title: MEDFORD: A human and machine readable metadata markup language

abstract:
  Reproducibility of research is essential for science. However, in the way
modern computational biology research is done, it is easy to lose track of
small, but extremely critical, details. Key details, such as the specific
version of a software used or iteration of a genome can easily be lost in the
shuffle, or perhaps not noted at all. Much work is being done on the database
and storage side of things, ensuring that there exists a space to store
experiment-specific details, but current mechanisms for recording details are
cumbersome for scientists to use. We propose a new metadata description
language, named MEDFORD, in which scientists can record all details relevant to
their research. Human-readable, easily-editable, and templatable, MEDFORD
serves as a collection point for all notes that a researcher could find
relevant to their research, be it for internal use or for future replication.
MEDFORD has been applied to coral research, documenting research from RNA-seq
analyses to photo collections.


title:
Optical Atomic Clock aboard an Earth-orbiting Space Station (OACESS):
  Enhancing searches for physics beyond the standard model in space

abstract:
  We present a concept for a high-precision optical atomic clock (OAC)
operating on an Earth-orbiting space station. This pathfinder science mission
will compare the space-based OAC with one or more ultra-stable terrestrial OACs
to search for space-time-dependent signatures of dark scalar fields that
manifest as anomalies in the relative frequencies of station-based and
ground-based clocks. This opens the possibility of probing models of new
physics that are inaccessible to purely ground-based OAC experiments such as
models where a dark scalar field is strongly screened near Earth's surface.

categories: physics.atom-ph gr-qc hep-ph physics.space-ph


title:
Comparison theorems for Lorentzian length spaces with lower timelike
  curvature bounds

abstract:
  In this article we introduce a notion of normalized angle for Lorentzian
pre-length spaces. This concept allows us to prove some equivalences to the
definition of timelike curvature bounds from below for Lorentzian pre-length
spaces. Specifically, we establish some comparison theorems known as the local
Lorentzian version of the Toponogov theorem and the Alexandrov convexity
property. Finally, as an application we obtain a first variation Formula for
non-negatively curved globally hyperbolic Lorentzian length spaces.


title:
The Dense Gas Mass Fraction and the Relationship to Star Formation in
  M51

abstract:
  Observations of 12CO J=1-0 and HCN J=1-0 emission from NGC 5194 (M51) made
with the 50~meter Large Millimeter Telescope and the SEQUOIA focal plane array
are presented. Using the HCN to CO ratio, we examine the dense gas mass
fraction over a range of environmental conditions within the galaxy. Within the
disk, the dense gas mass fraction varies along spiral arms but the average
value over all spiral arms is comparable to the mean value of interarm regions.
We suggest that the near constant dense gas mass fraction throughout the disk
arises from a population of density stratified, self gravitating molecular
clouds and the required density threshold to detect each spectral line. The
measured dense gas fraction significantly increases in the central bulge in
response to the effective pressure, P_e, from the weight from the stellar and
gas components. This pressure modifies the dynamical state of the molecular
cloud population and possibly, the HCN emitting regions, in the central bulge
from self-gravitating to diffuse configurations in which P_e is greater than
the gravitational energy density of individual clouds. Diffuse molecular clouds
comprise a significant fraction of the molecular gas mass in the central bulge,
which may account for the measured sublinear relationships between the surface
densities of the star formation rate and molecular and dense gas.


title:
Observation of $a_0(1710)^+ \to K_S^0K^+$ in study of the $D_s^+\to
  K_S^0K^+\pi^0$ decay

abstract:
  Using $e^+e^-$ annihilation data corresponding to an integrated luminosity of
6.32 fb$^{-1}$ collected at center-of-mass energies between 4.178 GeV and 4.226
GeV with the BESIII detector, we perform the first amplitude analysis of the
decay $D_s^+\to K_S^0K^+\pi^0$ and determine the relative branching fractions
and phases for intermediate processes. We observe the $a_0(1710)^+$, the
isovector partner of the $f_0(1710)$ and $f_0(1770)$ mesons, in its decay to
$K_S^0K^+$ for the first time. In addition, we measure the ratio
$\frac{\mathcal{B}(D_{s}^{+} \to
\bar{K}^{*}(892)^{0}K^{+})}{\mathcal{B}(D_{s}^{+} \to
\bar{K}^{0}K^{*}(892)^{+})}$ to be $2.35^{+0.42}_{-0.23\text{stat.}}\pm
0.10_{\rm syst.}$. Finally, we provide a precision measurement of the absolute
branching fraction $\mathcal{B}(D_s^+\to K_S^0K^+\pi^0) = (1.46\pm
0.06_{\text{stat.}}\pm 0.05_{\text{syst.}})\%$.


title:
Dissipative stabilization of linear input delay systems via dynamical
  state feedback controllers: an optimization based approach

abstract:
  In this note, we present an effective solution to stabilize linear input
delay systems subject to dissipative constraints while all the effect of input
delay is compensated by a novel controller. The method is inspired by the
recent development in the mathematical treatment of distributed delays and
predictor controllers, which are critical for the derivation of the solution.
An important conceptual innovation is the use of a parameterized dynamical
state feedback controller, where the dimension of the controller equals the
dimension of the control input. A sufficient condition for the existence of a
dissipative dynamical state feedback controller is obtained via the Krasovskii
functional approach, where the condition includes a bilinear matrix inequality
(BMI). To solve the BMI, we apply an inner convex approximation algorithm which
can be initialized based on an explicit construction of a predictor controller
gain. The dynamical state controller in this paper can be considered as an
extension of the classical predictor controller, thereby capable of
compensating all the effects of the pointwise input delay while satisfying
dissipative constraints. A numerical example is given to illustrate the
effectiveness of our proposed methodology.


title: Assembly Planning from Observations under Physical Constraints

abstract:
  This paper addresses the problem of copying an unknown assembly of primitives
with known shape and appearance using information extracted from a single
photograph by an off-the-shelf procedure for object detection and pose
estimation. The proposed algorithm uses a simple combination of physical
stability constraints, convex optimization and Monte Carlo tree search to plan
assemblies as sequences of pick-and-place operations represented by STRIPS
operators. It is efficient and, most importantly, robust to the errors in
object detection and pose estimation unavoidable in any real robotic system.
The proposed approach is demonstrated with thorough experiments on a UR5
manipulator.


title:
CALI: Coarse-to-Fine ALIgnments Based Unsupervised Domain Adaptation of
  Traversability Prediction for Deployable Autonomous Navigation

abstract:
  Traversability prediction is a fundamental perception capability for
autonomous navigation. The diversity of data in different domains imposes
significant gaps to the prediction performance of the perception model. In this
work, we make efforts to reduce the gaps by proposing a novel coarse-to-fine
unsupervised domain adaptation (UDA) model - CALI. Our aim is to transfer the
perception model with high data efficiency, eliminate the prohibitively
expensive data labeling, and improve the generalization capability during the
adaptation from easy-to-obtain source domains to various challenging target
domains. We prove that a combination of a coarse alignment and a fine alignment
can be beneficial to each other and further design a first-coarse-then-fine
alignment process. This proposed work bridges theoretical analyses and
algorithm designs, leading to an efficient UDA model with easy and stable
training. We show the advantages of our proposed model over multiple baselines
in several challenging domain adaptation setups. To further validate the
effectiveness of our model, we then combine our perception model with a visual
planner to build a navigation system and show the high reliability of our model
in complex natural environments where no labeled data is available.


title:
Robin-Dirichlet alternating iterative procedure for solving the Cauchy
  problem for Helmholtz equation in an unbounded domain

abstract:
  We consider the Cauchy problem for the Helmholtz equation with a domain in
R^d, d>2 with N cylindrical outlets to infinity with bounded inclusions in
R^{d-1}. Cauchy data are prescribed on the boundary of the bounded domains and
the aim is to find solution on the unbounded part of the boundary. In 1989,
Kozlov and Maz'ya proposed an alternating iterative method for solving Cauchy
problems associated with elliptic,self-adjoint and positive-definite operators
in bounded domains. Different variants of this method for solving Cauchy
problems associated with Helmholtz-type operators exists. We consider the
variant proposed by Mpinganzima et al. for bounded domains and derive the
necessary conditions for the convergence of the procedure in unbounded domains.
For the numerical implementation, a finite difference method is used to solve
the problem in a simple rectangular domain in R^2 that represent a truncated
infinite strip. The numerical results shows that by appropriate truncation of
the domain and with appropriate choice of the Robin parameters, the
Robin-Dirichlet alternating iterative procedure is convergent.


title:
Interaction-Enhanced Topological Hall Effects in Strained Twisted
  Bilayer Graphene

abstract:
  We analyze the effects of the long-range Coulomb interaction on the
distribution of Berry curvature among the bands near charge neutrality of
twisted bilayer graphene (TBG) closely aligned with hexagonal boron nitride
(hBN). Due to the suppressed dispersion of the narrow bands, the band structure
is strongly renormalized by electron-electron interactions, and thus, the
associated topological properties of the bands are sensitive to filling. Using
a Hartree formalism, we calculate the linear and nonlinear Hall conductivities,
and find that for certain fillings, the remote bands contribute substantially
to the Hall currents while the contribution from the central bands is
suppressed. In particular, we find that these currents are generically
substantial near regions of energies where the bands are highly entangled with
each other, often featuring doping-induced band inversions. Our results
demonstrate that topological transport in TBG/hBN is substantially modified by
electron-electron interactions, which offer a simple explanation to recent
experimental results.


title: Estimating city-wide hourly bicycle flow using a hybrid LSTM MDN

abstract:
  Cycling can reduce greenhouse gas emissions and air pollution and increase
public health. With this in mind, policy-makers in cities worldwide seek to
improve the bicycle mode-share. However, they often struggle against the fear
and the perceived riskiness of cycling. Efforts to increase the bicycle's
mode-share involve many measures, one of them being the improvement of cycling
safety. This requires the analysis of the factors surrounding accidents and the
outcome. However, meaningful analysis of cycling safety requires accurate
bicycle flow data that is generally sparse or not even available at a segment
level. Therefore, safety engineers often rely on aggregated variables or
calibration factors that fail to account for variations in the cycling traffic
caused by external factors. This paper fills this gap by presenting a Deep
Learning based approach, the Long Short-Term Memory Mixture Density Network
(LSTMMDN), to estimate hourly bicycle flow in Copenhagen, conditional on
weather, temporal and road conditions at the segment level. This method
addresses the shortcomings in the calibration factor method and results in
66-77\% more accurate bicycle traffic estimates. To quantify the impact of more
accurate bicycle traffic estimates in cycling safety analysis, we estimate
bicycle crash risk models to evaluate bicycle crashes in Copenhagen. The models
are identical except for the exposure variables being used. One model is
estimated using the LSTMMDN estimates, one using the calibration-based
estimates, and one using yearly mean traffic estimates. The results show that
investing in more advanced methods for obtaining bicycle volume estimates can
benefit the quality, mitigating efforts by improving safety analyses and other
performance measures.


title:
Extraction of Unaliased High-Frequency Micro-Doppler Signature using
  FMCW radar

abstract:
  Micro-Doppler signature is a potent feature that has been used for target
identification and micro-motion parameter estimation. The extraction of high
frequency micro-Doppler signature from frequency modulated continuous wave
(FMCW) radar along with the target range and velocity is the problem considered
in this article. The severe aliasing of the high micro-Doppler frequency spread
is circumvented by the fast time processing in the proposed method. The use of
range-Doppler (RD) filtering and empirical mode decomposition (EMD) enables
effective out-of-band and in-band noise suppression. Simulation studies and
experimental results present the effectiveness of the proposed approach.


title:
A Brief Guide to Designing and Evaluating Human-Centered Interactive
  Machine Learning

abstract:
  Interactive machine learning (IML) is a field of research that explores how
to leverage both human and computational abilities in decision making systems.
IML represents a collaboration between multiple complementary human and machine
intelligent systems working as a team, each with their own unique abilities and
limitations. This teamwork might mean that both systems take actions at the
same time, or in sequence. Two major open research questions in the field of
IML are: "How should we design systems that can learn to make better decisions
over time with human interaction?" and "How should we evaluate the design and
deployment of such systems?" A lack of appropriate consideration for the humans
involved can lead to problematic system behaviour, and issues of fairness,
accountability, and transparency. Thus, our goal with this work is to present a
human-centred guide to designing and evaluating IML systems while mitigating
risks. This guide is intended to be used by machine learning practitioners who
are responsible for the health, safety, and well-being of interacting humans.
An obligation of responsibility for public interaction means acting with
integrity, honesty, fairness, and abiding by applicable legal statutes. With
these values and principles in mind, we as a machine learning research
community can better achieve goals of augmenting human skills and abilities.
This practical guide therefore aims to support many of the responsible
decisions necessary throughout the iterative design, development, and
dissemination of IML systems.


title:
Preventing technical debt with the TAP framework for Technical Debt
  Aware Management

abstract:
  Context. Technical Debt (TD) is a metaphor for technical problems that are
not visible to users and customers but hinder developers in their work, making
future changes more difficult. TD is often incurred due to tight project
deadlines and can make future changes more costly or impossible. Project
Management usually focuses on customer benefits and pays less attention to
their IT systems' internal quality. TD prevention should be preferred over TD
repayment because subsequent refactoring and re-engineering are expensive.
Objective. This paper evaluates a framework focusing on both TD prevention and
TD repayment in the context of agile-managed projects. The framework was
developed and applied in an IT unit of a publishing house. The unique
contribution of this framework is the integration of TD management into project
management. Method. The evaluation was performed as a comparative case study
based on ticket statistics and two structured surveys. The surveys were
conducted in the observed IT unit using the framework and a comparison unit not
using the framework. The first survey targeted team members, the second one IT
managers. Results. The evaluation shows that in this IT unit, the TAP framework
led to a raised awareness for the incurrence of TD. Decisions to incur TD are
intentional, and TD is repaid timelier. Unintentional TD incurred by
unconscious decisions is prevented. Furthermore, better communication and
better planning of the project pipeline can be observed. Conclusions. We
provide an insight into practitioners' ways to identify, monitor, prevent and
repay TD. The presented framework includes a feasible method for TD prevention
despite tight timelines by making TD repayment part of project management.


title: An Efficient Wait-free Resizable Hash Table

abstract:
  This paper presents an efficient wait-free resizable hash table. To achieve
high throughput at large core counts, our algorithm is specifically designed to
retain the natural parallelism of concurrent hashing, while providing wait-free
resizing. An extensive evaluation of our hash table shows that in the common
case where resizing actions are rare, our implementation outperforms all
existing lock-free hash table implementations while providing a stronger
progress guarantee.


title:
Controlling mode orientations and frequencies in levitated cavity
  optomechanics

abstract:
  Cavity optomechanics offers quantum cooling, quantum control and measurement
of small mechanical oscillators. However the optical backactions that underpin
quantum control can significantly disturb the oscillator modes: mechanical
frequencies are shifted by the optical spring effect and light-matter
hybridisation in strong coupling regimes; mechanical modes hybridise with each
other via the cavity mode. This is even more pertinent in the field of
levitated optomechanics, where optical trapping fully determines the mechanical
modes and their frequencies. Here, using the coherent-scattering (CS) set-up
that allowed quantum ground state cooling of a levitated nanoparticle, we show
that -- when trapping away from a node of the cavity standing wave -- the CS
field opposes optical spring shifts and mechanical mode hybridisation. At an
optimal cancellation point, independent of most experimental parameters, we
demonstrate experimentally that it is possible to strongly cavity cool and
control the {\em unperturbed} modes. Suppression of the cavity-induced mode
hybridisation in the $x-y$ plane is quantified by measuring the
$S_{xy}(\omega)$ correlation spectra which are seen to always be
anti-correlated except at the cancellation point where they become
uncorrelated. The findings have implications for directional force sensing
using CS set-ups.


title:
The origin of UV/optical emission in the black hole low-mass X-ray
  binary Swift J1753.5-0127

abstract:
  The emission from the accreting black holes (BHs) in low-mass X-ray binaries
(LMXBs) covers a broad energy band from radio to X-rays. Studying the
correlations between emission in different energy bands during outbursts can
provide valuable information about the accretion process. We analyse the
simultaneous optical, ultraviolet (UV) and X-ray data of the BH-LMXB Swift
J1753.5-0127 during its $\sim$ 12-year long outburst with the {\it Neil Gehrels
Swift Observatory}. We find that the UV/optical and X-ray emission are strongly
correlated during the hard states of the outburst. We fit the relation with a
power-law function $F_{UV/optical} \propto F_{X}^{\beta}$ and find that the
power-law index $\beta$ increases from $\sim$ 0.24 to $\sim$ 0.33 as the
UV/optical wavelength decreases from $\sim$ 5400 \r{A} (V) to $\sim$ 2030 \r{A}
(UVW2). We explore the possible reasons for this and suggest that in Swift
J1753.5-0127 the UV/optical emission is dominated by a viscously heated
accretion disc at large radii. We find that the data that deviate from the
correlation correspond to the low-intensity peaks appeared in the X-ray band
during the outburst, and suggest that these deviations are driven by the
emission from the inner part of the accretion disc.


title: Infrared and Optical Detectability of Dyson Spheres at White Dwarf Stars

abstract:
  It has been hypothesized that advanced technological civilizations will
construct giant space colonies and supporting infrastructures to orbit about
their home stars. With data from recent satellites that operate at infrared and
optical wavelengths (Spitzer, WISE, TESS, Kepler), in company with a few modest
assumptions, it is now possible to begin to constrain observationally the
frequency of such space-based civilizations in our Milky Way Galaxy.


title:
Formation and Abundance of Late Forming Primordial Black Holes as Dark
  Matter

abstract:
  We propose a novel mechanism where Primordial Black Hole (PBH) dark matter is
formed much later in the history of the universe between the epoch of Big Bang
Nucleosynthesis (BBN) and Cosmic Microwave Background (CMB) photon decoupling.
In our setup, one does not need to modify the scale-invariant inflationary
power spectra; rather, a late phase transition in strongly interacting
fermion-scalar fluid (which naturally occurs around red-shift $ 10^6 \leq \,
z_{\scriptscriptstyle T} \, \leq 10^8$ ) creates an instability in the density
perturbation as sound speed turns imaginary. As a result, the dark matter
perturbation grows exponentially in sub-Compton scales. This follows the
immediate formation of early dense dark matter halo, which finally evolves into
PBH due to cooling through scalar radiation. We calculate the variance of the
density perturbations and PBH fractional abundances $f(M)$ by using a
non-monochromatic mass function. We find the peak of our PBH mass function lies
between $10^{-16} - 10^{-14}$ solar mass for $ z_{\scriptscriptstyle T} \simeq
10^6$, and thus it can be the entire dark matter of the universe. In PBH
formation, one would expect a temporary phase where an attractive scalar
balances the Fermi pressure. We numerically confirm that such a state indeed
exists, and we find the radius and density profile of the temporary static
structure of the dark matter halo, which finally evolves to PBH due to cooling
through scalar radiation.


title:
The shared universality of charged black holes and the many many-body
  SYK model

abstract:
  We investigate the charged $q/2$-body interacting Sachdev-Ye-Kitaev (SYK)
model in the grand-canonical ensemble. By treating $q$ as a large parameter, we
are able to analytically study its phase diagram. By varying the chemical
potential or temperature, we find that the system undergoes a phase transition
between low and high entropies, in the maximally chaotic regime. A similar
transition in entropy is seen in charged AdS black holes transitioning between
a large and small event horizon. Approaching zero temperature, we find a
first-order chaotic-to-integrable quantum phase transition, where the finite
extensive entropy drops to zero. This again has a gravitational analogue -- the
Hawking-Page (HP) transition between a large black hole and thermal radiation.
  An analytical study of the critical phenomena associated with the continuous
phase transition provides us with two sets of critical exponents. These sets
define two separate universality classes, both of which include several charged
AdS black hole phase-transitions. Together, these findings indicate a
connection between the charged $q\to\infty$ SYK model and black holes.


title: $L_p$-$L_q$-theory for a quasilinear non-isothermal Westervelt equation

abstract:
  We investigate a quasilinear system consisting of the Westervelt equation
from nonlinear acoustics and Pennes bioheat equation, subject to Dirichlet or
Neumann boundary conditions. The concept of maximal regularity of type
$L_p$-$L_q$ is applied to prove local and global well-posedness. Moreover, we
show by a parameter trick that the solutions regularize instantaneously.
Finally, we compute the equilibria of the system and investigate the long-time
behaviour of solutions starting close to equilibria.


title:
An Optimization algorithm for nonsmooth nonconvex problems with
  upper-C^2 objective

abstract:
  An optimization algorithm for nonsmooth nonconvex constrained optimization
problems with upper-C2 objective functions is proposed and analyzed. Upper-C2
is a weakly concave property that exists in difference of convex (DC) functions
and arises naturally in many applications, particularly certain classes of
solutions to parametric optimization problems [34, 4], e.g., recourse of
stochastic programming [36] and projection into closed sets [34]. The algorithm
can be viewed as a bundle method specialized for upper-C2 problems and is
globally convergent with bounded algorithm parameters. Compared to conventional
bundle methods, the proposed method is both simpler and computationally more
efficient. The algorithm handles general smooth constraints similarly to
sequential quadratic programming (SQP) methods and uses a line search to ensure
progress. The potential inconsistencies from the linearization of the
constraints are addressed through a penalty method. The capabilities of the
algorithm are demonstrated by solving both simple upper-C2 problems and
real-world optimal power flow problems used in current power grid industry
practices.


title:
Discontinuous Galerkin methods for stochastic Maxwell equations with
  multiplicative noise

abstract:
  In this paper we propose and analyze finite element discontinuous Galerkin
methods for the one- and two-dimensional stochastic Maxwell equations with
multiplicative noise. The discrete energy law of the semi-discrete DG methods
were studied. Optimal error estimate of the semi-discrete method is obtained
for the one-dimensional case, and the two-dimensional case on both rectangular
meshes and triangular meshes under certain mesh assumptions. Strong Taylor 2.0
scheme is used as the temporal discretization. Both one- and two-dimensional
numerical results are presented to validate the theoretical analysis results.


title:
SurvLatent ODE : A Neural ODE based time-to-event model with competing
  risks for longitudinal data improves cancer-associated Deep Vein Thrombosis
  (DVT) prediction

abstract:
  Effective learning from electronic health records (EHR) data for prediction
of clinical outcomes is often challenging because of features recorded at
irregular timesteps and loss to follow-up as well as competing events such as
death or disease progression. To that end, we propose a generative
time-to-event model, SurvLatent ODE, which adopts an Ordinary Differential
Equation-based Recurrent Neural Networks (ODE-RNN) as an encoder to effectively
parameterize a latent representation under irregularly sampled data. Our model
then utilizes the latent representation to flexibly estimate survival times for
multiple competing events without specifying shapes of event-specific hazard
function. We demonstrate competitive performance of our model on MIMIC-III, a
freely-available longitudinal dataset collected from critical care units, on
predicting hospital mortality as well as the data from the Dana-Farber Cancer
Institute (DFCI) on predicting onset of Deep Vein Thrombosis (DVT), a
life-threatening complication for patients with cancer, with death as a
competing event. SurvLatent ODE outperforms the current clinical standard
Khorana Risk scores for stratifying DVT risk groups.


title: Clotho-AQA: A Crowdsourced Dataset for Audio Question Answering

abstract:
  Audio question answering (AQA) is a multimodal translation task where a
system analyzes an audio signal and a natural language question, to generate a
desirable natural language answer. In this paper, we introduce Clotho-AQA, a
dataset for Audio question answering consisting of 1991 audio files each
between 15 to 30 seconds in duration selected from the Clotho dataset [1]. For
each audio file, we collect six different questions and corresponding answers
by crowdsourcing using Amazon Mechanical Turk. The questions and answers are
produced by different annotators. Out of the six questions for each audio, two
questions each are designed to have 'yes' and 'no' as answers, while the
remaining two questions have other single-word answers. For each question, we
collect answers from three different annotators. We also present two baseline
experiments to describe the usage of our dataset for the AQA task - an
LSTM-based multimodal binary classifier for 'yes' or 'no' type answers and an
LSTM-based multimodal multi-class classifier for 828 single-word answers. The
binary classifier achieved an accuracy of 62.7% and the multi-class classifier
achieved a top-1 accuracy of 54.2% and a top-5 accuracy of 93.7%. Clotho-AQA
dataset is freely available online at https://zenodo.org/record/6473207.


title: LIGHTYEAR: Using Modularity to Scale BGP Control Plane Verification

abstract:
  Current network control plane verification tools cannot scale to large
networks, because of the complexity of jointly reasoning about the behaviors of
all nodes in the network. In this paper we present a modular approach to
control plane verification, whereby end-to-end network properties are verified
via a set of purely local checks on individual nodes and edges. The approach
targets the verification of safety properties for BGP configurations and
provides guarantees in the face of both arbitrary external route announcements
from neighbors and arbitrary node/link failures. We have proven the approach
correct and also implemented it in a tool called Lightyear. Experimental
results show that Lightyear scales dramatically better than prior control plane
verifiers. Further, we have used Lightyear to verify three properties of the
wide area network of a major cloud provider, containing hundreds of routers and
tens of thousands of edges. To our knowledge no prior tool has been
demonstrated to provide such guarantees at that scale. Finally, in addition to
the scaling benefits, our modular approach to verification makes it easy to
localize the causes of configuration errors and to support incremental
re-verification as configurations are updated


title: Residual Mixture of Experts

abstract:
  Mixture of Experts (MoE) is able to scale up vision transformers effectively.
However, it requires prohibiting computation resources to train a large MoE
transformer. In this paper, we propose Residual Mixture of Experts (RMoE), an
efficient training pipeline for MoE vision transformers on downstream tasks,
such as segmentation and detection. RMoE achieves comparable results with the
upper-bound MoE training, while only introducing minor additional training cost
than the lower-bound non-MoE training pipelines. The efficiency is supported by
our key observation: the weights of an MoE transformer can be factored into an
input-independent core and an input-dependent residual. Compared with the
weight core, the weight residual can be efficiently trained with much less
computation resource, e.g., finetuning on the downstream data. We show that,
compared with the current MoE training pipeline, we get comparable results
while saving over 30% training cost. When compared with state-of-the-art non-
MoE transformers, such as Swin-T / CvT-13 / Swin-L, we get +1.1 / 0.9 / 1.0
mIoU gain on ADE20K segmentation and +1.4 / 1.6 / 0.6 AP gain on MS-COCO object
detection task with less than 3% additional training cost.


title: Electronic properties and phase transition in Kagome metal, Yb0.5Co3Ge3

abstract:
  The Kagome lattice is an important fundamental structure in condensed matter
physics for investigating the interplay of electron correlation, topology, and
frustrated magnetism. Recent work on Kagome metals in the AV3Sb5 (A = K, Rb,
Cs) family, has shown a multitude of correlation-driven distortions, including
symmetry breaking charge density waves and nematic superconductivity at low
temperatures. Here we study the new Kagome metal Yb0.5Co3Ge3 and find a
temperature-dependent kink in the resistivity that is highly similar to the
AV3Sb5 behavior and is commensurate with an in-plane structural distortion of
the Co Kagome lattice along with a doubling of the c-axis. The space group is
found to lower from P6/mmm to P63/m below the transition temperature, breaking
the in-plane mirror planes and C6 rotation, while gaining a screw axis along
the c-direction. At very low temperatures, anisotropic negative
magnetoresistance is observed, which may be related to anisotropic magnetism.
This raises questions about the types of the distortions in Kagome nets and
their resulting physical properties including superconductivity and magnetism.


title: Factors Hindering the Adoption of DevOps in the Saudi Software Industry

abstract:
  DevOps has gained high importance in the global software industry due to the
ease of software development, testing and deployment it provides. However, the
Saudi software industry has not been able to adopt DevOps at a great pace due
to various factors. This study, thus, aimed to examine different factors that
hindered the adoption of DevOps in the Saudi software industry. Also,
recommendations are provided at the end for Saudi Arabia to enhance the
adoption of DevOps in its software industry. To accomplish the aims, this study
used a literature review and interviews to gather data and examine it to
produce the findings. The findings of the study highlight lack of support from
organizational management and lack of laws as the major factors for the
adoption of DevOps in the Saudi software industry.


title: Planar graphs without cycles of length from 4 to 7 are near-bipartite

abstract:
  A graph is near-bipartite if its vertex set can be partitioned into an
independent set and a set which induces a forest. In this paper, planar graphs
without cycles of length from 4 to 7 are shown to be near-bipartite.


title:
An Interpretable Probabilistic Autoregressive Neural Network Model for
  Time Series Forecasting

abstract:
  Forecasting time series data presents an emerging field of data science that
has its application ranging from stock price and exchange rate prediction to
the early prediction of epidemics. Numerous statistical and machine learning
methods have been proposed in the last five decades with the demand for
generating high-quality and reliable forecasts. However, in real-life
prediction problems, situations exist in which a model based on one of the
above paradigms is preferable, and therefore, hybrid solutions are needed to
bridge the gap between classical forecasting methods and scalable neural
network models. We introduce an interpretable probabilistic autoregressive
neural network model for an explainable, scalable, and "white box-like"
framework that can handle a wide variety of irregular time series data (e.g.,
nonlinearity and nonstationarity). Sufficient conditions for asymptotic
stationarity and geometric ergodicity are obtained by considering the
asymptotic behavior of the associated Markov chain. During computational
experiments, PARNN outperforms standard statistical, machine learning, and deep
learning models on a diverse collection of real-world datasets coming from
economics, finance, and epidemiology, to mention a few. Furthermore, the
proposed PARNN model improves forecast accuracy significantly for 10 out of 12
datasets compared to state-of-the-art models for short to long-term forecasts.


title:
Softening molecular bonds through the giant optomechanical spring effect
  in plasmonic nanocavities

abstract:
  Molecular vibrations couple to visible light only weakly, have small mutual
interactions, and hence are often ignored for nonlinear optics. Here we show
the extreme confinement provided by plasmonic nanocavities can sufficiently
enhance optomechanical coupling so that intense laser illumination drastically
softens the molecular bonds. This optomechanical pumping regime produces strong
distortions of the Raman vibrational spectrum that are related to giant
vibrational frequency shifts from an 'optical spring effect' which is
hundred-fold larger than in traditional cavities, as predicted by an
optomechanical theory fully accounting for the multimodal nanocavity response
as well as for near-field-induced collective phonon interactions that hybridize
the vibrations of hundreds of identical molecules. The theoretical results are
consistent with the strongly non-linear behavior exhibited in the Raman spectra
of molecular monolayers placed in more than a thousand nanoparticle-on-a-mirror
constructs illuminated by microwatt ultrafast laser pulses. Driving this
collective phonon in the nanocavity paves the way to control reversible bond
softening, as well as irreversible chemistry. Optomechanical dressing thus
provides a new tool for molecular dynamics distinct from coherent control,
vibrational strong coupling, or vibrational heating.


title:
A label-state formulation of stochastic graphon games and approximate
  equilibria on large networks

abstract:
  This paper studies stochastic games on large graphs and their graphon limits.
We propose a new formulation of graphon games based on a single typical
player's label-state distribution. In contrast, other recently proposed models
of graphon games work directly with a continuum of players, which involves
serious measure-theoretic technicalities. In fact, by viewing the label as a
component of the state process, we show in our formulation that graphon games
are a special case of mean field games, albeit with certain inevitable
degeneracies and discontinuities that make most existing results on mean field
games inapplicable. Nonetheless, we prove existence of Markovian graphon
equilibria under fairly general assumptions, as well as uniqueness under a
monotonicity condition. Most imporantly, we show how our notion of graphon
equilibrium can be used to construct approximate equilibria for large finite
games set on any (weighted, directed) graph which converges in cut norm. The
lack of players' exchangeability necessitates a careful definition of
approximate equilibrium, allowing heterogeneity among the players'
approximation errors, and we show how various regularity properties of the
model inputs and underlying graphon lead naturally to different strengths of
approximation.


title: Carrollian and Non-relativistic Jackiw-Teitelboim Supergravity

abstract:
  We present non- and ultra-relativistic Jackiw-Teitelboim (JT) supergravity as
metric BF theories based on the extended Newton-Hooke and extended AdS Carroll
superalgebras in two spacetime dimensions, respectively. The extended
Newton-Hooke structure, and, in particular, the invariant metric necessary for
the BF construction of non-relativistic JT supergravity, is obtained by
performing an expansion of the $\mathcal{N}=2$ AdS$_2$ superalgebra.
Subsequently, we introduce the extended AdS$_2$ Carroll superalgebra, and the
associated invariant metric, as a suitable redefinition of the extended
Newton-Hooke superalgebra. The mapping involved can be seen as the
supersymmetric extension of the duality existing at the purely bosonic level
between the extended Newton-Hooke algebra with (positive) negative cosmological
constant and the extended (A)dS Carroll algebra in two dimensions. Finally, we
provide the Carrollian JT supergravity action in the BF formalism. Moreover, we
show that both the non-relativistic and the ultra-relativistic theories
presented can also be obtained by direct expansion of $\mathcal{N}=2$ JT
supergravity.


title: Inverse-designed dielectric cloaks for entanglement generation

abstract:
  We investigate the generation of entanglement between two quantum emitters
through the inverse-design engineering of their photonic environment. By means
of a topology-optimization approach acting at the level of the electromagnetic
Dyadic Green's function, we generate dielectric cloaks operating at different
inter-emitter distances and incoherent pumping strengths. We show that the
structures obtained maximize the dissipative coupling between the emitters
under extremely different Purcell factor conditions, and yield steady-state
concurrence values much larger than those attainable in free space. Finally, we
benchmark our design strategy by proving that the entanglement enabled by our
devices approaches the limit of maximum-entangled-mixed-states.


title:
Nonblockers for hereditarily decomposable continua with the property of
  Kelley

abstract:
  Given a continuum $X$, let $\mathcal{NB} (\mathcal{F}_1(X))$ be the
hyperspace of nonblockers of $\mathcal{F}_1(X)$. In this paper, we show that if
$X$ is hereditarily decomposable with the property of Kelley such that
$\mathcal{NB} (\mathcal{F}_1(X))$ is a continuum, then $X$ is a simple closed
curve. Thus, we characterize the simple closed curve as the unique hereditarily
decomposable continuum with the property of Kelley $X$ such that its hyperspace
$\mathcal{NB} (\mathcal{F}_1(X))$ is a continuum.


title: Collective modes of gluons in an anisotropic thermo-magnetic medium

abstract:
  We study the collective modes of gluon in an anisotropic thermal medium in
presence of a constant background magnetic field using the hard-thermal loop
(HTL) perturbation theory. The momentum space anisotropy of the medium has been
incorporated through the generalized $`$Romatschke-Strickland' form of the
distribution function, whereas, the magnetic modification arising from the
quark loop contribution has been taken into account in the lowest Landau level
approximation. We consider two special cases: (i) a spheroidal anisotropy with
the anisotropy vector orthogonal to the external magnetic field and (ii) an
ellipsoidal anisotropy with two mutually orthogonal vectors describing
aniostropies along and orthogonal to the field direction. The general structure
of the polarization tensor in both cases are equivalent and consists of six
independent basis tensors. We find that the introduction of momentum anisotropy
ingrains azimuthal angular dependence in the thermo-magnetic collective modes.
Our study suggests that the presence of a strong background magnetic field can
significantly reduce the growth rate of the unstable modes which may have
important implications in the equilibration of magnetized quark-gluon plasma.


title: Parametric Models for DOA Trajectory Localization

abstract:
  Directions of arrival (DOA) estimation or localization of sources is an
important problem in many applications for which numerous algorithms have been
proposed. Most localization methods use block-level processing that combines
multiple data snapshots to estimate DOA within a block. The DOAs are assumed to
be constant within the block duration. However, these assumptions are often
violated due to source motion. In this paper, we propose a signal model that
captures the linear variations in DOA within a block. We applied conventional
beamforming (CBF) algorithm to this model to estimate linear DOA trajectories.
Further, we formulate the proposed signal model as a block sparse model and
subsequently derive sparse Bayesian learning (SBL) algorithm. Our simulation
results show that this linear parametric DOA model and corresponding algorithms
capture the DOA trajectories for moving sources more accurately than
traditional signal models and methods.


title: One-Class Model for Fabric Defect Detection

abstract:
  An automated and accurate fabric defect inspection system is in high demand
as a replacement for slow, inconsistent, error-prone, and expensive human
operators in the textile industry. Previous efforts focused on certain types of
fabrics or defects, which is not an ideal solution. In this paper, we propose a
novel one-class model that is capable of detecting various defects on different
fabric types. Our model takes advantage of a well-designed Gabor filter bank to
analyze fabric texture. We then leverage an advanced deep learning algorithm,
autoencoder, to learn general feature representations from the outputs of the
Gabor filter bank. Lastly, we develop a nearest neighbor density estimator to
locate potential defects and draw them on the fabric images. We demonstrate the
effectiveness and robustness of the proposed model by testing it on various
types of fabrics such as plain, patterned, and rotated fabrics. Our model also
achieves a true positive rate (a.k.a recall) value of 0.895 with no false
alarms on our dataset based upon the Standard Fabric Defect Glossary.


title:
BliMe: Verifiably Secure Outsourced Computation with Hardware-Enforced
  Taint Tracking

abstract:
  We present Blinded Memory (BliMe), a way to realize efficient and secure
outsourced computation. BliMe consists of a novel and minimal set of ISA
extensions that uses taint tracking to ensure the confidentiality of sensitive
(client) data even in the presence of server malware, run-time attacks, and
side-channel attacks.
  To secure outsourced computation, the BliMe extensions can be used together
with an attestable, fixed-function trusted execution environment (TEE) and an
encryption engine that provides atomic decrypt-and-taint and
encrypt-and-untaint operations. The TEE engages in an attestation and key
agreement protocol with the client. It provides the resulting client-specific
keys to the encryption engine. Clients rely on remote attestation to ensure
that their data will always be protected by BliMe's taint tracking policy after
decryption.
  We provide a machine-checked security proof and an FPGA implementation
(BliMe-Ibex) of BliMe's taint tracking policy. We show that BliMe-Ibex does not
reduce performance relative to the unmodified core, and incurs only minor
increases in resource consumption in terms of power (${\approx}2.1\%$), LUTs
(${\approx}1.0\%$), and registers (${\approx}2.3\%$).


title:
Time-delayed neutrino emission from supernovae as a probe of dark
  matter-neutrino interactions

abstract:
  Thermal MeV neutrino emission from core-collapse supernovae offers a unique
opportunity to probe physics beyond the Standard Model in the neutrino sector.
The next generation of neutrino experiments, such as DUNE and Hyper-Kamiokande,
can detect $\mathcal{O}(10^3)$ and $\mathcal{O}(10^4)$ neutrinos in the event
of a Galactic supernova, respectively. As supernova neutrinos propagate to
Earth, they may interact with the local dark matter via hidden mediators and
may be delayed with respect to the initial neutrino signal. We show that for
sub-MeV dark matter, the presence of dark matter-neutrino interactions may lead
to neutrino echoes with significant time delays. The absence or presence of
this feature in the light curve of MeV neutrinos from a supernova allows us to
probe parameter space that has not been explored by dark matter direct
detection experiments.


title:
Quasi-infinite divisibility of a class of distributions with discrete
  part

abstract:
  We consider distributions on $\mathbb{R}$ that can be written as the sum of a
non-zero discrete distribution and an absolutely continuous distribution. We
show that such a distribution is quasi-infinitely divisible if and only if its
characteristic function is bounded away from zero, thus giving a new class of
quasi-infinitely divisible distributions. Moreover, for this class of
distributions we characterize the existence of the $g$-moment for certain
functions $g$.


title:
The TalkMoves Dataset: K-12 Mathematics Lesson Transcripts Annotated for
  Teacher and Student Discursive Moves

abstract:
  Transcripts of teaching episodes can be effective tools to understand
discourse patterns in classroom instruction. According to most educational
experts, sustained classroom discourse is a critical component of equitable,
engaging, and rich learning environments for students. This paper describes the
TalkMoves dataset, composed of 567 human-annotated K-12 mathematics lesson
transcripts (including entire lessons or portions of lessons) derived from
video recordings. The set of transcripts primarily includes in-person lessons
with whole-class discussions and/or small group work, as well as some online
lessons. All of the transcripts are human-transcribed, segmented by the speaker
(teacher or student), and annotated at the sentence level for ten discursive
moves based on accountable talk theory. In addition, the transcripts include
utterance-level information in the form of dialogue act labels based on the
Switchboard Dialog Act Corpus. The dataset can be used by educators,
policymakers, and researchers to understand the nature of teacher and student
discourse in K-12 math classrooms. Portions of this dataset have been used to
develop the TalkMoves application, which provides teachers with automated,
immediate, and actionable feedback about their mathematics instruction.


title:
On the Transferability of Pre-trained Language Models for Low-Resource
  Programming Languages

abstract:
  A recent study by Ahmed and Devanbu reported that using a corpus of code
written in multilingual datasets to fine-tune multilingual Pre-trained Language
Models (PLMs) achieves higher performance as opposed to using a corpus of code
written in just one programming language. However, no analysis was made with
respect to fine-tuning monolingual PLMs. Furthermore, some programming
languages are inherently different and code written in one language usually
cannot be interchanged with the others, i.e., Ruby and Java code possess very
different structure. To better understand how monolingual and multilingual PLMs
affect different programming languages, we investigate 1) the performance of
PLMs on Ruby for two popular Software Engineering tasks: Code Summarization and
Code Search, 2) the strategy (to select programming languages) that works well
on fine-tuning multilingual PLMs for Ruby, and 3) the performance of the
fine-tuned PLMs on Ruby given different code lengths.
  In this work, we analyze over a hundred of pre-trained and fine-tuned models.
Our results show that 1) multilingual PLMs have a lower Performance-to-Time
Ratio (the BLEU, METEOR, or MRR scores over the fine-tuning duration) as
compared to monolingual PLMs, 2) our proposed strategy to select target
programming languages to fine-tune multilingual PLMs is effective: it reduces
the time to fine-tune yet achieves higher performance in Code Summarization and
Code Search tasks, and 3) our proposed strategy consistently shows good
performance on different code lengths.


title:
LAMNER: Code Comment Generation Using Character Language Model and Named
  Entity Recognition

abstract:
  Code comment generation is the task of generating a high-level natural
language description for a given code method or function. Although researchers
have been studying multiple ways to generate code comments automatically,
previous work mainly considers representing a code token in its entirety
semantics form only (e.g., a language model is used to learn the semantics of a
code token), and additional code properties such as the tree structure of a
code are included as an auxiliary input to the model. There are two
limitations: 1) Learning the code token in its entirety form may not be able to
capture information succinctly in source code, and 2) The code token does not
contain additional syntactic information, inherently important in programming
languages.
  In this paper, we present LAnguage Model and Named Entity Recognition
(LAMNER), a code comment generator capable of encoding code constructs
effectively and capturing the structural property of a code token. A
character-level language model is used to learn the semantic representation to
encode a code token. For the structural property of a token, a Named Entity
Recognition model is trained to learn the different types of code tokens. These
representations are then fed into an encoder-decoder architecture to generate
code comments. We evaluate the generated comments from LAMNER and other
baselines on a popular Java dataset with four commonly used metrics. Our
results show that LAMNER is effective and improves over the best baseline model
in BLEU-1, BLEU-2, BLEU-3, BLEU-4, ROUGE-L, METEOR, and CIDEr by 14.34%,
18.98%, 21.55%, 23.00%, 10.52%, 1.44%, and 25.86%, respectively. Additionally,
we fused LAMNER's code representation with the baseline models, and the fused
models consistently showed improvement over the non-fused models. The human
evaluation further shows that LAMNER produces high-quality code comments.


title: Syntax-informed Question Answering with Heterogeneous Graph Transformer

abstract:
  Large neural language models are steadily contributing state-of-the-art
performance to question answering and other natural language and information
processing tasks. These models are expensive to train. We propose to evaluate
whether such pre-trained models can benefit from the addition of explicit
linguistics information without requiring retraining from scratch.
  We present a linguistics-informed question answering approach that extends
and fine-tunes a pre-trained transformer-based neural language model with
symbolic knowledge encoded with a heterogeneous graph transformer. We
illustrate the approach by the addition of syntactic information in the form of
dependency and constituency graphic structures connecting tokens and virtual
vertices.
  A comparative empirical performance evaluation with BERT as its baseline and
with Stanford Question Answering Dataset demonstrates the competitiveness of
the proposed approach. We argue, in conclusion and in the light of further
results of preliminary experiments, that the approach is extensible to further
linguistics information including semantics and pragmatics.


title: A Fast Post-Training Pruning Framework for Transformers

abstract:
  Pruning is an effective way to reduce the huge inference cost of large
Transformer models. However, prior work on model pruning requires retraining
the model. This can add high cost and complexity to model deployment, making it
difficult to use in many practical situations. To address this, we propose a
fast post-training pruning framework for Transformers that does not require any
retraining. Given a resource constraint and a sample dataset, our framework
automatically prunes the Transformer model using structured sparsity methods.
To retain high accuracy without retraining, we introduce three novel
techniques: (i) a lightweight mask search algorithm that finds which heads and
filters to prune based on the Fisher information; (ii) mask rearrangement that
complements the search algorithm; and (iii) mask tuning that reconstructs the
output activations for each layer. We apply our method to BERT-BASE and
DistilBERT, and we evaluate its effectiveness on GLUE and SQuAD benchmarks. Our
framework achieves up to 2.0x reduction in FLOPs and 1.56x speedup in inference
latency, while maintaining < 1% loss in accuracy. Importantly, our framework
prunes Transformers in less than 3 minutes on a single GPU, which is over two
orders of magnitude faster than existing pruning approaches that retrain. Our
code is publicly available.


title: The MIT Voice Name System

abstract:
  This RFC white Paper summarizes our progress on the MIT Voice Name System
(VNS) and Huey. The VNS, similar in name and function to the DNS, is a system
to reserve and use "wake words" to activate Artificial Intelligence (AI)
devices. Just like you can say "Hey Siri" to activate Apple's personal
assistant, we propose using the VNS in smart speakers and other devices to
route wake requests based on commands such as "turn off", "open grocery
shopping list" or "271, start flash card review of my computer vision class".
We also introduce Huey, an unambiguous Natural Language to interact with AI
devices. We aim to standardize voice interactions to a universal reach similar
to that of other systems such as phone numbering, with an agreed world-wide
approach to assign and use numbers, or the Internet's DNS, with a standard
naming system, that has helped flourish popular services including the
World-Wide-Web, FTP, and email. Just like these standards are "neutral", we
also aim to endow the VNS with "wake neutrality" so that each participant can
develop its own digital voice. We focus on voice as a starting point to talk to
any IoT object and explain briefly how the VNS may be expanded to other AI
technologies enabling person-to-machine conversations (really
machine-to-machine), including computer vision or neural interfaces. We also
describe briefly considerations for a broader set of standards, MIT Open AI
(MOA), including a reference architecture to serve as a starting point for the
development of a general conversational commerce infrastructure that has
standard "Wake Words", NLP commands such as "Shopping Lists" or "Flash Card
Reviews", and personalities such as Pi or 271. Privacy and security are key
elements considered because of speech-to-text errors and the amount of personal
information contained in a voice sample.


title: Generative Design Ideation: A Natural Language Generation Approach

abstract:
  This paper aims to explore a generative approach for knowledge-based design
ideation by applying the latest pre-trained language models in artificial
intelligence (AI). Specifically, a method of fine-tuning the generative
pre-trained transformer using the USPTO patent database is proposed. The
AI-generated ideas are not only in concise and understandable language but also
able to synthesize the target design with external knowledge sources with
controllable knowledge distance. The method is tested in a case study of
rolling toy design and the results show good performance in generating ideas of
varied novelty with near-field and far-field source knowledge.


title:
Design and Development of Rule-based open-domain Question-Answering
  System on SQuAD v2.0 Dataset

abstract:
  Human mind is the palace of curious questions that seek answers.
Computational resolution of this challenge is possible through Natural Language
Processing techniques. Statistical techniques like machine learning and deep
learning require a lot of data to train and despite that they fail to tap into
the nuances of language. Such systems usually perform best on close-domain
datasets. We have proposed development of a rule-based open-domain
question-answering system which is capable of answering questions of any domain
from a corresponding context passage. We have used 1000 questions from SQuAD
2.0 dataset for testing the developed system and it gives satisfactory results.
In this paper, we have described the structure of the developed system and have
analyzed the performance.


title: Medical Dataset Classification for Kurdish Short Text over Social Media

abstract:
  The Facebook application is used as a resource for collecting the comments of
this dataset, The dataset consists of 6756 comments to create a Medical Kurdish
Dataset (MKD). The samples are comments of users, which are gathered from
different posts of pages (Medical, News, Economy, Education, and Sport). Six
steps as a preprocessing technique are performed on the raw dataset to clean
and remove noise in the comments by replacing characters. The comments (short
text) are labeled for positive class (medical comment) and negative class
(non-medical comment) as text classification. The percentage ratio of the
negative class is 55% while the positive class is 45%.


title: Gravitational Waves from Cosmological Phase Transitions

abstract:
  In the present thesis, the author reviews the physics of cosmological
first-order phase transitions that may have occured shortly after the Big Bang.
Such transitions proceed via the nucleation and expansion of true vacuum
bubbles and give rise to a rich phenomenology, for instance the emission of a
stochastic gravitational-wave background caused by bubble collisions. The
author discusses, in depth, the formalism of the effective scalar potential and
its different contributions in the loop expansion, points out the necessary
ingredients for a first-order transition, and assesses the detectability of the
associated gravitational-wave spectrum via future space-based observatories and
pulsar timing arrays. He then applies the the developed phenomenological
toolbox to investigate the detection prospect for phase transitions in the
context of specific theories such as the Vev Flip-Flop (a dark matter
mechanism) and the Dark Photon Model.


title:
Stability threshold of the Couette flow for Navier-Stokes Boussinesq
  system with large Richardson number $\gamma^2>\frac{1}{4}$

abstract:
  In this paper, we study the nonlinear asymptotic stability of the Couette
flow in the stably stratified regime, namely the Richardson number
$\gamma^2>\frac{1}{4}$. Precisely, we prove that if the initial perturbation
$(u_{in},\vartheta_{in})$ of the Couette flow $v_s=(y,0)$ and the linear
temperature $\rho_s=-\gamma^2y+1$ satisfies
$\|u_{in}\|_{H^{s+1}}+\|\vartheta_{in}\|_{H^{s+2}}\leq
\epsilon_0\nu^{\frac{1}{2}}$, then the asymptotic stability holds.


title: Lagrangian multiforms on Lie groups and non-commuting flows

abstract:
  We describe a variational framework for non-commuting flows, extending the
theories of Lagrangian multiforms and pluri-Lagrangian systems, which have
gained prominence in recent years as a variational description of integrable
systems in the sense of multidimensional consistency. In the context of
non-commuting flows, the manifold of independent variables, often called
multi-time, is a Lie group whose bracket structure corresponds to the
commutation relations between the vector fields generating the flows. Natural
examples are provided by super-integrable systems for the case of Lagrangian
1-form structures, and integrable hierarchies on loop groups in the case of
Lagrangian 2-forms. As particular examples we discuss the Kepler problem, the
rational Calogero-Moser system, and a generalisation of the
Ablowitz-Kaup-Newell-Segur system with non-commuting flows. We view this
endeavour as a first step towards a purely variational approach to Lie group
actions on manifolds.


title:
Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs
  Locally Adaptive?

abstract:
  We study the theory of neural network (NN) from the lens of classical
nonparametric regression problems with a focus on NN's ability to adaptively
estimate functions with heterogeneous smoothness -- a property of functions in
Besov or Bounded Variation (BV) classes. Existing work on this problem requires
tuning the NN architecture based on the function spaces and sample sizes. We
consider a "Parallel NN" variant of deep ReLU networks and show that the
standard weight decay is equivalent to promoting the $\ell_p$-sparsity
($0<p<1$) of the coefficient vector of an end-to-end learned function bases,
i.e., a dictionary. Using this equivalence, we further establish that by tuning
only the weight decay, such Parallel NN achieves an estimation error
arbitrarily close to the minimax rates for both the Besov and BV classes.
Notably, it gets exponentially closer to minimax optimal as the NN gets deeper.
Our research sheds new lights on why depth matters and how NNs are more
powerful than kernel methods.


title: Demonstration of Superconducting Optoelectronic Single-Photon Synapses

abstract:
  Superconducting optoelectronic hardware is being explored as a path towards
artificial spiking neural networks with unprecedented scales of complexity and
computational ability. Such hardware combines integrated-photonic components
for few-photon, light-speed communication with superconducting circuits for
fast, energy-efficient computation. Monolithic integration of superconducting
and photonic devices is necessary for the scaling of this technology. In the
present work, superconducting-nanowire single-photon detectors are
monolithically integrated with Josephson junctions for the first time, enabling
the realization of superconducting optoelectronic synapses. We present circuits
that perform analog weighting and temporal leaky integration of single-photon
presynaptic signals. Synaptic weighting is implemented in the electronic domain
so that binary, single-photon communication can be maintained. Records of
recent synaptic activity are locally stored as current in superconducting
loops. Dendritic and neuronal nonlinearities are implemented with a second
stage of Josephson circuitry. The hardware presents great design flexibility,
with demonstrated synaptic time constants spanning four orders of magnitude
(hundreds of nanoseconds to milliseconds). The synapses are responsive to
presynaptic spike rates exceeding 10 MHz and consume approximately 33 aJ of
dynamic power per synapse event before accounting for cooling. In addition to
neuromorphic hardware, these circuits introduce new avenues towards realizing
large-scale single-photon-detector arrays for diverse imaging, sensing, and
quantum communication applications.

categories: physics.app-ph cond-mat.supr-con cs.AI cs.NE physics.ins-det


title: A random Hall-Paige conjecture

abstract:
  A complete mapping of a group $G$ is a bijection $\phi\colon G\to G$ such
that $x\mapsto x\phi(x)$ is also bijective. Hall and Paige conjectured in 1955
that a finite group $G$ has a complete mapping whenever $\prod_{x\in G} x$ is
the identity in the abelianization of $G$. This was confirmed in 2009 by
Wilcox, Evans, and Bray with a proof using the classification of finite simple
groups. In this paper, we give a combinatorial proof of a far-reaching
generalisation of the Hall-Paige conjecture for large groups. We show that for
random-like and equal-sized subsets $A,B,C$ of a group $G$, there exists a
bijection $\phi\colon A\to B$ such that $x\mapsto x\phi(x)$ is a bijection from
$A$ to $C$ whenever $\prod_{a\in A} a \prod_{b\in B} b=\prod_{c\in C} c$ in the
abelianization of $G$. Using this result, we settle the following conjectures
for sufficiently large groups. (1) We confirm in a strong form a conjecture of
Snevily by characterising large subsquares of multiplication tables of finite
groups that admit transversals. Previously, this characterisation was known
only for abelian groups of odd order. (2) We characterise the abelian groups
that can be partitioned into zero-sum sets of specific sizes, solving a problem
of Tannenbaum, and confirming a conjecture of Cichacz. (3) We characterise
harmonious groups, that is, groups with an ordering in which the product of
each consecutive pair of elements is distinct, solving a problem of Evans. (4)
We characterise the groups with which any path can be assigned a cordial
labelling. In the case of abelian groups, this confirms a conjecture of Patrias
and Pechenik.


title:
Sim-2-Sim Transfer for Vision-and-Language Navigation in Continuous
  Environments

abstract:
  Recent work in Vision-and-Language Navigation (VLN) has presented two
environmental paradigms with differing realism -- the standard VLN setting
built on topological environments where navigation is abstracted away, and the
VLN-CE setting where agents must navigate continuous 3D environments using
low-level actions. Despite sharing the high-level task and even the underlying
instruction-path data, performance on VLN-CE lags behind VLN significantly. In
this work, we explore this gap by transferring an agent from the abstract
environment of VLN to the continuous environment of VLN-CE. We find that this
sim-2-sim transfer is highly effective, improving over the prior state of the
art in VLN-CE by +12% success rate. While this demonstrates the potential for
this direction, the transfer does not fully retain the original performance of
the agent in the abstract setting. We present a sequence of experiments to
identify what differences result in performance degradation, providing clear
directions for further improvement.


title:
A double-peaked Lyman-$\alpha$ emitter with a strong blue peak multiply
  imaged by the galaxy cluster RXC J0018.5+1626

abstract:
  We report the discovery of a double-peaked Lyman-$\alpha$ (Ly$\alpha$)
emitter (LAE) at $z=3.2177\pm0.0001$ in VLT/MUSE data. The galaxy is strongly
lensed by the galaxy cluster RXC~J0018.5+1626 recently observed in the RELICS
survey, and the double-peaked Ly$\alpha$ emission is clearly detected in the
two counter images in the MUSE field-of-view. We measure a relatively high
Ly$\alpha$ rest-frame equivalent width (EW) of
$\mathrm{EW}_{\mathrm{Ly}\alpha,0}=(63\pm2)\,\mathring{\mathrm{A}}$. Additional
near-infrared (NIR) spectroscopy allows us to measure the H$\beta$,
[OIII]$\lambda4959\,\mathring{\mathrm{A}}$ and
[OIII]$\lambda5007\,\mathring{\mathrm{A}}$ emission lines, which show moderate
rest-frame EWs of the order of a few $\sim10-100\,\mathring{\mathrm{A}}$, an
[OIII]$\lambda5007\,\mathring{\mathrm{A}}$/H$\beta$ ratio of $4.8\pm0.7$, and a
lower limit on the [OIII]/[OII] ratio of $>5.6$. The galaxy has very blue
UV-continuum slopes of $\beta_{\mathrm{FUV}}=-2.23\pm0.06$ and
$\beta_{\mathrm{NUV}}=-3.0\pm0.2$, and is magnified by factors $\mu\sim7-10$ in
each of the two images, thus enabling a view into a low-mass
($M_{\star}\simeq10^{7.5}\,\mathrm{M}_{\odot}$) high-redshift galaxy analog.
Notably, the blue peak of the Ly$\alpha$ profile is significantly stronger than
the red peak, which suggests an inflow of matter and possibly very low HI
column densities in its circumgalactic gas. Combined with the high lensing
magnification and image multiplicity, these properties make this galaxy a prime
candidate for follow-up observations to search for LyC emission and constrain
the LyC photon escape fraction.


title: Interface effects on titanium growth on graphene

abstract:
  Poor quality interfaces between metal and graphene cause non-linearity and
impairs the carrier mobility in graphene devices. Here, we use aberration
corrected scanning transmission electron microscopy to observe hexagonally
close-packed Ti nano-islands grown on atomically clean graphene, and establish
a 30{\deg} epitaxial relationship between the lattices. Due to the strong
binding of Ti on graphene, at the limit of a monolayer, the Ti lattice constant
is mediated by the graphene epitaxy, and compared to bulk Ti, is strained by
ca. 3.7% to a value of 0.306(3) nm. The resulting interfacial strain is
slightly greater than what has been predicted by density functional theory
calculations. Our early growth stage investigations also reveal that, in
contrast to widespread assumptions, Ti does not fully wet graphene but grows
initially in clusters with a thickness of 1-2 layers. Raman spectroscopy
implies charge transfer between the Ti islands and graphene substrate.


title:
Characterizing and Mitigating Anti-patterns of Alerts in Industrial
  Cloud Systems

abstract:
  Alerts are crucial for requesting prompt human intervention upon cloud
anomalies. The quality of alerts significantly affects the cloud reliability
and the cloud provider's business revenue. In practice, we observe on-call
engineers being hindered from quickly locating and fixing faulty cloud services
because of the vast existence of misleading, non-informative, non-actionable
alerts. We call the ineffectiveness of alerts "anti-patterns of alerts". To
better understand the anti-patterns of alerts and provide actionable measures
to mitigate anti-patterns, in this paper, we conduct the first empirical study
on the practices of mitigating anti-patterns of alerts in an industrial cloud
system. We study the alert strategies and the alert processing procedure at
Huawei Cloud, a leading cloud provider. Our study combines the quantitative
analysis of millions of alerts in two years and a survey with eighteen
experienced engineers. As a result, we summarized four individual anti-patterns
and two collective anti-patterns of alerts. We also summarize four current
reactions to mitigate the anti-patterns of alerts, and the general preventative
guidelines for the configuration of alert strategy. Lastly, we propose to
explore the automatic evaluation of the Quality of Alerts (QoA), including the
indicativeness, precision, and handleability of alerts, as a future research
direction that assists in the automatic detection of alerts' anti-patterns. The
findings of our study are valuable for optimizing cloud monitoring systems and
improving the reliability of cloud services.


title:
Singlet-Doublet Fermion Origin of Dark Matter, Neutrino Mass and W-Mass
  Anomaly

abstract:
  Motivated by the recently reported anomaly in W boson mass by the CDF
collaboration with $7\sigma$ statistical significance, we consider a
singlet-doublet (SD) Majorana fermion dark matter (DM) model where the required
correction to W boson mass arises from radiative corrections induced by SD
fermions. While a single generation of SD fermions, odd under an unbroken $Z_2$
symmetry, can not explain the W boson mass anomaly while being consistent with
DM phenomenology, two generations of SD fermions can do so with the heavier
generation playing the dominant role in W-mass correction and lighter
generation playing the role in DM phenomenology. Additionally, such multiple
generations of SD fermions can also generate light neutrino masses radiatively
if a $Z_2$-odd singlet scalar is included.


title: TropeTwist: Trope-based Narrative Structure Generation

abstract:
  Games are complex, multi-faceted systems that share common elements and
underlying narratives, such as the conflict between a hero and a big bad enemy
or pursuing some goal that requires overcoming challenges. However, identifying
and describing these elements together is non-trivial as they might differ in
certain properties and how players might encounter the narratives. Likewise,
generating narratives also pose difficulties when encoding, interpreting,
analyzing, and evaluating them. To address this, we present TropeTwist, a
trope-based system that can describe narrative structures in games in a more
abstract and generic level, allowing the definition of games' narrative
structures and their generation using interconnected tropes, called narrative
graphs. To demonstrate the system, we represent the narrative structure of
three different games. We use MAP-Elites to generate and evaluate novel
quality-diverse narrative graphs encoded as graph grammars, using these three
hand-made narrative structures as targets. Both hand-made and generated
narrative graphs are evaluated based on their coherence and interestingness,
which are improved through evolution.


title: Toward a minimal information reporting standard about new cell types

abstract:
  The classification of cell diversity using ontologies is a core step for
managing biomedical data. While technical tools to represent knowledge about
cell types are available, there is still a gap between classes mentioned in
biomedical literature and what has been cataloged by the Cell Ontology. Here we
introduce the Minimal Information Reporting About a CelL (MIRACL) standard to
bridge the gap between natural language reports and formal ontologies. The
standard currently outlines four free-text fields (label, diagnostic
description, reference, and additional information) and three formal fields
(scientific taxon, UBERON anatomical structure, and Cell Ontology superclass).
The standard is open to contributors.


title:
Measurement of the vertical atmospheric density profile from the X-ray
  Earth occultation of the Crab Nebula with Insight-HXMT

abstract:
  In this paper, the X-ray Earth occultation (XEO) of the Crab Nebula is
investigated by using the Hard X-ray Modulation Telescope (Insight-HXMT). The
pointing observation data on the 30th September, 2018 recorded by the Low
Energy X-ray telescope (LE) of Insight-HXMT are selected and analyzed. The
extinction lightcurves and spectra during the X-ray Earth occultation process
are extracted. A forward model for the XEO lightcurve is established and the
theoretical observational signal for lightcurve is predicted. The atmospheric
density model is built with a scale factor to the commonly used MSIS density
profile within a certain altitude range. A Bayesian data analysis method is
developed for the XEO lightcurve modeling and the atmospheric density
retrieval. The posterior probability distribution of the model parameters is
derived through the Markov Chain Monte Carlo (MCMC) algorithm with the
NRLMSISE-00 model and the NRLMSIS 2.0 model as basis functions and the best-fit
density profiles are retrieved respectively. It is found that in the altitude
range of 105--200 km, the retrieved density profile is 88.8% of the density of
NRLMSISE-00 and 109.7% of the density of NRLMSIS 2.0 by fitting the lightcurve
in the energy range of 1.0--2.5 keV based on XEOS method. In the altitude range
of 95--125 km, the retrieved density profile is 81.0% of the density of
NRLMSISE-00 and 92.3% of the density of NRLMSIS 2.0 by fitting the lightcurve
in the energy range of 2.5--6.0 keV based on XEOS method. In the altitude range
of 85--110 km, the retrieved density profile is 87.7% of the density of
NRLMSISE-00 and 101.4% of the density of NRLMSIS 2.0 by fitting the lightcurve
in the energy range of 6.0--10.0 keV based on XEOS method. This study
demonstrates that the XEOS from the X-ray astronomical satellite Insight-HXMT
can provide an approach for the study of the upper atmosphere.


title:
Optimize_Prime@DravidianLangTech-ACL2022: Abusive Comment Detection in
  Tamil

abstract:
  This paper tries to address the problem of abusive comment detection in
low-resource indic languages. Abusive comments are statements that are
offensive to a person or a group of people. These comments are targeted toward
individuals belonging to specific ethnicities, genders, caste, race, sexuality,
etc. Abusive Comment Detection is a significant problem, especially with the
recent rise in social media users. This paper presents the approach used by our
team - Optimize_Prime, in the ACL 2022 shared task "Abusive Comment Detection
in Tamil." This task detects and classifies YouTube comments in Tamil and
Tamil- English Codemixed format into multiple categories. We have used three
methods to optimize our results: Ensemble models, Recurrent Neural Networks,
and Transformers. In the Tamil data, MuRIL and XLM-RoBERTA were our best
performing models with a macro-averaged f1 score of 0.43. Furthermore, for the
Code-mixed data, MuRIL and M-BERT provided sub-lime results, with a
macro-averaged f1 score of 0.45.


title:
Spatially-Preserving Flattening for Location-Aware Classification of
  Findings in Chest X-Rays

abstract:
  Chest X-rays have become the focus of vigorous deep learning research in
recent years due to the availability of large labeled datasets. While
classification of anomalous findings is now possible, ensuring that they are
correctly localized still remains challenging, as this requires recognition of
anomalies within anatomical regions. Existing deep learning networks for
fine-grained anomaly classification learn location-specific findings using
architectures where the location and spatial contiguity information is lost
during the flattening step before classification. In this paper, we present a
new spatially preserving deep learning network that preserves location and
shape information through auto-encoding of feature maps during flattening. The
feature maps, auto-encoder and classifier are then trained in an end-to-end
fashion to enable location aware classification of findings in chest X-rays.
Results are shown on a large multi-hospital chest X-ray dataset indicating a
significant improvement in the quality of finding classification over
state-of-the-art methods.


title: Astronomy with Chaucer: using an astrolabe to determine planetary orbits

abstract:
  Armed with an astrolabe and Kepler's laws one can arrive at accurate
estimates of the orbits of planets.


title:
Anisotropic Bianchi Type V Bulk Viscous Universe with Variable G and
  Lambda

abstract:
  The present study deals with spatially homogeneous and totally anisotropic
Bianchi type V cosmological model in presence of bulk viscous fluid source with
time dependent gravitational constant G and cosmological term Lambda. The
coefficient of bulk viscosity zeta is considered to be a quadratic function of
Hubble parameter H (i.e. zeta=zeta_0+zeta_1H+zeta_2H^2, where zeta_0, zeta_1
and zeta_2 are constants). Exact solution of Einstein's field equations are
obtained by using a particular form of Hubble parameter H i.e. H(a)=m(a^-n +1),
where m>0, n>1, a being a scale factor and H=(da/dt)/a which provides an early
deceleration and late time acceleration of the universe. From state finder
diagnosis {r, s} the behavior of the universe in different stages during the
evolution is exhibits.


title:
FS-NCSR: Increasing Diversity of the Super-Resolution Space via
  Frequency Separation and Noise-Conditioned Normalizing Flow

abstract:
  Super-resolution suffers from an innate ill-posed problem that a single
low-resolution (LR) image can be from multiple high-resolution (HR) images.
Recent studies on the flow-based algorithm solve this ill-posedness by learning
the super-resolution space and predicting diverse HR outputs. Unfortunately,
the diversity of the super-resolution outputs is still unsatisfactory, and the
outputs from the flow-based model usually suffer from undesired artifacts which
causes low-quality outputs. In this paper, we propose FS-NCSR which produces
diverse and high-quality super-resolution outputs using frequency separation
and noise conditioning compared to the existing flow-based approaches. As the
sharpness and high-quality detail of the image rely on its high-frequency
information, FS-NCSR only estimates the high-frequency information of the
high-resolution outputs without redundant low-frequency components. Through
this, FS-NCSR significantly improves the diversity score without significant
image quality degradation compared to the NCSR, the winner of the previous
NTIRE 2021 challenge.


title:
Emergent simulation of cell-like shapes satisfying the conditions of
  life using lattice-type multiset chemical model

abstract:
  It is one of the great challenges of science to clarify when, where, why, and
how the first life arose, and what the first life was. Here, assumed the
conditions of life are 1) bounded, 2) replicating, 3) able to inherit
information, and 4) metabolizing energy. The various hypotheses also provide
little explanation of how the four conditions for life were established. It is
not always clear how a chemical process that simultaneously satisfies all four
conditions emerged after the materials for life were in place. This study
considered a model (Multi-set chemical lattice model) that allows virtual
molecules of multiple types to be placed in each cell on a two-dimensional
space. Using only the processes of molecular diffusion, reaction, and
polymerization, by modeling chemical reactions by 15 kinds of molecules and two
kinds of polymerized molecules, and using the morphogenesis rule of the Turing
model, it was able to model and show the process of the emergence of cell-like
form having the four conditions of life. This model will allow us to revisit
and refine each of the hypotheses of the birth of life in the future.


title:
Far-ultraviolet investigation into the galactic globular cluster M30
  (NGC 7099): II. Potential X-ray counterparts and variable sources

abstract:
  We present a far-ultraviolet (FUV) study of the globular cluster M30 (NGC
7099). The images were obtained using the Advanced Camera for Surveys (ACS/SBC,
F150LP, FUV) and the Wide Field Planetary Camera 2 (WFPC2, F300W, UV) on board
the Hubble Space Telescope (HST). We compare the catalogue of FUV objects to
ten known X-ray sources and find six confident matches of two cataclysmic
variables (CVs), one RS CVn, one red giant with strong FUV emission and two
sources only detected in the FUV. We also searched for variable sources in our
dataset and found a total of seven blue stragglers (BSs), four horizontal
branch (HB) stars, five red giant branch stars, 28 main sequence stars and four
gap objects that demonstrated variability. One BS star is a known W-UMa contact
binary, one of the gap objects is a known CV identified in this work to be a
dwarf nova, and the three other gap sources are weak variables. The periods and
positions of two of the variable HB stars match them to two previously known RR
Lyrae variables of types RRab and RRc.


title: Dirac pairings, one-form symmetries and Seiberg-Witten geometries

abstract:
  The Coulomb phase of a quantum field theory, when present, illuminates the
analysis of its line operators and one-form symmetries. For 4d $\mathcal{N}=2$
field theories the low energy physics of this phase is encoded in the special
K\"ahler geometry of the moduli space of Coulomb vacua. We clarify how the
information on the allowed line operator charges and one-form symmetries is
encoded in the special K\"ahler structure. We point out the important
difference between the lattice of charged states and the homology lattice of
the abelian variety fibered over the moduli space, which, when principally
polarized, is naturally identified with a choice of the lattice of mutually
local line operators. This observation illuminates how the distinct S-duality
orbits of global forms of $\mathcal{N}=4$ theories are encoded geometrically.


title:
3D Hydrodynamical Simulations of Helium-Ignited Double-degenerate White
  Dwarf Mergers

abstract:
  The origins of type Ia supernovae (SNe Ia) are still debated. Some of the
leading scenarios involve a double detonation in double white dwarf (WD)
systems. In these scenarios, helium shell detonation occurs on top of a
carbon-oxygen (CO) WD, which then drives the detonation of the CO-core,
producing a SN Ia. Extensive studies have been done on the possibility of a
double helium detonation, following a dynamical helium mass-transfer phase onto
a CO-WD. However, 3D self-consistent modeling of the double-WD system, the mass
transfer, and the helium shell detonation have been little studied. Here we use
3D hydrodynamical simulations to explore this case in which a helium detonation
occurs near the point of Roche lobe overflow of the donor WD and may lead to an
SN Ia through the dynamically driven double-degenerate double-detonation (D6)
mechanism. We find that the helium layer of the accreting primary WD does
undergo a detonation, while the underlying carbon-oxygen core does not, leading
to an extremely rapid and faint nova-like transient instead of a luminous SN Ia
event. This failed core detonation suggests that D6 SNe Ia may be restricted to
the most massive carbon-oxygen primary WDs. We highlight the nucleosynthesis of
the long-lived radioisotope $^{44}$Ti during explosive helium burning, which
may serve as a hallmark both of successful as well as failed D6 events which
subsequently detonate as classical double-degenerate mergers.


title:
Partons as unique ground states of quantum Hall parent Hamiltonians: The
  case of Fibonacci anyons

abstract:
  We present microscopic, multiple Landau level, (frustration-free and positive
semi-definite) parent Hamiltonians whose ground states, realizing different
quantum Hall fluids, are parton-like and whose excitations display either
Abelian or non-Abelian braiding statistics. We prove ground state energy
monotonicity theorems for systems with different particle numbers, demonstrate
S-duality in the case of toroidal geometry and establish an exact zero-energy
mode counting. The emergent Entangled Pauli Principle, introduced in Phys. Rev.
B 98, 161118(R) (2018) and which defines the "DNA" of the quantum Hall fluid,
is behind the exact determination of the topological characteristics of the
fluid, including charge and braiding statistics of excitations, and effective
edge theory descriptions. When the closed-shell condition is satisfied, the
densest (i.e., the highest density and lowest total angular momentum)
zero-energy mode is a unique parton state. As a corollary, it follows that the
Moore-Read Pfaffian and Read-Rezayi states (both of which may be expressed as
linear combinations of parton-like states) cannot be densest ground states of
two-body parent Hamiltonians. We conjecture, based on the algebra of
polynomials in holomormorphic and anti-holomorphic complex variables, that
parton-like states span the subspace of many-body wave functions with the
two-body $M$-clustering property, that is, wave functions with $M$th-order
coincidence plane zeroes. We illustrate our framework by presenting a parent
Hamiltonian whose excitations are rigorously proven to be Fibonacci anyons and
show how to extract the DNA of the fluid whose entanglement pattern manifests
in the form of a matrix product state.

categories: cond-mat.str-el cond-mat.mes-hall math-ph math.MP physics.comp-ph quant-ph


title: Higher spin JT gravity and a matrix model dual

abstract:
  We propose a generalization of the Saad-Shenker-Stanford duality relating
matrix models and JT gravity to the case in which the bulk includes higher spin
fields. Using a $\textsf{PSL}(N,\mathbb{R})$ BF theory we compute the disk and
generalization of the trumpet partition function in this theory. We then study
higher genus corrections and show how this differs from the usual JT gravity
calculations. In particular, the usual quotient by the mapping class group is
not enough to ensure finite answers and so we propose to extend this group with
additional elements that make the gluing integrals finite. These elements can
be thought of as large higher spin diffeomorphisms. The cylinder contribution
to the spectral form factor then behaves as $T^{N-1}$ at late times $T$,
signaling a deviation from conventional random matrix theory. To account for
this deviation, we propose that the bulk theory is dual to a matrix model
consisting of $N-1$ commuting matrices associated to the $N-1$ conserved higher
spin charges. We find further evidence for the existence of the additional
mapping class group elements by interpreting the bulk gauge theory
geometrically and employing the formalism developed by Gomis et al. in the
nineties. This formalism introduces additional (auxiliary) boundary times so
that each conserved charge generates translations in those new directions. This
allows us to find an explicit description for the $\textsf{PSL}(3,\mathbb{R})$
Schwarzian theory for the disk and trumpet and view the additional mapping
class group elements as ordinary Dehn twists, but in higher dimensions.


title: Magnon drag in a metal-antiferromagnet bilayer

abstract:
  We study a bilayer structure consisting of an antiferromagnetic insulator and
a normal metal. An electron current is driven in the normal metal with
direction parallel to the interface between the materials. Due to interfacial
exchange coupling between the localized spins in the antiferromagnet and the
itinerant electrons in the normal metal, a magnon current can then be induced
in the antiferromagnet. Using an uncompensated antiferromagnetic interface,
creating an asymmetry in the interfacial coupling to the two degenerate magnon
modes, we find that it is possible to generate a magnon spin-current. The
magnon spin-current can be enhanced by increasing the temperature or by
spin-splitting the magnon modes.


title:
3D hydrodynamic simulations for the formation of the Local Group
  satellite planes

abstract:
  The existence of mutually correlated thin and rotating planes of satellite
galaxies around both the Milky Way (MW) and Andromeda (M31) calls for an
explanation. Previous work in Milgromian dynamics (MOND) indicated that a past
MW-M31 encounter might have led to the formation of these satellite planes. We
perform the first-ever hydrodynamical MOND simulation of the Local Group using
Phantom of RAMSES. We show that an MW-M31 encounter at $z \approx 1$, with a
perigalactic distance of about 80 kpc, can yield two disc galaxies at $z=0$
oriented similarly to the observed galactic discs and separated similarly to
the observed M31 distance. Importantly, the tidal debris are distributed in
phase space similarly to the observed MW and M31 satellite planes, with the
correct preferred orbital pole for both. The MW-M31 orbital geometry is
consistent with the presently observed M31 proper motion despite this not being
considered as a constraint when exploring the parameter space. The mass of the
tidal debris around the MW and M31 at $z=0$ compare well with the mass observed
in their satellite systems. The remnant discs of the two galaxies have
realistic radial scale lengths and velocity dispersions, and the simulation
naturally produces a much hotter stellar disc in M31 than in the MW. However,
reconciling this scenario with the ages of stellar populations in satellite
galaxies would require that a higher fraction of stars previously formed in the
outskirts of the progenitors ended up within the tidal debris, or that the
MW-M31 interaction occurred at $z>1$.


title: Mean field theory of failed thermalizing avalanches

abstract:
  We show that localization in quasiperiodically modulated, two-dimensional
systems is stable to the presence of a finite density of ergodic grains. This
contrasts with the case of randomly modulated systems, where such grains seed
thermalizing avalanches. These results are obtained within a quantitatively
accurate, self-consistent entanglement mean field theory which analytically
describes two level systems connected to a central ergodic grain. The theory
predicts the distribution of entanglement entropies of each two level system
across eigenstates, and the late time values of dynamical observables. In
addition to recovering the known phenomenology of avalanches, the theory
reproduces exact diagonalization data, and predicts the spatial profile of the
thermalized region when the avalanche fails.


title:
Multiqubit State Learning with Entangling Quantum Generative Adversarial
  Networks

abstract:
  The increasing success of classical generative adversarial networks (GANs)
has inspired several quantum versions of GANs. Fully quantum mechanical
applications of such quantum GANs have been limited to one- and two-qubit
systems. In this paper, we investigate the entangling quantum GAN (EQ-GAN) for
multiqubit learning. We show that the EQ-GAN can learn a circuit more
efficiently compared to a swap test. We also consider the EQ-GAN for learning
VQE-approximated eigenstates, and find that it generates excellent overlap
matrix elements when learning VQE states of small molecules. However, this does
not directly translate to a good estimate of the energy due to a lack of phase
estimation. Finally, we consider random state learning with the EQ-GAN for up
to six qubits, using different two-qubit gates, and show that it is capable of
learning completely random quantum states, something which could be useful in
quantum state loading.


title: Bubbles of nothing in binary black holes and black rings, and viceversa

abstract:
  We argue that expanding bubbles of nothing are a widespread feature of
systems of black holes with multiple or non-spherical horizons, appearing as a
limit of regions that are narrowly enclosed by the horizons. The bubble is a
minimal cycle that links the Einstein-Rosen bridges in the system, and its
expansion occurs through the familiar stretching of space in black hole
interiors. We demonstrate this idea (which does not involve any Wick rotations)
with explicit constructions in four and five dimensions. The geometries of
expanding bubbles in these dimensions arise as a limit of, respectively, static
black hole binaries and black rings. The limit is such that the separation
between the two black holes, or the inner hole of the black ring, becomes very
small, and the horizons of the black holes correspond to acceleration horizons
of the bubbles. We also explain how a five-dimensional black hole binary gives
rise to a different type of expanding bubble. We then show that bubble
spacetimes can host black hole binaries and black rings in static equilibrium,
with their gravitational attraction being balanced against the background
spacetime expansion. Similar constructions are expected in six or more
dimensions, but most of these solutions can be obtained only numerically.
Finally, we argue that the Nariai solution can be regarded as containing an
expanding circular bubble of nothing.


title: Twisted bilayer U(1) Dirac spin liquids

abstract:
  When two layers of two-dimensional materials are assembled with a relative
twist, moir\'e patterns arise, inducing a tremendous wealth of exotic
phenomena. In this work, we consider twisting two triangular lattices hosting
Dirac quantum spin liquids. Using recent results for the monopole scaling
dimensions and quantum numbers of the Dirac spin liquid state, we show that, in
the bilayer system, interlayer monopole tunneling is a symmetry-allowed
relevant perturbation and can lead to ordered bilayer states. We analyze the
effect of this interlayer tunneling using a mean field theory in both the
weak-coupling and strong-coupling regimes. In particular, when the spin
monopoles can tunnel, we find a tunable lattice of magnetic vortices at strong
coupling.


title:
The Age of Discovery with the James Webb: Excavating the Spectral
  Signatures of the First Massive Black Holes

abstract:
  The James Webb Space Telescope (JWST) will open a new window of the most
distant universe and unveil the early growth of supermassive black holes (BHs)
in the first galaxies. In preparation for deep JWST imaging surveys, it is
crucial to understand the color selection of high-redshift accreting seed BHs.
We model the spectral energy distribution of super-Eddington accreting BHs with
millions of solar masses in metal-poor galaxies at $z\gtrsim 8$, applying
post-process line transfer calculations to radiation hydrodynamical simulation
results. Ten kilosecond exposures with the NIRCam and MIRI broad-band filters
are sufficient to detect the radiation flux from the seed BHs with bolometric
luminosities of $L_{\rm bol}\simeq 10^{45}~{\rm erg~s}^{-1}$. While the
continuum colors are similar to those of typical low-$z$ quasars, strong
H$\alpha$ line emission with a rest-frame equivalent width ${\rm EW}_{\rm
rest}\simeq 1300~\r{A}$ is so prominent that the line flux affects the
broad-band colors significantly. The unique colors, for instance F356W$-$F560W
$\gtrsim 1$ at $7<z<8$ and F444W$-$F770W $\gtrsim 1$ at $9<z<12$, provide
robust criteria for photometric selection of the rapidly growing seed BHs.
Moreover, NIRSpec observations of low-ionization emission lines can test
whether the BH is fed via a dense accretion disk at super-Eddington rates.