title: SafePicking: Learning Safe Object Extraction via Object-Level Mapping

abstract:
  Robots need object-level scene understanding to manipulate objects while
reasoning about contact, support, and occlusion among objects. Given a pile of
objects, object recognition and reconstruction can identify the boundary of
object instances, giving important cues as to how the objects form and support
the pile. In this work, we present a system, SafePicking, that integrates
object-level mapping and learning-based motion planning to generate a motion
that safely extracts occluded target objects from a pile. Planning is done by
learning a deep Q-network that receives observations of predicted poses and a
depth-based heightmap to output a motion trajectory, trained to maximize a
safety metric reward. Our results show that the observation fusion of poses and
depth-sensing gives both better performance and robustness to the model. We
evaluate our methods using the YCB objects in both simulation and the real
world, achieving safe object extraction from piles.


title:
Active Privacy-Utility Trade-off Against Inference in Time-Series Data
  Sharing

abstract:
  Internet of things (IoT) devices, such as smart meters, smart speakers and
activity monitors, have become highly popular thanks to the services they
offer. However, in addition to their many benefits, they raise privacy concerns
since they share fine-grained time-series user data with untrusted third
parties. In this work, we consider a user releasing her data containing
personal information in return of a service from an honest-but-curious service
provider (SP). We model user's personal information as two correlated random
variables (r.v.'s), one of them, called the secret variable, is to be kept
private, while the other, called the useful variable, is to be disclosed for
utility. We consider active sequential data release, where at each time step
the user chooses from among a finite set of release mechanisms, each revealing
some information about the user's personal information, i.e., the true values
of the r.v.'s, albeit with different statistics. The user manages data release
in an online fashion such that the maximum amount of information is revealed
about the latent useful variable as quickly as possible, while the confidence
for the sensitive variable is kept below a predefined level. For privacy
measure, we consider both the probability of correctly detecting the true value
of the secret and the mutual information (MI) between the secret and the
released data. We formulate both problems as partially observable Markov
decision processes (POMDPs), and numerically solve them by advantage
actor-critic (A2C) deep reinforcement learning (DRL). We evaluate the
privacy-utility trade-off (PUT) of the proposed policies on both the synthetic
data and smoking activity dataset, and show their validity by testing the
activity detection accuracy of the SP modeled by a long short-term memory
(LSTM) neural network.


title: Predicting Out-of-Distribution Error with the Projection Norm

abstract:
  We propose a metric -- Projection Norm -- to predict a model's performance on
out-of-distribution (OOD) data without access to ground truth labels.
Projection Norm first uses model predictions to pseudo-label test samples and
then trains a new model on the pseudo-labels. The more the new model's
parameters differ from an in-distribution model, the greater the predicted OOD
error. Empirically, our approach outperforms existing methods on both image and
text classification tasks and across different network architectures.
Theoretically, we connect our approach to a bound on the test error for
overparameterized linear models. Furthermore, we find that Projection Norm is
the only approach that achieves non-trivial detection performance on
adversarial examples. Our code is available at
https://github.com/yaodongyu/ProjNorm.


title:
Final State Observability in Banach spaces with applications to
  Subordination and Semigroups induced by L{\'e}vy processes

abstract:
  This paper generalizes the abstract method of proving an observability
estimate by combining an uncertainty principle and a dissipation estimate. In
these estimates we allow for a large class of growth/decay rates satisfying an
integrability condition. In contrast to previous results, we use an iterative
argument which enables us to give an asymptotically sharp estimate for the
observation constant and which is explicit in the model parameters. We give two
types of applications where the extension of the growth/decay rates naturally
appear. By exploiting subordination techniques we show how the dissipation
estimate of a semigroup transfers to subordinated semigroups. Furthermore, we
apply our results to semigroups related to L{\'e}vy processes.


title:
Galaxies lacking dark matter produced by close encounters in a
  cosmological simulation

abstract:
  The standard cold dark matter plus cosmological constant model predicts that
galaxies form within dark-matter haloes, and that low-mass galaxies are more
dark-matter dominated than massive ones. The unexpected discovery of two
low-mass galaxies lacking dark matter immediately provoked concerns about the
standard cosmology and ignited explorations of alternatives, including
self-interacting dark matter and modified gravity. Apprehension grew after
several cosmological simulations using the conventional model failed to form
adequate numerical analogues with comparable internal characteristics (stellar
masses, sizes, velocity dispersions and morphologies). Here we show that the
standard paradigm naturally produces galaxies lacking dark matter with internal
characteristics in agreement with observations. Using a state-of-the-art
cosmological simulation and a meticulous galaxy-identification technique, we
find that extreme close encounters with massive neighbours can be responsible
for this. We predict that approximately 30 percent of massive central galaxies
(with at least 1e11 solar masses in stars) harbour at least one
dark-matter-deficient satellite (with 1e8 - 1e9 solar masses in stars). This
distinctive class of galaxies provides an additional layer in our understanding
of the role of interactions in shaping galactic properties. Future observations
surveying galaxies in the aforementioned regime will provide a crucial test of
this scenario.


title:
The nodal basis of $C^m$-$P_{k}^{(3)}$ and $C^m$-$P_{k}^{(4)}$ finite
  elements on tetrahedral and 4D simplicial grids

abstract:
  We construct the nodal basis of $C^m$-$P_{k}^{(3)}$ ($k \ge 2^3m+1$) and
$C^m$-$P_{k}^{(4)}$ ($k \ge 2^4m+1$) finite elements on 3D tetrahedral and 4D
simplicial grids, respectively. $C^m$-$P_{k}^{(n)}$ stands for the space of
globally $C^m$ ($m\ge1$) and locally piecewise $n$-dimensional polynomials of
degree $k$ on $n$-dimensional simplicial grids. We prove the uni-solvency and
the $C^m$ continuity of the constructed $C^m$-$P_{k}^{(3)}$ and
$C^m$-$P_{k}^{(4)}$ finite element spaces. A computer code is provided which
generates the index set for the nodal basis of $C^m$-$P_k^{(n)}$ finite
elements on $n$-dimensional simplicial grids.


title: Applications of Machine Learning to Lattice Quantum Field Theory

abstract:
  There is great potential to apply machine learning in the area of numerical
lattice quantum field theory, but full exploitation of that potential will
require new strategies. In this white paper for the Snowmass community planning
process, we discuss the unique requirements of machine learning for lattice
quantum field theory research and outline what is needed to enable exploration
and deployment of this approach in the future.


title: Abstraction for Deep Reinforcement Learning

abstract:
  We characterise the problem of abstraction in the context of deep
reinforcement learning. Various well established approaches to analogical
reasoning and associative memory might be brought to bear on this issue, but
they present difficulties because of the need for end-to-end differentiability.
We review developments in AI and machine learning that could facilitate their
adoption.


title: On Electromagnetic Turbulence

abstract:
  Combining a generalized current in the set of Maxwell's equations offers a
useful framework to address the complex phenomena of electromagnetic
turbulence. The fluidic-electromagnetic analogy implies that diffraction is the
analog phenomenon of EM turbulence and indicates norms to design suitable
plasmon circuity to control electromagnetic turbulence in stealth technology
and propulsion machines.


title: Entropic fictitious play for mean field optimization problem

abstract:
  It is well known that the training of the neural network can be viewed as a
mean field optimization problem. In this paper we are inspired by the
fictitious play, a classical algorithm in the game theory for learning the Nash
equilibria, and propose a new algorithm, different from the conventional
gradient-descent ones, to solve the mean field optimization. We rigorously
prove its (exponential) convergence, and show some simple numerical examples.


title:
Corrigenda to: A nonconventional scenario for thermal equilibrium
  (Foundations of Physics, 2007)

abstract:
  Abstract of the original article: A nonuniform superconducting loop poses a
challenge to statistical mechanics: assuming thermal equilibrium and applying
the accepted rules, we obtain that the heat flow does not vanish. In the
present note, two required corrections are raised, but it turns out that these
corrections do not change the original results.


title: Fast Model-based Policy Search for Universal Policy Networks

abstract:
  Adapting an agent's behaviour to new environments has been one of the primary
focus areas of physics based reinforcement learning. Although recent approaches
such as universal policy networks partially address this issue by enabling the
storage of multiple policies trained in simulation on a wide range of
dynamic/latent factors, efficiently identifying the most appropriate policy for
a given environment remains a challenge. In this work, we propose a Gaussian
Process-based prior learned in simulation, that captures the likely performance
of a policy when transferred to a previously unseen environment. We integrate
this prior with a Bayesian Optimisation-based policy search process to improve
the efficiency of identifying the most appropriate policy from the universal
policy network. We empirically evaluate our approach in a range of continuous
and discrete control environments, and show that it outperforms other competing
baselines.


title: Uncertainty Aware System Identification with Universal Policies

abstract:
  Sim2real transfer is primarily concerned with transferring policies trained
in simulation to potentially noisy real world environments. A common problem
associated with sim2real transfer is estimating the real-world environmental
parameters to ground the simulated environment to. Although existing methods
such as Domain Randomisation (DR) can produce robust policies by sampling from
a distribution of parameters during training, there is no established method
for identifying the parameters of the corresponding distribution for a given
real-world setting. In this work, we propose Uncertainty-aware policy search
(UncAPS), where we use Universal Policy Network (UPN) to store
simulation-trained task-specific policies across the full range of
environmental parameters and then subsequently employ robust Bayesian
optimisation to craft robust policies for the given environment by combining
relevant UPN policies in a DR like fashion. Such policy-driven grounding is
expected to be more efficient as it estimates only task-relevant sets of
parameters. Further, we also account for the estimation uncertainties in the
search process to produce policies that are robust against both aleatoric and
epistemic uncertainties. We empirically evaluate our approach in a range of
noisy, continuous control environments, and show its improved performance
compared to competing baselines.


title: Brain Webs for Brane Webs

abstract:
  We propose a new technique for classifying 5d Superconformal Field Theories
arising from brane webs in Type IIB String Theory, using technology from
Machine Learning to identify different webs giving rise to the same theory. We
concentrate on webs with three external legs, for which the problem is
analogous to that of classifying sets of 7-branes. Training a Siamese Neural
Network to determine equivalence between any two brane webs shows an improved
performance when webs are considered equivalent under a weaker set of
conditions. Thus, Machine Learning teaches us that the conjectured
classification of 7-brane sets is not complete, which we confirm with explicit
examples.


title:
A dispersive analysis of $\eta'\to\pi^+\pi^-\gamma$ and $\eta'\to
  \ell^+\ell^-\gamma$

abstract:
  We present a dispersive representation of the $\eta'$ transition form factor
that allows one to account, in a consistent way, for the effects of
$\rho$-$\omega$ mixing in both the isoscalar and the isovector contributions.
Using this formalism, we analyze recent data on $\eta'\to \pi^+\pi^-\gamma$ to
constrain the isovector part of the form factor, individually and in
combination with data for the pion vector form factor, which suggests a tension
in the $\rho$-$\omega$ mixing parameter. As a first application, we use our
results, in combination with the most recent input for the isoscalar part of
the form factor, to predict the corresponding spectrum of
$\eta'\to\ell^+\ell^-\gamma$, in particular we find the slope parameter
$b_{\eta'}=1.455(24)\text{GeV}^{-2}$. With forthcoming data on the latter
process, our results establish the necessary framework to improve the
evaluation of the $\eta'$-pole contribution to the anomalous magnetic moment of
the muon using experimental input from both $\eta'$ decay channels.


title: Coherent quantum annealing in a programmable 2000-qubit Ising chain

abstract:
  Quantum simulation has emerged as a valuable arena for demonstrating and
understanding the capabilities of near-term quantum computers. Quantum
annealing has been used successfully in simulating a range of open quantum
systems, both at equilibrium and out of equilibrium. However, in all previous
experiments, annealing has been too slow to simulate a closed quantum system
coherently, due to the onset of thermal effects from the environment. Here we
demonstrate coherent evolution through a quantum phase transition in the
paradigmatic setting of the 1D transverse-field Ising chain, using up to 2000
superconducting flux qubits in a programmable quantum annealer. In large
systems we observe the quantum Kibble-Zurek mechanism with theoretically
predicted kink statistics, as well as characteristic positive kink-kink
correlations, independent of system temperature. In small chains, excitation
statistics validate the picture of a Landau-Zener transition at a minimum gap.
In both cases, results are in quantitative agreement with analytical solutions
to the closed-system quantum model. For slower anneals we observe
anti-Kibble-Zurek scaling in a crossover to the open quantum regime. These
experiments demonstrate that large-scale quantum annealers can be operated
coherently, paving the way to exploiting coherent dynamics in quantum
optimization, machine learning, and simulation tasks.


title:
Intermediate- and high-velocity clouds in the Milky Way I: covering
  factors and vertical heights

abstract:
  Intermediate- and high-velocity clouds (IVCs, HVCs) are a potential source of
fuel for star formation in the Milky Way (MW), but their origins and fates
depend sensitively on their distances. We search for IVC and HVC in HST
high-resolution ultraviolet spectra of 55 halo stars at vertical heights $|z|
\gtrsim 1$ kpc. We show that IVCs ($40 \leq |v_{\rm LSR}| <90$ km/s) have a
high detection rate - the covering factor, $f_c$ - that is about constant ($f_c
=0.90\pm 0.04$) from $z=1.5$ to $14$ kpc, implying IVCs are essentially
confined to $|z|\lesssim 1.5$ kpc. For the HVCs ($90 \leq |v_{\rm LSR}|
\lesssim 170$ km/s), we find $f_c$ increases from $f_c \simeq 0.14\pm 0.10$ at
$|z|\lesssim 2-3$ kpc to $f_c =0.60\pm 0.15$ at $5\lesssim |z|\lesssim 14$ kpc,
the latter value being similar to that found towards QSOs. In contrast, the
covering factor of very high-velocity clouds (VHVCs, $|v_{\rm LSR}|\gtrsim 170$
km/s) is $f_c<4\%$ in the stellar sample compared to 20\% in a QSO sample,
implying these clouds must be at $d\gtrsim 10-15$ kpc ($|z|\gtrsim 10$ kpc).
Gas clouds with $|v_{\rm LSR}|>40$ km/s at $|b|\gtrsim 15^\circ$ have therefore
$|v_{\rm LSR}|$ decreasing with decreasing $|z|$. Assuming each feature
originates from a single cloud, we derive scale-heights of $1.0 \pm 0.3$ and
$2.8 \pm 0.3$ kpc for the IVCs and HVCs, respectively. Our findings provide
support to the "rain" and galactic fountain models. In the latter scenario,
VHVCs may mostly serve as fuel for the MW halo. In view of their locations and
high covering factors, IVCs and HVCs are good candidates to sustain star
formation in the MW.


title:
Resolving Combinatorial Ambiguities in Dilepton $t \bar t$ Event
  Topologies with Neural Networks

abstract:
  We study the potential of deep learning to resolve the combinatorial problem
in SUSY-like events with two invisible particles at the LHC. As a concrete
example, we focus on dileptonic $t \bar t$ events, where the combinatorial
problem becomes an issue of binary classification: pairing the correct lepton
with each $b$ quark coming from the decays of the tops. We investigate the
performance of a number of machine learning algorithms, including
attention-based networks, which have been used for a similar problem in the
fully-hadronic channel of $t\bar t$ production; and the Lorentz Boost Network,
which is motivated by physics principles. We then consider the general case
when the underlying mass spectrum is unknown, and hence no kinematic endpoint
information is available. Compared against existing methods based on kinematic
variables, we demonstrate that the efficiency for selecting the correct pairing
is greatly improved by utilizing deep learning techniques.


title: Fragmented Cooper pair condensation in striped superconductors

abstract:
  Condensation of bosons in Bose-Einstein condensates or Cooper pairs in
superconductors refers to a macroscopic occupation of a few single- or
two-particle states. A condensate is called "fragmented" if not a single, but
multiple states are macroscopically occupied. While fragmentation is known to
occur in particular Bose-Einstein condensates, we propose that fragmentation
naturally takes place in striped superconductors. To this end, we investigate
the nature of the superconducting ground state realized in the two-dimensional
$t$-$t^\prime$-$J$ model. In the presence of charge density modulations, the
condensate is shown to be fragmented and composed of partial condensates
located on the stripes. The fragments of the condensates hybridize to form an
extended macroscopic wave function across the system. The results are obtained
from evaluating the singlet-pairing two-particle density matrix of the ground
state on finite cylinders computed via the density matrix renormalization group
(DMRG) method. Our results shed light on the intricate relation between stripe
order and superconductivity in systems of strongly correlated electrons.


title:
Resonant light enhances phase coherence in a cavity QED simulator of
  fermionic superfluidity

abstract:
  Cavity QED experiments are natural hosts for non-equilibrium phases of matter
supported by photon-mediated interactions. In this work, we consider a cavity
QED simulation of the BCS model of superfluidity, by studying regimes where the
cavity photons act as dynamical degrees of freedom instead of mere mediators of
the interaction via virtual processes. We find an enhancement of long time
coherence following a quench whenever the cavity frequency is tuned into
resonance with the atoms. We discuss how this is equivalent to enhancement of
non-equilibrium superfluidity and highlight similarities to an analogous
phenomena recently studied in solid state quantum optics. We also discuss the
conditions for observing this enhanced resonant pairing in experiments by
including the effect of photon losses and inhomogeneous coupling in our
analysis.


title:
Intermediate- and high-velocity clouds in the Milky Way II: evidence for
  a Galactic fountain with collimated outflows and diffuse inflows

abstract:
  We model the kinematics of the high- and intermediate- velocity clouds (HVCs
and IVCs) observed in absorption towards a sample of 55 Galactic halo stars
with accurate distance measurements. We employ a simple model of a thick disc
whose main free parameters are the gas azimuthal, radial and vertical
velocities ($v_\phi$, $v_{\rm R}$ and $v_{\rm z}$), and apply it to the data by
fully accounting for the distribution of the observed features in the
distance-velocity space. We find that at least two separate components are
required to reproduce the data. A scenario where the HVCs and the IVCs are
treated as distinct populations provides only a partial description of the
data, which suggests that a pure velocity-based separation may give a biased
vision of the gas physics at the Milky Way's disc-halo interface. Instead, the
data are best described by a combination of an inflow and an outflow
components, both characterised by rotation with $v_\phi$ comparable to that of
the disc and $v_{\rm z}$ of 50-100 km/s. Features associated with the inflow
appear to be diffused across the sky, while those associated with the outflow
are mostly confined within a bi-cone pointing towards ($l\!=\!220^{\circ}$,
$b\!=\!+40^{\circ}$) and ($l\!=\!40^{\circ}$, $b\!=\!-40^{\circ}$). Our
findings indicate that the lower ($|z|\!\lesssim\!10$ kpc) Galactic halo is
populated by a mixture of diffuse inflowing gas and collimated outflowing
material, which are likely manifestations of a galaxy-wide gas cycle triggered
by stellar feedback, that is, the galactic fountain.


title: Cosmic Kite: Auto-encoding the Cosmic Microwave Background

abstract:
  In this work we present the results of the study of the cosmic microwave
background TT power spectrum through auto-encoders in which the latent
variables are the cosmological parameters. This method was trained and
calibrated using a data-set composed by 80000 power spectra from random
cosmologies computed numerically with the CAMB code. Due to the specific
architecture of the auto-encoder, the encoder part is a model that estimates
the maximum-likelihood parameters from a given power spectrum. On the other
hand, the decoder part is a model that computes the power spectrum from the
cosmological parameters and can be used as a forward model in a fully Bayesian
analysis. We show that the encoder is able to estimate the true cosmological
parameters with a precision varying from $\approx 0.004\% $ to $\approx 0.2\% $
(depending on the cosmological parameter), while the decoder computes the power
spectra with a mean percentage error of $\approx 0.0018\% $ for all the
multipole range. We also demonstrate that the decoder recovers the expected
trends when varying the cosmological parameters one by one, and that it does
not introduce any significant bias on the estimation of cosmological parameters
through a Bayesian analysis. These studies gave place to the Cosmic Kite python
software that is publicly available and can be downloaded and installed from
https://github.com/Martindelosrios/cosmic-kite. Although this algorithm does
not improve the precision of the measurements compared with the traditional
methods, it reduces significantly the computation time and represents the first
attempt towards forcing the latent variables to have a physical interpretation.


title: Moir\'e Landau fans and magic zeros

abstract:
  We study the energy spectrum of moir\'e systems under a uniform magnetic
field. The superlattice potential generally broadens Landau levels into Chern
bands with finite bandwidth. However, we find that these Chern bands become
flat at a discrete set of magnetic fields which we dub "magic zeros". The flat
band subspace is generally different from the Landau level subspace in the
absence of the moir\'e superlattice. By developing a semiclassical quantization
method and taking account of superlattice induced Bragg reflection, we prove
that magic zeros arise from the simultaneous quantization of two distinct
$k$-space orbits. The flat bands at magic zeros provide a new setting for
exploring crystalline fractional quantum Hall physics.


title:
Proposal for asymmetric photoemission and tunneling spectroscopies in
  Fermi-Hubbard model on triangular optical lattices

abstract:
  Recent realization of well-controlled triangular optical lattices loaded with
ultracold Fermions and the more advanced techniques to probe it pave the way
for studying frustrated Fermi-Hubbard physics. Here, we theoretically predict
asymmetric photoemission and tunneling spectroscopies for a lightly hole-doped
and electron-doped triangular Mott antiferromagnet, and reveal two distinct
types of magnetic polarons: a \emph{lightly} renormalized quasiparticle with
the same momentum as the spin background and a \emph{heavily} renormalized
quasiparticle with a shifted momentum and a nearly flat band, using both
analytical and unbiased numerical methods. We propose these theoretical
findings to be verified in frustrated optical lattices and Moir\'e
superlattices by probing various observables including the spectral function,
the density of states, the energy dispersion and the quasiparticle weight.
Moreover, we reveal the asymmetric response of the spin background against
charge doping, demonstrating that the interplay between the local spin and
charge degrees of freedom plays a vital role in doped triangular Mott
antiferromagnets.


title: Model selection and signal extraction using Gaussian Process regression

abstract:
  We present a novel computational approach for extracting weak signals, whose
exact location and width may be unknown, from complex background distributions
with an arbitrary functional form. We focus on datasets that can be naturally
presented as binned integer counts, demonstrating our approach on the CERN open
dataset from the ATLAS collaboration at the Large Hadron Collider, which
contains the Higgs boson signature. Our approach is based on Gaussian Process
(GP) regression - a powerful and flexible machine learning technique that
allowed us to model the background without specifying its functional form
explicitly, and to separate the background and signal contributions in a robust
and reproducible manner. Unlike functional fits, our GP-regression-based
approach does not need to be constantly updated as more data becomes available.
We discuss how to select the GP kernel type, considering trade-offs between
kernel complexity and its ability to capture the features of the background
distribution. We show that our GP framework can be used to detect the Higgs
boson resonance in the data with more statistical significance than a
polynomial fit specifically tailored to the dataset. Finally, we use Markov
Chain Monte Carlo (MCMC) sampling to confirm the statistical significance of
the extracted Higgs signature.


title:
Detection of a radio-filled X-ray cavity within the interstellar medium
  of NGC 5141

abstract:
  We present the first Chandra detection of a single X-ray cavity within the
interstellar medium of the small Fanaroff-Riley type I (FRI) radio galaxy NGC
5141. The X-ray surface brightness depression, located $\approx 4$ kpc away
from the galaxy center, is projected on the northern radio lobe, which is
completely contained within the galaxy. The thermal gas surrounding the cavity,
which extends to $\approx$ 20 kpc, has a bolometric X-ray luminosity (0.1 - 100
keV) of L${_X}\approx2\times10^{40}$ erg s$^{-1}$ and a temperature of
$kT\approx0.8$ keV. We calculated the total energy (E$_{cav} = 4PV \approx
10^{55}$ erg) required to inflate the cavity and its age ($t_{cav}\approx 9$
Myrs), assuming that it is filled with relativistic particles and rises
buoyantly. The inferred total cavity power is as low as
P$_{cav}=E_{cav}/t_{cav}\approx6\times10^{40}$ erg s$^{-1}$, which is the
lowest one among the radio-filled systems. Comparing $P_{cav}$ to the
bolometric X-ray luminosity (i.e., the cooling luminosity), we conclude that
NGC 5141's central active galactic nucleus can heat the interstellar medium and
balance its cooling luminosity, confirming that the $P_{cav}-L_{cool}$
relation, mainly tested on groups and clusters, also works for such a low-power
system.


title:
Detectability of Chlorofluorocarbons in the Atmospheres of Habitable
  M-dwarf Planets

abstract:
  The presence of chlorofluorocarbons (CFCs) in Earth's atmosphere is a direct
result of technology. Ozone-depleting CFCs have been banned by most countries,
but some CFCs have persistent in elevated concentrations due to their long
stratospheric lifetimes. CFCs are effective greenhouse gases and could serve as
a remotely detectable spectral signature of technology. Here we use a
three-dimensional climate model and a synthetic spectrum generator to assess
the detectability of CFC-11 and CFC-12 as a technosignature on exoplanets. We
consider the case of TRAPPIST-1e as well as a habitable Earth-like planet
around a 3300 K M-dwarf star, with CFC abundances ranging from one to five
times present-day levels. Assuming an optimistic James Webb Space Telescope
(JWST) Mid Infrared Instrument (MIRI) low resolution spectrometer (LRS) noise
floor level of 10 ppm to multiple co-added observations, we find that spectral
features potentially attributable to present or historic Earth-level CFC
features could be detected with a SNR $\ge 3-5$ on TRAPPIST-1e, if present, in
$\sim 100$ hours of in-transit time. However, applying a very conservative 50
ppm noise floor to co-added observations, even a 5x Earth-level CFC would not
be detectable no matter the observation time. Such observations could be
carried out simultaneously and at no additional cost with searches for
biosignature gases. Non-detection would place upper limits on the CFC
concentration. We find that with the launch of JWST, humanity may be
approaching the cusp of being able to detect passive atmospheric
technosignatures equal in strength to its own around the nearest stars.


title:
Many-Body Quantum Muon Effects and Quadrupolar Coupling in Solid
  Nitrogen

abstract:
  Quantum zero-point motion (ZPM) of light particles in materials has only
recently begun to be explored from an ab initio perspective, through several
competing approximations. Here we develop a unified description of muon (or
light nucleus) ZPM and establish the regimes of anharmonicity and positional
quantum entanglement where different approximation schemes apply. Via density
functional theory and path-integral molecular dynamics simulations we
demonstrate that in solid nitrogen, $\alpha$--N$_2$, muon ZPM is both strongly
anharmonic and many-body in character, with the muon forming an extended
electric-dipole polaron around a central, quantum-entangled
[N$_2$--$\mu$--N$_2$]$^+$ complex. By combining this quantitative description
of quantum muon ZPM with precision muon quadrupolar level-crossing resonance
experiments, we independently determine the static $^{14}$N nuclear quadrupolar
coupling constant of pristine $\alpha$--N$_2$ to be $-5.36(2)$ MHz, a
significant improvement in accuracy over the previously-accepted value of
$-5.39(5)$ MHz.


title:
Many-particle covalency, ionicity, and atomicity on example of simple
  molecules -- revisited

abstract:
  We analyze two-particle binding factors of H2, LiH, and HeH+ molecules/ions
with the help of our original exact diagonalization ab intio (EDABI) approach.
The interelectronic correlations are taken into account rigorously within the
second quantization scheme for restricted basis of renormalized single-particle
wave functions, i.e., with their size readjusted in the correlated state. This
allows us to determine the many-particle covalency and ionicity factors in a
natural and intuitive manner in terms of the microscopic single-particle and
interaction parameters, also determined within our method. We discuss the
limitations of those basic characteristics and introduce the concept of
atomicity, corresponding to the Mott and Hubbard criterion concerning
localization threshold in many-particle systems. This addition introduces an
atomic ingredient into the electron states and thus removes a spurious behavior
of covalency with the increasing interatomic distance, as well as provides a
more complete physical interpretation of bonding.


title: A uniform stellar origin for binary black holes revealed by lensing

abstract:
  Although most gravitational wave events are claimed to be mergers of
unusually massive, $25-65M_\odot$, black holes, it is now clear that 20\% of
all reported events comprise modest mass black holes, $5-15M_\odot$, like the
stellar black holes in the Milky Way. We show that such stellar mass black hole
binaries (BBH) if magnified by lensing galaxies can be detected at high
redshift, 1$< $z$ <$5, with chirp masses increased by $1+z$, accounting for the
majority of apparently high mass BBH events. This simple lensing explanation is
manifested by the evident bimodality of BBH chirp masses now visible, with 80\%
of BBH events in a broad peak centered on $m_{chirp} \simeq 35M_\odot$, and
20\% of BBH events in a narrow, low mass peak at $m_{chirp} \simeq 8.5M_\odot$,
matching well our prediction for lensed and unlensed events respectively. This
lensing interpretation is reinforced by the "graveyard plot" when ranked by
chirp mass, revealing a jump in chirp mass at $m_{chirp} \simeq 10M_\odot$ that
we show is caused by the large redshift difference between unlensed events with
$z<0.3$ and lensed events above $z>1$. Furthermore, nearly all BBH events are
seen to share a component mass ratio of $m_1/m_2=1.45\pm0.03$, indicating a
common stellar origin for BBH events across all chirp masses. This observed
component mass uniformity implies most binary black holes seldom pair up by
random capture, instead we may conclude that massive progenitor stars of BBH
black holes typically formed in-situ, in a well defined way over the full span
of cosmic time accessed through gravitational lensing.

categories: astro-ph.GA astro-ph.CO astro-ph.HE gr-qc


title:
Particle production during inflation: A Bayesian analysis with CMB data
  from Planck 2018

abstract:
  A class of inflationary models that involve rapid bursts of particle
productions predict observational signatures, such as bump-like features in the
primordial scalar power spectrum. In this work, we analyze such models by
comparing their predictions with the latest CMB data from Planck 2018. We
consider two scenarios of particle production. The first one is a simple
scenario consisting of a single burst of particle production during observable
inflation. The second one consists of multiple bursts of particle production
that lead to a series of bump-like features in the primordial power spectrum.
We find that the second scenario of the multi-bump model gives better fit to
the CMB data compared to the concordance $\Lambda$CDM model. We carried out
model comparisons using Bayesian evidences. From the observational constraints
on the amplitude of primordial features of the multi-bump model, we find that
the coupling parameter $g$ responsible for particle production is bound to be
$g< 0.05$.


title:
Motion Correction and Volumetric Reconstruction for Fetal Functional
  Magnetic Resonance Imaging Data

abstract:
  Motion correction is an essential preprocessing step in functional Magnetic
Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts
caused by fetal movement and maternal breathing and consequently to suppress
erroneous signal correlations. Current motion correction approaches for fetal
fMRI choose a single 3D volume from a specific acquisition timepoint with least
motion artefacts as reference volume, and perform interpolation for the
reconstruction of the motion corrected time series. The results can suffer, if
no low-motion frame is available, and if reconstruction does not exploit any
assumptions about the continuity of the fMRI signal. Here, we propose a novel
framework, which estimates a high-resolution reference volume by using
outlier-robust motion correction, and by utilizing Huber L2 regularization for
intra-stack volumetric reconstruction of the motion-corrected fetal brain fMRI.
We performed an extensive parameter study to investigate the effectiveness of
motion estimation and present in this work benchmark metrics to quantify the
effect of motion correction and regularised volumetric reconstruction
approaches on functional connectivity computations. We demonstrate the proposed
framework's ability to improve functional connectivity estimates,
reproducibility and signal interpretability, which is clinically highly
desirable for the establishment of prognostic noninvasive imaging biomarkers.
The motion correction and volumetric reconstruction framework is made available
as an open-source package of NiftyMIC.


title: Grid-based methods for chemistry modelling on a quantum computer

abstract:
  We explore grid-based techniques for simulating multi-electron dynamics using
a quantum computer, an approach sometimes called first quantized chemistry.
Using a split-operator Fourier transform (SO-FT) method on exactly-emulated
quantum computers with up to 36 qubits, we model the dynamics of 2D and 3D
atoms with single and paired particles interacting via the Coulomb potential.
The grid-based method enables direct visualisation of quantum dynamics,
including the behaviour of bound states, electron ionization by an applied
electric field, and electron-electron scattering. We exploit ancilla-qubit
measurements for iterative phase estimation, state preparation and for
implementing complex (absorbing) potentials. Ancilla-based methods for
obtaining observables such as binding energies are found to be far more
efficient and robust than direct sampling. We also introduce an augmented
split-operator (ASO) method where the usual kinetic and potential operators are
supplemented by a core-stabilisation operator that dramatically improves
wavepacket propagation near a Coulomb singularity. Overall, the grid-based
method scales well in terms of the quantum processor's size and execution time;
we extrapolate the resource requirements for accurate modelling of
classically-intractable systems from our numerical results, and we describe
suitable quantum architectures. We conclude by noting that the approach should
be successful in far broader settings, from high energy physics to financial
portfolio modelling.


title: Quantum information scrambling and quantum chaos in little string theory

abstract:
  In the current manuscript we perform a systematic investigation about the
effects of nonlocal interaction to the spread of quantum information in many
body system. In particular, we have studied how nonlocality influence the
existing bound on the growth rate of the commutator involving two local
operators, the butterfly velocity. For this purpose, we consider the nonlocal
theory on the worldvolume of $N\gg 1$, NS$5$ branes arising in the limit of
vanishing string coupling, the `little string theory'. A direct evidence of
nonlocality can be realized from the `volume law' behavior for the most
dominant part of holographic entanglement entropy. We obtain the butterfly
velocity by studying the dynamics of the near horizon geometry backreacted by a
high energy quanta in the form of a shockwave resulting from an early
perturbation on the corresponding thermofield double state. We observe that the
butterfly velocity increases with the nonlocal scale of little string theory,
the inverse Hagedorn temperature $\beta_{h}$, indicating a faster rate of
information spread due to the nonlocal interaction. The same conclusion follows
as the disruption of two sided mutual information is observed to occur at a
faster rate for higher values of $\beta_{h}$. Finally, we realize a direct
connection between the parameters of quantum chaos and the quasinormal modes
for collective excitations through the phenomenon of `pole skipping'.


title: Generalized Symmetry Breaking Scales and Weak Gravity Conjectures

abstract:
  We explore the notion of approximate global symmetries in quantum field
theory and quantum gravity. We show that a variety of conjectures about quantum
gravity, including the weak gravity conjecture, the distance conjecture, and
the magnetic and axion versions of the weak gravity conjecture can be motivated
by the assumption that generalized global symmetries should be strongly broken
within the context of low-energy effective field theory, i.e. at a
characteristic scale less than the Planck scale where quantum gravity effects
become important. For example, the assumption that the electric one-form
symmetry of Maxwell theory should be strongly broken below the Planck scale
implies the weak gravity conjecture. Similarly, the violation of generalized
non-invertible symmetries is closely tied to analogs of this conjecture for
non-abelian gauge theory. This reasoning enables us to unify these conjectures
with the absence of global symmetries in quantum gravity.


title: Photodissociation and X-Ray Dominated Regions

abstract:
  The radiation from stars and active galactic nuclei (AGN) creates
photodissociation regions (PDRs) and X-ray dominated regions (XDRs), where the
chemistry or heating are dominated by far-ultraviolet (FUV) radiation or X-ray
radiation, respectively. PDRs include a wide range of environments from the
diffuse interstellar medium to dense star-forming regions. XDRs are found in
the center of galaxies hosting AGN, in protostellar disks, and in the vicinity
of X-ray binaries. In this review, we describe the dominant thermal, chemical,
and radiation transfer processes in PDRs and XDRs, as well as a brief
description of models and their use to analyze observations. We then present
recent results from Milky Way, nearby extragalactic, and high-redshift
observations. Several important results are:
  $\bullet$ Velocity resolved PDR lines reveal the kinematics of the neutral
atomic gas and provide constraints on the stellar feedback process. Their
interpretation is, however, in dispute as observations suggest a prominent role
for stellar winds while they are much less important in theoretical models.
  $\bullet$ A significant fraction of molecular mass resides in CO-dark gas
especially in low-metallicity/highly irradiated environments.
  $\bullet$ The CO ladder and CI/CII ratios can determine if FUV or X-rays
dominate the ISM heating of extragalactic sources.
  $\bullet$ With ALMA, PDR and XDR tracers are now routinely detected on
galactic scales over cosmic time. This makes it possible to link the star
formation history of the Universe to the evolution of the physical and chemical
properties of the gas.


title:
Blocking Techniques for Sparse Matrix Multiplication on Tensor
  Accelerators

abstract:
  Tensor accelerators have gained popularity because they provide a cheap and
efficient solution for speeding up computational-expensive tasks in Deep
Learning and, more recently, in other Scientific Computing applications.
However, since their features are specifically designed for tensor algebra
(typically dense matrix-product), it is commonly assumed that they are not
suitable for applications with sparse data. To challenge this viewpoint, we
discuss methods and present solutions for accelerating sparse matrix
multiplication on such architectures. In particular, we present a 1-dimensional
blocking algorithm with theoretical guarantees on the density, which builds
dense blocks from arbitrary sparse matrices. Experimental results show that,
even for unstructured and highly-sparse matrices, our block-based solution
which exploits Nvidia Tensor Cores is faster than its sparse counterpart. We
observed significant speed-ups of up to two orders of magnitude on real-world
sparse matrices.


title:
The short ionizing photon mean free path at z=6 in Cosmic Dawn III, a
  new fully-coupled radiation-hydrodynamical simulation of the Epoch of
  Reionization

abstract:
  Recent determinations of the mean free path of ionising photons (mfp) in the
intergalactic medium (IGM) at $\rm z=6$ are lower than many theoretical
predictions. In order to gain insight into this issue, we investigate the
evolution of the mfp in our new massive fully coupled radiation hydrodynamics
cosmological simulation of reionization: Cosmic Dawn III (CoDaIII). CoDaIII's
scale ($\rm 94^3 \, cMpc^3$) and resolution ($\rm 8192^3$ grid) make it
particularly suitable to study the evolution of the IGM during the Epoch of
Reionization (EoR). The simulation was performed with RAMSES-CUDATON on Summit,
and used 131072 processors coupled to 24576 GPUs, making it the largest EoR
simulation, and largest RAMSES simulation ever performed. A superior agreement
with global constraints on reionization is obtained in CoDaIII over CoDaII
especially for the evolution of the neutral hydrogen fraction and the cosmic
photo-ionization rate, thanks to an improved calibration, later end of
reionization ($\rm z=5.6$), and higher spatial resolution. Analyzing the mfp,
we find that CoDaIII reproduces the most recent observations very well, from
$\rm z=6$ to $\rm z=4.6$. We show that the distribution of the mfp in CoDaIII
is bimodal, with short (neutral) and long (ionized) mfp modes, respectively,
due to the patchiness of reionization and the co-existence of neutral versus
ionized regions during the EoR. The neutral mode peaks at sub-kpc to kpc scales
of mfp, while the ionized mode peak evolves from $\rm 0.1 Mpc/h$ at $\rm z=7$
to $\sim 10$ Mpc/h at $\rm z=5.2$. Computing the mfp as the average of the
ionized mode provides the best match to the recent observational
determinations. The distribution reduces to a single neutral (ionized) mode at
$\rm z>13$ ($\rm z<5$).


title: Microscopic density-functional approach to nonlinear elasticity theory

abstract:
  Starting from a general classical model of many interacting particles we
present a well defined step by step procedure to derive the continuum-mechanics
equations of nonlinear elasticity theory with fluctuations which describe the
macroscopic phenomena of a solid crystal. As the relevant variables we specify
the coarse-grained densities of the conserved quantities and a properly defined
displacement field which describes the local translations, rotations, and
deformations. In order to stay within the framework of the conventional
density-functional theory we first and mainly consider the isothermal case and
omit the effects of heat transport and entropy production where later we extend
our theory to the general case and include these effects. We proceed in two
steps. First, we apply the concept of local thermodynamic equilibrium and
minimize the free energy functional under the constraints that the macroscopic
relevant variables are fixed. As results we obtain the local free energy
density and we derive explicit formulas for the elastic constants which are
exact within the framework of density-functional theory. Second, we apply the
methods of nonequilibrium statistical mechanics with projection-operator
techniques. We extend the projection operators in order to include the effects
of coarse-graining and the displacement field. As a result we obtain the
time-evolution equations for the relevant variables with three kinds of terms
on the right-hand sides: reversible, dissipative, and fluctuating terms. We
find explicit formulas for the transport coefficients which are exact in the
limit of continuum mechanics if the projection operators are properly defined.
By construction the theory allows the diffusion of particles in terms of point
defects where, however, in a normal crystal this diffusion is suppressed.


title: Chapman-Enskog derivation of multicomponent Navier-Stokes equations

abstract:
  There are several reasons to extend the presentation of Navier-Stokes
equations to multicomponent systems. Many technological applications are based
on physical phenomena that are present neither in pure elements nor in binary
mixtures. Whereas Fourier's law must already be generalized in binaries, it is
only with more than two components that Fick's law breaks down in its simple
form. The emergence of dissipative phenomena affects also the inertial
confinement fusion configurations, designed as prototypes for the future fusion
nuclear plants hopefully replacing the fission ones.
  This important topic can be described in much simpler terms than in many
textbooks since the publication of the formalism put forward recently by Snider
in \textit{Phys. Rev. E} \textbf{82}, 051201 (2010). In a very natural way, it
replaces the linearly dependent atomic fractions by the independent set of
partial densities. Then, the Chapman-Enskog procedure is hardly more
complicated for multicomponent mixtures than for pure elements. Moreover, the
recent proposal of a convergent kinetic equation by Baalrud and Daligault in
\textit{Phys. Plasmas} \textbf{26}, 082106 (2019) demonstrates that Boltzmann
equation with the potential of mean force is a far better choice in situations
close to equilibrium, as described by the Navier-Stokes equations, than Landau
or Lenard-Balescu equations.
  In our comprehensive presentation, we emphasize the physical arguments behind
Chapman-Enskog derivation and keep the mathematics as simple as possible. This
excludes as a technical non-essential aspect the solution of the linearized
Boltzmann equation through an expansion in Hermite polynomials. We discuss the
link with the second principle of Thermodynamics of entropy increase, and what
can be learned from this exposition.


title:
REST: Integrating Term Rewriting with Program Verification (Extended
  Version)

abstract:
  We introduce REST, a novel term rewriting technique for theorem proving that
uses online termination checking and can be integrated with existing program
verifiers. REST enables flexible but terminating term rewriting for theorem
proving by: (1) exploiting newly-introduced term orderings that are more
permissive than standard rewrite simplification orderings; (2) dynamically and
iteratively selecting orderings based on the path of rewrites taken so far; and
(3) integrating external oracles that allow steps that cannot be justified with
rewrite rules. Our REST approach is designed around an easily implementable
core algorithm, parameterizable by choices of term orderings and their
implementations; in this way our approach can be easily integrated into
existing tools. We implemented REST as a Haskell library and incorporated it
into Liquid Haskell's evaluation strategy, extending Liquid Haskell with
rewriting rules. We evaluated our REST implementation by comparing it against
both existing rewriting techniques and E-matching and by showing that it can be
used to supplant manual lemma application in many existing Liquid Haskell
proofs.


title:
A note on best constants for Weighted Integral Hardy inequalities on
  homogeneous groups

abstract:
  The main aim of this note is to prove sharp weighted integral Hardy
inequality and conjugate integral Hardy inequality on homogeneous Lie groups
with any quasi-norm for the range $1<p\leq q<\infty.$ We also calculate the
precise value of sharp constants in respective inequalities, improving the
result of $[19]$ in the case of homogeneous groups.


title: Symmetry-resolved entanglement in a long-range free-fermion chain

abstract:
  We investigate the symmetry resolution of entanglement in the presence of
long-range couplings. To this end, we study the symmetry-resolved entanglement
entropy in the ground state of a fermionic chain that has dimerised long-range
hoppings with power-like decaying amplitude -- a long-range generalisation of
the Su-Schrieffer-Heeger model. This is a system that preserves the number of
particles. The entropy of each symmetry sector is calculated via the charged
moments of the reduced density matrix. We exploit some recent results on block
Toeplitz determinants generated by a discontinuous symbol to obtain
analytically the asymptotic behaviour of the charged moments and of the
symmetry-resolved entropies for a large subsystem. At leading order we find
entanglement equipartition, but comparing with the short-range counterpart its
breaking occurs at a different order and it does depend on the hopping
amplitudes.


title: On weakly \'etale morphisms

abstract:
  We show that the weakly \'etale morphisms, used to define the pro-\'etale
site of a scheme, are characterized by a lifting property similar to the one
which characterizes formally \'etale morphisms. In order to prove this, we
prove a theorem called Henselian descent which is a "Henselized version" of the
fact that a scheme defines a sheaf for the fpqc topology. Finally, we show that
weakly \'etale algebras over regular rings arising in geometry are ind-\'etale
and that weakly \'etale algebras do not always lift along surjective ring
homomorphisms.


title: Group testing via residuation and partial geometries

abstract:
  The motivation for this paper comes from the ongoing SARS-CoV-2 Pandemic. Its
goal is to present a previously neglected approach to non-adaptive group
testing and describes it in terms of residuated pairs on partially ordered
sets. Our investigation has the advantage, as it naturally yields an efficient
decision scheme (decoder) for any given testing scheme. This decoder allows to
detect a large amount of infection patterns. Apart from this, we devise a
construction of good group testing schemes that are based on incidence matrices
of finite partial linear spaces. The key idea is to exploit the structure of
these matrices and make them available as test matrices for group testing.
These matrices may generally be tailored for different estimated disease
prevalence levels. As an example, we discuss the group testing schemes based on
generalized quadrangles. In the context at hand, we state our results only for
the error-free case so far. An extension to a noisy scenario is desirable and
will be treated in a subsequent account on the topic.


title: Blind leads Blind: A Zero-Knowledge Attack on Federated Learning

abstract:
  Attacks on Federated Learning (FL) can severely reduce the quality of the
generated models and limit the usefulness of this emerging learning paradigm
that enables on-premise decentralized learning. There have been various
untargeted attacks on FL, but they are not widely applicable as they i) assume
that the attacker knows every update of benign clients, which is indeed sent in
encrypted form to the central server, or ii) assume that the attacker has a
large dataset and sufficient resources to locally train updates imitating
benign parties. In this paper, we design a zero-knowledge untargeted attack
(ZKA), which synthesizes malicious data to craft adversarial models without
eavesdropping on the transmission of benign clients at all or requiring a large
quantity of task-specific training data. To inject malicious input into the FL
system by synthetic data, ZKA has two variants. ZKA-R generates adversarial
ambiguous data by reversing engineering from the global models. To enable
stealthiness, ZKA-G trains the local model on synthetic data from the generator
that aims to synthesize images different from a randomly chosen class.
Furthermore, we add a novel distance-based regularization term for both attacks
to further enhance stealthiness. Experimental results on Fashion-MNIST and
CIFAR-10 show that the ZKA achieves similar or even higher attack success rate
than the state-of-the-art untargeted attacks against various defense
mechanisms, namely more than 50% for Cifar-10 for all considered defense
mechanisms. As expected, ZKA-G is better at circumventing defenses, even
showing a defense pass rate of close to 90% when ZKA-R only achieves 70%.
Higher data heterogeneity favours ZKA-R since detection becomes harder.


title: A generalised distributed-order Maxwell model

abstract:
  In this work we present a generalised viscoelastic model using
distributed-order derivatives. The model consists of two distributed-order
elements (distributed springpots) connected in series, as in the Maxwell model.
The new model generalises the fractional viscoelastic model presented in [H.
Schiessel, A. Blumen, Hierarchical analogues to fractional relaxation
equations, Journal of Physics A: Mathematical and General 26 (1993) 5057-50]
and allows for a more broad and accurate description of complex fluids when a
proper weighting function of the order of the derivatives is chosen. We discuss
the connection between classical, fractional, and viscoelastic models of
distributed order and highlight the fundamental concepts that support these
constitutive equations. We also derive the relaxation modulus, the storage and
loss modulus, and the creep compliance for specific weighting functions.


title:
Nuclear-induced dephasing and signatures of hyperfine effects in
  isotopically purified $^{13}$C graphene

abstract:
  The hyperfine interaction between the spins of electrons and nuclei is both a
blessing and a curse. It can provide a wealth of information when used as an
experimental probing technique but it can also be destructive when it acts as a
dephasive perturbation on the electronic system. In this work, we fabricated
large scale single and multilayer isotopically-purified $^{13}$C graphene Hall
bars to search for interaction effects between the nuclear magnetic moments and
the electronic system. We find signatures of nuclei with a spin in the analysis
of the weak localization phenomenon that shows a significant dichotomy in the
scattering times of monolayer $^{12}$C and $^{13}$C graphene close the Dirac
point. Microwave-induced electron spin flips were exploited to transfer
momentum to the nuclei and build-up a nuclear field. The presence of a very
weak nuclear field is encoded in a modulation of the electron Zeeman energy
which shifts the energy for resonant absorption and reduces the $g$-factor.


title: The complexity of computing optimum labelings for temporal connectivity

abstract:
  A graph is temporally connected if there exists a strict temporal path, i.e.
a path whose edges have strictly increasing labels, from every vertex $u$ to
every other vertex $v$. In this paper we study temporal design problems for
undirected temporally connected graphs. The basic setting of these optimization
problems is as follows: given a connected undirected graph $G$, what is the
smallest number $|\lambda|$ of time-labels that we need to add to the edges of
$G$ such that the resulting temporal graph $(G,\lambda)$ is temporally
connected? As it turns out, this basic problem, called MINIMUM LABELING (ML),
can be optimally solved in polynomial time. However, exploiting the temporal
dimension, the problem becomes more interesting and meaningful in its following
variations, which we investigate in this paper. First we consider the problem
MIN. AGED LABELING (MAL) of temporally connecting the graph when we are given
an upper-bound on the allowed age (i.e. maximum label) of the obtained temporal
graph $(G,\lambda)$. Second we consider the problem MIN. STEINER LABELING
(MSL), where the aim is now to have a temporal path between any pair of
"terminals" vertices which lie in a subset $R\subseteq V$. This relaxed problem
resembles STEINER TREE in static graphs. However, due to the requirement of
strictly increasing labels in a temporal path, STEINER TREE is not a special
case of MSL. Finally we consider the age-restricted version of MSL, namely MIN.
AGED STEINER LABELING (MASL). Our main results are threefold: we prove that (i)
MAL becomes NP-complete on undirected graphs, while (ii) MASL becomes W[1]-hard
with respect to the number $|R|$ of terminals. On the other hand we prove that
(iii) although the age-unrestricted problem MSL is NP-hard, it is in FPT with
respect to the number $|R|$ of terminals. That is, adding the age restriction,
makes the above problems strictly harder.


title: Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets

abstract:
  Online ad platforms offer budget management tools for advertisers that aim to
maximize the number of conversions given a budget constraint. As the volume of
impressions, conversion rates and prices vary over time, these budget
management systems learn a spend plan (to find the optimal distribution of
budget over time) and run a pacing algorithm which follows the spend plan.
  This paper considers two models for impressions and competition that varies
with time: a) an episodic model which exhibits stationarity in each episode,
but each episode can be arbitrarily different from the next, and b) a model
where the distributions of prices and values change slowly over time. We
present the first learning theoretic guarantees on both the accuracy of spend
plans and the resulting end-to-end budget management system. We present four
main results: 1) for the episodic setting we give sample complexity bounds for
the spend rate prediction problem: given $n$ samples from each episode, with
high probability we have $|\widehat{\rho}_e - \rho_e| \leq
\tilde{O}(\frac{1}{n^{1/3}})$ where $\rho_e$ is the optimal spend rate for the
episode, $\widehat{\rho}_e$ is the estimate from our algorithm, 2) we extend
the algorithm of Balseiro and Gur (2017) to operate on varying, approximate
spend rates and show that the resulting combined system of optimal spend rate
estimation and online pacing algorithm for episodic settings has regret that
vanishes in number of historic samples $n$ and the number of rounds $T$, 3) for
non-episodic but slowly-changing distributions we show that the same approach
approximates the optimal bidding strategy up to a factor dependent on the
rate-of-change of the distributions and 4) we provide experiments showing that
our algorithm outperforms both static spend plans and non-pacing across a wide
variety of settings.


title:
Picking the low-hanging fruit: testing new physics at scale with active
  learning

abstract:
  Since the discovery of the Higgs boson, testing the many possible extensions
to the Standard Model has become a key challenge in particle physics. This
paper discusses a new method for predicting the compatibility of new physics
theories with existing experimental data from particle colliders. Using machine
learning, the technique obtained comparable results to previous methods (>90%
precision and recall) with only a fraction of their computing resources (<10%).
This makes it possible to test models that were impossible to probe before, and
allows for large-scale testing of new physics theories.


title: Local ice-like structure at the liquid water surface

abstract:
  Experiments and computer simulations have established that liquid water's
surfaces can deviate in important ways from familiar bulk behavior. Even in the
simplest case of an air-water interface, distinctive layering, orientational
biases, and hydrogen bond arrangements have been reported, but an overarching
picture of their origins and relationships has been incomplete. Here we show
that a broad set of such observations can be understood through an analogy with
the basal face of crystalline ice. Using simulations, we specifically
demonstrate that water and ice surfaces share a set of structural features
suggesting the presence of nanometer-scale ice-like domains at the air-water
interface. Most prominent is a shared characteristic layering of molecular
density and orientation perpendicular to the interface. Similarities in
two-point correlations of hydrogen bond network geometry point to shared
ice-like intermolecular structure in the parallel direction as well. Our
results bolster and significantly extend previous conceptions of ice-like
structure at the liquid's boundary, and suggest that the much-discussed
quasi-liquid layer on ice evolves subtly above the melting point into a
quasi-ice layer at the surface of liquid water.


title: Unusual electrical and magnetic properties in layered EuZn2As2

abstract:
  Eu-based compounds often exhibit unusual magnetism, which is critical for
nontrivial topological properties seen in materials such as EuCd2As2. We
investigate the structure and physical properties of EuZn2As2 through
measurements of the electrical resistivity, Hall effect, magnetization, and
neutron diffraction. Our data show that EuZn2As2 orders antiferromagnetically
with an A-type spin configuration below TN = 19 K. Surprisingly, there is
strong evidence for dominant ferromagnetic fluctuations above TN, as reflected
by positive Curie-Weiss temperature and extremely large negative
magnetoresistance (MR) between TN and Tfl {\guillemotright} 200 K. Furthermore,
the angle dependence of the MRab indicates field-induced spin reorientation
from the ab-plane to a direction approximately 45{\deg} from the ab plane.
Compared to EuCd2As2, the doubled TN and Tfl make EuZn2As2 a better platform
for exploring topological properties in both magnetic fluctuation (TN < T <
Tfl) and ordered (T < TN) regimes.


title: Equilibrium Defaultable Corporate Debt and Investment

abstract:
  In dynamic capital structure models with an investor break-even condition,
the firm's Bellman equation may not generate a contraction mapping, so the
standard existence and uniqueness conditions do not apply. First, we provide an
example showing the problem in a classical trade-off model. The firm can issue
one-period defaultable debt, invest in capital and pay a dividend. If the firm
cannot meet the required debt payment, it is liquidated. Second, we show how to
use a dual to the original problem and a change of measure, such that existence
and uniqueness can be proved. In the unique Markov-perfect equilibrium, firm
decisions reflect state-dependent capital and debt targets. Our approach may be
useful for other dynamic firm models that have an investor break-even
condition.


title:
Radio-Morphing: a fast, efficient and accurate tool to compute the radio
  signals from air-showers

abstract:
  Radio detection of air-showers is a mature technique that has gained momentum
over the past decades. With increasingly large-scale experiments, massive
air-shower simulations are needed to evaluate the radio signal at each antenna
position. Radio Morphing was developed for this purpose. It is a
semi-analytical tool that enables a fast computation of the radio signal
emitted by any air-shower at any location, from the simulation data of one
single reference shower at given positions. It relies on simple electromagnetic
scaling laws of the radio emission (i.e., electric field) at the antenna level
and then an interpolation of the radio pulse at the desired positions. We
present here major improvements on the Radio Morphing method that have been
implemented recently. The upgraded version is based on revised and refined
scaling laws, derived from physical principles. It also includes
shower-to-shower fluctuations and a new spatial interpolation technique, thanks
to which an excellent signal timing accuracy of a fraction of nanosecond can be
reached. This new implementation, provides simulated signals with relative
differences on the peak-to-peak amplitude of ZHAireS simulations below 10\%
(respectively 25\%) for 91\% (99\%) of antennas while the computation time was
reduced by more than 2 orders of magnitude compared to standard simulations.
This makes Radio Morphing an efficient tool that allows for a fast and accurate
computation of air-shower radio signals. Further implementation of Askaryan
emission or enabling to use an input value of the geomagnetic field should
reduce relative differences with ZHAireS by few percents and make the method
more universal.


title:
An Invariant Set Construction Method, Applied to Safe Coordination of
  Thermostatic Loads

abstract:
  We consider the problem of coordinating a collection of switched subsystems
under both local and global constraints for safe operation of the system.
Although an invariant set can be leveraged to construct a safety-guaranteed
controller for this kind of problem, computing an invariant set is not scalable
to high-dimensional systems. In this paper, we introduce a strategy to obtain
an implicit representation of a controlled invariant set for a collection of
switched subsystems, and construct a safety-guaranteed controller to coordinate
the subsystems using the representation. Specifically, we incorporate the
invariant set into a model predictive controller to guarantee safety and
recursive feasibility. Since the amount of computations is independent of the
number of subsystems, this approach scales to large collections of switched
subsystems. We use our approach to safely control a collection of
thermostatically controlled loads to provide grid balancing services. The
problem includes constraints on each load's temperature and duration it must
remain in a mode after a switch, and also on aggregate power consumption to
ensure network safety. Numerical simulations show that the proposed approach
outperforms benchmark strategies in terms of safety and recursive feasibility.


title: Statistical Limits for Testing Correlation of Hypergraphs

abstract:
  In this paper, we consider the hypothesis testing of correlation between two
$m$-uniform hypergraphs on $n$ unlabelled nodes. Under the null hypothesis, the
hypergraphs are independent, while under the alternative hypothesis, the
hyperdges have the same marginal distributions as in the null hypothesis but
are correlated after some unknown node permutation. We focus on two scenarios:
the hypergraphs are generated from the Gaussian-Wigner model and the dense
Erd\"{o}s-R\'{e}nyi model. We derive the sharp information-theoretic testing
threshold. Above the threshold, there exists a powerful test to distinguish the
alternative hypothesis from the null hypothesis. Below the threshold, the
alternative hypothesis and the null hypothesis are not distinguishable. The
threshold involves $m$ and decreases as $m$ gets larger. This indicates testing
correlation of hypergraphs ($m\geq3$) becomes easier than testing correlation
of graphs ($m=2$)


title: Embeddability of centrosymmetric matrices

abstract:
  In this paper, we discuss the embedding problem for centrosymmetric matrices,
which are higher order generalizations of the matrices occurring in Strand
Symmetric Models. These models capture the substitution symmetries arising from
the double helix structure of the DNA. Deciding whether a transition matrix is
embeddable or not enables us to know if the observed substitution probabilities
are consistent with a homogeneous continuous time substitution model, such as
the Kimura models, the Jukes-Cantor model or the general time-reversible model.
On the other hand, the generalization to higher order matrices is motivated by
the setting of synthetic biology, which works with different sizes of genetic
alphabets.


title:
Modeling bacterial flagellar motor with new structure information:
  Rotational dynamics of two interacting protein nano-rings

abstract:
  In this article, we develop a mathematical model for the rotary bacterial
flagellar motor (BFM) based on the recently discovered structure of the stator
complex (MotA$_5$MotB$_2$). The structure suggested that the stator also
rotates. The BFM is modeled as two rotating nano-rings that interact with each
other. Specifically, translocation of protons through the stator complex drives
rotation of the MotA pentamer ring, which in turn drives rotation of the FliG
ring in the rotor via interactions between the MotA ring of the stator and the
FliG ring of the rotor. Preliminary results from the structure-informed model
are consistent with the observed torque-speed relation. More importantly, the
model predicts distinctive rotor and stator dynamics and their load dependence,
which may be tested by future experiments. Possible approaches to verify and
improve the model to further understanding of the molecular mechanism for
torque generation in BFM are also discussed.


title:
Cooperative Solutions to Exploration Tasks Under Speed and Budget
  Constraints

abstract:
  We present a multi-agent system where agents can cooperate to solve a system
of dependent tasks, with agents having the capability to explore a solution
space, make inferences, as well as query for information under a limited
budget. Re-exploration of the solution space takes place by an agent when an
older solution expires and is thus able to adapt to dynamic changes in the
environment. We investigate the effects of task dependencies, with
highly-dependent graph $G_{40}$ (a well-known program graph that contains $40$
highly interlinked nodes, each representing a task) and less-dependent graphs
$G_{18}$ (a program graph that contains $18$ tasks with fewer links),
increasing the speed of the agents and the complexity of the problem space and
the query budgets available to agents. Specifically, we evaluate trade-offs
between the agent's speed and query budget. During the experiments, we observed
that increasing the speed of a single agent improves the system performance to
a certain point only, and increasing the number of faster agents may not
improve the system performance due to task dependencies. Favoring faster agents
during budget allocation enhances the system performance, in line with the
"Matthew effect." We also observe that allocating more budget to a faster agent
gives better performance for a less-dependent system, but increasing the number
of faster agents gives a better performance for a highly-dependent system.


title:
POSYDON: A General-Purpose Population Synthesis Code with Detailed
  Binary-Evolution Simulations

abstract:
  Most massive stars are members of a binary or a higher-order stellar systems,
where the presence of a binary companion can decisively alter their evolution
via binary interactions. Interacting binaries are also important astrophysical
laboratories for the study of compact objects. Binary population synthesis
studies have been used extensively over the last two decades to interpret
observations of compact-object binaries and to decipher the physical processes
that lead to their formation. Here, we present POSYDON, a novel, binary
population synthesis code that incorporates full stellar-structure and
binary-evolution modeling, using the MESA code, throughout the whole evolution
of the binaries. The use of POSYDON enables the self-consistent treatment of
physical processes in stellar and binary evolution, including: realistic
mass-transfer calculations and assessment of stability, internal
angular-momentum transport and tides, stellar core sizes, mass-transfer rates
and orbital periods. This paper describes the detailed methodology and
implementation of POSYDON, including the assumed physics of stellar- and
binary-evolution, the extensive grids of detailed single- and binary-star
models, the post-processing, classification and interpolation methods we
developed for use with the grids, and the treatment of evolutionary phases that
are not based on pre-calculated grids. The first version of POSYDON targets
binaries with massive primary stars (potential progenitors of neutron stars or
black holes) at solar metallicity.


title: The Inert Drift Atlas Model

abstract:
  Consider a massive (inert) particle impinged from above by N Brownian
particles that are instantaneously reflected upon collision with the inert
particle.
  The velocity of the inert particle increases due to the influence of an
external Newtonian potential (e.g. gravitation) and decreases in proportion to
the total local time of collisions with the Brownian particles.
  This system models a semi-permeable membrane in a fluid having microscopic
impurities (Knight (2001)).
  We study the long-time behavior of the process $(V,\mathbf{Z})$, where $V$ is
the velocity of the inert particle and $\mathbf{Z}$ is the vector of gaps
between successive particles ordered by their relative positions. The system is
not hypoelliptic, not reversible, and has singular form interactions. Thus the
study of stability behavior of the system requires new ideas. We show that this
process has a unique stationary distribution that takes an explicit product
form which is Gaussian in the velocity component and Exponential in the other
components. We also show that convergence in total variation distance to the
stationary distribution happens at an exponential rate. We further obtain
certain law of large numbers results for the particle locations and
intersection local times.


title:
Failure Prediction with Statistical Guarantees for Vision-Based Robot
  Control

abstract:
  We are motivated by the problem of performing failure prediction for
safety-critical robotic systems with high-dimensional sensor observations
(e.g., vision). Given access to a blackbox control policy (e.g., in the form of
a neural network) and a dataset of training environments, we present an
approach for synthesizing a failure predictor with guaranteed bounds on
false-positive and false-negative errors. In order to achieve this, we utilize
techniques from Probably Approximately Correct (PAC)-Bayes generalization
theory. In addition, we present novel class-conditional bounds that allow us to
tradeoff the relative rates of false-positive vs. false-negative errors. We
propose algorithms that train failure predictors (that take as input the
history of sensor observations) by minimizing our theoretical error bounds. We
demonstrate the resulting approach using extensive simulation and hardware
experiments for vision-based navigation with a drone and grasping objects with
a robotic manipulator equipped with a wrist-mounted RGB-D camera. These
experiments illustrate the ability of our approach to (1) provide strong bounds
on failure prediction error rates (that closely match empirical error rates),
and (2) improve safety by predicting failures.


title:
Privacy Limits in Power-Law Bipartite Networks under Active
  Fingerprinting Attacks

abstract:
  This work considers the fundamental privacy limits under active
fingerprinting attacks in power-law bipartite networks. The scenario arises
naturally in social network analysis, tracking user mobility in wireless
networks, and forensics applications, among others. A stochastic growing
network generation model -- called the popularity-based model -- is
investigated, where the bipartite network is generated iteratively, and in each
iteration vertices attract new edges based on their assigned popularity values.
It is shown that using the appropriate choice of initial popularity values, the
node degree distribution follows a power-law distribution with arbitrary
parameter $\alpha>2$, i.e. fraction of nodes with degree $d$ is proportional to
$d^{-\alpha}$. An active fingerprinting deanonymization attack strategy called
the augmented information threshold attack strategy (A-ITS) is proposed which
uses the attacker's knowledge of the node degree distribution along with the
concept of information values for deanonymization. Sufficient conditions for
the success of the A-ITS, based on network parameters, are derived. It is shown
through simulations that the proposed attack significantly outperforms the
state-of-the-art attack strategies.


title: Calabi-Yau metrics, CFTs and random matrices

abstract:
  Calabi-Yau manifolds have played a key role in both mathematics and physics,
and are particularly important for deriving realistic models of particle
physics from string theory. Unfortunately, very little is known about the
explicit metrics on these spaces, leaving us unable, for example, to compute
particle masses or couplings in these models. We review recent progress in this
direction on using numerical approximations to compute the spectrum of the
Laplacian on these spaces. We give an example of what one can do with this new
"data", giving a surprising link between Calabi-Yau metrics and random matrix
theory.


title: On the maximal autocorrelation of Rudin-Shapiro sequences

abstract:
  In this paper, we prove that the maximal aperiodic autocorrelation of the
$m$-th Rudin-Shapiro sequence is of the same order as $\lambda^{m}$, where
$\lambda$ is the real root of $x^{3} + x^{2} - 2x - 4$. This proof was
developed independently of the recently published proof given by Katz and van
der Linden (2021). A proof of this result for the related periodic
autocorrelation is given by Allouche, Choi, Denise, Erd\'elyi, and Saffari
(2019) and Choi (2020) using a translation of the problem into linear algebra.
Our approach modifies this linear algebraic translation to deal with aperiodic
autocorrelation and provides an alternative method of dealing with the
computations given by Choi.


title:
Identifying Processes Governing Damage Evolution in Quasi-Static
  Elasticity. Part 2 -- Numerical Simulations

abstract:
  We investigate numerically a quasi-static elasticity system of Kachanov-type.
To do so we propose an Euler time discretization combined with a suitable
finite elements scheme (FEM) to handle the discretization is space. We use
ODE-type arguments to prove the consistency of the scheme as well as its
convergence rate. We rely on the computational platform FEniCS to perform the
FEM discretizations in space needed to compute the model output. The simulation
results show a good agreement with both the physics of the problem and with our
previous qualitative mathematical analysis results obtained for precisely the
same problem setting. Furthermore, our implementation recovers nicely the
theoretically expected convergence rate. This is a preliminary study preparing
the framework for the rigorous numerical identification of the damage process
in Kachanov-type models.


title: Sheaves over complexes of groups and developability

abstract:
  We define the notion of a sheaf over a complex of groups. As an application,
we give a criterion for the developability of a complex of groups. When the
developability is witnessed by a morphism to $\mathrm{GL}(V)$ for some $V$, our
criterion is a characterization of developability.


title:
The Role of Disk Tearing and Precession in the Observed Variability of
  Pleione

abstract:
  We acquired H$\alpha$ spectroscopic observations from 2005 to 2019 showing
Pleione has transitioned from a Be phase to a Be-shell phase during this
period. Using the radiative transfer code \hdust\ we created a grid of
$\sim100,000$ disk models for Pleione. We successfully reproduced the observed
transition with a disk model that varies in inclination while maintaining an
equatorial density of $\rho_0(r) = 3\times 10^{-11}
(r/R_{eq})^{-2.7}~\rm{g~cm^{-3}}$, and an H$\alpha$ emitting region extending
to $15~\rm{R_{eq}}$. We use a precessing disk model to extrapolate the changing
disk inclination over $120$ years and follow the variability in archival
observations. The best-fit disk model precesses over a line of sight
inclination between $\sim25\rm{^{\circ}}$ and $\sim144\rm{^{\circ}}$ with a
precessional period of $\sim80.5$ years. Our precessing models match some of
the observed variability but fail to reproduce all of the historical data
available. Therefore, we propose an ad-hoc model based on our precessing disk
model inspired by recent SPH simulations of similar systems, where the disk
tears due to the tidal influence of a companion star. In this model, a single
disk is slowly tilted to an angle of $30^{\circ}$ from the stellar equator over
$34$ years. Then, the disk is torn by the companion's tidal torque, with the
outer region separating from the innermost disk. The small inner disk returns
to the stellar equator as mass injection remains constant. The outer disk
precesses for $\sim15$ years before gradually dissipating. The process repeats
every $34$ years and reproduces all trends in Pleione's variability.


title: Identification of Flux Rope Orientation via Neural Networks

abstract:
  Geomagnetic disturbance forecasting is based on the identification of solar
wind structures and accurate determination of their magnetic field orientation.
For nowcasting activities, this is currently a tedious and manual process.
Focusing on the main driver of geomagnetic disturbances, the twisted internal
magnetic field of interplanetary coronal mass ejections (ICMEs), we explore a
convolutional neural network's (CNN) ability to predict the embedded magnetic
flux rope's orientation once it has been identified from in situ solar wind
observations. Our work uses CNNs trained with magnetic field vectors from
analytical flux rope data. The simulated flux ropes span many possible
spacecraft trajectories and flux rope orientations. We train CNNs first with
full duration flux ropes and then again with partial duration flux ropes. The
former provides us with a baseline of how well CNNs can predict flux rope
orientation while the latter provides insights into real-time forecasting by
exploring how accuracy is affected by percentage of flux rope observed. The
process of casting the physics problem as a machine learning problem is
discussed as well as the impacts of different factors on prediction accuracy
such as flux rope fluctuations and different neural network topologies.
Finally, results from evaluating the trained network against observed ICMEs
from Wind during 1995-2015 are presented.


title: Stellar activity correction using PCA decomposition of shells

abstract:
  Context. Stellar activity and instrumental signals are the main limitations
to the detection of Earth-like planets using the radial velocity (RV)
technique. Recent studies show that the key to mitigating those perturbing
effects might reside in analysing the spectra themselves, rather than the RV
time series and a few activity proxies. Aims. The goal of this paper is to
demonstrate that we can reach further improvement in RV precision by performing
a principal component analysis (PCA) decomposition of the shell time series,
with the shell as the projection of a spectrum onto the spacenormalised flux
versus flux gradient. Methods. By performing a PCA decomposition of shell time
series, it is possible to obtain a basis of first-order spectral variations
that are not related to Keplerian motion. The time coeffcients associated with
this basis can then be used to correct for non-Dopplerian signatures in RVs.
Results. We applied this new method on the YARARA post-processed spectra time
series of HD10700 and HD128621. On HD10700, we demonstrate, thanks to planetary
signal injections, that this new approach can successfully disentangle real
Dopplerian signals from instrumental systematics. The application of this new
methodology on HD128621 shows that the strong stellar activity signal seen at
the stellar rotational period and one-year aliases becomes insignificant in a
periodogram analysis. The RV root mean square on the five-year data is reduced
from 2.44 m/s down to 1.73 m/s. This new approach allows us to strongly
mitigate stellar activity, however, noise injections tests indicate that rather
high signal-to-noise ratio (S/N>250) is required to correct for the observed
activity signal on HD128621.


title:
Can We Talk? An Exploratory Study of Gender and Network Ties in a Local
  Government Setting

abstract:
  We explore the influence of gender and formal organizational status on the
formation of discussion ties. Network data, gathered through surveying
employees from a municipal organization in the United States, garnered a 92%
response rate (n=143). Results of exponential random graph modeling indicate
women supervisors are more likely to send discussion ties, while women in
general are more likely to receive discussion ties. These exploratory results
suggest women may be perceived as more approachable for work discussions, but
not as supervisors. Finally, the results identified a consistent homophily
effect of gender in the discussion network.


title: Positive scalar curvature -- constructions and obstructions

abstract:
  This is a survey of the current state of the question "Which closed connected
manifolds of dimension $n\ge 5$ admit Riemannian metrics whose scalar curvature
function is everywhere positive?" The introduction gives a brief overview of
these results, while the body of the paper discusses the methods used in the
proofs of these results. We mention the two flavors of topological obstructions
to the existence of \pscm s: one is a consequence of the Weizenb\"ock formula
for the Dirac operator, the other is obtained by considering stable minimal
hypersurfaces. We talk about geometric constructions of \pscm s (the
surgery/bordism theorem), which shows that the answer to the question above
depends only the bordism class of the manifold in a suitable bordism group. Via
the Pontryagin-Thom construction this can be translated into stable homotopy
theory, and solved completely in some cases, in particular for simply connected
manifolds, or manifolds with very special fundamental groups. The last section
discusses some open questions.


title:
Experimental investigations of linear and nonlinear periodic traveling
  waves in a viscous fluid conduit

abstract:
  Conduits generated by the buoyant dynamics between two miscible, Stokes
fluids with high viscosity contrast exhibit rich nonlinear wave dynamics.
However, little is known about the fundamental wave dispersion properties of
the medium. In the present work, a pump is used to inject a time-periodic flow
that results in the excitation of propagating small and large amplitude
periodic traveling waves along the conduit interface. This wavemaker problem is
used as a means to measure the linear and nonlinear dispersion relations and
corresponding periodic traveling wave profiles. Measurements are favorably
compared with predictions from a fully nonlinear, long-wave model (the conduit
equation) and the analytically computed linear dispersion relation for
two-Stokes flow. A critical frequency is observed, marking the threshold
between propagating and non-propagating (spatially decaying) waves.
Measurements of wave profiles and the wavenumber-frequency dispersion relation
quantitatively agree with wave solutions of the conduit equation. An upshift
from the conduit equation's predicted critical frequency is observed and is
hypothesized to be explained by incorporating a weak recirculating flow into
the full two-Stokes flow model which we observe to be operable in the
experiment. When the boundary condition corresponds to the temporal profile of
a nonlinear periodic traveling wave solution of the conduit equation, weakly
nonlinear and strongly nonlinear, cnoidal-type waves are observed that
quantitatively agree with the conduit nonlinear dispersion relation and wave
profiles. This wavemaker problem is an important precursor to the experimental
investigation of more general boundary value problems in viscous fluid conduit
nonlinear wave dynamics.


title: Towards Build Verifiability for Java-based Systems

abstract:
  Build verifiability refers to the property that the build of a software
system can be verified by independent third parties and it is crucial for the
trustworthiness of a software system. Various efforts towards build
verifiability have been made to C/C++-based systems, yet the techniques for
Java-based systems are not systematic and are often specific to a particular
build tool (e.g., Maven). In this study, we present a systematic approach
towards build verifiability on Java-based systems. Our approach consists of
three parts: a unified build process, a tool that dynamically controls
non-determinism during the build process, and another tool that eliminates
non-equivalences by post-processing the build artifacts. We apply our approach
on 46 unverified open source projects from Reproducible Central and 13 open
source projects that are widely used by Huawei commercial products. As a
result, 91% of the unverified Reproducible Central projects and 100% of the
commercially adopted OSS projects are successfully verified with our approach.
In addition, based on our experience in analyzing thousands of builds for both
commercial and open source Java-based systems, we present 14 patterns that
introduce non-equivalences in generated build artifacts and their respective
mitigation strategies. Among these patterns, 11 (78%) are unique for Java-based
system, whereas the remaining 3 (22%) are common with C/C++-based systems. The
approach and the findings of this paper are useful for both practitioners and
researchers who are interested in build verifiability.


title: Fast and perfect sampling of subgraphs and polymer systems

abstract:
  We give an efficient perfect sampling algorithm for weighted, connected
induced subgraphs (or graphlets) of rooted, bounded degree graphs under a
vertex-percolation subcriticality condition. We show that this subcriticality
condition is optimal in the sense that the problem of (approximately) sampling
weighted rooted graphlets becomes impossible for infinite graphs and
intractable for finite graphs if the condition does not hold. We apply our
rooted graphlet sampling algorithm as a subroutine to give a fast perfect
sampling algorithm for polymer models and a fast perfect sampling algorithm for
weighted non-rooted graphlets in finite graphs, two widely studied yet very
different problems. We apply this polymer model algorithm to give improved
sampling algorithms for spin systems at low temperatures on expander graphs and
other structured families of graphs: under the least restrictive conditions
known we give near linear-time algorithms, while previous algorithms in these
regimes required large polynomial running times.


title:
Towards the Maximum Traffic Demand and Throughput Supported by
  Relay-Assisted mmWave Backhaul Networks

abstract:
  This paper investigates the throughput performance issue of the
relay-assisted mmWave backhaul network. The maximum traffic demand of
small-cell base stations (BSs) and the maximum throughput at the macro-cell BS
have been found in a tree-style backhaul network through linear programming
under different network settings, which concern both the number of radio chains
available on BSs and the interference relationship between logical links in the
backhaul network. A novel interference model for the relay-assisted mmWave
backhaul network in the dense urban environment is proposed, which demonstrates
the limited interference footprint of mmWave directional communications.
Moreover, a scheduling algorithm is developed to find the optimal scheduling
for tree-style mmWave backhaul networks. Extensive numerical analysis and
simulations are conducted to show and validate the network throughput
performance and the scheduling algorithm.


title:
Habitable planet formation around low-mass stars: Rapid accretion, rapid
  debris removal and the essential contribution of external giants

abstract:
  In recent years a paradigm shift has occurred in exoplanet science, wherein
low-mass stars are increasingly viewed as a foundational pillar of the search
for potentially habitable worlds in the solar neighborhood. However, the
formation processes of this rapidly accumulating sample of planet systems are
still poorly understood. Moreover, it is unclear whether tenuous primordial
atmospheres around these Earth-analogs could have survived the intense epoch of
heightened stellar activity that is typical for low-mass stars. We present new
simulations of in-situ planet formation across the M-dwarf mass spectrum, and
derive leftover debris populations of small bodies that might source delayed
volatile delivery. We then follow the evolution of this debris with
high-resolution models of real systems of habitable zone planets around
low-mass stars such as TRAPPIST-1, Proxima Centauri and TOI-700. While debris
in the radial vicinity of the habitable zone planets is removed rapidly, thus
making delayed volatile delivery highly unlikely, we find that material
ubiquitously scattered into an exo-asteroid belt region during the planet
formation process represents a potentially lucrative reservoir of icy small
bodies. Thus, the presence of external ~Neptune-Saturn mass planets capable of
dynamically perturbing these asteroids would be a sign that habitable zone
worlds around low-mass stars might have avoided complete desiccation. However,
we also find that such giant planets significantly limit the efficiency of
asteroidal implantation during the planet formation process. In the coming
decade, long-baseline radial velocity studies and Roman Space Telescope
microlensing observations will undoubtedly further constrain this process.


title: Multi-level Latent Space Structuring for Generative Control

abstract:
  Truncation is widely used in generative models for improving the quality of
the generated samples, at the expense of reducing their diversity. We propose
to leverage the StyleGAN generative architecture to devise a new truncation
technique, based on a decomposition of the latent space into clusters, enabling
customized truncation to be performed at multiple semantic levels. We do so by
learning to re-generate W-space, the extended intermediate latent space of
StyleGAN, using a learnable mixture of Gaussians, while simultaneously training
a classifier to identify, for each latent vector, the cluster that it belongs
to. The resulting truncation scheme is more faithful to the original
untruncated samples and allows a better trade-off between quality and
diversity. We compare our method to other truncation approaches for StyleGAN,
both qualitatively and quantitatively.