title: Entanglement Features of Random Neural Network Quantum States

abstract:
  Restricted Boltzmann machines (RBMs) are a class of neural networks that have
been successfully employed as a variational ansatz for quantum many-body
wavefunctions. Here, we develop an analytic method to study quantum many-body
spin states encoded by random RBMs with independent, identically-distributed
complex Gaussian weights. By mapping the computation of ensemble-averaged
observables to statistical mechanics models, we are able to investigate the
parameter space of the RBM ensemble in the thermodynamic limit. We discover
qualitatively distinct wavefunctions by varying RBM parameters, which
correspond to distinct phases in the equivalent statistical mechanics model.
Notably, there is a regime in which the entanglement entropy exhibits
volume-law scaling and approaches the maximal bound expected for random quantum
states in the thermodynamic limit. However, these near-maximal entropy states
do not form quantum state designs and feature no level repulsion in the
entanglement spectrum. These fine-grained properties are capable of
discriminating between random Gaussian RBM states and truly random quantum
states.


title: Secondary GWs and PBHs in string inflation: formation and detectability

abstract:
  We derive the spectrum and analyse the detectability prospects of secondary
gravity waves (GWs) associated to primordial black hole (PBH) production in a
class of string inflationary models called Fibre Inflation. The inflationary
potential features a near inflection point that induces a period of ultra
slow-roll responsible for an enhancement of the scalar perturbations which can
lead to PBHs with different masses and contributions to dark matter (DM) in
agreement with current observational bounds, including CMB constraints on the
scalar spectral index and the tensor-to-scalar ratio. This enhancement of the
curvature perturbations sources secondary GWs which can be detected by either
LISA or DECIGO and BBO, depending on the GW frequency but regardless of the
amount of PBH DM since secondary GWs remain detectable even if the PBH
contribution to DM is exponentially suppressed. The possibility to see a
secondary GW signal is instead due to the presence of an ultra slow-roll epoch
between CMB horizon exit and the end of inflation.


title: Fractional soft limits

abstract:
  It is a common lore that the amplitude for a scattering process involving one
soft Nambu-Goldstone boson should scale like an integer power of the soft
momentum. We revisit this expectation by considering the $2 \to 2$ scattering
of phonons in solids. We show that, depending on the helicities of the phonons
involved in the scattering process, the scattering amplitude may in fact vanish
like a fractional power of the soft momentum. This is a peculiarity of the
$4$-point amplitude, which can be traced back to (1) the (spontaneous or
explicit) breaking of Lorentz invariance, and (2) the approximately collinear
kinematics arising when one of the phonons becomes soft. Our results extend to
the general class of non-relativistic shift-invariant theories of a vector
field.


title: Global Kinetic Modeling of the Intrabinary Shock in Spider Pulsars

abstract:
  Spider pulsars are compact binary systems composed of a millisecond pulsar
and a low-mass companion. The relativistic magnetically-dominated pulsar wind
impacts onto the companion, ablating it and slowly consuming its atmosphere.
The interaction forms an intrabinary shock, a proposed site of particle
acceleration. We perform global fully-kinetic particle-in-cell simulations of
the intrabinary shock, assuming that the pulsar wind consists of plane-parallel
stripes of alternating polarity and that the shock wraps around the companion.
We find that particles are efficiently accelerated via shock-driven
reconnection. We extract first-principles synchrotron spectra and lightcurves
which are in good agreement with X-ray observations: (1) the synchrotron
spectrum is nearly flat, $F_\nu\propto {\rm const}$; (2) when the pulsar spin
axis is nearly aligned with the orbital angular momentum, the light curve
displays two peaks, just before and after the pulsar eclipse (pulsar superior
conjunction), separated in phase by $\sim 0.8\, {\rm rad}$; (3) the peak flux
exceeds the one at inferior conjunction by a factor of ten. We demonstrate that
the double-peaked signature in the lightcurve is due to Doppler boosting in the
post-shock flow.


title: An absolute $\nu$ mass measurement with the DUNE experiment

abstract:
  Time of flight delay in the supernova neutrino signal offers a unique tool to
set model-independent constraints on the absolute neutrino mass. The presence
of a sharp time structure during a first emission phase, the so-called
neutronization burst in the electron neutrino flavor time distribution, makes
this channel a very powerful one. Large liquid argon underground detectors will
provide precision measurements of the time dependence of the electron neutrino
fluxes. We derive here a new $\nu$ mass sensitivity attainable at the future
DUNE far detector from a future supernova collapse in our galactic
neighborhood, finding a sub-eV reach under favorable scenarios. These values
are competitive with those expected for laboratory direct neutrino mass
searches.


title: MUSE spectroscopic observations of the Young Massive Cluster NGC1850

abstract:
  NGC1850 is the nearest Young Massive Cluster of the Local Group with a mass
similar to those of Galactic globular clusters. Recent studies have revealed an
extended morphology of its MSTO, which can be interpreted as a spread in either
age or internal rotation. An accurate spectroscopic determination of its
chemical properties is still missing. We analyse spectra obtained with MUSE in
adaptive optics mode of 1167 stars in both components of this cluster (NGC1850A
and NGC1850B). Thanks to this dataset, we measure an average metallicity of
<[M/H]>=-0.31 +/- 0.01, a mean Ba abundance of <[Ba/Fe]>=+0.40 +/- 0.02 and a
systemic radial velocity of <v_{LOS}>=251.1 +/- 0.3 km/s. The dispersion of the
radial velocities suggests a dynamical mass of log(M/Ms)=4.84 +/- 0.1, while no
significant systemic rotation is detected. We detect a significant bimodality
in OI line strength among the TO stars of NGC1850A with ~66% of stars with
[O/Fe]~-0.16 and the rest with no detectable line. The majority of O-weak stars
populate preferentially the red side of the MSTO and show H lines in emission,
suggesting that they are Be stars rotating close to their critical velocity.
Among normal MSTO stars, red stars have on average broader line profiles than
blue ones, suggesting a correlation between colour and rotational velocity. The
mean metallicity of this cluster lies at the metal-rich side of the metallicity
distribution of the LMC following its age-metallicity relation. The Ba and O
abundances agree with those measured in the bar of this galaxy. The observed
spread in OI line width among its MS stars can be interpreted as an effect of
rotational mixing occurring in the envelopes of O-weak stars. The correlation
between line broadening and colour suggests that the observed colour spread
among turn-off stars can be due to a wide range in rotational velocity covered
by these stars.


title:
Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and
  Equivariant Set-Based Neural Networks

abstract:
  Theoretical uncertainty limits our ability to extract cosmological
information from baryonic fields such as the thermal Sunyaev-Zel'dovich (tSZ)
effect. Being sourced by the electron pressure field, the tSZ effect depends on
baryonic physics that is usually modeled by expensive hydrodynamic simulations.
We train neural networks on the IllustrisTNG-300 cosmological simulation to
predict the continuous electron pressure field in galaxy clusters from
gravity-only simulations. Modeling clusters is challenging for neural networks
as most of the gas pressure is concentrated in a handful of voxels and even the
largest hydrodynamical simulations contain only a few hundred clusters that can
be used for training. Instead of conventional convolutional neural net (CNN)
architectures, we choose to employ a rotationally equivariant DeepSets
architecture to operate directly on the set of dark matter particles. We argue
that set-based architectures provide distinct advantages over CNNs. For
example, we can enforce exact rotational and permutation equivariance,
incorporate existing knowledge on the tSZ field, and work with sparse fields as
are standard in cosmology. We compose our architecture with separate,
physically meaningful modules, making it amenable to interpretation. For
example, we can separately study the influence of local and cluster-scale
environment, determine that cluster triaxiality has negligible impact, and
train a module that corrects for mis-centering. Our model improves by 70 % on
analytic profiles fit to the same simulation data. We argue that the electron
pressure field, viewed as a function of a gravity-only simulation, has inherent
stochasticity, and model this property through a conditional-VAE extension to
the network. This modification yields further improvement by 7 %, it is limited
by our small training set however. (abridged)


title:
Monitored Open Fermion Dynamics: Exploring the Interplay of Measurement,
  Decoherence and Free Hamiltonian Evolution

abstract:
  The interplay of unitary evolution and local measurements in many-body
systems gives rise to a stochastic state evolution, and to measurement-induced
phase transitions in the pure state entanglement. In realistic settings,
however, this dynamics may be spoiled by decoherence, e.g., dephasing, due to
coupling to an environment or measurement imperfections. We investigate the
impact of dephasing, and the inevitable evolution into a non-Gaussian, mixed
state, on the dynamics of monitored fermions. We approach it from three
complementary perspectives: (i) the exact solution of the conditional
master-equation for small systems; (ii) quantum trajectory simulations of
Gaussian states for large systems and (iii) a renormalization group analysis of
a bosonic replica field theory. For weak dephasing, constant monitoring
preserves a weakly mixed state, which displays a robust measurement-induced
phase transition between a critical and a pinned phase, as in the
decoherence-free case. At strong dephasing, we observe the emergence of a new
scale describing an effective temperature, which is accompanied with an
increased mixedness of the fermion density matrix. Remarkably, observables such
as density-density correlation functions or the subsystem parity still display
scale invariant behavior even in this strongly mixed phase. We interpret this
as a signature of gapless, classical diffusion, which is stabilized by the
balanced interplay of Hamiltonian dynamics, measurements and decoherence.


title:
DWIFOB: A Dynamically Weighted Inertial Forward-Backward Algorithm for
  Monotone Inclusions

abstract:
  We propose a novel dynamically weighted inertial forward-backward algorithm
(DWIFOB) for solving structured monotone inclusion problems. The scheme
exploits the globally convergent forward-backward algorithm with deviations in
[26] as the basis and combines it with the extrapolation technique used in
Anderson acceleration to improve local convergence. We also present a globally
convergent primal-dual variant of DWIFOB and numerically compare its
performance to the primal-dual method of Chambolle-Pock and a Tikhonov
regularized version of Anderson acceleration applied to the same problem. In
all our numerical evaluations, the primal-dual variant of DWIFOB outperforms
the Chambolle-Pock algorithm. Moreover, our numerical experiments suggest that
our proposed method is much more robust than the regularized Anderson
acceleration, which can fail to converge and be sensitive to algorithm
parameters. These numerical experiments highlight that our method performs very
well while still being robust and reliable.


title:
The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion
  Protostars VI. Insights from Radiative Transfer Modeling

abstract:
  We present Markov Chain Monte Carlo radiative transfer modeling of a joint
ALMA 345 GHz and spectral energy distribution dataset for a sample of 97
protostellar disks from the VLA and ALMA Nascent Disk and Multiplicity Survey
of Orion Protostars. From this modeling, we derive disk and envelope properties
for each protostar, allowing us to examine the bulk properties of a population
of young protostars. We find that disks are small, with a median dust radius of
$ 29.4^{+ 4.1}_{- 2.7}$ au and a median dust mass of $ 5.8^{+ 4.6}_{- 2.7}$
M$_{\oplus}$. We find no statistically significant difference between most
properties of Class 0, I, and Flat Spectrum sources with the exception of
envelope dust mass and inclination. The distinction between inclination is an
indication that the Class 0/I/Flat Spectrum system may be difficult to tie
uniquely to the evolutionary state of protostars. When comparing with Class II
disk dust masses in Taurus from similar radiative transfer modeling, we further
find that the trend of disk dust mass decreasing from Class 0 to Class II disks
is no longer present, though it remains unclear whether such a comparison is
fair due to differences in star forming region and modeling techniques.
Moreover, the disks we model are broadly gravitationally stable. Finally, we
compare disk masses and radii with simulations of disk formation and find that
magnetohydrodynamical effects may be important for reproducing the observed
properties of disks.


title: Spatio-temporal Vision Transformer for Super-resolution Microscopy

abstract:
  Structured illumination microscopy (SIM) is an optical super-resolution
technique that enables live-cell imaging beyond the diffraction limit.
Reconstruction of SIM data is prone to artefacts, which becomes problematic
when imaging highly dynamic samples because previous methods rely on the
assumption that samples are static. We propose a new transformer-based
reconstruction method, VSR-SIM, that uses shifted 3-dimensional window
multi-head attention in addition to channel attention mechanism to tackle the
problem of video super-resolution (VSR) in SIM. The attention mechanisms are
found to capture motion in sequences without the need for common motion
estimation techniques such as optical flow. We take an approach to training the
network that relies solely on simulated data using videos of natural scenery
with a model for SIM image formation. We demonstrate a use case enabled by
VSR-SIM referred to as rolling SIM imaging, which increases temporal resolution
in SIM by a factor of 9. Our method can be applied to any SIM setup enabling
precise recordings of dynamic processes in biomedical research with high
temporal resolution.


title: The complexity of quantum support vector machines

abstract:
  Quantum support vector machines employ quantum circuits to define the kernel
function. It has been shown that this approach offers a provable exponential
speedup compared to any known classical algorithm for certain data sets. The
training of such models corresponds to solving a convex optimization problem
either via its primal or dual formulation. Due to the probabilistic nature of
quantum mechanics, the training algorithms are affected by statistical
uncertainty, which has a major impact on their complexity. We show that the
dual problem can be solved in $\mathcal{O}(M^{4.67}/\varepsilon^2)$ quantum
circuit evaluations, where $M$ denotes the size of the data set and
$\varepsilon$ the solution accuracy. We prove under an empirically motivated
assumption that the kernelized primal problem can alternatively be solved in
$\mathcal{O}(\min \{ M^2/\varepsilon^6, \, 1/\varepsilon^{10} \})$ evaluations
by employing a generalization of a known classical algorithm called Pegasos.
Accompanying empirical results demonstrate these analytical complexities to be
essentially tight. In addition, we investigate a variational approximation to
quantum support vector machines and show that their heuristic training achieves
considerably better scaling in our experiments.


title:
Order, Disorder and Monopole Confinement in the Spin-$1/2$ XXZ Model on
  a Pyrochlore Tube

abstract:
  We study the ground state and thermodynamic properties of the spin-half XXZ
model, with an Ising interaction $J_z$ and a transverse exchange interaction
$J_{x}$, on a pyrochlore tube obtained by joining together elementary cubes in
a one-dimensional array. Periodic boundary conditions in the transverse
directions ensure that the bulk of the system consists of corner-sharing
tetrahedra, with the same local geometry as the pyrochlore lattice. We use
exact diagonalization, the density matrix renormalization group (DMRG), and
minimally entangled typical thermal states (METTS) methods to study the system.
When $J_z$ is antiferromagnetic ($J_{z}>0$) and $J_x$ is ferromagnetic
($J_{x}<0$), we find a transition from a spin liquid to an XY ferromagnet,
which has power-law correlations at $T=0$. For $J_{z}<0$ and $J_{x}>0$,
spin-two excitations are found to have lower energy than spin-one at the
transition away from the fully polarized state, showing evidence for incipient
spin-nematic order. When both interactions are antiferromagnetic, we find a
non-degenerate ground state with no broken symmetries and a robust energy gap.
The low energy spectra evolve smoothly from predominantly Ising to
predominantly XY interactions. In the spin-liquid regime of small $|J_{x}|$, we
study the confinement of monopole-anti-monopole pairs and find that the
confinement length scale is larger for $J_x<0$ than for $J_{x}>0$, although
both length scales are very short. These results are consistent with a local
spin-liquid phase for the Heisenberg antiferromagnet with no broken symmetries.


title: QCD Analysis of $\Delta S=0$ Hadronic Parity Violation

abstract:
  We present a QCD analysis of the effective weak Hamiltonian at hadronic
energy scales for strangeness-nonchanging ($\Delta S=0$) hadronic processes.
Performing a leading-order renormalization group analysis in QCD from the $W$
to the ${\cal O}(2\,\rm GeV)$ energy scale, we derive the pertinent effective
Hamiltonian for hadronic parity violation, including the effects of both
neutral and charged weak currents. We compute the complete renormalization
group evolution of all isosectors and the evolution through heavy-flavor
thresholds for the first time. We show that the additional four-quark operators
that enter below the $W$ mass scale from QCD operator mixing effects form a
closed set, and they result in a 12X12 anomalous dimension matrix. We use the
resulting effective Hamiltonian to determine the parity-violating meson-nucleon
coupling constants, $h_\pi^1$, $h_\rho^{0,1,2}$, $h_\omega^{0,1}$, employing
the factorization Ansatz and assessments of the pertinent quark charges of the
nucleon in lattice QCD at the 2 GeV scale. Using these results we connect to
earlier calculations of low-energy, hadronic parity-violating observables in
few-nucleon systems to make theoretical predictions that we compare with
experimental results, which affirm important roles for each of the isosectors.


title:
Influence of tidal dissipation on outcomes of binary-single encounters
  between stars and black holes in stellar clusters

abstract:
  In the cores of dense stellar clusters, close gravitational encounters
between binary and single stars can frequently occur. Using the Tsunami code,
we computed the outcome of a large number of binary-single interactions
involving two black holes (BHs) and a star to check how the inclusion of
orbital energy losses due to tidal dissipation can change the outcome of these
chaotic interactions. Each interaction was first simulated without any
dissipative processes and then we systematically added orbital energy losses
due to gravitational wave emission (using post-Newtonian (PN) corrections) and
dynamical tides and recomputed the interactions. We find that the inclusion of
tides increases the number of BH-star mergers by up to 75 per cent but it does
not affect the number of BH-BH mergers. These results highlight the importance
of including orbital energy dissipation due to dynamical tides during few-body
encounters and evolution of close binary systems within stellar cluster
simulations. Consistent with previous studies, we find that the inclusion of PN
terms increases the number of BH-BH mergers during binary-single encounters.
However, BH-star mergers are largely unaffected by the inclusion of these
terms.


title:
Can Mean Field Control (MFC) Approximate Cooperative Multi Agent
  Reinforcement Learning (MARL) with Non-Uniform Interaction?

abstract:
  Mean-Field Control (MFC) is a powerful tool to solve Multi-Agent
Reinforcement Learning (MARL) problems. Recent studies have shown that MFC can
well-approximate MARL when the population size is large and the agents are
exchangeable. Unfortunately, the presumption of exchangeability implies that
all agents uniformly interact with one another which is not true in many
practical scenarios. In this article, we relax the assumption of
exchangeability and model the interaction between agents via an arbitrary
doubly stochastic matrix. As a result, in our framework, the mean-field `seen'
by different agents are different. We prove that, if the reward of each agent
is an affine function of the mean-field seen by that agent, then one can
approximate such a non-uniform MARL problem via its associated MFC problem
within an error of $e=\mathcal{O}(\frac{1}{\sqrt{N}}[\sqrt{|\mathcal{X}|} +
\sqrt{|\mathcal{U}|}])$ where $N$ is the population size and $|\mathcal{X}|$,
$|\mathcal{U}|$ are the sizes of state and action spaces respectively. Finally,
we develop a Natural Policy Gradient (NPG) algorithm that can provide a
solution to the non-uniform MARL with an error
$\mathcal{O}(\max\{e,\epsilon\})$ and a sample complexity of
$\mathcal{O}(\epsilon^{-3})$ for any $\epsilon >0$.


title: 4-8 GHz Fourier-domain Searches for Galactic Center Pulsars

abstract:
  The Galactic Center (GC), with its high density of massive stars, is a
promising target for radio transient searches. In particular, the discovery and
timing of a pulsar orbiting the central supermassive black hole (SMBH) of our
Galaxy will enable stringent strong-field tests of gravity and accurate
measurements of SMBH properties. We performed multi-epoch 4-8 GHz observations
of the inner $\approx$ 15 pc of our Galaxy using the Robert C. Byrd Green Bank
Telescope in 2019 August-September. Our investigations constitute the most
sensitive 4-8 GHz GC pulsar survey conducted to date, reaching down to a 6.1
GHz pseudo-luminosity threshold of $\approx$ 1 mJy kpc$^2$ for a pulse duty
cycle of 2.5$\%$. We searched our data in the Fourier domain for periodic
signals incorporating a constant or linearly changing line-of-sight pulsar
acceleration. We report the successful detection of the GC magnetar PSR
J1745$-$2900 in our data. Our pulsar searches yielded a non-detection of novel
periodic astrophysical emissions above a 6$\sigma$ detection threshold in
harmonic-summed power spectra. We reconcile our non-detection of GC pulsars
with inadequate sensitivity to a likely GC pulsar population dominated by
millisecond pulsars. Alternatively, close encounters with compact objects in
the dense GC environment may scatter pulsars away from the GC. The dense
central interstellar medium may also favorably produce magnetars over pulsars.


title:
Fluid-structure interaction of a bio-inspired passively deployable flap
  for lift enhancement

abstract:
  Birds have a remarkable ability to perform complex maneuvers at post-stall
angles of attack. The passive deployment of self-actuating covert feathers in
response to unsteady flow separation while performing such maneuvers provides a
passive flow control paradigm for these aerodynamic capabilities. Most studies
involving covert-feathers-inspired passive flow control have modeled the
feathers as a rigidly attached or a freely moving flap on a wing. A flap
mounted via a torsional spring enables a configuration more emblematic of the
finite stiffness associated with the covert-feather dynamics. The performance
benefits and flow physics associated with this more general case remain largely
unexplored. In this work, we model covert feathers as a passively deployable,
torsionally hinged flap on the suction surface of a stationary airfoil. We
numerically investigate this airfoil-flap system at a low Reynolds number of
$Re=1{,}000$ and angle of attack of $20^\circ$ by performing high-fidelity
nonlinear simulations using a projection-based immersed boundary method. A
parametric study performed by varying the stiffness of the spring, mass of the
flap and location of the hinge yielded lift improvements as high as 27%
relative to the baseline flap-less case and revealed two dominant flow
behavioral regimes. A detailed analysis revealed that the stiffness-dependent
mean flap deflection and inertia-dependent amplitude and phase of flap
oscillations altered the dominant flow characteristics in both the regimes. Of
special interest for performance benefits were the flap parameters that
enhanced the lift-conducive leading-edge vortex while weakening the
trailing-edge vortex and associated detrimental effect of upstream propagation
of reverse flow. These parameters also yielded a favorable temporal
synchronization of flap oscillations with the vortex-shedding process in both
regimes.


title: Entanglement based tomography to probe new macroscopic forces

abstract:
  Quantum entanglement provides a novel way to test short distance physics in
the non-relativistic regime. We will provide a protocol to {\it potentially}
test new physics by bringing two charged massive Schr\"odinger cat states
adjacent to each other. Being charged, the two superpositions will be entangled
via electromagnetic interactions mediated by the photons, including the Coulomb
and the Casimir-Polder potential. We will bring a method of {\it entanglement
based tomography} to seek time evolution of very small entanglement phases to
probe new physical effects mediated by {\it hitherto unknown macroscopic force}
which might be responsible for entangling the two charged superpositions
modelled by the Yukawa type potential. We will be able to constrain the Yukawa
couplings $\alpha \geq 10^{-35}{\rm Kg~m^2~s^{-2}}$ for $r\geq 10^{-6}$m for
new physics occurring in the electromagnetic sector, and in the gravitational
potential $\alpha_g \geq 10^{-8}$ for $r \geq 10^{-3}$m.


title: Manipulating organic semiconductor morphology with visible light

abstract:
  We present a method to manipulate the final morphology of roll-to-roll
slot-die coated poly(3-hexylthiophene) (P3HT) by optically exciting the p-type
polymer in solution while coating. Our results provide a comprehensive picture
of the entire knowledge chain, from demonstrating how to apply our method to a
fundamental understanding of the changes in morphology and physical properties
induced by exciting P3HT while coating. By combining results from density
functional theory and molecular dynamics simulations with a variety of X-ray
experiments, absorption spectroscopy, and THz spectroscopy, we demonstrate the
relationship between morphology and physical properties of the thin film.
Specifically, in P3HT films excited with light during deposition, we observe
changes in crystallinity and texture with more face-on orientation and
increased out-of-plane charge mobility.


title:
FIRE-3: Updated Stellar Evolution Models, Yields, & Microphysics and
  Fitting Functions for Applications in Galaxy Simulations

abstract:
  Increasingly, uncertainties in predictions from galaxy formation simulations
(at sub-Milky Way masses) are dominated by uncertainties in stellar evolution
inputs. In this paper, we present the full set of updates from the FIRE-2
version of the Feedback In Realistic Environments (FIRE) project code, to the
next version, FIRE-3. While the transition from FIRE-1 to FIRE-2 focused on
improving numerical methods, here we update the stellar evolution tracks used
to determine stellar feedback inputs, e.g. stellar mass-loss (O/B and AGB),
spectra (luminosities and ionization rates), and supernova rates (core-collapse
and Ia), as well as detailed mass-dependent yields. We also update the
low-temperature cooling and chemistry, to enable improved accuracy at $T
\lesssim 10^{4}\,$K and densities $n\gg 1\,{\rm cm^{-3}}$, and the
meta-galactic ionizing background. All of these synthesize newer empirical
constraints on these quantities and updated stellar evolution and yield models
from a number of groups, addressing different aspects of stellar evolution. To
make the updated models as accessible as possible, we provide fitting functions
for all of the relevant updated tracks, yields, etc, in a form specifically
designed so they can be directly 'plugged in' to existing galaxy formation
simulations. We also summarize the default FIRE-3 implementations of 'optional'
physics, including spectrally-resolved cosmic rays and supermassive black hole
growth and feedback.

categories: astro-ph.GA astro-ph.CO astro-ph.HE astro-ph.IM astro-ph.SR


title:
A Recurrent Differentiable Engine for Modeling Tensegrity Robots
  Trainable with Low-Frequency Data

abstract:
  Tensegrity robots, composed of rigid rods and flexible cables, are difficult
to accurately model and control given the presence of complex dynamics and high
number of DoFs. Differentiable physics engines have been recently proposed as a
data-driven approach for model identification of such complex robotic systems.
These engines are often executed at a high-frequency to achieve accurate
simulation. Ground truth trajectories for training differentiable engines,
however, are not typically available at such high frequencies due to
limitations of real-world sensors. The present work focuses on this frequency
mismatch, which impacts the modeling accuracy. We proposed a recurrent
structure for a differentiable physics engine of tensegrity robots, which can
be trained effectively even with low-frequency trajectories. To train this new
recurrent engine in a robust way, this work introduces relative to prior work:
(i) a new implicit integration scheme, (ii) a progressive training pipeline,
and (iii) a differentiable collision checker. A model of NASA's icosahedron
SUPERballBot on MuJoCo is used as the ground truth system to collect training
data. Simulated experiments show that once the recurrent differentiable engine
has been trained given the low-frequency trajectories from MuJoCo, it is able
to match the behavior of MuJoCo's system. The criterion for success is whether
a locomotion strategy learned using the differentiable engine can be
transferred back to the ground-truth system and result in a similar motion.
Notably, the amount of ground truth data needed to train the differentiable
engine, such that the policy is transferable to the ground truth system, is 1%
of the data needed to train the policy directly on the ground-truth system.


title:
A Call to Arms Control: Synergies between Nonproliferation Applications
  of Neutrino Detectors and Large-Scale Fundamental Neutrino Physics
  Experiments

abstract:
  The High Energy Physics community can benefit from a natural synergy in
research activities into next-generation large-scale water and scintillator
neutrino detectors, now being studied for remote reactor monitoring, discovery
and exclusion applications in cooperative nonproliferation contexts.
  Since approximately 2010, US nonproliferation researchers, supported by the
National Nuclear Security Administration (NNSA), have been studying a range of
possible applications of relatively large (100 ton) to very large (hundreds of
kiloton) water and scintillator neutrino detectors.
  In parallel, the fundamental physics community has been developing detectors
at similar scales and with similar design features for a range of high-priority
physics topics, primarily in fundamental neutrino physics. These topics include
neutrino oscillation studies at beams and reactors, solar, and geological
neutrino measurements, supernova studies, and others.
  Examples of ongoing synergistic work at U.S. national laboratories and
universities include prototype gadolinium-doped water and water-based and
opaque scintillator test-beds and demonstrators, extensive testing and industry
partnerships related to large area fast position-sensitive photomultiplier
tubes, and the development of concepts for a possible underground kiloton-scale
water-based detector for reactor monitoring and technology demonstrations.
  Some opportunities for engagement between the two communities include
bi-annual Applied Antineutrino Physics conferences, collaboration with U.S.
National Laboratories engaging in this research, and occasional NNSA funding
opportunities supporting a blend of nonproliferation and basic science R&D,
directed at the U.S. academic community.


title:
Chance-constrained OPF: A Distributed Method with Confidentiality
  Preservation

abstract:
  Given the increased percentage of wind power in power systems,
chance-constrained optimal power flow (CC-OPF) calculation, as a means to take
wind power uncertainty into account with a guaranteed security level, is being
promoted. Compared to the local CC-OPF within a regional grid, the global
CC-OPF of a multi-regional interconnected grid is able to coordinate across
different regions and therefore improve the economic efficiency when
integrating high percentage of wind power generation. In this global problem,
however, multiple regional independent system operators (ISOs) participate in
the decision-making process, raising the need for distributed but coordinated
approaches. Most notably, due to regulation restrictions, commercial interest,
and data security, regional ISOs may refuse to share confidential information
with others, including generation cost, load data, system topologies, and line
parameters. But this information is needed to build and solve the global CC-OPF
spanning multiple areas. To tackle these issues, this paper proposes a
distributed CC-OPF method with confidentiality preservation, which enables
regional ISOs to determine the optimal dispatchable generations within their
regions without disclosing confidential data. This method does not require
parameter tunings and will not suffer from convergence challenges. Results from
IEEE test cases show that this method is highly accurate.


title:
Thermodynamic modeling of the Pd-Zn system with uncertainty
  quantification and its implication to tailor catalysts

abstract:
  Pd-Zn intermetallic catalysts show encouraging combinations of activity and
selectivity on well-defined active site ensembles. Thermodynamic description of
the Pd-Zn system, delineating phase boundaries, and enumerating site
occupancies within intermediate alloy phases, are essential to determining the
ensembles of Pd-Zn atoms as a function of composition and temperature.
Combining the present extensive first-principles calculations based on density
functional theory (DFT) and available experimental data, the Pd-Zn system was
remodeled using the CALculation of PHAse Diagrams (CALPHAD) approach. High
throughput modeling tools with uncertainty quantification, i.e., ESPEI and
PyCalphad, were incorporated in the phase analysis. The site occupancies across
the {\gamma}-phase composition region were given special attention. A
four-sublattice model was used for the {\gamma}-phase owing to its four Wyckoff
positions, i.e., the outer tetrahedral (OT) site 8c, the inner tetrahedral (IT)
site 8c, the octahedral (OH) 12e, and the cuboctahedral (CO) site 24g. The site
fractions of Pd and Zn calculated from the present thermodynamic model show the
occupancy preference of Pd in the OT and OH sublattices in agreement with
experimental observations. The force constants obtained from DFT-based phonon
calculations further supports the tendency of Pd occupying the OH sublattice
compared with the IT and CO sublattice. The catalytic assembles changing from
Pd monomers (Pd1) to trimers (Pd3) on the surface of the {\gamma}-phase is
attributed to the increase of Pd occupancy in the OH sublattice.


title:
Control-aware Probabilistic Load Flow for Transmission Systems: An
  Analytical Method

abstract:
  Probabilistic load flow (PLF) calculation, as a fundamental tool to analyze
transmission system behavior, has been studied for decades. Despite a variety
of available methods, existing PLF approaches rarely take system control into
account. However, system control, as an automatic buffer between the
fluctuations in random variables and the variations in system states, has a
significant impact on the final PLF result. To consider control actions'
influence, this paper proposes the first analytical PLF method for the
transmission grid that takes into account primary and secondary frequency
controls. This method is based on a high-precision linear power flow model,
whose precision is even further improved in this paper by an original
correction approach. This paper also proves that if the joint probability
distribution (JPD) of random variables is expressed by a Gaussian mixture model
(GMM), then the JPD of system states (e.g., nodal voltages) is an infinite GMM.
By leveraging this proposition, the proposed method can generate the joint PLF
of the whole system, is applicable to random variables obeying any
distributions, and is capable of capturing their correlation. The high accuracy
and satisfactory efficiency of this method are verified on test cases scaling
from 14 to 1354 buses.


title:
Voxelmorph++ Going beyond the cranial vault with keypoint supervision
  and multi-channel instance optimisation

abstract:
  The majority of current research in deep learning based image registration
addresses inter-patient brain registration with moderate deformation
magnitudes. The recent Learn2Reg medical registration benchmark has
demonstrated that single-scale U-Net architectures, such as VoxelMorph that
directly employ a spatial transformer loss, often do not generalise well beyond
the cranial vault and fall short of state-of-the-art performance for abdominal
or intra-patient lung registration. Here, we propose two straightforward steps
that greatly reduce this gap in accuracy. First, we employ keypoint
self-supervision with a novel network head that predicts a discretised heatmap
and robustly reduces large deformations for better robustness. Second, we
replace multiple learned fine-tuning steps by a single instance optimisation
with hand-crafted features and the Adam optimiser. Different to other related
work, including FlowNet or PDD-Net, our approach does not require a fully
discretised architecture with correlation layer. Our ablation study
demonstrates the importance of keypoints in both self-supervised and
unsupervised (using only a MIND metric) settings. On a multi-centric
inspiration-exhale lung CT dataset, including very challenging COPD scans, our
method outperforms VoxelMorph by improving nonlinear alignment by 77% compared
to 19% - reaching target registration errors of 2 mm that outperform all but
one learning methods published to date. Extending the method to semantic
features sets new stat-of-the-art performance on inter-subject abdominal CT
registration.


title:
Local and Global GANs with Semantic-Aware Upsampling for Image
  Generation

abstract:
  In this paper, we address the task of semantic-guided image generation. One
challenge common to most existing image-level generation methods is the
difficulty in generating small objects and detailed local textures. To address
this, in this work we consider generating images using local context. As such,
we design a local class-specific generative network using semantic maps as
guidance, which separately constructs and learns subgenerators for different
classes, enabling it to capture finer details. To learn more discriminative
class-specific feature representations for the local generation, we also
propose a novel classification module. To combine the advantages of both global
image-level and local class-specific generation, a joint generation network is
designed with an attention fusion module and a dual-discriminator structure
embedded. Lastly, we propose a novel semantic-aware upsampling method, which
has a larger receptive field and can take far-away pixels that are semantically
related for feature upsampling, enabling it to better preserve semantic
consistency for instances with the same semantic labels. Extensive experiments
on two image generation tasks show the superior performance of the proposed
method. State-of-the-art results are established by large margins on both tasks
and on nine challenging public benchmarks. The source code and trained models
are available at https://github.com/Ha0Tang/LGGAN.


title: Multi-modal Alignment using Representation Codebook

abstract:
  Aligning signals from different modalities is an important step in
vision-language representation learning as it affects the performance of later
stages such as cross-modality fusion. Since image and text typically reside in
different regions of the feature space, directly aligning them at instance
level is challenging especially when features are still evolving during
training. In this paper, we propose to align at a higher and more stable level
using cluster representation. Specifically, we treat image and text as two
"views" of the same entity, and encode them into a joint vision-language coding
space spanned by a dictionary of cluster centers (codebook). We contrast
positive and negative samples via their cluster assignments while
simultaneously optimizing the cluster centers. To further smooth out the
learning process, we adopt a teacher-student distillation paradigm, where the
momentum teacher of one view guides the student learning of the other. We
evaluated our approach on common vision language benchmarks and obtain new SoTA
on zero-shot cross modality retrieval while being competitive on various other
transfer tasks.


title:
Towards Targeted Change Detection with Heterogeneous Remote Sensing
  Images for Forest Mortality Mapping

abstract:
  In this paper we develop a method for mapping forest mortality in the
forest-tundra ecotone using satellite data from heterogeneous sensors. We use
medium resolution imagery in order to provide the complex pattern of forest
mortality in this sparsely forested area, which has been induced by an outbreak
of geometrid moths. Specifically, Landsat-5 Thematic Mapper images from before
the event are used, with RADARSAT-2 providing the post-event images. We obtain
the difference images for both multispectral optical and synthetic aperture
radar (SAR) by using a recently developed deep learning method for translating
between the two domains. These differences are stacked with the original pre-
and post-event images in order to let our algorithm also learn how the areas
appear before and after the change event. By doing this, and focusing on
learning only the changes of interest with one-class classification (OCC), we
obtain good results with very little training data.


title: Magnetising the ${\cal N}=4$ Super Yang-Mills plasma

abstract:
  We investigate the thermodynamics of the anisotropic magnetic $\rm AdS_5$
black brane solution found by D'Hoker and Kraus arXiv:0908.3875. This solution
is the gravity dual of a strongly coupled ${\cal N}=4$ Super Yang-Mills plasma
in ${\mathbb R}^{3,1}$, with temperature $T$, in the presence of a magnetic
field ${\cal B}$. Following the procedure of holographic renormalisation we
calculate the Gibbs free energy and the holographic stress tensor of the
conformal plasma. We evaluate several thermodynamic quantities including the
magnetisation, the anisotropic pressures and the speeds of sound. Our results
are consistent with an RG flow from a perturbed $\rm AdS_5$ black brane at
small ${\cal B}/T^2$ to a $\rm \bf BTZ \times {\mathbb R}^2$ black brane at
large ${\cal B}/T^2$. We also perform a phenomenological analysis where we
compare the thermodynamics of a magnetised conformal plasma against the lattice
QCD results for the thermodynamics of the magnetised quark-gluon plasma.


title: ERF: Explicit Radiance Field Reconstruction From Scratch

abstract:
  We propose a novel explicit dense 3D reconstruction approach that processes a
set of images of a scene with sensor poses and calibrations and estimates a
photo-real digital model. One of the key innovations is that the underlying
volumetric representation is completely explicit in contrast to neural
network-based (implicit) alternatives. We encode scenes explicitly using clear
and understandable mappings of optimization variables to scene geometry and
their outgoing surface radiance. We represent them using hierarchical
volumetric fields stored in a sparse voxel octree. Robustly reconstructing such
a volumetric scene model with millions of unknown variables from registered
scene images only is a highly non-convex and complex optimization problem. To
this end, we employ stochastic gradient descent (Adam) which is steered by an
inverse differentiable renderer.
  We demonstrate that our method can reconstruct models of high quality that
are comparable to state-of-the-art implicit methods. Importantly, we do not use
a sequential reconstruction pipeline where individual steps suffer from
incomplete or unreliable information from previous stages, but start our
optimizations from uniformed initial solutions with scene geometry and radiance
that is far off from the ground truth. We show that our method is general and
practical. It does not require a highly controlled lab setup for capturing, but
allows for reconstructing scenes with a vast variety of objects, including
challenging ones, such as outdoor plants or furry toys. Finally, our
reconstructed scene models are versatile thanks to their explicit design. They
can be edited interactively which is computationally too costly for implicit
alternatives.


title:
Gaussian quantum estimation of the lossy parameter in a thermal
  environment

abstract:
  Lossy bosonic channels play an important role in a number of quantum
information tasks, since they well approximate thermal dissipation in an
experiment. Here, we characterize their metrological power in the idler-free
and entanglement-assisted cases, using respectively single- and two-mode
Gaussian states as probes. In the problem of estimating the lossy parameter, we
study the energy-constrained quantum Fisher information (QFI) for generic
temperature and lossy parameter regimes, showing qualitative behaviours of the
optimal probes. We show semi-analytically that the two-mode squeezed-vacuum
state optimizes the QFI for any value of the lossy parameter and temperature.
We discuss the optimization of the {\it total} QFI, where the number of probes
is allowed to vary by keeping the total energy-constrained. In this context, we
elucidate the role of the "shadow-effect" for reaching a quantum advantage. We
also consider a photon-number normalization for the environment, widely used in
the analysis of quantum illumination and quantum reading protocols. With this
normalization, we prove that the large bandwidth TMSV state is the optimal
probe for any parameter value. Here, the quantum advantage is of at most a
factor of $2$, and is reached in the bright environment case for {\it any}
lossy parameter values. Finally, we discuss the implications of our results for
quantum illumination and quantum reading applications.


title:
Fast Bayesian estimation of brain activation with cortical surface and
  subcortical fMRI data using EM

abstract:
  Analysis of brain imaging scans is critical to understanding the way the
human brain functions, which can be leveraged to treat injuries and conditions
that affect the quality of life for a significant portion of the human
population. In particular, functional magnetic resonance imaging (fMRI) scans
give detailed data on a living subject at high spatial and temporal
resolutions. Due to the high cost involved in the collection of these scans,
robust methods of analysis are of critical importance in order to produce
meaningful inference. Bayesian methods in particular allow for the inclusion of
expected behavior from prior study into an analysis, increasing the power of
the results while circumventing problems that arise in classical analyses,
including the effects of smoothing results and sensitivity to multiple
comparison testing corrections. Recent development of a surface-based spatial
Bayesian general linear model for cortical surface fMRI (cs-fMRI) data provides
the desired power increase in task fMRI data using stochastic partial
differential equation (SPDE) priors. This model relies on the computational
efficiencies of the integrated nested Laplace approximation (INLA) to perform
powerful analyses that have been validated to outperform classical analyses. In
this article, we develop an exact Bayesian analysis method for the GLM,
employing an expectation-maximization (EM) algorithm to find maximum a
posteriori (MAP) estimates of task-based regressors on cs-fMRI and subcortical
fMRI data while using minimal computational resources. Our proposed method is
compared to the INLA implementation of the Bayesian GLM, as well as a classical
GLM on simulated data. A validation of the method on data from the Human
Connectome Project is also provided.


title: LISA: Learning Interpretable Skill Abstractions from Language

abstract:
  Learning policies that effectually utilize language instructions in complex,
multi-task environments is an important problem in imitation learning. While it
is possible to condition on the entire language instruction directly, such an
approach could suffer from generalization issues. To encode complex
instructions into skills that can generalize to unseen instructions, we propose
Learning Interpretable Skill Abstractions (LISA), a hierarchical imitation
learning framework that can learn diverse, interpretable skills from
language-conditioned demonstrations. LISA uses vector quantization to learn
discrete skill codes that are highly correlated with language instructions and
the behavior of the learned policy. In navigation and robotic manipulation
environments, LISA is able to outperform a strong non-hierarchical baseline in
the low data regime and compose learned skills to solve tasks containing unseen
long-range instructions. Our method demonstrates a more natural way to
condition on language in sequential decision-making problems and achieve
interpretable and controllable behavior with the learned skills.


title:
Risk-averse controller design against data injection attacks on
  actuators for uncertain control systems

abstract:
  In this paper, we consider the optimal controller design problem against data
injection attacks on actuators for an uncertain control system. We consider
attacks that aim at maximizing the attack impact while remaining stealthy in
the finite horizon. To this end, we use the Conditional Value-at-Risk to
characterize the risk associated with the impact of attacks. The worst-case
attack impact is characterized using the recently proposed output-to-output
$\ell_2$-gain (OOG). We formulate the design problem and observe that it is
non-convex and hard to solve. Using the framework of scenario-based
optimization and a convex proxy for the OOG, we propose a convex optimization
problem that approximately solves the design problem with probabilistic
certificates. Finally, we illustrate the results through a numerical example.


title: An Empirical Study on Explanations in Out-of-Domain Settings

abstract:
  Recent work in Natural Language Processing has focused on developing
approaches that extract faithful explanations, either via identifying the most
important tokens in the input (i.e. post-hoc explanations) or by designing
inherently faithful models that first select the most important tokens and then
use them to predict the correct label (i.e. select-then-predict models).
Currently, these approaches are largely evaluated on in-domain settings. Yet,
little is known about how post-hoc explanations and inherently faithful models
perform in out-of-domain settings. In this paper, we conduct an extensive
empirical study that examines: (1) the out-of-domain faithfulness of post-hoc
explanations, generated by five feature attribution methods; and (2) the
out-of-domain performance of two inherently faithful models over six datasets.
Contrary to our expectations, results show that in many cases out-of-domain
post-hoc explanation faithfulness measured by sufficiency and comprehensiveness
is higher compared to in-domain. We find this misleading and suggest using a
random baseline as a yardstick for evaluating post-hoc explanation
faithfulness. Our findings also show that select-then predict models
demonstrate comparable predictive performance in out-of-domain settings to
full-text trained models.


title: Differentiable Matrix Elements with MadJax

abstract:
  MadJax is a tool for generating and evaluating differentiable matrix elements
of high energy scattering processes. As such, it is a step towards a
differentiable programming paradigm in high energy physics that facilitates the
incorporation of high energy physics domain knowledge, encoded in simulation
software, into gradient based learning and optimization pipelines. MadJax
comprises two components: (a) a plugin to the general purpose matrix element
generator MadGraph that integrates matrix element and phase space sampling code
with the JAX differentiable programming framework, and (b) a standalone
wrapping API for accessing the matrix element code and its gradients, which are
computed with automatic differentiation. The MadJax implementation and example
applications of simulation based inference and normalizing flow based matrix
element modeling, with capabilities enabled uniquely with differentiable matrix
elements, are presented.

categories: hep-ph cs.LG physics.comp-ph physics.data-an


title: Singular solutions of the r-Camassa-Holm equation

abstract:
  This paper introduces the r-CH equation, which describes a geodesic flow on
the manifold of diffeomorphisms acting on the real line induced by the $W^1_r$
metric. The conserved energy is $\|u\|_{W^1_r}^r$ for the Eulerian velocity $u$
and the case $r=2$ recovers the Camassa-Holm (CH) equation. The paper also
introduces singular weak solutions of the r-CH equation for $r\geq 2$ and
demonstrates their robustness in numerical simulations of their nonlinear
interactions in both overtaking collisions and head-on collisions. Several open
questions are formulated about the unexplored properties of the r-CH weak
singular solutions, including the question of whether they would emerge from
smooth initial conditions.


title:
Large-charge conformal dimensions at the $O(N)$ Wilson-Fisher fixed
  point

abstract:
  Recent work using a large-charge expansion for the $O(N)$ Wilson-Fisher
conformal field theory has shown that the anomalous dimensions of large-charge
operators can be expressed in terms of a few low-energy constants (LECs) of a
large-charge effective field theory (EFT). By performing lattice Monte Carlo
computations at the $O(N)$ Wilson-Fisher fixed point, we compute the anomalous
dimensions of large-charge operators up to $N=8$ and charge $Q=10$, and extract
the leading and subleading LECs of the $O(N)$ large-charge EFT. To alleviate
the signal-to-noise ratio problem present in the large-charge sector of
conventional lattice formulations of the $O(N)$ theory, we employ a recently
developed qubit formulation of the $O(N)$ nonlinear sigma models with a worm
algorithm. This enables us to test the validity of the large-charge expansion
and the recent large-$N$ predictions for the coefficients of the large-charge
EFT.


title:
Strongly anisotropic electronic and magnetic structures in oxide
  dichlorides RuOCl$_2$ and OsOCl$_2$

abstract:
  Here, using density functional theory and density matrix renormalization
group methods, we investigate the electronic and magnetic properties of
RuOCl$_2$ and OsOCl$_2$ with $d^4$ electronic configurations. Different from a
previous study using VOI$_2$ with $d^1$ configuration, these systems with
$4d^4$ or $5d^4$ do not exhibit a ferroelectric instability along the $a$-axis.
Due to the fully-occupied $d_{xy}$ orbital in RuOCl$_2$ and OsOCl$_2$, the
Peierls instability distortion disappears along the $b$-axis, leading to an
undistorted I${\rm mmm}$ phase (No. 71). Furthermore, we observe strongly
anisotropic electronic and magnetic structures along the $a$-axis. The large
crystal-field splitting energy (between $d_{xz/yz}$ and $d_{xy}$ orbitals) and
large hopping between nearest-neighbor Ru and Os atoms suppresses the
spin-orbital effect in $M$OCl$_2$ ($M$ = Ru or Os) with electronic density $n =
4$, resulting in a spin-1 system instead of a $J = 0$ singlet ground state.
Moreover, we find staggered antiferromagnetic order with $\pi$ wavevector along
the $M$-O chain direction ($a$-axis) while the magnetic coupling along the
$b$-axis is weak. Based on Wannier functions from first-principles
calculations, we calculated the relevant hopping amplitudes and crystal-field
splitting energies of the $t_{2g}$ orbitals for the Os atoms to construct a
multi-orbital Hubbard model for the $M$-O chains. Staggered AFM with
$\uparrow$-$\downarrow$-$\uparrow$-$\downarrow$ spin structure dominates in our
DMRG calculations, in agreement with DFT calculations.


title:
Coefficients of Catalan States of Lattice Crossing I: $\Theta_{A}$-state
  Expansion

abstract:
  Plucking polynomial for plane rooted trees was introduced by J.H. Przytycki
in 2014. As it was shown later, this polynomial can be used to find
coefficients $C(A)$ of Catalan states $C$ of $m \times n$-lattice crossing
$L(m,n)$ without returns on one side. In this paper, we show that $C(A)$ for
any $C$ can be found by using $\Theta_{A}$-state expansion which represents
$C(A)$ as a linear combination of coefficients of Catalan states with no top
returns over $\mathbb{Q}(A)$. We also provide an algorithm for finding
$\Theta_{A}$-state expansions and examples of its applications. Finally, an
example of a Catalan state with non-unimodal coefficient is given.


title: Robust Causal Inference of Drug-drug Interactions

abstract:
  There is growing interest in developing causal inference methods for
multi-valued treatments with a focus on pairwise average treatment effects.
Here we focus on a clinically important, yet less-studied estimand: causal
drug-drug interactions (DDIs), quantified by the change of the causal effect of
drug $A$ when switching drug $B$ status from "off" to "on". Control of
confounding by measured variables when studying the effects of DDIs can be
accomplished via inverse probability of treatment weighting (IPTW), a standard
approach to confounding adjustment, originally developed for binary treatments
and later generalized to multi-valued treatments. However, this approach
generally results in biased results when the propensity score model is
misspecified. Motivated by the need for more robust techniques, we propose two
empirical likelihood-based weighting approaches that allow for specifying a set
of propensity score models, with the second method balancing user-specified
covariates directly, by incorporating additional, nonparametric constraints.
The resulting estimators from both methods are consistent when the postulated
set of propensity score models contains a correct one; this property has been
termed multiple robustness. In this paper, we derive two multiply-robust
estimators of the causal DDI, and develop inference procedures. We then
evaluate the finite sample performance of the proposed estimators through
simulation. In doing so, we demonstrate that the proposed estimators outperform
the standard IPTW method in terms of both robustness and efficiency. Finally,
we apply the proposed method to evaluate the impact of renin-angiotensin system
inhibitors (RAS-I) on the comparative nephrotoxicity of nonsteroidal
anti-inflammatory drugs (NSAID) and opioids, using data derived from electronic
medical records from a large multi-hospital health system.


title: Structure from Voltage

abstract:
  Effective resistance (ER) is an attractive way to interrogate the structure
of graphs. It is an alternative to computing the eigen-vectors of the graph
Laplacian. Graph laplacians are used to find low dimensional structures in high
dimensional data. Here too, ER based analysis has advantages over eign-vector
based methods. Unfortunately Von Luxburg et al. (2010) show that, when vertices
correspond to a sample from a distribution over a metric space, the limit of
the ER between distant points converges to a trivial quantity that holds no
information about the structure of the graph. We show that by using scaling
resistances in a graph with $n$ vertices by $n^2$, one gets a meaningful limit
of the voltages and of effective resistances. We also show that by adding a
"ground" node to a metric graph one gets a simple and natural way to compute
all of the distances from a chosen point to all other points.


title: Piezoelectric Strain FET (PeFET) based Non-Volatile Memories

abstract:
  We propose non-volatile memory (NVM) designs based on Piezoelectric Strain
FET (PeFET) utilizing a piezoelectric/ferroelectric (PE/FE such as PZT) coupled
with 2D Transition Metal Dichalcogenide (2D-TMD such as MoS2) transistor. The
proposed NVMs store bit information in the form of polarization (P) of the
FE/PE, use electric-field driven P-switching for write and employ
piezoelectricity induced dynamic bandgap modulation of 2D-TMD channel for bit
sensing. We analyze PeFET with COMSOL based 3D modeling showing that the
circuit-driven optimization of PeFET geometry is essential to achieve effective
hammer-and-nail effect and adequate bandgap modulation for NVM read. Our
results show that distinguishability of binary states to up to 11X is achieved
in PeFETs.We propose various flavors of PeFET NVMs, namely (a) high density
(HD) NVM featuring a compact access-transistor-less bit-cell, (b) 1T-1PeFET NVM
with segmented architecture, targeted for optimized write energy and latency
and (c) cross-coupled (CC) NVM offering a trade-off between area and
latency.PeFET NVMs offer up to 7X smaller cell area, 66% lower write energy,
87% lower read energy and 44% faster read compared to 2D-FET SRAM. This comes
at the cost of high write latency in PeFET NVMs, which can be minimized by
virtue of optimized PE geometry.


title: Self-Repelling Elastic Manifolds with Low Dimensional Range

abstract:
  We consider self-repelling elastic manifolds with a domain $[-N,N]^d \cap
\mathbb{Z}^d$, that take values in $\mathbb{R}^D$. Our main result states that
when the dimension of the domain is $d=2$ and the dimension of the range is
$D=1$, the effective radius $R_N$ of the manifold is approximately $N^{4/3}$.
This verifies the conjecture of Kantor, Kardar and Nelson [7]. Our results for
the case where $d \geq 3$ and $D <d$ give a lower bound on $R_N$ of order
$N^{\frac{1}{D} \left(d-\frac{2(d-D)}{D+2} \right)}$ and an upper bound
proportional to $N^{\frac{d}{2}+\frac{d-D}{D+2}}$. These results imply that
self-repelling elastic manifolds with a low dimensional range undergo a
significantly stronger stretching than in the case where $d=D$, which was
studied by the authors in [10].


title: Magnetic Interactions of the Centrosymmetric Skyrmion Material Gd2PdSi3

abstract:
  The experimental realization of magnetic skyrmions in centrosymmetric
materials has been driven by theoretical understanding of how a delicate
balance of anisotropy and frustration can stabilize topological spin structures
in applied magnetic fields. Recently, the centrosymmetric material
Gd$_{2}$PdSi$_{3}$ was shown to host a field-induced skyrmion phase, but the
skyrmion stabilization mechanism remains unclear. Here, we employ
neutron-scattering measurements on an isotopically-enriched polycrystalline
Gd$_{2}$PdSi$_{3}$ sample to quantify the interactions that drive skyrmion
formation. Our analysis reveals spatially-extended interactions in triangular
planes that are consistent with an RKKY mechanism, and large ferromagnetic
inter-planar magnetic interactions that are modulated by the Pd/Si
superstructure. The skyrmion phase emerges from a zero-field helical magnetic
order with magnetic moments perpendicular to the magnetic propagation vector,
indicating that the magnetic dipolar interaction plays a significant role. Our
experimental results establish an interaction space that can promote skyrmion
formation, facilitating identification and design of centrosymmetric skyrmion
materials.


title:
Spin dynamics in the square-lattice cupola system
  Ba(TiO)Cu$_4$(PO$_4$)$_4$

abstract:
  We report high-resolution single-crystal inelastic neutron scattering
measurements on the spin-1/2 antiferromagnet Ba(TiO)Cu$_4$(PO$_4$)$_4$. This
material is formed from layers of four-site \cupola" structures, oriented
alternately upwards and downwards, which constitute a rather special
realization of two-dimensional (2D) square-lattice magnetism. The strong
Dzyaloshinskii-Moriya (DM) interaction within each cupola, or plaquette, unit
has a geometry largely unexplored among the numerous studies of magnetic
properties in 2D Heisenberg models with spin and spatial anisotropies. We have
measured the magnetic excitations at zero field and in fields up to 5 T,
finding a complex mode structure with multiple characteristic features that
allow us to extract all the relevant magnetic interactions by modelling within
the linear spin-wave approximation. We demonstrate that
Ba(TiO)Cu$_4$(PO$_4$)$_4$ is a checkerboard system with almost equal intra- and
inter-plaquette couplings, in which the intra-plaquette DM interaction is
instrumental both in enforcing robust magnetic order and in opening a large gap
at the Brillouin-zone center. We place our observations in the perspective of
generalized phase diagrams for spin-1/2 square-lattice models and materials,
where exploring anisotropies and frustration as routes to quantum disorder
remains a frontier research problem.


title: Perturbation of invariant subspaces for ill-conditioned eigensystem

abstract:
  Given a diagonalizable matrix $A$, we study the stability of its invariant
subspaces when its matrix of eigenvectors is ill-conditioned. Let
$\mathcal{X}_1$ be some invariant subspace of $A$ and $X_1$ be the matrix
storing the right eigenvectors that spanned $\mathcal{X}_1$. It is generally
believed that when the condition number $\kappa_2(X_1)$ gets large, the
corresponding invariant subspace $\mathcal{X}_1$ will become unstable to
perturbation. This paper proves that this is not always the case. Specifically,
we show that the growth of $\kappa_2(X_1)$ alone is not enough to destroy the
stability. As a direct application, our result ensures that when $A$ gets
closer to a Jordan form, one may still estimate its invariant subspaces from
the noisy data stably.


title:
Optimal Transport-based Graph Matching for 3D retinal OCT image
  registration

abstract:
  Registration of longitudinal optical coherence tomography (OCT) images
assists disease monitoring and is essential in image fusion applications. Mouse
retinal OCT images are often collected for longitudinal study of eye disease
models such as uveitis, but their quality is often poor compared with human
imaging. This paper presents a novel but efficient framework involving an
optimal transport based graph matching (OT-GM) method for 3D mouse OCT image
registration. We first perform registration of fundus-like images obtained by
projecting all b-scans of a volume on a plane orthogonal to them, hereafter
referred to as the x-y plane. We introduce Adaptive Weighted Vessel Graph
Descriptors (AWVGD) and 3D Cube Descriptors (CD) to identify the correspondence
between nodes of graphs extracted from segmented vessels within the OCT
projection images. The AWVGD comprises scaling, translation and rotation, which
are computationally efficient, whereas CD exploits 3D spatial and frequency
domain information. The OT-GM method subsequently performs the correct
alignment in the x-y plane. Finally, registration along the direction
orthogonal to the x-y plane (the z-direction) is guided by the segmentation of
two important anatomical features peculiar to mouse b-scans, the Internal
Limiting Membrane (ILM) and the hyaloid remnant (HR). Both subjective and
objective evaluation results demonstrate that our framework outperforms other
well-established methods on mouse OCT images within a reasonable execution
time.


title: Choosing on Sequences

abstract:
  The standard economic model of choice assumes that a decision maker chooses
from sets of alternatives. A new branch of literature has considered the
problem of choosing from lists i.e. ordered sets. In this paper, we propose a
new framework that considers choice from infinite sequences. Our framework
provides a natural way to model decision making in settings where choice relies
on a string of recommendations. We introduce three broad classes of choice
rules in this framework. Our main result shows that bounded attention is due to
the continuity of the choice functions with respect to a natural topology. We
introduce some natural choice rules in this framework and provide their
axiomatic characterizations. Finally, we introduce the notion of computability
of a choice function using Turing machines and show that computable choice
rules can be implemented by a finite automaton.


title: A graph related to the sum of element orders of a finite group

abstract:
  A finite group is called $\psi$-divisible iff $\psi(H)|\psi(G)$ for any
subgroup $H$ of a finite group $G$. Here, $\psi(G)$ is the sum of element
orders of $G$. For now, the only known examples of such groups are the cyclic
ones of square-free order. The existence of non-abelian $\psi$-divisible groups
still constitutes an open question. The aim of this paper is to make a
connection between the $\psi$-divisibility property and graph theory. Hence,
for a finite group $G$, we introduce a simple undirected graph called the
$\psi$-divisibility graph of $G$. We denote it by $\psi_G$. Its vertices are
the non-trivial subgroups of $G$, while two distinct vertices $H$ and $K$ are
adjacent iff $H\subset K$ and $\psi(H)|\psi(K)$ or $K\subset H$ and
$\psi(K)|\psi(H)$. We prove that $G$ is $\psi$-divisible iff $\psi_G$ has a
universal (dominating) vertex. Also, we study various properties of $\psi_G$,
when $G$ is a finite cyclic group. The choice of restricting our study to this
specific class of groups is motivated in the paper.


title: Parametrized and equivariant higher algebra

abstract:
  We develop the rudiments of a theory of parametrized $\infty$-operads,
including parametrized generalizations of monoidal envelopes, Day convolution,
operadic left Kan extensions, results on limits and colimits of algebras, and
the symmetric monoidal Yoneda embedding.


title: Structure Extraction in Task-Oriented Dialogues with Slot Clustering

abstract:
  Extracting structure information from dialogue data can help us better
understand user and system behaviors. In task-oriented dialogues, dialogue
structure has often been considered as transition graphs among dialogue states.
However, annotating dialogue states manually is expensive and time-consuming.
In this paper, we propose a simple yet effective approach for structure
extraction in task-oriented dialogues. We first detect and cluster possible
slot tokens with a pre-trained model to approximate dialogue ontology for a
target domain. Then we track the status of each identified token group and
derive a state transition structure. Empirical results show that our approach
outperforms unsupervised baseline models by far in dialogue structure
extraction. In addition, we show that data augmentation based on extracted
structures enriches the surface formats of training data and can achieve a
significant performance boost in dialogue response generation.


title:
How Well Can We Measure Galaxy Dust Attenuation Curves? The Impact of
  the Assumed Star-Dust Geometry Model in SED Fitting

abstract:
  One of the most common methods for inferring galaxy attenuation curves is via
spectral energy distribution (SED) modeling, where the dust attenuation
properties are modeled simultaneously with other galaxy physical properties. In
this paper, we assess the ability of SED modeling to infer these dust
attenuation curves from broadband photometry, and suggest a new flexible model
that greatly improves the accuracy of attenuation curve derivations. To do
this, we fit mock SEDs generated from the Simba cosmological simulation with
the Prospector SED fitting code. We consider the impact of the commonly-assumed
uniform screen model and introduce a new non-uniform screen model parameterized
by the fraction of unobscured stellar light. This non-uniform screen model
allows for a non-zero fraction of stellar light to remain unattenuated,
resulting in a more flexible attenuation curve shape by decoupling the shape of
the UV attenuation curve from the optical attenuation curve. The ability to
constrain the dust attenuation curve is significantly improved with the use of
a non-uniform screen model, with the median offset in UV attenuation decreasing
from $-0.30$ dex with a uniform screen model to $-0.17$ dex with the
non-uniform screen model. With this increase in dust attenuation modeling
accuracy, we also improve the star formation rates (SFRs) inferred with the
non-uniform screen model, decreasing the SFR offset on average by $0.12$ dex.
We discuss the efficacy of this new model, focusing on caveats with modeling
star-dust geometries and the constraining power of available SED observations.


title:
Long-time asymptotics and regularity estimates for weak solutions to a
  doubly degenerate thin-film equation in the Taylor-Couette setting

abstract:
  We study the dynamic behaviour of solutions to a fourth-order quasilinear
degenerate parabolic equation for large times arising in fluid dynamical
applications. The degeneracy occurs both with respect to the unknown and with
respect to the sum of its first and third spatial derivative. The modelling
equation appears as a thin-film limit for the interface separating two
immiscible viscous fluid films confined between two cylinders rotating at small
relative angular velocity. More precisely, the fluid occupying the layer next
to the outer cylinder is considered to be Newtonian, i.e. it has constant
viscosity, while we assume that the layer next to the inner cylinder is filled
by a shear-thinning power-law fluid.
  Using energy methods, Fourier analysis and suitable regularity estimates for
higher-order parabolic equations, we prove global existence of positive weak
solutions in the case of low initial energy. Moreover, these global solutions
are polynomially stable, in the sense that interfaces which are initially close
to a circle, converge at rate $1/t^{1/\beta}$ for some $\beta > 0$ to a circle,
as time tends to infinity.
  In addition, we provide regularity estimates for general nonlinear degenerate
parabolic equations of fourth order.


title: Robust Multi-Agent Bandits Over Undirected Graphs

abstract:
  We consider a multi-agent multi-armed bandit setting in which $n$ honest
agents collaborate over a network to minimize regret but $m$ malicious agents
can disrupt learning arbitrarily. Assuming the network is the complete graph,
existing algorithms incur $O( (m + K/n) \log (T) / \Delta )$ regret in this
setting, where $K$ is the number of arms and $\Delta$ is the arm gap. For $m
\ll K$, this improves over the single-agent baseline regret of
$O(K\log(T)/\Delta)$.
  In this work, we show the situation is murkier beyond the case of a complete
graph. In particular, we prove that if the state-of-the-art algorithm is used
on the undirected line graph, honest agents can suffer (nearly) linear regret
until time is doubly exponential in $K$ and $n$. In light of this negative
result, we propose a new algorithm for which the $i$-th agent has regret $O( (
d_{\text{mal}}(i) + K/n) \log(T)/\Delta)$ on any connected and undirected
graph, where $d_{\text{mal}}(i)$ is the number of $i$'s neighbors who are
malicious. Thus, we generalize existing regret bounds beyond the complete graph
(where $d_{\text{mal}}(i) = m$), and show the effect of malicious agents is
entirely local (in the sense that only the $d_{\text{mal}}(i)$ malicious agents
directly connected to $i$ affect its long-term regret).


title:
One Model is All You Need: Multi-Task Learning Enables Simultaneous
  Histology Image Segmentation and Classification

abstract:
  The recent surge in performance for image analysis of digitised pathology
slides can largely be attributed to the advance of deep learning. Deep models
can be used to initially localise various structures in the tissue and hence
facilitate the extraction of interpretable features for biomarker discovery.
However, these models are typically trained for a single task and therefore
scale poorly as we wish to adapt the model for an increasing number of
different tasks. Also, supervised deep learning models are very data hungry and
therefore rely on large amounts of training data to perform well. In this paper
we present a multi-task learning approach for segmentation and classification
of nuclei, glands, lumen and different tissue regions that leverages data from
multiple independent data sources. While ensuring that our tasks are aligned by
the same tissue type and resolution, we enable simultaneous prediction with a
single network. As a result of feature sharing, we also show that the learned
representation can be used to improve downstream tasks, including nuclear
classification and signet ring cell detection. As part of this work, we use a
large dataset consisting of over 600K objects for segmentation and 440K patches
for classification and make the data publicly available. We use our approach to
process the colorectal subset of TCGA, consisting of 599 whole-slide images, to
localise 377 million, 900K and 2.1 million nuclei, glands and lumen
respectively. We make this resource available to remove a major barrier in the
development of explainable models for computational pathology.


title:
Elliptical Slice Sampling for Probabilistic Verification of Stochastic
  Systems with Signal Temporal Logic Specifications

abstract:
  Autonomous robots typically incorporate complex sensors in their
decision-making and control loops. These sensors, such as cameras and Lidars,
have imperfections in their sensing and are influenced by environmental
conditions. In this paper, we present a method for probabilistic verification
of linearizable systems with Gaussian and Gaussian mixture noise models (e.g.
from perception modules, machine learning components). We compute the
probabilities of task satisfaction under Signal Temporal Logic (STL)
specifications, using its robustness semantics, with a Markov Chain Monte-Carlo
slice sampler. As opposed to other techniques, our method avoids
over-approximations and double-counting of failure events. Central to our
approach is a method for efficient and rejection-free sampling of signals from
a Gaussian distribution such that satisfy or violate a given STL formula. We
show illustrative examples from applications in robot motion planning.


title:
Direct observation of discommensurate charge density wave modulation in
  the quasi-1D Weyl semimetal candidate NbTe$_4$

abstract:
  The transition-metal tetrachalcogenides are a model system to explore the
conjunction of correlated electronic states such as charge density waves (CDW),
with topological phases of matter. Understanding the connection between these
phases requires a thorough understanding of the individual states, which for
the case of the CDW in this system, is still missing. In this paper we combine
phonon-structure calculations and scanning tunneling microscopy measurements of
NbTe$_4$ in order to provide a full characterization of the CDW state. We find
that, at short range, the superstructure formed by the CDW is fully
commensurate with the lattice parameters. Moreover, our data reveals the
presence of phase-slip domain-walls separating regions of commensurate-CDW in
the nanoscale, indicating that the CDW in this compound is discommensurate at
long-range. Our results solve a long-standing discussion about the nature of
the CDW in these materials, and provide a strong basis for the study of the
interplay between this state and other novel quantum electronic states.

categories: cond-mat.mtrl-sci cond-mat.mes-hall cond-mat.other cond-mat.str-el


title: Deep Camera Pose Regression Using Pseudo-LiDAR

abstract:
  An accurate and robust large-scale localization system is an integral
component for active areas of research such as autonomous vehicles and
augmented reality. To this end, many learning algorithms have been proposed
that predict 6DOF camera pose from RGB or RGB-D images. However, previous
methods that incorporate depth typically treat the data the same way as RGB
images, often adding depth maps as additional channels to RGB images and
passing them through convolutional neural networks (CNNs). In this paper, we
show that converting depth maps into pseudo-LiDAR signals, previously shown to
be useful for 3D object detection, is a better representation for camera
localization tasks by projecting point clouds that can accurately determine
6DOF camera pose. This is demonstrated by first comparing localization
accuracies of a network operating exclusively on pseudo-LiDAR representations,
with networks operating exclusively on depth maps. We then propose FusionLoc, a
novel architecture that uses pseudo-LiDAR to regress a 6DOF camera pose.
FusionLoc is a dual stream neural network, which aims to remedy common issues
with typical 2D CNNs operating on RGB-D images. The results from this
architecture are compared against various other state-of-the-art deep pose
regression implementations using the 7 Scenes dataset. The findings are that
FusionLoc performs better than a number of other camera localization methods,
with a notable improvement being, on average, 0.33m and 4.35{\deg} more
accurate than RGB-D PoseNet. By proving the validity of using pseudo-LiDAR
signals over depth maps for localization, there are new considerations when
implementing large-scale localization systems.


title:
Asymptotic Normality of Gini Correlation in High Dimension with
  Applications to the K-sample Problem

abstract:
  The categorical Gini correlation proposed by Dang et al. is a dependence
measure between a categorical and a numerical variables, which can characterize
independence of the two variables. The asymptotic distributions of the sample
correlation under the dependence and independence have been established when
the dimension of the numerical variable is fixed. However, its asymptotic
distribution for high dimensional data has not been explored. In this paper, we
develop the central limit theorem for the Gini correlation for the more
realistic setting where the dimensionality of the numerical variable is
diverging. We then construct a powerful and consistent test for the K-sample
problem based on the asymptotic normality. The proposed test not only avoids
computation burden but also gains power over the permutation procedure.
Simulation studies and real data illustrations show that the proposed test is
more competitive to existing methods across a broad range of realistic
situations, especially in unbalanced cases.


title:
High-precision real-space simulation of electrostatically-confined
  few-electron states

abstract:
  In this paper we present a computational procedure that utilizes real-space
grids to obtain high precision approximations of electrostatically confined
few-electron states such as those that arise in gated semiconductor quantum
dots. We use the Full Configuration Interaction (FCI) method with a
continuously adapted orthonormal orbital basis to approximate the ground and
excited states of such systems. We also introduce a benchmark problem based on
a realistic analytical electrostatic potential for quantum dot devices. We show
that our approach leads to highly precise computed energies and energy
differences over a wide range of model parameters. The analytic definition of
the benchmark allows for a collection of tests that are easily replicated, thus
facilitating comparisons with other computational approaches.


title: Sampling-Based Winner Prediction in District-Based Elections

abstract:
  In a district-based election, we apply a voting rule $r$ to decide the
winners in each district, and a candidate who wins in a maximum number of
districts is the winner of the election. We present efficient sampling-based
algorithms to predict the winner of such district-based election systems in
this paper. When $r$ is plurality and the margin of victory is known to be at
least $\varepsilon$ fraction of the total population, we present an algorithm
to predict the winner. The sample complexity of our algorithm is
$\mathcal{O}\left(\frac{1}{\varepsilon^4}\log
\frac{1}{\varepsilon}\log\frac{1}{\delta}\right)$. We complement this result by
proving that any algorithm, from a natural class of algorithms, for predicting
the winner in a district-based election when $r$ is plurality, must sample at
least $\Omega\left(\frac{1}{\varepsilon^4}\log\frac{1}{\delta}\right)$ votes.
We then extend this result to any voting rule $r$. Loosely speaking, we show
that we can predict the winner of a district-based election with an extra
overhead of
$\mathcal{O}\left(\frac{1}{\varepsilon^2}\log\frac{1}{\delta}\right)$ over the
sample complexity of predicting the single-district winner under $r$. We
further extend our algorithm for the case when the margin of victory is
unknown, but we have only two candidates. We then consider the median voting
rule when the set of preferences in each district is single-peaked. We show
that the winner of a district-based election can be predicted with
$\mathcal{O}\left(\frac{1}{\varepsilon^4}\log\frac{1}{\varepsilon}\log\frac{1}{\delta}\right)$
samples even when the harmonious order in different districts can be different
and even unknown. Finally, we also show some results for estimating the margin
of victory of a district-based election within both additive and multiplicative
error bounds.


title:
Competitors-Aware Stochastic Lap Strategy Optimisation for Race Hybrid
  Vehicles

abstract:
  World Endurance Championship (WEC) racing events are characterised by a
relevant performance gap among competitors. The fastest vehicles category,
consisting in hybrid vehicles, has to respect energy usage constraints set by
the technical regulation. Considering absence of competitors, i.e. traffic
conditions, the optimal energy usage strategy for lap time minimisation is
typically computed through a constrained optimisation problem. To the best of
our knowledge, the majority of state-of-the-art works neglects competitors.
This leads to a mismatch with the real world, where traffic generates
considerable time losses. To bridge this gap, we propose a new framework to
offline compute optimal strategies for the powertrain energy management
considering competitors. Through analysis of the available data from previous
events, statistics on the sector times and overtaking probabilities are
extracted to encode the competitors' behaviour. Adopting a multi-agent model,
the statistics are then used to generate realistic Monte Carlo (MC) simulation
of their position along the track. The simulator is then adopted to identify
the optimal strategy as follows. We develop a longitudinal vehicle model for
the ego-vehicle and implement an optimisation problem for lap time minimisation
in absence of traffic, based on Genetic Algorithms. Solving the optimisation
problem for a variety of constraints generates a set of candidate optimal
strategies. Stochastic Dynamic Programming is finally implemented to choose the
best strategy considering competitors, whose motion is generated by the MC
simulator. Our approach, validated on data from a real stint of race, allows to
significantly reduce the lap time.


title: On energy for accelerating observers in black hole spacetimes

abstract:
  Quasi-local energies for constantly accelerating observers in Ba\~nados,
Teitelboim, and Zanelli (BTZ), Schwarzschild and Schwarzschild-de Sitter
spacetimes are derived. The energies are expressed in terms of acceleration,
cosmological constant, and area, quantities measurable by the observers. Based
on results from quantum fields in curved spacetime for the redshifted Hawking
temperature, entropy and thermodynamic-like laws are briefly explored in the
three spacetimes.


title: Pippi: Practical Protocol Instantiation

abstract:
  A protocol specifies interactions between roles, which together constitute a
multiagent system (MAS). Enacting a protocol presupposes that agents are bound
to the its roles. Existing protocol-based approaches, however, do not
adequately treat the practical aspects of how roles bindings come about.
  Pippi addresses this problem of MAS instantiation. It proposes the notion of
a metaprotocol, enacting which instantiates a MAS suitable for enacting a given
protocol. Pippi demonstrates the subtleties involved in instantiating MAS
arising from protocol composition, correlation, and decentralization. To
address these subtleties and further support practical application patterns, we
introduce an enhanced protocol language, with support for parameter types
(including role and protocol typed parameters, for metaprotocols), interface
flexibility, and binding constraints. We discuss the realization of our
approach through an extended agent architecture, including the novel concept of
a MAS adapter for contact management. We evaluate Pippi's expressiveness by
demonstrating common patterns for agent discovery.


title:
Using Multivariate Imputation by Chained Equations to Predict Redshifts
  of Active Galactic Nuclei

abstract:
  Redshift measurement of active galactic nuclei (AGNs) remains a
time-consuming and challenging task, as it requires follow up spectroscopic
observations and detailed analysis. Hence, there exists an urgent requirement
for alternative redshift estimation techniques. The use of machine learning
(ML) for this purpose has been growing over the last few years, primarily due
to the availability of large-scale galactic surveys. However, due to
observational errors, a significant fraction of these data sets often have
missing entries, rendering that fraction unusable for ML regression
applications. In this study, we demonstrate the performance of an imputation
technique called Multivariate Imputation by Chained Equations (MICE), which
rectifies the issue of missing data entries by imputing them using the
available information in the catalog. We use the Fermi-LAT Fourth Data Release
Catalog (4LAC) and impute 24% of the catalog. Subsequently, we follow the
methodology described in Dainotti et al. (2021) and create an ML model for
estimating the redshift of 4LAC AGNs. We present results which highlight
positive impact of MICE imputation technique on the machine learning models
performance and obtained redshift estimation accuracy.


title:
Virtual Reference Feedback Tuning for linear discrete-time systems with
  robust stability guarantees based on Set Membership

abstract:
  In this paper we propose a novel methodology that allows to design, in a
purely data-based fashion and for linear single-input and single-output
systems, both robustly stable and performing control systems for tracking
piecewise constant reference signals. The approach uses both (i) Virtual
Reference Feedback Tuning for enforcing suitable performances and (ii) the Set
Membership framework for providing a-priori robust stability guarantees.
Indeed, an uncertainty set for the system parameters is obtained through Set
Membership identification, where an algorithm based on the scenario approach is
proposed to estimate the inflation parameter in a probabilistic way. Based on
this set, robust stability conditions are enforced as Linear Matrix Inequality
constraints within an optimization problem whose linear cost function relies on
Virtual Reference Feedback Tuning. To show the generality and effectiveness of
our approach, we apply it to two of the most widely used yet simple control
schemes, i.e., where tracking is achieved thanks to (i) a static feedforward
action and (ii) an integrator in closed-loop. The proposed method is not fully
direct due to the Set Membership identification. However, the uncertainty set
is used with the only objective of providing robust stability guarantees for
the closed-loop system and it is not directly used for the performances
optimization, which instead is totally based on data. The effectiveness of the
developed method is demonstrated with reference to two simulation examples. A
comparison with other data-driven methods is also carried out.


title: Amortized Proximal Optimization

abstract:
  We propose a framework for online meta-optimization of parameters that govern
optimization, called Amortized Proximal Optimization (APO). We first interpret
various existing neural network optimizers as approximate stochastic proximal
point methods which trade off the current-batch loss with proximity terms in
both function space and weight space. The idea behind APO is to amortize the
minimization of the proximal point objective by meta-learning the parameters of
an update rule. We show how APO can be used to adapt a learning rate or a
structured preconditioning matrix. Under appropriate assumptions, APO can
recover existing optimizers such as natural gradient descent and KFAC. It
enjoys low computational overhead and avoids expensive and numerically
sensitive operations required by some second-order optimizers, such as matrix
inverses. We empirically test APO for online adaptation of learning rates and
structured preconditioning matrices for regression, image reconstruction, image
classification, and natural language translation tasks. Empirically, the
learning rate schedules found by APO generally outperform optimal fixed
learning rates and are competitive with manually tuned decay schedules. Using
APO to adapt a structured preconditioning matrix generally results in
optimization performance competitive with second-order methods. Moreover, the
absence of matrix inversion provides numerical stability, making it effective
for low precision training.


title: Assigned rational functions of a rooted tree

abstract:
  We investigate the spectral properties of rooted trees with the intention of
improving the currently existing results that deal with this matter. The
concept of an assigned rational function is recursively defined for each vertex
of a rooted tree. Afterwards, two mathematical formulas are given which show
how the characteristic polynomials of the adjacency and Laplacian matrix can be
represented as products of the aforementioned rational functions. In order to
demonstrate their general use case scenario, the obtained formulas are
subsequently implemented on balanced trees, with a special focus on the Bethe
trees. In the end, some of the previously derived results are used in order to
construct a tree merging procedure which preserves the spectra of all of the
starting trees.


title: Dynamic N:M Fine-grained Structured Sparse Attention Mechanism

abstract:
  Transformers are becoming the mainstream solutions for various tasks like NLP
and Computer vision. Despite their success, the high complexity of the
attention mechanism hinders them from being applied to latency-sensitive tasks.
Tremendous efforts have been made to alleviate this problem, and many of them
successfully reduce the asymptotic complexity to linear. Nevertheless, most of
them fail to achieve practical speedup over the original full attention under
moderate sequence lengths and are unfriendly to finetuning. In this paper, we
present DFSS, an attention mechanism that dynamically prunes the full attention
weight matrix to N:M fine-grained structured sparse pattern. We provide both
theoretical and empirical evidence that demonstrates DFSS is a good
approximation of the full attention mechanism. We propose a dedicated CUDA
kernel design that completely eliminates the dynamic pruning overhead and
achieves speedups under arbitrary sequence length. We evaluate the 1:2 and 2:4
sparsity under different configurations and achieve 1.27~ 1.89x speedups over
the full-attention mechanism. It only takes a couple of finetuning epochs from
the pretrained model to achieve on par accuracy with full attention mechanism
on tasks from various domains under different sequence lengths from 384 to
4096.


title: Explicit Non-Abelian Gerbes with Connections

abstract:
  We derive the complete cocycle description for non-Abelian gerbes with
connections whose structure 2-group is a 2-group with adjustment datum. We
depart from the common fake-flat connections and employ adjusted connections,
which is important for physical applications, especially in the context of
supergravity. We give a number of explicit examples; in particular, we lift the
principal bundle corresponding to an instanton-anti-instanton pair to a string
2-group bundle. We also outline how categorified forms of Bogomolny monopoles
known as self-dual strings can be obtained via a Penrose-Ward transform of
string bundles over twistor space.