title: On-Robot Policy Learning with $\mathrm{O}(2)$-Equivariant SAC

abstract:
  Recently, equivariant neural network models have been shown to be useful in
improving sample efficiency for tasks in computer vision and reinforcement
learning. This paper explores this idea in the context of on-robot policy
learning where a policy must be learned entirely on a physical robotic system
without reference to a model, a simulator, or an offline dataset. We focus on
applications of $\mathrm{SO}(2)$-Equivariant SAC to robotic manipulation and
explore a number of variations of the algorithm. Ultimately, we demonstrate the
ability to learn several non-trivial manipulation tasks completely through
on-robot experiences in less than an hour or two of wall clock time.


title:
Quantum advantage for multi-option portfolio pricing and valuation
  adjustments

abstract:
  A critical problem in the financial world deals with the management of risk,
from regulatory risk to portfolio risk. Many such problems involve the analysis
of securities modelled by complex dynamics that cannot be captured
analytically, and hence rely on numerical techniques that simulate the
stochastic nature of the underlying variables. These techniques may be
computationally difficult or demanding. Hence, improving these methods offers a
variety of opportunities for quantum algorithms. In this work, we study the
problem of Credit Valuation Adjustments (CVAs) which have significant
importance in the valuation of derivative portfolios. We propose quantum
algorithms that accelerate statistical sampling processes to approximate the
CVA under different measures of dispersion, using known techniques in Quantum
Monte Carlo (QMC) and analyse the conditions under which we may employ these
techniques.


title: Correlated quantization for distributed mean estimation and optimization

abstract:
  We study the problem of distributed mean estimation and optimization under
communication constraints. We propose a correlated quantization protocol whose
error guarantee depends on the deviation of data points instead of their
absolute range. The design doesn't need any prior knowledge on the
concentration property of the dataset, which is required to get such dependence
in previous works. We show that applying the proposed protocol as sub-routine
in distributed optimization algorithms leads to better convergence rates. We
also prove the optimality of our protocol under mild assumptions. Experimental
results show that our proposed algorithm outperforms existing mean estimation
protocols on a diverse set of tasks.


title:
Autoregressive models for time series of random sums of positive
  variables: application to tree growth as a function of climate and insect
  outbreaks

abstract:
  We present a broad class of semi-parametric models for time series of random
sums of positive variables. Our methodology allows the number of terms inside
the sum to be time-varying and is therefore well suited to many examples
encountered in the natural sciences. We study the stability properties of the
models and provide a valid statistical inference procedure to estimate the
model parameters. It is shown that the proposed quasi-maximum likelihood
estimator is consistent and asymptotically normally distributed. This work is
complemented by simulation results and applied to annual growth rate time
series of white spruce (Picea glauca) trees from a few dozen sites in Quebec
spanning 41 years, including one major spruce budworm (Choristoneura
fumiferana) outbreak from around 1968 to 1991. We found significant growth
reductions due to budworm-induced by defoliation up to two years in the past.
Our results also revealed positive effects of maximum temperature,
precipitation and the climate moisture index in the summer, as well as negative
effects of the climate moisture index in the spring and the maximum temperature
in the previous summer. However, considering the interaction of climate and
defoliation on growth did not improve the model's performance on this dataset.
This study represent a major advances and our result represent an useful tool
in the understanding of the combined effects of climate and insect defoliation
on tree growth in the face of climate change, where the frequency and the
severity of outbreaks, as well as an increase of temperature is expected.


title: Investigation of Factorized Optical Flows as Mid-Level Representations

abstract:
  In this paper, we introduce a new concept of incorporating factorized flow
maps as mid-level representations, for bridging the perception and the control
modules in modular learning based robotic frameworks. To investigate the
advantages of factorized flow maps and examine their interplay with the other
types of mid-level representations, we further develop a configurable
framework, along with four different environments that contain both static and
dynamic objects, for analyzing the impacts of factorized optical flow maps on
the performance of deep reinforcement learning agents. Based on this framework,
we report our experimental results on various scenarios, and offer a set of
analyses to justify our hypothesis. Finally, we validate flow factorization in
real world scenarios.


title: DISCO: Comprehensive and Explainable Disinformation Detection

abstract:
  Disinformation refers to false information deliberately spread to influence
the general public, and the negative impact of disinformation on society can be
observed for numerous issues, such as political agendas and manipulating
financial markets. In this paper, we identify prevalent challenges and advances
related to automated disinformation detection from multiple aspects, and
propose a comprehensive and explainable disinformation detection framework
called DISCO. It leverages the heterogeneity of disinformation and addresses
the prediction opaqueness. Then we provide a demonstration of DISCO on a
real-world fake news detection task with satisfactory detection accuracy and
explanation. The demo video and source code of DISCO is now publicly available.
We expect that our demo could pave the way for addressing the limitations of
identification, comprehension, and explainability as a whole.


title: Finiteness for Hecke algebras of $p$-adic groups

abstract:
  Let $G$ be a reductive group over a non-archimedean local field $F$ of
residue characteristic $p$. We prove that the Hecke algebras of $G(F)$ with
coefficients in a ${\mathbb Z}_{\ell}$-algebra $R$ for $\ell$ not equal to $p$
are finitely generated modules over their centers, and that these centers are
finitely generated $R$-algebras. Following Bernstein's original strategy, we
then deduce that "second adjointness" holds for smooth representations of
$G(F)$ with coefficients in any ring $R$ in which $p$ is invertible. These
results had been conjectured for a long time. The crucial new tool that unlocks
the problem is the Fargues-Scholze morphism between a certain "excursion
algebra" defined on the Langlands parameters side and the Bernstein center of
$G(F)$. Using this bridge, our main results are representation theoretic
counterparts of the finiteness of certain morphisms between coarse moduli
spaces of local Langlands parameters that we also prove here, which may be of
independent interest


title:
Triangular Character Animation Sampling with Motion, Emotion, and
  Relation

abstract:
  Dramatic progress has been made in animating individual characters. However,
we still lack automatic control over activities between characters, especially
those involving interactions. In this paper, we present a novel energy-based
framework to sample and synthesize animations by associating the characters'
body motions, facial expressions, and social relations. We propose a
Spatial-Temporal And-Or graph (ST-AOG), a stochastic grammar model, to encode
the contextual relationship between motion, emotion, and relation, forming a
triangle in a conditional random field. We train our model from a labeled
dataset of two-character interactions. Experiments demonstrate that our method
can recognize the social relation between two characters and sample new scenes
of vivid motion and emotion using Markov Chain Monte Carlo (MCMC) given the
social relation. Thus, our method can provide animators with an automatic way
to generate 3D character animations, help synthesize interactions between
Non-Player Characters (NPCs), and enhance machine emotion intelligence (EQ) in
virtual reality (VR).


title:
The Assouad spectrum of Kleinian limit sets and Patterson-Sullivan
  measure

abstract:
  The Assouad dimension of the limit set of a geometrically finite Kleinian
group with parabolics may exceed the Hausdorff and box dimensions. The Assouad
\emph{spectrum} is a continuously parametrised family of dimensions which
`interpolates' between the box and Assouad dimensions of a fractal set. It is
designed to reveal more subtle geometric information than the box and Assouad
dimensions considered in isolation. We conduct a detailed analysis of the
Assouad spectrum of limit sets of geometrically finite Kleinian groups and the
associated Patterson-Sullivan measure. Our analysis reveals several novel
features, such as interplay between horoballs of different rank not seen by the
box or Assouad dimensions.


title: On the character ring of a quasireductive Lie superalgebra

abstract:
  We study character rings of quasireductive Lie superalgebras and give a new
proof of the Sergeev-Veselov theorem describing the character rings of
finite-dimensional Kac-Moody superalgebras.


title:
A giant shell of ionized gas discovered near M82 with the Dragonfly
  Spectral Line Mapper pathfinder

abstract:
  We present the discovery of a giant cloud of ionized gas in the field of the
starbursting galaxy M82. Emission from the cloud is seen in H$\alpha$ and
[NII]$\lambda$6583 in data obtained though a small pathfinder instrument used
to test the key ideas that will be implemented in the Dragonfly Spectral Line
Mapper, an upcoming ultranarrow-bandpass imaging version of the Dragonfly
Telephoto Array. The discovered cloud has a shell-like morphology with a linear
extent of $0.8^{\circ}$ and is positioned $0.6^{\circ}$ northwest of M82. At
the heliocentric distance of the M81 group, the cloud's extent corresponds to
55 kpc and its projected distance from the nucleus of M82 is 40 kpc. The cloud
has an average H$\alpha$ surface brightness of $2\times10^{-18}$ $\mathrm{erg}
~\mathrm{cm}^{-2} ~\mathrm{s}^{-1} ~\mathrm{arcsec}^{-2}$. The
[NII]$\lambda$6583/H$\alpha$ line ratio varies from [NII]/H$\alpha$ $\sim0.2$
to [NII]/H$\alpha$ $\sim1.0$ across the cloud, with higher values found in its
eastern end. Follow-up spectra obtained with Keck-LRIS confirm the existence of
the cloud and yield line ratios of [NII]$\lambda$6583/H$\alpha$ = 0.340 $\pm$
0.003 and [SII]$\lambda$6716,6731/H$\alpha$ = 0.64 $\pm$ 0.03 in the cloud.
This giant cloud of material could be lifted from M82 by tidal interactions or
by its powerful starburst. Alternatively, it may be gas infalling from the
cosmic web, potentially precipitated by the superwinds of M82. Deeper data are
needed to test these ideas further. The upcoming Dragonfly Spectral Line Mapper
will have 120 lenses, $40\times$ more than in the pathfinder instrument used to
obtain the data presented here.


title:
The environmental dependence of the stellar and gas-phase
  mass-metallicity relation at 2 < z < 4

abstract:
  In the local universe, galaxies in clusters show different properties
compared to more isolated systems. Understanding how this difference originates
and whether it is already in place at high redshift is still a matter of
debate. Thanks to uniquely deep optical spectra from the VANDELS survey, we
investigate environmental effects on the stellar mass-metallicity relation
(MZR) for a sample of ~1000 star-forming galaxies at 2<z<4. We complement our
dataset with MOSFIRE follow-up of 21 galaxies to study the environmental
dependence of the gas-phase MZR. Robust stellar and gas metallicities are
derived, respectively, from well-calibrated photospheric absorptions features
at 1501 and 1719 \AA in the stacked spectra, and from optical emission lines
([OII]3726-3729, [OIII]5007, and Hbeta) in individual systems. We characterize
the environment through multiple criteria by using the local galaxy density
maps previously derived in VANDELS. We find that environmental effects are weak
at these redshifts, and more important around the densest overdensity
structures, where galaxies have a lower stellar Z (by 0.2 dex) and a lower
gas-phase Z (by 0.1 dex) compared to the field, with a significance of 1 and 2
sigma, respectively. Crucially, this offset cannot be explained by a selection
effect due to a higher SFR, a fainter UV continuum, or different dust
attenuations and stellar ages. Despite the still low S/N of our results, we
propose a combination of increased mergers and high-speed encounters, more
efficient AGN feedback in dense cores, and cold gas inflows as viable
mechanisms diluting the metal content of overdense galaxies or expelling their
metals to the IGM. Finally, some tensions remain with semi-analytic models and
hydrodynamical simulations, which predict no significant offset as a function
of host halo mass, suggesting that an explicit implementation of environmental
processes is needed.


title:
Deep Generative Models for Downlink Channel Estimation in FDD Massive
  MIMO Systems

abstract:
  It is well accepted that acquiring downlink channel state information in
frequency division duplexing (FDD) massive multiple-input multiple-output
(MIMO) systems is challenging because of the large overhead in training and
feedback. In this paper, we propose a deep generative model (DGM)-based
technique to address this challenge. Exploiting the partial reciprocity of
uplink and downlink channels, we first estimate the frequency-independent
underlying channel parameters, i.e., the magnitudes of path gains, delays,
angles-of-arrivals (AoAs) and angles-of-departures (AoDs), via uplink training,
since these parameters are common in both uplink and downlink. Then, the
frequency-specific underlying channel parameters, namely, the phase of each
propagation path, are estimated via downlink training using a very short
training signal. In the first step, we incorporate the underlying distribution
of the channel parameters as a prior into our channel estimation algorithm. We
use DGMs to learn this distribution. Simulation results indicate that our
proposed DGM-based channel estimation technique outperforms, by a large gap,
the conventional channel estimation techniques in practical ranges of
signal-to-noise ratio (SNR). In addition, a near-optimal performance is
achieved using only few downlink pilot measurements.


title:
Thermal conductivity of a weakly interacting Bose gas by quasi-one
  dimensionality

abstract:
  Transport coefficients are typically divergent for quantum integrable systems
in one dimension, such as a Bose gas with a two-body contact interaction.
However, when a one-dimensional system is realized by confining bosons into a
tight matter waveguide, an effective three-body interaction inevitably arises
as leading perturbation to break the integrability. This fact motivates us to
study the thermal conductivity of a Bose gas in one dimension with both
two-body and three-body interactions. In particular, we evaluate the Kubo
formula exactly to the lowest order in perturbation by summing up all
contributions that are naively higher orders in perturbation but become
comparable in the zero-frequency limit due to the pinch singularity.
Consequently, a self-consistent equation for a vertex function is derived,
showing that the thermal conductivity in one dimension is dominated by the
three-body interaction rather than the two-body interaction. Furthermore, the
resulting thermal conductivity in the weak-coupling limit proves to be
identical to that computed based on the quantum Boltzmann equation and its
temperature dependence is numerically determined.


title:
Addressing Bias in Visualization Recommenders by Identifying Trends in
  Training Data: Improving VizML Through a Statistical Analysis of the Plotly
  Community Feed

abstract:
  Machine learning is a promising approach to visualization recommendation due
to its high scalability and representational power. Researchers can create a
neural network to predict visualizations from input data by training it over a
corpus of datasets and visualization examples. However, these machine learning
models can reflect trends in their training data that may negatively affect
their performance. Our research project aims to address training bias in
machine learning visualization recommendation systems by identifying trends in
the training data through statistical analysis.


title:
Form factors of $B\to\pi\ell\nu$ and a determination of $|V_{ub}|$ with
  M\"{o}bius domain-wall-fermions

abstract:
  Using a fully relativistic lattice fermion action, we compute the form
factors of the semileptonic decay $B\to\pi\ell\nu$, which is required for the
determination of the Cabibbo-Kobayashi-Maskawa matrix element $|V_{ub}|$. We
employ the M\"{o}bius domain-wall fermion formalism for the generation of
lattice ensembles with 2+1 sea quark flavours as well as for the valence heavy
and light quarks. We compute the form factors at various values of the lattice
spacing and multiple light and heavy quark masses, and extrapolate the results
to the physical point. We combine our lattice results with the available
experimental data to obtain $|V_{ub}| = (3.93\pm 0.41)\times 10^{-3}$.


title:
The interplay between structural, magnetic and electronic states in the
  pyrochlore iridate Eu2Ir2O7

abstract:
  We address the concomitant metal-insulator transition (MIT) and
antiferromagnetic ordering in the novel pyrochlore iridate Eu2Ir2O7 by
combining x-ray absorption spectroscopy, x-ray and neutron diffractions and
density functional theory (DFT) based calculations. The temperature dependent
powder x-ray diffraction clearly rules out any change in the lattice symmetry
below the MIT, nevertheless a clear anomaly in the Ir-O-Ir bond angle and Ir-O
bond length is evident at the onset of MIT. From the x-ray absorption near edge
structure (XANES) spectroscopic study of Ir-L3 and L2 edges, the effective
spin-orbit coupling is found to be intermediate, at least quite far from the
strong atomic spin-orbit coupling limit. Powder neutron diffraction measurement
is in line with an all-in-all-out magnetic structure of the Ir-tetrahedra in
this compound, which is quite common among rare-earth pyrochlore iridates. The
sharp change in the Ir-O-Ir bond angle around the MIT possibly arises from the
exchange striction mechanism, which favors an enhanced electron correlation via
weakening of Ir-Ir orbital overlap and an insulating phase below TMI . The
theoretical calculations indicate an insulating state for shorter bond angle
validating the experimental observation. Our DFT calculations show a
possibility of intriguing topological phase below a critical value of the Ir-O
distance, which is shorter than the experimentally observed bond length.
Therefore, a topological state may be realized in bulk Eu2Ir2O7 sample if the
Ir-O bond length can be reduced by the application of sufficient external
pressure.


title: Data-Efficient Structured Pruning via Submodular Optimization

abstract:
  Structured pruning is an effective approach for compressing large pre-trained
neural networks without significantly affecting their performance, which
involves removing redundant regular regions of weights. However, current
structured pruning methods are highly empirical in nature, do not provide any
theoretical guarantees, and often require fine-tuning, which makes them
inapplicable in the limited-data regime. We propose a principled data-efficient
structured pruning method based on submodular optimization. In particular, for
a given layer, we select neurons/channels to prune and corresponding new
weights for the next layer, that minimize the change in the next layer's input
induced by pruning. We show that this selection problem is a weakly submodular
maximization problem, thus it can be provably approximated using an efficient
greedy algorithm. Our method is one of the few in the literature that uses only
a limited-number of training data and no labels. Our experimental results
demonstrate that our method outperforms popular baseline methods in various
one-shot pruning settings.


title:
Vanadium doped beta-Ga2O3 single crystals: Growth, Optical and Terahertz
  characterization

abstract:
  We report the growth of electrically-resistive vanadium-doped beta-Ga2O3
single crystals via the optical floating zone technique. By carefully
controlling the growth parameters V-doped crystals with very high electrical
resistivity compared to the usual n-type V-doped beta-Ga2O3 (ne~10^(18)/cm^3)
can be synthesized. The optical properties of such high resistive V-doped
b-Ga2O3 are significantly different compared to the undoped and n-doped
crystals. We study the polarization-dependent Raman spectra,
polarization-dependent transmission, temperature-dependent photoluminescence in
the optical wavelength range and the THz transmission properties in the 0.2 -
2.6 THz range. The V-doped insulating Ga2O3 crystals show strong birefringence
with refractive index contrast Dn of 0.3+-0.02 at 1 THz, suggesting it to be an
ideal material for optical applications in the THz region.


title: Topological defects in nonlocal field theories

abstract:
  In this paper, we study for the first time topological defects in the context
of nonlocal field theories in which Lagrangians contain infinite-order
differential operators. In particular, we analyze domain walls. Despite the
complexity of non-linear infinite-order differential equations, we are able to
find an approximate analytic solution. We first determine the asymptotic
behavior of the nonlocal domain wall close to the vacua. Then, we find a
linearized nonlocal solution by perturbing around the well-known local 'kink',
and show that it is consistent with the asymptotic behavior. We develop a
formalism to study the solution around the origin, and use it to verify the
validity of the linearized solution. We find that nonlocality makes the width
of the domain wall thinner, and the energy per unit area smaller as compared to
the local case. For the specific domain wall solution under investigation, we
derive a theoretical constraint on the energy scale of nonlocality which must
be larger than the corresponding symmetry-breaking scale. We also briefly
comment on other topological defects like string and monopole.


title: Assouad type dimensions of parabolic Julia sets

abstract:
  We prove that the Assouad dimension of a parabolic Julia set is $\max\{1,h\}$
where $h$ is the Hausdorff dimension of the Julia set. Since $h$ may be
strictly less than 1, this provides examples where the Assouad and Hausdorff
dimension are distinct. The box and packing dimensions of the Julia set are
known to coincide with $h$ and, moreover, $h$ can be characterised by a
topological pressure function. This distinctive behaviour of the Assouad
dimension invites further analysis of the Assouad type dimensions, including
the Assouad and lower spectra. We compute all of the Assouad type dimensions
for parabolic Julia sets and the associated $h$-conformal measure. Further, we
show that if a Julia set has a Cremer point, then the Assouad dimension is 2.


title:
Polar nature of the ferro-electric nematic studied by dielectric
  spectroscopy

abstract:
  The nematic-nematic transitions in a nematic compound DIO are studied in
homogeneously planar and homeotropic aligned cells using dielectric
spectroscopy in the frequency range 0.1 Hz to 10 MHz over a wide range of
temperatures. Three relaxation processes are identified in this material. All
the three relaxation processes show large jumps in the dielectric strength and
discontinuity in frequency at the NX to NF transition, indicative of it being a
first order phase transition. Unlike a conventional nematic liquid crystalline
material that usually shows molecular relaxation processes at higher
frequencies, this material shows three processes at relatively lower
frequencies. The three processes are found to be collective in nature. The
least frequency process is proven to be due to the conductivity of ions in the
medium and due to the accumulation of charge at the alignment layers unlike
mistakenly reported in the literature otherwise. The mode at the highest
frequency is proven to be due to the ferroelectric domains in the NF phase.
This is evidenced by its dielectric strength two orders of magnitude higher in
a homeotropic aligned cell than planar aligned cell. Its mechanism is soft mode
like in the N phase. The intermediate frequency mode is proven to be due to the
correlation of molecules of the medium as is normally observed in liquid
crystalline cybotactic clusters.


title:
Searching for the Origin of Double-peaked Broad Emission Lines in a
  Merging Galaxy with EVN

abstract:
  The current cosmological structure formation models predict that galaxies
evolve through frequent mergers. During these events, the supermassive black
holes (SMBHs) residing in the centres of the galaxies shrink to the central
region, while losing energy via dynamical friction. Detection of SMBHs in these
galaxy mergers is straightforward if they are actively accreting matter from
their surroundings as active galactic nuclei (AGN). Currently, only a few dual
AGN are known. One way to identify dual AGN candidates is by detecting
double-peaked emission lines in their spectra. If these are broad spectral
lines, it may indicate the existence of two broad line regions associated with
two AGN. 2MASS J165939.7+183436 is a merging system, where the detected
double-peaked broad emission lines can be explained by a dual AGN with an
estimated separation of $0.085"$. Radio emission from this object was detected
in the Faint Images of the Radio Sky at Twenty-centimeters survey with a flux
density of 2.6 mJy. We used the European Very Long Baseline Interferometer
Network and the enhanced Multi-Element Remotely Linked Interferometer Network
to image 2MASS J165939.7+183436 at 1.7 GHz. We did not detect compact radio
emission from the source.


title:
On the surprising tradeoff between ImageNet accuracy and perceptual
  similarity

abstract:
  Perceptual distances between images, as measured in the space of pre-trained
deep features, have outperformed prior low-level, pixel-based metrics on
assessing image similarity. While the capabilities of older and less accurate
models such as AlexNet and VGG to capture perceptual similarity are well known,
modern and more accurate models are less studied. First, we observe a
surprising inverse correlation between ImageNet accuracy and Perceptual Scores
of modern networks such as ResNets, EfficientNets, and Vision Transformers:
that is better classifiers achieve worse Perceptual Scores. Then, we perform a
large-scale study and examine the ImageNet accuracy/Perceptual Score
relationship on varying the depth, width, number of training steps, weight
decay, label smoothing, and dropout. Higher accuracy improves Perceptual Score
up to a certain point, but we uncover a Pareto frontier between accuracies and
Perceptual Score in the mid-to-high accuracy regime. We explore this
relationship further using distortion invariance, spatial frequency
sensitivity, and alternative perceptual functions. Interestingly we discover
shallow ResNets, trained for less than 5 epochs only on ImageNet, whose
emergent Perceptual Score matches the prior best networks trained directly on
supervised human perceptual judgements.


title: Trouble in Paradox

abstract:
  Recent developments in holography have suggested a potential resolution to
the black hole information paradox within the context of gravitational
effective field theory. We emphasize the non-local nature of this proposed
resolution, and highlight the ways in which observations outside the black hole
can detect it and conclude that the black hole is not radiating like an
ordinary body would.


title: Fragile boundaries of tailored surface codes

abstract:
  Biased noise is common in physical qubits, and tailoring a quantum code to
the bias by locally modifying stabilizers or changing boundary conditions has
been shown to greatly increase error correction thresholds. In this work, we
explore the challenges of using a specific tailored code, the XY surface code,
for fault-tolerant quantum computation. We introduce efficient and
fault-tolerant decoders, belief-matching and belief-find, which exploit
correlated hyperedge fault mechanisms present in circuit-level noise. Using
belief-matching, we find that the XY surface code has a higher threshold and
lower overhead than the square CSS surface code for moderately biased noise.
However, the rectangular CSS surface code has a lower qubit overhead than the
XY surface code when below threshold. We identify a contributor to the reduced
performance that we call fragile boundary errors. These are string-like errors
that can occur along spatial or temporal boundaries in planar architectures or
during logical state preparation and measurement. While we make partial
progress towards mitigating these errors by deforming the boundaries of the XY
surface code, our work suggests that fragility could remain a significant
obstacle, even for other tailored codes. We expect that our decoders will have
other uses; belief-find has an almost-linear running time, and we show that it
increases the threshold of the surface code to 0.937(2)% in the presence of
circuit-level depolarising noise, compared to 0.817(5)% for the more
computationally expensive minimum-weight perfect matching decoder.


title:
Star formation of X-ray AGN in COSMOS: The role of AGN activity and
  galaxy stellar mass

abstract:
  We use $\sim 1,000$ X-ray sources in the COSMOS-Legacy survey and study the
position of the AGN relative to the star forming main sequence (MS). We also
construct a galaxy (non-AGN) reference sample that includes $\sim 90,000$
sources. We apply the same photometric selection criteria on both datasets and
construct their spectral energy distributions (SEDs) using optical to
far-infrared photometry compiled by the HELP project. We perform SED fitting,
using the X-CIGALE algorithm and the same parametric grid for both datasets, to
measure the star formation rate (SFR) and stellar mass of the sources. The mass
completeness of the data is calculated at different redshift intervals and is
applied on both samples. We define our own main sequence, based on the
distributions of the specific SFR at different redshift ranges and exclude
quiescent galaxies from our analysis. These allow us to compare the SFR of the
two populations in a uniform manner, minimizing systematics and selection
effects. Our results show that at low to moderate X-ray luminosities, AGN tend
to have lower or, at most, equal star formation rates compared to non-AGN
systems with similar stellar mass and redshift. At higher ($\rm L_{X,2-10keV} >
2-3\times 10^{44}\,erg\,s^{-1}$), we observe an increase of the SFR of AGN, for
systems that have $\rm 10.5 < log\,[M_*(M_\odot)] < 11.5$.


title: Renyi Fair Information Bottleneck for Image Classification

abstract:
  We develop a novel method for ensuring fairness in machine learning which we
term as the Renyi Fair Information Bottleneck (RFIB). We consider two different
fairness constraints - demographic parity and equalized odds - for learning
fair representations and derive a loss function via a variational approach that
uses Renyi's divergence with its tunable parameter $\alpha$ and that takes into
account the triple constraints of utility, fairness, and compactness of
representation. We then evaluate the performance of our method for image
classification using the EyePACS medical imaging dataset, showing it
outperforms competing state of the art techniques with performance measured
using a variety of compound utility/fairness metrics, including accuracy gap
and Rawls' minimal accuracy.


title:
Learning from Physical Human Feedback: An Object-Centric One-Shot
  Adaptation Method

abstract:
  For robots to be effectively deployed in novel environments and tasks, they
must be able to understand the feedback expressed by humans during
intervention. This can either correct undesirable behavior or indicate
additional preferences. Existing methods either require repeated episodes of
interactions or assume prior known reward features, which is data-inefficient
and can hardly transfer to new tasks. We relax these assumptions by describing
human tasks in terms of object-centric sub-tasks and interpreting physical
interventions in relation to specific objects. Our method, Object Preference
Adaptation (OPA), is composed of two key stages: 1) pre-training a base policy
to produce a wide variety of behaviors, and 2) online-updating only certain
weights in the model according to human feedback. The key to our fast, yet
simple adaptation is that general interaction dynamics between agents and
objects are fixed, and only object-specific preferences are updated. Our
adaptation occurs online, requires only one human intervention (one-shot), and
produces new behaviors never seen during training. Trained on cheap synthetic
data instead of expensive human demonstrations, our policy demonstrates
impressive adaptation to human perturbations on challenging, realistic tasks in
our user study. Videos, code, and supplementary material provided.


title:
Semiclassical Real-Time Nuclear-Electronic Orbital Dynamics for
  Molecular Polaritons: Unified Theory of Electronic and Vibrational Strong
  Couplings

abstract:
  Molecular polaritons have become an emerging platform for remotely
controlling molecular properties through strong light-matter interactions.
Herein, a semiclassical approach is developed for describing molecular
polaritons by self-consistently propagating the real-time dynamics of classical
cavity modes and a quantum molecular subsystem described by the
nuclear-electronic orbital (NEO) method, where electrons and specified nuclei
are treated quantum mechanically on the same level. This semiclassical
real-time NEO approach provides a unified description of electronic and
vibrational strong couplings and describes the impact of the cavity on coupled
nuclear-electronic dynamics while including nuclear quantum effects. For a
single o-hydroxybenzaldehyde molecule under electronic strong coupling, this
approach shows that the cavity suppression of excited state intramolecular
proton transfer is influenced not only by the polaritonic potential energy
surface but also by the timescale of the chemical reaction. This work provides
the foundation for exploring collective strong coupling in nuclear-electronic
quantum dynamical systems within optical cavities.


title:
Minimal obstructions for polarity, monopolarity, unipolarity and
  $(s,1)$-polarity in generalizations of cographs

abstract:
  Every hereditary property can be characterized by finitely many minimal
obstructions when restricted to either the class of cographs or the class of
$P_4$-reducible graphs. In this work we prove that also when restricted to the
classes of $P_4$-sparse graphs and $P_4$-extendible graphs (both of which
extend $P_4$-reducible graphs) every hereditary property can be characterized
by finitely many minimal obstructions.
  We present complete lists of $P_4$-sparse and $P_4$-extendible minimal
obstructions for polarity, monopolarity, unipolarity, and $(s,1)$-polarity,
where $s$ is a positive integer. In parallel to the case of $P_4$-reducible
graphs, all the $P_4$-sparse minimal obstructions for these hereditary
properties are cographs.


title:
An entropic generalization of Caffarelli's contraction theorem via
  covariance inequalities

abstract:
  The optimal transport map between the standard Gaussian measure and an
$\alpha$-strongly log-concave probability measure is $\alpha^{-1/2}$-Lipschitz,
as first observed in a celebrated theorem of Caffarelli. In this paper, we
apply two classical covariance inequalities (the Brascamp-Lieb and Cram\'er-Rao
inequalities) to prove a sharp bound on the Lipschitz constant of the map that
arises from entropically regularized optimal transport. In the limit as the
regularization tends to zero, we obtain an elegant and short proof of
Caffarelli's original result. We also extend Caffarelli's theorem to the
setting in which the Hessians of the log-densities of the measures are bounded
by arbitrary positive definite commuting matrices.


title: Temporal Difference Learning for Model Predictive Control

abstract:
  Data-driven model predictive control has two key advantages over model-free
methods: a potential for improved sample efficiency through model learning, and
better performance as computational budget for planning increases. However, it
is both costly to plan over long horizons and challenging to obtain an accurate
model of the environment. In this work, we combine the strengths of model-free
and model-based methods. We use a learned task-oriented latent dynamics model
for local trajectory optimization over a short horizon, and use a learned
terminal value function to estimate long-term return, both of which are learned
jointly by temporal difference learning. Our method, TD-MPC, achieves superior
sample efficiency and asymptotic performance over prior work on both state and
image-based continuous control tasks from DMControl and Meta-World. Code and
video results are available at https://nicklashansen.github.io/td-mpc.


title: Derivatives of Sub-Riemannian Geodesics are $L_p$-H\"older Continuous

abstract:
  The article is devoted to the long-standing problem on the smoothness of
sub-Riemannian geodesics. We prove that the derivatives of sub-Riemannian
geodesics are always $L_p$-H\"older continuous. This result has a number of
interesting implications concerning (i) the Fourier coefficients decay on
abnormal controls, (ii) the rate of their approximation by smooth functions,
(iii) the corresponding generalization of the Poincare inequality, and (iv) the
compact embedding of the set of shortest paths into the space of Bessel
potentials.


title:
A continuous multiple hypothesis testing framework for optimal exoplanet
  detection

abstract:
  The detection of exoplanets is hindered by the presence of complex
astrophysical and instrumental noises. Given the difficulty of the task, it is
important to ensure that the data are exploited to their fullest potential. In
the present work, we search for an optimal exoplanet detection criterion. We
adopt a general Bayesian multiple hypothesis testing framework, where the
hypotheses are indexed by continuous variables. This framework is adaptable to
the different observational methods used to detect exoplanets as well as other
data analysis problems. We describe the data as a combination of several
parametrized patterns and nuisance signals. We wish to determine which patterns
are present, and for a detection to be valid, the parameters of the claimed
pattern have to correspond to a true one with a certain accuracy. We search for
a detection criterion minimizing false and missed detections, either as a
function of their relative cost, or when the expected number of false
detections is bounded. We find that if the patterns can be separated in a
technical sense, the two approaches lead to the same optimal procedure. We
apply it to the retrieval of periodic signals in unevenly sampled time series,
emulating the search for exoplanets in radial velocity data. We show on a
simulation that, for a given tolerance to false detections, the new criterion
leads to 15 to 30\% more true detections than other criteria, including the
Bayes factor.


title:
Fluid registration between lung CT and stationary chest tomosynthesis
  images

abstract:
  Registration is widely used in image-guided therapy and image-guided surgery
to estimate spatial correspondences between organs of interest between planning
and treatment images. However, while high-quality computed tomography (CT)
images are often available at planning time, limited angle acquisitions are
frequently used during treatment because of radiation concerns or imaging time
constraints. This requires algorithms to register CT images based on limited
angle acquisitions. We, therefore, formulate a 3D/2D registration approach
which infers a 3D deformation based on measured projections and digitally
reconstructed radiographs of the CT. Most 3D/2D registration approaches use
simple transformation models or require complex mathematical derivations to
formulate the underlying optimization problem. Instead, our approach entirely
relies on differentiable operations which can be combined with modern
computational toolboxes supporting automatic differentiation. This then allows
for rapid prototyping, integration with deep neural networks, and to support a
variety of transformation models including fluid flow models. We demonstrate
our approach for the registration between CT and stationary chest tomosynthesis
(sDCT) images and show how it naturally leads to an iterative image
reconstruction approach.


title:
ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for
  Multiple Sclerosis Lesion Segmentation with Missing Modalities

abstract:
  Multiple Sclerosis (MS) is a chronic neuroinflammatory disease and
multi-modality MRIs are routinely used to monitor MS lesions. Many automatic MS
lesion segmentation models have been developed and have reached human-level
performance. However, most established methods assume the MRI modalities used
during training are also available during testing, which is not guaranteed in
clinical practice. A training strategy termed Modality Dropout (ModDrop) has
been applied to MS lesion segmentation to achieve the state-of-the-art
performance for missing modality. We present a novel method dubbed ModDrop++ to
train a unified network adaptive to an arbitrary number of input MRI sequences.
Moreover, ModDrop++ can be easily applied to any existing model architectures.
Specifically, ModDrop++ upgrades the main idea of ModDrop in two key ways.
First, we devise a plug-and-play dynamic head and adopt a filter scaling
strategy to improve the expressiveness of the network. Second, we design a
co-training strategy to leverage the intra-subject relation between full
modality and missing modality. In particular, the intra-subject co-training
strategy aims to guide the dynamic head to generate similar feature
representations between the full- and missing-modality data from the same
subject. We use two public MS datasets to show the superiority of ModDrop++.
Source code and trained models are available at
https://github.com/han-liu/ModDropPlusPlus.


title:
Memory-augmented Deep Unfolding Network for Guided Image
  Super-resolution

abstract:
  Guided image super-resolution (GISR) aims to obtain a high-resolution (HR)
target image by enhancing the spatial resolution of a low-resolution (LR)
target image under the guidance of a HR image. However, previous model-based
methods mainly takes the entire image as a whole, and assume the prior
distribution between the HR target image and the HR guidance image, simply
ignoring many non-local common characteristics between them. To alleviate this
issue, we firstly propose a maximal a posterior (MAP) estimation model for GISR
with two types of prior on the HR target image, i.e., local implicit prior and
global implicit prior. The local implicit prior aims to model the complex
relationship between the HR target image and the HR guidance image from a local
perspective, and the global implicit prior considers the non-local
auto-regression property between the two images from a global perspective.
Secondly, we design a novel alternating optimization algorithm to solve this
model for GISR. The algorithm is in a concise framework that facilitates to be
replicated into commonly used deep network structures. Thirdly, to reduce the
information loss across iterative stages, the persistent memory mechanism is
introduced to augment the information representation by exploiting the Long
short-term memory unit (LSTM) in the image and feature spaces. In this way, a
deep network with certain interpretation and high representation ability is
built. Extensive experimental results validate the superiority of our method on
a variety of GISR tasks, including Pan-sharpening, depth image
super-resolution, and MR image super-resolution.


title:
Sharing Generative Models Instead of Private Data: A Simulation Study on
  Mammography Patch Classification

abstract:
  Early detection of breast cancer in mammography screening via deep-learning
based computer-aided detection systems shows promising potential in improving
the curability and mortality rates of breast cancer. However, many clinical
centres are restricted in the amount and heterogeneity of available data to
train such models to (i) achieve promising performance and to (ii) generalise
well across acquisition protocols and domains. As sharing data between centres
is restricted due to patient privacy concerns, we propose a potential solution:
sharing trained generative models between centres as substitute for real
patient data. In this work, we use three well known mammography datasets to
simulate three different centres, where one centre receives the trained
generator of Generative Adversarial Networks (GANs) from the two remaining
centres in order to augment the size and heterogeneity of its training dataset.
We evaluate the utility of this approach on mammography patch classification on
the test set of the GAN-receiving centre using two different classification
models, (a) a convolutional neural network and (b) a transformer neural
network. Our experiments demonstrate that shared GANs notably increase the
performance of both transformer and convolutional classification models and
highlight this approach as a viable alternative to inter-centre data sharing.


title: Learning the Degradation Distribution for Blind Image Super-Resolution

abstract:
  Synthetic high-resolution (HR) \& low-resolution (LR) pairs are widely used
in existing super-resolution (SR) methods. To avoid the domain gap between
synthetic and test images, most previous methods try to adaptively learn the
synthesizing (degrading) process via a deterministic model. However, some
degradations in real scenarios are stochastic and cannot be determined by the
content of the image. These deterministic models may fail to model the random
factors and content-independent parts of degradations, which will limit the
performance of the following SR models. In this paper, we propose a
probabilistic degradation model (PDM), which studies the degradation
$\mathbf{D}$ as a random variable, and learns its distribution by modeling the
mapping from a priori random variable $\mathbf{z}$ to $\mathbf{D}$. Compared
with previous deterministic degradation models, PDM could model more diverse
degradations and generate HR-LR pairs that may better cover the various
degradations of test images, and thus prevent the SR model from over-fitting to
specific ones. Extensive experiments have demonstrated that our degradation
model can help the SR model achieve better performance on different datasets.
The source codes are released at \url{git@github.com:greatlog/UnpairedSR.git}.


title: Neural Data-Dependent Transform for Learned Image Compression

abstract:
  Learned image compression has achieved great success due to its excellent
modeling capacity, but seldom further considers the Rate-Distortion
Optimization (RDO) of each input image. To explore this potential in the
learned codec, we make the first attempt to build a neural data-dependent
transform and introduce a continuous online mode decision mechanism to jointly
optimize the coding efficiency for each individual image. Specifically, apart
from the image content stream, we employ an additional model stream to generate
the transform parameters at the decoder side. The presence of a model stream
enables our model to learn more abstract neural-syntax, which helps cluster the
latent representations of images more compactly. Beyond the transform stage, we
also adopt neural-syntax based post-processing for the scenarios that require
higher quality reconstructions regardless of extra decoding overhead. Moreover,
the involvement of the model stream further makes it possible to optimize both
the representation and the decoder in an online way, i.e. RDO at the testing
time. It is equivalent to a continuous online mode decision, like coding modes
in the traditional codecs, to improve the coding efficiency based on the
individual input image. The experimental results show the effectiveness of the
proposed neural-syntax design and the continuous online mode decision
mechanism, demonstrating the superiority of our method in coding efficiency
compared to the latest conventional standard Versatile Video Coding (VVC) and
other state-of-the-art learning-based methods.


title:
Metastatic Cancer Outcome Prediction with Injective Multiple Instance
  Pooling

abstract:
  Cancer stage is a large determinant of patient prognosis and management in
many cancer types, and is often assessed using medical imaging modalities, such
as CT and MRI. These medical images contain rich information that can be
explored to stratify patients within each stage group to further improve
prognostic algorithms. Although the majority of cancer deaths result from
metastatic and multifocal disease, building imaging biomarkers for patients
with multiple tumors has been a challenging task due to the lack of annotated
datasets and standard study framework. In this paper, we process two public
datasets to set up a benchmark cohort of 341 patient in total for studying
outcome prediction of multifocal metastatic cancer. We identify the lack of
expressiveness in common multiple instance classification networks and propose
two injective multiple instance pooling functions that are better suited to
outcome prediction. Our results show that multiple instance learning with
injective pooling functions can achieve state-of-the-art performance in the
non-small-cell lung cancer CT and head and neck CT outcome prediction
benchmarking tasks. We will release the processed multifocal datasets, our code
and the intermediate files i.e. extracted radiomic features to support further
transparent and reproducible research.


title: Bardeen-Kiselev black hole with cosmological constant

abstract:
  In this work we analyze a solution that mimics the Bardeen solution with a
cosmological constant surrounded by quintessence. We show that this solution
can be obtained by Einstein equations coupled with nonlinear electrodynamics.
We also show that the solution is not always regular and what the conditions
for regularity are. We analyze the thermodynamics associated with this type of
solution by establishing the form of the Smarr formula and the first law of
thermodynamics. We obtain some thermodynamic quantities such as pressure,
temperature, heat capacity and isothermal compressibility. Once we have these
thermodynamic quantities, we check if this solution has phase transitions and
how it behaves at the points where the transitions occur. For some values of
the parameters, we find that the solution exhibits a first-order phase
transition, like a van der Waals fluid.


title:
Linear-Time and Constant-Space Algorithms to compute Multi-Sequences
  that arise in Enumerative Combinatorics (and Elsewhere)

abstract:
  How many ways, exactly, can a Chess King, always moving forward (i.e. with
steps [1,0],[0,1],[1,1]) walk to [100000,200000]? Thanks to the amazing
Apagodu-Zeilberger extension of the Almkvist-Zeilberger algorithm, adapted in
this article for combinatorial applications, this 104492-digit number, can be
computed in less than 33 seconds. But not just this particular number. Many
other numbers that come up in enumerative combinatorics, can be computed just
as efficiently


title: UNeXt: MLP-based Rapid Medical Image Segmentation Network

abstract:
  UNet and its latest extensions like TransUNet have been the leading medical
image segmentation methods in recent years. However, these networks cannot be
effectively adopted for rapid image segmentation in point-of-care applications
as they are parameter-heavy, computationally complex and slow to use. To this
end, we propose UNeXt which is a Convolutional multilayer perceptron (MLP)
based network for image segmentation. We design UNeXt in an effective way with
an early convolutional stage and a MLP stage in the latent stage. We propose a
tokenized MLP block where we efficiently tokenize and project the convolutional
features and use MLPs to model the representation. To further boost the
performance, we propose shifting the channels of the inputs while feeding in to
MLPs so as to focus on learning local dependencies. Using tokenized MLPs in
latent space reduces the number of parameters and computational complexity
while being able to result in a better representation to help segmentation. The
network also consists of skip connections between various levels of encoder and
decoder. We test UNeXt on multiple medical image segmentation datasets and show
that we reduce the number of parameters by 72x, decrease the computational
complexity by 68x, and improve the inference speed by 10x while also obtaining
better segmentation performance over the state-of-the-art medical image
segmentation architectures. Code is available at
https://github.com/jeya-maria-jose/UNeXt-pytorch


title: Projective measurement of black holes

abstract:
  We study the effect of projective measurements on the entanglement structure
of quantum black holes. It is shown that the entanglement verification in
monitored quantum circuits, recently discussed in condensed matter physics, is
equivalent to the information recovery from a black hole with projective
measurements. This correspondence provides useful predictions about
non-perturbative effects on quantum gravity and some insights on the black hole
interior as well as the final state proposal.

categories: hep-th cond-mat.stat-mech gr-qc quant-ph


title:
Exoplanet atmospheres at high resolution through a modest-size
  telescope. Fe II in MASCARA-2b and KELT-9b with FIES on the Nordic Optical
  Telescope

abstract:
  Ground-based, high-resolution spectrographs are providing us with an
unprecedented view of the dynamics and chemistry of the atmospheres of planets
outside the Solar System. While there is a large number of stable and precise
high-resolution spectrographs on modest-size telescopes, it is the
spectrographs at observatories with apertures larger than 3.5 metres that
dominate the atmospheric follow-up of exoplanets. In this work, we explore the
potential of characterising exoplanetary atmospheres with FIES, a
high-resolution spectrograph at the 2.56 metre Nordic Optical Telescope. We
observed two transits of MASCARA-2 b (also known as KELT-20 b) and one transit
of KELT-9 b to search for atomic iron, a species that has been recently
discovered in both neutral and ionised forms in the atmospheres of these
ultra-hot Jupiters using large telescopes. Using a cross-correlation method, we
detect a signal of Fe II at the $4.5\sigma$ and $4.0\sigma$ level in the
transits of MASCARA-2 b. We also detect Fe II in the transit of KELT-9 b at the
$8.5\sigma$ level. Although we do not find any significant Doppler shift in the
signal of MASCARA-2 b, we do measure a moderate blueshift (3-6 km/s) of the
feature in KELT-9 b, which might be a manifestation of high-velocity winds
transporting Fe II from the planetary dayside to the nightside. Our work
demonstrates the feasibility of investigating exoplanet atmospheres with FIES,
potentially unlocking a wealth of additional atmosphere detections with this
and other high-resolution spectrographs mounted on similar-size telescopes.


title:
Tidally Tilted Pulsations in HD 265435, a subdwarf B Star with a Close
  White Dwarf Companion

abstract:
  Tidally tilted pulsators (TTPs) are an intriguing new class of oscillating
stars in binary systems; in such stars, the pulsation axis coincides with the
line of apsides, or semi-major axis, of the binary. All three TTPs discovered
so far have been $\delta$~Scuti stars. In this Letter, we report the first
conclusive discovery of tidally tilted pulsations in a subdwarf B (sdB) star.
HD 265435 is an sdB--white dwarf binary with a 1.65-hr period that has been
identified and characterized as the nearest potential Type Ia supernova
progenitor. Using TESS 20-s cadence data from Sectors 44 and 45, we show that
the pulsation axis of the sdB star has been tidally tilted into the orbital
plane and aligned with the tidal axis of the binary. We identify 31 independent
pulsation frequencies, 27 of which have between 1 and 7 sidebands separated by
the orbital frequency ($\nu_{\rm orb}$), or multiples thereof. Using the
observed amplitude and phase variability due to tidal tilting, we assign $\ell$
and $m$ values to most of the observed oscillation modes and use these mode
identifications to generate preliminary asteroseismic constraints. Our work
significantly expands our understanding of TTPs, as we now know that (i) they
can be found in stars other than $\delta$~Scuti pulsators, especially
highly-evolved stars that have lost their H-rich envelopes, and (ii) tidally
tilted pulsations can be used to probe the interiors of stars in very tight
binaries.


title:
Constrains on the physics of the prompt emission from a distant and
  energetic {\gamma}-ray burst GRB 220101A

abstract:
  The emission region of $\rm \gamma$-ray bursts (GRBs) is poorly constrained.
The uncertainty on the size of the dissipation site spans over 4 orders of
magnitude ($\rm 10^{12}-10^{16}$ cm) depending on the unknown energy
composition of the GRB jets. The joint multi-band analysis from soft X-rays to
high energies (up to $\rm \sim$ 1 GeV) of one of the most energetic and distant
GRB 220101A (z = 4.618) allows us for an accurate distinction between prompt
and early afterglow emissions. The enormous amount of energy released by GRB
220101A ($\rm E_{iso} \approx 3 \times10^{54}$ erg) and the spectral cutoff at
$\rm E_{cutoff} = 80_{-14}^{+44}$ MeV observed in the prompt emission spectrum
constrains the parameter space of GRB dissipation site. We put stringent
constraints on the prompt emission site, requiring $\rm \Gamma_0 \sim 900$ and
$\rm R_\gamma \sim 4.5 \times 10^{13}$ cm. Our findings favor the proton
synchrotron model, also consistent with the observed spectral shape. Deeper
measurements of the time variability of GRBs together with accurate high-energy
observations (MeV-GeV) would unveil the nature of the prompt emission.


title: Exceptional moduli spaces for exceptional $\mathcal{N}=3$ theories

abstract:
  It is expected on general grounds that the moduli space of 4d $\mathcal{N}=3$
theories is of the form $\mathbb{C}^{3r}/\Gamma$, with $r$ the rank and
$\Gamma$ a crystallographic complex reflection group (CCRG). As in the case of
Lie algebras, the space of CCRGs consists of several infinite families,
together with some exceptionals. To date, no 4d $\mathcal{N}=3$ theory with
moduli space labelled by an exceptional CCRG (excluding Weyl groups) has been
identified. In this work we show that the 4d $\mathcal{N}=3$ theories proposed
in \cite{Garcia-Etxebarria:2016erx}, constructed via non-geometric quotients of
type-$\mathfrak{e}$ 6d (2,0) theories, realize nearly all such exceptional
moduli spaces. In addition, we introduce an extension of this construction to
allow for twists and quotients by outer automorphism symmetries. This gives new
examples of 4d $\mathcal{N}=3$ theories going beyond simple S-folds.


title: The Spacetime Geometry of Fixed-Area States in Gravitational Systems

abstract:
  The concept of fixed-area states has proven useful for recent studies of
quantum gravity, especially in connection with gravitational holography. We
explore the Lorentz-signature spacetime geometry intrinsic to such fixed-area
states in this paper. This contrasts with previous treatments which focused
instead on Euclidean-signature saddles for path integrals that prepare such
states. We analyze general features of fixed-area state geometries and
construct explicit examples. The spacetime metrics are real at real times and
have no conical singularities. With enough symmetry the classical metrics are
in fact smooth, though more generally their curvatures feature power-law
divergences along null congruences launched orthogonally from the fixed-area
surface. While we argue that such divergences are not problematic at the
classical level, quantum fields in fixed-area states feature stronger
divergences. At the quantum level we thus expect fixed-area states to be
well-defined only when the fixed-area surface is appropriately smeared.


title: Traces of a Heavy Field in Gravitational Waves

abstract:
  We discuss gravitational waves (GWs) induced by a heavy spectator field that
starts to oscillate during inflation. During the oscillation of the spectator
field, its effective mass can also oscillate in some potentials. This mass
oscillation can resonantly amplify the spectator field fluctuations. We show
that these amplified fluctuations can induce GWs, which could be investigated
by future gravitational wave observations. This kind of induced GWs can be
produced even if the spectator field does not have any interaction with other
fields except for gravitational interaction.


title: Quantifying Grover speed-ups beyond asymptotic analysis

abstract:
  The usual method for studying run-times of quantum algorithms is via an
asymptotic, worst-case analysis. Whilst useful, such a comparison can often
fall short: it is not uncommon for algorithms with a large worst-case run-time
to end up performing well on instances of practical interest. To remedy this it
is necessary to resort to run-time analyses of a more empirical nature, which
for sufficiently small input sizes can be performed on a quantum device or a
simulation thereof. For larger input sizes, alternative approaches are
required.
  In this paper we consider an approach that combines classical emulation with
rigorous complexity bounds: simulating quantum algorithms by running classical
versions of the sub-routines, whilst simultaneously collecting information
about what the run-time of the quantum routine would have been if it were run
instead. To do this accurately and efficiently for very large input sizes, we
describe an estimation procedure that provides provable guarantees on the
estimates that it obtains. A nice feature of this approach is that it allows
one to compare the performance of quantum and classical algorithms on
particular inputs of interest, rather than only on those that allow for an
easier mathematical analysis.
  We apply our method to some simple quantum speedups of classical heuristic
algorithms for solving the well-studied MAX-k-SAT optimization problem. To do
this we first obtain some rigorous bounds (including all constants) on the
expected- and worst-case complexities of two important quantum sub-routines,
which improve upon existing results and might be of broader interest: Grover
search with an unknown number of marked items, and quantum maximum-finding. Our
results suggest that such an approach can provide insightful and meaningful
information, in particular when the speedup is of a small polynomial nature.


title:
The ionizing properties of two bright Ly$\alpha$ emitters in the BDF
  reionized bubble at z=7

abstract:
  We investigate the ionizing properties of the pair of bright Ly$\alpha$
emitting galaxies BDF521 and BDF2195 at z=7.012 in order to constrain their
contribution to the formation of the BDF "reionized bubble". We obtain
constraints on UV emission lines (CIV$\lambda 1548$ doublet, HeII$\lambda
1640$, OIII]$\lambda 1660$ doublet, and CIII]$\lambda 1909$ doublet) from deep
VLT-XSHOOTER observations and compare them to those available for other
high-redshift objects, and to models with mixed stellar and AGN emission. We
use this spectroscopic information together with the photometry available in
the field to constrain the physical properties of the two objects using the
spectro-photometric fitting code BEAGLE. We do not detect any significant
emission at the expected position of UV lines, with 3$\sigma$ upper limits of
EW$\lesssim$2-7 AA rest-frame. We find that the two objects have lower CIII]
emission than expected on the basis of the correlation between the Ly$\alpha$
and CIII] EWs. The EW limits on CIV and HeII emission exclude pure AGN
templates at $\sim2-3\sigma$ significance, and only models with a $\lesssim$40%
AGN contribution are compatible with the observations. The two objects are
found to be relatively young ($\sim$20-30 Myrs) and metal-poor ($\lesssim 0.3
Z_{\odot}$) with stellar masses of a few $10^9M_{\odot}$. Their production rate
of hydrogen ionizing photons per intrinsic UV luminosity is
log($\xi_{ion}^*$/Hz erg$^{-1}$)=25.02-25.26, consistent with values typically
found in high-redshift galaxies, but more than twice lower than values measured
in $z>$7 galaxies with strong CIII] and/or optical line emission
($\simeq$25.6-25.7). The two BDF emitters have no evidence of higher than
average ionizing capabilities and are not capable of reionizing their
surroundings by their own means under realistic assumptions on the escape
fraction of ionizing photons. (abridged)


title:
Signature of Supersonic Turbulence in Galaxy Clusters Revealed by
  AGN-driven H$\alpha$ Filaments

abstract:
  The hot intracluster medium (ICM) is thought to be quiescent with low
observed velocity dispersions. Surface brightness fluctuations of the ICM also
suggest that its turbulence is subsonic with a Kolmogorov scaling relation,
indicating that the viscosity is suppressed and the kinetic energy cascades to
small scales unscathed. However, recent observations of the cold gas filaments
in galaxy clusters find that the scaling relations are steeper than that of the
hot plasma, signaling kinetic energy losses and the presence of supersonic
flows. In this work we use high-resolution simulations to explore the turbulent
velocity structure of the cold filaments at the cores of galaxy clusters. Our
results indicate that supersonic turbulent structures can be "frozen" in the
cold gas that cools and fragments out of a fast, $10^7$ K outflow driven by the
central active galactic nucleus (AGN), when the radiative cooling time is
shorter than the dynamical sound-crossing time. After the cold gas formation,
however, the slope of the velocity structure function (VSF) flattens
significantly over short, 10 Myr timescales. The lack of flattened VSF in
observations of H$\alpha$ filaments indicates that the H$\alpha$-emitting phase
is short-lived for the cold gas in galaxy clusters. On the other hand, the
ubiquity of supersonic turbulence revealed by observed filaments strongly
suggests that supersonic outflows are an integral part of AGN-ICM interaction,
and that AGN activity plays a crucial role at driving turbulence in galaxy
clusters.


title:
Parameterized Two-Qubit Gates for Enhanced Variational Quantum
  Eigensolver

abstract:
  The variational quantum eigensolver is a prominent hybrid quantum-classical
algorithm expected to impact near-term quantum devices. They are usually based
on a circuit ansatz consisting of parameterized single-qubit gates and fixed
two-qubit gates. We study the effect of parameterized two-qubit gates in the
variational quantum eigensolver. We simulate a variational quantum eigensolver
algorithm using fixed and parameterized two-qubit gates in the circuit ansatz
and show that the parameterized versions outperform the fixed versions for a
range of Hamiltonians with applications in quantum chemistry and materials
science.


title:
REQUIEM-2D: A diversity of formation pathways in a sample of
  spatially-resolved massive quiescent galaxies at z~2

abstract:
  REQUIEM-2D (REsolving QUIEscent Magnified galaxies with 2D grism
spectroscopy) is comprised of a sample of 8 massive ($\log M_*/M_\odot > 10.6$)
strongly lensed quiescent galaxies at $z\sim2$. REQUIEM-2D combines the natural
magnification from strong gravitational lensing with the high
spatial-resolution grism spectroscopy of \emph{Hubble Space Telescope} through
a spectrophotometric fit to study spatially resolved stellar populations. We
show that quiescent galaxies in the REQUIEM-2D survey have diverse formation
histories manifesting as a gradient in stellar ages, including examples of (1)
a younger central region supporting outside-in formation, (2) flat age
gradients that show evidence for both spatially-uniform early formation or
inside-out quenching, and (3) regions at a fixed radial distance having
different ages (such asymmetries cannot be recovered when averaging stellar
population measurements azimuthally). The typical dust attenuation curve for
the REQUIEM-2D galaxies is constrained to be steeper than Calzetti's law in the
UV and generally consistent with $A_V<1$. Combined together and accounting for
the different physical radial distances and formation time-scales, we find that
the REQUIEM-2D galaxies that formed earlier in the universe exhibit slow and
uniform growth in their inner core, whereas the galaxies that formed later have
rapid inner growth in their inner core with younger ages relative to the
outskirts. These results challenge the currently accepted paradigm of how
massive quiescent galaxies form, where the earliest galaxies are thought to
form most rapidly. Significantly larger samples close to the epoch of formation
with similar data quality and higher spectral resolution are required to
validate this finding.


title:
From dawn till disk: Milky Way's turbulent youth revealed by the
  APOGEE+Gaia data

abstract:
  We use accurate estimates of aluminium abundance provided as part of the
APOGEE Data Release 17 and Gaia Early Data Release 3 astrometry to select a
highly pure sample of stars with metallicity $-1.5\lesssim {\rm [Fe/H]}\lesssim
0.5$ born in-situ in the Milky Way proper. We show that the low-metallicity
([Fe/H]$\lesssim -1.3$) in-situ component that we dub Aurora is kinematically
hot with an approximately isotropic velocity ellipsoid and a modest net
rotation. Aurora stars exhibit large scatter in metallicity and in a number of
element abundance ratios. The median tangential velocity of the in-situ stars
increases sharply with increasing metallicity between [Fe/H]$=-1.3$ and $-0.9$,
the transition that we call the spin-up. The observed and theoretically
expected age-metallicity correlations imply that this increase reflects a rapid
formation of the Milky Way disk over $\approx 1-2$ Gyrs. The transformation of
the stellar kinematics as a function of [Fe/H] is accompanied by a qualitative
change in chemical abundances: the scatter drops sharply once the Galaxy builds
up a disk during later epochs corresponding to [Fe/H]$>-0.9$. Results of galaxy
formation models presented in this and other recent studies strongly indicate
that the trends observed in the Milky Way reflect generic processes during the
early evolution of progenitors of MW-sized galaxies: a period of chaotic
pre-disk evolution, when gas is accreted along cold narrow filaments and when
stars are born in irregular configurations, and subsequent rapid disk
formation. The latter signals formation of a stable hot gaseous halo around the
MW progenitor, which changes the mode of gas accretion and allows development
of coherently rotating disk.


title: Snowmass White Paper: Micro- and Macro-Structure of Black Holes

abstract:
  The black-hole information paradox provides a stringent test of would-be
theories of quantum gravity. String theory has made significant progress toward
a resolution of this paradox, and has led to the fuzzball and microstate
geometry programs. The central thesis of these programs is that only string
theory has sufficiently many degrees of freedom to resolve black-hole
microstructure, and that horizons and singularities are artifacts of attempting
to describe gravity using a theory that has too few degrees of freedom to
resolve the physics. Fuzzballs and microstate geometries recast black holes
within string theory as horizonless and singularity-free objects that not only
resolve the paradox but provide new insight into the underlying microstructure.
We give an overview of this approach, summarize its current status and describe
future prospects and insights that are now within reach.


title:
Mapping Progenitors of Binary Black Holes and Neutron Stars with Binary
  Population Synthesis

abstract:
  The first directly observed gravitational wave event, GW150914, featuring the
merger of two massive black holes, highlighted the need to determine how these
systems of compact remnant binaries are formed. We use the binary population
synthesis code COSMIC (Compact Object Synthesis and Monte Carlo Investigation
Code) to predict the types of massive stars that will show significant radial
velocity variations, indicative of a potential compact object (i.e. a black
hole or neutron star) orbiting the star. We "observe" the binaries generated in
the populations with a similar number of epochs and RV accuracy as planned for
the Milky Way Mapper. In this analysis, we are especially interested in systems
where a compact remnant is orbiting a massive O or B star as these systems
survived the first supernova and neutron star kick. We test the ability of the
Milky Way Mapper observing strategy to distinguish among different mass loss
and kick prescriptions. We find that Wolf-Rayet stars or hot subdwarfs in
binaries could be detectable (i.e. luminous, high delta RV max), viable
progenitors of such objects, while the different prescriptions primarily affect
the number of sources.


title: Modeling hadronization using machine learning

abstract:
  We present the first steps in the development of a new class of hadronization
models utilizing machine learning techniques. We successfully implement,
validate, and train a conditional sliced-Wasserstein autoencoder to replicate
the Pythia generated kinematic distributions of first-hadron emissions, when
the Lund string model of hadronization implemented in Pythia is restricted to
the emissions of pions only. The trained models are then used to generate the
full hadronization chains, with an IR cutoff energy imposed externally. The
hadron multiplicities and cumulative kinematic distributions are shown to match
the Pythia generated ones. We also discuss possible future generalizations of
our results.


title: Circuit connectivity boosts by quantum-classical-quantum interfaces

abstract:
  High-connectivity circuits are a major roadblock for current quantum
hardware. We propose a hybrid classical-quantum algorithm to simulate such
circuits without swap-gate ladders. As main technical tool, we introduce
quantum-classical-quantum interfaces. These replace an experimentally
problematic gate (e.g. a long-range one) by single-qubit random measurements
followed by state-preparations sampled according to a classical
quasi-probability simulation of the noiseless gate. Each interface introduces a
multiplicative statistical overhead which is remarkably independent of the
on-chip qubit distance. Hence, by applying interfaces to the longest range
gates in a target circuit, significant reductions in circuit depth and gate
infidelity can be attained. We numerically show the efficacy of our method for
a Bell-state circuit for two increasingly distant qubits and a variational
ground-state solver for the transverse-field Ising model on a ring. Our
findings provide a versatile toolbox for error-mitigation and circuit boosts
tailored for noisy, intermediate-scale quantum computation.


title:
The feasibility of constraining DM interactions with high-redshift
  observations by JWST

abstract:
  Observations of the high redshift universe provide a promising avenue for
constraining the nature of the dark matter (DM). This will be even more true
following the now successful launch of the James Webb Space Telescope (JWST).
We run cosmological simulations of galaxy formation as a part of the Effective
Theory of Structure Formation (ETHOS) project to compare the properties of high
redshift galaxies in Cold (CDM) and alternative DM models which have varying
relativistic coupling and self-interaction strengths. Phenomenologically, the
interacting DM scenarios result in a cutoff in the linear power spectrum on
small-scales, followed by a series of "dark acoustic oscillations" (DAOs). We
find that DM interactions suppress the abundance of galaxies below $M_\star
\sim 10^8\, M_\odot$ for the models considered. The cutoff in the linear power
spectrum generally causes a delay in structure formation relative to CDM.
Objects in ETHOS that end up at the same final masses as their CDM counterparts
are characterised by a more vigorous phase of early star formation. While
galaxies with $M_\star \lesssim 10^6\,{M_\odot}$ make up more than 60 per cent
of star formation in CDM at $z\approx 10$, they contribute only about half the
star formation density in ETHOS. These differences in star formation diminish
with decreasing redshift. We find that the effects of DM self-interactions are
negligible compared to effects of relativistic coupling (i.e. the effective
initial conditions for galaxy formation) in all properties of the galaxy
population we examine. Finally, we show that the clustering strength of
galaxies at high redshifts depends sensitively on DM physics, although these
differences are manifest on scales that may be too small to be measurable by
JWST.


title: Designer Meron Lattice on the Surface of a Topological Insulator

abstract:
  We present a promising new route to realize spontaneous magnetic order on the
surface of a 3D topological insulator by applying a superlattice potential. The
superlattice potential creates tunable van Hove singularities, which, when
combined with strong spin-orbit coupling and Coulomb repulsion give rise to a
topological meron lattice spin texture. The periodicity of this designer meron
lattice can be tuned by varying the periodicity of the superlattice potential.
We characterize the magnetic order using Ginzburg-Landau theory and show that
the magnetic transition temperature reaches experimentally accessible values.
Our work introduces a new direction to realize exotic quantum order by
engineering interacting Dirac electrons in a superlattice potential, with
promising applications to spintronics.


title:
Ionization of HeII in star-forming galaxies by X-rays from cluster winds
  and superbubbles

abstract:
  The nature of the sources powering nebular HeII emission in star-forming
galaxies remains debated, and various types of objects have been considered,
including Wolf-Rayet stars, X-ray binaries, and Population III stars. Modern
X-ray observations show the ubiquitous presence of hot gas filling star-forming
galaxies. We use a collisional ionization plasma code to compute the specific
HeII ionizing flux produced by hot gas and show that if its temperature is not
too high (less than 2.5 MK), then the observed levels of soft diffuse X-ray
radiation could explain HeII ionization in galaxies. To gain a physical
understanding of this result, we propose a model that combines the
hydrodynamics of cluster winds and hot superbubbles with observed populations
of young massive clusters in galaxies. We find that in low-metallicity
galaxies, the temperature of hot gas is lower and the production rate of HeII
ionizing photons is higher compared to high-metallicity galaxies. The reason is
that the slower stellar winds of massive stars in lower-metallicity galaxies
input less mechanical energy in the ambient medium. Furthermore, we show that
ensembles of star clusters up to 10-20 Myr old in galaxies can produce enough
soft X-rays to induce nebular HeII emission. We discuss observations of the
template low-metallicity galaxy I Zw 18 and suggest that the HeII nebula in
this galaxy is powered by a hot superbubble. Finally, appreciating the complex
nature of stellar feedback, we suggest that soft X-rays from hot superbubbles
are among the dominant sources of HeII ionizing flux in low-metallicity
star-forming galaxies.


title: Data-Enhanced Variational Monte Carlo for Rydberg Atom Arrays

abstract:
  Rydberg atom arrays are programmable quantum simulators capable of preparing
interacting qubit systems in a variety of quantum states. Due to long
experimental preparation times, obtaining projective measurement data can be
relatively slow for large arrays, which poses a challenge for state
reconstruction methods such as tomography. Today, novel groundstate
wavefunction ans\"atze like recurrent neural networks (RNNs) can be efficiently
trained not only from projective measurement data, but also through
Hamiltonian-guided variational Monte Carlo (VMC). In this paper, we demonstrate
how pretraining modern RNNs on even small amounts of data significantly reduces
the convergence time for a subsequent variational optimization of the
wavefunction. This suggests that essentially any amount of measurements
obtained from a state prepared in an experimental quantum simulator could
provide significant value for neural-network-based VMC strategies.


title: Generalised entropy accumulation

abstract:
  Consider a sequential process in which each step outputs a system $A_i$ and
updates a side information register $E$. We prove that if this process
satisfies a natural "non-signalling" condition between past outputs and future
side information, the min-entropy of the outputs $A_1, \dots, A_n$ conditioned
on the side information $E$ at the end of the process can be bounded from below
by a sum of von Neumann entropies associated with the individual steps. This is
a generalisation of the entropy accumulation theorem (EAT), which deals with a
more restrictive model of side information: there, past side information cannot
be updated in subsequent rounds, and newly generated side information has to
satisfy a Markov condition. Due to its more general model of side-information,
our generalised EAT can be applied more easily and to a broader range of
cryptographic protocols. As examples, we give the first multi-round security
proof for blind randomness expansion and a simplified analysis of the E91 QKD
protocol. The proof of our generalised EAT relies on a new variant of Uhlmann's
theorem and new chain rules for the Renyi divergence and entropy, which might
be of independent interest.


title:
Universal, low temperature, $T$-linear resistivity in two-dimensional
  quantum-critical metals from spatially random interactions

abstract:
  We consider two-dimensional metals of fermions coupled to critical scalars,
the latter representing order parameters at zero crystal momentum or emergent
gauge fields. We show that at low temperatures ($T$), such metals generically
exhibit a $T$-linear resistivity arising from spatially random fluctuations in
the fermion-scalar Yukawa couplings about a non-zero spatial average. We also
find a $T\ln (1/T)$ specific heat, and a rationale for the Planckian bound on
the transport scattering time. These results are obtained in the large $N$
expansion of an ensemble of critical metals.


title:
Essential role of quantum speed limit in violation of Leggett-Garg
  inequality across a PT-transition

abstract:
  We study Leggett-Garg inequality (LGI) of a two level system (TLS) undergoing
non-Hermitian dynamics governed by a non-linear Bloch equation (derived in J.
Phys. A: Math. Theor. 54, 115301 (2021)) across a PT-transition. We present an
algebraic identification of the parameter space for the maximum violation of
LGI (in particular $K_{3}$). In the PT-symmetric regime the maximum allowed
value for $K_{3}$ is always found to be greater than the quantum bound
(L\"{u}ders bound) of $3/2$ but it does not reach the algebraic maximum of
$K_{3}=3$ in general. However, in the limit where PT-symmetry breaking
parameter approaches the exceptional point from the PT-symmetric side, $K_{3}$
is found to asymptotically approach its algebraic maximum of 3. In contrast,
the maximum value of $K_{3}$ always reaches its algebraic maximum in the
PT-broken phase $i.e.$ $K_{3}\rightarrow 3$. We find that (i) the speed of
evolution (SOE) must reach its maximum value (in the parameter space of initial
state and the time interval between successive measurements) to facilitate the
value of $K_{3} \rightarrow 3$, (ii) together with the constraint that its
minimum value must run into SOE equals to zero during the evolution of the
state. In fact we show that the minimum speed of evolution can serve as an
order parameter which is finite on the PT-symmetric side and identically zero
on the PT-broken side. Finally, we discuss a possible experimental realization
of this dynamics by quantum measurement followed by post-selection procedure in
a three level atom coupled to cavity mode undergoing a Lindbladian dynamics.


title: Effective drag in rotating, poorly conducting plasma turbulence

abstract:
  Despite the increasing sophistication of numerical models of hot Jupiter
atmospheres, the large time-scale separation required in simulating the wide
range in electrical conductivity between the dayside and nightside has made it
difficult to run fully consistent magnetohydrodynamic (MHD) models. This has
led to many studies that resort to drag parametrizations of MHD. In this study,
we revisit the question of the Lorentz force as an effective drag by running a
series of direct numerical simulations of a weakly rotating, poorly conducting
flow in the presence of a misaligned, strong background magnetic field. We find
that the drag parametrization fails once the time-scale associated with the
Lorentz force becomes shorter than the dynamical time-scale in the system,
beyond which the effective drag coefficient remains roughly constant, despite
orders-of-magnitude variation in the Lorentz (magnetic) time-scale. We offer an
improvement to the drag parametrization by considering the relevant asymptotic
limit of low conductivity and strong background magnetic field, known as the
quasi-static MHD approximation of the Lorentz force. This approximation removes
the fast time-scale associated with magnetic diffusion, but retains a more
complex version of the Lorentz force, which could be utilized in future
numerical models of hot Jupiter atmospheric circulation.


title:
Security of quantum key distribution from generalised entropy
  accumulation

abstract:
  The goal of quantum key distribution (QKD) is to establish a secure key
between two parties connected by an insecure quantum channel. To use a QKD
protocol in practice, one has to prove that it is secure against general
attacks: even if an adversary performs a complicated attack involving all of
the rounds of the protocol, they cannot gain useful information about the key.
A much simpler task is to prove security against collective attacks, where the
adversary is assumed to behave the same in each round. Using a recently
developed information-theoretic tool called generalised entropy accumulation,
we show that for a very broad class of QKD protocols, security against
collective attacks implies security against general attacks. Compared to
existing techniques such as the quantum de Finetti theorem or a previous
version of entropy accumulation, our result can be applied much more broadly
and easily: it does not require special assumptions on the protocol such as
symmetry or a Markov property between rounds, its bounds are independent of the
dimension of the underlying Hilbert space, and it can be applied to
prepare-and-measure protocols directly without switching to an
entanglement-based version.


title:
A Comparative Analysis To Deal With Missing Spectral Information Caused
  By RFI In Cosmological HI 21CM Observations

abstract:
  We investigate the effect of radio-frequency interference (RFI) excision in
estimating the cosmological \hi\ 21 cm power spectrum. Flagging of
RFI-contaminated channels results in a non-uniform sampling of the instrumental
bandpass response. Hence, the Fourier transformation (FT) of visibilities from
frequency to delay domain contaminates the higher foreground-free delay modes,
and separating the spectrally fluctuating \hi\ signal from spectrally smooth
foregrounds becomes challenging. We have done a comparative analysis between
two algorithms, one-dimensional CLEAN and Least Square Spectral Analysis
(LSSA), which have been used widely to solve this issue in the literature. We
test these algorithms using the simulated SKA-1 low observations in the
presence of different RFI flagging scenarios. We find that in the presence of
random flagging of data, both algorithms perform well and can mitigate the
foreground leakage issue. But, CLEAN fails to restrict the foreground leakage
in the presence of periodic and periodic plus broadband RFI flagging and gives
an extra bias to the estimated power spectrum. However, LSSA can restrict the
foreground leakage for these RFI flagging scenarios and gives an unbiased
estimate of the \hi\ 21 cm power spectrum. We have also applied these
algorithms to the upgraded GMRT observation and found that both CLEAN and LSSA
give consistent results in the presence of realistic random flagging scenarios
for this observed data set. This comparative analysis demonstrates the
effectiveness and robustness of these two algorithms in estimating the \hi\ 21
cm power spectrum from the data set affected by different RFI scenarios.


title: CUBES: A Parallel Synthesizer for SQL Using Examples

abstract:
  In recent years, more and more people see their work depend on data
manipulation tasks. However, many of these users do not have the background in
programming required to write complex programs, particularly SQL queries. One
way of helping these users is to automatically synthesize the SQL query given a
small set of examples provided by the user - a task known as Query Reverse
Engineering. In the last decade, a large plethora of program synthesizers for
SQL have been proposed, but none of the current tools take advantage of the
increased number of cores per processor. This paper proposes CUBES, a parallel
program synthesizer for the domain of SQL queries using input-output examples.
CUBES extends current sequential query synthesizers with new pruning techniques
and a divide-and-conquer approach, splitting the search space into smaller
independent sub-problems.
  Examples are an under-specification, and the synthesized query may not match
the user's intent. We improve the accuracy of CUBES by developing a
disambiguation procedure based on fuzzing that interacts with the user and
increases our confidence that the returned query matches the user intent.
  We perform an extensive evaluation on around 4000 SQL queries from different
domains. Experimental results show that our sequential version can solve more
instances than other state-of-the-art SQL synthesizers. Moreover, the parallel
approach can scale up to 16 processes with super-linear speedups for many hard
instances. Our disambiguation approach is critical to achieving an accuracy of
around 75%, significantly larger than other SQL synthesizers.


title:
All-optical switching on the nanometer scale excited and probed with
  femtosecond extreme ultraviolet pulses

abstract:
  Ultrafast control of magnetization on the nanometer length scale, in
particular all-optical switching, is key to putting ultrafast magnetism on the
path towards future technological application in data storage technology.
However, magnetization manipulation with light on this length scale is
challenging due to the wavelength limitations of optical radiation. Here, we
excite transient magnetic gratings in a GdFe alloy with a periodicity of 87 nm
by interference of two coherent femtosecond light pulses in the extreme
ultraviolet spectral range at the free electron laser facility FERMI. The
subsequent ultrafast evolution of the magnetization pattern is probed by
diffraction of a third, time-delayed pulse tuned to the Gd N-edge at a
wavelength of 8.3 nm. By examining the simultaneously recorded first and second
diffraction orders and by performing reference real-space measurements with a
wide-field magneto-optical microscope with femtosecond time resolution, we can
conclusively demonstrate the ultrafast emergence of all-optical switching on
the nanometer length scale.


title: High Frequency Waves in Chromospheric Spicules

abstract:
  Using high cadence observations from the Hydrogen-alpha Rapid Dynamics camera
imaging system on the Dunn Solar Telescope, we present an investigation of the
statistical properties of transverse oscillations in spicules captured above
the solar limb. At five equally separated atmospheric heights, spanning
approximately 4900-7500 km, we have detected a total of 15 959 individual wave
events, with a mean displacement amplitude of 151 +/- 124 km, a mean period of
54 +/- 45 s, and a mean projected velocity amplitude of 21 +/- 13 km s^-1. We
find that both the displacement and velocity amplitudes increase with height
above the solar limb, ranging from 132 +/- 111 km and 17.7 +/- 10.6 km s^-1 at
4900 km, and 168 +/- 125 km and 26.3 +/- 14.1 km s^-1 at 7500 km, respectively.
Following the examination of neighboring oscillations in time and space, we
find 45% of the waves to be upwardly propagating, 49% to be downwardly
propagating, and 6% to be standing, with mean absolute phase velocities for the
propagating waves on the order of 75-150 km s^-1. While the energy flux of the
waves propagating downwards does not appear to depend on height, we find the
energy flux of the upwardly propagating waves decreases with atmospheric height
at a rate of -13 200 +/- 6500 W m^-2 /Mm. As a result, this decrease in energy
flux as the waves propagate upwards may provide significant thermal input into
the local plasma.


title: Cooperative subwavelength molecular quantum emitter arrays

abstract:
  Dipole-coupled subwavelength quantum emitter arrays respond cooperatively to
external light fields as they may host collective delocalized excitations (a
form of excitons) with super- or subradiant character. Deeply subwavelength
separations typically occur in molecular ensembles, where in addition to
photon-electron interactions, electron-vibron couplings and vibrational
relaxation processes play an important role. We provide analytical and
numerical results on the modification of super- and subradiance in molecular
rings of dipoles including excitations of the vibrational degrees of freedom.
While vibrations are typically considered detrimental to coherent dynamics, we
show that molecular dimers or rings can be operated as platforms for the
preparation of long-lived dark superposition states aided by vibrational
relaxation. In closed ring configurations, we extend previous predictions for
the generation of coherent light from ideal quantum emitters to molecular
emitters, quantifying the role of vibronic coupling onto the output intensity
and coherence.


title: The Ca II H&K Rotation-Activity Relation in 53 mid-to-late type M-Dwarfs

abstract:
  In the canonical theory of stellar magnetic dynamo, the tachocline in
partially convective stars serves to arrange small-scale fields, generated by
stochastic movement of plasma into a coherent large-scale field. Mid-to-late
M-dwarfs, which are fully convective, show more magnetic activity than
classical magnetic dymano theory predicts. However, mid-to-late M-dwarfs show
tight correlations between rotation and magnetic activity, consistent with
elements of classical dynamo theory. We use data from Magellan Inamori Kyocera
Echelle (MIKE) Spectrograph to detail the relation between Ca II H\&K flux and
rotation period for these low-mass stars. We measure $R'_{HK}$ values for 53
spectroscopically identified M-dwarfs selected from the MEarth survey; these
stars span spectral classes from M5.0 to M3.5 and have rotation periods ranging
from hours to months. We present the rotation--activity relationship as traced
through these data. We find power law and saturated regimes consistent to
within one sigma of previously published results and observe a mass dependence
in $R'_{HK}$.


title: Notes on altermagnetism and superconductivity

abstract:
  A brief discussion of possible interplay between altermagnetism (a third type
of collinear magnetism, besides ferro- and antiferromagnetism), and
superconductivity.


title:
Dynamics of topological defects after a photo-induced melting of a
  charge-density wave

abstract:
  Charge-density-wave order in a solid can be temporarily "melted" by a strong
laser pulse. Here we use the discrete Gross-Pitaevskii equation on a cubic
lattice to simulate the recovery of the CDW long-range phase coherence
following such a pulse. Our simulations indicate that the recovery process is
dramatically slowed down by the three-dimensional topological defects - CDW
dislocations - created as a result of strongly nonequilibrium heating and
cooling of the system. Overall, the simulated CDW recovery was found to be
strongly reminiscent of a recent pump-probe experiment in LaTe$_3$.

categories: cond-mat.str-el cond-mat.mes-hall cond-mat.quant-gas cond-mat.supr-con quant-ph


title:
Ultrafast carrier-coupled interlayer contraction, coherent intralayer
  motions, and phonon thermalization dynamics of black phosphorus

abstract:
  Black phosphorus (BP) exhibits highly anisotropic properties and dynamical
behavior that are unique even among two-dimensional and van der Waals (vdW)
layered materials. Here, we show that an interlayer lattice contraction and
concerted, symmetric intralayer vibrations take place concurrently within few
picoseconds following the photoinjection and relaxation of carriers, using
ultrafast electron diffraction in the reflection geometry to probe the
out-of-plane motions. A strong coupling between the photocarriers and BP's
puckered structure, with the alignment of the electronic band structure, is at
work for such directional atomic motions without a photoinduced phase
transition. Three temporal regimes can be identified for the phonon
thermalization dynamics where a quasi-equilibrium without anisotropy is reached
in about 50 ps, followed by propagation of coherent acoustic phonons and heat
diffusion into the bulk. The early-time out-of-plane dynamics reported here
have important implications for single- and few-layer BP and other vdW
materials with strong electronic-lattice correlations.