title:
Benchmark Evaluation of Counterfactual Algorithms for XAI: From a White
  Box to a Black Box

abstract:
  Counterfactual explanations have recently been brought to light as a
potentially crucial response to obtaining human-understandable explanations
from predictive models in Explainable Artificial Intelligence (XAI). Despite
the fact that various counterfactual algorithms have been proposed, the state
of the art research still lacks standardised protocols to evaluate the quality
of counterfactual explanations. In this work, we conducted a benchmark
evaluation across different model agnostic counterfactual algorithms in the
literature (DiCE, WatcherCF, prototype, unjustifiedCF), and we investigated the
counterfactual generation process on different types of machine learning models
ranging from a white box (decision tree) to a grey-box (random forest) and a
black box (neural network). We evaluated the different counterfactual
algorithms using several metrics including proximity, interpretability and
functionality for five datasets. The main findings of this work are the
following: (1) without guaranteeing plausibility in the counterfactual
generation process, one cannot have meaningful evaluation results. This means
that all explainable counterfactual algorithms that do not take into
consideration plausibility in their internal mechanisms cannot be evaluated
with the current state of the art evaluation metrics; (2) the counterfactual
generated are not impacted by the different types of machine learning models;
(3) DiCE was the only tested algorithm that was able to generate actionable and
plausible counterfactuals, because it provides mechanisms to constraint
features; (4) WatcherCF and UnjustifiedCF are limited to continuous variables
and can not deal with categorical data.


title:
Quantum Approximate Optimization Algorithm for Bayesian network
  structure learning

abstract:
  Bayesian network structure learning is an NP-hard problem that has been faced
by a number of traditional approaches in recent decades. Currently, quantum
technologies offer a wide range of advantages that can be exploited to solve
optimization tasks that cannot be addressed in an efficient way when utilizing
classic computing approaches. In this work, a specific type of variational
quantum algorithm, the quantum approximate optimization algorithm, was used to
solve the Bayesian network structure learning problem, by employing $3n(n-1)/2$
qubits, where $n$ is the number of nodes in the Bayesian network to be learned.
Our results showed that the quantum approximate optimization algorithm approach
offers competitive results with state-of-the-art methods and quantitative
resilience to quantum noise. The approach was applied to a cancer benchmark
problem, and the results justified the use of variational quantum algorithms
for solving the Bayesian network structure learning problem.


title:
Differentiable Control Barrier Functions for Vision-based End-to-End
  Autonomous Driving

abstract:
  Guaranteeing safety of perception-based learning systems is challenging due
to the absence of ground-truth state information unlike in state-aware control
scenarios. In this paper, we introduce a safety guaranteed learning framework
for vision-based end-to-end autonomous driving. To this end, we design a
learning system equipped with differentiable control barrier functions (dCBFs)
that is trained end-to-end by gradient descent. Our models are composed of
conventional neural network architectures and dCBFs. They are interpretable at
scale, achieve great test performance under limited training data, and are
safety guaranteed in a series of autonomous driving scenarios such as lane
keeping and obstacle avoidance. We evaluated our framework in a sim-to-real
environment, and tested on a real autonomous car, achieving safe lane following
and obstacle avoidance via Augmented Reality (AR) and real parked vehicles.


title: Band-Mott mixing hybridizes the gap in Fe$_2$Mo$_3$O$_8$

abstract:
  We combined optical spectroscopy and first principles electronic structure
calculations to reveal the charge gap in the polar magnet Fe$_2$Mo$_3$O$_8$.
Iron occupation on the octahedral site draws the gap strongly downward compared
to the Zn parent compound, and subsequent occupation of the tetrahedral site
creates a narrow resonance near the Fermi energy that draws the gap downward
even further. This resonance is a many-body effect that emanates from a flat
valence band in a Mott-like state due to screening of the local moment -
similar to expectations for a Zhang-Rice singlet, except that here, it appears
in a semi-conductor. We discuss the unusual hybridization in terms of orbital
occupation and character as well as the structure-property relationships that
can be unveiled in various metal-substituted systems (Ni, Mn, Co, Zn).


title:
Examining the Effects of Irradiation Temperature on Defect Generation
  and the Nature of Dislocation Loops

abstract:
  Unlike the vast amount of irradiated material data that exists for stainless
steel internals from LWRs, with high fast neutron flux and an irradiation
temperature of~330oC, the CANDU reactor is unique with a high thermal spectrum
stainless steel components peripheral to the core. In particular, the CANDU
design contains an austenitic stainless steel calandria vessel, which contains
the heavy water moderator at a temperature of 60-80oC. This article explores
the effects of low (60-80oC) and moderate (300-360oC) irradiation temperature
on irradiation induced defects and defect sinks, both in terms of
microstructure and mechanical properties of Grade 304L stainless steel,
irradiated with 3 MeV protons. State-of-the-art microscopy has been applied to
characterize the irradiation defects, and nano-indentation performed to provide
a link with the mechanical properties. It is hypothesized that the interstitial
type of loops that develop is highly temperature dependent, and the formation
of the defect loops is strongly linked with the amount of radiation induced
segregation.


title: A posteriori validation of generalized polynomial chaos expansions

abstract:
  Generalized polynomial chaos expansions are a powerful tool to study
differential equations with random coefficients, allowing in particular to
efficiently approximate random invariant sets associated to such equations. In
this work, we use ideas from validated numerics in order to obtain rigorous a
posteriori error estimates together with existence results about gPC expansions
of random invariant sets. This approach also provides a new framework for
conducting validated continuation, i.e. for rigorously computing isolated
branches of solutions in parameter-dependent systems, which generalizes in a
straightforward way to multi-parameter continuation. We illustrate the proposed
methodology by rigorously computing random invariant periodic orbits in the
Lorenz system, as well as branches and 2-dimensional manifolds of steady states
of the Swift-Hohenberg equation.


title: Averaging principle for stochastic complex Ginzburg-Landau equations

abstract:
  Averaging principle is an effective method for investigating dynamical
systems with highly oscillating components. In this paper, we study three types
of averaging principle for stochastic complex Ginzburg-Landau equations.
Firstly, we prove that the solution of the original equation converges to that
of the averaged equation on finite intervals as the time scale $\varepsilon$
goes to zero when the initial data are the same. Secondly, we show that there
exists a unique recurrent solution (in particular, periodic, almost periodic,
almost automorphic, etc.) to the original equation in a neighborhood of the
stationary solution of averaged equation when the time scale is small. Finally,
we establish the global averaging principle in weak sense, i.e. we show that
the attractor of original system tends to that of the averaged equation in
probability measure space as $\varepsilon$ goes to zero.


title: Research Opportunities in Plasma Astrophysics

abstract:
  Major scientific questions and research opportunities are described on 10
unprioritized plasma astrophysics topics: (1) magnetic reconnection, (2)
collisionless shocks and particle acceleration, (3) waves and turbulence, (4)
magnetic dynamos, (5) interface and shear instabilities, (6) angular momentum
transport, (7) dusty plasmas, (8) radiative hydrodynamics, (9) relativistic,
pair-dominated and strongly magnetized plasmas, (10) jets and outflows. Note
that this is a conference report from a Workshop on Opportunities in Plasma
Astrophysics (WOPA, https://w3.pppl.gov/conferences/2010/WOPA/) in January
2010, that attracted broad representation from the community and was supported
by the U.S. Department of Energy, National Aeronautics and Space
Administration, National Science Foundation, American Physical Society's
Topical Group for Plasma Astrophysics and Division of Plasma Physics, and
Center for Magnetic Self-Organization in Laboratory and Astrophysical Plasmas.
Although there has been much planning and many developments in both science and
infrastructure since the report was written, most of the motivation,
priorities, problems and technical challenges discussed therein remain
unaddressed and are relevant at the time of posting.

categories: physics.plasm-ph astro-ph.HE astro-ph.IM astro-ph.SR physics.space-ph


title:
Sparse InSAR Data 3D Inpainting for Ground Deformation Detection Along
  the Rail Corridor

abstract:
  Monitoring of ground movement close to the rail corridor, such as that
associated with landslips caused by ground subsidence and/or uplift, is of
great interest for the detection and prevention of possible railway faults.
Interferometric synthetic-aperture radar (InSAR) data can be used to measure
ground deformation, but its use poses distinct challenges, as the data is
highly sparse and can be particularly noisy. Here we present a scheme for
processing and interpolating noisy, sparse InSAR data into a dense
spatio-temporal stack, helping suppress noise and opening up the possibility
for treatment with deep learning and other image processing methods.


title: Gauge fields renormalization groups and thermofractals

abstract:
  The perturbative approach to QCD has been shown to be limited, and the
difficulties to obtain accurate calculations in the low-energy region seems to
be insurmountable. A recent approach uses the fractal structures of Yang-Mills
Field Theory to circumvent those difficulties, allowing for the determination
of an analytic expression for the running coupling. The results obtained are in
agreement with several experimental findings, and explain many of the observed
phenomena at high-energy collisions. In this work, we address some of the
conceptual aspects of the fractal approach, which are expressed in terms of the
renormalization group equation and the self-energy corrections to the parton
mass. We associate these well-known concepts with the origins of the fractal
structure in the quantum field theory.


title: From spin foams to area metric dynamics to gravitons

abstract:
  Although spin foams arose as quantizations of the length metric degrees of
freedom, the quantum configuration space is rather based on areas as more
fundamental variables. This is also highlighted by the semi-classical limit of
four-dimensional spin foam models, which is described by the Area Regge action.
Despite its central importance to spin foams the dynamics encoded by the Area
Regge action is only poorly understood, in particular in the continuum limit.
We perform here a systematic investigation of the dynamics defined by the Area
Regge action on a regular centrally subdivided hypercubical lattice. This
choice of lattice avoids many problems of the non-subdivided hypercubical
lattice, for which the Area Regge action is singular. The regularity of the
lattice allows to extract the continuum limit and its corrections, order by
order in the lattice constant. We show that, contrary to widespread
expectations which arose from the so-called flatness problem of spin foams, the
continuum limit of the Area Regge action does describe to leading order the
same graviton dynamics as general relativity. The next-to-leading order
correction to the effective action for the length metric is of second order in
the lattice constant, and is given by a quadratic term in the Weyl curvature
tensor. This correction can be understood to originate from an underlying
dynamics of area metrics. This suggests that the continuum limit of spin foam
dynamics does lead to massless gravitons, and that the leading order quantum
corrections can be understood to emerge from a generalization of the
configuration space from length to area metrics.


title:
A circumcentered-reflection method for finding common fixed points of
  firmly nonexpansive operators

abstract:
  The circumcentered-reflection method (CRM) has been recently proposed as a
methodology for accelerating several algorithms for solving the Convex
Feasibility Problem (CFP), equivalent to finding a common fixed-point of the
orthogonal projections onto a finite number of closed and convex sets. In this
paper, we apply CRM to the more general Fixed Point Problem (denoted as FPP),
consisting of finding a common fixed-point of operators belonging to a larger
family of operators, namely firmly nonexpansive operators. We prove than in
this setting, CRM is globally convergent to a common fixed-point (supposing at
least one exists). We also establish linear convergence of the sequence
generated by CRM applied to FPP, under a not too demanding error bound
assumption, and provide an estimate of the asymptotic constant. We provide
solid numerical evidence of the superiority of CRM when compared to the
classical Parallel Projections Method (PPM). Additionally, we present certain
results of convex combination of orthogonal projections, of some interest on
its own.


title:
A normal form for frequency comb and localized states in time-delayed
  Kerr-Gires-Tournois interferometers

abstract:
  We elucidate the mechanisms that underly the formation of temporal localized
states and frequency combs in vertical external-cavity Kerr-Gires-Tournois
interferometers. We reduce our first principle model based upon delay algebraic
equations to a minimal pattern formation scenario. It consists in a real cubic
Ginzburg-Landau equation modified by high-order effects such as third order
dispersion and nonlinear drift. The latter are responsible for generating
localized states via the locking of domain walls connecting the high and low
intensity levels of the injected micro-cavity. We interpret the effective
parameters of the normal form in relation with the configuration of the optical
setup. Comparing the two models, we observe an excellent agreement close to the
onset of bistability.


title:
Size-dependent mass absorption cross-section of soot particles from
  various sources

abstract:
  The mass absorption cross-section (MAC) of combustion-generated soot is used
in pollution and emissions measurements to quantify the mass concentration of
soot and in atmospheric modelling to predict the radiative effects of soot on
climate. Previous work has suggested that the MAC of soot particles may change
with their size, due to (1) internal scattering among monomers in the soot
aggregate, (2) the correlation of soot primary-particle diameter with aggregate
size, (3) quantum confinement effects, or (4) a size-dependent degree of soot
graphitization. Here, we report a size-dependent MAC for ex-situ soot sampled
from two commercially available diffusion-flame soot generators, one aviation
turbine engine, and one diesel generator. We also incorporate literature data.
We show that the MAC increases with aggregate size until a plateau is reached
at single particle masses between 4 and 30 fg (approximately 300-650 nm soot
mobility diameter). The smallest particles may have MACs 50% to 80% smaller
than the largest, depending on the source, while the largest particles have
MACs within the range reported by previous measurements on polydisperse
samples. Moreover, we show that models of hypotheses (1), (2), and (3) do not
describe our measurement results, leaving hypothesis (4) as the only remaining
candidate.


title: Shear and bulk acceleration viscosities in simple fluids

abstract:
  Inhomogeneities in the velocity field of a moving fluid are dampened by the
inherent viscous behaviour of the system. Both bulk and shear effects, related
to the divergence and the curl of the velocity field, are relevant. On
molecular time scales, beyond the Navier-Stokes description, memory plays an
important role. We demonstrate here on the basis of molecular and overdamped
Brownian dynamics many-body simulations that analogous viscous effects act on
the acceleration field. This acceleration viscous behaviour is associated with
the divergence and the curl of the acceleration field and it can be
quantitatively described using simple exponentially decaying memory kernels.
The simultaneous use of velocity and acceleration fields enables the
description of fast dynamics on molecular scales.


title:
Consequences of the Dresden-II reactor data for the weak mixing angle
  and new physics

abstract:
  The Dresden-II reactor experiment has recently reported a suggestive evidence
for the observation of coherent elastic neutrino-nucleus scattering, using a
germanium detector. Given the low recoil energy threshold, these data are
particularly interesting for a low-energy determination of the weak mixing
angle and for the study of new physics leading to spectral distortions at low
momentum transfer. Using two hypotheses for the quenching factor, we study the
impact of the data on: (i) The weak mixing angle at a renormalization scale of
$\sim 10\,\text{MeV}$, (ii) neutrino generalized interactions with light
mediators, (iii) the sterile neutrino dipole portal. The results for the weak
mixing angle show a strong dependence on the quenching factor choice. Although
still with large uncertainties, the Dresden-II data provide for the first time
a determination of $\sin^2\theta_W$ at such scale using coherent elastic
neutrino-nucleus scattering data. Tight upper limits are placed on the light
vector, scalar and tensor mediator scenarios. Kinematic constraints implied by
the reactor anti-neutrino flux and the ionization energy threshold allow the
sterile neutrino dipole portal to produce up-scattering events with sterile
neutrino masses up to $\sim 8\,$MeV. In this context, we find that limits are
also sensitive to the quenching factor choice, but in both cases competitive
with those derived from XENON1T data and more stringent that those derived with
COHERENT data, in the same sterile neutrino mass range.


title: Instantaneous support propagation for $\Lambda$-Fleming-Viot processes

abstract:
  For a probability-measure-valued neutral Fleming-Viot process $Z_t$ with
L\'evy mutation and resampling mechanism associated to a general
$\Lambda$-coalescent with multiple collisions, we prove the instantaneous
propagation of supports. That is, at any fixed time $t>0$, with probability one
the closed support $S(Z_t)$ of the Fleming-Viot process satisfies $S(\nu * Z_t)
\subseteq S(Z_t)$, where $\nu$ is the L\'evy measure of the mutation process.
To show this result, we apply Donnelly-Kurtz's lookdown particle representation
for Fleming-Viot process.


title: Nonreciprocal directional dichroism at telecom wavelengths

abstract:
  Magnetoelectrics with ultra-low symmetry and spin-orbit coupling are well
known to display a number of remarkable properties including nonreciprocal
directional dichroism. As a polar and chiral magnet, Ni$_3$TeO$_6$ is predicted
to host this effect in three fundamentally different configurations, although
only two have been experimentally verified. Inspired by the opportunity to
unravel the structure-property relations of such a unique light-matter
interaction, we combined magneto-optical spectroscopy and first-principles
calculations to reveal nonreciprocity in the toroidal geometry and compared our
findings with the chiral configurations. We find that formation of Ni toroidal
moments is responsible for the largest effects near 1.1 eV - a tendency that is
captured by our microscopic model and computational implementation. At the same
time, we demonstrate deterministic control of nonreciprocal directional
dichroism in Ni$_3$TeO$_6$ across the entire telecom wavelength range. This
discovery will accelerate the development of photonics applications that take
advantage of unusual symmetry characteristics.


title: Non-Markovian Quantum State Diffusion for Spin Environments

abstract:
  We introduce a theory of non-Markovian quantum state diffusion (NMQSD) for
quantum systems coupled to a class of non-Gaussian environments consisting of
spins. This allows us to express the time evolved reduced state of the system
as the average of an ensemble of stochastically evolving pure states. We
develop the theory for arbitrary linear spin environments at zero and also at
finite temperature. Moreover, we introduce a hierarchical expansion which
constitutes a method to numerically compute the time evolution of the
stochastic pure states, and thus to solve the open system problem.


title:
Off-shell Higgs Interpretations Task Force: Models and Effective Field
  Theories Subgroup Report

abstract:
  This report presents the results of the activities of the Models and
Effective Field Theories Subgroup of the Off-Shell Interpretations Task Force
in the LHC Higgs Working Group. The main goal of the subgroup was to discuss
and advance the potential impact of off-shell Higgs measurements on searches
for BSM physics carried out in the EFT framework or as benchmark model studies.
In the first contribution, the off-shell potential to resolve flat directions
in parameter space for on-shell measurements is studied. Furthermore, the
sensitivity of off-shell measurements to SMEFT dimension-6 operators for the gg
$\to$ ZZ process is discussed, and studies of explicit models that are testable
in off-shell production are reviewed. In the second contribution, the SMEFT
effects in the off-shell gluon fusion and electroweak processes are discussed.
Subsequently, the computation of integrated and differential effects using
SMEFT@NLO and MG5_aMC@NLO, or JHUGen and MCFM, is demonstrated. On that basis,
a study of the prospects of obtaining additional SMEFT constraints - beyond
those from global fits - by utilising the off-shell process is presented. For
clarification, a revised introduction, definition and discussion of the Higgs
basis parametrisation of the SMEFT is given in the third contribution. In short
notes on the SMEFT, the Higgs basis with an additional constraint is discussed
and relations between the Higgs and Warsaw bases are presented. Lastly, an
overview of EFT calculations and tools is given.


title:
Momentum-Resolved Exciton Coupling and Valley Polarization Dynamics in
  Monolayer WS$_2$

abstract:
  Coupling between exciton states across the Brillouin zone in monolayer
transition metal dichalcogenides can lead to ultrafast valley depolarization.
Using time- and angle-resolved photoemission, we present momentum- and
energy-resolved measurements of exciton coupling in monolayer WS$_2$. By
comparing full 4D ($k_x, k_y, E, t$) data sets after both linearly and
circularly polarized excitation, we are able to disentangle intervalley and
intravalley exciton coupling dynamics. Recording in the exciton binding energy
basis instead of excitation energy, we observe strong mixing between the
B$_{1s}$ exciton and A$_{n>1}$ states. The photoelectron energy and momentum
distributions observed from excitons populated via intervalley coupling (e.g.
K$^-$ $\rightarrow$ K$^+$) indicate that the dominant valley depolarization
mechanism conserves the exciton binding energy and center-of-mass momentum,
consistent with intervalley Coulomb exchange. On longer timescales, exciton
relaxation is accompanied by contraction of the momentum space distribution.


title:
Frequency Distribution of Acoustic Oscillation in the Solar Atmosphere
  During Flare Event

abstract:
  We present a study of multi-wavelength observations, of a C 2.3 solar flare
in Active Region NOAA 12353, observed on 2015 May 23, which reveal new
properties of acoustic waves in the flaring region. The space-, and
ground-based data measured by the HELioseismological Large Regions
Interferometric Device, operating at the Vacuum Tower Telescope, the
Atmospheric Imaging Assembly, and Helioseismic and Magnetic Imager on board the
Solar Dynamic Observatory, were used in this paper. First, using power spectra
of solar oscillations, we identified the dominant frequencies and their
location at seven different atmospheric levels before and after the flare
event. Second, based on AIA observations taken in six Extreme Ultraviolet
filters, we derived Differential Emission Measure (DEM) profiles and DEM maps
of the flare. Finally, we confirm the {\sigma}-classification of the magnetic
field in the active area, directly related to the flare. Our results are as
follows: the high-frequency waves ({\nu}>5 mHz) in the photosphere, in both
cases, before and after the flare, are generated at the foot-points of the
chromospheric loop, while in the chromosphere (H{\alpha} line), before the
event the power enhancement exhibits for maximum of flare emission, and after
the eruption the enhancement by all frequencies is observed only in the post
flare loop area. Moreover, the power of oscillation in the pores surrounding
area before the flare has a random character, while after the flare
oscillation's power is concentrated in the pore, and weakened outside of. We
conclude that the accurate detection of high-frequency acoustic waves in the
active regions can lead to faster and easier prediction of high-energy events.


title: Snowmass White Paper: S-matrix Bootstrap

abstract:
  The S-matrix Bootstrap originated on the idea that the S-matrix might be
fully constrained by global symmetries, crossing, unitarity, and analyticity
without relying on an underlying dynamical theory that may or may not be a
quantum field theory. Recently this approach was revived from a somewhat
different point of view. Using the same constraints, one numerically maps out
the (infinite-dimensional) space of allowed S-matrices that should contain all
consistent quantum field theories (and quantum theories of gravity). Moreover,
in the best case scenario one finds special points in the space that can be
identified with a certain quantum field theory of interest. In that case, the
approach allows the numerical computation of the S-matrix without relying on
the particular Lagrangian of the theory.
  In this white paper we summarize the state of the art and discuss the future
of the topic.


title: Some Results on Factorization of Monoids

abstract:
  Factorizations of monoids are studied. Two necessary and sufficient
conditions in terms of so-called descent 1-cocyles for a monoid to be
factorized through two submonoids are found. A full classification of those
factorizations of a monoid whose one factor is a subgroup of the monoid is
obtained. The relationship between monoid factorizations and non-abelian
cohomology of monoids is analyzed. Some applications to semi-direct product of
monoids are given.


title: Mirror Symmetry for open r-spin invariants

abstract:
  We show that a generating function for open $r$-spin enumerative invariants
produces a universal unfolding of the polynomial $x^r$. Further, the
coordinates parametrizing this universal unfolding are flat coordinates on the
Frobenius manifold associated to the Landau-Ginzburg model $(\mathbb{C},x^r)$
via Saito-Givental theory. This result provides evidence for the same
phenomenon to occur in higher dimension, proven in a sequel paper.


title: R-GCN: The R Could Stand for Random

abstract:
  The inception of Relational Graph Convolutional Networks (R-GCNs) marked a
milestone in the Semantic Web domain as it allows for end-to-end training of
machine learning models that operate on Knowledge Graphs (KGs). R-GCNs generate
a representation for a node of interest by repeatedly aggregating parametrised,
relation-specific transformations of its neighbours. However, in this paper, we
argue that the the R-GCN's main contribution lies in this "message passing"
paradigm, rather than the learned parameters. To this end, we introduce the
"Random Relational Graph Convolutional Network" (RR-GCN), which constructs
embeddings for nodes in the KG by aggregating randomly transformed random
information from neigbours, i.e., with no learned parameters. We empirically
show that RR-GCNs can compete with fully trained R-GCNs in both node
classification and link prediction settings. The implications of these results
are two-fold: on the one hand, our technique can be used as a quick baseline
that novel KG embedding methods should be able to beat. On the other hand, it
demonstrates that further research might reveal more parameter-efficient
inductive biases for KGs.


title:
Fractional Calder\'on problems and Poincar\'e inequalities on unbounded
  domains

abstract:
  We generalize many recent uniqueness results on the fractional Calder\'on
problem to cover the cases of all domains with nonempty exterior. The highlight
of our work is the characterization of uniqueness and nonuniqueness of partial
data inverse problems for the fractional conductivity equation on domains that
are bounded in one direction for conductivities supported in the whole
Euclidean space and decaying to a constant background conductivity at infinity.
We generalize the uniqueness proof for the fractional Calder\'on problem by
Ghosh, Salo and Uhlmann to a general abstract setting in order to use the full
strength of their argument. This allows us to observe that there are also
uniqueness results for many inverse problems for higher order local
perturbations of a lower order fractional Laplacian. We give concrete example
models to illustrate these curious situations and prove Poincar\'e inequalities
for the fractional Laplacians of any order on domains that are bounded in one
direction. We establish Runge approximation results in these general settings,
improve regularity assumptions also in the cases of bounded sets and prove
general exterior determination results. Counterexamples to uniqueness in the
inverse fractional conductivity problem with partial data are constructed in
another companion work.


title:
de Sitter Black Holes as Constrained States in the Euclidean Path
  Integral

abstract:
  Schwarzschild-de Sitter black holes have two horizons that are at different
temperatures for generic values of the black hole mass. Since the horizons are
out of equilibrium the solutions do not admit a smooth Euclidean continuation
and it is not immediately clear what role they play in the gravitational path
integral. We show that Euclidean SdS is a genuine saddle point of a certain
constrained path integral, providing a consistent Euclidean computation of the
probability $\sim e^{-(S_{dS}-S_{SdS})}$ to find a black hole in the de Sitter
bath.


title: Scaling and adiabaticity in a rapidly expanding gluon plasma

abstract:
  In this work we aim to gain qualitative insight on the far-from-equilibrium
behavior of the gluon plasma produced in the early stages of a heavy-ion
collision. It was recently discovered arXiv:1810.10554 that the distribution
functions of quarks and gluons in QCD effective kinetic theory (EKT) exhibit
self-similar "scaling" evolution with time-dependent scaling exponents long
before those exponents reach their pre-hydrodynamic fixed-point values. In this
work we shed light on the origin of this time-dependent scaling phenomenon in
the small-angle approximation to the Boltzmann equation. We first solve the
Boltzmann equation numerically and find that time-dependent scaling is a
feature of this kinetic theory, and that it captures key qualitative features
of the scaling of hard gluons in QCD EKT. We then proceed to study scaling
analytically and semi-analytically in this equation. We find that an
appropriate momentum rescaling allows the scaling distribution to be identified
as the instantaneous ground state of the operator describing the evolution of
the distribution function, and the approach to the scaling function is
described by the decay of the excited states. That is to say, there is a frame
in which the system evolves adiabatically. Furthermore, from the conditions for
adiabaticity we can derive evolution equations for the time-dependent scaling
exponents. In addition to the known free-streaming and BMSS fixed points, we
identify a new "dilute" fixed point when the number density becomes small
before hydrodynamization. Corrections to the fixed point exponents in the
small-angle approximation agree quantitatively with those found previously in
QCD EKT and arise from the evolution of the ratio between hard and soft scales.


title: Stabilization of light bullets in nonlinear metamaterial waveguides

abstract:
  In this paper, we carry out a theoretical investigation on the propagation of
spatio-temporal solitons (light bullets) in the nonlinear metamaterial
waveguides. Our theoretical study is based on the formulation of Lagrangian
variational analysis with a suitable ansatz followed by a split-step Fourier
method in confirming the outcomes former numerically. A particular emphasis is
given to obtain the conditions on the system parameters for stable dynamics in
negative as well as positive index regimes of metamaterial waveguides. Similar
to the conventional medium, the three-dimensional (3D) light bullets are highly
unstable in metamaterials with the Kerr type nonlinearity alone. However, in
the negative index regime of metamaterials, stable propagation of light bullets
may occur in the normal dispersion regime balancing with defocusing cubic
nonlinearity and focusing quintic nonlinearity. As in the conventional case,
the stable dynamics is also observed in the case of anomalous dispersion with
focusing cubic nonlinearity and defocusing quintic nonlinearity in the positive
index regime. To test the solitonic nature of the 3D light bullets in the
metamaterials, we also numerically investigate the collision dynamics of two
light bullets. The study shows that the spatio-temporal soliton propagates
without any change except perhaps some phase shift after a collision with
another spatio-temporal soliton in competing cubic and quintic nonlinear
metamaterials. The improper balancing between the linear and nonlinear effects
results to form the bullet molecules in a distorted form with a large amount of
energy after interaction and in the long run, oscillations of the light bullets
grow and the bullets become filaments. We have observed the same collision
dynamics in both the negative and positive refractive index regimes of the
metamaterial.


title: String topology in three flavours

abstract:
  We describe two major string topology operations, the Chas-Sullivan product
and the Goresky-Hingston coproduct, from geometric and algebraic perspectives.
The geometric construction uses Thom-Pontrjagin intersection theory while the
algebraic construction is phrased in terms of Hochschild homology. We give
computations of products and coproducts on lens spaces via geometric
intersection, and deduce that the coproduct distinguishes 3-dimensional lens
spaces. Algebraically, we describe the structure these operations define
together on the Tate-Hochschild complex. We use rational homotopy theory
methods to sketch the equivalence between the geometric and algebraic
definitions for simply connected manifolds and real coefficients, emphasizing
the role of configuration spaces. Finally, we study invariance properties of
the operations, both algebraically and geometrically.


title: Characterizing Renal Structures with 3D Block Aggregate Transformers

abstract:
  Efficiently quantifying renal structures can provide distinct spatial context
and facilitate biomarker discovery for kidney morphology. However, the
development and evaluation of the transformer model to segment the renal
cortex, medulla, and collecting system remains challenging due to data
inefficiency. Inspired by the hierarchical structures in vision transformer, we
propose a novel method using a 3D block aggregation transformer for segmenting
kidney components on contrast-enhanced CT scans. We construct the first cohort
of renal substructures segmentation dataset with 116 subjects under
institutional review board (IRB) approval. Our method yields the
state-of-the-art performance (Dice of 0.8467) against the baseline approach of
0.8308 with the data-efficient design. The Pearson R achieves 0.9891 between
the proposed method and manual standards and indicates the strong correlation
and reproducibility for volumetric analysis. We extend the proposed method to
the public KiTS dataset, the method leads to improved accuracy compared to
transformer-based approaches. We show that the 3D block aggregation transformer
can achieve local communication between sequence representations without
modifying self-attention, and it can serve as an accurate and efficient
quantification tool for characterizing renal structures.


title: Rethinking Efficient Lane Detection via Curve Modeling

abstract:
  This paper presents a novel parametric curve-based method for lane detection
in RGB images. Unlike state-of-the-art segmentation-based and point
detection-based methods that typically require heuristics to either decode
predictions or formulate a large sum of anchors, the curve-based methods can
learn holistic lane representations naturally. To handle the optimization
difficulties of existing polynomial curve methods, we propose to exploit the
parametric B\'ezier curve due to its ease of computation, stability, and high
freedom degrees of transformations. In addition, we propose the deformable
convolution-based feature flip fusion, for exploiting the symmetry properties
of lanes in driving scenes. The proposed method achieves a new state-of-the-art
performance on the popular LLAMAS benchmark. It also achieves favorable
accuracy on the TuSimple and CULane datasets, while retaining both low latency
(> 150 FPS) and small model size (< 10M). Our method can serve as a new
baseline, to shed the light on the parametric curves modeling for lane
detection. Codes of our model and PytorchAutoDrive: a unified framework for
self-driving perception, are available at:
https://github.com/voldemortX/pytorch-auto-drive .


title: Improving \textit{Tug-of-War} sketch using Control-Variates method

abstract:
  Computing space-efficient summary, or \textit{a.k.a. sketches}, of large
data, is a central problem in the streaming algorithm. Such sketches are used
to answer \textit{post-hoc} queries in several data analytics tasks. The
algorithm for computing sketches typically requires to be fast, accurate, and
space-efficient. A fundamental problem in the streaming algorithm framework is
that of computing the frequency moments of data streams. The frequency moments
of a sequence containing $f_i$ elements of type $i$, are the numbers
$\mathbf{F}_k=\sum_{i=1}^n {f_i}^k,$ where $i\in [n]$. This is also called as
$\ell_k$ norm of the frequency vector $(f_1, f_2, \ldots f_n).$ Another
important problem is to compute the similarity between two data streams by
computing the inner product of the corresponding frequency vectors. The seminal
work of Alon, Matias, and Szegedy~\cite{AMS}, \textit{a.k.a. Tug-of-war} (or
AMS) sketch gives a randomized sublinear space (and linear time) algorithm for
computing the frequency moments, and the inner product between two frequency
vectors corresponding to the data streams. However, the variance of these
estimates typically tends to be large. In this work, we focus on minimizing the
variance of these estimates. We use the techniques from the classical
Control-Variate method~\cite{Lavenberg} which is primarily known for variance
reduction in Monte-Carlo simulations, and as a result, we are able to obtain
significant variance reduction, at the cost of a little computational overhead.
We present a theoretical analysis of our proposal and complement it with
supporting experiments on synthetic as well as real-world datasets.


title:
The Machine Learning for Combinatorial Optimization Competition (ML4CO):
  Results and Insights

abstract:
  Combinatorial optimization is a well-established area in operations research
and computer science. Until recently, its methods have focused on solving
problem instances in isolation, ignoring that they often stem from related data
distributions in practice. However, recent years have seen a surge of interest
in using machine learning as a new approach for solving combinatorial problems,
either directly as solvers or by enhancing exact solvers. Based on this
context, the ML4CO aims at improving state-of-the-art combinatorial
optimization solvers by replacing key heuristic components. The competition
featured three challenging tasks: finding the best feasible solution, producing
the tightest optimality certificate, and giving an appropriate solver
configuration. Three realistic datasets were considered: balanced item
placement, workload apportionment, and maritime inventory routing. This last
dataset was kept anonymous for the contestants.


title:
A Comprehensive Analysis of WASP-17b's Transmission Spectrum from
  Space-Based Observations

abstract:
  Due to its 1770 K equilibrium temperature, WASP-17b, a 1.99 $R_\mathrm{Jup}$,
0.486 $M_\mathrm{Jup}$ exoplanet, sits at the critical juncture between hot and
ultra-hot Jupiters. We present its 0.3-5 $\mu m$ transmission spectrum, with
newly obtained with Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3)
measurements, and, taking advantage of improved analysis techniques, reanalysed
HST Space Telescope Imaging Spectrograph (STIS) and Spitzer Space Telescope
Infrared Array Camera (IRAC) observations. We achieve a median precision of 132
ppm, with a mean of 272 ppm across the whole spectrum. We additionally make use
of Transiting Exoplanet Survey Satellite (TESS) and ground-based transit
observations to refine the orbital period of WASP-17b. To interpret the
observed atmosphere, we make use of free and equilibrium chemistry retrievals
using the POSEIDON and ATMO retrieval codes respectively. We detect absorption
due to H$_2$O at $>7 \sigma$, and find evidence of absorption due to CO$_2$ at
$>3 \sigma$. We see no evidence of previously detected Na and K absorption.
Across an extensive suite of retrieval configurations, we find the data favours
a bimodal solution with high or low metallicity modes as a result of poor
constraints in the optical, and demonstrate the importance of using multiple
statistics for model selection. Future James Webb Space Telescope (JWST) GTO
observations, combined with the presented transmission spectrum, will enable
precise constraints on WASP-17b's atmosphere.


title: Open FJRW Theory and Mirror Symmetry

abstract:
  We construct an open enumerative theory for the Landau-Ginzburg (LG) model
$(\mathbb{C}^2, \mu_r\times \mu_s, x^r+y^s)$. The invariants are defined as
integrals of multisections of a Witten bundle with descendents over a moduli
space that is a real orbifold with corners. In turn, a generating function for
these open invariants yields the mirror LG model and a versal deformation of it
with flat coordinates. After establishing an open topological recursion result,
we prove an LG/LG open mirror symmetry theorem in dimension two with all
descendents. The open invariants we define are not unique but depend on
boundary conditions that, when altered, exhibit wall-crossing phenomena for the
invariants. We describe an LG wall-crossing group classifying the wall-crossing
transformations that can occur.


title: Bending the rules of low-temperature thermometry with periodic driving

abstract:
  There exist severe limitations on the accuracy of low-temperature
thermometry, which poses a major challenge for future quantum-technological
applications. Low-temperature sensitivity might be manipulated by tailoring the
interactions between probe and sample. Unfortunately, the tunability of these
interactions is usually very restricted. Here, we focus on a more practical
solution to boost thermometric precision -- driving the probe. Specifically, we
solve for the limit cycle of a periodically modulated linear probe in an
equilibrium sample. We treat the probe-sample interactions \textit{exactly} and
hence, our results are valid for arbitrarily low temperatures $ T $ and any
spectral density. We find that weak near-resonant modulation strongly enhances
the signal-to-noise ratio of low-temperature measurements, while causing
minimal back action on the sample. Furthermore, we show that near-resonant
driving changes the power law that governs thermal sensitivity over a broad
range of temperatures, thus `bending' the fundamental precision limits and
enabling more sensitive low-temperature thermometry. We then focus on a
concrete example -- impurity thermometry in an atomic condensate. We
demonstrate that periodic driving allows for a sensitivity improvement of
several orders of magnitude in sub-nanokelvin temperature estimates drawn from
the density profile of the impurity atoms. We thus provide a feasible upgrade
that can be easily integrated into low-$T$ thermometry experiments.


title:
One-to-one correspondence between thermal structure factors and coupling
  constants of general bilinear Hamiltonians

abstract:
  A theorem that establishes a one-to-one relation between zero-temperature
static spin-spin correlators and coupling constants for a general class of
quantum spin Hamiltonians bilinear in the spin operators has been recently
established by J. Quintanilla, using an argument in the spirit of the
Hohenberg-Kohn theorem in density functional theory. Quintanilla's theorem
gives a firm theoretical foundation to quantum spin Hamiltonian learning using
spin structure factors as input data. Here we extend the validity of the
theorem in two directions. First, following the same approach as Mermin, the
proof is extended to the case of finite-temperature spin structure factors,
thus ensuring that the application of this theorem to experimental data is
sound. Second, we note that this theorem applies to all types of Hamiltonians
expressed as sums of bilinear operators, so that it can also relate the
density-density correlators to the Coulomb matrix elements for interacting
electrons in the lowest Landau level.


title:
Distinct excitatory and inhibitory bump wandering in a stochastic neural
  field

abstract:
  Localized persistent cortical neural activity is a validated neural substrate
of parametric working memory. Such activity `bumps' represent the continuous
location of a cue over several seconds. Pyramidal (excitatory) and
interneuronal (inhibitory) subpopulations exhibit tuned bumps of activity,
linking neural dynamics to behavioral inaccuracies observed in memory recall.
However, many bump attractor models collapse these subpopulations into a single
joint excitatory/inhibitory (lateral inhibitory) population, and do not
consider the role of interpopulation neural architecture and noise
correlations. Both factors have a high potential to impinge upon the stochastic
dynamics of these bumps, ultimately shaping behavioral response variance. In
our study, we consider a neural field model with separate excitatory/inhibitory
(E/I) populations and leverage asymptotic analysis to derive a nonlinear
Langevin system describing E/I bump interactions. While the E bump attracts the
I bump, the I bump stabilizes but can also repel the E bump, which can result
in prolonged relaxation dynamics when both bumps are perturbed. Furthermore,
the structure of noise correlations within and between subpopulations strongly
shapes the variance in bump position. Surprisingly, higher interpopulation
correlations reduce variance.


title:
Comparing Generator Unavailability Models with Empirical Distributions
  from Open Energy Datasets

abstract:
  The modelling of power station outages is an integral part of power system
planning. In this work, models of the unavailability of the fleets of eight
countries in Northwest Europe are constructed and subsequently compared against
empirical distributions derived using data from the open-access ENTSO-e
Transparency Platform. Summary statistics of non-sequential models highlight
limitations with the empirical modelling, with very variable results across
countries. Additionally, analysis of time sequential models suggests a clear
need for fleet-specific analytic model parameters. Despite a number of
challenges and ambiguities associated with the empirical distributions, it is
suggested that a range of valuable qualitative and quantitative insights can be
gained by comparing these two complementary approaches for modelling and
understanding generator unavailabilities.


title: Properties of the Be-type stars in 30 Doradus

abstract:
  The evolutionary status of Be-type stars remains unclear, with both
single-star and binary pathways having been proposed. Here, VFTS spectroscopy
of 73 Be-type stars, in the spectral-type range, B0--B3, is analysed to
estimate projected rotational velocities, radial velocities and stellar
parameters. They are found to be rotating faster than the corresponding VFTS
B-type sample but simulations imply that their projected rotational velocities
are inconsistent with them all rotating at near critical velocities. The
de-convolution of the projected rotational velocities estimates leads to a mean
rotational velocity estimate of 320-350 km/s, approximately 100 km/s larger
than that for the corresponding B-type sample. There is a dearth of targets
with rotational velocities less than 0.4 of the critical velocity, with a broad
distribution reaching up to critical rotation. Our best estimate for the mean
or median of the rotational velocitiy is 0.68 of the critical velocity.
Rapidly-rotating B-type stars are more numerous than their Be-type
counterparts, whilst the observed frequency of Be-type stars identified as
binary systems is significantly lower than that for normal B-type stars,
consistent with their respective radial-velocity dispersions. The
semi-amplitudes for the Be-type binaries are also smaller. Similar results are
found for a SMC Be-type sample centred on NGC346 with no significant
differences being found between the two samples. These results are compared
with the predictions of single and binary stellar evolutionary models for
Be-type stars. Assuming that a single mechanism dominated the production of
classical Be-type stars, our comparison would favour a binary evolutionary
history.


title:
MISSILES: an Efficient Resolution of the Co-simulation Coupling
  Constraint on Nearly Linear Differential Systems through a Global Linear
  Formulation

abstract:
  In a co-simulation context, interconnected systems of differential equations
are solved separately but they regularly communicate data to one another during
these resolutions. Iterative co-simulation methods have been developed in order
to enhance both stability and accuracy. Such methods imply that the systems
must integrate one or more times per co-simulation step (the interval between
two consecutive communications) in order to find the best satisfying interface
values for exchanged data (according to a given coupling constraint). This
requires that every system involved in the modular model is capable of
rollback: the ability to re-integrate a time interval that has already been
integrated with different input commands. In a paper previously introduced by
Eguillon et al. in 2022, the COSTARICA process is presented and consists in
replacing the non-rollback-capable systems by an estimator on the non-last
integrations of the iterative process. The MISSILES algorithm, introduced in
this paper, consists in applying the COSTARICA process on every system of a
modular model simulated with the IFOSMONDI-JFM iterative co-simulation method
(introduced by Eguillon et al. in 2021). Indeed, in this case, the iterative
part on the estimators of each system can be avoided as the global resolution
on a co-simulation step can be written as a single global linear system to
solve. Consequently, MISSILES is a non-iterative method that leads to the same
solution than the IFOSMONDI-JFM iterative co-simulation method applied to
systems using the COSTARICA process to emulate the rollback.


title:
Counterexamples to uniqueness in the inverse fractional conductivity
  problem with partial data

abstract:
  We construct counterexamples for the partial data inverse problem for the
fractional conductivity equation in all dimensions on general bounded open
sets. In particular, we show that for any bounded domain $\Omega \subset
\mathbb{R}^n$ and any disjoint open sets $W_1,W_2 \Subset \mathbb{R}^n
\setminus \overline{\Omega}$ there always exist two positive, bounded, smooth,
conductivities $\gamma_1,\gamma_2$, $\gamma_1 \neq \gamma_2$, with equal
partial exterior Dirichlet-to-Neumann maps $\Lambda_{\gamma_1}f|_{W_2} =
\Lambda_{\gamma_2}f|_{W_2}$ for all $f \in C_c^\infty(W_1)$. The proof uses the
characterization of equal exterior data from another work of the authors in
combination with the maximum principle of fractional Laplacians. The main
technical difficulty arises from the requirement that the conductivities should
be strictly positive and have a special regularity property $\gamma_i^{1/2}-1
\in H^{2s,\frac{n}{2s}}(\mathbb{R}^n)$ for $i=1,2$. We also provide
counterexamples on domains that are bounded in one direction when $n \geq 4$ or
$s \in (0,n/4]$ when $n=2,3$ using a modification of the argument on bounded
domains.


title: Cyclic products and optimal traps in cyclic birth and death chains

abstract:
  A birth-death chain is a discrete-time Markov chain on the integers whose
transition probabilities $p_{i,j}$ are non-zero if and only if $|i-j|=1$. We
consider birth-death chains whose birth probabilities $p_{i,i+1}$ form a
periodic sequence, so that $p_{i,i+1}=p_{i \mod m}$ for some $m$ and
$p_0,\ldots,p_{m-1}$. The trajectory $(X_n)_{n=0,1,\ldots}$ of such a chain
satisfies a strong law of large numbers and a central limit theorem. We study
the effect of reordering the probabilities $p_0,\ldots,p_{m-1}$ on the velocity
$v=\lim_{n\to\infty} X_n/n$. The sign of $v$ is not affected by reordering, but
its magnitude in general is. We show that for Lebesgue almost every choice of
$(p_0,\ldots,p_{m-1})$, exactly $(m-1)!/2$ distinct speeds can be obtained by
reordering. We make an explicit conjecture of the ordering that minimises the
speed, and prove it for all $m\leq 7$. This conjecture is implied by a purely
combinatorial conjecture that we think is of independent interest.


title: Symmetry enhanced variational quantum eigensolver

abstract:
  The variational quantum-classical algorithms are the most promising approach
for achieving quantum advantage on near-term quantum simulators. Among these
methods, the variational quantum eigensolver has attracted a lot of attention
in recent years. While it is very effective for simulating the ground state of
many-body systems, its generalization to excited states becomes very resource
demanding. Here, we show that this issue can significantly be improved by
exploiting the symmetries of the Hamiltonian. The improvement is even more
effective for higher energy eigenstates. We introduce two methods for
incorporating the symmetries. In the first approach, called hardware symmetry
preserving, all the symmetries are included in the design of the circuit. In
the second approach, the cost function is updated to include the symmetries.
The hardware symmetry preserving approach indeed outperforms the second
approach. However, integrating all symmetries in the design of the circuit
could be extremely challenging. Therefore, we introduce hybrid symmetry
preserving method in which symmetries are divided between the circuit and the
classical cost function. This allows to harness the advantage of symmetries
while preventing sophisticated circuit design.


title: SFPN: Synthetic FPN for Object Detection

abstract:
  FPN (Feature Pyramid Network) has become a basic component of most SoTA one
stage object detectors. Many previous studies have repeatedly proved that FPN
can caputre better multi-scale feature maps to more precisely describe objects
if they are with different sizes. However, for most backbones such VGG, ResNet,
or DenseNet, the feature maps at each layer are downsized to their quarters due
to the pooling operation or convolutions with stride 2. The gap of
down-scaling-by-2 is large and makes its FPN not fuse the features smoothly.
This paper proposes a new SFPN (Synthetic Fusion Pyramid Network) arichtecture
which creates various synthetic layers between layers of the original FPN to
enhance the accuracy of light-weight CNN backones to extract objects' visual
features more accurately. Finally, experiments prove the SFPN architecture
outperforms either the large backbone VGG16, ResNet50 or light-weight backbones
such as MobilenetV2 based on AP score.


title:
AutoMap: Automatic Medical Code Mapping for Clinical Prediction Model
  Deployment

abstract:
  Given a deep learning model trained on data from a source site, how to deploy
the model to a target hospital automatically? How to accommodate heterogeneous
medical coding systems across different hospitals? Standard approaches rely on
existing medical code mapping tools, which have significant practical
limitations.
  To tackle this problem, we propose AutoMap to automatically map the medical
codes across different EHR systems in a coarse-to-fine manner: (1)
Ontology-level Alignment: We leverage the ontology structure to learn a coarse
alignment between the source and target medical coding systems; (2) Code-level
Refinement: We refine the alignment at a fine-grained code level for the
downstream tasks using a teacher-student framework.
  We evaluate AutoMap using several deep learning models with two real-world
EHR datasets: eICU and MIMIC-III. Results show that AutoMap achieves relative
improvements up to 3.9% (AUC-ROC) and 8.7% (AUC-PR) for mortality prediction,
and up to 4.7% (AUC-ROC) and 3.7% (F1) for length-of-stay estimation. Further,
we show that AutoMap can provide accurate mapping across coding systems.
Lastly, we demonstrate that AutoMap can adapt to the two challenging scenarios:
(1) mapping between completely different coding systems and (2) between
completely different hospitals.


title:
The coherent Ising machine with quantum feedback: the total and
  conditional master equation methods

abstract:
  We give a detailed theoretical derivation of the quantum master equation for
the coherent Ising machine. This is a quantum computational network with
feedback, that solves NP hard combinatoric problems, including the traveling
salesman problem and various extensions and analogs. There are two types of
master equation that are obtained, either conditional on the feedback current
or unconditional. We show that both types can be accurately simulated in a
scalable way using stochastic equations in the positive-P phase-space
representation. This depends on the nonlinearity present, and we use parameter
values that are typical of current experiments. However, while the two
approaches are in excellent agreement, they are not equivalent with regard to
efficiency. We find that unconditional simulation has much greater efficiency,
and is scalable to larger sizes. This is a case where too much knowledge is a
dangerous thing. Conditioning the simulations on the feedback current is not
essential to determining the success probability, but it greatly increases the
computational complexity. To illustrate the speed improvements obtained with
the unconditional approach, we carry out full quantum simulations of the
quantum master equation with up to 1000 nodes.


title:
The transition regime between traveling-wave and resonant parametric
  amplifier

abstract:
  Traveling wave parametric amplifiers based on kinetic or Josephson nonlinear
inductance are known to be microwave quantum limited amplifiers. Usually, a
perfectly impedance-matched model is used to describe their characteristics in
terms of standard coupled mode theory. In practice, the amplifiers are
unmatched nonlinear devices with finite length, exhibiting ripples in the
transmission. Since commonly used models fail to describe the ripples of real
parametric amplifiers, here we are introducing a theoretical approach with
non-negligible reflections, which provides their gain and bandwidth properly
for both 3-wave and 4-wave mixing. Predictions of the model are experimentally
demonstrated on two types of TWPA, based on coplanar waveguides with a central
wire consisting of i) high kinetic inductance superconductor, and ii) array of
2000 Josephson junctions.


title: Cumulant Methods for Electron-Phonon Problems I: Perturbative Expansions

abstract:
  In this work we investigate the ability of the cumulant expansion (CE) to
capture one-particle spectral information in electron-phonon coupled systems at
both zero and finite temperatures. In particular, we present a comprehensive
study of the second- and fourth-order CE for the one-dimensional Holstein model
as compared with numerically exact methods. We investigate both finite sized
systems as well as the approach to the thermodynamic limit, drawing
distinctions and connections between the behavior of systems in and away from
the thermodynamic limit that enable a greater understanding of the ability of
the CE to capture real-frequency information across the full range of wave
vectors. We find that for zero electronic momentum, the spectral function is
well described by the second-order CE at low and high temperatures. However,
for non-zero electronic momenta, the CE is only accurate at high temperature.
We analyze the fourth-order cumulant, and find that while it improves the
description of the short-time dynamics encoded in the one-particle Green's
function, it can introduce divergences in the time domain as well as unphysical
negative spectral weight in the spectral function. When well-behaved, the
fourth-order CE does provide notable accurate corrections to the second-order
CE. Finally, we use our results to comment on the use of the CE as a tool for
calculating transport behavior in the realistic ab initio modeling of
materials.


title:
Cumulant Methods for Electron-Phonon Problems II: The Self-Consistent
  Cumulant Expansion

abstract:
  In this work we present a self-consistent cumulant expansion (SC-CE) and
investigate its accuracy for the one-dimensional Holstein model with and
without phonon dispersion. We show that for finite lattices sizes, the
numerical integration of the SC-CE equations becomes unstable at long times.
This defect is partially ameliorated when studying systems in the thermodynamic
limit, enabling the demonstration that the SC-CE corrects many deficits of the
standard perturbative CE in the (non-dispersive) Holstein model. The natural
phonon damping that arises in the more realistic dispersed Holstein model
renders the SC-CE stable, allowing for a complete assessment of the method.
Here we find that self-consistency dramatically corrects many of the failures
found in the perturbative CE, but also introduces some unphysical features.
Finally, we comment on the potential use of SC-CE as a tool for calculating
Green's functions in generic many-body problems.


title: Mott Quantum Critical Points at finite doping

abstract:
  Strongly correlated materials often undergo a Mott metal-insulator
transition, which is tipically first-order, as a function of control parameters
like pressure. Upon doping, rich phase diagrams with competing instabilities
are found. Yet, the conceptual link between the interaction-driven Mott
transition and the finite-doping behavior lacks a clear connection with the
theory of critical phenomena. In a prototypical case of a first-order Mott
transition the surface associated with the equation of state for the
homogeneous system is "folded" so that in a range of parameters stable metallic
and insulating phases exist and are connected by an unstable metallic branch.
Here we show that tuning the chemical potential the zero-temperature equation
of state gradually unfolds. Under general conditions, we find that the Mott
transition evolves into a first-order transition between two metals, associated
to a phase separation region ending in a quantum critical point (QCP) at finite
doping. This scenario is here demonstrated solving a simple multi-orbital
Hubbard model relevant for the Iron-based superconductors, but its origin - the
splitting of the atomic ground state multiplet by a small energy scale, here
Hund's coupling - is much more general. A strong analogy with cuprate
superconductors is traced.


title:
Contextformer: A Transformer with Spatio-Channel Attention for Context
  Modeling in Learned Image Compression

abstract:
  Entropy modeling is a key component for high-performance image compression
algorithms. Recent developments in autoregressive context modeling helped
learning-based methods to surpass their classical counterparts. However, the
performance of those models can be further improved due to the underexploited
spatio-channel dependencies in latent space, and the suboptimal implementation
of context adaptivity. Inspired by the adaptive characteristics of the
transformers, we propose a transformer-based context model, a.k.a.
Contextformer, which generalizes the de facto standard attention mechanism to
spatio-channel attention. We replace the context model of a modern compression
framework with the Contextformer and test it on the widely used Kodak image
dataset. Our experimental results show that the proposed model provides up to
10% rate savings compared to the standard Versatile Video Coding (VVC) Test
Model (VTM) 9.1, and outperforms various learning-based models.


title:
Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost
  Monocular UAV

abstract:
  Unmanned aerial vehicles (UAVs) have been used for many applications in
recent years, from urban search and rescue, to agricultural surveying, to
autonomous underground mine exploration. However, deploying UAVs in tight,
indoor spaces, especially close to humans, remains a challenge. One solution,
when limited payload is required, is to use micro-UAVs, which pose less risk to
humans and typically cost less to replace after a crash. However, micro-UAVs
can only carry a limited sensor suite, e.g. a monocular camera instead of a
stereo pair or LiDAR, complicating tasks like dense mapping and markerless
multi-person 3D human pose estimation, which are needed to operate in tight
environments around people. Monocular approaches to such tasks exist, and dense
monocular mapping approaches have been successfully deployed for UAV
applications. However, despite many recent works on both marker-based and
markerless multi-UAV single-person motion capture, markerless single-camera
multi-person 3D human pose estimation remains a much earlier-stage technology,
and we are not aware of existing attempts to deploy it in an aerial context. In
this paper, we present what is thus, to our knowledge, the first system to
perform simultaneous mapping and multi-person 3D human pose estimation from a
monocular camera mounted on a single UAV. In particular, we show how to loosely
couple state-of-the-art monocular depth estimation and monocular 3D human pose
estimation approaches to reconstruct a hybrid map of a populated indoor scene
in real time. We validate our component-level design choices via extensive
experiments on the large-scale ScanNet and GTA-IM datasets. To evaluate our
system-level performance, we also construct a new Oxford Hybrid Mapping dataset
of populated indoor scenes.


title:
Optimal parabolic upper bound for the energy-momentum relation of a
  strongly coupled polaron

abstract:
  We consider the large polaron described by the Fr\"ohlich Hamiltonian and
study its energy-momentum relation defined as the lowest possible energy as a
function of the total momentum. Using a suitable family of trial states, we
derive an optimal parabolic upper bound for the energy-momentum relation in the
limit of strong coupling. The upper bound consists of a momentum independent
term that agrees with the predicted two-term expansion for the ground state
energy of the strongly coupled polaron at rest, and a term that is quadratic in
the momentum with coefficient given by the inverse of twice the classical
effective mass introduced by Landau and Pekar.


title: On the rank of the distance matrix of graphs

abstract:
  Let $G$ be a connected graph with $V(G)=\{v_1,\ldots,v_n\}$. The
$(i,j)$-entry of the distance matrix $D(G)$ of $G$ is the distance between
$v_i$ and $v_j$. In this article, using the well-known Ramsey's theorem, we
prove that for each integer $k\ge 2$, there is a finite amount of graphs whose
distance matrices have rank $k$. We exhibit the list of graphs with distance
matrices of rank $2$ and $3$. Besides, we study the rank of the distance
matrices of graphs belonging to a family of graphs with their diameters at most
two, the trivially perfect graphs. We show that for each $\eta\ge 1$ there
exists a trivially perfect graph with nullity $\eta$. We also show that for
threshold graphs, which are a subfamily of the family of trivially perfect
graphs, the nullity is bounded by one.


title:
Effect of magnetic field on fermions in graphene through
  time-oscillating potential

abstract:
  We study the effect of a magnetic field on Dirac fermions in graphene subject
to a scalar potential oscillating in time. Using the Floquet theory and
resonance approximation, we show that the energy spectrum exhibits extra
subbands resulted from the oscillating potential in addition to quantized
Landau levels. It is found that a current density can be generated in $ x $ and
$ y $-directions that is strongly dependent on the magnetic field and
potential. Our numerical analysis show that the energy spectrum possesses a
symmetry and the current density oscillates with different amplitudes under
various conditions.


title: The Arithmetic-Periodicity of \textsc{cut} for $\mathcal{C}=\{1,2c\}$

abstract:
  \textsc{cut} is a class of partition games played on a finite number of
finite piles of tokens. Each version of \textsc{cut} is specified by a cut-set
$\mathcal{C}\subseteq\mathbb{N}$. A legal move consists of selecting one of the
piles and partitioning it into $d+1$ nonempty piles, where $d\in\mathcal{C}$.
No tokens are removed from the game. It turns out that the nim-set for any
$\mathcal{C}=\{1,2c\}$ with $c\geq 2$ is arithmetic-periodic, which answers an
open question of \cite{par}. The key step is to show that there is a
correspondence between the nim-sets of \textsc{cut} for $\mathcal{C}=\{1,6\}$
and the nim-sets of \textsc{cut} for $\mathcal{C}=\{1,2c\}, c\geq 4$. The
result easily extends to the case of $\mathcal{C} = \{1, 2c_1, 2c_2, 2c_3,
...\}$, where $c_1,c_2, ... \geq 2$.


title:
Comprehension of Subtitles from Re-Translating Simultaneous Speech
  Translation

abstract:
  In simultaneous speech translation, one can vary the size of the output
window, system latency and sometimes the allowed level of rewriting. The effect
of these properties on readability and comprehensibility has not been tested
with modern neural translation systems. In this work, we propose an evaluation
method and investigate the effects on comprehension and user preferences. It is
a pilot study with 14 users on 2 hours of German documentaries or speeches with
online translations into Czech. We collect continuous feedback and answers on
factual questions. Our results show that the subtitling layout or flicker have
a little effect on comprehension, in contrast to machine translation itself and
individual competence. Other results show that users with a limited knowledge
of the source language have different preferences to stability and latency than
the users with zero knowledge. The results are statistically insignificant,
however, we show that our method works and can be reproduced in larger volume.


title:
From Simultaneous to Streaming Machine Translation by Leveraging
  Streaming History

abstract:
  Simultaneous Machine Translation is the task of incrementally translating an
input sentence before it is fully available. Currently, simultaneous
translation is carried out by translating each sentence independently of the
previously translated text. More generally, Streaming MT can be understood as
an extension of Simultaneous MT to the incremental translation of a continuous
input text stream. In this work, a state-of-the-art simultaneous sentence-level
MT system is extended to the streaming setup by leveraging the streaming
history. Extensive empirical results are reported on IWSLT Translation Tasks,
showing that leveraging the streaming history leads to significant quality
gains. In particular, the proposed system proves to compare favorably to the
best performing systems.


title:
Error Distribution Of The Euler Approximation Scheme For Stochastic
  Volterra Integral Equations

abstract:
  The purpose of this paper is to establish the convergence in distribution of
the normalized error in the Euler approximation scheme for stochastic Volterra
equations driven by a standard Brownian motion, with a kernel of the form
$(t-s)^\alpha$, where $\alpha \in (-\frac 12, \frac 12)$.


title: A Theory of Protocol Composition

abstract:
  Real-world communication protocols are often built out of a number of simpler
protocols that cater for some specific functionality (e.g., banking,
authentication). However much of the formal definitions of protocols used for
program verification treat protocols as monolithic units. Composition is
considered for implementations of a protocol, but not for the protocols
themselves as engineering components. We propose primitives and techniques for
the modular composition of protocols. Our notion of composition defines an
interleaving of two or more protocols in a way that satisfies user-specified
context-dependent constraints which serve to explain "contact points" between
the protocols. The resulting approach gives a theoretical basis for protocol
(re-)engineering based on a process calculus with constraint annotations. We
have implemented our approach as a tool for Erlang that supports generation of
protocol compositions with formal guarantees, and code generation/extraction.


title: Algebraic models for classifying spaces of fibrations

abstract:
  We prove new structural results for the rational homotopy type of the
classifying space $B\operatorname{aut}(X)$ of fibrations with fiber a simply
connected finite CW-complex $X$. We first study nilpotent covers of
$B\operatorname{aut}(X)$ and show that their rational cohomology groups are
algebraic representations of the associated transformation groups. For the
universal cover, this yields an extension of the Sullivan-Wilkerson theorem to
higher homotopy and cohomology groups. For the cover corresponding to the
kernel of the homology representation, this proves algebraicity of the
cohomology of the homotopy Torelli space. For the cover that classifies what we
call normal unipotent fibrations, we then prove the stronger result that there
exists a nilpotent dg Lie algebra $\mathfrak{g}(X)$ in algebraic
representations that models its equivariant rational homotopy type. This leads
to an algebraic model for the space $B\operatorname{aut}(X)$ and to a
description of its rational cohomology ring as the cohomology of a certain
arithmetic group $\Gamma(X)$ with coefficients in the Chevalley-Eilenberg
cohomology of $\mathfrak{g}(X)$. This has strong structural consequences for
the cohomology ring and, in certain cases, allows it to be completely
determined using invariant theory and calculations with modular forms. We
illustrate these points with concrete examples. As another application, we
significantly improve on certain results of Berglund-Madsen on self-homotopy
equivalences of highly connected even-dimensional manifolds, and we prove
parallel new results in odd dimensions.


title: Essential ideals represented by mod-annihilators of modules

abstract:
  Let $R$ be a commutative ring with unity, $M$ be a unitary $R$-module and $G$
a finite abelian group (viewed as a $\mathbb{Z}$-module). The main objective of
this paper is to study properties of mod-annihilators of $M$. For $x \in M$, we
study the ideals $[x : M] =\{r\in R | rM\subseteq Rx\}$ of $R$ corresponding to
mod-annihilator of $M$. We investigate that when $[x : M]$ is an essential
ideal of $R$. We prove that arbitrary intersection of essential ideals
represented by mod-annihilators is an essential ideal. We observe that $[x :
M]$ is injective if and only if $R$ is non-singular and the radical of $R/[x :
M]$ is zero. Moreover, if essential socle of $M$ is non-zero, then we show that
$[x : M]$ is the intersection of maximal ideals and $[x : M]^2 = [x : M]$.
Finally, we discuss the correspondence of essential ideals of $R$ and vertices
of the annihilating graphs realized by $M$ over $R$.


title:
Surviving The Barren Plateau in Variational Quantum Circuits with
  Bayesian Learning Initialization

abstract:
  Variational quantum-classical hybrid algorithms are seen as a promising
strategy for solving practical problems on quantum computers in the near term.
While this approach reduces the number of qubits and operations required from
the quantum machine, it places a heavy load on a classical optimizer. While
often under-appreciated, the latter is a computationally hard task due to the
barren plateau phenomenon in parameterized quantum circuits. The absence of
guiding features like gradients renders conventional optimization strategies
ineffective as the number of qubits increases. Here, we introduce the
fast-and-slow algorithm, which uses Bayesian Learning to identify a promising
region in parameter space. This is used to initialize a fast local optimizer to
find the global optimum point efficiently. We illustrate the effectiveness of
this method on the Bars-and-Stripes (BAS) quantum generative model, which has
been studied on several quantum hardware platforms. Our results move
variational quantum algorithms closer to their envisioned applications in
quantum chemistry, combinatorial optimization, and quantum simulation problems.


title:
Low-order preconditioning for the high-order finite element de Rham
  complex

abstract:
  In this paper we present a unified framework for constructing spectrally
equivalent low-order-refined discretizations for the high-order finite element
de Rham complex. This theory covers diffusion problems in $H^1$, $H({\rm
curl})$, and $H({\rm div})$, and is based on combining a low-order
discretization posed on a refined mesh with a high-order basis for N\'ed\'elec
and Raviart-Thomas elements that makes use of the concept of polynomial
histopolation (polynomial fitting using prescribed mean values over certain
regions). This spectral equivalence, coupled with algebraic multigrid methods
constructed using the low-order discretization, results in highly scalable
matrix-free preconditioners for high-order finite element problems in the full
de Rham complex. Additionally, a new lowest-order (piecewise constant)
preconditioner is developed for high-order interior penalty discontinuous
Galerkin (DG) discretizations, for which spectral equivalence results and
convergence proofs for algebraic multigrid methods are provided. In all cases,
the spectral equivalence results are independent of polynomial degree and mesh
size; for DG methods, they are also independent of the penalty parameter. These
new solvers are flexible and easy to use; any "black-box" preconditioner for
low-order problems can be used to create an effective and efficient
preconditioner for the corresponding high-order problem. A number of numerical
experiments are presented, based on an implmentation in the finite element
library MFEM. The theoretical properties of these preconditioners are
corroborated, and the flexibility and scalability of the method are
demonstrated on a range of challenging three-dimensional problems.


title: Random Information Sharing over Social Networks

abstract:
  This work studies the learning process over social networks under partial and
random information sharing. In traditional social learning, agents exchange
full information with each other while trying to infer the true state of
nature. We study the case where agents share information about only one
hypothesis, i.e., the trending topic, which can be randomly changing at every
iteration. We show that agents can learn the true hypothesis even if they do
not discuss it, at rates comparable to traditional social learning. We also
show that using one's own belief as a prior for estimating the neighbors'
non-transmitted components might create opinion clusters that prevent learning
with full confidence. This practice however avoids the complete rejection of
the truth.


title: Constant-roll, cosmic acceleration, and massive neutrinos

abstract:
  We propose a model to describe the late-time cosmic acceleration in the
context of the constant-roll model. By considering a coupling between massive
neutrinos and the quintessence, the onset of evolution of the quintessence is
related to the neutrinos' behavior. When the neutrinos become non-relativistic,
the dark energy density increases from zero and results in late-time cosmic
acceleration alleviating the coincidence problem. The quintessence potential is
such that it evolves under the constant-roll condition giving rise to
persistent late-time acceleration.


title:
Symbolic State Estimation with Predicates for Contact-Rich Manipulation
  Tasks

abstract:
  Manipulation tasks often require a robot to adjust its sensorimotor skills
based on the state it finds itself in. Taking peg-in-hole as an example: once
the peg is aligned with the hole, the robot should push the peg downwards.
While high level execution frameworks such as state machines and behavior trees
are commonly used to formalize such decision-making problems, these frameworks
require a mechanism to detect the high-level symbolic state. Handcrafting
heuristics to identify symbolic states can be brittle, and using data-driven
methods can produce noisy predictions, particularly when working with limited
datasets, as is common in real-world robotic scenarios. This paper proposes a
Bayesian state estimation method to predict symbolic states with predicate
classifiers. This method requires little training data and allows fusing noisy
observations from multiple sensor modalities. We evaluate our framework on a
set of real-world peg-in-hole and connector-socket insertion tasks,
demonstrating its ability to classify symbolic states and to generalize to
unseen tasks, outperforming baseline methods. We also demonstrate the ability
of our method to improve the robustness of manipulation policies on a real
robot.


title:
Do specific ion effects influence the physical chemistry of aqueous
  graphene-based supercapacitors? Perspectives from multiscale QMMD simulations

abstract:
  Whether or not specific ion effects determine the charge storage properties
of aqueous graphene and graphite based supercapcitors remains a highly debated
topic. In this work we present a multiscale quantum mechanics classical
molecular dynamics (QMMD) investigation of aqueous mono- and divalent salt
electrolytes in contact with fully polarizable charged graphene sheets. By
combining the electrochemical double layer (EDL) and quantum capacitance we
observe an increasing electrode specific capacitance with cationic radii and
charge. Counterintuitively, we determine that a switch in the cation adsorption
mechanism from inner to outer Helmholtz layers leads to negligible changes to
the EDL capacitance. However, the ability of ions (such as K) with a relatively
low hydration free energy to penetrate the inner Helmholtz plane and adsorb
directly on the electrode surface is found to slow their diffusion dynamics.
Our results show that surface effects underpin the behaviour of cations at the
interface and add a vital new perspective on trends in ion mobilities seen
under confinement.


title: What is a random surface?

abstract:
  Given $2n$ unit equilateral triangles, there are finitely many ways to glue
each edge to a partner. We obtain a random sphere-homeomorphic surface by
sampling uniformly from the gluings that produce a topological sphere. As $n$
tends to infinity, these random surfaces (appropriately scaled) converge in
law. The limit is a "canonical" sphere-homeomorphic random surface, much the
way Brownian motion is a canonical random path.
  Depending on how the surface space and convergence topology are specified,
the limit is the Brownian sphere, the peanosphere, the pure Liouville quantum
gravity sphere, or a certain conformal field theory. All of these objects have
concise definitions, and are all in some sense equivalent, but the equivalence
is highly non-trivial, building on hundreds of math and physics papers over the
past half century.
  More generally, the "continuum random surface embedded in $d$-dimensional
Euclidean space" makes a kind of sense for $d \in (-\infty, 25)$ even when $d$
is not a positive integer; and this can be extended to higher genus surfaces,
surfaces with boundary, and surfaces with marked points or other decoration.
  These constructions have deep roots in both mathematics and physics, drawing
from classical graph theory, complex analysis, probability and representation
theory, as well as string theory, planar statistical physics, random matrix
theory and a simple model for two-dimensional quantum gravity.
  We present here an informal, colloquium-level overview of the subject, which
we hope will be accessible to both newcomers and experts. We aim to answer, as
cleanly as possible, the fundamental question. What is a random surface?


title: Graph clustering with Boltzmann machines

abstract:
  Graph clustering is the process of grouping vertices into densely connected
sets called clusters. We tailor two mathematical programming formulations from
the literature, to this problem. In doing so, we obtain a heuristic
approximation to the intra-cluster density maximization problem. We use two
variations of a Boltzmann machine heuristic to obtain numerical solutions. For
benchmarking purposes, we compare solution quality and computational
performances to those obtained using a commercial solver, Gurobi. We also
compare clustering quality to the clusters obtained using the popular Louvain
modularity maximization method. Our initial results clearly demonstrate the
superiority of our problem formulations. They also establish the superiority of
the Boltzmann machine over the traditional exact solver. In the case of smaller
less complex graphs, Boltzmann machines provide the same solutions as Gurobi,
but with solution times that are orders of magnitude lower. In the case of
larger and more complex graphs, Gurobi fails to return meaningful results
within a reasonable time frame. Finally, we also note that both our clustering
formulations, the distance minimization and $K$-medoids, yield clusters of
superior quality to those obtained with the Louvain algorithm.


title: Backreacted D0/D4 background

abstract:
  We construct a supergravity background corresponding to a backreacted
D0/D4-brane system. The background is holographically dual to the Venecianno
limit of the Berkoos-Douglas matrix model. It is known that the localized D0/D4
system is unstable when the D0-branes are within the D4-branes. To circumvent
this difficulty we separate the D4s from the D0s, which are placed at the
origin, and restore the symmetry of the combined system by distributing the
D4-branes on a spherical shell around the D0-branes. The backreacted solution
is first obtained perturbatively in N_f/N_c and displayed analytically to 1st
order. A non-perturbative numerical solution is then presented.


title: Interpretable Off-Policy Learning via Hyperbox Search

abstract:
  Personalized treatment decisions have become an integral part of modern
medicine. Thereby, the aim is to make treatment decisions based on individual
patient characteristics. Numerous methods have been developed for learning such
policies from observational data that achieve the best outcome across a certain
policy class. Yet these methods are rarely interpretable. However,
interpretability is often a prerequisite for policy learning in clinical
practice. In this paper, we propose an algorithm for interpretable off-policy
learning via hyperbox search. In particular, our policies can be represented in
disjunctive normal form (i.e., OR-of-ANDs) and are thus intelligible. We prove
a universal approximation theorem that shows that our policy class is flexible
enough to approximate any measurable function arbitrarily well. For
optimization, we develop a tailored column generation procedure within a
branch-and-bound framework. Using a simulation study, we demonstrate that our
algorithm outperforms state-of-the-art methods from interpretable off-policy
learning in terms of regret. Using real-word clinical data, we perform a user
study with actual clinical experts, who rate our policies as highly
interpretable.


title:
Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning
  Algorithms

abstract:
  Understanding generalization in modern machine learning settings has been one
of the major challenges in statistical learning theory. In this context, recent
years have witnessed the development of various generalization bounds
suggesting different complexity notions such as the mutual information between
the data sample and the algorithm output, compressibility of the hypothesis
space, and the fractal dimension of the hypothesis space. While these bounds
have illuminated the problem at hand from different angles, their suggested
complexity notions might appear seemingly unrelated, thereby restricting their
high-level impact. In this study, we prove novel generalization bounds through
the lens of rate-distortion theory, and explicitly relate the concepts of
mutual information, compressibility, and fractal dimensions in a single
mathematical framework. Our approach consists of (i) defining a generalized
notion of compressibility by using source coding concepts, and (ii) showing
that the `compression error rate' can be linked to the generalization error
both in expectation and with high probability. We show that in the `lossless
compression' setting, we recover and improve existing mutual information-based
bounds, whereas a `lossy compression' scheme allows us to link generalization
to the rate-distortion dimension -- a particular notion of fractal dimension.
Our results bring a more unified perspective on generalization and open up
several future research directions.


title: Cooperative Task and Motion Planning for Multi-Arm Assembly Systems

abstract:
  Multi-robot assembly systems are becoming increasingly appealing in
manufacturing due to their ability to automatically, flexibly, and quickly
construct desired structural designs. However, effectively planning for these
systems in a manner that ensures each robot is simultaneously productive, and
not idle, is challenging due to (1) the close proximity that the robots must
operate in to manipulate the structure and (2) the inherent structural partial
orderings on when each part can be installed. In this paper, we present a task
and motion planning framework that jointly plans safe, low-makespan plans for a
team of robots to assemble complex spatial structures. Our framework takes a
hierarchical approach that, at the high level, uses Mixed-integer Linear
Programs to compute an abstract plan comprised of an allocation of robots to
tasks subject to precedence constraints and, at the low level, builds on a
state-of-the-art algorithm for Multi-Agent Path Finding to plan collision-free
robot motions that realize this abstract plan. Critical to our approach is the
inclusion of certain collision constraints and movement durations during
high-level planning, which better informs the search for abstract plans that
are likely to be both feasible and low-makespan while keeping the search
tractable. We demonstrate our planning system on several challenging assembly
domains with several (sometimes heterogeneous) robots with grippers or suction
plates for assembling structures with up to 23 objects involving Lego bricks,
bars, plates, or irregularly shaped blocks.


title: Active Phase for Activated Random Walks on the Lattice in all Dimensions

abstract:
  We show that the critical density of the Activated Random Walk model on
$\mathbb{Z}^d$ is strictly less than one when the sleep rate $\lambda$ is small
enough, and tends to $0$ when $\lambda\to 0$, in any dimension $d\geqslant 1$.
As far as we know, the result is new for $d=2$.
  We prove this by showing that, for high enough density and small enough sleep
rate, the stabilization time of the model on the $d$-dimensional torus is
exponentially large. To do so, we fix the the set of sites where the particles
eventually fall asleep, which reduces the problem to a simpler model with
density one. Taking advantage of the Abelian property of the model, we show
that the stabilization time stochastically dominates the escape time of a
one-dimensional random walk with a negative drift. We then check that this slow
phase for the finite volume dynamics implies the existence of an active phase
on the infinite lattice.


title: Interactions of zeros of of polynomials and multiplicity matrices

abstract:
  An $(m+1) \times (n+1)$ multiplicity matrix is a matrix $M = \begin{matrix}
\mu_{i,j} \end{matrix}$ with rows numerated by $i = 0,1,2,\ldots, m$ and
columns enumerated by $j = 0,1,2,\ldots, n$ whose coordinates are nonnegative
integers satisfying the two properties: (1) If $\mu_{i,j} \geq 1$, then $j \leq
n$ and $\mu_{i,j+1} = \mu_{i,j}-1$, and (2) $\sum_{i=0}^m \mu_{i.j} \leq n-j$
for all $j$.
  Let $f(x)$ be a polynomial of degree $n$ and let $f^{(j)}(x)$ be the $j$th
derivative of $f(x)$. Let $\Lambda = (\lambda_0, \lambda_1,\ldots, \lambda_m)$
be a set of real numbers. For $i \in \{1,\ldots, m\}$ and $j \in \{0,1,\ldots,
n\}$, let $ \mu_{i,j}$ be the multiplicity of $\lambda_i$ as a root of the
polynomial $f^{(j)}(x)$. The $(m+1) \times (n+1)$ matrix $M_f(\Lambda) =
\begin{matrix} \mu_{i,j} \end{matrix}$ is called the multiplicity matrix of the
polynomial $f(x)$ with respect to $\Lambda$. The problem is to describe the
multiplicity matrices that are multiplicity matrices of polynomials.


title: The Sherali-Adams Hierarchy for Promise CSPs through Tensors

abstract:
  We study the Sherali-Adams linear programming hierarchy in the context of
promise constraint satisfaction problems (PCSPs). We characterise when a level
of the hierarchy accepts an instance in terms of a homomorphism problem for an
appropriate multilinear structure obtained through a tensor power of the
constraint language. The geometry of this structure, which consists in a space
of tensors satisfying certain symmetries, allows then to establish
non-solvability of the approximate graph colouring problem via constantly many
levels of Sherali-Adams.
  Besides this primary application, our tensorisation construction introduces a
new tool to the study of hierarchies of algorithmic relaxations for
computational problems within (and, possibly, beyond) the context of constraint
satisfaction. In particular, we see it as a key step towards the algebraic
characterisation of the power of Sherali-Adams for PCSPs.


title:
Evaluation of Portable Programming Models to Accelerate LArTPC Detector
  Simulations

abstract:
  The Liquid Argon Time Projection Chamber (LArTPC) technology is widely used
in high energy physics experiments, including the upcoming Deep Underground
Neutrino Experiment (DUNE). Accurately simulating LArTPC detector responses is
essential for analysis algorithm development and physics model interpretations.
Accurate LArTPC detector response simulations are computationally demanding,
and can become a bottleneck in the analysis workflow. Compute devices such as
General-Purpose Graphics Processing Units (GPGPUs) have the potential to
substantially accelerate simulations compared to traditional CPU-only
processing. The software development that requires often carries the cost of
specialized code refactorization and porting to match the target hardware
architecture. With the rapid evolution and increased diversity of the computer
architecture landscape, it is highly desirable to have a portable solution that
also maintains reasonable performance. We report our ongoing effort in
evaluating Kokkos as a basis for this portable programming model using LArTPC
simulations in the context of the Wire-Cell Toolkit, a C++ library for LArTPC
simulations, data analysis, reconstruction and visualization.


title:
Didn't see that coming: a survey on non-verbal social human behavior
  forecasting

abstract:
  Non-verbal social human behavior forecasting has increasingly attracted the
interest of the research community in recent years. Its direct applications to
human-robot interaction and socially-aware human motion generation make it a
very attractive field. In this survey, we define the behavior forecasting
problem for multiple interactive agents in a generic way that aims at unifying
the fields of social signals prediction and human motion forecasting,
traditionally separated. We hold that both problem formulations refer to the
same conceptual problem, and identify many shared fundamental challenges:
future stochasticity, context awareness, history exploitation, etc. We also
propose a taxonomy that comprises methods published in the last 5 years in a
very informative way and describes the current main concerns of the community
with regard to this problem. In order to promote further research on this
field, we also provide a summarised and friendly overview of audiovisual
datasets featuring non-acted social interactions. Finally, we describe the most
common metrics used in this task and their particular issues.