title: Social Science Theories in Software Engineering Research

abstract:
  As software engineering research becomes more concerned with the
psychological, sociological and managerial aspects of software development,
relevant theories from reference disciplines are increasingly important for
understanding the field's core phenomena of interest. However, the degree to
which software engineering research draws on relevant social sciences remains
unclear. This study therefore investigates the use of social science theories
in five influential software engineering journals over 13 years. It analyzes
not only the extent of theory use but also what, how and where these theories
are used. While 87 different theories are used, less than two percent of papers
use a social science theory, most theories are used in only one paper, most
social sciences are ignored, and the theories are rarely tested for
applicability to software engineering contexts. Ignoring relevant social
science theories may (1) undermine the community's ability to generate,
elaborate and maintain a cumulative body of knowledge; and (2) lead to
oversimplified models of software engineering phenomena. More attention to
theory is needed for software engineering to mature as a scientific discipline.


title:
Discrete-time Flatness-based Controller Design using an Implicit
  Euler-discretization

abstract:
  In this contribution, we present a constructive method to derive flat
sampled-data models for continuous-time flat systems through an implicit
Euler-discretization. We show how the sampled-data model can be used
subsequently for a flatness-based controller design, and illustrate our results
with the well-known planar VTOL aircraft example.


title:
5G Enabled Fault Detection and Diagnostics: How Do We Achieve
  Efficiency?

abstract:
  The 5th-generation wireless networks (5G) technologies and mobile edge
computing (MEC) provide great promises of enabling new capabilities for the
industrial Internet of Things. However, the solutions enabled by the 5G
ultra-reliable low-latency communication (URLLC) paradigm come with challenges,
where URLLC alone does not necessarily guarantee the efficient execution of
time-critical fault detection and diagnostics (FDD) applications. Based on the
Tennessee Eastman Process model, we propose the concept of the
communication-edge-computing (CEC) loop and a system model for evaluating the
efficiency of FDD applications. We then formulate an optimization problem for
achieving the defined CEC efficiency and discuss some typical solutions to the
generic CEC-based FDD services, and propose a new uplink-based communication
protocol called "ReFlexUp". From the performance analysis and numerical
results, the proposed ReFlexUp protocol shows its effectiveness compared to the
typical protocols such as Selective Repeat ARQ, HARQ, and "Occupy CoW" in terms
of the key metrics such as latency, reliability, and efficiency. These results
are further convinced from the mmWave-based simulations in a typical 5G
MEC-based implementation.


title:
Role of indistinguishability and entanglement in Hong-Ou-Mandel
  interference and finite bandwidth effects of frequency entangled photons

abstract:
  We investigate the relation between indistinguishability and quantum
entanglement in Hong-Ou-Mandel-interference experiments theoretically and
relate these quantum mechanical principles to the theorem of entanglement
monogamy. We use Glauber's theory of quantum coherence to predict the detection
statistics in HOM-interference of photons produced by three different
photon-pair sources: sources of spectrally indistinguishable frequency
uncorrelated photons, sources of spectrally distinguishable frequency detuned
photons and sources of spectrally indistinguishable frequency entangled
photons. In the case of frequency entangled photon sources, we find a new
additional term in the coincidence detection probability of HOM-interference
compared to existing results, which is relevant for the case of low frequency
separations or large single-photon bandwidths. Compared to previous work in
that context we treat all photonic degrees of freedom (DOF) on equal footing.


title: SpaIn-Net: Spatially-Informed Stereophonic Music Source Separation

abstract:
  With the recent advancements of data driven approaches using deep neural
networks, music source separation has been formulated as an instrument-specific
supervised problem. While existing deep learning models implicitly absorb the
spatial information conveyed by the multi-channel input signals, we argue that
a more explicit and active use of spatial information could not only improve
the separation process but also provide an entry-point for many
user-interaction based tools. To this end, we introduce a control method based
on the stereophonic location of the sources of interest, expressed as the
panning angle. We present various conditioning mechanisms, including the use of
raw angle and its derived feature representations, and show that spatial
information helps. Our proposed approaches improve the separation performance
compared to location agnostic architectures by 1.8 dB SI-SDR in our Slakh-based
simulated experiments. Furthermore, the proposed methods allow for the
disentanglement of same-class instruments, for example, in mixtures containing
two guitar tracks. Finally, we also demonstrate that our approach is robust to
incorrect source panning information, which can be incurred by our proposed
user interaction.


title:
Deciphering stellar chorus: apollinaire, a Python 3 module for Bayesian
  peakbagging in helio- and asteroseismology

abstract:
  Since the asteroseismic revolution, availability of efficient and reliable
methods to extract stellar-oscillation mode parameters has been one of the
keystone of modern stellar physics. In the helio- and asteroseismology fields,
these methods are usually referred as peakbagging. We introduce in this paper
the apollinaire module, a new Python 3 open-source Markov Chains Monte Carlo
(MCMC) framework dedicated to peakbagging. The theoretical framework necessary
to understand MCMC peakbagging methods for integrated helio- and asteroseismic
observations are extensively described. In particular, we present the models
that are used to compute the posterior probability in a peakbagging framework.
A description of the apollinaire module is then provided. We explain how the
module enables stellar background, p-mode global pattern and individual-mode
parameters extraction. By taking into account instrumental particularities,
stellar inclination angle, rotational splittings, and asymmetries, the module
allows fitting a large variety of p-mode models suited for solar as well as
stellar data analysis with different instruments. After having been validated
through a Monte Carlo fitting trial on synthetic data, the module is
benchmarked by comparing its outputs with results obtained with other
peakbagging codes. An analysis of the PSD of 89 one-year subseries of GOLF
observations is performed. Six stars are also selected from the Kepler LEGACY
sample in order to demonstrate the code abilities on asteroseismic data. The
parameters we extract with apollinaire are in good agreement with those
presented in the literature and demonstrate the precision and reliability of
the module.


title: Diagrams and harmonic maps, revisited

abstract:
  We extend many known results for harmonic maps from the 2-sphere into a
Grassmannian to harmonic maps of finite uniton number from an arbitrary Riemann
surface. Our method relies on a new theory of nilpotent cycles arising from the
diagrams of F.E. Burstall and the second author associated to such harmonic
maps; these properties arise from a criterion for finiteness of the uniton
number found recently by the authors with A. Aleman. Applications include a new
classification result on minimal surfaces of constant curvature and a constancy
result for finite type harmonic maps.


title:
Estimating linear radiance indicators from the zenith night sky
  brightness: on the Posch ratio for natural and light polluted skies

abstract:
  Estimating the horizontal irradiance from measurements of the zenith night
sky radiance is a useful operation for basic and applied studies in observatory
site assessment, atmospheric optics and environmental sciences. The ratio
between these two quantities, also known as Posch ratio, has been previously
studied for some canonical cases and reported for a few observational sites. In
this work we (a) generalize the Posch ratio concept, extending it to any pair
of radiance-related linear indicators, (b) describe its main algebraic
properties, and (c) provide analytical expressions and numerical evaluations
for its three basic nighttime components (moonlight, starlight and other
astrophysical light sources, and artificial light). We show that the horizontal
irradiance (or any other linear radiance indicator) is generally correlated
with the zenith radiance, enabling its estimation from zenith measurements if
some a priori information on the atmospheric state is available.


title: Isobarycentric Inequalities

abstract:
  We prove the following isoperimetric type inequality: Given a finite
absolutely continuous Borel measure on ${\mathbb R}^n$, halfspaces have maximal
measure among all subsets with prescribed barycenter. As a consequence, we make
progress towards a solution to a problem of Henk and Pollehn, which is
equivalent to a Log-Minkowski inequality for a parallelotope and a centered
convex body. Our probabilistic approach to the problem also gives rise to
several inequalities and conjectures concerning the truncated mean of certain
log-concave random variables.


title:
Experimental observations and modeling of sub-Hinze bubble production by
  turbulent bubble break-up

abstract:
  We present experiments on large air cavities spanning a wide range of sizes
relative to the Hinze scale $d_\mathrm{H}$, the scale at which turbulent
stresses are balanced by surface tension, disintegrating in turbulence. For
cavities with initial sizes $d_0$ much larger than $d_\mathrm{H}$ (probing up
to $d_0 / d_\mathrm{H} = 8.3$), the size distribution of bubbles smaller than
$d_\mathrm{H}$ follows $N(d) \propto d^{-3/2}$, with $d$ the bubble diameter.
The capillary instability of ligaments involved in the deformation of the large
bubbles is shown visually to be responsible for the creation of the small ones.
Turning to dynamical, three-dimensional measurements of individual break-up
events, we describe the break-up child size distribution and the number of
child bubbles formed as a function of $d_0 / d_\mathrm{H}$. Then, to model the
evolution of a population of bubbles produced by turbulent bubble break-up, we
propose a population balance framework in which break-up involves two physical
processes: an inertial deformation to the parent bubble that sets the size of
large child bubbles, and a capillary instability that sets the size of small
child bubbles. A Monte Carlo approach is used to construct the child size
distribution, with simulated stochastic break-ups constrained by our
experimental measurements and the understanding of the role of capillarity in
small bubble production. This approach reproduces the experimental time
evolution of the bubble size distribution during the disintegration of large
air cavities in turbulence.


title:
Counterexamples to the characterisation of graphs with equal
  independence and annihilation number

abstract:
  We disprove the characterisation of graphs with equal independence and
annihilation number by Larson and Pepper (2011). Series of counterexamples with
arbitrary number of vertices, arbitrary number of components, arbitrary large
independence number and arbitrary large difference between the critical and the
regular independence number are provided. Furthermore, we point out the error
in the proof of the theorem. However, we show that the theorem still holds for
bipartite graphs and connected claw-free graphs.


title: Optimal Algorithms for Stochastic Multi-Level Compositional Optimization

abstract:
  In this paper, we investigate the problem of stochastic multi-level
compositional optimization, where the objective function is a composition of
multiple smooth but possibly non-convex functions. Existing methods for solving
this problem either suffer from sub-optimal sample complexities or need a huge
batch size. To address this limitation, we propose a Stochastic Multi-level
Variance Reduction method (SMVR), which achieves the optimal sample complexity
of $\mathcal{O}\left(1 / \epsilon^{3}\right)$ to find an $\epsilon$-stationary
point for non-convex objectives. Furthermore, when the objective function
satisfies the convexity or Polyak-{\L}ojasiewicz (PL) condition, we propose a
stage-wise variant of SMVR and improve the sample complexity to
$\mathcal{O}\left(1 / \epsilon^{2}\right)$ for convex functions or
$\mathcal{O}\left(1 /(\mu\epsilon)\right)$ for non-convex functions satisfying
the $\mu$-PL condition. The latter result implies the same complexity for
$\mu$-strongly convex functions. To make use of adaptive learning rates, we
also develop Adaptive SMVR, which achieves the same optimal complexities but
converges faster in practice. All our complexities match the lower bounds not
only in terms of $\epsilon$ but also in terms of $\mu$ (for PL or strongly
convex functions), without using a large batch size in each iteration.


title: Control and stabilization of geometrically exact beams

abstract:
  We study well-posedness, stabilization and control problems involving freely
vibrating beams that may undergo motions of large magnitude -- i.e. large
displacements of the reference line and large rotations of the cross sections.
Such beams, shearable and very flexible, are often called geometrically exact
beams and are especially needed in modern highly flexible light-weight
structures, where one cannot neglect these large motions. We view these beams
from two perspectives. The first perspective is one in which the beam is
described in terms of the position of its reference line and the orientation of
its cross sections (expressed in some fixed coordinate system). This is the
generally encountered model, due to Eric Reissner and Juan C. Simo. Of second
order in time and space, it is a quasilinear system of six equations. The
second perspective is one in which the beam is rather described by intrinsic
variables -- velocities and strains or internal forces and moments -- which are
moreover expressed in a moving coordinate system attached to the beam. This
system, proposed in its most general form by Dewey H. Hodges, consists of twice
as many equations, but is of first order in time and space, hyperbolic and only
semilinear (quadratic). From the definition of the state of the latter model,
one can see that both perspectives are linked by a nonlinear transformation.
The questions of well-posedness, stabilization and control are addressed for
beams governed by the intrinsic model, while by using the transformation we
also prove that the existence and uniqueness of a classical solution to the
intrinsic model implies that of a classical solution to the model written in
terms of positions and rotations. In particular, this enables us to deduce
corresponding results for the latter model. We also also address these
questions for networks of beams attached to each other by means of rigid
joints.


title:
Closing the Management Gap for Satellite-Integrated Community Networks:
  A Hierarchical Approach to Self-Maintenance

abstract:
  Community networks (CNs) have become an important paradigm for providing
essential Internet connectivity in unserved and underserved areas across the
world. However, an indispensable part for CNs is network management, where
responsive and autonomous maintenance is much needed. With the technological
advancement in telecommunications networks, a classical satellite-dependent CN
is envisioned to be transformed into a satellite-integrated CN (SICN), which
will embrace significant autonomy, intelligence, and scalability in network
management. This article discusses the machine-learning (ML) based hierarchical
approach to enabling autonomous self-maintenance for SICNs. The approach is
split into the anomaly identification and anomaly mitigation phases, where the
related ML methods, data collection means, deployment options, and mitigation
schemes are presented. With the case study, we discuss a typical scenario using
satellite and fixed connections as backhaul options and show the effectiveness
and performance improvements of the proposed approach with recurrent neural
network and ensemble methods


title: Diversity and fitness uncertainty allow for faster evolutionary rates

abstract:
  I derive uncertainty relations that bound the speed of evolutionary processes
in mutation-less and mutation-driven dynamics, modeled by the replicator and
replicator-mutator equations. The speed limits derived imply that diversity in
the population of a system allows for faster evolutionary rates. In particular,
the uncertainty of the fitness function is singled out as necessary for fast
evolution. These results generalize Fisher's fundamental theorem of natural
selection to dynamics that allow for mutations in terms of uncertainty
relations that constrain evolutionary rates.

categories: q-bio.PE cond-mat.stat-mech physics.bio-ph q-bio.QM


title:
Pressure and temperature dependence of fluorescence anisotropy of Green
  Fluorescent Protein

abstract:
  We have studied the effect of high hydrostatic pressure and temperature on
the steady state fluorescence anisotropy of Green Fluorescent Protein (GFP). We
find that the fluorescence anisotropy of GFP at a constant temperature
decreases with increasing pressure. At atmospheric pressure, anisotropy
decreases with increasing temperature but exhibits a maximum with temperature
for pressure larger than 20 MPa. The temperature corresponding to the maximum
of anisotropy increases with increasing pressure. By taking into account of the
rotational correlation time changes of GFP with the pressure-temperature
dependent viscosity of the solvent, we argue that viscosity increase with
pressure is not a major contributing factor to the decrease in anisotropy with
pressure. Our results suggest that the decrease of fluorescence anisotropy with
pressure may result from changes in H-bonding environment around the
chromophore.


title:
Time-dependent variational Monte Carlo study of the dynamic structure
  factor for bosons in an optical lattice

abstract:
  We study the dynamics of a one-dimensional Bose gas at unit filling in both
shallow and deep optical lattices and obtain the dynamic structure factor
$S(k,\omega)$ by monitoring the linear response to a weak probe pulse. In order
to do that we introduce a new procedure, based on the time-dependent
variational Monte Carlo method (tVMC), which allows to evolve the system in
real time, using as a variational model a Jastrow-Feenberg wave function that
includes pair correlations. Comparison with exact diagonalization results of
$S(k,\omega)$ obtained on a lattice in the Bose-Hubbard limit shows good
agreement of the dispersion relation for sufficiently deep optical lattices,
while for shallow lattices we observe the influence of higher Bloch bands. We
also investigate non-linear response and obtain the excitation spectrum, albeit
broadened, by higher harmonic generation after a strong pulse with a single low
wave number. As a remarkable feature of our simulations we demonstrate that the
full excitation spectrum can be retrieved from the stochastic noise inherent in
any Monte Carlo method, without applying an actual perturbation.


title:
Testing the Cosmological Principle: Astrometric Limits on Systemic
  Motion of Quasars at Different Cosmological Epochs

abstract:
  A sample of $60,410$ bona fide optical quasars with astrometric proper
motions in Gaia EDR3 and spectroscopic redshifts above 0.5 in an oval 8400
square degree area of the sky is constructed. Using orthogonal Zernike
functions of polar coordinates, the proper motion fields are fitted in a
weighted least-squares adjustment of the entire sample and of six equal bins of
sorted redshifts. The overall fit with 37 Zernike functions reveals a
statistically significant pattern, which is likely to be of instrumental
origin. The main feature of this pattern is a chain of peaks and dips mostly in
the R.A. component with an amplitude of 25~$\mu$as yr$^{-1}$. This field is
subtracted from each of the six analogous fits for quasars grouped by redshifts
covering the range 0.5 through 7.03, with median values 0.72, 1.00, 1.25, 1.52,
1.83, 2.34. The resulting residual patterns are noisier, with formal
uncertainties up to 8~$\mu$as yr$^{-1}$ in the central part of the area. We
detect a single high-confidence Zernike term for R.A. proper motion components
of quasars with redshifts around 1.52 representing a general gradient of 30
$\mu$as yr$^{-1}$ over $150\degr$ on the sky. We do not find any small- or
medium-scale systemic variations of the residual proper motion field as
functions of redshift above the $2.5\,\sigma$ significance level.


title: Information-Theoretic Analysis of Minimax Excess Risk

abstract:
  Two main concepts studied in machine learning theory are generalization gap
(difference between train and test error) and excess risk (difference between
test error and the minimum possible error). While information-theoretic tools
have been used extensively to study the generalization gap of learning
algorithms, the information-theoretic nature of excess risk has not yet been
fully investigated. In this paper, some steps are taken toward this goal. We
consider the frequentist problem of minimax excess risk as a zero-sum game
between algorithm designer and the world. Then, we argue that it is desirable
to modify this game in a way that the order of play can be swapped. We prove
that, under some regularity conditions, if the world and designer can play
randomly the duality gap is zero and the order of play can be changed. In this
case, a Bayesian problem surfaces in the dual representation. This makes it
possible to utilize recent information-theoretic results on minimum excess risk
in Bayesian learning to provide bounds on the minimax excess risk. We
demonstrate the applicability of the results by providing information theoretic
insight on two important classes of problems: classification when the
hypothesis space has finite VC-dimension, and regularized least squares.


title:
Orientated energy absorption from mid-infrared laser pulses in
  constrained water systems

abstract:
  The energy acquisition based on resonant excitations are of great importance
in chemical and biological systems. Here, the intramolecular resonant
absorption of polarized mid-infrared pulses by bulk water and surface water is
investigated using molecular dynamics simulation. The consequent heating based
on the OH stretching vibrations is found to be very prompt, achieving more than
100 K temperature jump under irradiation of a pulse with 1 ps width and maximum
intensity of 0.5 V/nm. A general anisotropic phenomenon is manifested as a
result of preferential resonant excitation of symmetric or asymmetric OH
stretching vibration, depending on the relationships between the orientations
of water molecules and the polarized direction of the pulse. In the case of
water molecules with the preferred dipole orientation, constrained by applied
static electric field or spacial confinement, parallel to the polarized
direction of the pulses, the energy absorption is dominated by the symmetric
stretching mode (around 99 THz), while in the perpendicular case, the
asymmetric stretching mode (around 101 THz) is more efficient. Since orientated
water molecules are prevalent in chemical and biological systems, these
findings concerning orientation-dependent excitation of intramolecular
vibrations are of special significance to understood the energy absorption and
transition in relevant biochemical processes.


title:
Tau longitudinal and transverse polarizations from visible kinematics in
  (anti-)neutrino nucleus scattering

abstract:
  Since the $\nu_\tau(\bar\nu_\tau) A_Z \to \tau^\mp X$ reaction is notoriously
difficult to be directly measured, the information on the dynamics of this
nuclear process should be extracted from the analysis of the energy and angular
distributions of the tau decay visible products. These distributions depend on
the components of the tau-polarization vector. We give, for the first time, the
general expression for the outgoing hadron (pion or rho meson) energy and
angular differential cross section for the sequential $\nu_\tau A_Z \to
\tau^-(\pi^- \nu_\tau, \rho^-\nu_\tau) X$ and $\bar\nu_\tau A_Z \to
\tau^+(\pi^+ \bar\nu_\tau, \rho^+ \bar\nu_\tau) X$ reactions. Though all
possible nuclear reaction mechanisms contribute to the distribution, it may be
possible to isolate/enhance one of them by implementing appropriate selection
criteria. For the case of the quasi-elastic reaction off oxygen and neutrino
energies below 6 GeV, we show that the pion distributions are sensitive to the
details of the tau-polarization components. We find significant differences
between the full calculation, where the longitudinal and transverse components
of the tau polarization vector vary with the energy and the scattering angle of
the produced tau, and the simplified scheme in which the polarizations are set
to one and zero (respective asymptotic values in the high energy regime). In
addition to its potential impact on neutrino oscillation analyses, this result
can be used to further test different nuclear models, since these observables
provide complementary information to that obtained by means of the inclusive
nuclear weak charged-current differential cross section. We also study the
effects on the cross section of the $W_4$, $W_5$ nuclear structure functions,
which contributions are proportional to the charged lepton mass, and therefore
difficult to constrain in muon and electron neutrino experiments.


title:
Light-Cone Sum Rules Analysis of
  $\Xi_{QQ^{\prime}q}\to\Sigma_{Q^{\prime}}^{*}$ Weak Decays

abstract:
  In this work, we investigate the semi-leptonic weak decays of spin-1/2
doubly-heavy baryons $\Xi_{QQ^{\prime}}$ into spin-3/2 singly-heavy baryons
$\Sigma_{Q^{\prime}}^*$ within light-cone sum rules. Using the parallel
components of the light-cone distribution amplitudes of
$\Sigma_{Q^{\prime}}^*$, the transition form factors for these decays are
calculated both analytically and numerically. The numerical results for these
semi-leptonic weak decays widths and branching ratios are also predicted, which
are compared with the same predictions by other theoretical approaches in the
literatures. These phenomenology predictions can be tested by the experiments
in the future.


title: Coding and Bounds for Partially Defective Memory Cells

abstract:
  This paper considers coding for so-called partially stuck (defect) memory
cells. Such memory cells can only store partial information as some of their
levels cannot be used fully due to, e.g., wearout. First, we present new
constructions that are able to mask $u$ partially stuck cells while correcting
at the same time $t$ random errors. The process of "masking" determines a word
whose entries coincide with writable levels at the (partially) stuck cells. For
$u>1$ and alphabet size $q>2$, our new constructions improve upon the required
redundancy of known constructions for $t=0$, and require less redundancy for
masking partially stuck cells than former works required for masking fully
stuck cells (which cannot store any information). Second, we show that treating
some of the partially stuck cells as erroneous cells can decrease the required
redundancy for some parameters. Lastly, we derive Singleton-like,
sphere-packing-like, and Gilbert--Varshamov-like bounds. Numerical comparisons
state that our constructions match the Gilbert--Varshamov-like bounds for
several code parameters, e.g., BCH codes that contain all-one word by our first
construction.


title:
The ATM implied volatility slope, the (dual) volatility swap, and the
  (dual) zero vanna implied volatility

abstract:
  Exact relationships between the short time-to-maturity ATM implied volatility
slope, the (dual) volatility swap, and the (dual) zero vanna implied volatility
are given.


title: BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer

abstract:
  Memes are prevalent on the internet and continue to grow and evolve alongside
our culture. An automatic understanding of memes propagating on the internet
can shed light on the general sentiment and cultural attitudes of people. In
this work, we present team BLUE's solution for the second edition of the
MEMOTION shared task. We showcase two approaches for meme classification (i.e.
sentiment, humour, offensive, sarcasm and motivation levels) using a text-only
method using BERT, and a Multi-Modal-Multi-Task transformer network that
operates on both the meme image and its caption to output the final scores. In
both approaches, we leverage state-of-the-art pretrained models for text (BERT,
Sentence Transformer) and image processing (EfficientNetV4, CLIP). Through our
efforts, we obtain first place in task A, second place in task B and third
place in task C. In addition, our team obtained the highest average score for
all three tasks.


title:
Tailoring propagation of light via spin-orbit interactions in correlated
  disorder

abstract:
  Based on the fundamental interplay between spatial wavefronts and
polarization degrees of freedom, spin-orbit interactions (SOI) of light
constitute a novel tool for optical control at the nanoscale. While well
described in simple geometries, SOI of light in disordered environments, where
only a partial knowledge of the material's microscopy is available, remain
largely unexplored. Here, we show that in transversally random media, the
disorder correlation can be exploited to tailor a variety of trajectories for
ballistic beams via SOI. In particular, we unveil the existence of an
oscillating spin Hall effect, stemming from the deformation of the phase of the
wavefront due to SOI. In combination with a weak measurement, this phenomenon
can also be maximized by an optimal choice of the disorder correlation.


title: Virtualization of Electromagnetic Operations (VEMO)

abstract:
  Today's operations in the spectrum occur across many disparate and unique
devices. For example, a military or commercial maritime platform can have
dozens of apertures used for various functions. Utilizing software-defined
radios and dynamic analog front ends, we propose that future systems involving
multiple applications for RF, virtualize operations to gain performance
efficiencies. The concept of virtualization of electromagnetic operations
(VEMO) is considered a means to enable flexible use of hardware and spectrum
resources to achieve diverse mission requirements in military and commercial
settings. This will result in more efficient use of resources, including
size/weight/power and integration of objectives across systems that employ the
electromagnetic spectrum.


title: Anisotropic Dark Energy from a Coupled 2-form Field

abstract:
  In this paper, we study the cosmological dynamics of a 2-form field coupled
to a cold dark matter component in a Bianchi I background. Using a dynamical
system analysis, we show that our model has two attractor solutions
corresponding to late-time accelerated universes. These solutions have an
important distinction between each other: one is isotropic and the other one is
not, i.e. there is a non-zero shear contribution induced by the 2-form field.
Due to the coupling in the dark sector, we also obtain a scaling solution where
the 2-form field behaves as a pressure-less fluid. For a particular set of
parameters and initial conditions, we numerically study the expansion history
of the model. We find that the value of the shear at redshift zero is within
the current observational bounds, which could be a potential alleviation to the
quadrupole anomaly, and that the equation of state of dark energy is exactly
-1.


title: Nano-scale simulation of neuronal damage by galactic cosmic rays

abstract:
  The effects of complex, mixed-ion radiation fields on neuronal function
remain largely unexplored. Here, we present a complete analysis of the
nano-scale physics associated with broad-spectrum galactic cosmic ray (GCR)
irradiation in a realistic cornu ammonis 1 (CA1) pyramidal neuron geometry.
  We simulate the entire 33 ion-energy beam fluence distribution currently in
use at the NASA Space Radiation Laboratory galactic cosmic ray simulator
(GCRSim). We use the TOol for PArticle Simulation (TOPAS) and TOPAS-nBio Monte
Carlo-based track structure simulation toolkits to assess the dosimetry,
physics processes, and fluence statistics of different neuronal compartments at
the nanometer scale. We also make comparisons between the full GCRSim
distribution and a simplified 6 ion-energy spectrum (SimGCRSim).
  We show that across all physics processes, ionizations mediate the majority
of the energy deposition $(68 \pm 1\%)$, though vibrational excitations are the
most abundant ($70 \pm 2\%$ of all energy deposition events). We report that
neuronal energy deposition by proton and $\alpha$-particle tracks declines
approximately hyperbolically with increasing primary particle energy at
mission-relevant energies. We also demonstrate an inverted exponential
relationship between dendritic segment irradiation probability and neuronal
absorbed dose. Finally, we find that there are no significant differences in
the average physical responses between the GCRSim and SimGCRSim fluence
distributions.
  To our knowledge, this is the first nano-scale simulation study of a
realistic neuron geometry using the GCRSim and SimGCRSim fluence distributions.
The results presented here are expected to aid in the interpretation of future
experimental results and help guide future study designs.


title: Long term radio and X-ray evolution of the magnetar Swift J1818.0-1607

abstract:
  We report on the the long term monitoring campaign of the seemingly youngest
magnetar Swift~J1818.0-1607 at radio and X-ray wavelengths over a span of one
year. We obtained a coherent timing solution for the magnetar over the same
time span. The frequency derivative of the magnetar shows systematic variation
with the values oscillating about a mean value of $-$1.37$\times$10$^{-11}$ Hz
s$^{-1}$. The magnitude of the variation in the frequency derivative reduces
with time before converging on the mean value. We were able to identify four
states in the spin-frequency derivative that were quantified by the amount of
modulation about the mean value and the transition between these states seem to
be correlated with the change in the radio emission of the magnetar while no
correlation is seen in the average radio profile variability on a shorter
timescale (days). The 0.5--12 keV X-ray flux shows a monotonic decrease that
can be attributed to thermal emission from a hot-spot on the surface of the
neutron star that is reducing in size. Such decrease is consistent with what is
seen in other magnetars. The potential correlation between the radio emission
mode and the behaviour of the spin-down rate hints to a global change in the
magnetopshere of the magnetar akin to the correlation seen in a subset of
mode-changing radio pulsars and suggests a physical link between the two
sub-populations.


title: Robust Multi-Objective Bayesian Optimization Under Input Noise

abstract:
  Bayesian optimization (BO) is a sample-efficient approach for tuning design
parameters to optimize expensive-to-evaluate, black-box performance metrics. In
many manufacturing processes, the design parameters are subject to random input
noise, resulting in a product that is often less performant than expected.
Although BO methods have been proposed for optimizing a single objective under
input noise, no existing method addresses the practical scenario where there
are multiple objectives that are sensitive to input perturbations. In this
work, we propose the first multi-objective BO method that is robust to input
noise. We formalize our goal as optimizing the multivariate value-at-risk
(MVaR), a risk measure of the uncertain objectives. Since directly optimizing
MVaR is computationally infeasible in many settings, we propose a scalable,
theoretically-grounded approach for optimizing MVaR using random
scalarizations. Empirically, we find that our approach significantly
outperforms alternative methods and efficiently identifies optimal robust
designs that will satisfy specifications across multiple metrics with high
probability.


title:
Label fusion and training methods for reliable representation of
  inter-rater uncertainty

abstract:
  Medical tasks are prone to inter-rater variability due to multiple factors
such as image quality, professional experience and training, or guideline
clarity. Training deep learning networks with annotations from multiple raters
is a common practice that mitigates the model's bias towards a single expert.
Reliable models generating calibrated outputs and reflecting the inter-rater
disagreement are key to the integration of artificial intelligence in clinical
practice. Various methods exist to take into account different expert labels.
We focus on comparing three label fusion methods: STAPLE, average of the
rater's segmentation, and random sampling of each rater's segmentation during
training. Each label fusion method is studied using both the conventional
training framework and the recently published SoftSeg framework that limits
information loss by treating the segmentation task as a regression. Our
results, across 10 data splittings on two public datasets, indicate that
SoftSeg models, regardless of the ground truth fusion method, had better
calibration and preservation of the inter-rater rater variability compared with
their conventional counterparts without impacting the segmentation performance.
Conventional models, i.e., trained with a Dice loss, with binary inputs, and
sigmoid/softmax final activate, were overconfident and underestimated the
uncertainty associated with inter-rater variability. Conversely, fusing labels
by averaging with the SoftSeg framework led to underconfident outputs and
overestimation of the rater disagreement. In terms of segmentation performance,
the best label fusion method was different for the two datasets studied,
indicating this parameter might be task-dependent. However, SoftSeg had
segmentation performance systematically superior or equal to the conventionally
trained models and had the best calibration and preservation of the inter-rater
variability.


title: Fair Division of Indivisible Goods: A Survey

abstract:
  Allocating resources to individuals in a fair manner has been a topic of
interest since the ancient times, with most of the early rigorous mathematical
work on the problem focusing on infinitely divisible resources. Recently, there
has been a surge of papers studying computational questions regarding various
different notions of fairness for the indivisible case, like maximin share
fairness (MMS) and envy-freeness up to any good (EFX). We survey the most
important results in the discrete fair division literature, focusing on the
case of additive valuation functions and paying particular attention to the
progress made in the last 10 years.


title: A Theory of PAC Learnability under Transformation Invariances

abstract:
  Transformation invariances are present in many real-world problems. For
example, image classification is usually invariant to rotation and color
transformation: a rotated car in a different color is still identified as a
car. Data augmentation, which adds the transformed data into the training set
and trains a model on the augmented data, is one commonly used technique to
build these invariances into the learning process. However, it is unclear how
data augmentation performs theoretically and what the optimal algorithm is in
presence of transformation invariances. In this paper, we study PAC
learnability under transformation invariances in three settings according to
different levels of realizability: (i) A hypothesis fits the augmented data;
(ii) A hypothesis fits only the original data and the transformed data lying in
the support of the data distribution; (iii) Agnostic case. One interesting
observation is that distinguishing between the original data and the
transformed data is necessary to achieve optimal accuracy in setting (ii) and
(iii), which implies that any algorithm not differentiating between the
original and transformed data (including data augmentation) is not optimal.
Furthermore, this type of algorithms can even "harm" the accuracy. In setting
(i), although it is unnecessary to distinguish between the two data sets, data
augmentation still does not perform optimally. Due to such a difference, we
propose two combinatorial measures characterizing the optimal sample complexity
in setting (i) and (ii)(iii) and provide the optimal algorithms.


title:
On Deciding Feature Membership in Explanations of SDD & Related
  Classifiers

abstract:
  When reasoning about explanations of Machine Learning (ML) classifiers, a
pertinent query is to decide whether some sensitive features can serve for
explaining a given prediction. Recent work showed that the feature membership
problem (FMP) is hard for $\Sigma_2^P$ for a broad class of classifiers. In
contrast, this paper shows that for a number of families of classifiers, FMP is
in NP. Concretely, the paper proves that any classifier for which an
explanation can be computed in polynomial time, then deciding feature
membership in an explanation can be decided with one NP oracle call. The paper
then proposes propositional encodings for classifiers represented with
Sentential Decision Diagrams (SDDs) and for other related propositional
languages. The experimental results confirm the practical efficiency of the
proposed approach.


title:
Between Stochastic and Adversarial Online Convex Optimization: Improved
  Regret Bounds via Smoothness

abstract:
  Stochastic and adversarial data are two widely studied settings in online
learning. But many optimization tasks are neither i.i.d. nor fully adversarial,
which makes it of fundamental interest to get a better theoretical
understanding of the world between these extremes. In this work we establish
novel regret bounds for online convex optimization in a setting that
interpolates between stochastic i.i.d. and fully adversarial losses. By
exploiting smoothness of the expected losses, these bounds replace a dependence
on the maximum gradient length by the variance of the gradients, which was
previously known only for linear losses. In addition, they weaken the i.i.d.
assumption by allowing adversarially poisoned rounds or shifts in the data
distribution. To accomplish this goal, we introduce two key quantities
associated with the loss sequence, that we call the cumulative stochastic
variance and the adversarial variation. Our upper bounds are attained by
instances of optimistic follow the regularized leader, and we design adaptive
learning rates that automatically adapt to the cumulative stochastic variance
and adversarial variation. In the fully i.i.d. case, our bounds match the rates
one would expect from results in stochastic acceleration, and in the fully
adversarial case they gracefully deteriorate to match the minimax regret. We
further provide lower bounds showing that our regret upper bounds are tight for
all intermediate regimes for the cumulative stochastic variance and the
adversarial variation.


title:
Vanishing sums of roots of unity and the Favard length of self-similar
  product sets

abstract:
  We improve a special case of the Lam-Leung lower bound on the number of
elements in a vanishing sum of $N$-th roots of unity. Using this result, we
extend the Favard length estimates due to Bond, {\L}aba, and Volberg to a new
class of rational product Cantor sets in $\mathbb{R}^2$.


title: Resonant phase lags of a Duffing oscillator

abstract:
  This paper revisits the resonant behavior of a harmonically-forced Duffing
oscillator with a specific attention to phase resonance and to its relation
with amplitude resonance. To this end, the different families of resonances,
namely primary (1:1), superharmonic (k:1), subharmonic (1:{\nu}) and
ultra-subharmonic (k:{\nu}) resonances are carefully studied using first and
higher-order averaging. When the phase lag is calculated between the k-th
harmonic of the displacement and the harmonic forcing, this study evidences
that phase resonance occurs when the phase lag is equal to either {\pi}/2
(phase quadrature) or 3{\pi}/4{\nu}.


title:
$\texttt{matryoshka}$ II: Accelerating Effective Field Theory Analyses
  of the Galaxy Power Spectrum

abstract:
  In this paper we present an extension to the $\texttt{matryoshka}$ suite of
neural network based emulators. The new editions have been developed to
accelerate EFTofLSS analyses of galaxy power spectrum multipoles in redshift
space. They are collectively referred to as the $\texttt{EFTEMU}$. We test the
$\texttt{EFTEMU}$ at the power spectrum level and achieve a prediction accuracy
of better than 1\% with BOSS-like bias parameters and counterterms on scales
$0.001\ h\ \mathrm{Mpc}^{-1} \leq k \leq 0.19\ h\ \mathrm{Mpc}^{-1}$. We also
run a series of mock full shape analyses to test the $\texttt{EFTEMU}$ at the
inference level. Through these mock analyses we verify that the
$\texttt{EFTEMU}$ recovers the true cosmology within $1\sigma$ at several
redshifts ($z=[0.38,0.51,0.61]$), and with several noise levels (the most
stringent of which being a Gaussian covariance associated with a volume of
$5000^3 \ \mathrm{Mpc}^3 \ h^{-3}$). We compare mock inference results from the
$\texttt{EFTEMU}$ to those obtained with a fully analytic EFTofLSS model and
again find no significant bias, whilst speeding up the inference by three
orders of magnitude. The $\texttt{EFTEMU}$ is publicly available as part of the
$\texttt{matryoshka}$ $\texttt{Python}$ package
https://github.com/JDonaldM/Matryoshka.


title: Greedy lattice paths with negative values

abstract:
  Let $\{X_{v}:v\in\mathbb{Z}^d\}$ be i.i.d. random variables. Let
$S(\pi)=\sum_{v\in\pi}X_v$ be the weight of a self-avoiding lattice path $\pi$.
Let $M_n=\max\{S(\pi):\pi\text{ has length }n\text{ and starts from the
origin}\}$. Under the two assumptions that $\exists\alpha>0$,
$E(X_v^{+})^d(\log^{+}X_v^{+})^{d+\alpha}$ and that $E[(X_v^{-})^4]<+\infty$,
we prove that there exists a finite real number $M$ such that $M_n/n$ converges
to $M$ a.s. and in $L^{1}$ as $n$ tends to infinity.


title: Unsupervised Learning of Group Invariant and Equivariant Representations

abstract:
  Equivariant neural networks, whose hidden features transform according to
representations of a group G acting on the data, exhibit training efficiency
and an improved generalisation performance. In this work, we extend group
invariant and equivariant representation learning to the field of unsupervised
deep learning. We propose a general learning strategy based on an
encoder-decoder framework in which the latent representation is disentangled in
an invariant term and an equivariant group action component. The key idea is
that the network learns the group action on the data space and thus is able to
solve the reconstruction task from an invariant data representation, hence
avoiding the necessity of ad-hoc group-specific implementations. We derive the
necessary conditions on the equivariant encoder, and we present a construction
valid for any G, both discrete and continuous. We describe explicitly our
construction for rotations, translations and permutations. We test the validity
and the robustness of our approach in a variety of experiments with diverse
data types employing different network architectures.


title: Equilibrium Fluctuations in Mean-field Disordered Models

abstract:
  Mean-field models of glasses that present a random first order transition
exhibit highly non-trivial fluctuations. Building on previous studies that
focused on the critical scaling regime, we here obtain a fully quantitative
framework for all equilibrium conditions. By means of the replica method we
evaluate Gaussian fluctuations of the overlaps around the thermodynamic limit,
decomposing them in thermal fluctuations inside each state and heterogeneous
fluctuations between different states. We first test and compare our analytical
results with numerical simulation results for the $p$-spin spherical model and
the random orthogonal model, and then analyze the random Lorentz gas. In all
cases, a strong quantitative agreement is obtained. Our analysis thus provides
a robust scheme for identifying the key finite-size (or finite-dimensional)
corrections to the mean-field treatment of these paradigmatic glass models, and
opens the way for later identifying instantonic corrections.


title:
Exponential reduction of the sign problem at finite density in the 2+1D
  XY model via contour deformations

abstract:
  We study the 2+1 dimensional XY model at nonzero chemical potential $\mu$ on
deformed integration manifolds, with the aim of alleviating its sign problem.
We investigate several proposals for the deformations, and considerably improve
on the severity of the sign problem with respect to standard reweighting
approaches. We present numerical evidence that the reduction of the sign
problem is exponential both in $\mu^2$ and in the spatial volume. We also
present a new approach to the optimization procedure based on reweighting, that
sensibly reduces its computational cost.


title:
Improving the repeatability of deep learning models with Monte Carlo
  dropout

abstract:
  The integration of artificial intelligence into clinical workflows requires
reliable and robust models. Repeatability is a key attribute of model
robustness. Repeatable models output predictions with low variation during
independent tests carried out under similar conditions. During model
development and evaluation, much attention is given to classification
performance while model repeatability is rarely assessed, leading to the
development of models that are unusable in clinical practice. In this work, we
evaluate the repeatability of four model types (binary classification,
multi-class classification, ordinal classification, and regression) on images
that were acquired from the same patient during the same visit. We study the
performance of binary, multi-class, ordinal, and regression models on four
medical image classification tasks from public and private datasets: knee
osteoarthritis, cervical cancer screening, breast density estimation, and
retinopathy of prematurity. Repeatability is measured and compared on ResNet
and DenseNet architectures. Moreover, we assess the impact of sampling Monte
Carlo dropout predictions at test time on classification performance and
repeatability. Leveraging Monte Carlo predictions significantly increased
repeatability for all tasks on the binary, multi-class, and ordinal models
leading to an average reduction of the 95\% limits of agreement by 16% points
and of the disagreement rate by 7% points. The classification accuracy improved
in most settings along with the repeatability. Our results suggest that beyond
about 20 Monte Carlo iterations, there is no further gain in repeatability. In
addition to the higher test-retest agreement, Monte Carlo predictions were
better calibrated which leads to output probabilities reflecting more
accurately the true likelihood of being correctly classified.


title: Multipliers of Nilpotent Diassociative Algebras

abstract:
  The paper concerns nilpotent associative dialgebras and their corresponding
diassociative Schur multipliers. Using Lie (and group) theory as a guide, we
first extend a classic five-term cohomological sequence under alternative
conditions in the nilpotent setting. This main result is then applied to obtain
a new proof for a previous extension of the same sequence. It also yields a
different extension of the sequence that involves terms in the upper central
series. Furthermore, we use the main result to obtain a collection of dimension
bounds on the multiplier of a nilpotent diassociative algebra. These differ
notably from the Lie case. Since diassociative algebras generalize associative
algebras, we obtain an associative analogue of the results herein. We conclude
by computing both the associative and diassociative multipliers of an
associative algebra. This paper is part of an ongoing project to advance
extension theory in the context of several Loday algebras.


title:
Stablecoins and Central Bank Digital Currencies: Policy and Regulatory
  Challenges

abstract:
  Stablecoins and central bank digital currencies are on the horizon in Asia,
and in some cases have already arrived. This paper provides new analysis and a
critique of the use case for both forms of digital currency. It provides
time-varying estimates of devaluation risk for the leading stablecoin, Tether,
using data from the futures market. It describes the formidable obstacles to
widespread use of central bank digital currencies in cross-border transactions,
the context in which their utility is arguably greatest. The bottom line is
that significant uncertainties continue to dog the region's digital currency
initiatives.


title:
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement
  Learning

abstract:
  Safe reinforcement learning (RL) is still very challenging since it requires
the agent to consider both return maximization and safe exploration. In this
paper, we propose CUP, a Conservative Update Policy algorithm with a
theoretical safety guarantee. We derive the CUP based on the new proposed
performance bounds and surrogate functions. Although using bounds as surrogate
functions to design safe RL algorithms have appeared in some existing works, we
develop them at least three aspects: (i) We provide a rigorous theoretical
analysis to extend the surrogate functions to generalized advantage estimator
(GAE). GAE significantly reduces variance empirically while maintaining a
tolerable level of bias, which is an efficient step for us to design CUP; (ii)
The proposed bounds are tighter than existing works, i.e., using the proposed
bounds as surrogate functions are better local approximations to the objective
and safety constraints. (iii) The proposed CUP provides a non-convex
implementation via first-order optimizers, which does not depend on any convex
approximation. Finally, extensive experiments show the effectiveness of CUP
where the agent satisfies safe constraints. We have opened the source code of
CUP at https://github.com/RL-boxes/Safe-RL.


title: Emittance self-compensation in blow-out mode

abstract:
  We report an unusual regime of emittance self-compensation in an electron
bunch generated in blow-out mode by a radio-frequency photocathode gun.
Simulations clearly show an initial growth and a subsequent self-compensation
of projected emittance in a divergent electron bunch originating from the
effects of: (i) strong space-charge forces of mirror charges on the cathode,
(ii) an energy chirp in the bunch and (iii) substantial re-shaping of the
electron bunch. Furthermore, we show analytically and numerically how a complex
interplay between these effects leads to emittance self-compensation in free
space -- the effect that is normally observed only in a focusing magnetic
field.


title: Hypergraph removal with polynomial bounds

abstract:
  Given a fixed $k$-uniform hypergraph $F$, the $F$-removal lemma states that
every hypergraph with few copies of $F$ can be made $F$-free by the removal of
few edges. Unfortunately, for general $F$, the constants involved are given by
incredibly fast-growing Ackermann-type functions. It is thus natural to ask for
which $F$ one can prove removal lemmas with polynomial bounds. One trivial case
where such bounds can be obtained is when $F$ is $k$-partite. Alon proved that
when $k=2$ (i.e. when dealing with graphs), only bipartite graphs have a
polynomial removal lemma. Kohayakawa, Nagle and R\"odl conjectured in 2002 that
Alon's result can be extended to all $k>2$, namely, that the only $k$-graphs
$F$ for which the hypergraph removal lemma has polynomial bounds are the
trivial cases when $F$ is $k$-partite. In this paper we prove this conjecture.


title:
StratDef: a strategic defense against adversarial attacks in malware
  detection

abstract:
  Over the years, most research towards defenses against adversarial attacks on
machine learning models has been in the image processing domain. The malware
detection domain has received less attention despite its importance. Moreover,
most work exploring defenses focuses on feature-based, gradient-based or
randomized methods but with no strategy when applying them. In this paper, we
introduce StratDef, which is a strategic defense system tailored for the
malware detection domain based on a Moving Target Defense and Game Theory
approach. We overcome challenges related to the systematic construction,
selection and strategic use of models to maximize adversarial robustness.
StratDef dynamically and strategically chooses the best models to increase the
uncertainty for the attacker, whilst minimizing critical aspects in the
adversarial ML domain like attack transferability. We provide the first
comprehensive evaluation of defenses against adversarial attacks on machine
learning for malware detection, where our threat model explores different
levels of threat, attacker knowledge, capabilities, and attack intensities. We
show that StratDef performs better than other defenses even when facing the
peak adversarial threat. We also show that, from the existing defenses, only a
few adversarially-trained models provide substantially better protection than
just using vanilla models but are still outperformed by StratDef.


title:
Constant-weight PIR: Single-round Keyword PIR via Constant-weight
  Equality Operators

abstract:
  Equality operators are an essential building block in tasks over secure
computation such as private information retrieval. In private information
retrieval (PIR), a user queries a database such that the server does not learn
which element is queried. In this work, we propose \emph{equality operators for
constant-weight codewords}. A constant-weight code is a collection of codewords
that share the same Hamming weight. Constant-weight equality operators have a
multiplicative depth that depends only on the Hamming weight of the code, not
the bit-length of the elements. In our experiments, we show how these equality
operators are up to 10 times faster than existing equality operators.
Furthermore, we propose PIR using the constant-weight equality operator or
\emph{constant-weight PIR}, which is a PIR protocol using an approach
previously deemed impractical. We show that for private retrieval of large,
streaming data, constant-weight PIR has a smaller communication complexity and
lower runtime compared to SEALPIR and MulPIR, respectively, which are two
state-of-the-art solutions for PIR. Moreover, we show how constant-weight PIR
can be extended to keyword PIR. In keyword PIR, the desired element is
retrieved by a unique identifier pertaining to the sought item, e.g., the name
of a file. Previous solutions to keyword PIR require one or multiple rounds of
communication to reduce the problem to normal PIR. We show that constant-weight
PIR is the first practical single-round solution to single-server keyword PIR.


title:
ScoreNet: Learning Non-Uniform Attention and Augmentation for
  Transformer-Based Histopathological Image Classification

abstract:
  Progress in digital pathology is hindered by high-resolution images and the
prohibitive cost of exhaustive localized annotations. The commonly used
paradigm to categorize pathology images is patch-based processing, which often
incorporates multiple instance learning (MIL) to aggregate local patch-level
representations yielding image-level prediction. Nonetheless, diagnostically
relevant regions may only take a small fraction of the whole tissue, and
current MIL-based approaches often process images uniformly, discarding the
inter-patches interactions. To alleviate these issues, we propose ScoreNet, a
new efficient transformer that exploits a differentiable recommendation stage
to extract discriminative image regions and dedicate computational resources
accordingly. The proposed transformer leverages the local and global attention
of a few dynamically recommended high-resolution regions at an efficient
computational cost. We further introduce a novel Mixup-based data-augmentation,
namely ScoreMix, by leveraging the image's semantic distribution to guide the
data mixing and produce coherent sample-label pairs. ScoreMix is embarrassingly
simple and mitigates the pitfalls of previous augmentations, which assume a
uniform semantic distribution and risk mislabeling the samples. Thorough
experiments and ablation studies on three breast cancer histology datasets of
Haematoxylin & Eosin (H&E) have validated the superiority of our approach over
prior arts, including transformer-based models on tumour regions-of-interest
(TRoIs) classification. ScoreNet equipped with proposed ScoreMix augmentation
demonstrates better generalization capabilities and achieves new
state-of-the-art (SOTA) results with only 50% of the data compared to other
Mixup augmentation variants. Finally, ScoreNet yields high efficacy and
outperforms SOTA efficient transformers, namely TransPath and SwinTransformer.


title:
Constraining Brans-Dicke cosmology with the CSST galaxy clustering
  spectroscopic survey

abstract:
  The Brans-Dicke (BD) theory is the simplest Scalar-Tensor theory of gravity,
which can be considered as a candidate of modified Einstein's theory of general
relativity. In this work, we forecast the constraints on BD theory in the CSST
galaxy clustering spectroscopic survey with a magnitude limit $\sim 23$ AB mag
for point-source 5$\sigma$ detection. We generate mock data based on the
zCOSMOS catalog and consider the observational and instrumental effects of the
CSST spectroscopic survey. We predicate galaxy power spectra in the BD theory
from $z=0$ to 1.5, and the galaxy bias and other systematical parameters are
also included. The Markov Chain Monte Carlo (MCMC) technique is employed to
find the best-fits and probability distributions of the cosmological and
systematical parameters. A Brans-Dicke parameter $\zeta$ is introduced, which
satisfies $\zeta=\ln \left(1+\frac{1}{\omega}\right)$. We find that the CSST
spectroscopic galaxy clustering survey can give $|\zeta|<10^{-2}$, or
equivalently $|\omega|>\mathcal{O}(10^2)$ and $|\dot{G}/G|<10^{-13}$, under the
assumption $\zeta = 0$. These constraints are almost at the same order of
magnitude compared to the joint constraints using the current cosmic microwave
background (CMB), baryon acoustic oscillations (BAO), and Type Ia supernova (SN
Ia) data, indicating that the CSST galaxy clustering spectroscopic survey would
be powerful to constrain the BD theory and other modified gravity theories.


title: On Representation Learning with Feedback

abstract:
  This note complements the author's recent paper "Robust representation
learning with feedback for single image deraining" by providing heuristically
theoretical explanations on the mechanism of representation learning with
feedback, namely an essential merit of the works presented in this recent
article. This note facilitates understanding of key points in the mechanism of
representation learning with feedback.


title:
Existence of standing and traveling waves in quantum hydrodynamics with
  viscosity

abstract:
  We prove existence of standing waves for two quantum hydrodynamics systems
with linear and nonlinear viscosity. Moreover, global existence of traveling
waves is proved for the former without restrictions on the viscosity and
dispersion parameters, thanks to a suitable Lyapunov function. This is an
improvement with respect to the global existence result in \cite{LMZ2020},
where it was required that the viscosity is sufficiently strong.


title: Damped Online Newton Step for Portfolio Selection

abstract:
  We revisit the classic online portfolio selection problem, where at each
round a learner selects a distribution over a set of portfolios to allocate its
wealth. It is known that for this problem a logarithmic regret with respect to
Cover's loss is achievable using the Universal Portfolio Selection algorithm,
for example. However, all existing algorithms that achieve a logarithmic regret
for this problem have per-round time and space complexities that scale
polynomially with the total number of rounds, making them impractical. In this
paper, we build on the recent work by Haipeng et al. 2018 and present the first
practical online portfolio selection algorithm with a logarithmic regret and
whose per-round time and space complexities depend only logarithmically on the
horizon. Behind our approach are two key technical novelties of independent
interest. We first show that the Damped Online Newton steps can approximate
mirror descent iterates well, even when dealing with time-varying regularizers.
Second, we present a new meta-algorithm that achieves an adaptive logarithmic
regret (i.e. a logarithmic regret on any sub-interval) for mixable losses.


title: Forecasting Global Weather with Graph Neural Networks

abstract:
  We present a data-driven approach for forecasting global weather using graph
neural networks. The system learns to step forward the current 3D atmospheric
state by six hours, and multiple steps are chained together to produce skillful
forecasts going out several days into the future. The underlying model is
trained on reanalysis data from ERA5 or forecast data from GFS. Test
performance on metrics such as Z500 (geopotential height) and T850
(temperature) improves upon previous data-driven approaches and is comparable
to operational, full-resolution, physical models from GFS and ECMWF, at least
when evaluated on 1-degree scales and when using reanalysis initial conditions.
We also show results from connecting this data-driven model to live,
operational forecasts from GFS.


title:
Accuracy of Restricted Boltzmann Machines for the one-dimensional
  $J_1-J_2$ Heisenberg model

abstract:
  Neural networks have been recently proposed as variational wave functions for
quantum many-body systems [G. Carleo and M. Troyer, Science 355, 602 (2017)].
In this work, we focus on a specific architecture, known as Restricted
Boltzmann Machine (RBM), and analyse its accuracy for the spin-1/2 $J_1-J_2$
antiferromagnetic Heisenberg model in one spatial dimension. The ground state
of this model has a non-trivial sign structure, especially for $J_2/J_1>0.5$,
forcing us to work with complex-valued RBMs. Two variational Ans\"atze are
discussed: one defined through a fully complex RBM, and one in which two
different real-valued networks are used to approximate modulus and phase of the
wave function. In both cases, translational invariance is imposed by
considering linear combinations of RBMs, giving access also to the
lowest-energy excitations at fixed momentum $k$. We perform a systematic study
on small clusters to evaluate the accuracy of these wave functions in
comparison to exact results, providing evidence for the supremacy of the fully
complex RBM. Our calculations show that this kind of Ans\"atze is very flexible
and describes both gapless and gapped ground states, also capturing the
incommensurate spin-spin correlations and low-energy spectrum for
$J_2/J_1>0.5$. The RBM results are also compared to the ones obtained with
Gutzwiller-projected fermionic states, often employed to describe quantum spin
models [F. Ferrari, A. Parola, S. Sorella and F. Becca, Phys. Rev. B 97, 235103
(2018)]. Contrary to the latter class of variational states, the
fully-connected structure of RBMs hampers the transferability of the wave
function from small to large clusters, implying an increase of the
computational cost with the system size.


title: Weighted Programming

abstract:
  We study weighted programming, a programming paradigm for specifying
mathematical models. More specifically, the weighted programs we investigate
are like usual imperative programs with two additional features: (1)
nondeterministic branching and (2) weighting execution traces. Weights can be
numbers but also other objects like words from an alphabet, polynomials, formal
power series, or cardinal numbers. We argue that weighted programming as a
paradigm can be used to specify mathematical models beyond probability
distributions (as is done in probabilistic programming).
  We develop weakest-precondition- and weakest-liberal-precondition-style
calculi \`{a} la Dijkstra for reasoning about mathematical models specified by
weighted programs. We present several case studies. For instance, we use
weighted programming to model the ski rental problem - an optimization problem.
We model not only the optimization problem itself, but also the best
deterministic online algorithm for solving this problem as weighted programs.
By means of weakest-precondition-style reasoning, we can determine the
competitive ratio of the online algorithm on source code level.


title:
A law of large numbers for local patterns in Schur measures and a Schur
  process

abstract:
  The aim of this note is to prove a law of large numbers for local patterns in
discrete point processes. We investigate two different situations: a class of
point processes on the one dimensional lattice including certain Schur
measures, and a model of random plane partitions, introduced by Okounkov and
Reshetikhin. The results state in both cases that the linear statistic of a
function, weighted by the appearance of a fixed pattern in the random
configuration and conveniently normalized, converges to the deterministic
integral of that function weighted by the expectation with respect to the limit
process of the appearance of the pattern.


title:
Numerical simulation of the coalescence-induced polymeric droplet
  jumping on superhydrophobic surfaces

abstract:
  Self-propelled jumping of two polymeric droplets on superhydrophobic surfaces
is investigated by three-dimensional direct numerical simulations. Two
identical droplets of a viscoelastic fluid slide, meet and coalesce on a
surface with contact angle 180 degrees. The droplets are modelled by the
Giesekus constitutive equation, introducing both viscoelasticity and a
shear-thinning effects. The Cahn-Hilliard Phase-Field method is used to capture
the droplet interface. The simulations capture the spontaneous coalescence and
jumping of the droplets. The effect of elasticity and shear-thinning on the
coalescence and jumping is investigated at capillary-inertial and viscous
regimes. The results reveal that the elasticity of the droplet changes the
known capillary-inertial velocity scaling of the Newtonian drops at large
Ohnesorge numbers; the resulting viscoelastic droplet jumps from the surface at
larger Ohnesorge numbers than a Newtonian drop, when elasticity gives rise to
visible shape oscillations of the merged droplet. The numerical results show
that polymer chains are stretched during the coalescence and prior to the
departure of two drops, and the resulting elastic stresses at the interface
induce the jumping of the liquid out of the surface. This study shows that
viscoelasticity, typical of many biological and industrial applications,
affects the droplet behaviour on superhydrophobic and self-cleaning surfaces.


title: Wetterich equation on Lorentzian manifolds

abstract:
  We discuss the scaling of the effective action for the interacting scalar
quantum field theory on generic spacetimes with Lorentzian signature and in a
generic state (including vacuum and thermal states, if they exist). This is
done constructing a flow equation, which is very close to the renown Wetterich
equation, by means of techniques recently developed in the realm of
perturbative Algebraic Quantum Field theory (pAQFT). The key ingredient that
allows one to obtain an equation which is meaningful on generic Lorentzian
backgrounds is the use of a local regulator, which keeps the theory covariant.
As a proof of concept, the developed methods are used to show that non-trivial
fixed points arise in quantum field theories in a thermal state and in the case
of quantum fields in the Bunch-Davies state on the de Sitter spacetime.


title:
Bayesian Stochastic Gradient Descent for Stochastic Optimization with
  Streaming Input Data

abstract:
  We consider stochastic optimization under distributional uncertainty, where
the unknown distributional parameter is estimated from streaming data that
arrive sequentially over time. Moreover, data may depend on the decision of the
time when they are generated. For both decision-independent and
decision-dependent uncertainties, we propose an approach to jointly estimate
the distributional parameter via Bayesian posterior distribution and update the
decision by applying stochastic gradient descent on the Bayesian average of the
objective function. Our approach converges asymptotically over time and
achieves the convergence rates of classical SGD in the decision-independent
case. We demonstrate the empirical performance of our approach on both
synthetic test problems and a classical newsvendor problem.


title: Monoidal Width

abstract:
  We introduce monoidal width as a measure of the difficulty of decomposing
morphisms in monoidal categories. For graphs, we show that monoidal width and
two variations capture existing notions, namely branch width, tree width and
path width. Through these and other examples, we propose that monoidal width:
(i) is a promising concept that, while capturing known measures, can similarly
be instantiated in other settings, avoiding the need for ad-hoc domain-specific
definitions and (ii) comes with a general, formal algebraic notion of
decomposition using the language of monoidal categories.


title:
Crypto-ransomware detection using machine learning models in
  file-sharing network scenario with encrypted traffic

abstract:
  Ransomware is considered as a significant threat for most enterprises since
the past few years. In scenarios wherein users can access all files on a shared
server, one infected host can lock the access to all shared files. We propose a
tool to detect ransomware infection based on file-sharing traffic analysis. The
tool monitors the traffic exchanged between the clients and the file servers
and using machine learning techniques it searches for patterns in the traffic
that betray ransomware actions while reading and overwriting files. The
proposal is designed to work for clear text and for encrypted file-sharing
protocols. We compare three machine learning models and choose the best for
validation. We train and test the detection model using more than 70 ransomware
binaries from 26 different strains and more than 2500 hours of not infected
traffic from real users. The results reveal that the proposed tool can detect
all ransomware binaries, including those not used in training phase (unseen).
This paper provides a validation of the algorithm by studying the false
positive rate and the amount of information from user files that the ransomware
could encrypt before being detected.


title:
Multi-class granular approximation by means of disjoint and adjacent
  fuzzy granules

abstract:
  In granular computing, fuzzy sets can be approximated by granularly
representable sets that are as close as possible to the original fuzzy set
w.r.t. a given closeness measure. Such sets are called granular approximations.
In this article, we introduce the concepts of disjoint and adjacent granules
and we examine how the new definitions affect the granular approximations.
First, we show that the new concepts are important for binary classification
problems since they help to keep decision regions separated (disjoint granules)
and at the same time to cover as much as possible of the attribute space
(adjacent granules). Later, we consider granular approximations for multi-class
classification problems leading to the definition of a multi-class granular
approximation. Finally, we show how to efficiently calculate multi-class
granular approximations for {\L}ukasiewicz fuzzy connectives. We also provide
graphical illustrations for a better understanding of the introduced concepts.


title:
Production of forward heavy-flavour dijets at the LHCb within
  $k_{T}$-factorization approach

abstract:
  We calculate differential cross sections for $c \bar c$- and $b \bar b$-dijet
production in $pp$-scattering at $\sqrt{s} = 13$ TeV in the $k_T$-factorization
and hybrid approaches with different unintegrated parton distribution functions
(uPDFs). We present distributions in transverse momentum and pseudorapidity of
the leading jet, rapidity difference between the jets and the dijet invariant
mass. Our results are compared to recent LHCb data on forward production of
heavy flavour dijets, measured recently for the first time individually for
both, charm and bottom flavours. We found that an agreement between the
predictions and the data within the full $k_T$-factorization is strongly
related to the modelling of the large-$x$ behaviour of the gluon uPDFs which is
usually not well constrained. The problem may be avoided following the hybrid
factorization approach. Then a good description of the measured distributions
is obtained with the Parton-Branching, the Kimber-Martin-Ryskin, the
Kutak-Sapeta and the Jung setA0 CCFM gluon uPDFs. We calculate also
differential distributions for the ratio of $c \bar c$ and $b \bar b$ cross
sections. In all cases we obtain the ratio close to 1 which is caused by the
condition on minimal jet transverse momentum ($p_{T}^{\mathrm{jet}} > 20$ GeV)
introduced in the experiment, that makes the role of heavy quark mass almost
negligible. The LHCb experimental ratio seems a bit larger. We discuss
potentially important for the ratio effect of $c$- or $b$-quark gluon radiative
corrections related to emission outside of the jet cone. The found effect seems
rather small. More refine calculation requires full simulation of $c$- and
$b$-jets which goes beyond of the scope of the present paper.


title:
Deep Generative model with Hierarchical Latent Factors for Time Series
  Anomaly Detection

abstract:
  Multivariate time series anomaly detection has become an active area of
research in recent years, with Deep Learning models outperforming previous
approaches on benchmark datasets. Among reconstruction-based models, most
previous work has focused on Variational Autoencoders and Generative
Adversarial Networks. This work presents DGHL, a new family of generative
models for time series anomaly detection, trained by maximizing the observed
likelihood by posterior sampling and alternating back-propagation. A top-down
Convolution Network maps a novel hierarchical latent space to time series
windows, exploiting temporal dynamics to encode information efficiently.
Despite relying on posterior sampling, it is computationally more efficient
than current approaches, with up to 10x shorter training times than RNN based
models. Our method outperformed current state-of-the-art models on four popular
benchmark datasets. Finally, DGHL is robust to variable features between
entities and accurate even with large proportions of missing values, settings
with increasing relevance with the advent of IoT. We demonstrate the superior
robustness of DGHL with novel occlusion experiments in this literature. Our
code is available at https://github.com/cchallu/dghl.


title:
Shifting Trends of COVID-19 Tweet Sentiment with Respect to Voting
  Preferences in the 2020 Election Year of the United States

abstract:
  COVID-19 related policies were extensively politicized during the 2020
election year of the United States, resulting in polarizing viewpoints. Twitter
users were particularly engaged during the 2020 election year. Here we
investigated whether COVID-19 related tweets were associated with the overall
election results at the state level during the period leading up to the
election day. We observed weak correlations between the average sentiment of
COVID-19 related tweets and popular votes in two-week intervals, and the trends
gradually become opposite. We then compared the average sentiments of COVID-19
related tweets between states called in favor of Republican (red states) or
Democratic parties (blue states). We found that at the beginning of lockdowns
sentiments in the blue states were much more positive than those in the red
states. However, sentiments in the red states gradually become more positive
during the summer of 2020 and persisted until the election day.


title:
On a coupled Kadomtsev--Petviashvili system associated with an elliptic
  curve

abstract:
  The higher-dimensional elliptic integrable system proposed by Date, Jimbo and
Miwa [J. Phys. Soc. Jpn., 52:766--771, 1983] is reinvestigated within the
direct linearisation framework. We show that such an elliptic integrable model
can effectively be reformulated as an elliptic extension of the
$(2+1)$-dimensional coupled Kadomtsev--Petviashvili system (also known as the
$D_\infty$-type Kadomtsev--Petviashvili equation). The direct linearisation
scheme also allows us to construct a Lax pair composed of differential
operators in $2\times2$ matrix form and simultaneously the corresponding
elliptic $N$-soliton solutions, which guarantees the integrability of this
elliptic model. By performing dimensional reductions, two novel elliptic
integrable models, namely the elliptic coupled Korteweg--de Vries and
Boussinesq systems, are obtained from the elliptic coupled
Kadomtsev--Petviashvili system.


title: Theoretical description of pygmy (dipole) resonances

abstract:
  Stable and unstable nuclei with neutron excess ($N>Z$) show - in the
isovector dipole transition strength distribution - a small hump around the
neutron emission threshold energy known as Pygmy Dipole Resonance (PDR). One of
its main features is the isospin mixing allowing the experimental studies with
both isovector and isoscalar probes. Different theoretical approaches and
methodologies are used to deduce the characteristics of the PDR. In this
Chapter, the various mean-field theories and their extensions, devoted to
understand and reproduce the strength distribution of these low-lying dipole
states, are summarised. Special attention is dedicated to the calculations of
the inelastic cross section, aspect that is particularly important in the
investigation with isoscalar probes, such as $\alpha$-particles or $^{17}$O.
The relevance of the radial form factors is presented in relation to the
inelastic cross-section calculations.


title:
Identifying equivalent Calabi--Yau topologies: A discrete challenge from
  math and physics for machine learning

abstract:
  We review briefly the characteristic topological data of Calabi--Yau
threefolds and focus on the question of when two threefolds are equivalent
through related topological data. This provides an interesting test case for
machine learning methodology in discrete mathematics problems motivated by
physics.


title: Enhancing Healthcare System Using Blockchain Smart Contracts

abstract:
  The concept of blockchain has emerged as an effective solution for
data-sensitive domains, such as healthcare, financial services, etc., due to
its various attributes like immutability, non-repudiation, and availability.
Thus, implementation of this technology in various domains rose exponentially;
one of such fields is the healthcare supply chain. Managing healthcare supply
chain processes effectively is very crucial for the healthcare system. Despite
various innovations in the method of treatment methodologies, the healthcare
supply chain management system is not up to the mark and lacks efficiency. The
traditional healthcare supply chain system is time-consuming and lacks the work
synergy among the various stakeholders of the supply chain. Thus, In this
paper, we propose a framework based on blockchain smart contracts and
decentralized storage to connect all the supply chain stakeholders. Smart
contracts in the framework enforce and depict various interactions and
transactions among the stakeholders, thus helping to automate these processes,
promote transparency, improve efficiency, and minimize service time. The
preliminary results show that the proposed framework is more efficient, secure,
and economically feasible.


title:
Deep Convolutional Autoencoder for Assessment of Anomalies in
  Multi-stream Sensor Data

abstract:
  A fully convolutional autoencoder is developed for the detection of anomalies
in multi-sensor vehicle drive-cycle data from the powertrain domain.
Preliminary results collected on real-world powertrain data show that the
reconstruction error of faulty drive cycles deviates significantly relative to
the reconstruction of healthy drive cycles using the trained autoencoder. The
results demonstrate applicability for identifying faulty drive-cycles, and for
improving the accuracy of system prognosis and predictive maintenance in
connected vehicles.


title:
The dependency of spectral gaps on the convergence of the inverse
  iteration for a nonlinear eigenvector problem

abstract:
  In this paper we consider the generalized inverse iteration for computing
ground states of the Gross-Pitaevskii eigenvector problem (GPE). For that we
prove explicit linear convergence rates that depend on the maximum eigenvalue
in magnitude of a weighted linear eigenvalue problem. Furthermore, we show that
this eigenvalue can be bounded by the first spectral gap of a linearized
Gross-Pitaevskii operator, recovering the same rates as for linear eigenvector
problems. With this we establish the first local convergence result for the
basic inverse iteration for the GPE without damping. We also show how our
findings directly generalize to extended inverse iterations, such as the
Gradient Flow Discrete Normalized (GFDN) proposed in [W. Bao, Q. Du, SIAM J.
Sci. Comput., 25 (2004)] or the damped inverse iteration suggested in [P.
Henning, D. Peterseim, SIAM J. Numer. Anal., 53 (2020)]. Our analysis also
reveals why the inverse iteration for the GPE does not react favourably to
spectral shifts. This empirical observation can now be explained with a blow-up
of a weighting function that crucially contributes to the convergence rates.
Our findings are illustrated by numerical experiments.


title: Vortex Excitations of Dirac Bose-Einstein Condensates

abstract:
  We explore vortices in non-equilibrium Dirac Bose-Einstein condensates (Dirac
BEC) described by a stationary Dirac Gross-Pitaevskii equations (GPE). We find
that the multi-component structure of Dirac equation enables the difference in
phase winding of two condensates with respective phase winding number differing
by one, $\ell_a - \ell_b = \pm 1$. We observe three classes of vortex states
distinguished by their far-field behavior: A ring soliton on either of the two
components in combination with a vortex on the other component, and, in the
case of strong inter-component interactions, a vortex profile on both
components. The latter are multiple core vortices due to the phase winding
difference between the components. We also address the role of a Haldane gap on
these vortices, which has a similar effect than inter-component by making the
occupation on either sublattice more costly. We employ a numerical shooting
method to reliably identify vortex solutions and use it to scan large parts of
the phase space. We then use a classification algorithm on the integrated
wavefunctions to establish a phase diagram of the different topological
sectors.


title: Bayesian Optimisation for Active Monitoring of Air Pollution

abstract:
  Air pollution is one of the leading causes of mortality globally, resulting
in millions of deaths each year. Efficient monitoring is important to measure
exposure and enforce legal limits. New low-cost sensors can be deployed in
greater numbers and in more varied locations, motivating the problem of
efficient automated placement. Previous work suggests Bayesian optimisation is
an appropriate method, but only considered a satellite data set, with data
aggregated over all altitudes. It is ground-level pollution, that humans
breathe, which matters most. We improve on those results using hierarchical
models and evaluate our models on urban pollution data in London to show that
Bayesian optimisation can be successfully applied to the problem.


title:
A General Framework for Modelling Conditional Reasoning -- Preliminary
  Report

abstract:
  We introduce and investigate here a formalisation for conditionals that
allows the definition of a broad class of reasoning systems. This framework
covers the most popular kinds of conditional reasoning in logic-based KR: the
semantics we propose is appropriate for a structural analysis of those
conditionals that do not satisfy closure properties associated to classical
logics.


title:
Contrasting pseudo-criticality in the classical two-dimensional
  Heisenberg and $\mathrm{RP}^2$ models: zero-temperature phase transition
  versus finite-temperature crossover

abstract:
  Tensor-network methods are used to perform a comparative study of the
two-dimensional classical Heisenberg and $\mathrm{RP}^2$ models. We demonstrate
that uniform matrix product states (MPS) with explicit $\mathrm{SO}(3)$
symmetry can probe correlation lengths up to $\mathcal{O}(10^3)$ sites
accurately, and we study the scaling of entanglement entropy and universal
features of MPS entanglement spectra. For the Heisenberg model, we find no
signs of a finite-temperature phase transition, supporting the scenario of
asymptotic freedom. For the $\mathrm{RP}^2$ model we observe an abrupt onset of
scaling behaviour, consistent with hints of a finite-temperature phase
transition reported in previous studies. A careful analysis of the softening of
the correlation length divergence, the scaling of the entanglement entropy and
the MPS entanglement spectra shows that our results are inconsistent with true
criticality, but are rather in agreement with the scenario of a crossover to a
pseudo-critical region which exhibits strong signatures of nematic
quasi-long-range order at length scales below the true correlation length. Our
results reveal a fundamental difference in scaling behaviour between the
Heisenberg and $\mathrm{RP}^2$ models: Whereas the emergence of scaling in the
former shifts to zero temperature if the bond dimension is increased, it occurs
at a finite bond-dimension independent crossover temperature in the latter.


title: Blazar-Boosted Dark Matter at Super-Kamiokande

abstract:
  Dark matter particles near the center of a blazar, after being accelerated by
the elastic collisions with relativistic electrons and protons in the blazar
jet, can be energetic enough to trigger detectable signals at terrestrial
detectors. In this work, focusing on the blazars TXS 0506+056 and BL Lacertae,
we derive novel limits on the cross section of the elastic scattering between
dark matter and electrons by means of the available Super-Kamiokande data.
Thanks to the large blazar-boosted dark matter flux, the limit on the dark
matter-electron scattering cross section for dark matter masses below 100 MeV
can be as low as $\sim10^{-38}~\text{cm}^2$, that is about 5 orders of
magnitude stronger than the analogous results from galactic cosmic rays.


title:
Measuring frequency and period separations in red-giant stars using
  machine learning

abstract:
  Asteroseismology is used to infer the interior physics of stars. The
\textit{Kepler} and TESS space missions have provided a vast data set of
red-giant light curves, which may be used for asteroseismic analysis. These
data sets are expected to significantly grow with future missions such as
\textit{PLATO}, and efficient methods are therefore required to analyze these
data rapidly. Here, we describe a machine learning algorithm that identifies
red giants from the raw oscillation spectra and captures \textit{p} and
\textit{mixed} mode parameters from the red-giant power spectra. We report
algorithmic inferences for large frequency separation ($\Delta \nu$), frequency
at maximum amplitude ($\nu_{max}$), and period separation ($\Delta \Pi$) for an
ensemble of stars. In addition, we have discovered $\sim$25 new probable red
giants among 151,000 \textit{Kepler} long-cadence stellar-oscillation spectra
analyzed by the method, among which four are binary candidates which appear to
possess red-giant counterparts. To validate the results of this method, we
selected $\sim$ 3,000 \textit{Kepler} stars, at various evolutionary stages
ranging from subgiants to red clumps, and compare inferences of $\Delta \nu$,
$\Delta \Pi$, and $\nu_{max}$ with estimates obtained using other techniques.
The power of the machine-learning algorithm lies in its speed: it is able to
accurately extract seismic parameters from 1,000 spectra in $\sim$5 seconds on
a modern computer (single core of the Intel Xeon Platinum 8280 CPU).


title: Bayesian Imitation Learning for End-to-End Mobile Manipulation

abstract:
  In this work we investigate and demonstrate benefits of a Bayesian approach
to imitation learning from multiple sensor inputs, as applied to the task of
opening office doors with a mobile manipulator. Augmenting policies with
additional sensor inputs, such as RGB + depth cameras, is a straightforward
approach to improving robot perception capabilities, especially for tasks that
may favor different sensors in different situations. As we scale multi-sensor
robotic learning to unstructured real-world settings (e.g. offices, homes) and
more complex robot behaviors, we also increase reliance on simulators for cost,
efficiency, and safety. Consequently, the sim-to-real gap across multiple
sensor modalities also increases, making simulated validation more difficult.
We show that using the Variational Information Bottleneck (Alemi et al., 2016)
to regularize convolutional neural networks improves generalization to held-out
domains and reduces the sim-to-real gap in a sensor-agnostic manner. As a side
effect, the learned embeddings also provide useful estimates of model
uncertainty for each sensor. We demonstrate that our method is able to help
close the sim-to-real gap and successfully fuse RGB and depth modalities based
on understanding of the situational uncertainty of each sensor. In a real-world
office environment, we achieve 96% task success, improving upon the baseline by
+16%.


title: The Calabi-Yau problem for minimal surfaces with Cantor ends

abstract:
  We show that every connected compact or bordered Riemann surface contains a
Cantor set whose complement admits a complete conformal minimal immersion in
$\mathbb R^3$ with bounded image. The analogous result holds for holomorphic
immersions into any complex manifold of dimension at least $2$, for holomorphic
null immersions into $\mathbb C^n$ with $n\ge 3$, for holomorphic Legendrian
immersions into an arbitrary complex contact manifold, and for superminimal
immersions in an arbitrary self-dual or anti-self-dual Einstein four-manifold.


title:
Accelerating the convergence of Dynamic Iteration method with Restricted
  Additive Schwarz splitting for the solution of RLC circuits

abstract:
  The dynamic iteration method with a restricted additive Schwarz splitting is
investigated to co-simulate linear differential algebraic equations system
coming from RLC electrical circuit with linear components. We show the pure
linear convergence or divergence of the method with respect to the linear
operator belonging to the restricted additive Schwarz interface. It allows us
to accelerate it toward the true solution with the Aitken's technique for
accelerating convergence. This provides a dynamic iteration method less
sensitive to the splitting. Numerical examples with convergent and divergent
splitting show the efficiency of the proposed approach. We also test it on a
linear RLC circuit combining different types of circuit modeling (Transient
Stability model and Electro-Magnetic Transient model) with overlapping
partitions. Finally, some results for a weakly nonlinear differential algebraic
equations system are also provided.


title:
Fairness Indicators for Systematic Assessments of Visual Feature
  Extractors

abstract:
  Does everyone equally benefit from computer vision systems? Answers to this
question become more and more important as computer vision systems are deployed
at large scale, and can spark major concerns when they exhibit vast performance
discrepancies between people from various demographic and social backgrounds.
Systematic diagnosis of fairness, harms, and biases of computer vision systems
is an important step towards building socially responsible systems. To initiate
an effort towards standardized fairness audits, we propose three fairness
indicators, which aim at quantifying harms and biases of visual systems. Our
indicators use existing publicly available datasets collected for fairness
evaluations, and focus on three main types of harms and bias identified in the
literature, namely harmful label associations, disparity in learned
representations of social and demographic traits, and biased performance on
geographically diverse images from across the world.We define precise
experimental protocols applicable to a wide range of computer vision models.
These indicators are part of an ever-evolving suite of fairness probes and are
not intended to be a substitute for a thorough analysis of the broader impact
of the new computer vision technologies. Yet, we believe it is a necessary
first step towards (1) facilitating the widespread adoption and mandate of the
fairness assessments in computer vision research, and (2) tracking progress
towards building socially responsible models. To study the practical
effectiveness and broad applicability of our proposed indicators to any visual
system, we apply them to off-the-shelf models built using widely adopted model
training paradigms which vary in their ability to whether they can predict
labels on a given image or only produce the embeddings. We also systematically
study the effect of data domain and model size.