temperature and electrically
pumped topological insulator laser. This laser array, using a structure that
mimics the quantum spin Hall effect for photons, generates light at telecom
wavelengths and exhibits single frequency emission. Our work is expected to
lead to further developments in laser science and technology, while opening up
new possibilities in topological photonics.


Abstract:
  The unprecedented behavioural responses of societies have been evidently
shaping the COVID-19 pandemic, yet it is a significant challenge to accurately
monitor the continuously changing social mixing patterns in real-time. Contact
matrices, usually stratified by age, summarise interaction motifs efficiently,
but their collection relies on conventional representative survey techniques,
which are expensive and slow to obtain. Here we report a data collection effort
involving over $2.3\%$ of the Hungarian population to simultaneously record
contact matrices through a longitudinal online and sequence of representative
phone surveys. To correct non-representative biases characterising the online
data, by using census data and the representative samples we develop a
reconstruction method to provide a scalable, cheap, and flexible way to
dynamically obtain closer-to-representative contact matrices. Our results
demonstrate the potential of combined online-offline data collections to
understand the changing behavioural responses determining the future evolution
of the outbreak, and inform epidemic models with crucial data.


Abstract:
  Star-forming galaxies (SFGs) emit non-thermal radiation from radio to
gamma-rays. We aim to investigate the main mechanisms of global CR transport
and cooling in SFGs. The way they contribute in shaping the relations between
non-thermal luminosities and SFR could shed light onto their nature. We develop
a model to compute the CR populations of SFGs, taking into account their
production, transport, and cooling. The model is parameterised only through
global galaxy properties, and describes the non-thermal emission in both radio
and gamma-rays. We focus on the role of diffusive and advective transport by
galactic winds, either driven by turbulent or thermal instabilities. We compare
model predictions to observations, for which we compile a homogeneous set of
luminosities in these radio bands, and update those available in gamma-rays.
Our model reproduces reasonably well the observed relations between the
gamma-ray or 1.4 GHz radio luminosities and the SFR, assuming a single
power-law scaling of the magnetic field with the latter with index beta=0.3,
and winds blowing either at Alfvenic speeds or typical starburst wind
velocities. Escape of CR is negligible for > 30 Mo/yr. A constant ionisation
fraction of the interstellar medium fails to reproduce the 150 MHz radio
luminosity throughout the whole SFR range. Our results reinforce the idea that
galaxies with high SFR are CR calorimeters, and that the main mechanism driving
proton escape is diffusion, whereas electron escape also proceeds via wind
advection. They also suggest that these winds should be CR or thermally-driven
at low and intermediate SFR, respectively. Our results globally support that
magnetohydrodynamic turbulence is responsible for the dependence of the
magnetic field strength on the SFR and that the ionisation fraction is strongly
disfavoured to be constant throughout the whole SFR range.


Abstract:
  Link Prediction, addressing the issue of completing KGs with missing facts,
has been broadly studied. However, less light is shed on the ubiquitous
hyper-relational KGs. Most existing hyper-relational KG embedding models still
tear an n-ary fact into smaller tuples, neglecting the indecomposability of
some n-ary facts. While other frameworks work for certain arity facts only or
ignore the significance of primary triple. In this paper, we represent an n-ary
fact as a whole, simultaneously keeping the integrity of n-ary fact and
maintaining the vital role that the primary triple plays. In addition, we
generalize hyperbolic Poincar\'e embedding from binary to arbitrary arity data,
which has not been studied yet. To tackle the weak expressiveness and high
complexity issue, we propose HYPER^2 which is qualified for capturing the
interaction between entities within and beyond triple through information
aggregation on the tangent space. Extensive experiments demonstrate HYPER^2
achieves superior performance to its translational and deep analogues,
improving SOTA by up to 34.5\% with relatively few dimensions. Moreover, we
study the side effect of literals and we theoretically and experimentally
compare the computational complexity of HYPER^2 against several best performing
baselines, HYPER^2 is 49-61 times quicker than its counterparts.


Abstract:
  All global symmetries are expected to be explicitly broken by quantum
gravitational effects, and yet may play an important role in Particle Physics
and Cosmology. As such, any evidence for a well-preserved global symmetry would
give insight into an important feature of gravity. We argue that a recently
reported $2.4\sigma$ detection of cosmic birefringence in the Cosmic Microwave
Background could be the first observational indication of a well-preserved
(although spontaneously broken) global symmetry in nature. A compelling
solution to explain this measurement is a very light pseudoscalar field that
interacts with electromagnetism. In order for gravitational effects not to lead
to large corrections to the mass of this scalar field, we show that the
breaking of global symmetries by gravity should be bounded above. Finally, we
highlight that any bound of this type would have clear implications for the
construction of theories of quantum gravity, as well as for many particle
physics scenarios.


Abstract:
  Intrusion Detection Systems (IDS) are now an essential element when it comes
to securing computers and networks. Despite the huge research efforts done in
the field, handling sources' reliability remains an open issue. To address this
problem, this paper proposes a novel contextual discounting method based on
sources' reliability and their distinguishing ability between normal and
abnormal behavior. Dempster-Shafer theory, a general framework for reasoning
under uncertainty, is used to construct an evidential classifier. The NSL-KDD
dataset, a significantly revised and improved version of the existing KDDCUP'99
dataset, provides the basis for assessing the performance of our new detection
approach. While giving comparable results on the KDDTest+ dataset, our approach
outperformed some other state-of-the-art methods on the KDDTest-21 dataset
which is more challenging.


Abstract:
  We study the fragmentation of He$_2$ dimers into He$^+$ ions by relativistic
highly-charged projectiles. It is demonstrated that the interaction between an
ultrafast projectile with an extremely extended object -- the helium dimer --
possesses interesting features which are absent in collisions with "normal"
molecules. It is also shown that such projectiles, due to their enormous
interaction range, can accurately probe the ground state of the dimer and even
be used for a precise determination of its binding energy.


Abstract:
  Let $E$ be a holomorphic vector bundle over a compact K\"{a}hler manifold
$(X,\omega)$ with negative sectional curvature $sec\leq -K<0$, $\Delta_{E}$ be
the Chern connection on $E$. In this article we show that if
$C:=|[\Lambda,i\Theta(E)]|\leq c_{n}K$, then $(X,E)$ satisfy a family of Chern
number inequalities. The main idea in our proof is study the $L^{2}$
$\bar{\partial}_{\tilde{E}}$-harmonic forms on lifting bundle $\tilde{E}$ over
the universal covering space $\tilde{X}$. We also observe that there is a
closely relationship between the eigenvalue of the Laplace-Beltrami operator
$\Delta_{\bar{\partial}_{\tilde{E}}}$ and the Euler characteristic of $X$.
Precisely, if there is a line bundle $L$ on $X$ such that
$\chi^{p}(X,L^{\otimes m})$ is not constant for some integers $p\in[0,n]$, then
the Euler characteristic of $X$ satisfies $(-1)^{n}\chi(X)\geq
(n+1)+\lfloor\frac{c_{n}K}{2nC} \rfloor$.


Abstract:
  Music information is often conveyed or recorded across multiple data
modalities including but not limited to audio, images, text and scores.
However, music information retrieval research has almost exclusively focused on
single modality recognition, requiring development of separate models for each
modality. Some multi-modal works require multiple coexisting modalities given
to the model as inputs, constraining the use of these models to the few cases
where data from all modalities are available. To the best of our knowledge, no
existing model has the ability to take inputs from varying modalities, e.g.
images or sounds, and classify them into unified music categories. We explore
the use of cross-modal retrieval as a pretext task to learn modality-agnostic
representations, which can then be used as inputs to classifiers that are
independent of modality. We select instrument classification as an example task
for our study as both visual and audio components provide relevant semantic
information. We train music instrument classifiers that can take both images or
sounds as input, and perform comparably to sound-only or image-only
classifiers. Furthermore, we explore the case when there is limited labeled
data for a given modality, and the impact in performance by using labeled data
from other modalities. We are able to achieve almost 70% of best performing
system in a zero-shot setting. We provide a detailed analysis of experimental
results to understand the potential and limitations of the approach, and
discuss future steps towards modality-agnostic classifiers.


Abstract:
  One of the most celebrated contributions to the study of the mechanical
behavior of materials is due to J.D. Eshelby, who in the late 50s
revolutionized our understanding of the elastic stress and strain fields due to
an ellipsoidal inclusion/inhomogeneity that undergoes a transformation of shape
and size. While Eshelby's work laid the foundation for significant advancements
in various fields, including fracture mechanics, theory of phase transitions,
and homogenization methods, its extension into the range of large deformations,
and to situations in which the material can actively reorganize in response to
the finite transformation strain, is in a nascent state. Beyond the theoretical
difficulties imposed by highly nonlinear material response, a major hindrance
has been the absence of experimental observations that can elucidate the
intricacies that arise in this regime. To address this limitation, our
experimental observations reveal the key morphogenesis steps of Vibrio cholerae
biofilms embedded in hydrogels, as they grow by four orders of magnitude from
their initial size. Using the biofilm growth as a case study, our theoretical
model considers various growth scenarios and employs two different and
complimentary methods -- a minimal analytical model and finite element
computations -- to obtain approximate equilibrium solutions. A particular
emphasis is put on determining the natural growth path of an inclusion that
optimizes its shape in response to the confinement, and the onset of damage in
the matrix, which together explain the observed behavior of biofilms. Beyond
bacterial biofilms, this work sheds light on the role of mechanics in
determining the morphogenesis pathways of confined growing bodies and thus
applies to a broad range of phenomena that are ubiquitous in both natural and
engineered material systems.


Abstract:
  In this paper we define the algebraic entropy test for face-centered quad
equations, which are equations defined on vertices of a quadrilateral plus an
additional interior vertex. This notion of algebraic entropy is applied to a
recently introduced class of these equations that satisfy a new form of
multidimensional consistency called consistency-around-a-face-centered-cube
(CAFCC), whereby the system of equations is consistent on a face-centered cubic
unit cell. It is found that for certain arrangements of equations (or pairs of
equations) in the square lattice, all known CAFCC equations pass the algebraic
entropy test possessing either quadratic or linear growth.


Abstract:
  Stress and driving are a dangerous combination which can lead to crashes, as
evidenced by the large number of road traffic crashes that involve stress.
Motivated by the need to address the significant costs of driver stress, it is
essential to build a practical system that can classify driver stress level
with high accuracy. However, the performance of an accurate driving stress
levels classification system depends on hyperparameter optimization choices
such as data segmentation (windowing hyperparameters). The configuration
setting of hyperparameters, which has an enormous impact on the system
performance, are typically hand-tuned while evaluating the algorithm. This
tuning process is time consuming and often depends on personal experience.
There are also no generic optimal values for hyperparameters values. In this
work, we propose a meta-heuristic approach to support automated hyperparameter
optimization and provide a real-time driver stress detection system. This is
the first systematic study of optimizing windowing hyperparameters based on
Electrocardiogram (ECG) signal in the domain of driving safety. Our approach is
to propose a framework based on Particle Swarm Optimization algorithm (PSO) to
select an optimal/near optimal windowing hyperparameters values. The
performance of the proposed framework is evaluated on two datasets: a public
dataset (DRIVEDB dataset) and our collected dataset using an advanced
simulator. DRIVEDB dataset was collected in a real time driving scenario, and
our dataset was collected using an advanced driving simulator in the control
environment. We demonstrate that optimising the windowing hyperparameters
yields significant improvement in terms of accuracy. The most accurate built
model applied to the public dataset and our dataset, based on the selected
windowing hyperparameters, achieved 92.12% and 77.78% accuracy, respectively.


Abstract:
  This paper investigates the quadratic irrationals that arise as periodic
points of the Gauss type shift associated to the odd continued fraction
expansion (Schweiger). It is shown that these numbers, which we call O-reduced,
are equidistributed with respect to the Lebesgue absolutely continuous
invariant probability measure of the Odd Gauss shift.


Abstract:
  We calculate the energy levels and corresponding eigenstates of an
interacting scalar quantum field theory on a lattice using a
continuous-variable version of the quantum imaginary time evolution algorithm.
Only a single qumode is needed for the simulation of the field at each point on
the lattice. Our quantum algorithm avoids the use of non-Gaussian quantum gates
and relies, instead, on detectors projecting onto eigenstates of the
photon-number operator. Using Xanadu's Strawberry Fields simulator, we obtain
results on energy levels that are in very good agreement with results from
exact calculations. We propose an experimental setup that can be realized with
existing technology.


Abstract:
  Current computational-emotion research has focused on applying acoustic
properties to analyze how emotions are perceived mathematically or used in
natural language processing machine learning models. While recent interest has
focused on analyzing emotions from the spoken voice, little experimentation has
been performed to discover how emotions are recognized in the singing voice --
both in noiseless and noisy data (i.e., data that is either inaccurate,
difficult to interpret, has corrupted/distorted/nonsense information like
actual noise sounds in this case, or has a low ratio of usable/unusable
information). Not only does this ignore the challenges of training machine
learning models on more subjective data and testing them with much noisier
data, but there is also a clear disconnect in progress between advancing the
development of convolutional neural networks and the goal of emotionally
cognizant artificial intelligence. By training a new model to include this type
of information with a rich comprehension of psycho-acoustic properties, not
only can models be trained to recognize information within extremely noisy
data, but advancement can be made toward more complex biofeedback applications
-- including creating a model which could recognize emotions given any human
information (language, breath, voice, body, posture) and be used in any
performance medium (music, speech, acting) or psychological assistance for
patients with disorders such as BPD, alexithymia, autism, among others. This
paper seeks to reflect and expand upon the findings of related research and
present a stepping-stone toward this end goal.


Abstract:
  The present paper is a follow-up of our previous paper that derives a
slightly simplified model equation for the Klein-Gordon equation, describing
the propagation of a scalar field of mass $\mu$ in the background of a rotating
black hole and, among others, supports the instability of the field down to
$a/M \approx 0.97$. The latter result was derived numerically. This paper gives
corresponding rigorous results, supporting instability of the field down to
$a/M \approx 0.979796$.


Abstract:
  Building neural network classifiers with an ability to distinguish between in
and out-of distribution inputs is an important step towards faithful deep
learning systems. Some of the successful approaches for this, resort to
architectural novelties, such as ensembles, with increased complexities in
terms of the number of parameters and training procedures. Whereas some other
approaches make use of surrogate samples, which are easy to create and work as
proxies for actual out-of-distribution (OOD) samples, to train the networks for
OOD detection. In this paper, we propose a very simple approach for enhancing
the ability of a pretrained network to detect OOD inputs without even altering
the original parameter values. We define a probabilistic trust interval for
each weight parameter of the network and optimize its size according to the
in-distribution (ID) inputs. It allows the network to sample additional weight
values along with the original values at the time of inference and use the
observed disagreement among the corresponding outputs for OOD detection. In
order to capture the disagreement effectively, we also propose a measure and
establish its suitability using empirical evidence. Our approach outperforms
the existing state-of-the-art methods on various OOD datasets by considerable
margins without using any real or surrogate OOD samples. We also analyze the
performance of our approach on adversarial and corrupted inputs such as
CIFAR-10-C and demonstrate its ability to clearly distinguish such inputs as
well. By using fundamental theorem of calculus on neural networks, we explain
why our technique doesn't need to observe OOD samples during training to
achieve results better than the previous works.


Abstract:
  A path system $\mathcal{P}$ in a graph $G=(V,E)$ is said to be irreducible if
there does not exist a partition $V= A\sqcup B$ such that $\mathcal{P}$
restricts to a path system on both $G[A]$ and $G[B]$. In this paper, we
construct an infinite family of non-metrizable irreducible path systems defined
on certain Paley graphs.


Abstract:
  Moir\'e superlattices in van der Waals heterostructures are gaining
increasing attention because they offer new opportunities to tailor and explore
unique electronic phenomena when stacking 2D materials with small twist angles.
Here, we reveal local surface potentials associated with stacking domains in
twisted double bilayer graphene (TDBG) moir\'e superlattices. Using a
combination of both lateral Piezoresponse Force Microscopy (LPFM) and Scanning
Kelvin Probe Microscopy (SKPM), we distinguish between Bernal (ABAB) and
rhombohedral (ABCA) stacked graphene and directly correlate these stacking
configurations with local surface potential. We find that the surface potential
of the ABCA domains is ~15 mV higher (smaller work function) than that of the
ABAB domains. First-principles calculations based on density functional theory
further show that the different work functions between ABCA and ABAB domains
arise from the stacking dependent electronic structure. We show that, while the
moir\'e superlattice visualized by LPFM can change with time, imaging the
surface potential distribution via SKPM appears more stable, enabling the
mapping of ABAB and ABCA domains without tip-sample contact-induced effects.
Our results provide a new means to visualize and probe local domain stacking in
moir\'e superlattices along with its impact on electronic properties.


Abstract:
  Many techniques have been proposed for quickly detecting and containing
malware-generated network attacks such as large-scale denial of service
attacks; unfortunately, much damage is already done within the first few
minutes of an attack, before it is identified and contained. There is a need
for an early warning system that can predict attacks before they actually
manifest, so that upcoming attacks can be prevented altogether by blocking the
hosts that are likely to engage in attacks. However, blocking responses may
disrupt legitimate processes on blocked hosts; in order to minimise user
inconvenience, it is important to also foretell the time when the predicted
attacks will occur, so that only the most urgent threats result in
auto-blocking responses, while less urgent ones are first manually
investigated. To this end, we identify a typical infection sequence followed by
modern malware; modelling this sequence as a Markov chain and training it on
real malicious traffic, we are able to identify behaviour most likely to lead
to attacks and predict 98\% of real-world spamming and port-scanning attacks
before they occur. Moreover, using a Semi-Markov chain model, we are able to
foretell the time of upcoming attacks, a novel capability that allows
accurately predicting the times of 97% of real-world malware attacks. Our work
represents an important and timely step towards enabling flexible threat
response models that minimise disruption to legitimate users.


Abstract:
  A classic application of description length is for model selection with the
minimum description length (MDL) principle. The focus of this paper is to
extend description length for data analysis beyond simple model selection and
sequences of scalars. More specifically, we extend the description length for
data analysis in Gaussian graphical models. These are powerful tools to model
interactions among variables in a sequence of i.i.d Gaussian data in the form
of a graph. Our method uses universal graph coding methods to accurately
account for model complexity, and therefore provide a more rigorous approach
for graph model selection. The developed method is tested with synthetic and
electrocardiogram (ECG) data to find the graph model and anomaly in Gaussian
graphical models. The experiments show that our method gives better performance
compared to commonly used methods.


Abstract:
  In this article we have conducted a reanalysis of the phase III aducanumab
(ADU) summary statistics announced by Biogen, in particular the result of the
Clinical Dementia Rating-Sum of Boxes (CDR-SB). The results showed that the
evidence on the efficacy of the drug is very low and a more clearer view of the
results of clinical trials are presented in the Bayesian framework that can be
useful for future development and research in the field.


Abstract:
  Fuzzy dark matter (FDM) is an attractive dark matter candidate motivated by
small scale problems in astrophysics and with a rich phenomenology on those
scales. We scrutinize the FDM model, more specifically the mass of the FDM
particle, through a dynamical analysis for the Galactic ultra-faint dwarf (UFD)
galaxies. We use a sample of 18 UFDs to place the strongest constraints to date
on the mass of the FDM particle, updating on previous bounds using a subset of
the sample used here. We find that most of the sample UFDs prefer a FDM
particle mass heavier than $10^{-21}\mathrm{eV}$. In particular, Segue 1
provides the strongest constraint, with
$m_\psi=1.1^{+8.3}_{-0.7}\times10^{-19}\mathrm{eV}$. The constraints found here
are the first that are compatible with various other independent cosmological
and astrophysical bounds found in the literature, in particular with the latest
bounds using the Lyman-$\alpha$ forest. We also find that the constraints
obtained in this work are not compatible with the bounds from luminous dwarf
galaxies, as already pointed out in the previous work using UFDs. This could
indicate that although a viable dark matter model, it might be challenging for
the FDM model to solve the small scale problems.


Abstract:
  We demonstrate that a quantum annealer can be used to solve the NP-complete
problem of graph partitioning into subgraphs containing Hamiltonian cycles of
constrained length. We present a method to find a partition of a given directed
graph into Hamiltonian subgraphs with three or more vertices, called vertex
3-cycle cover. We formulate the problem as a quadratic unconstrained binary
optimisation and run it on a D-Wave Advantage quantum annealer. We test our
method on synthetic graphs constructed by adding a number of random edges to a
set of disjoint cycles. We show that the probability of solution is independent
of the cycle length, and a solution is found for graphs up to 4000 vertices and
5200 edges, close to the number of physical working qubits available on the
quantum annealer.


Abstract:
  We study the link between lobbying and industrial concentration. Using data
for the past 20 years in the US, we show how lobbying increases when an
industry becomes more concentrated, using mergers as shocks to concentration.
This holds true both for expenditures on federal lobbying as well as
expenditures on campaign contributions. Results are in line with the
predictions of a model where lobbying is akin to a public good for incumbents,
and thus typically underprovided, while a merger solves the coordination
problem.


Abstract:
  Most data is automatically collected and only ever "seen" by algorithms. Yet,
data compressors preserve perceptual fidelity rather than just the information
needed by algorithms performing downstream tasks. In this paper, we
characterize the bit-rate required to ensure high performance on all predictive
tasks that are invariant under a set of transformations, such as data
augmentations. Based on our theory, we design unsupervised objectives for
training neural compressors. Using these objectives, we train a generic image
compressor that achieves substantial rate savings (more than $1000\times$ on
ImageNet) compared to JPEG on 8 datasets, without decreasing downstream
classification performance.


Abstract:
  Approximate Dynamic Programming (ADP) is a methodology to solve multi-stage
stochastic optimization problems in multi-dimensional discrete or continuous
spaces. ADP approximates the optimal value function by adaptively sampling both
action and state space. It provides a tractable approach to very large
problems, but can suffer from the exploration-exploitation dilemma. We propose
a novel approach for selecting actions using importance sampling weighted by
the value function approximation in continuous decision spaces to address this
dilemma. An advantage of this approach is it balances exploration and
exploitation without any tuning parameters when sampling actions compared to
other exploration approaches such as Epsilon Greedy, instead relying only on
the approximate value function. We compare the proposed algorithm with other
exploration strategies in continuous action space in the context of a
multi-stage generation expansion planning problem under uncertainty.


Abstract:
  Network embedding has been widely used in social recommendation and network
analysis, such as recommendation systems and anomaly detection with graphs.
However, most of previous approaches cannot handle large graphs efficiently,
due to that (i) computation on graphs is often costly and (ii) the size of
graph or the intermediate results of vectors could be prohibitively large,
rendering it difficult to be processed on a single machine. In this paper, we
propose an efficient and effective distributed algorithm for network embedding
on large graphs using Apache Spark, which recursively partitions a graph into
several small-sized subgraphs to capture the internal and external structural
information of nodes, and then computes the network embedding for each subgraph
in parallel. Finally, by aggregating the outputs on all subgraphs, we obtain
the embeddings of nodes in a linear cost. After that, we demonstrate in various
experiments that our proposed approach is able to handle graphs with billions
of edges within a few hours and is at least 4 times faster than the
state-of-the-art approaches. Besides, it achieves up to $4.25\%$ and $4.27\%$
improvements on link prediction and node classification tasks respectively. In
the end, we deploy the proposed algorithms in two online games of Tencent with
the applications of friend recommendation and item recommendation, which
improve the competitors by up to $91.11\%$ in running time and up to $12.80\%$
in the corresponding evaluation metrics.


Abstract:
  Practical learning-based autonomous driving models must be capable of
generalizing learned behaviors from simulated to real domains, and from
training data to unseen domains with unusual image properties. In this paper,
we investigate transfer learning methods that achieve robustness to domain
shifts by taking advantage of the invariance of spatio-temporal features across
domains. In this paper, we propose a transfer learning method to improve
generalization across domains via transfer of spatio-temporal features and
salient data augmentation. Our model uses a CNN-LSTM network with Inception
modules for image feature extraction. Our method runs in two phases: Phase 1
involves training on source domain data, while Phase 2 performs training on
target domain data that has been supplemented by feature maps generated using
the Phase 1 model. Our model significantly improves performance in unseen test
cases for both simulation-to-simulation transfer as well as simulation-to-real
transfer by up to +37.3\% in test accuracy and up to +40.8\% in steering angle
prediction, compared to other SOTA methods across multiple datasets.


Abstract:
  In this article we study a coarse version of the $K$-theoretic Farrell--Jones
conjecture we call coarse or bounded isomorphism conjecture. Using controlled
category theory we are able to translate this conjecture for asymptotically
faithful covers into a more familiar form. This allows us to prove the
conjecture for box spaces of residually finite groups whose Farrell--Jones
assembly map with coefficients is an isomorphism.


Abstract:
  This article presents stability and convergence analyses of subgrid
multiscale stabilized finite element formulation of non-Newtonian power-law
fluid flow model strongly coupled with variable coefficients
Advection-Diffusion-Reaction ($VADR$) equation. Considering the highly
non-linear viscosity coefficient as solute concentration dependent makes the
coupling two way. The stabilized formulation of the transient coupled system is
developed based upon time dependent subscales, which ensures inherent
consistency of the method. The proposed algebraic expressions of the
stabilization parameters appropriately shape up the apriori and aposteriori
error estimates. Both the shear thinning and shear thickening properties,
indicated by different power-law indices are properly highlighted in
theoretical derivations as well as in numerical validations. The numerical
experiments carried out for different combinations of small and large Reynolds
numbers and power-law indices establish far better performance of time
dependent $ASGS$ method in approximating the solution of this coupled system
for all the cases over the other well known stabilized finite element methods.


Abstract:
  We present a novel approach for time series classification where we represent
time series data as plot images and feed them to a simple CNN, outperforming
several state-of-the-art methods. We propose a simple and highly replicable way
of plotting the time series, and feed these images as input to a non-optimized
shallow CNN, without any normalization or residual connections. These
representations are no more than default line plots using the time series data,
where the only pre-processing applied is to reduce the number of white pixels
in the image. We compare our method with different state-of-the-art methods
specialized in time series classification on two real-world non public
datasets, as well as 98 datasets of the UCR dataset collection. The results
show that our approach is very promising, achieving the best results on both
real-world datasets and matching / beating the best state-of-the-art methods in
six UCR datasets. We argue that, if a simple naive design like ours can obtain
such good results, it is worth further exploring the capabilities of using
image representation of time series data, along with more powerful CNNs, for
classification and other related tasks.


Abstract:
  Theoretical predictions of top-quark cross sections depend on the values of
the parameters of the quantum chromodynamics Lagrangian, such as the strong
coupling constant and the mass of the top quark, but also on parameters of the
electroweak sector of the standard model and parton distribution functions.
Comparisons between state-of-the-art calculations and recent measurements at
the ATLAS and CMS experiments at the CERN LHC allow for the precise
determination of these parameters. In this proceeding, the most recent results
by the two collaborations are summarized and discussed.


Abstract:
  We adapt the argument of Dodson-Murphy to give a simple proof of scattering
below the ground state for the intercritical inhomogeneous nonlinear
Schr\"odinger equation. The decaying factor in the nonlinearity obviates the
need for a radial assumption.


Abstract:
  We consider a class of inhomogeneous self-similar cosmological models in
which the perfect fluid flow is tangential to the orbits of a three-parameter
similarity group. We restrict the similarity group to possess both an Abelian
$G_{2}$, and a single hypersurface orthogonal Killing vector field, and we
restrict the fluid flow to be orthogonal to the orbits of the Abelian $G_{2}$.
The temporal evolution of the models is forced to be power law, due to the
similarity group, and the Einstein field equations reduce to a
three-dimensional autonomous system of ordinary differential equations which is
qualitatively analysed in order to determine the spatial structure of the
models. The existence of two classes of well-behaved models is demonstrated.
The first of these is asymptotically spatially homogeneous and matter
dominated, and the second is vacuum dominated and either asymptotically
spatially homogeneous or acceleration dominated, at large spatial distances.


Abstract:
  In dimension two, we investigate a free energy and the ground state energy of
the Schr\"odinger-Poisson system coupled with a logarithmic nonlinearity in
terms of underlying functional inequalities which take into account the scaling
invariances of the problem. Such a system can be considered as a nonlinear
Schr\"odinger equation with a cubic but nonlocal Poisson nonlinearity, and a
local logarithmic nonlinearity. Both cases of repulsive and attractive forces
are considered. We also assume that there is an external potential with minimal
growth at infinity, which turns out to have a logarithmic growth. Our estimates
rely on new logarithmic interpolation inequalities which combine logarithmic
Hardy-Littlewood-Sobolev and logarithmic Sobolev inequalities. The
two-dimensional model appears as a limit case of more classical problems in
higher dimensions.


Abstract:
  Training deep neural models in the presence of corrupted supervision is
challenging as the corrupted data points may significantly impact the
generalization performance. To alleviate this problem, we present an efficient
robust algorithm that achieves strong guarantees without any assumption on the
type of corruption and provides a unified framework for both classification and
regression problems. Unlike many existing approaches that quantify the quality
of the data points (e.g., based on their individual loss values), and filter
them accordingly, the proposed algorithm focuses on controlling the collective
impact of data points on the average gradient. Even when a corrupted data point
failed to be excluded by our algorithm, the data point will have a very limited
impact on the overall loss, as compared with state-of-the-art filtering methods
based on loss values. Extensive experiments on multiple benchmark datasets have
demonstrated the robustness of our algorithm under different types of
corruption.


Abstract:
  We present a method for assessing the sensitivity of the true causal effect
to unmeasured confounding. The method requires the analyst to set two intuitive
parameters. Otherwise, the method is assumption-free. The method returns an
interval that contains the true causal effect, and whose bounds are sharp, i.e.
attainable. We show experimentally that our bounds can be sharper than those
obtained by the method of Ding and VanderWeele (2016a) which, moreover,
requires to set one more parameter than our method. Finally, we extend our
method to bound the natural direct and indirect effects when there are measured
mediators and unmeasured exposure-outcome confounding.


Abstract:
  Suppose $G$ is a finite group acting on an Abelian variety $A$ such that the
coarse moduli $A/G$ is smooth. Using the recent classification result due to
Auffarth, Lucchini, and Quezada, we construct a motivic semiorthogonal
decomposition for $\mathcal{D}[A/G]$ provided $G = T\rtimes H$ with $T$ a
subgroup of translations and $H$ is a subgroup of group automorphisms.


Abstract:
  It is shown that the Cotton tensor can describe the effects of gravity beyond
general relativity. Any solution of the Einstein equations with or without the
cosmological constant satisfies the field equations described by the Cotton
tensor. It implies that the cosmological constant is an integration constant. A
vacuum of a theory is represented by the vanishing of the Cotton tensor, rather
than the vanishing of the Ricci tensor. An exact Schwarzschild-like solution
for a static and spherically symmetric source is discovered. Although the field
equations involve the third order of derivative, it is found that they reduce
to the second-order differential equations due to a variational principle.


Abstract:
  Nanostructures can be used for boosting the light outcoupling of color
centers in diamond; however, the fiber coupling performance of these
nanostructures is rarely investigated. Here, we use a finite element method for
computing the emission from color centers in inverted nanocones and the overlap
of this emission with the propagation mode in a single-mode fiber. Using
different figures of merit, the inverted nanocone parameters are optimized to
obtain maximal fiber coupling efficiency, free-space collection efficiency, or
rate enhancement. The optimized inverted nanocone designs show promising
results with 66% fiber coupling or 83% free-space coupling efficiency at the
tin-vacancy center zero-phonon line wavelength of 619 nm. Moreover, when
evaluated for broadband performance, the optimized designs show 55% and 76% for
fiber coupling and free-space efficiencies respectively, for collecting the
full tin-vacancy emission spectrum at room temperature. An analysis of
fabrication insensitivity indicates that these nanostructures are robust
against imperfections. For maximum emission rate into a fiber mode, a design
with a Purcell factor of 2.34 is identified. Finally, possible improvements
offered by a hybrid inverted nanocone, formed by patterning into two different
materials, are investigated, and increases the achievable fiber coupling
efficiency to 71%.


Abstract:
  Many popular models from the networks literature can be viewed through a
common lens. We describe it here and call the class of models log-linear ERGMs.
It includes degree-based models, stochastic blockmodels, and combinations of
these. Given the interest in combined node and block effects in network
formation mechanisms, we introduce a general directed relative of the
degree-corrected stochastic blockmodel: an exponential family model we call
$p_1$-SBM. It is a generalization of several well-known variants of the
blockmodel.
  We study the problem of testing model fit for the log-linear ERGM class.
  The model fitting approach we take, through the use of quick estimation
algorithms borrowed from the contingency table literature and effective
sampling methods rooted in graph theory and algebraic statistics, results in an
exact test whose $p$-value can be approximated efficiently in networks of
moderate sizes.
  We showcase the performance of the method on two data sets from biology: the
connectome of \emph{C. elegans} and the interactome of \emph{Arabidopsis
thaliana}. These two networks, a neuronal network and a protein-protein
interaction network, have been popular examples in the network science
literature, but a model-based approach to studying them has been missing thus
far.


Abstract:
  Artificial neural networks (ANNs) have gained significant popularity in the
last decade for solving narrow AI problems in domains such as healthcare,
transportation, and defense. As ANNs become more ubiquitous, it is imperative
to understand their associated safety, security, and privacy vulnerabilities.
Recently, it has been shown that ANNs are susceptible to a number of
adversarial evasion attacks--inputs that cause the ANN to make high-confidence
misclassifications despite being almost indistinguishable from the data used to
train and test the network. This work explores to what degree finding these
examples maybe aided by using side-channel information, specifically switching
power consumption, of hardware implementations of ANNs. A black-box threat
scenario is assumed, where an attacker has access to the ANN hardware's input,
outputs, and topology, but the trained model parameters are unknown. Then, a
surrogate model is trained to have similar functional (i.e. input-output
mapping) and switching power characteristics as the oracle (black-box) model.
Our results indicate that the inclusion of power consumption data increases the
fidelity of the model extraction by up to 30 percent based on a mean square
error comparison of the oracle and surrogate weights. However, transferability
of adversarial examples from the surrogate to the oracle model was not
significantly affected.


Abstract:
  This manuscript explores a new class of non-autonomous second-order
stochastic inclusions of Clarke's subdifferential form with non-instantaneous
impulses (NIIs), unbounded delay and the Rosenblatt process in Hilbert spaces.
The existence of a solution is deduced by employing a fixed point strategy for
a set-valued map together with the evolution operator and stochastic analysis
approach. An example is analyzed for theoretical developments.


Abstract:
  Combined bounds on the Majorana neutrino mass for light and heavy neutrino
exchange mechanisms are derived from current neutrinoless double beta decay
(0{\nu}\b{eta}\b{eta}) search results for a variety of nuclear matrix element
(NME) models. The approach requires self-consistency of a given model to
predict NMEs across different isotopes. The derived bounds are notably stronger
than those from any single experiment and show less model-to-model variation,
highlighting the advantages of using multiple isotopes in such searches.
Projections indicate that the combination of near-term experiments should be
able to probe well into the inverted mass hierarchy region. A method to
visually represent 0{\nu}\b{eta}\b{eta} experimental results is also suggested
to more transparently compare across different isotopes and explicitly track
model dependencies.


Abstract: We show that all longest cycles intersect in 2-connected partial 3-trees.


Abstract:
  Structural materials are broadly used in applications such as nuclear
vessels, high-temperature processes, and civil construction. Usually, during
their placing and lifespan, they may present free or chemically bonded liquid
phases in their structure, demanding careful attention when exposed to high
heating rates. Their behavior in such conditions is a challenging problem as it
comprises numerous highly nonlinear properties (not easily measured via
experimental tests), strongly coupled equations and unreliable experimental
benchmarks. Nonetheless, such simulations are of great interest. This work aims
to provide a numerical study, checking whether its solution indeed converges
and yields reliable results. Additionally, as the model needs several input
parameters, this work conducts a sensitivity analysis and also assesses its
applicability to more complex scenarios, as such issues remain open in the
literature. In order to do that, a simple model that can be easily adapted for
mixed formulations and complex geometries was proposed. It was found out that
when considering unidimensional models the choices regarding the interpolation
of the sorption isotherms are not essential to the numerical stability of the
system. Besides that, the permeability and thermal conductivity of the material
are the most important parameters that affect the simulation results of
pressure, temperature and evaporable water content profiles. Finally, the 2D
mesoscale simulation of concrete with polymeric fibers (based on the mixed
formulation of the problem) yielded results that agreed with experimental
observations. Thus, the model proposed herein can provide a solid base for
future works and also important insights towards simpler methodologies.


Abstract:
  Data-driven, model-free analytics are natural choices for discovery and
forecasting of complex, nonlinear systems. Methods that operate in the system
state-space require either an explicit multidimensional state-space, or, one
approximated from available observations. Since observational data are
frequently sampled with noise, it is possible that noise can corrupt the
state-space representation degrading analytical performance. Here, we evaluate
the synthesis of empirical mode decomposition with empirical dynamic modeling,
which we term empirical mode modeling, to increase the information content of
state-space representations in the presence of noise. Evaluation of a
mathematical, and, an ecologically important geophysical application across
three different state-space representations suggests that empirical mode
modeling may be a useful technique for data-driven, model-free, state-space
analysis in the presence of noise.


Abstract:
  During the COVID-19 epidemic, many health professionals started using mass
communication on social media to relay critical information and persuade
individuals to adopt preventative health behaviors. Our group of clinicians and
nurses developed and recorded short video messages to encourage viewers to stay
home for the Thanksgiving and Christmas Holidays. We then conducted a two-stage
clustered randomized controlled trial in 820 counties (covering 13 States) in
the United States of a large-scale Facebook ad campaign disseminating these
messages. In the first level of randomization, we randomly divided the counties
into two groups: high intensity and low intensity. In the second level, we
randomly assigned zip codes to either treatment or control such that 75% of zip
codes in high intensity counties received the treatment, while 25% of zip codes
in low intensity counties received the treatment. In each treated zip code, we
sent the ad to as many Facebook subscribers as possible (11,954,109 users
received at least one ad at Thanksgiving and 23,302,290 users received at least
one ad at Christmas). The first primary outcome was aggregate holiday travel,
measured using mobile phone location data, available at the county level: we
find that average distance travelled in high-intensity counties decreased by
-0.993 percentage points (95% CI -1.616, -0.371, p-value 0.002) the three days
before each holiday. The second primary outcome was COVID-19 infection at the
zip-code level: COVID-19 infections recorded in the two-week period starting
five days post-holiday declined by 3.5 percent (adjusted 95% CI [-6.2 percent,
-0.7 percent], p-value 0.013) in intervention zip codes compared to control zip
codes.


Abstract:
  We prove some $C^\infty$ and Gevrey well-posedness results for hyperbolic
equations whose coefficients lose regularity at one point.


Abstract:
  Software traceability establishes and leverages associations between diverse
development artifacts. Researchers have proposed the use of deep learning trace
models to link natural language artifacts, such as requirements and issue
descriptions, to source code; however, their effectiveness has been restricted
by availability of labeled data and efficiency at runtime. In this study, we
propose a novel framework called Trace BERT (T-BERT) to generate trace links
between source code and natural language artifacts. To address data sparsity,
we leverage a three-step training strategy to enable trace models to transfer
knowledge from a closely related Software Engineering challenge, which has a
rich dataset, to produce trace links with much higher accuracy than has
previously been achieved. We then apply the T-BERT framework to recover links
between issues and commits in Open Source Projects. We comparatively evaluated
accuracy and efficiency of three BERT architectures. Results show that a
Single-BERT architecture generated the most accurate links, while a
Siamese-BERT architecture produced comparable results with significantly less
execution time. Furthermore, by learning and transferring knowledge, all three
models in the framework outperform classical IR trace models. On the three
evaluated real-word OSS projects, the best T-BERT stably outperformed the VSM
model with average improvements of 60.31% measured using Mean Average Precision
(MAP). RNN severely underperformed on these projects due to insufficient
training data, while T-BERT overcame this problem by using pretrained language
models and transfer learning.


Abstract:
  Understanding the high-tech industrial agglomeration from a spatial-spillover
perspective is essential for cities to gain economic and technological
competitive advantages. Along with rapid urbanization and the development of
fast transportation networks, socioeconomic interactions between cities have
been ever-increasing, traditional spatial metrics are not enough to describe
actual inter-city connections. High-skilled labor flow between cities strongly
influences the high-tech industrial agglomeration, yet receives less attention.
By exploiting unique large-scale datasets and tools from complex network and
data mining, we construct an inter-city high-skilled labor flow network, which
was integrated into spatial econometric models. Our regression results indicate
that spatial-spillover effects exist in the development of high-tech industries
in the Yangtze River Delta Urban Agglomeration region. Moreover, the
spatial-spillover effects are stronger among cities with a higher volume of
high-skilled labor flows than among cities with just stronger geographic
connections. Additionally, we investigate the channels for the spillover
effects and discover that inadequate local government expenses on science and
technology likely hamper the high-tech industrial agglomeration, so does the
inadequate local educational provision. The increasing foreign direct
investments in one city likely encourages the high-tech industrial
agglomeration in other cities because of the policy inertia toward traditional
industries.


Abstract:
  We prove how the universal enveloping algebra constructions for Lie-Rinehart
algebras and anchored Lie algebras are naturally left adjoint functors. This
provides a conceptual motivation for the universal properties these
constructions satisfy. As a supplement, the categorical approach offers new
insights into the definitions of Lie-Rinehart algebra morphisms, of modules
over Lie-Rinehart algebras and of the infinitesimal gauge algebra of a module.


Abstract:
  Analysis-suitable T-splines (AST-splines) are a promising candidate to
achieve a seamless integration between the design and the analysis of
thin-walled structures in industrial settings. In this work, we generalize
AST-splines to allow multiple extraordinary points within the same face. This
generalization drastically increases the flexibility to build geometries using
AST-splines; e.g., much coarser meshes can be generated to represent a certain
geometry. The AST-spline spaces detailed in this work have $C^1$ inter-element
continuity near extraordinary points and $C^2$ inter-element continuity
elsewhere. We mathematically show that AST-splines with multiple extraordinary
points per face are linearly independent and their polynomial basis functions
form a non-negative partition of unity. We numerically show that AST-splines
with multiple extraordinary points per face lead to optimal convergence rates
for second- and fourth-order linear elliptic problems. To illustrate a possible
isogeometric framework that is already available, we design the B-pillar and
the side outer panel of a car using T-splines with the commercial software
Autodesk Fusion360, import the control nets into our in-house code to build
AST-splines, and import the B\'ezier extraction information into the commercial
software LS-DYNA to solve eigenvalue problems. The results are compared with
conventional finite elements. Good agreement is found, but conventional finite
elements require significantly more degrees of freedom to reach a converged
solution than AST-splines.


Abstract:
  This paper develops asymptotic normality results for individual coordinates
of robust M-estimators with convex penalty in high-dimensions, where the
dimension $p$ is at most of the same order as the sample size $n$, i.e,
$p/n\le\gamma$ for some fixed constant $\gamma>0$. The asymptotic normality
requires a bias correction and holds for most coordinates of the M-estimator
for a large class of loss functions including the Huber loss and its smoothed
versions regularized with a strongly convex penalty.
  The asymptotic variance that characterizes the width of the resulting
confidence intervals is estimated with data-driven quantities. This estimate of
the variance adapts automatically to low ($p/n\to0)$ or high ($p/n \le \gamma$)
dimensions and does not involve the proximal operators seen in previous works
on asymptotic normality of M-estimators. For the Huber loss, the estimated
variance has a simple expression involving an effective degrees-of-freedom as
well as an effective sample size. The case of the Huber loss with Elastic-Net
penalty is studied in details and a simulation study confirms the theoretical
findings. The asymptotic normality results follow from Stein formulae for
high-dimensional random vectors on the sphere developed in the paper which are
of independent interest.


Abstract:
  The presence of disorder and inhomogeneities in quantum networks has often
been unexpectedly beneficial for both quantum and classical resources. Here, we
experimentally realize a controllable inhomogenous Quantum Walk dynamics, which
can be exploited to investigate the effect of coherent disorder on the quantum
correlations between two indistinguishable photons. Through the imposition of
suitable disorder configurations, we observe two photon states which exhibit an
enhancement in the quantum correlations between two modes of the network,
compared to the case of an ordered Quantum Walk. Different configurations of
disorder can steer the system towards different realizations of such an
enhancement, thus allowing spatial and temporal manipulation of quantum
correlations.


Abstract:
  Aerial pixel-wise scene perception of the surrounding environment is an
important task for UAVs (Unmanned Aerial Vehicles). Previous research works
mainly adopt conventional pinhole cameras or fisheye cameras as the imaging
device. However, these imaging systems cannot achieve large Field of View
(FoV), small size, and lightweight at the same time. To this end, we design a
UAV system with a Panoramic Annular Lens (PAL), which has the characteristics
of small size, low weight, and a 360-degree annular FoV. A lightweight
panoramic annular semantic segmentation neural network model is designed to
achieve high-accuracy and real-time scene parsing. In addition, we present the
first drone-perspective panoramic scene segmentation dataset Aerial-PASS, with
annotated labels of track, field, and others. A comprehensive variety of
experiments shows that the designed system performs satisfactorily in aerial
panoramic scene parsing. In particular, our proposed model strikes an excellent
trade-off between segmentation performance and inference speed suitable,
validated on both public street-scene and our established aerial-scene
datasets.


Abstract:
  We introduce and study the dynamics of an \emph{immortal} critical branching
process. In the classic, critical branching process, particles give birth to a
single offspring or die at the same rates. Even though the average population
is constant in time, the ultimate fate of the population is extinction. We
augment this branching process with immortality by positing that either: (a) a
single particle cannot die, or (b) there exists an immortal stem cell that
gives birth to ordinary cells that can subsequently undergo critical branching.
We discuss the new dynamical aspects of this immortal branching process.


Abstract:
  Gaussian processes (GPs) are non-parametric Bayesian models that are widely
used for diverse prediction tasks. Previous work in adding strong privacy
protection to GPs via differential privacy (DP) has been limited to protecting
only the privacy of the prediction targets (model outputs) but not inputs. We
break this limitation by introducing GPs with DP protection for both model
inputs and outputs. We achieve this by using sparse GP methodology and
publishing a private variational approximation on known inducing points. The
approximation covariance is adjusted to approximately account for the added
uncertainty from DP noise. The approximation can be used to compute arbitrary
predictions using standard sparse GP techniques. We propose a method for
hyperparameter learning using a private selection protocol applied to
validation set log-likelihood. Our experiments demonstrate that given
sufficient amount of data, the method can produce accurate models under strong
privacy protection.


Abstract:
  This paper presents a methodology for using LLVM-based tools to tune the
DCA++ (dynamical clusterapproximation) application that targets the new ARM
A64FX processor. The goal is to describethe changes required for the new
architecture and generate efficient single instruction/multiple data(SIMD)
instructions that target the new Scalable Vector Extension instruction set.
During manualtuning, the authors used the LLVM tools to improve code
parallelization by using OpenMP SIMD,refactored the code and applied
transformation that enabled SIMD optimizations, and ensured thatthe correct
libraries were used to achieve optimal performance. By applying these code
changes, codespeed was increased by 1.98X and 78 GFlops were achieved on the
A64FX processor. The authorsaim to automatize parts of the efforts in the
OpenMP Advisor tool, which is built on top of existingand newly introduced LLVM
tooling.


Abstract:
  Experiments with diblock co-polymer melts display undulated bilayers that
emanate from defects such as triple junctions and endcaps,
\cite{batesjain_2004}. Undulated bilayers are characterized by oscillatory
perturbations of the bilayer width, which decay on a spatial length scale that
is long compared to the bilayer width. We mimic defects within the
functionalized Cahn-Hillard free energy by introducing spatially localized
inhomogeneities within its parameters. For length parameter $\varepsilon\ll1$,
we show that this induces undulated bilayer solutions whose width perturbations
decay on an $O\!\left(\varepsilon^{-1/2}\right)$ inner length scale that is
long in comparison to the $O(1)$ scale that characterizes the bilayer width.


Abstract:
  In this paper we investigate whether the features of the non-equilibrium
cascade, which have been identified in recent studies using high-fidelity
tools, can be captured in the case of the classical dissipation scaling by
turbulence closures based on the statistical description of freely decaying
isotropic turbulence. Numerical results obtained using the EDQNM model over a
very large range of Reynolds numbers (from $Re_{\lambda}=50$ up to
$Re_{\lambda}=10^6$) are analyzed to perform an extensive investigation of the
scaling region identified as inertial range in Kolmogorov's theory. It is
observed that EDQNM results are in agreement with the results of Lundgren's
matched asymptotic expansion approach to the Karman-Howarth equation. Both
predict that the Kolmogorov inertial range equilibrium is never obtained
irrespective of Reynolds number. Equilibrium is reached in the vicinity of the
Taylor length $\lambda$ (which depends on viscosity) as Reynolds number tends
to infinity and there is a gradual departure from equilibrium as the length
scale moves away from $\lambda$, in particular towards scales larger than
$\lambda$ all the way to the integral length-scale.


Abstract:
  Cache coherence protocols such as MESI that use writer-initiated invalidation
have high complexity and sometimes have poor performance and energy usage,
especially under false sharing. Such protocols require numerous transient
states, a shared directory, and support for core-to-core communication, while
also suffering under false sharing. An alternative to MESI's writer-initiated
invalidation is self-invalidation, which achieves lower complexity than MESI
but adds high performance costs or relies on programmer annotations or specific
data access patterns.
  This paper presents Neat, a low-complexity, efficient cache coherence
protocol. Neat uses self-invalidation, thus avoiding MESI's transient states,
directory, and core-to-core communication requirements. Neat uses novel
mechanisms that effectively avoid many unnecessary self-invalidations. An
evaluation shows that Neat is simple and has lower verification complexity than
the MESI protocol. Neat not only outperforms state-of-the-art self-invalidation
protocols, but its performance and energy consumption are comparable to MESI's,
and it outperforms MESI under false sharing.


Abstract:
  This White Paper summarizes the authors' discussion regarding objectionable
content for the University of Houston (UH) Research Team to outline a strategy
for building an extensive repository of online videos to support research into
automated multimodal approaches to detect objectionable content. The workshop
focused on defining what harmful content is, to whom it is harmful, and why it
is harmful.


Abstract:
  I present a numerical study of the crossover between the low temperature
chirally broken phase and the high temperature chirally restored phase in
$SU(N_c)$ gauge theory with $N_c=3-5$ colors and $N_f=2$ degenerate fermion
flavors. Fermion masses span a range of intermediate values (represented by the
squared ratio of pseudoscalar to vector meson masses $(m_{PS}/m_V)^2\sim 0.25$
to 0.63). Observables include the temperature dependent chiral condensate and
screening masses. At each fermion mass these quantities show nearly identical
temperature dependence across $N_c$.


Abstract:
  Recently, artificial neural networks have been gaining momentum in the field
of gravitational wave astronomy, for example in surrogate modelling of
computationally expensive waveform models for binary black hole inspiral and
merger. Surrogate modelling yields fast and accurate approximations of
gravitational waves and neural networks have been used in the final step of
interpolating the coefficients of the surrogate model for arbitrary waveforms
outside the training sample. We investigate the existence of underlying
structures in the empirical interpolation coefficients using autoencoders. We
demonstrate that when the coefficient space is compressed to only two
dimensions, a spiral structure appears, wherein the spiral angle is linearly
related to the mass ratio. Based on this finding, we design a spiral module
with learnable parameters, that is used as the first layer in a neural network,
which learns to map the input space to the coefficients. The spiral module is
evaluated on multiple neural network architectures and consistently achieves
better speed-accuracy trade-off than baseline models. A thorough experimental
study is conducted and the final result is a surrogate model which can evaluate
millions of input parameters in a single forward pass in under 1ms on a desktop
GPU, while the mismatch between the corresponding generated waveforms and the
ground-truth waveforms is better than the compared baseline methods. We
anticipate the existence of analogous underlying structures and corresponding
computational gains also in the case of spinning black hole binaries.


Abstract:
  The question of finding sets of monomials which are removable from a generic
homogeneous polynomial through a linear change of coordinates was raised by E.
K. Wakeford in [32]. This linear algebra question motivated C. K. Fan and J.
Losonczy to define the concept of acyclic matchings in $\mathbb{Z}^n$ in [16]
which was later generalized to abelian groups by the latter author [26].
Concepts of matchings and acyclic matchings have linear analogues developed in
the context of vector subspaces in a field extension [13, 1]. We discuss the
acyclic matching and weak acyclic matching properties and we provide results on
the existence of acyclic matchings infinite cyclic groups. As for field
extensions, we classify field extensions with the linear acyclic matching
property generalizing a theorem from [1]. The analogy between matchings in
abelian groups and in field extensions is highlighted throughout the paper and
numerous open questions are presented for further inquiry.


Abstract:
  The Weyl double copy is a procedure for relating exact solutions in biadjoint
scalar, gauge and gravity theories, and relates fields in spacetime directly.
Where this procedure comes from, and how general it is, have until recently
remained mysterious. In this paper, we show how the current form and scope of
the Weyl double copy can be derived from a certain procedure in twistor space.
The new formalism shows that the Weyl double copy is more general than
previously thought, applying in particular to gravity solutions with arbitrary
Petrov types. We comment on how to obtain anti-self-dual as well as self-dual
fields, and clarify some conceptual issues in the twistor approach.


Abstract:
  We evaluate what will be the effectiveness of the ExoMars Raman Laser
Spectrometer (RLS) to determine the degree of serpentinization of olivine-rich
units on Mars. We selected terrestrial analogues of martian ultramafic rocks
from the Leka Ophiolite Complex (LOC) and analyzed them with both laboratory
and flight-like analytical instruments. We first studied the mineralogical
composition of the samples (mostly olivine and serpentine) with
state-of-the-art diffractometric and spectroscopic laboratory systems. We
compared these results with those obtained using our RLS ExoMars Simulator. Our
work shows that the RLS ExoMars Simulator successfully identified all major
phases. Moreover, when emulating the automatic operating mode of the flight
instrument, the RLS ExoMars simulator also detected several minor compounds,
some of which were not observed by NIR and XRD. Thereafter, we produced RLS
dedicated calibration curves (R2 between 0.9993 and 0.9995 with an uncertainty
between 3.0% and 5.2% with a confidence interval of 95%) to estimate the
relative content of olivine and serpentine in the samples. Our results show
that RLS can be very effective to identify serpentine, a scientific target of
primary importance for the potential detection of biosignatures on Mars the
main objective of the ExoMars rover mission.