title: Toward accurate polarization estimation in nanoscopic systems

abstract:
  The nanoscopic characterization of ferroelectric thin films is crucial from
their device application point of view. Standard characterization techniques
are based on detecting the nanoscopic charge compensation current (switching
current) caused by the polarization reversal in the ferroelectric. Owing to
various surface and bulk limited mechanisms, leakage currents commonly appear
during such measurements, which are frequently subtracted using the device I-V
characteristic by employing positive-up-negative-down (PUND) measurement
scheme. By performing nanoscopic switching current measurements on a commonly
used ferroelectric, BiFeO3, we show that such characterization methods may be
prone to large errors in the polarization estimation on ferro-resistive
samples, due to current background subtraction issues. Especially, when
ferro-resistive behavior is associated with the polarization reversal of the
ferroelectric thin film, background current subtraction is not accurate due to
the mismatch of the I-V characteristics for the two polarization states. We
show instead that removing the background current by an asymmetric least
squares subtraction method, though not perfect, gives a much better estimation
of the ferroelectric properties of the sample under study.


title: Personalized Execution Time Optimization for the Scheduled Jobs

abstract:
  Scheduled batch jobs have been widely used on the asynchronous computing
platforms to execute various enterprise applications, including the scheduled
notifications and the candidate computation for the modern recommender systems.
It is important to deliver or update the information to the users at the right
time to maintain the user experience and the execution impact. However, it is
challenging to provide a versatile execution time optimization solution for the
user-basis scheduled jobs to satisfy various product scenarios while
maintaining reasonable infrastructure resource consumption. In this paper, we
describe how we apply a pointwise learning-to-rank approach plus a "best time
policy" in the best time selection. In addition, we propose a value model
approach to efficiently leverage multiple streams of user activity signals in
our scheduling decisions of the execution time. Our optimization approach has
been successfully tested with production traffic that serves billions of users
per day, with statistically significant improvements in various product
metrics, including the notifications and content candidate generation. To the
best of our knowledge, our study represents the first ML-based multi-tenant
solution to the execution time optimization problem for the scheduled jobs at a
large industrial scale.


title: Numerical Study of the Roberge-Weiss Transition

abstract:
  We study numerically the Roberge-Weiss (RW) phase transition associated with
the discontinuities in the quark-number density at a definite series of the
values of the imaginary part of the quark chemical potential. We use polynomial
fit functions to parametrize the quark number density for computing the
canonical partition functions and show that they provide a good framework for
the analysis of lattice QCD data at finite density and $T>T_{RW}$. We argue
that at high temperatures the Lee-Yang zeroes (LYZ) lie in the negative real
semiaxis provided that the high-quark-number contributions to the great
canonical partition function do not vanish. Nonzero linear density of the LYZ
distribution signals the RW phase transition.


title: Axion Instability Supernovae

abstract:
  New particles coupled to the Standard Model can equilibrate in stellar cores
if they are sufficiently heavy and strongly coupled. In this work, we
investigate the astrophysical consequences of such a scenario for massive stars
by incorporating new contributions to the equation of state into a state of the
art stellar structure code. We focus on axions in the "cosmological triangle",
a region of parameter space with $300{\rm\,keV} \lesssim m_a \lesssim 2$ MeV,
$g_{a\gamma\gamma}\sim 10^{-5}$ GeV$^{-1}$ that is not presently excluded by
other considerations. We find that for axion masses $m_a \sim m_e $, axion
production in the core drives a new stellar instability that results in
explosive nuclear burning that either drives a series of mass-shedding
pulsations or completely disrupts the star resulting in a new type of optical
transient -- an \textit{Axion Instability Supernova}. We predict that the upper
black hole mass gap would be located at $37{\rm M}_\odot \le M\le 107{\rm
M}_\odot$ in these theories, a large shift down from the standard prediction,
which is disfavored by the detection of the mass gap in the LIGO/Virgo/KAGRA
GWTC-2 gravitational wave catalog beginning at $46_{-6}^{+17}{\rm M}_\odot$.
Furthermore, axion-instability supernovae are more common than pair-instability
supernovae, making them excellent candidate targets for JWST. The methods
presented in this work can be used to investigate the astrophysical
consequences of any theory of new physics that contains heavy bosonic particles
of arbitrary spin. We provide the tools to facilitate such studies.

categories: hep-ph astro-ph.CO astro-ph.HE astro-ph.SR gr-qc


title:
SolO/EUI Observations of Ubiquitous Fine-scale Bright Dots in an
  Emerging Flux Region: Comparison with a Bifrost MHD Simulation

abstract:
  We report on the presence of numerous tiny bright dots in and around an
emerging flux region (an X-ray/coronal bright point) observed with SolO's
EUI/\hri\ in 174 \AA. These dots are roundish, have a diameter of 675$\pm$300
km, a lifetime of 50$\pm$35 seconds, and an intensity enhancement of 30\%
$\pm$10\% above their immediate surroundings. About half of the dots remain
isolated during their evolution and move randomly and slowly ($<$10 \kms). The
other half show extensions, appearing as a small loop or surge/jet, with
intensity propagations below 30\,\kms. Many of the bigger and brighter \hri\
dots are discernible in SDO/AIA 171 \AA\ channel, have significant emissivity
in the temperature range of 1--2 MK, and are often located at polarity
inversion lines observed in HMI LOS magnetograms. Although not as pervasive as
in observations, Bifrost MHD simulation of an emerging flux region do show dots
in synthetic \fe\ images. These dots in simulation show distinct Doppler
signatures -- blueshifts and redshifts coexist, or a redshift of the order of
10 \kms\ is followed by a blueshift of similar or higher magnitude. The
synthetic images of \oxy\ and \siiv\ lines, which represent transition region
radiation, also show the dots that are observed in \fe\ images, often expanded
in size, or extended as a loop, and always with stronger Doppler velocities (up
to 100 \kms) than that in \fe\ lines. Our observation and simulation results,
together with the field geometry of dots in the simulation, suggest that most
dots in emerging flux regions form in the lower solar atmosphere (at $\approx$1
Mm) by magnetic reconnection between emerging and pre-existing/emerged magnetic
field. Some dots might be manifestations of magneto-acoustic shocks through the
line formation region of \fe\ emission.


title:
The VVV survey: Long-period variable stars I. Photometric catalog of ten
  VVV/OGLE tiles

abstract:
  Long-period variable stars (LPVs) are pulsating red giants, primarily in the
asymptotic giant branch phase, and they include both Miras and semi-regular
variables (SRVs). Their period-age and period-luminosity relations enable us to
trace different stellar populations, as they are intrinsically very bright and
cover a wide range in distances and ages. The purpose of this study is to
establish a census of LPV stars in a region close to the Galactic center, using
the six-year database of the Vista Variables in the V\'ia L\'actea (VVV) ESO
Public Survey, as well as to describe the methodology that was employed to
search for and characterize LPVs using VVV data. Near-IR surveys such as VVV
provide a unique opportunity to probe the high-extinction innermost regions of
the Milky Way. The detection and analysis of the intrinsically bright Miras in
this region could provide us with an excellent probe of the properties of the
Milky Way far behind its bulge. We used point-spread function photometry for
all available $K_{s}$-band images in ten VVV tiles, covering $16.4~\deg^2$ in
total, overlapping fields observed in the course of the Optical Gravitational
Lensing Experiment (OGLE)-III survey. We designed a method to select LPV
candidates, and we used the known variables from OGLE-III and other known
variables from the literature to test our approach. The reduced $\chi^2$
statistic, along with the flux-independent index $K_{(fi)}$, were used in our
analysis. The Lomb-Scargle period search method, Fourier analysis, template
fitting, and visual inspection were then performed to refine our sample and
characterize the properties of the stars included in our catalog. A final
sample of 130 Mira candidates, of which 129 are new discoveries, was thus
obtained, with periods in the range between about 80 and 1400~days.


title:
Magic angles in twisted bilayer graphene near commensuration: towards a
  hyper-magic manifold

abstract:
  The Bistritzer-MacDonald continuum model (BM model) describes the low-energy
moir\'e bands for twisted bilayer graphene (TBG) at small twist angles. We
derive a generalized continuum model for TBG near any commensurate twist angle,
which is characterized by a complex inter-layer hopping at commensurate $AA$
stackings (rather than the real hopping in the BM model), a real inter-layer
hopping at commensurate $AB/BA$ stackings, and a global energy shift. The
complex phase of the $AA$ stacking hopping and the twist angle together define
a single angle parameter $\phi_0$. We compute the model parameters for the
first six distinct commensurate TBG configurations, among which the
$38.2^\circ$ configuration may be within experimentally observable energy
scales. We identify the first magic angle for any $\phi_0$ at a condition
similar to that of the BM model. At this angle, the lowest two moir\'e bands at
charge neutrality become flat (except in the vicinity of the
$\boldsymbol\Gamma_M$ point) and retain fragile topology, but lose
particle-hole symmetry. We further identify a hyper-magic manifold in the
parameter space at $\phi_0=\pm\pi/2$, where seven or more moir\'e bands around
charge neutrality become flat simultaneously. The lowest two moir\'e flat bands
in the hyper-magic manifold have fragile (trivial) topology when close to (far
from) the chiral limit with zero $AA$ hopping.


title: Higgs Factory Considerations

abstract:
  We discuss considerations that can be used to formulate recommendations for
initiating a lepton collider project that would provide precision studies of
the Higgs boson and related electroweak phenomena.


title:
Quantum Thermal Transport Beyond Second Order with the Reaction
  Coordinate Mapping

abstract:
  Standard quantum master equation techniques such as the Redfield or Lindblad
equations are perturbative to second order in the microscopic system-reservoir
coupling parameter $\lambda$. As a result, characteristics of dissipative
systems, which are beyond second order in $\lambda$, are not captured by such
tools. Moreover, if the leading order in the studied effect is
higher-than-quadratic in $\lambda$, a second-order description fundamentally
fails, even at weak coupling. Here, using the reaction coordinate (RC) quantum
master equation framework, we are able to investigate and classify
higher-than-second order transport mechanisms. This technique, which relies on
the redefinition of the system-environment boundary, allows for the effects of
system-bath coupling to be included to high orders. We study steady-state heat
current beyond second-order in two models: The generalized spin-boson model
with non-commuting system-bath operators and a three-level ladder system. In
the latter model heat enters in one transition and it is extracted from a
different one. Crucially, we identify two transport pathways: (i) System's
current, where heat conduction is mediated by transitions in the system, with
the heat current scaling as $j_q \propto \lambda^2$ to lowest order in
$\lambda$. (ii) Inter-bath current, with the thermal baths directly exchanging
energy between them, facilitated by the bridging quantum system. To the lowest
order in $\lambda$, this current scales as $j_q \propto \lambda^4$. These
mechanisms are uncovered and examined using numerical and analytical tools. We
contend that the RC mapping brings, already at the level of the mapped
Hamiltonian, much insights on transport characteristics.


title: Weihrauch Complexity and the Hagen School of Computable Analysis

abstract:
  Weihrauch complexity is now an established and active part of mathematical
logic. It can be seen as a computability-theoretic approach to classifying the
uniform computational content of mathematical problems. This theory has become
an important interface between more proof-theoretic and more
computability-theoretic studies in the realm of reverse mathematics. Here we
present a historical account of the early developments of Weihrauch complexity
by the Hagen school of computable analysis that started more than thirty years
ago, and we indicate how this has influenced, informed, and anticipated more
recent developments of the subject.


title: Algebraic canonical quantization of lumped superconducting networks

abstract:
  We present a systematic canonical quantization procedure for lumped-element
superconducting networks by making use of a redundant configuration-space
description. The algorithm is based on an original, explicit, and constructive
implementation of the symplectic diagonalization of positive semidefinite
Hamiltonian matrices, a particular instance of Williamson's theorem. With it,
we derive canonically quantized discrete-variable descriptions of passive
causal systems. We exemplify the algorithm with representative singular
electrical networks, a nonreciprocal extension for the black-box quantization
method, as well as an archetypal Landau quantization problem.


title: Cheeger Inequalities for Vertex Expansion and Reweighted Eigenvalues

abstract:
  The classical Cheeger's inequality relates the edge conductance $\phi$ of a
graph and the second smallest eigenvalue $\lambda_2$ of the Laplacian matrix.
Recently, Olesker-Taylor and Zanetti discovered a Cheeger-type inequality
$\psi^2 / \log |V| \lesssim \lambda_2^* \lesssim \psi$ connecting the vertex
expansion $\psi$ of a graph $G=(V,E)$ and the maximum reweighted second
smallest eigenvalue $\lambda_2^*$ of the Laplacian matrix.
  In this work, we first improve their result to $\psi^2 / \log d \lesssim
\lambda_2^* \lesssim \psi$ where $d$ is the maximum degree in $G$, which is
optimal assuming the small-set expansion conjecture. Also, the improved result
holds for weighted vertex expansion, answering an open question by
Olesker-Taylor and Zanetti. Building on this connection, we then develop a new
spectral theory for vertex expansion. We discover that several interesting
generalizations of Cheeger inequalities relating edge conductances and
eigenvalues have a close analog in relating vertex expansions and reweighted
eigenvalues. These include an analog of Trevisan's result on bipartiteness, an
analog of higher order Cheeger's inequality, and an analog of improved
Cheeger's inequality.
  Finally, inspired by this connection, we present negative evidence to the
$0/1$-polytope edge expansion conjecture by Mihail and Vazirani. We construct
$0/1$-polytopes whose graphs have very poor vertex expansion. This implies that
the fastest mixing time to the uniform distribution on the vertices of these
$0/1$-polytopes is almost linear in the graph size. This does not provide a
counterexample to the conjecture, but this is in contrast with known positive
results which proved poly-logarithmic mixing time to the uniform distribution
on the vertices of subclasses of $0/1$-polytopes.


title:
LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text
  Retrieval

abstract:
  In this paper, we propose LaPraDoR, a pretrained dual-tower dense retriever
that does not require any supervised data for training. Specifically, we first
present Iterative Contrastive Learning (ICoL) that iteratively trains the query
and document encoders with a cache mechanism. ICoL not only enlarges the number
of negative instances but also keeps representations of cached examples in the
same hidden space. We then propose Lexicon-Enhanced Dense Retrieval (LEDR) as a
simple yet effective way to enhance dense retrieval with lexical matching. We
evaluate LaPraDoR on the recently proposed BEIR benchmark, including 18
datasets of 9 zero-shot text retrieval tasks. Experimental results show that
LaPraDoR achieves state-of-the-art performance compared with supervised dense
retrieval models, and further analysis reveals the effectiveness of our
training strategy and objectives. Compared to re-ranking, our lexicon-enhanced
approach can be run in milliseconds (22.5x faster) while achieving superior
performance.


title: Passive self-propulsion and enhanced annihilation of $p-$atic defects

abstract:
  We investigate the effects of hydrodynamics on the motion of topological
defects in $p-$atic liquid crystals: i.e. two-dimensional liquid crystals
characterised by $p-$fold rotational symmetry. Using numerical simulations and
analytical work, we demonstrate the existence of a generic passive
self-propulsion mechanism for defects of winding number $s=(p-1)/p$ and
arbitrary $p$ values. Remarkably, whereas this mechanism is not unique to
nematics (i.e. $p=2$), nematics are the only type of liquid crystals where
passive self-propulsion is thermodynamically stable. Furthermore, we find that
hydrodynamics can accelerate the annihilation dynamics of pairs of $\pm 1/p$
defects and that this effect increases with $p$ before approaching a plateau.


title:
(In-)Approximability Results for Interval, Resource Restricted, and Low
  Rank Scheduling

abstract:
  We consider variants of the restricted assignment problem where a set of jobs
has to be assigned to a set of machines, for each job a size and a set of
eligible machines is given, and the jobs may only be assigned to eligible
machines with the goal of makespan minimization. For the variant with interval
restrictions, where the machines can be arranged on a path such that each job
is eligible on a subpath, we present the first better than $2$-approximation
and an improved inapproximability result. In particular, we give a
$(2-\frac{1}{24})$-approximation and show that no better than
$9/8$-approximation is possible, unless P=NP. Furthermore, we consider
restricted assignment with $R$ resource restrictions and rank $D$ unrelated
scheduling. In the former problem, a machine may process a job if it can meet
its resource requirements regarding $R$ (renewable) resources. In the latter,
the size of a job is dependent on the machine it is assigned to and the
corresponding processing time matrix has rank at most $D$. The problem with
interval restrictions includes the 1 resource variant, is encompassed by the 2
resource variant, and regarding approximation the $R$ resource variant is
essentially a special case of the rank $R+1$ problem. We show that no better
than $3/2$, $8/7$, and $3/2$-approximation is possible (unless P=NP) for the 3
resource, 2 resource, and rank 3 variant, respectively. Both the approximation
result for the interval case and the inapproximability result for the rank 3
variant are solutions to open challenges stated in previous works. Lastly, we
also consider the reverse objective, that is, maximizing the minimal load any
machine receives, and achieve similar results.


title: Deep AutoAugment

abstract:
  While recent automated data augmentation methods lead to state-of-the-art
results, their design spaces and the derived data augmentation strategies still
incorporate strong human priors. In this work, instead of fixing a set of
hand-picked default augmentations alongside the searched data augmentations, we
propose a fully automated approach for data augmentation search named Deep
AutoAugment (DeepAA). DeepAA progressively builds a multi-layer data
augmentation pipeline from scratch by stacking augmentation layers one at a
time until reaching convergence. For each augmentation layer, the policy is
optimized to maximize the cosine similarity between the gradients of the
original and augmented data along the direction with low variance. Our
experiments show that even without default augmentations, we can learn an
augmentation policy that achieves strong performance with that of previous
works. Extensive ablation studies show that the regularized gradient matching
is an effective search method for data augmentation policies. Our code is
available at: https://github.com/MSU-MLSys-Lab/DeepAA .


title: Masked Visual Pre-training for Motor Control

abstract:
  This paper shows that self-supervised visual pre-training from real-world
images is effective for learning motor control tasks from pixels. We first
train the visual representations by masked modeling of natural images. We then
freeze the visual encoder and train neural network controllers on top with
reinforcement learning. We do not perform any task-specific fine-tuning of the
encoder; the same visual representations are used for all motor control tasks.
To the best of our knowledge, this is the first self-supervised model to
exploit real-world images at scale for motor control. To accelerate progress in
learning from pixels, we contribute a benchmark suite of hand-designed tasks
varying in movements, scenes, and robots. Without relying on labels,
state-estimation, or expert demonstrations, we consistently outperform
supervised encoders by up to 80% absolute success rate, sometimes even matching
the oracle state performance. We also find that in-the-wild images, e.g., from
YouTube or Egocentric videos, lead to better visual representations for various
manipulation tasks than ImageNet images.


title:
Quantifying the barren plateau phenomenon for a model of unstructured
  variational ans\"{a}tze

abstract:
  Quantifying the flatness of the objective-function landscape associated with
unstructured parameterized quantum circuits is important for understanding the
performance of variational algorithms utilizing a "hardware-efficient ansatz",
particularly for ensuring that a prohibitively flat landscape -- a so-called
"barren plateau" -- is avoided. For a model of such ans\"{a}tze, we relate the
typical landscape flatness to a certain family of random walks, enabling us to
derive a Monte Carlo algorithm for efficiently, classically estimating the
landscape flatness for any architecture. The statistical picture additionally
allows us to prove new analytic bounds on the barren plateau phenomenon, and
more generally provides novel insights into the phenomenon's dependence on the
ansatz depth, architecture, qudit dimension, and Hamiltonian combinatorial and
spatial locality. Our analysis utilizes techniques originally developed by
Dalzell et al. to study anti-concentration in random circuits.


title:
Spectroscopic Analysis Tool for intEgraL fieLd unIt daTacubEs
  (satellite): Case studies of NGC 7009 and NGC 6778 with MUSE

abstract:
  Integral field spectroscopy (IFS) provides a unique capability to
spectroscopically study extended sources over a 2D field of view, but it also
requires new techniques and tools. In this paper, we present an automatic code,
Spectroscopic Analysis Tool for intEgraL fieLd unIt daTacubEs, SATELLITE,
designed to fully explore such capability in the characterization of extended
objects, such as planetary nebulae, H II regions, galaxies, etc. SATELLITE
carries out 1D and 2D spectroscopic analysis through a number of pseudo-slits
that simulate slit spectrometry, as well as emission line imaging. The 1D
analysis permits direct comparison of the integral field unit (IFU) data with
previous studies based on long-slit spectroscopy, while the 2D analysis allows
the exploration of physical properties in both spatial directions. Interstellar
extinction, electron temperatures and densities, ionic abundances from
collisionally excited lines, total elemental abundances and ionization
correction factors are computed employing the Pyneb package. A Monte Carlo
approach is implemented in the code to compute the uncertainties for all the
physical parameters. SATELLITE provides a powerful tool to extract physical
information from IFS observations in an automatic and user configurable way.
The capabilities and performance of SATELLITE are demonstrated by means of a
comparison between the results obtained from the Multi Unit Spectroscopic
Explorer (MUSE) data of the planetary nebula NGC 7009 with the results obtained
from long-slit and IFU data available in the literature. The SATELLITE
characterization of NGC 6778 based on MUSE data is also presented.


title:
More Than a Toy: Random Matrix Models Predict How Real-World Neural
  Representations Generalize

abstract:
  Of theories for why large-scale machine learning models generalize despite
being vastly overparameterized, which of their assumptions are needed to
capture the qualitative phenomena of generalization in the real world? On one
hand, we find that most theoretical analyses fall short of capturing these
qualitative phenomena even for kernel regression, when applied to kernels
derived from large-scale neural networks (e.g., ResNet-50) and real data (e.g.,
CIFAR-100). On the other hand, we find that the classical GCV estimator (Craven
and Wahba, 1978) accurately predicts generalization risk even in such
overparameterized settings. To bolster this empirical finding, we prove that
the GCV estimator converges to the generalization risk whenever a local random
matrix law holds. Finally, we apply this random matrix theory lens to explain
why pretrained representations generalize better as well as what factors govern
scaling laws for kernel regression. Our findings suggest that random matrix
theory, rather than just being a toy model, may be central to understanding the
properties of neural representations in practice.


title: BAO scale inference from biased tracers using the EFT likelihood

abstract:
  The physical scale corresponding to baryon acoustic oscillations (BAO), the
size of the sound horizon at recombination, is precisely determined by CMB
experiments. Measuring the apparent size of the BAO scale imprinted in the
clustering of galaxies gives us a direct estimate of the angular-diameter
distance and the Hubble parameter as a function of redshift. The BAO feature is
damped by non-linear structure formation, which reduces the precision with
which we can infer the BAO scale from standard galaxy clustering analysis
methods. Many methods to undo this damping via the so-called BAO reconstruction
have so far been proposed; however, they all rely on backward modeling. In this
paper, we present the first results of BAO inference from rest-frame halo
catalogs using forward modeling combined with the EFT likelihood, in the case
where the initial phases of the density field are fixed. We show that the
remaining systematic bias is less than 2% when we consider cutoff values of
$\Lambda \leq 0.25 \,h\,{\rm Mpc}^{-1}$ for all halo samples considered, and
below 1% and consistent with zero for all but the most highly biased samples.
We also demonstrate that, when compared to the standard power spectrum
likelihood approach under the same assumption of fixed phases, the 1$\sigma$
errors associated to the field level inference of the BAO scale are 1.1 to 3.3
times smaller, depending on the value of the cutoff and the halo sample. Our
analysis therefore unveils another promising feature of using field-level
inference for high-precision cosmology


title: Modeling of Solids

abstract:
  This text is the support for the course of Modeling of Solids, of the Master
of Mechanics of the University Paris-Saclay - Curriculum MMM: Mathematical
Methods for Mechanics, held at Versailles. The course is the continuation of
the course Continuum Mechanics - Solids, and as such it is an introduction, for
graduate students, to some typical topics of the theory of solid bodies. The
different arguments are dealt with in a simple, succinct way, the objective
being to give to students the fundamentals of each argument. Only static
problems are considered, being the dynamic of structures dealt with in other
courses.


title:
Classical and quantum billiard inside the square with gravitational
  field

abstract:
  The classical and quantum behavior of a particle inside a square box under
the influence of the gravitational field is studied. Detailed calculations on
periodic orbits, probability densities as well as expectation values and
uncertainties are carried out.

categories: quant-ph math.DS nlin.CD physics.class-ph


title: Quantum Computing and the Future Internet

abstract:
  One of the biggest concerns among cybersecurity professionals these days is
the hype around quantum computing, its incomprehensible power, and its
implications. The advancement in quantum computing has the potential to
revolutionize our daily lives, but it can also completely break down the
Internet as we know it. Mathematicians and physicists have developed algorithms
based on quantum computing, which can change the Internet security paradigm.
This article discusses quantum computing key concepts, with a special focus on
quantum Internet, quantum key distribution, and related challenges.


title: Bogoliubov's causal perturbative QFT with Hida operators

abstract:
  We will present the axioms of Bogoliubov's causal perturbative QFT in which
the creation-annihilation operators are interpreted as Hida operators. We will
shortly present the results that can be achieved in this theory: 1. Removal of
UV and IR infinity in the scattering operator, 2. Existence of the adiabatic
limit for interacting fields in QED, 3. Proof that charged particles have
non-zero mass, 4. Existence of infrared and ultraviolet asymptotics for QED.


title: Causal perturbative QED and white noise. The inductive step

abstract:
  We present the Bogoliubov's causal perturbative QFT, which includes only one
refinement: the creation-annihilation operators at a point, \emph{i.e.} for a
specific momentum, are mathematically interpreted as the Hida operators from
the white noise analysis. We leave the rest of the theory completely unchanged.
This allows avoiding infrared -- and ultraviolet -- divergences in the
transition to the adiabatic limit for interacting fields. Here we present the
inductive step. We are especially concentrated with the analysis of the second
order contribution to the scattering operator, and give explicit computations
using the calculus of integral kernel operators, \emph{i.e} contractions of
their vector-valued kernels, and splittings of the contractions into advanced
and retarded parts. Explicit example of QED is provided, together with the
detailed analysis of the key point in avoiding UV divergence.


title:
Tactile-ViewGCN: Learning Shape Descriptor from Tactile Data using Graph
  Convolutional Network

abstract:
  For humans, our "senses of touch" have always been necessary for our ability
to precisely and efficiently manipulate objects of all shapes in any
environment, but until recently, not many works have been done to fully
understand haptic feedback. This work proposed a novel method for getting a
better shape descriptor than existing methods for classifying an object from
multiple tactile data collected from a tactile glove. It focuses on improving
previous works on object classification using tactile data. The major problem
for object classification from multiple tactile data is to find a good way to
aggregate features extracted from multiple tactile images. We propose a novel
method, dubbed as Tactile-ViewGCN, that hierarchically aggregate tactile
features considering relations among different features by using Graph
Convolutional Network. Our model outperforms previous methods on the STAG
dataset with an accuracy of 81.82%.


title:
Semi-supervised classification of medical ultrasound images based on
  generative adversarial network

abstract:
  Medical ultrasound (US) is one of the most widely used imaging modalities in
clinical practice. However, its use presents unique challenges such as variable
imaging quality. Deep learning (DL) can be used as an advanced medical US
images analysis tool, while the performance of the DL model is greatly limited
by the scarcity of big datasets. Here, we develop semi-supervised
classification enhancement (SSCE) structures by constructing seven
convolutional neural network (CNN) models and one of the most state-of-the-art
generative adversarial network (GAN) models, StyleGAN2-ADA, to address this
problem. A breast cancer dataset with 780 images is used as our base dataset.
The results show that our SSCE structures obtain an accuracy of up to 97.9%,
showing a maximum 21.6% improvement compared with utilizing CNN models alone
and outperforming the previous methods using the same dataset by up to 23.9%.
We believe our proposed state-of-the-art method can be regarded as a potential
auxiliary tool for on-the-fly diagnoses of medical US images.


title:
Across the Green Valley with HST grisms: colour evolution, crossing
  time-scales and the growth of the red sequence at $z=1.0-1.8$

abstract:
  We measure the colour evolution and quenching time-scales of $z=1.0-1.8$
galaxies across the green valley. We derive rest-frame $NUVrK$ colours and
select blue-cloud, green-valley and red-sequence galaxies from the spectral
energy distribution modelling of CANDELS GOODS-South and UDS multi-band
photometry. Separately, we constrain the star-formation history (SFH)
parameters (ages, $\tau$) of these galaxies by fitting their deep archival HST
grism spectroscopy. We derive the galaxy colour-age relation and show that only
rapidly evolving galaxies with characteristic delayed-$\tau$ SFH time-scales of
$<0.5$ Gyr reach the red sequence at these redshifts, after a period of
accelerated colour evolution across the green valley. These results indicate
that the stellar mass build-up of these galaxies stays minimal after leaving
the blue cloud and entering the green valley (i.e., it may represent $\lesssim
5\%$ of the galaxies' final, quiescent masses). Visual inspection of
age-sensitive features in the stacked spectra also supports the view that these
galaxies follow a quenching sequence along the blue-cloud $\rightarrow$
green-valley $\rightarrow$ red-sequence track. For this rapidly evolving
population, we measure a green-valley crossing time-scale of
$0.99^{+0.42}_{-0.25}$ Gyr and a crossing rate at the bottom of the green
valley of $0.82^{+0.27}_{-0.25}$ mag/Gyr. Based on these time-scales, we
estimate that the number density of massive ($M_\star>10^{10} M_\odot$)
red-sequence galaxies doubles every Gyr at these redshifts, in remarkable
agreement with the evolution of the quiescent galaxy stellar mass function.
These results offer a new approach to measuring galaxy quenching over time and
represent a pathfinder study for future JWST, Euclid, and Roman Space Telescope
programs.


title: Schwarzschild and Ledoux are equivalent on evolutionary timescales

abstract:
  Stellar evolution models calculate convective boundaries using either the
Schwarzschild or Ledoux criterion, but confusion remains regarding which
criterion to use. Here we present a 3D hydrodynamical simulation of a
convection zone and adjacent radiative zone, including both thermal and
compositional buoyancy forces. As expected, regions which are unstable
according to the Ledoux criterion are convective. Initially, the radiative zone
adjacent to the convection zone is Schwarzschild-unstable but Ledoux-stable due
to a composition gradient. Over many convective overturn timescales the
convection zone grows via entrainment. The convection zone saturates at the
size originally predicted by the Schwarzschild criterion, although in this
final state the Schwarzschild and Ledoux criteria agree. Therefore, the
Schwarzschild criterion should be used to determine the size of stellar
convection zones, except possibly during short-lived evolutionary stages in
which entrainment persists.


title: Effects of an Immortal Stellar Population in AGN Disks

abstract:
  Stars are likely embedded in the gas disks of Active Galactic Nuclei (AGN).
Theoretical models predict that in the inner regions of the disk these stars
accrete rapidly, with fresh gas replenishing hydrogen in their cores faster
than it is burned into helium, effectively stalling their evolution at hydrogen
burning. We produce order-of-magnitude estimates of the number of such stars in
a fiducial AGN disk. We find numbers of order $10^{2-4}$, confined to the inner
$r_{\rm cap} \sim 3000 r_s \sim 0.03\rm pc$. These stars can profoundly alter
the chemistry of AGN disks, enriching them in helium and depleting them in
hydrogen, both by order-unity amounts. We further consider mergers between
these stars and other disk objects, suggesting that star-star mergers result in
rapid mass loss from the remnant to restore an equilibrium mass, while
star-compact object mergers may result in exotic outcomes and even host binary
black hole mergers within themselves. Finally, we examine how these stars react
as the disk dissipates towards the end of its life, and find that they may
return mass to the disk fast enough to extend its lifetime by a factor of
several and/or may drive powerful outflows from the disk. Post-AGN, these stars
rapidly lose mass and form a population of stellar mass black holes around
$10M_{\odot}$. Due to the complex and uncertain interactions between embedded
stars and the disk, their plausible ubiquity, and their order unity impact on
disk structure and evolution, they must be included in realistic disk models.


title:
Hilbert space shattering and dynamical freezing in the quantum Ising
  model

abstract:
  We discuss quantum dynamics in the transverse field Ising model in two
spatial dimensions. We show that, up to a prethermal timescale, which we
quantify, the Hilbert space 'shatters' into dynamically disconnected
subsectors. We identify this shattering as originating from the interplay of a
$\mathrm{U}(1)$ conservation law and a one-form $\mathbb{Z}_2$ constraint. We
show that the number of dynamically disconnected sectors is exponential in
system volume, and includes a subspace exponential in system volume within
which the dynamics is exactly localized, even in the absence of quenched
disorder. Depending on the emergent sector in which we work, the shattering can
be weak (such that typical initial conditions thermalize with respect to their
emergent symmetry sector), or strong (such that typical initial conditions
exhibit localized dynamics). We present analytical and numerical evidence that
a first order-like 'freezing transition' between weak and strong shattering
occurs as a function of the symmetry sector, in a non-standard thermodynamic
limit. We further numerically show that on the 'weak' (melted) side of the
transition domain wall dynamics follows ordinary diffusion.


title:
A Tale of Two Butterflies: An Exact Equivalence in Higher-Derivative
  Gravity

abstract:
  We prove the equivalence of two holographic computations of the butterfly
velocity in higher-derivative theories with Lagrangian built from arbitrary
contractions of curvature tensors. The butterfly velocity characterizes the
speed at which local perturbations grow in chaotic many-body systems and can be
extracted from the out-of-time-order correlator. This leads to a holographic
computation in which the butterfly velocity is determined from a localized
shockwave on the horizon of a dual black hole. A second holographic computation
uses entanglement wedge reconstruction to define a notion of operator size and
determines the butterfly velocity from certain extremal surfaces. By direct
computation, we show that these two butterfly velocities match precisely in the
aforementioned class of gravitational theories. We also present evidence
showing that this equivalence holds in all gravitational theories. Along the
way, we prove a number of general results on shockwave spacetimes.


title:
Generalised model-independent characterisation of strong gravitational
  lenses VII: impact of source properties and higher-order lens properties on
  the local lens reconstruction

abstract:
  We investigate the impact of higher-order gravitational lens properties and
properties of the background source on our approach to directly infer local
lens properties from observables in multiple images of strong gravitationally
lensed extended, static background sources developed in papers I to VI. As the
degeneracy between local lens and source properties only allows to determine
relative local lens properties between the multiple image positions, we cannot
distinguish common scalings and distortions caused by lensing from intrinsic
source characteristics. The consequences of this degeneracy for lens modelling
and our approach and ways to break it are detailed here. We also set up
quantitative measures around the critical curve to find clear limits on the
validity of the approximation that source properties are negligible to infer
local lens properties at critical points. The impact of the source on the local
lens properties depends on the reduced shear at the image position and the
amplitude and orientation of the source ellipticity, as we derive in this
paper. Similarly, we investigate the role of third-order lens properties
(flexion), in two galaxy-cluster simulations and in the Lenstool-reconstruction
of the galaxy-cluster lens CL0024. In all three cases, we find that flexion is
negligible in over 90% of all pixels of the lensing region for our current
imprecision of local lens properties of about 10%. Decreasing the imprecision
to 2%, higher-order terms start to play a role, especially in regions with
shear components close to zero.


title:
Revisiting BD-06 1339b: A Likely False Positive Caused by Stellar
  Activity

abstract:
  As long as astronomers have searched for exoplanets, the intrinsic
variability of host stars has interfered with the ability to reliably detect
and confirm exoplanets. One particular source of false positives is the
presence of stellar magnetic or chromospheric activity that can mimic the
radial-velocity reflex motion of a planet. Here we present the results of a
photometric data analysis for the known planet hosting star, BD-06 1339,
observed by the Transiting Exoplanet Survey Satellite (TESS) during Sector 6 at
2 minute cadence. We discuss evidence that suggests the observed 3.9 day
periodic radial velocity signature may be caused by stellar activity rather
than a planetary companion, since variability detected in the photometric data
are consistent with the periodic signal. We conclude that the previously
reported planetary signature is likely the result of a false positive signal
resulting from stellar activity, and discuss the need for more data to confirm
this conclusion.


title: Supercurrent induced resonant optical response

abstract:
  The optical conductivity encodes the current response to a time dependent
electric field. We develop a theory of the optical conductivity
$\sigma(\omega)$ in presence of a dc supercurrent. Current induced optical
response is prohibited by Galilean invariance from occurring in systems with a
single parabolic band. However, we show that lattice effects give rise to a
pronounced current dependent peak in $\sigma(\omega)$ at the gap edge $\omega =
2 \Delta$, which diverges in the clean limit. We demonstrate this in a model of
a multi-band superconductor. Our theory explains the recent observation of a
current induced peak in the optical conductivity in NbN by Nakamura et al.
(2019), and provides a new mechanism for direct activation of the Higgs mode
with light.


title:
Strategy to Extract Kitaev Interaction using Symmetry in Honeycomb Mott
  Insulators

abstract:
  The Kitaev spin liquid, a ground state of the bond-dependent Kitaev model in
a honeycomb lattice has been a centre of attraction, since a microscopic theory
to realize such interaction in solid-state materials was discovered. A
challenge in real materials though is the presence of the Heisenberg and
another bond-dependent Gamma interactions detrimental to the Kitaev spin
liquid, and there has been much debate on their relative strengths. Here we
offer a strategy to extract the Kitaev interaction out of a full microscopic
model by utilizing the symmetries of the Hamiltonian. Two tilted magnetic field
directions above and below the plane related by a two-fold rotational symmetry
generate distinct spin excitations originated from a specific combination of
the Kitaev and Gamma interactions. Together with the in- and out-of-plane
magnetic anisotropy, one can determine the Kitaev and Gamma interactions
separately. Dynamic spin structure factors obtained by exact diagonalization
are presented to motivate future experiments that probe spin excitations such
as angle-dependent ferromagnetic resonance and inelastic neutron scattering
spectroscopy. The proposed setups will advance the search for Kitaev materials.


title:
LEGA-C: analysis of dynamical masses from ionized gas and stellar
  kinematics at z~0.8

abstract:
  We compare dynamical mass estimates based on spatially extended stellar and
ionized gas kinematics ($\mathrm{M_{dyn,*}}$ and $\mathrm{M_{dyn,eml}}$,
respectively) of 157 star forming galaxies at $0.6\leq z<1$. Compared to
$z\sim0$, these galaxies have enhanced star formation rates, with stellar
feedback likely affecting the dynamics of the gas. We use LEGA-C DR3, the
highest redshift dataset providing sufficiently deep measurements of a
$K_s-$band limited sample. For $\mathrm{M_{dyn,*}}$ we use Jeans Anisotropic
Multi-Gaussian Expansion models. For $\mathrm{M_{dyn,eml}}$ we first fit a
custom model of a rotating exponential disk with uniform dispersion, whose
light is projected through a slit and corrected for beam smearing. We then
apply an asymmetric drift correction based on assumptions common in the
literature to the fitted kinematic components to obtain the circular velocity,
assuming hydrostatic equilibrium. Within the half-light radius,
$\mathrm{M_{dyn,eml}}$ is on average lower than $\mathrm{M_{dyn,*}}$, with a
mean offset of $-0.15\pm0.016$ dex and galaxy-to-galaxy scatter of $0.19$ dex,
reflecting the combined random uncertainty. While data of higher spatial
resolution are needed to understand this small offset, it supports the
assumption that the galaxy-wide ionized gas kinematics do not predominantly
originate from disruptive events such as star formation driven outflows.
However, a similar agreement can be obtained without modeling from the
integrated emission line dispersions for axis ratios $q<0.8$. This suggests
that our current understanding of gas kinematics is not sufficient to
efficiently apply asymmetric drift corrections to improve dynamical mass
estimates compared to observations lacking the $S/N$ required for spatially
extended dynamics.


title:
The ALMA Frontier Fields Survey. VI. Lensing-corrected 1.1mm number
  counts in Abell 2744, MACSJ0416.1-2403, MACSJ1149.5+2223, Abell 370 and Abell
  S1063

abstract:
  [abridged] Probing the faint end of the number counts at mm wavelengths is
important to identify the origin of the extragalactic background light in this
regime. Aided by strong gravitational lensing, ALMA observations towards
massive galaxy clusters have opened a window to disentangle this origin,
allowing to resolve sub-mJy dusty star-forming galaxies. We aim to derive
number counts at 1.1 mm down to flux densities fainter than 0.1 mJy, based on
ALMA observations towards five Hubble Frontier Fields (FF) galaxy clusters,
following a statistical approach to correct for lensing effects. We created a
source catalog that includes 29 ALMA 1.1 mm continuum detections down to a
4.5sigma significance. We derived source intrinsic flux densities using public
lensing models. We folded the uncertainties in both magnifications and source
redshifts into the number counts through Monte Carlo simulations. We derive
cumulative number counts over two orders of magnitude down to 0.01 mJy after
correction for lensing effects. Cosmic variance estimates are all exceeded by
uncertainties in our median combined cumulative counts that come from both our
Monte Carlo simulations and Poisson statistics. Our number counts are
consistent to 1sigma with most of recent ALMA estimates and galaxy evolution
models. However, below 0.1 mJy, they are lower by 0.4 dex compared to all deep
ALMA studies but ASPECS-LP. Importantly, the flattening found for our
cumulative counts extends further to 0.01 mJy. Our results bring further
support in line of the flattening of the number counts reported previously by
us and ASPECS-LP, which has been interpreted by a recent galaxy evolution model
as a measurement of the "knee" of the infrared luminosity function at high
redshift. Our estimates of the contribution to the EBL in the FFs suggest that
we may be resolving most of the EBL at 1.1mm down to 0.01 mJy.


title: Efficient quantum interpolation of smooth distributions

abstract:
  We present an efficient method to interpolate smooth distributions with a
quantum computer that aims to complement data uploading techniques. The quantum
algorithm operates in four steps: i) a small sample of the distribution is
uploaded onto a reduced set of qubits; ii) a Quantum Fourier Transform (QFT) is
applied; iii) qubits encoding vanishing high frequencies are added to the
register; and iv) an inverse QFT is operated on the total register. This
leverages on the efficiency of the QFT and opens the door of quantum advantage
to a relevant family of distributions. We demonstrate the power of the method
with an efficient interpolation of a toy gaussian model, discussing uploading
methods and precision. We then apply the QFT interpolation technique to quantum
encoded images and present further advantages when processing data in
superposition.


title: Searching for Kerr in the 2PM amplitude

abstract:
  The classical scattering of spinning objects is well described by the
spinor-helicity formalism for heavy particles. Using these variables, we derive
spurious-pole-free, all-spin opposite-helicity Compton amplitudes (factorizing
on physical poles to the minimal, all-spin three-point amplitudes of ref.
\cite{Arkani-Hamed:2017jhn}) in the classical limit for QED, QCD, and gravity.
The cured amplitudes are subject to deformations by contact terms, the vast
majority of whose contributions we can fix by imposing a relation between spin
structures -- motivated by lower spin multipoles of black hole scattering -- at
the second post-Minkowskian (2PM) order. For QED and gravity, this leaves a
modest number of unfixed coefficients parametrizing contact-term deformations,
while the QCD amplitude is uniquely determined. Our gravitational Compton
amplitude allows us to push the state-of-the-art of spinning-2PM scattering to
any order in the spin vectors of both objects; we present results here and in
the auxiliary file \texttt{2PMSpin8Aux.nb} up to eighth order in the spin
vectors. Interestingly, despite leftover coefficients in the Compton amplitude,
imposing the aforementioned relation between spin structures uniquely fixes
some higher-spin parts of the 2PM amplitude.


title:
Disorder-free localization transition in a two-dimensional lattice gauge
  theory

abstract:
  Disorder-free localization is a novel mechanism for ergodicity breaking which
can occur in interacting quantum many-body systems such as lattice gauge
theories (LGTs). While the nature of the quantum localization transition (QLT)
is still debated for conventional many-body localization, here we provide the
first comprehensive characterization of the QLT in two dimensions (2D) for a
disorder-free case. Disorder-free localization can appear in homogeneous 2D
LGTs such as the U(1) quantum link model (QLM) due to an underlying classical
percolation transition fragmenting the system into disconnected real-space
clusters. Building on the percolation model, we characterize the QLT in the
U(1) QLM through a detailed study of the ergodicity properties of finite-size
real-space clusters via level-spacing statistics and localization in
configuration space. We argue for the presence of two regimes - one in which
large finite-size clusters effectively behave non-ergodically, a result
naturally accounted for as an interference phenomenon in configuration space
and the other in which all large clusters behave ergodically. As one central
result, in the latter regime we claim that the QLT is equivalent to the
classical percolation transition and is hence continuous. Utilizing this
equivalence we determine the universality class and critical behaviour of the
QLT from a finite-size scaling analysis of the percolation problem.


title: The faint light in groups and clusters of galaxies

abstract:
  The diffuse light that spreads through groups and clusters of galaxies is
made of free-floating stars not bound to any galaxy. This is known as the
intracluster light (ICL) and holds important clues for understanding the
evolution of these large structures. The study of this light has gained
traction in the past 20 years thanks to technological and data processing
advances that have permitted us to reach unprecedented observational depths.
This progress has led to ground-breaking results in the field, such as
pinpointing the origin of the ICL and its potential to map dark matter in
clusters of galaxies. We now enter an era of deep and wide surveys that promise
to uncover the faint Universe as never seen before, adding to our growing
understanding of the properties of the ICL and, consequently, of the formation
of the largest gravitationally bound structures in the Universe. The goal of
this Review is to summarize the most recent results on ICL, synthesizing the
current knowledge in the field and providing a global perspective that may
benefit future ICL studies.


title:
Snowmass2021 Cosmic Frontier White Paper: Observational Facilities to
  Study Dark Matter

abstract:
  We present an overview of future observational facilities that will
significantly enhance our understanding of the fundamental nature of dark
matter. These facilities span a range of observational techniques including
optical/near-infrared imaging and spectroscopy, measurements of the cosmic
microwave background, pulsar timing, 21-cm observations of neutral hydrogen at
high redshift, and the measurement of gravitational waves. Such facilities are
a critical component of a multi-pronged experimental program to uncover the
nature of dark matter, while often providing complementary measurements of dark
energy, neutrino physics, and inflation.


title:
Exploring supermassive black hole physics and galaxy quenching across
  halo mass in FIRE cosmological zoom simulations

abstract:
  Feedback from accreting supermassive black holes (SMBHs) is thought to be a
primary driver of quenching in massive galaxies, but the best way to implement
SMBH physics into galaxy formation simulations remains ambiguous. As part of
the Feedback in Realistic Environments (FIRE) project, we explore the effects
of different modeling choices for SMBH accretion and feedback in a suite of
$\sim500$ cosmological zoom-in simulations across a wide range of halo mass
(10^10-10^13 Msun). Within the suite, we vary the numerical schemes for BH
accretion and feedback, the accretion efficiency, and the strength of
mechanical, radiative, and cosmic ray feedback independently. We then compare
the outcomes to observed galaxy scaling relations. We find several models that
satisfy the observational constraints, and for which the energetics in
different feedback channels are physically plausible. Interestingly, cosmic
rays accelerated by SMBHs play an important role in many successful models.
However, it is non-trivial to reproduce scaling relations across halo mass, and
many model variations produce qualitatively incorrect results regardless of
parameter choices. The growth of stellar and BH mass are closely related: for
example, over-massive BHs tend to over-quench galaxies. BH mass is most
strongly affected by the choice of accretion efficiency in high-mass halos, but
by feedback efficiency in low-mass halos. The amount of star formation
suppression by SMBH feedback in low-mass halos is determined primarily by the
time-integrated feedback energy. For massive galaxies, the "responsiveness" of
a model (i.e. how quickly and powerfully the BH responds to gas available for
accretion) is an additional important factor for quenching.


title: Binary Dynamics Through the Fifth Power of Spin at $\mathcal{O}(G^2)$

abstract:
  We use a previously developed scattering-amplitudes-based framework to
determine the two-body Hamiltonian for generic binary systems with arbitrary
spin $S$. By construction this formalism bypasses difficulties with unphysical
singularities or higher-time derivatives. This framework had been previously
used to determine the exact velocity dependence of the $\mathcal O(G^2)$
quadratic-in-spin two-body Hamiltonian. We first evaluate the $S^3$ scattering
angle and two-body Hamiltonian at this order in $G$, including not only all
operators corresponding to the usual worldline operators, but also an
additional set due to an interesting subtlety. We then evaluate $S^4$ and $S^5$
contributions at $\mathcal O(G^2)$ which we confirm by comparing against
aligned-spin results. We conjecture that a certain shift symmetry together with
a constraint on the high-energy growth of the scattering amplitude specify the
Wilson coefficients for the Kerr black hole to all orders in the spin and
confirm that they reproduce the previously-obtained results through $S^4$.


title:
Probing the Inner Circumgalactic Medium and Quasar Illumination around
  the Reddest `Extremely Red Quasar' (ERQ)

abstract:
  Dusty quasars might be in a young stage of galaxy evolution with prominent
quasar feedback. We study a population of luminous, extremely red quasars at
redshifts $z\sim$~2--4 that has a suite of extreme spectral properties that may
all be related to exceptionally powerful quasar-driven outflows. We present
Keck/KCWI observations of the reddest known ERQ, at $z=$\,2.3184, with an
extremely fast [\ion{O}{III}]~$\lambda$5007 outflow at $\sim$6000~km~s$^{-1}$.
The Ly$\alpha$ halo spans $\sim$100~kpc. The halo is kinematically quiet, with
velocity dispersion $\sim$300~km~s$^{-1}$ and no broadening above the dark
matter circular velocity down to the spatial resolution $\sim$6~kpc from the
quasar. We detect spatially-resolved \ion{He}{II}~$\lambda$1640 and
\ion{C}{IV}~$\lambda$1549 emissions with kinematics similar to the Ly$\alpha$
halo and a narrow component in the [\ion{O}{III}]~$\lambda$5007. Quasar
reddening acts as a coronagraph allowing views of the innermost halo. A narrow
Ly$\alpha$ spike in the quasar spectrum is inner halo emission, confirming the
broad \ion{C}{IV}~$\lambda$1549 in the unresolved quasar is blueshifted by
$2242$~km~s$^{-1}$ relative to the halo frame. We propose the inner halo is
dominated by past/moderate-speed outflow and the outer halo dominated by
inflow. The high central concentration of the surface brightness and the
circularly symmetric morphology of the inner halo are consistent with the ERQ
being young. The \ion{He}{II}~$\lambda$1640/Ly$\alpha$ ratio of the inner halo
and the asymmetry level of the overall halo are dissimilar to Type II quasars,
implying central ionizing photons can escape along the line of sight. While
quasar dominates the ionization, we find no evidence of mechanical quasar
feedback on circumgalactic scales.


title:
When classifying grammatical role, BERT doesn't care about word order...
  except when it matters

abstract:
  Because meaning can often be inferred from lexical semantics alone, word
order is often a redundant cue in natural language. For example, the words
chopped, chef, and onion are more likely used to convey "The chef chopped the
onion," not "The onion chopped the chef." Recent work has shown large language
models to be surprisingly word order invariant, but crucially has largely
considered natural prototypical inputs, where compositional meaning mostly
matches lexical expectations. To overcome this confound, we probe grammatical
role representation in English BERT and GPT-2, on instances where lexical
expectations are not sufficient, and word order knowledge is necessary for
correct classification. Such non-prototypical instances are naturally occurring
English sentences with inanimate subjects or animate objects, or sentences
where we systematically swap the arguments to make sentences like "The onion
chopped the chef". We find that, while early layer embeddings are largely
lexical, word order is in fact crucial in defining the later-layer
representations of words in semantically non-prototypical positions. Our
experiments isolate the effect of word order on the contextualization process,
and highlight how models use context in the uncommon, but critical, instances
where it matters.


title:
New families satisfying the Dynamical Uniform Boundedness Principle over
  function fields

abstract:
  We extend a technique, originally due to the first author and Poonen, for
proving cases of the Strong Uniform Boundedness Principle (SUBP) in algebraic
dynamics over function fields of positive characteristic. The original method
applied to unicritical polynomials for which the characteristic does not divide
the degree. We show that many new 1-parameter families of polynomials satisfy
the SUBP, including the family of all quadratic polynomials in even
characteristic. We also give the first nontrivial example of a family of
rational functions that satisfies the SUBP.


title:
Hopf type theorems for surfaces in the de Sitter-Schwarzschild and
  Reissner-Nordstrom manifolds

abstract:
  In 1951, H. Hopf proved that the only surfaces, homeomorphic to the sphere,
with constant mean curvature in the Euclidean space are the round (geometrical)
spheres. These results were generalized by S. S. Chern, and then by Eschenburg
and Tribuzy, for surfaces, homeomorphic to the sphere, in Riemannian manifolds
with constant sectional curvature whose mean curvature function satisfies some
bound on its differential. In this paper, using techniques partial differential
equations in the complex plane which generalizes the notion of holomorphy, we
extend these results for surfaces in a wide class of warped product manifolds,
which includes, besides the classical space forms of constant sectional
curvature, the de Sitter-Schwarzschild manifolds and the Reissner-Nordstrom
manifolds, which are time slices of solutions of the Einstein field equations
of the general relativity.


title: Probing heavy-flavor parton distribution functions at hadron colliders

abstract:
  Precision measurements of heavy-flavor hadroproduction at the Large Hadron
Collider (LHC) have the ability to probe heavy-flavor parton distribution
functions (PDFs) in the proton. Sensitivity of inclusive $B^\pm$ meson
production cross section measurements at LHCb 13 TeV to the bottom-quark PDF is
illustrated by using theory predictions obtained with a the S-ACOT-MPS general
mass variable flavor number (GMVFN) scheme within the QCD factorization
formula. This approach can easily be extended to other heavy-flavor production
processes, such as $Z$ boson production in association with a bottom- or a
charm-jet. The inclusion of such measurements will represent an important
improvement for future global QCD analyses that aim at reducing uncertainties
of heavy-flavor PDFs, provided that a consistent general mass treatment in $pp$
collisions is used.
  This work is related to the activities of the Snowmass topic groups EF03, and
EF06.


title: Quantum Algorithms for Community Detection and their Empirical Run-times

abstract:
  We apply our recent work on empirical estimates of quantum speedups to the
practical task of community detection in complex networks. We design several
quantum variants of a popular classical algorithm -- the Louvain algorithm for
community detection -- and first study their complexities in the usual way,
before analysing their complexities empirically across a variety of artificial
and real inputs. We find that this analysis yields insights not available to us
via the asymptotic analysis, further emphasising the utility in such an
empirical approach. In particular, we observe that a complicated quantum
algorithm with a large asymptotic speedup might not be the fastest algorithm in
practice, and that a simple quantum algorithm with a modest speedup might in
fact be the one that performs best. Moreover, we repeatedly find that overheads
such as those arising from the need to amplify the success probabilities of
quantum sub-routines such as Grover search can nullify any speedup that might
have been suggested by a theoretical worst- or expected-case analysis.


title:
Superconducting-semiconducting voltage-tunable qubits in the third
  dimension

abstract:
  We propose superconducting-semiconducting (super-semi) qubit and coupler
designs based on high-quality, compact through-silicon vias (TSVs). An
interposer "probe" wafer containing TSVs is used to contact a sample wafer
with, for example, a superconductor-proximitized, epitaxially-grown, germanium
quantum well. By utilizing the capacitance of the probe wafer TSVs, the
majority of the electric field in the qubits is pulled away from lossy regions
in the semiconducting wafer. Through simulations, we find that the probe wafer
can reduce the qubit's electric field participation in the sample wafer by an
order of magnitude for thin substrates and remains small even when the
epitaxial layer thickness approaches 100 $\mu$m. We also show how this scheme
is extensible to multi-qubit systems which have tunable qubit-qubit couplings
without magnetic fields. This approach shrinks the on-chip footprint of
voltage-tunable superconducting qubits and promises to accelerate the
understanding of super-semi heterostructures in a variety of systems.


title: Leveraging universality of jet taggers through transfer learning

abstract:
  A significant challenge in the tagging of boosted objects via
machine-learning technology is the prohibitive computational cost associated
with training sophisticated models. Nevertheless, the universality of QCD
suggests that a large amount of the information learnt in the training is
common to different physical signals and experimental setups. In this article,
we explore the use of transfer learning techniques to develop fast and
data-efficient jet taggers that leverage such universality. We consider the
graph neural networks LundNet and ParticleNet, and introduce two prescriptions
to transfer an existing tagger into a new signal based either on fine-tuning
all the weights of a model or alternatively on freezing a fraction of them. In
the case of $W$-boson and top-quark tagging, we find that one can obtain
reliable taggers using an order of magnitude less data with a corresponding
speed-up of the training process. Moreover, while keeping the size of the
training data set fixed, we observe a speed-up of the training by up to a
factor of three. This offers a promising avenue to facilitate the use of such
tools in collider physics experiments.


title: Staged Training for Transformer Language Models

abstract:
  The current standard approach to scaling transformer language models trains
each model size from a different random initialization. As an alternative, we
consider a staged training setup that begins with a small model and
incrementally increases the amount of compute used for training by applying a
"growth operator" to increase the model depth and width. By initializing each
stage with the output of the previous one, the training process effectively
re-uses the compute from prior stages and becomes more efficient. Our growth
operators each take as input the entire training state (including model
parameters, optimizer state, learning rate schedule, etc.) and output a new
training state from which training continues. We identify two important
properties of these growth operators, namely that they preserve both the loss
and the "training dynamics" after applying the operator. While the
loss-preserving property has been discussed previously, to the best of our
knowledge this work is the first to identify the importance of preserving the
training dynamics (the rate of decrease of the loss during training). To find
the optimal schedule for stages, we use the scaling laws from (Kaplan et al.,
2020) to find a precise schedule that gives the most compute saving by starting
a new stage when training efficiency starts decreasing. We empirically validate
our growth operators and staged training for autoregressive language models,
showing up to 22% compute savings compared to a strong baseline trained from
scratch. Our code is available at https://github.com/allenai/staged-training.


title: Engineering magnetic topological insulators in Eu$_5M_2X_6$ Zintls

abstract:
  Magnetic topological insulator provide a prominent material platform for
quantum anomalous Hall physics and axion electrodynamics. However, the lack of
material realizations with cleanly gapped surfaces hinders technological
utilization of these exotic quantum phenomena. Here, using the Zintl concept
and the properties of non-symmorphic space groups, we computationally engineer
magnetic topological insulators. Specifically, we explore Eu$_5M_2X_6$
($M$=metal, $X$=pnictide) Zintl compounds and find that Eu$_5$Ga$_2$Sb$_6$,
Eu$_5$Tl$_2$Sb$_6$ and Eu$_5$In$_2$Bi$_6$ form stable structures with
non-trivial $\mathbb{Z}_2$ indices. We also show that epitaxial and uniaxial
strain can be used to control the $\mathbb{Z}_2$ index and the bulk energy gap.
Finally, we discuss experimental progress towards the synthesis of the proposed
candidates and provide insights that can be used in the search for robust
magnetic topological insulators in Zintl compounds.


title:
TrafPS: A Visual Analysis System Interpreting Traffic Prediction in
  Shapley

abstract:
  In recent years, deep learning approaches have been proved good performance
in traffic flow prediction, many complex models have been proposed to make
traffic flow prediction more accurate. However, lacking transparency limits the
domain experts on understanding when and where the input data mainly impact the
results. Most urban experts and planners can only adjust traffic based on their
own experience and can not react effectively toward the potential traffic jam.
To tackle this problem, we adapt Shapley value and present a visualization
analysis system , which can provide experts with the interpretation of traffic
flow prediction. TrafPS consists of three layers, from data process to results
computation and visualization. We design three visualization views in TrafPS to
support the prediction analysis process. One demonstration shows that the
TrafPS supports an effective analytical pipeline on interpreting the prediction
flow to users and provides an intuitive visualization for decision making.


title:
On the spectrum and index of expanding and translating solitons of the
  mean curvature flow in $\mathbb{R}^3$

abstract:
  In this paper we prove that two-dimensional translating solitons in
$\mathbb{R}^3$ with finite $L$-index are homeomorphic to a plane or a cylinder
and that a two-dimensional self-expander with finite $L$-index and sub
exponential weighted volume growth has finite topology. We also prove that
translating solitons and self-expanders have finite topology, provided the
bottom of the spectrum of the $L$-stability operator is bounded from below and
their weighted volume have subexponential growth.


title:
Can I see an Example? Active Learning the Long Tail of Attributes and
  Relations

abstract:
  There has been significant progress in creating machine learning models that
identify objects in scenes along with their associated attributes and
relationships; however, there is a large gap between the best models and human
capabilities. One of the major reasons for this gap is the difficulty in
collecting sufficient amounts of annotated relations and attributes for
training these systems. While some attributes and relations are abundant, the
distribution in the natural world and existing datasets is long tailed. In this
paper, we address this problem by introducing a novel incremental active
learning framework that asks for attributes and relations in visual scenes.
While conventional active learning methods ask for labels of specific examples,
we flip this framing to allow agents to ask for examples from specific
categories. Using this framing, we introduce an active sampling method that
asks for examples from the tail of the data distribution and show that it
outperforms classical active learning methods on Visual Genome.


title:
Simulations of Silicon Radiation Detectors for High Energy Physics
  Experiments

abstract:
  Silicon radiation detectors are an integral component of current and planned
collider experiments in high energy physics. Simulations of these detectors are
essential for deciding operational configurations, for performing precise data
analysis, and for developing future detectors. In this white paper, we briefly
review the existing tools and discuss challenges for the future that will
require research and development to be able to cope with the foreseen extreme
radiation environments of the High Luminosity runs of the Large Hadron Collider
and future hadron colliders like FCC-hh and SPPC.


title:
Medical Image Segmentation on MRI Images with Missing Modalities: A
  Review

abstract:
  Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and
overcoming their negative repercussions is considered a hurdle in biomedical
imaging. The combination of a specified set of modalities, which is selected
depending on the scenario and anatomical part being scanned, will provide
medical practitioners with full information about the region of interest in the
human body, hence the missing MRI sequences should be reimbursed. The
compensation of the adverse impact of losing useful information owing to the
lack of one or more modalities is a well-known challenge in the field of
computer vision, particularly for medical image processing tasks including
tumour segmentation, tissue classification, and image generation. Various
approaches have been developed over time to mitigate this problem's negative
implications and this literature review goes through a significant number of
the networks that seek to do so. The approaches reviewed in this work are
reviewed in detail, including earlier techniques such as synthesis methods as
well as later approaches that deploy deep learning, such as common latent space
models, knowledge distillation networks, mutual information maximization, and
generative adversarial networks (GANs). This work discusses the most important
approaches that have been offered at the time of this writing, examining the
novelty, strength, and weakness of each one. Furthermore, the most commonly
used MRI datasets are highlighted and described. The main goal of this research
is to offer a performance evaluation of missing modality compensating networks,
as well as to outline future strategies for dealing with this issue.


title:
Article Processing Charges, Altmetrics and Citation Impact: Is there an
  economic rationale?

abstract:
  The present study aims to analyze 1) the relationship between Citation
Normalized Score of scientific publications and Article Processing Charges
(APCs) of Gold Open Access (OA) publications 2) the determinants of APCs. To do
so, we used APCs information provided by the OpenAPC database, citation scores
of publications from the WoS database and, for Altmetrics, data from
Altmetrics.com database, over the period from 2006 to 2019 for 83,752 articles
published in 4751 journals belonging to 267 distinct publishers. Results show
that contrary to common belief, paying high APCs does not necessarily increase
the impact of publications. First, large publishers with high impact are not
the most expensive. Second, publishers with the highest APCs are not
necessarily the best in terms of impact. Correlation between APCs and impact is
moderate. Regarding the determinants, results indicate that APCs are on average
50% higher in hybrid journals than in full OA journals. The results also
suggest that Altmetrics do not have a great impact: OA articles that have
garnered the most attention on internet are articles with relatively low APCs.
Another interesting result is that the ''number of readers'' indicator is more
effective as it is more correlated with classic bibliometrics indicators than
the Altmetrics score.


title: Testing neutrino flavor models

abstract:
  Finding a rationale behind the observed pattern of neutrino mixings has been
at the focus of neutrino flavor model building. Many different approaches have
been put forward including models based on symmetries. Among the most
predictive models based on symmetries are models which predict not only the
mixing parameters but also correlations between them. These mixing sum rules
allow to probe flavor models in the future. In this white paper we collect the
predictions for the mixing parameters from flavor models based on discrete
symmetries broken to certain residual symmetries of the lepton mass matrices
and from models with modular symmetries to contrast them with bounds from
current and future oscillations experiments.


title: Infrastructure-free, Deep Learned Urban Noise Monitoring at $\sim$100mW

abstract:
  The Sounds of New York City (SONYC) wireless sensor network (WSN) has been
fielded in Manhattan and Brooklyn over the past five years, as part of a larger
human-in-the-loop cyber-physical control system for monitoring, analyzing, and
mitigating urban noise pollution. We describe the evolution of the 2-tier SONYC
WSN from an acoustic data collection fabric into a 3-tier in situ noise
complaint monitoring WSN, and its current evaluation. The added tier consists
of long-range (LoRa), multi-hop networks of a new low-power acoustic mote, MKII
("Mach 2"), that we have designed and fabricated. MKII motes are notable in
three ways: First, they advance machine learning capability at mote-scale in
this application domain by introducing a real-time Convolutional Neural Network
(CNN) based embedding model that is competitive with alternatives while also
requiring 10$\times$ lesser training data and $\sim$2 orders of magnitude fewer
runtime resources. Second, they are conveniently deployed relatively far from
higher-tier base station nodes without assuming power or network infrastructure
support at operationally relevant sites (such as construction zones), yielding
a relatively low-cost solution. And third, their networking is frequency agile,
unlike conventional LoRa networks: it tolerates in a distributed,
self-stabilizing way the variable external interference and link fading in the
cluttered 902-928MHz ISM band urban environment by dynamically choosing good
frequencies using an efficient new method that combines passive and active
measurements.


title:
Some Notes on the Similarity of Priority Vectors Derived by the
  Eigenvalue Method and the Geometric Mean Method

abstract:
  The aim of this paper is to examine the differences in ordinal rankings
obtained from a pairwise comparison matrix using the eigenvalue method and the
geometric mean method. We introduce several propositions on the (dis)similarity
of both rankings with respect to the matrix size and its inconsistency
expressed by the Koczkodaj's inconsistency index. Further on, we examine the
relationship between differences in both rankings and Kendall's rank
correlation coefficient \(\tau\) and Spearman's rank coefficient \(\rho\).
Apart from theoretical results, intuitive numerical examples and Monte Carlo
simulations are provided as well.


title:
Learning cardiac activation maps from 12-lead ECG with multi-fidelity
  Bayesian optimization on manifolds

abstract:
  We propose a method for identifying an ectopic activation in the heart
non-invasively. Ectopic activity in the heart can trigger deadly arrhythmias.
The localization of the ectopic foci or earliest activation sites (EASs) is
therefore a critical information for cardiologists in deciding the optimal
treatment. In this work, we formulate the identification problem as a global
optimization problem, by minimizing the mismatch between the ECG predicted by a
cardiac model, when paced at a given EAS, and the observed ECG during the
ectopic activity. Our cardiac model amounts at solving an anisotropic eikonal
equation for cardiac activation and the forward bidomain model in the torso
with the lead field approach for computing the ECG. We build a Gaussian process
surrogate model of the loss function on the heart surface to perform Bayesian
optimization. In this procedure, we iteratively evaluate the loss function
following the lower confidence bound criterion, which combines exploring the
surface with exploitation of the minimum region. We also extend this framework
to incorporate multiple levels of fidelity of the model. We show that our
procedure converges to the minimum only after $11.7\pm10.4$ iterations (20
independent runs) for the single-fidelity case and $3.5\pm1.7$ iterations for
the multi-fidelity case. We envision that this tool could be applied in real
time in a clinical setting to identify potentially dangerous EASs.


title:
Generalized Key-Value Memory to Flexibly Adjust Redundancy in
  Memory-Augmented Networks

abstract:
  Memory-augmented neural networks enhance a neural network with an external
key-value memory whose complexity is typically dominated by the number of
support vectors in the key memory. We propose a generalized key-value memory
that decouples its dimension from the number of support vectors by introducing
a free parameter that can arbitrarily add or remove redundancy to the key
memory representation. In effect, it provides an additional degree of freedom
to flexibly control the trade-off between robustness and the resources required
to store and compute the generalized key-value memory. This is particularly
useful for realizing the key memory on in-memory computing hardware where it
exploits nonideal, but extremely efficient non-volatile memory devices for
dense storage and computation. Experimental results show that adapting this
parameter on demand effectively mitigates up to 44% nonidealities, at equal
accuracy and number of devices, without any need for neural network retraining.


title:
verBERT: Automating Brazilian Case Law Document Multi-label
  Categorization Using BERT

abstract:
  In this work, we carried out a study about the use of attention-based
algorithms to automate the categorization of Brazilian case law documents. We
used data from the Kollemata Project to produce two distinct datasets with
adequate class systems. Then, we implemented a multi-class and multi-label
version of BERT and fine-tuned different BERT models with the produced
datasets. We evaluated several metrics, adopting the micro-averaged F1-Score as
our main metric for which we obtained a performance value of F1-micro=0.72
corresponding to gains of 30 percent points over the tested statistical
baseline. In this work, we carried out a study about the use of attention-based
algorithms to automate the categorization of Brazilian case law documents. We
used data from the \textit{Kollemata} Project to produce two distinct datasets
with adequate class systems. Then, we implemented a multi-class and multi-label
version of BERT and fine-tuned different BERT models with the produced
datasets. We evaluated several metrics, adopting the micro-averaged F1-Score as
our main metric for which we obtained a performance value of $\langle
\mathcal{F}_1 \rangle_{micro}=0.72$ corresponding to gains of 30 percent points
over the tested statistical baseline.


title: High-dimensional Generalized Additive Mixed Model with Longitudinal Data

abstract:
  The problem of simultaneously estimating and selecting important variables is
challenging in the high-dimensional generalized additive mixed model (GAMM)
with longitudinal data because of the correlation structures. We give a
penalized estimation strategy for efficient variable selection when the number
of variables in the linear part and number of components in the non-linear part
can grow with the sample size. A Monte Carlo Newton-Raphson algorithm is
developed where a Metropolis step for generating random effects is used to
apply the proposed double-penalization technique in the high-dimensional GAMM.
In addition, to improve efficiency for regression coefficients, the estimation
of the working covariance matrix is involved in the proposed iterative
algorithm. We further study the asymptotic performance of the resulting
estimators and establish the oracle properties. We conduct extensive numerical
studies to assess the performance of our proposed estimation strategy and
numerically illustrate how efficient it is in selecting significant variables.


title:
Non-invasive measurement of nuclear relative stiffness from quantitative
  analysis of microscopy data

abstract:
  The connection between the properties of cell tissue and those of the single
constituent cells remains to be elucidated. At the purely mechanical level, the
degree of rigidity of different cellular components, such as the nucleus and
the cytoplasm, modulates the interplay between the cell inner processes and the
external environment, while simultaneously mediating the mechanical
interactions between neighboring cells. Being able to quantify the correlation
between single-cell and tissue properties would improve our mechanobiological
understanding of cell tissues. Here we develop a methodology to quantitatively
extract a set of structural and motility parameters from the analysis of
time-lapse movies of nuclei belonging to jammed and flocking cell monolayers.
We then study in detail the correlation between the dynamical state of the
tissue and the deformation of the nuclei. We observe that the nuclear
deformation rate linearly correlates with the local divergence of the velocity
field, which leads to a non-invasive estimate of the elastic modulus of the
nucleus relative to the one of the cytoplasm. We also find that nuclei
belonging to flocking monolayers, subjected to larger mechanical perturbations,
are about two times stiffer than nuclei belonging to dynamically arrested
monolayers, in agreement with atomic force microscopy results. Our results
demonstrate a non-invasive route to the determination of nuclear relative
stiffness for cells in a monolayer.


title:
Nonreciprocal Infrared Absorption via Resonant Magneto-optical Coupling
  to InAs

abstract:
  Nonreciprocal elements are a vital building block of electrical and optical
systems. In the infrared regime, there is a particular interest in structures
that break reciprocity because their thermal absorptive (and emissive)
properties should not obey the Kirchhoff thermal radiation law. In this work,
we break time-reversal symmetry and reciprocity in n-type doped magneto-optic
InAs with a static magnetic field where light coupling is mediated by a
guided-mode-resonator (GMR) structure whose resonant frequency coincides with
the epsilon-near-zero (ENZ) resonance of the doped InAs. Using this structure,
we observe the nonreciprocal absorptive behavior as a function of magnetic
field and scattering angle in the infrared. Accounting for resonant and
nonresonant optical scattering, we reliably model experimental results that
break reciprocal absorption relations in the infrared. The ability to design
such nonreciprocal absorbers opens an avenue to explore devices with unequal
absorptivity and emissivity in specific channels.


title:
CoDA21: Evaluating Language Understanding Capabilities of NLP Models
  With Context-Definition Alignment

abstract:
  Pretrained language models (PLMs) have achieved superhuman performance on
many benchmarks, creating a need for harder tasks. We introduce CoDA21 (Context
Definition Alignment), a challenging benchmark that measures natural language
understanding (NLU) capabilities of PLMs: Given a definition and a context each
for k words, but not the words themselves, the task is to align the k
definitions with the k contexts. CoDA21 requires a deep understanding of
contexts and definitions, including complex inference and world knowledge. We
find that there is a large gap between human and PLM performance, suggesting
that CoDA21 measures an aspect of NLU that is not sufficiently covered in
existing benchmarks.


title: Veracity: Declarative Multicore Programming with Commutativity

abstract:
  There is an ongoing effort to provide programming abstractions that ease the
burden of exploiting multicore hardware. Many programming abstractions (e.g.,
concurrent objects, transactional memory, etc.) simplify matters, but still
involve intricate engineering. We argue that some difficulty of multicore
programming can be meliorated through a declarative programming style in which
programmers directly express the independence of fragments of sequential
programs.
  In our proposed paradigm, programmers write programs in a familiar,
sequential manner, with the added ability to explicitly express the conditions
under which code fragments sequentially commute. Putting such commutativity
conditions into source code offers a new entry point for a compiler to exploit
the known connection between commutativity and parallelism. We give a semantics
for the programmer's sequential perspective and, under a correctness condition,
find that a compiler-transformed parallel execution is equivalent to the
sequential semantics. Serializability/linearizability are not the right fit for
this condition, so we introduce scoped serializability and show how it can be
enforced with lock synthesis techniques.
  We next describe a technique for automatically verifying and synthesizing
commute conditions via a new reduction from our commute blocks to logical
specifications, upon which symbolic commutativity reasoning can be performed.
We implemented our work in a new language called Veracity, implemented in
Multicore OCaml. We show that commutativity conditions can be automatically
generated across a variety of new benchmark programs, confirm the expectation
that concurrency speedups can be seen as the computation increases, and apply
our work to a small in-memory filesystem and an adaptation of a crowdfund
blockchain smart contract.


title: Half-isomorphisms of automorphic loops

abstract:
  Automorphic loops are loops in which all inner mappings are automorphisms.
This variety of loops includes groups and commutative Moufang loops. A
half-isomorphism $f : G \longrightarrow K$ between multiplicative systems $G$
and $K$ is a bijection from $G$ onto $K$ such that $f(ab)\in\{f(a)f(b),
f(b)f(a)\}$ for any $a,b\in G$. A half-isomorphism is trivial when it is either
an isomorphism or an anti-isomorphism. Consider the class of automorphic loops
such that the equation $x\cdot(x\cdot y) = (y\cdot x)\cdot x $ is equivalent to
$x\cdot y = y\cdot x$. Here we show that this class of loops includes
automorphic loops of odd order and uniquely $2$-divisible. Furthermore, we
prove that every half-isomorphism between loops in that class is trivial.


title:
Mechanical Characterization of Compliant Cellular Robots. Part I:
  Passive Stiffness

abstract:
  Modular Active Cell Robots (MACROs) are a design paradigm for modular robotic
hardware that uses only two components, namely actuators and passive compliant
joints. Under the MACRO approach, a large number of actuators and joints are
connected to create mesh-like cellular robotic structures that can be actuated
to achieve large deformation and shape-change. In this two-part paper, we study
the importance of different possible mesh topologies within the MACRO
framework. Regular and semi-regular tilings of the plane are used as the
candidate mesh topologies and simulated using Finite Element Analysis (FEA). In
Part 1, we use FEA to evaluate their passive stiffness characteristics. Using a
strain energy method, the homogenized material properties (Young's modulus,
shear modulus, and Poisson's ratio) of different mesh topologies are computed
and compared. The results show that the stiffnesses increase with increasing
nodal connectivity and that stretching-dominated topologies have higher
stiffness compared to bending-dominated ones. We also investigate the role of
relative actuator-node stiffness on the overall mesh characteristics. This
analysis shows that the stiffness of stretching-dominated topologies scale
directly with their cross-section area whereas the bending-dominated ones do
not have such a direct relationship.


title:
A counterexample to the monotone increasing behavior of an
  Alt-Caffarelli-Friedman formula in the Heisenberg group

abstract:
  In this paper we provide a counterexample about the existence of an
increasing monotonicity behavior of a function introduced in \cite{FeFo},
companion of the celebrated Alt-Caffarelli-Friedman monotonicity formula, in
the noncommutative framework.


title:
Automatic Parallelization of Python Programs for Distributed
  Heterogeneous Computing

abstract:
  This paper introduces a novel approach to automatic ahead-of-time (AOT)
parallelization and optimization of sequential Python programs for execution on
distributed heterogeneous platforms. Our approach enables AOT source-to-source
transformation of Python programs, driven by the inclusion of type hints for
function parameters and return values. These hints can be supplied by the
programmer or obtained by dynamic profiler tools; multi-version code generation
guarantees the correctness of our AOT transformation in all cases.
  Our compilation framework performs automatic parallelization and
sophisticated high-level code optimizations for the target distributed
heterogeneous hardware platform. It includes extensions to the polyhedral
framework that unify user-written loops and implicit loops present in
matrix/tensor operators, as well as automated section of CPU vs. GPU code
variants. Further, our polyhedral optimizations enable both intra-node and
inter-node parallelism. Finally, the optimized output code is deployed using
the Ray runtime for scheduling distributed tasks across multiple heterogeneous
nodes in a cluster.
  Our empirical evaluation shows significant performance improvements relative
to sequential Python in both single-node and multi-node experiments, with a
performance improvement of over 20,000$\times$ when using 24 nodes and 144 GPUs
in the OLCF Summit supercomputer for the Space-Time Adaptive Processing (STAP)
radar application.


title:
Mechanical Characterization of Compliant Cellular Robots. Part II:
  Active Strain

abstract:
  Modular Active Cell Robots (MACROs) is a design approach in which a large
number of linear actuators and passive compliant joints are assembled to create
an active structure with a repeating unit cell. Such a mesh-like robotic
structure can be actuated to achieve large deformation and shape-change. In
this two-part paper, we use Finite Element Analysis (FEA) to model the
deformation behavior of different MACRO mesh topologies and evaluate their
passive and active mechanical characteristics. In part 1, we presented the
passive stiffness characteristics of different MACRO meshes. Now, in this part
2 of the paper, we investigate the active strain characteristics of planar
MACRO meshes. Using FEA, we quantify and compare the strains generated for the
specific choice of MACRO mesh topology and further for the specific choice of
actuators actuated in that particular mesh. We simulate a series of actuation
modes that are based on the angular orientation of the actuators within the
mesh and show that such actuation modes result in deformation that is
independent of the size of the mesh. We also show that there exists a subset of
such actuation modes that spans the range of deformation behavior. Finally, we
compare the actuation effort required to actuate different MACRO meshes and
show that the actuation effort is related to the nodal connectivity of the
mesh.


title:
The Denjoy--Wolff set for holomorphic sequences, non-autonomous
  dynamical systems and wandering domains

abstract:
  There are many classical results, related to the Denjoy--Wolff Theorem,
concerning the relationship between orbits of interior points and orbits of
boundary points under iterates of holomorphic self-maps of the unit disc. Here
we address such questions in the much more general setting of sequences $(F_n)$
of holomorphic maps between simply connected domains. We show that, while some
classical results can be generalised, with an interesting dependence on the
geometry of the domains, a much richer variety of behaviours is possible. Some
of our results are new even in the classical setting.
  Our methods apply in particular to non-autonomous dynamical systems, when
$(F_n)$ are forward compositions of holomorphic maps, and to the study of
wandering domains in holomorphic dynamics.
  We construct examples to illustrate the possible behaviours that can occur.
Our proofs use techniques from geometric function theory, measure theory and
ergodic theory, and the examples involve a `weak independence' version of the
second Borel--Cantelli lemma and the concept from ergodic theory of `shrinking
targets'.


title:
Euler-MacLaurin summation formula on polytopes and expansions in
  multivariate Bernoulli polynomials

abstract:
  We provide a multidimensional weighted Euler--MacLaurin summation formula on
polytopes and a multidimensional generalization of a result due to L. J.
Mordell on the series expansion in Bernoulli polynomials. These results are
consequences of a more general series expansion; namely, if $\chi
_{\tau\mathcal{P}}$ denotes the characteristic function of a dilated integer
convex polytope $\mathcal{P}$ and $q$ is a function with suitable regularity,
we prove that the periodization of $q\chi_{\tau\mathcal{P}}$ admits an
expansion in terms of multivariate Bernoulli polynomials. These multivariate
polynomials are related to the Lerch Zeta function. In order to prove our
results we need to carefully study the asymptotic expansion of
$\widehat{q\chi_{\tau\mathcal{P}}}$, the Fourier transform of $q\chi
_{\tau\mathcal{P}}$.