title: Toward Improving Attentive Neural Networks in Legal Text Processing

abstract:
  In recent years, thanks to breakthroughs in neural network techniques
especially attentive deep learning models, natural language processing has made
many impressive achievements. However, automated legal word processing is still
a difficult branch of natural language processing. Legal sentences are often
long and contain complicated legal terminologies. Hence, models that work well
on general documents still face challenges in dealing with legal documents. We
have verified the existence of this problem with our experiments in this work.
In this dissertation, we selectively present the main achievements in improving
attentive neural networks in automatic legal document processing. Language
models tend to grow larger and larger, though, without expert knowledge, these
models can still fail in domain adaptation, especially for specialized fields
like law.


title:
Reconstructing Missing EHRs Using Time-Aware Within- and Cross-Visit
  Information for Septic Shock Early Prediction

abstract:
  Real-world Electronic Health Records (EHRs) are often plagued by a high rate
of missing data. In our EHRs, for example, the missing rates can be as high as
90% for some features, with an average missing rate of around 70% across all
features. We propose a Time-Aware Dual-Cross-Visit missing value imputation
method, named TA-DualCV, which spontaneously leverages multivariate
dependencies across features and longitudinal dependencies both within- and
cross-visit to maximize the information extracted from limited observable
records in EHRs. Specifically, TA-DualCV captures the latent structure of
missing patterns across measurements of different features and it also
considers the time continuity and capture the latent temporal missing patterns
based on both time-steps and irregular time-intervals. TA-DualCV is evaluated
using three large real-world EHRs on two types of tasks: an unsupervised
imputation task by varying mask rates up to 90% and a supervised 24-hour early
prediction of septic shock using Long Short-Term Memory (LSTM). Our results
show that TA-DualCV performs significantly better than all of the existing
state-of-the-art imputation baselines, such as DETROIT and TAME, on both types
of tasks.


title: Absorption Line Search Through Three Local Group Dwarf Galaxy Halos

abstract:
  Dwarf galaxies are missing nearly all of their baryons and metals from the
stellar disk, presumed to be in a bound halo or expelled beyond the virial
radius. The virial temperature for galaxies with $M_{\rm h} \sim 10^9 -
10^{10}$ $M_{\odot}$ is similar to the collisional ionization equilibrium
temperature for the C IV ion. We searched for UV absorption from C IV in six
sightlines toward three dwarf galaxies in the anti-M31 direction and at the
periphery of the Local Group ($D \approx$ 1.3 Mpc; Sextans A, Sextans B, and
NGC 3109). The C IV doublet is detected in only one of six sightlines, toward
Sextans A, with $\log N({\rm C IV})$ = $13.06 \pm 0.08$. This is consistent
with our gaseous halo models, where the halo gas mass is determined by the
cooling rate, feedback, and the star formation rate; the inclusion of
photoionization is an essential ingredient. This model can also reproduce the
higher detection rate of O VI absorption in other dwarf samples (beyond the
Local Group), and with C IV only detectable within $\sim 0.5R_{\rm vir}$.


title:
Vacuum Einstein field equations in smooth metric measure spaces: the
  isotropic case

abstract:
  On a smooth metric measure spacetime $(M,g,e^{-f} dvol_g)$, we define a
weighted Einstein tensor. It is given in terms of the Bakry-\'Emery Ricci
tensor as a tensor which is symmetric, divergence-free, concomitant of the
metric and the density function. We consider the associated vacuum weighted
Einstein field equations and show that isotropic solutions have nilpotent Ricci
operator. Moreover, the underlying manifold is a Brinkmann wave if it is
$2$-step nilpotent and a Kundt spacetime if it is $3$-step nilpotent. More
specific results are obtained in dimension $3$, where all isotropic solutions
are given in local coordinates as plane waves or Kundt spacetimes.


title:
Non-Linear Reinforcement Learning in Large Action Spaces: Structural
  Conditions and Sample-efficiency of Posterior Sampling

abstract:
  Provably sample-efficient Reinforcement Learning (RL) with rich observations
and function approximation has witnessed tremendous recent progress,
particularly when the underlying function approximators are linear. In this
linear regime, computationally and statistically efficient methods exist where
the potentially infinite state and action spaces can be captured through a
known feature embedding, with the sample complexity scaling with the
(intrinsic) dimension of these features. When the action space is finite,
significantly more sophisticated results allow non-linear function
approximation under appropriate structural constraints on the underlying RL
problem, permitting for instance, the learning of good features instead of
assuming access to them. In this work, we present the first result for
non-linear function approximation which holds for general action spaces under a
linear embeddability condition, which generalizes all linear and finite action
settings. We design a novel optimistic posterior sampling strategy, TS^3 for
such problems, and show worst case sample complexity guarantees that scale with
a rank parameter of the RL problem, the linear embedding dimension introduced
in this work and standard measures of the function class complexity.


title: Quantum curves, resurgence and exact WKB

abstract:
  We study the non-perturbative quantum geometry of the open and closed
topological string on the resolved conifold and its mirror. Our tools are
finite difference equations in the open and closed string moduli and the
resurgence analysis of their formal power series solutions. In the closed
setting, we derive new finite difference equations for the refined partition
function as well as its Nekrasov-Shatashvili (NS) limit. We write down a
distinguished analytic solution for the refined difference equation that
reproduces the expected non-perturbative content of the refined topological
string. We compare this solution to the Borel analysis of the free energy in
the NS limit. We find that the singularities of the Borel transform lie on
infinitely many rays in the Borel plane and that the Stokes jumps across these
rays encode the associated Donaldson-Thomas invariants of the underlying
Calabi-Yau geometry. In the open setting, the finite difference equation
corresponds to a canonical quantization of the mirror curve. We analyze this
difference equation using Borel analysis and exact WKB techniques and identify
the 5d BPS states in the corresponding exponential spectral networks. We
furthermore relate the resurgence analysis in the open and closed setting. This
guides us to a five-dimensional extension of the Nekrasov-Rosly-Shatashvili
proposal, in which the NS free energy is computed as a generating function of
q-difference opers in terms of a special set of spectral coordinates. Finally,
we examine two spectral problems describing the corresponding quantum
integrable system.


title:
Nematic single-component superconductivity and loop-current order from
  pair-density wave instability

abstract:
  We investigate the nematic and loop-current type orders that may arise as
vestigial precursor phases in a model with an underlying pair-density wave
(PDW) instability. We discuss how such a vestigial phase gives rise to a highly
anisotropic stiffness for a coexisting single-component superconductor with low
intrinsic stiffness, as is the case for the underdoped cuprate superconductors.
Next, focusing on a regime with a mean-field PDW ground state with loop-current
and nematic $xy$ (B$_{2g}$) order, we find a preemptive transition into a low
and high-temperature vestigial phase with loop-current and nematic order
corresponding to $xy$ (B$_{2g}$) and $x^2-y^2$ (B$_{1g}$) symmetry
respectively. Near the transition between the two phases, a state of soft
nematic order emerges for which we expect that the nematic director is readily
pinned away from the high-symmetry directions in the presence of an external
field. Results are discussed in relation to findings in the cuprates,
especially to the recently inferred highly anisotropic superconducting
fluctuations W{\aa}rdh et al.[1], giving additional evidence for an underlying
ubiquitous PDW instability in these materials.


title:
Flash: Fast and Light Motion Prediction for Autonomous Driving with
  Bayesian Inverse Planning and Learned Motion Profiles

abstract:
  Motion prediction of road users in traffic scenes is critical for autonomous
driving systems that must take safe and robust decisions in complex dynamic
environments. We present a novel motion prediction system for autonomous
driving. Our system is based on the Bayesian inverse planning framework, which
efficiently orchestrates map-based goal extraction, a classical control-based
trajectory generator and an ensemble of light-weight neural networks
specialised in motion profile prediction. In contrast to many alternative
methods, this modularity helps isolate performance factors and better interpret
results, without compromising performance. This system addresses multiple
aspects of interest, namely multi-modality, motion profile uncertainty and
trajectory physical feasibility. We report on several experiments with the
popular highway dataset NGSIM, demonstrating state-of-the-art performance in
terms of trajectory error. We also perform a detailed analysis of our system's
components, along with experiments that stratify the data based on behaviours,
such as change lane versus follow lane, to provide insights into the challenges
in this domain. Finally, we present a qualitative analysis to show other
benefits of our approach, such as the ability to interpret the outputs.


title:
Wind energy forecasting with missing values within a fully conditional
  specification framework

abstract:
  Wind power forecasting is essential to power system operation and electricity
markets. As abundant data became available thanks to the deployment of
measurement infrastructures and the democratization of meteorological
modelling, extensive data-driven approaches have been developed within both
point and probabilistic forecasting frameworks. These models usually assume
that the dataset at hand is complete and overlook missing value issues that
often occur in practice. In contrast to that common approach, we rigorously
consider here the wind power forecasting problem in the presence of missing
values, by jointly accommodating imputation and forecasting tasks. Our approach
allows inferring the joint distribution of input features and target variables
at the model estimation stage based on incomplete observations only. We place
emphasis on a fully conditional specification method owing to its desirable
properties, e.g., being assumption-free when it comes to these joint
distributions. Then, at the operational forecasting stage, with available
features at hand, one can issue forecasts by implicitly imputing all missing
entries. The approach is applicable to both point and probabilistic
forecasting, while yielding competitive forecast quality within both simulation
and real-world case studies. It confirms that by using a powerful universal
imputation method like fully conditional specification, the proposed approach
is superior to the common approach, especially in the context of probabilistic
forecasting.


title: Integrated System Models for Networks with Generators & Inverters

abstract:
  Synchronous generators and inverter-based resources are complex systems with
dynamics that cut across multiple intertwined physical domains and control
loops. Modeling individual generators and inverters is, in itself, a very
involved activity and has attracted dedicated attention from power engineers
and control theorists over the years. Control and stability challenges
associated with increasing penetration of grid-following inverters have
generated tremendous interest in grid-forming inverter technology. The
envisioned coexistence of inverter technologies alongside rotating machines
call for modeling frameworks that can accurately describe networked dynamics of
interconnected generators and inverters across timescales. We put forth a
comprehensive integrated system model for such a setting by: i) adopting a
combination of circuit- and system-theoretic constructs, ii) unifying
representations of three-phase signals across reference-frame transformations
and phasor types, and iii) leveraging domain-level knowledge, engineering
insights, and reasonable approximations. A running theme through our effort is
to offer a clear distinction between physics-based models and the task of
modeling. Among several insights spanning the spectrum from analytical to
practical, we highlight how differential-algebraic-equation models and
algebraic power-flow phasor models fall out of the detailed originating
electromagnetic transient models.


title:
The resistance of quantum entanglement to temperature in the
  Kugel--Khomskii model

abstract:
  The Kugel--Khomskii model with entangled spin and orbital degrees of freedom
is a good testing ground for many important features in quantum information
processing, such as robust gaps in the entanglement spectra. Here, we
demonstrate that the entanglement can be also robust under effect of
temperature within a wide range of parameters. It is shown, in particular, that
the temperature dependence of entanglement often exhibits a nonmonotonic
behavior. Namely, there turn out to be ranges of the model parameters, where
entanglement is absent at zero temperature, but then, with an increase in
temperature, it appears, passes through a maximum, and again vanishes.


title:
A 3D acoustic propagation model for shallow waters based on an indirect
  Boundary Element Method

abstract:
  The purpose of this work is twofold: (a) To present the theoretical
formulation of a 3D acoustic propagation model based on a Boundary Element
Method (BEM), which uses a half-space Green function in place of the more
conventional free-space Green function and (b) to show a numerical
implementation aimed to explore the formulation in simple idealized cases
--controlled by a few parameters--, and provides necessary tests for the
accuracy and performance of the model. The half-space Green's function, which
has been used previously in scattering and diffraction, adds terms to the usual
expressions of the integral operators without altering their continuity
properties. Verifications against the Pekeris waveguide suggest that the model
allows an adequately prediction for the acoustic field. Likewise, numerical
explorations in relation to the necessary mesh size for the description of the
water-sediment interface allow us to conclude that a TL prediction with
acceptable accuracy can be obtained with the use of a bounded mesh around the
desired evaluation region.


title: Distributed Design for Causal Inferences on Big Observational Data

abstract:
  A fundamental issue in causal inference for Big Observational Data is
confounding due to covariate imbalances between treatment groups. This can be
addressed by designing the data prior to analysis. Existing design methods,
developed for traditional observational studies with single designers, can
yield unsatisfactory designs with suboptimum covariate balance for Big
Observational Data due to their inability to accommodate the massive
dimensionality, heterogeneity, and volume of the Big Data. We propose a new
framework for the distributed design of Big Observational Data amongst
collaborative designers. Our framework first assigns subsets of the
high-dimensional and heterogeneous covariates to multiple designers. The
designers then summarize their covariates into lower-dimensional quantities,
share their summaries with the others, and design the study in parallel based
on their assigned covariates and the summaries they receive. The final design
is selected by comparing balance measures for all covariates across the
candidates. We perform simulation studies and analyze datasets from the 2016
Atlantic Causal Inference Conference Data Challenge to demonstrate the
flexibility and power of our framework for constructing designs with good
covariate balance from Big Observational Data.


title:
Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report
  Summarization

abstract:
  The IMPRESSIONS section of a radiology report about an imaging study is a
summary of the radiologist's reasoning and conclusions, and it also aids the
referring physician in confirming or excluding certain diagnoses. A cascade of
tasks are required to automatically generate an abstractive summary of the
typical information-rich radiology report. These tasks include acquisition of
salient content from the report and generation of a concise, easily consumable
IMPRESSIONS section. Prior research on radiology report summarization has
focused on single-step end-to-end models -- which subsume the task of salient
content acquisition. To fully explore the cascade structure and explainability
of radiology report summarization, we introduce two innovations. First, we
design a two-step approach: extractive summarization followed by abstractive
summarization. Second, we additionally break down the extractive part into two
independent tasks: extraction of salient (1) sentences and (2) keywords.
Experiments on English radiology reports from two clinical sites show our novel
approach leads to a more precise summary compared to single-step and to
two-step-with-single-extractive-process baselines with an overall improvement
in F1 score Of 3-4%.


title:
Role of self-generated magnetic fields in the inertial fusion ignition
  threshold

abstract:
  Magnetic fields spontaneously grow at unstable interfaces around hot-spot
asymmetries during inertial confinement fusion implosions. Although difficult
to measure, theoretical considerations and numerical simulations predict field
strengths exceeding 5kT in current national ignition facility experiments.
Magnetic confinement of electrons then reduces the hot-spot heat loss by >5%.
We demonstrate this via magnetic post-processing of two-dimensional xRAGE
hydrodynamic simulation data at bang time. We then derive a model for the
self-magnetization, finding that it varies with the square of the stagnation
temperature and inversely with the areal density. The self-magnetized Lawson
analysis then gives a slightly reduced ignition threshold. Time dependent
hot-spot energy balance models corroborate this finding, with the magnetic
field quadrupling the fusion yield for near threshold parameters. The inclusion
of magnetized multi-dimensional fluid instabilities could further alter the
ignition threshold, and will be the subject of future work.


title: Better Quality Estimation for Low Resource Corpus Mining

abstract:
  Quality Estimation (QE) models have the potential to change how we evaluate
and maybe even train machine translation models. However, these models still
lack the robustness to achieve general adoption. We show that State-of-the-art
QE models, when tested in a Parallel Corpus Mining (PCM) setting, perform
unexpectedly bad due to a lack of robustness to out-of-domain examples. We
propose a combination of multitask training, data augmentation and contrastive
learning to achieve better and more robust QE performance. We show that our
method improves QE performance significantly in the MLQE challenge and the
robustness of QE models when tested in the Parallel Corpus Mining setup. We
increase the accuracy in PCM by more than 0.80, making it on par with
state-of-the-art PCM methods that use millions of sentence pairs to train their
models. In comparison, we use a thousand times less data, 7K parallel sentences
in total, and propose a novel low resource PCM method.


title: SIR model with social gatherings

abstract:
  We introduce an extension to Kermack and McKendrick's classic
susceptible-infected-recovered (SIR) model in epidemiology, whose underlying
mechanism of infection consists of individuals attending randomly generated
social gatherings. This gives rise to a system of ODEs where the force of
infection term depends non-linearly on the proportion of infected individuals.
Some specific instances yield models already studied in the literature, to
which the present work provides a probabilistic foundation. The basic
reproduction number is seen to depend quadratically on the average size of the
gatherings, which may be helpful to understand how restrictions on social
gatherings affect the spread of the disease. We rigorously justify our model by
showing that the system of ODEs is the mean-field limit of the jump Markov
process corresponding to the evolution of the disease in a finite population.


title: Synthetic Hachimoji DNA Sequencing with Graphene Nanodevice

abstract:
  Based on combined density functional theory and nonequilibrium Greens
function quantum transport studies, we have demonstrated quantum interference
effects on the transverse conductance of Hachimoji synthetic nucleic acids
placed between the oxygen-terminated zigzag graphene nanoribbon nanoelectrodes.
We theorize that the QI effect could be well preserved in pi-pi coupling
between a nucleobase molecule and the carbon-based nanoelectrode. Our study
indicates that QI effects such as anti-resonance or Fano-resonance that affect
the variation of transverse conductance depending on the nucleobase
conformation. Further, a variation of up to 2-5 orders of magnitude is observed
in the conductance upon rotation for all the nucleobases. The current-voltage
characteristics results suggest a distinct variation in the electronic
tunnelling current across the proposed nanogap device for all the five
nucleobases with the applied bias voltage. The different rotation angles keep
the distinct feature of the nucleobases in both transverse conductance and
tunnelling current features. Both features could be utilized in an accurate
synthetic DNA sequencing device.

categories: cond-mat.mes-hall cond-mat.mtrl-sci physics.bio-ph physics.chem-ph


title: Fisher Forecasts for Primordial non-Gaussianity from Persistent Homology

abstract:
  We study the information content of summary statistics built from the
multi-scale topology of large-scale structures on primordial non-Gaussianity of
the local and equilateral type. We use halo catalogs generated from numerical
N-body simulations of the Universe on large scales as a proxy for observed
galaxies. Besides calculating the Fisher matrix for halos in real space, we
also check more realistic scenarios in redshift space. Without needing to take
a distant observer approximation, we place the observer on a corner of the box.
We also add redshift errors mimicking spectroscopic and photometric samples. We
perform several tests to assess the reliability of our Fisher matrix, including
the Gaussianity of our summary statistics and convergence. We find that the
marginalized 1-$\sigma$ uncertainties in redshift space are $\Delta f_{\rm
NL}^{\rm loc} \sim 16$ and $\Delta f_{\rm NL}^{\rm equi} \sim 41 $ on a survey
volume of $1$ $($Gpc$/h)^3$. These constraints are weakly affected by redshift
errors. We close by speculating as to how this approach can be made robust
against small-scale uncertainties by exploiting (non)locality.


title:
Performance Comparison of Python Translators for a Multi-threaded
  CPU-bound Application

abstract:
  Currently, Python is one of the most widely used languages in various
application areas. However, it has limitations when it comes to optimizing and
parallelizing applications due to the nature of its official CPython
interpreter, especially for CPU-bound applications. To solve this problem,
several alternative translators have emerged, each with a different approach
and its own cost-performance ratio. Due to the absence of comparative studies,
we have carried out a performance comparison of these translators using N-Body
as a case study (a well-known problem with high computational demand). The
results obtained show that CPython and PyPy presented poor performance due to
their limitations when it comes to parallelizing algorithms; while Numba and
Cython achieved significantly higher performance, proving to be viable options
to speed up numerical algorithms.


title:
Neural RF SLAM for unsupervised positioning and mapping with channel
  state information

abstract:
  We present a neural network architecture for jointly learning user locations
and environment mapping up to isometry, in an unsupervised way, from channel
state information (CSI) values with no location information. The model is based
on an encoder-decoder architecture. The encoder network maps CSI values to the
user location. The decoder network models the physics of propagation by
parametrizing the environment using virtual anchors. It aims at reconstructing,
from the encoder output and virtual anchor location, the set of time of flights
(ToFs) that are extracted from CSI using super-resolution methods. The neural
network task is set prediction and is accordingly trained end-to-end. The
proposed model learns an interpretable latent, i.e., user location, by just
enforcing a physics-based decoder. It is shown that the proposed model achieves
sub-meter accuracy on synthetic ray tracing based datasets with single anchor
SISO setup while recovering the environment map up to 4cm median error in a 2D
environment and 15cm in a 3D environment


title: The Frobenius character of the Orlik-Terao algebra of type A

abstract:
  We provide a new virtual description of the symmetric group action on the
cohomology of ordered configuration space on SU_2 up to translations. We use
this formula to prove the Moseley-Proudfoot-Young conjecture. As a consequence
we obtain the graded Frobenius character of the Orlik-Terao algebra of type
A_n.


title: Elastic wave manipulation in geodesic lenses

abstract:
  We investigate wave propagation in curved, thin elastic waveguides, where
curvature is shown to be equivalent to a spatially modulated refractive index.
We establish this relationship within a theoretical framework that leverages
non-Euclidean transformations. Theoretical predictions illustrate how curved
profiles can be designed to manipulate wave trajectories in analogy with
geometrical optics principles. Experiments on 3D printed curved membranes
confirm the theoretical predictions, and suggest curvature as a viable approach
for guiding, focusing and steering of elastic waves.


title:
2-speed network ensemble for efficient classification of incremental
  land-use/land-cover satellite image chips

abstract:
  The ever-growing volume of satellite imagery data presents a challenge for
industry and governments making data-driven decisions based on the timely
analysis of very large data sets. Commonly used deep learning algorithms for
automatic classification of satellite images are time and resource-intensive to
train. The cost of retraining in the context of Big Data presents a practical
challenge when new image data and/or classes are added to a training corpus.
Recognizing the need for an adaptable, accurate, and scalable satellite image
chip classification scheme, in this research we present an ensemble of: i) a
slow to train but high accuracy vision transformer; and ii) a fast to train,
low-parameter convolutional neural network. The vision transformer model
provides a scalable and accurate foundation model. The high-speed CNN provides
an efficient means of incorporating newly labelled data into analysis, at the
expense of lower accuracy. To simulate incremental data, the very large
(~400,000 images) So2Sat LCZ42 satellite image chip dataset is divided into
four intervals, with the high-speed CNN retrained every interval and the vision
transformer trained every half interval. This experimental setup mimics an
increase in data volume and diversity over time. For the task of automated
land-cover/land-use classification, the ensemble models for each data increment
outperform each of the component models, with best accuracy of 65% against a
holdout test partition of the So2Sat dataset. The proposed ensemble and
staggered training schedule provide a scalable and cost-effective satellite
image classification scheme that is optimized to process very large volumes of
satellite data.


title:
A constant lower bound for any quantum protocol for secure function
  evaluation

abstract:
  Secure function evaluation is a two-party cryptographic primitive where Bob
computes a function of Alice's and his respective inputs, and both hope to keep
their inputs private from the other party. It has been proven that perfect (or
near perfect) security is impossible, even for quantum protocols. We generalize
this no-go result by exhibiting a constant lower bound on the cheating
probabilities for any quantum protocol for secure function evaluation, and
present many applications from oblivious transfer to the millionaire's problem.
Constant lower bounds are of practical interest since they imply the
impossibility to arbitrarily amplify the security of quantum protocols by any
means.


title: Doubly-Robust Dynamic Treatment Regimen Estimation for Binary Outcomes

abstract:
  In precision medicine, Dynamic Treatment Regimes (DTRs) are treatment
protocols that adapt over time in response to a patient's observed
characteristics. A DTR is a set of decision functions that takes an individual
patient's information as arguments and outputs an action to be taken. Building
on observed data, the aim is to identify the DTR that optimizes expected
patient outcomes. Multiple methods have been proposed for optimal DTR
estimation with continuous outcomes. However, optimal DTR estimation with
binary outcomes is more complicated and has received comparatively little
attention. Solving a system of weighted generalized estimating equations, we
propose a new balancing weight criterion to overcome the misspecification of
generalized linear models' nuisance components. We construct binary
pseudo-outcomes, and develop a doubly-robust and easy-to-use method to estimate
an optimal DTR with binary outcomes. We also outline the underlying theory,
which relies on the balancing property of the weights; provide simulation
studies that verify the double-robustness of our method; and illustrate the
method in studying the effects of e-cigarette usage on smoking cessation, using
observational data from the Population Assessment of Tobacco and Health (PATH)
study.


title: Prospects for searches for Higgs boson decays to dark photons at the ILC

abstract:
  An interesting model of dark matter involves a hidden sector decoupled from
Standard Model (SM) fields except for some portal interaction. A concrete
realization of this is the Hidden Abelian Higgs Model, which gives rise to
decays of the SM Higgs boson into a pair of new bosons, called $Z_{\mathrm{d}}$
or dark photons. This note explores prospects for the search for such dark
photons at the ILC with $\sqrt{s}=250$ GeV, where the dark photons decay
promptly. For the $H\rightarrow Z_{\mathrm{d}}Z_{\mathrm{d}}\rightarrow 4\ell$
($\ell = e,\mu$) final state, it follows closely recent similar searches at the
LHC, while for the $2\ell2j$ and $4j$ final states a multivariate analysis
approach is used. This study has not been approved by the SiD consortium.


title: The strong coupling constant: State of the art and the decade ahead

abstract:
  This document provides a comprehensive summary of the state-of-the-art,
challenges, and prospects in the experimental and theoretical study of the
strong coupling $\alpha_s$. The current status of the seven methods presently
used to determine $\alpha_s$ based on: (i) lattice QCD, (ii) hadronic $\tau$
decays, (iii) deep-inelastic scattering and parton distribution functions fits,
(iv) electroweak boson decays, hadronic final-states in (v) e+e-, (vi) e-p, and
(vii) p-p collisions, and (viii) quarkonia decays and masses, are reviewed.
Novel $\alpha_s$ determinations are discussed, as well as the averaging method
used to obtain the PDG world-average value at the reference Z boson mass scale,
$\alpha_s(m^2_Z)$. Each of the extraction methods proposed provides a "wish
list" of experimental and theoretical developments required in order to achieve
an ideal permille precision on $\alpha_s(m^2_Z)$ within the next 10 years.


title: Active Exploration for Neural Global Illumination of Variable Scenes

abstract:
  Neural rendering algorithms introduce a fundamentally new approach for
photorealistic rendering, typically by learning a neural representation of
illumination on large numbers of ground truth images. When training for a given
variable scene, i.e., changing objects, materials, lights and viewpoint, the
space D of possible training data instances quickly becomes unmanageable as the
dimensions of variable parameters increase. We introduce a novel Active
Exploration method using Markov Chain Monte Carlo, which explores D, generating
samples (i.e., ground truth renderings) that best help training and interleaves
training and on-the-fly sample data generation. We introduce a self-tuning
sample reuse strategy to minimize the expensive step of rendering training
samples. We apply our approach on a neural generator that learns to render
novel scene instances given an explicit parameterization of the scene
configuration. Our results show that Active Exploration trains our network much
more efficiently than uniformly sampling, and together with our resolution
enhancement approach, achieves better quality than uniform sampling at
convergence. Our method allows interactive rendering of hard light transport
paths (e.g., complex caustics) -- that require very high samples counts to be
captured -- and provides dynamic scene navigation and manipulation, after
training for 5-18 hours depending on required quality and variations.


title: Bounds for spectral projectors on the Euclidean cylinder

abstract:
  We prove essentially optimal bounds for norms of spectral projectors on thin
spherical shells for the Laplacian on the cylinder (R/Z)*R. In contrast to
previous investigations into spectral projectors on tori, having one unbounded
dimension available permits a compact self-contained proof.


title:
Non-neural Models Matter: A Re-evaluation of Neural Referring Expression
  Generation Systems

abstract:
  In recent years, neural models have often outperformed rule-based and classic
Machine Learning approaches in NLG. These classic approaches are now often
disregarded, for example when new neural models are evaluated. We argue that
they should not be overlooked, since, for some tasks, well-designed non-neural
approaches achieve better performance than neural ones. In this paper, the task
of generating referring expressions in linguistic context is used as an
example. We examined two very different English datasets (WEBNLG and WSJ), and
evaluated each algorithm using both automatic and human evaluations. Overall,
the results of these evaluations suggest that rule-based systems with simple
rule sets achieve on-par or better performance on both datasets compared to
state-of-the-art neural REG systems. In the case of the more realistic dataset,
WSJ, a machine learning-based system with well-designed linguistic features
performed best. We hope that our work can encourage researchers to consider
non-neural models in future.


title:
Continuous and Coordinated Efforts of Structure Wakefield Acceleration
  (SWFA) Development for an Energy Frontier Machine

abstract:
  Structure wakefield acceleration (SWFA) is well suited for the linear
collider (LC) application due to its natural ability to accelerate positrons
and preserve emittance. Under the SWFA roadmap, which was developed in response
to Snowmass 2013 recommendations, four principal technologies: drive beam, main
beam, wakefield structure, and LC facility design, have been investigated. The
two SWFA schemes under development are the collinear wakefield accelerator
(CWA), in which the drive and main beam follow the same path through a
structure, and the two-beam accelerator (TBA), where the drive and main beam
pass through different structures. To further advance the SWFA technology in
the next decade, continuous and coordinated efforts must be carried out in a
more synchronized way. This whitepaper is written to address the research needs
in SWFA for preparation of Snowmass 2022.


title:
Design Considerations for Fermilab Multi-MW Proton Facility in the
  DUNE/LBNF era

abstract:
  Fermilab has submitted two Snowmass whitepapers on a future 2.4~MW upgrade
for DUNE/LBNF featuring a 2 GeV extension of the PIP-II linac and the
construction of a new rapid-cycling-synchrotron. This paper summarizes the
relationship between these two scenarios, emphasizing the commonalities and
tracing the differences to their original design questions. In addition to a
high-level summary of the two 2.4~MW upgrade scenarios, there is a brief
discussion of staging, beamline capabilities, subsequent upgrades, and relevant
R\&D. We are proposing a vigorous program to address various challenges
associated with each scenario and to down-select the concept, most suitable to
provide proton beams for years to come.


title:
Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement
  Learning

abstract:
  Most successes in robotic manipulation have been restricted to single-arm
robots, which limits the range of solvable tasks to pick-and-place, insertion,
and objects rearrangement. In contrast, dual and multi arm robot platforms
unlock a rich diversity of problems that can be tackled, such as laundry
folding and executing cooking skills. However, developing controllers for
multi-arm robots is complexified by a number of unique challenges, such as the
need for coordinated bimanual behaviors, and collision avoidance amongst
robots. Given these challenges, in this work we study how to solve bi-manual
tasks using reinforcement learning (RL) trained in simulation, such that the
resulting policies can be executed on real robotic platforms. Our RL approach
results in significant simplifications due to using real-time (4Hz) joint-space
control and directly passing unfiltered observations to neural networks
policies. We also extensively discuss modifications to our simulated
environment which lead to effective training of RL policies. In addition to
designing control algorithms, a key challenge is how to design fair evaluation
tasks for bi-manual robots that stress bimanual coordination, while removing
orthogonal complicating factors such as high-level perception. In this work, we
design a Connect Task, where the aim is for two robot arms to pick up and
attach two blocks with magnetic connection points. We validate our approach
with two xArm6 robots and 3D printed blocks with magnetic attachments, and find
that our system has 100% success rate at picking up blocks, and 65% success
rate at the Connect Task.


title: A New Charged Lepton Flavor Violation Program at Fermilab

abstract:
  The muon has played a central role in establishing the Standard Model of
particle physics, and continues to provide valuable information about the
nature of new physics. A new complex at Fermilab, the Advanced Muon Facility,
would provide the world's most intense positive and negative muon beams by
exploiting the full potential of PIP-II and the Booster upgrade. This facility
would enable a broad muon physics program, including studies of charged lepton
flavor violation, muonium-antimuonium transitions, a storage ring muon EDM
experiment, and muon spin rotation experiments. This document describes a
staged realization of this complex, together with a series of next-generation
experiments to search for charged lepton flavor violation.


title:
Splitting the square of homogeneous and elementary functions into their
  symmetric and anti-symmetric parts

abstract:
  We consider the expansion of the square of a complete homogeneous function
$h_\lambda$, or of an elementary symmetric function $e_\lambda$, in the basis
of Schur functions. This square also decomposes into two plethysms,
$s_2[h_\lambda]$ and $s_{11}[h_\lambda]$ (resp. $s_2[h_\lambda]$ and
$s_{11}[h_\lambda]$), which are called its symmetric and anti-symmetric parts,
respectively. We define a sign statistic on the set of tableaux that index the
Schur functions appearing in the square of those symmetric functions. This sign
statistic allows to determine to which plethysm each Schur function
contributes. We use mainly combinatorial tools on tableaux (product on tableau
and RSK) and basic manipulations on plethysm and symmetric functions.


title:
Data Transfer and Network Services management for Domain Science
  Workflows

abstract:
  This paper describes a vision and work in progress to elevate network
resources and data transfer management to the same level as compute and storage
in the context of services access, scheduling, life cycle management, and
orchestration. While domain science workflows often include active compute
resource allocation and management, the data transfers and associated network
resource coordination is not handled in a similar manner. As a result data
transfers can introduce a degree of uncertainty in workflow operations, and the
associated lack of network information does not allow for either the workflow
operations or the network use to be optimized. The net result is that domain
science workflow processes are forced to view the network as an opaque
infrastructure into which they inject data and hope that it emerges at the
destination with an acceptable Quality of Experience. There is little ability
for applications to interact with the network to exchange information,
negotiate performance parameters, discover expected performance metrics, or
receive status/troubleshooting information in real time. Developing mechanisms
to allow an application workflow to obtain information regarding the network
services, capabilities, and options, to a degree similar to what is possible
for compute resources is the primary motivation for this work. The initial
focus is on the Open Science Grid (OSG)/Compact Muon Solenoid (CMS) Large
Hadron Collider (LHC) workflows with Rucio/FTS/XRootD based data transfers and
the interoperation with the ESnet SENSE (Software-Defined Network for
End-to-end Networked Science at the Exascale) system.


title: Topological Modes in a Laser Cavity through Exceptional State Transfer

abstract:
  Shaping the light emission characteristics of laser systems is of great
importance in various areas of science and technology. In a typical lasing
arrangement, the spatial profile of the mode tends to remain self-similar
throughout the cavity. Here, we introduce a paradigm shift where a spatially
evolving mode is faithfully settled into a pair of bi-orthogonal states at the
two facets of a laser cavity. This is achieved by deliberately eliminating
non-adiabatic jumps in a purposely designed structure that features a dynamic
encirclement of a non-Hermitian exceptional point. The resulting state transfer
reflects the unique topology of the associated Riemann surfaces. Our approach
provides a route to develop versatile mode selective active


title:
Enabling Long mmWave Aerial Backhaul Links via Fixed-Wing UAVs:
  Performance and Design

abstract:
  We propose a fixed wing unmanned aerial vehicles (UAV)-based millimeter wave
(mmWave) backhaul links, that is offered as a cost effective and easy to deploy
solution, to connect a disaster or remote area to the nearest core network.
First, we fully characterize the single relay fixed-wing UAV-based
communication system by taking into account the effects of realistic physical
parameters, such as the UAV's circular path, critical points of the flight
path, heights and positions of obstacles, flight altitude, tracking error, the
severity of UAV's vibrations, the real 3D antenna pattern, mmWave atmospheric
channel loss, temperature and air pressure. Second, we derive the distribution
of the signal-to-noise ratio (SNR) metric, which is based on the sum of a
series of Dirac delta functions. Using the SNR distribution, we derive
closed-form expressions for the outage probability and the ergodic capacity of
the considered system as a function of all system parameters. To provide an
acceptable quality of service for longer link lengths, we extend the analytical
expressions to a multi-relay system. The accuracy of the closed-form
expressions are verified by Monte-Carlo simulations. Finally, by providing
sufficient simulation results, we investigate the effects of key channel
parameters such as antenna pattern gain and flight path on the performance of
the considered system; and we carefully analyze the relationships between these
parameters in order to maximize the average channel capacity.


title: Gain Induced Topological Response via Tailored Long-range Interactions

abstract:
  The ability to tailor the hopping interactions between the constituent
elements of a physical system could enable the observation of unusual phenomena
that are otherwise inaccessible in standard settings. In this regard, a number
of recent theoretical studies have indicated that an asymmetry in either the
short- or long-range complex exchange constants can lead to counterintuitive
effects, for example, the possibility of a Kramer's degeneracy even in the
absence of spin 1/2 or the breakdown of the bulk-boundary correspondence. Here,
we show how such tailored asymmetric interactions can be realized in photonic
integrated platforms by exploiting non-Hermitian concepts, enabling a class of
topological behaviors induced by optical gain. As a demonstration, we implement
the Haldane model, a canonical lattice that relies on asymmetric long-range
hopping in order to exhibit quantum Hall behavior without a net external
magnetic flux. The topological response observed in this lattice is a result of
gain and vanishes in a passive but otherwise identical structure. Our findings
not only enable the realization of a wide class of non-trivial phenomena
associated with tailored interactions, but also opens up avenues to study the
role of gain and nonlinearity in topological systems in the presence of quantum
noise.


title: A Personal Computer for a Distrustful World

abstract:
  Personal computer owners often want to be able to run security-critical
programs on the same machine as other untrusted and potentially malicious
programs. While ostensibly trivial, this requires users to trust hardware and
system software to correctly sandbox malicious programs, trust that is often
misplaced. Our goal is to minimize the number and complexity of hardware and
software components that a computer owner needs to trust to withstand
adversarial inputs. We present a hardware design, called the split-trust
machine model, which is composed of statically-partitioned, physically-isolated
trust domains. We introduce a few simple, formally-verified hardware components
to enable a program to gain provably exclusive and simultaneous access to both
computation and I/O on a temporary basis. To manage this hardware, we present
OctopOS, an OS composed of mutually distrustful subsystems.
  We present a prototype of this machine (hardware and OS) on a CPU-FPGA board
and show that it incurs a small hardware cost compared to modern SoCs. For
security-critical programs, we show that this machine significantly reduces the
required trust compared to mainstream TEEs, and for normal programs, we show
that it achieves similar performance as a legacy machine.


title: Majorana Bound State Cavities

abstract:
  Cavities play a fundamental role in optical physics by providing
spatio-temporal confinement of energy that facilitates light-matter
interactions. They are routinely utilized in a variety of settings, ranging
from lasers to spectral filters, to nonlinear wave mixers, and modulators.
While their resonant properties make them suitable for sensing, in many
applications, like in lasers, it is desired to alleviate their sensitivity in
order to make a device more resilient to perturbations. Along these lines there
have been several recent reports of using concepts from topological physics in
order to design laser arrays that are inherently more robust. Here, we present
a new class of topological cavities based on Majorana bound states that provide
unique scaling features as well as non-degenerate single-mode behaviors. These
cavities may lead to a new family of lasers and laser arrays that are robust to
defects, fabrication imperfections, and external perturbations.


title:
Role of bulk viscosity in deuteron production in ultrarelativistic
  nuclear collisions

abstract:
  We use a Bayesian-calibrated multistage viscous hydrodynamic model to explore
deuteron yield, mean transverse momentum and flow observables in LHC Pb-Pb
collisions. We explore theoretical uncertainty in the production of deuterons,
including (i) the contribution of thermal deuterons, (ii) models for the
subsequent formation of deuterons (hadronic transport vs coalescence) and (iii)
the overall sensitivity of the results to the hydrodynamic model -- in
particular to bulk viscosity, which is often neglected in studies of deuteron
production. Using physical parameters set by a comparison to only light hadron
observables, we find good agreement with measurements of the mean transverse
momentum $\langle p_T \rangle$ and elliptic flow $v_2$ of deuterons; however,
tension is observed with experimental data for the deuteron multiplicity in
central collisions. The results are found to be sensitive to each of the
mentioned theoretical uncertainties, with a particular sensitivity to bulk
viscosity, indicating that the latter is an important ingredient for an
accurate treatment of deuteron production.


title: LPV sequential loop closing for high-precision motion systems

abstract:
  Increasingly stringent throughput requirements in the industry necessitate
the need for lightweight design of high-precision motion systems to allow for
high accelerations, while still achieving accurate positioning of the
moving-body. The presence of position dependent dynamics in such motion systems
severely limits achievable position tracking performance using conventional
sequential loop closing (SLC) control design strategies. This paper presents a
novel extension of the conventional SLC design framework towards
linear-parameter-varying systems, which allows to circumvent limitations that
are introduced by position dependent effects in high-precision motion systems.
Advantages of the proposed control design approach are demonstrated in
simulation using a high-fidelity model of a moving-magnet planar actuator
system, which exhibits position dependency in both actuation and sensing.


title: Symplectic circle actions on manifolds with contact type boundary

abstract:
  Many of the existing results for closed Hamiltonian G-manifolds are based on
the analysis of the corresponding Hamiltonian functions using Morse-Bott
techniques. In general such methods fail for non-compact manifolds or for
manifolds with boundary.
  In this article, we consider circle actions only on symplectic manifolds that
have (convex) contact type boundary. In this situation we show that many of the
key ideas of Morse-Bott theory still hold, allowing us to generalize several
results from the closed setting.
  Among these, we show that in our situation any symplectic group action is
always Hamiltonian, we show several results about the topology of the
symplectic manifold and in particular about the connectedness of its boundary.
We also show that after attaching cylindrical ends, a level set of the
Hamiltonian of a circle action is either empty or connected.
  We concentrate mostly on circle actions, but we believe that with our methods
many of the classical results can be generalized from closed symplectic
manifolds to symplectic manifolds with contact type boundary.


title:
Driving Anomaly Detection Using Conditional Generative Adversarial
  Network

abstract:
  Anomaly driving detection is an important problem in advanced driver
assistance systems (ADAS). It is important to identify potential hazard
scenarios as early as possible to avoid potential accidents. This study
proposes an unsupervised method to quantify driving anomalies using a
conditional generative adversarial network (GAN). The approach predicts
upcoming driving scenarios by conditioning the models on the previously
observed signals. The system uses the difference of the output from the
discriminator between the predicted and actual signals as a metric to quantify
the anomaly degree of a driving segment. We take a driver-centric approach,
considering physiological signals from the driver and controller area
network-Bus (CAN-Bus) signals from the vehicle. The approach is implemented
with convolutional neural networks (CNNs) to extract discriminative feature
representations, and with long short-term memory (LSTM) cells to capture
temporal information. The study is implemented and evaluated with the driving
anomaly dataset (DAD), which includes 250 hours of naturalistic recordings
manually annotated with driving events. The experimental results reveal that
recordings annotated with events that are likely to be anomalous, such as
avoiding on-road pedestrians and traffic rule violations, have higher anomaly
scores than recordings without any event annotation. The results are validated
with perceptual evaluations, where annotators are asked to assess the risk and
familiarity of the videos detected with high anomaly scores. The results
indicate that the driving segments with higher anomaly scores are more risky
and less regularly seen on the road than other driving segments, validating the
proposed unsupervised approach.


title: Hadron Spectroscopy in Photoproduction

abstract:
  Recent decades have seen a resurge of interest in hadron spectroscopy, driven
by new, high-luminosity experiments which have identified many new hadrons,
both expected and unexpected. The large number of unexpected hadrons suggest
contributions from additional quark and gluonic degrees of freedom to the
hadronic spectrum beyond the basic quark model. Photoproduction has emerged as
an attractive process to study hadron spectroscopy, due to the range of states
accessible in new and planned experimental facilities, complimentary kinematics
to other experiments where rescattering effects near thresholds are reduced,
and advances in our theoretical understanding of these reactions. This white
paper reviews the prospects for hadron spectroscopy from three major existing
and proposed facilities: the current GlueX experiment, the planned Electron-Ion
Collider, and a proposed JLab 24 GeV upgrade.


title:
Error-Mitigated Simulation of Quantum Many-Body Scars on Quantum
  Computers with Pulse-Level Control

abstract:
  Quantum many-body scars are an intriguing dynamical regime in which quantum
systems exhibit coherent dynamics and long-range correlations when prepared in
certain initial states. We use this combination of coherence and many-body
correlations to benchmark the performance of present-day quantum computing
devices by using them to simulate the dynamics of an antiferromagnetic initial
state in mixed-field Ising chains of up to 19 sites. In addition to calculating
the dynamics of local observables, we also calculate the Loschmidt echo and a
nontrivial connected correlation function that witnesses long-range many-body
correlations in the scarred dynamics. We find coherent dynamics to persist over
up to 40 Trotter steps even in the presence of various sources of error. To
obtain these results, we leverage a variety of error mitigation techniques
including noise tailoring, zero-noise extrapolation, dynamical decoupling, and
physically motivated postselection of measurement results. Crucially, we also
find that using pulse-level control to implement the Ising interaction yields a
substantial improvement over the standard CNOT-based compilation of this
interaction. Our results demonstrate the power of error mitigation techniques
and pulse-level control to probe many-body coherence and correlation effects on
present-day quantum hardware.


title:
Bare Collapse, Formation of Neutron Star Binaries and Fast Optical
  Transients

abstract:
  {"Bare collapse", the collapse of a bare stellar core to a neutron star with
a very small mass ejection links two seemingly unrelated phenomena: the
formation of binary neutron star (BNS) systems and the observations of fast and
luminous optical transients. We carried out calculations of the collapse due to
electron-capture of both evolutionary and synthetic isentropic bare stellar
cores. We find that the collapse results in {the formation of} a light ~ 1.3
solar mass neutron star and {an} ejection of ~0.1 solar mass at ~0.1c. The
outer shell of the ejecta is composed of Ni56 that can power an ultra-stripped
supernova. The models we explored can explain most of the observed fast optical
flares but not the brightest ones. Collapse of cores surrounded by somewhat
more massive envelopes can produce larger amounts of Ni56 and explain brighter
flares. Alternatively, those events can arise due to interaction of the very
energetic ejecta with winds that were ejected from the progenitor a few days
before the collapse.


abstract:
  The Sanford Underground Research Facility (SURF) has been operating since
2007 supporting underground research in rare-process physics, as well as
offering research opportunities in other disciplines. SURF laboratory
facilities include a Surface Campus as well as campuses at the 4850-foot level
(1500 m, 4300 m.w.e.) that host a range of significant physics experiments,
including those studying dark matter, neutrino properties, and nuclear
astrophysics topics. SURF is also home to the Long-Baseline Neutrino Facility
(LBNF) that will host the international Deep Underground Neutrino Experiment
(DUNE). SURF offers an ultra-low background environment, low-background assay
capabilities, and electroformed copper is produced at the facility. SURF is
proposing additional underground space on the 4850L and 7400L (2300 m, 6500
m.w.e.), and initial engineering designs have been completed. SURF is a
dedicated research facility with significant expansion capability, and
applications from new experiments are welcome.


title:
Contrast mechanisms in secondary electron e-beam induced current
  (SEEBIC) imaging

abstract:
  Over the last few years, a new mode for imaging in the scanning transmission
electron microscope (STEM) has gained attention as it permits the direct
visualization of sample conductivity and electrical connectivity. When the
electron beam (e-beam) is focused on the sample in the STEM, secondary
electrons (SEs) are generated. If the sample is conductive and electrically
connected to an amplifier, the SE current can be measured as a function of the
e-beam position. This scenario is similar to the better-known scanning electron
microscopy (SEM)-based technique, electron beam induced current (EBIC) imaging
except the signal in STEM is generated by the emission of SEs, hence the name
SEEBIC, and in this case the current flows in the opposite direction. Here, we
provide a brief review of recent work in this area, examine the various
contrast generation mechanisms associated with SEEBIC, and illustrate its use
for the characterization of graphene nanoribbon devices.


title: Self-Distribution Distillation: Efficient Uncertainty Estimation

abstract:
  Deep learning is increasingly being applied in safety-critical domains. For
these scenarios it is important to know the level of uncertainty in a model's
prediction to ensure appropriate decisions are made by the system. Deep
ensembles are the de-facto standard approach to obtaining various measures of
uncertainty. However, ensembles often significantly increase the resources
required in the training and/or deployment phases. Approaches have been
developed that typically address the costs in one of these phases. In this work
we propose a novel training approach, self-distribution distillation (S2D),
which is able to efficiently train a single model that can estimate
uncertainties. Furthermore it is possible to build ensembles of these models
and apply hierarchical ensemble distillation approaches. Experiments on
CIFAR-100 showed that S2D models outperformed standard models and Monte-Carlo
dropout. Additional out-of-distribution detection experiments on LSUN, Tiny
ImageNet, SVHN showed that even a standard deep ensemble can be outperformed
using S2D based ensembles and novel distilled models.


title: On characteristic invariants of matrix pencils and linear relations

abstract:
  The relationship between linear relations and matrix pencils is investigated.
Given a linear relation, we introduce its Weyr characteristic. If the linear
relation is the range (or the kernel) representation of a given matrix pencil,
we show that there is a correspondence between this characteristic and the
Kronecker canonical form of the pencil. This relationship is exploited to
obtain estimations on the invariant characteristics of matrix pencils under
rank one perturbations.


title:
Snowmass2021 Cosmic Frontier: The landscape of low-threshold dark matter
  direct detection in the next decade

abstract:
  The search for particle-like dark matter with meV-to-GeV masses has developed
rapidly in the past few years. We summarize the science case for these
searches, the recent progress, and the exciting upcoming opportunities. Funding
for Research and Development and a portfolio of small dark matter projects will
allow the community to capitalize on the substantial recent advances in theory
and experiment and probe vast regions of unexplored dark-matter parameter space
in the coming decade.


title:
Modeling of Asymptotically Periodic Outbreaks: a long-term SIRW2
  description of COVID-19?

abstract:
  As the outbreak of COVID-19 enters its third year, we have now enough data to
analyse the behavior of the pandemic with mathematical models over a long
period of time. The pandemic alternates periods of high and low infections, in
a way that sheds a light on the nature of mathematical model that can be used
for reliable predictions. The main hypothesis of the model presented here is
that the oscillatory behavior is a structural feature of the outbreak, even
without postulating a time-dependence of the coefficients. As such, it should
be reflected by the presence of limit cycles as asymptotic solutions. This
stems from the introduction of (i) a non-linear waning immunity based on the
concept of immunity booster (already used for other pathologies); (ii) a fine
description of the compartments with a discrimination between individuals
infected/vaccinated for the first time, and individuals already
infected/vaccinated, undergoing to new infections/doses. We provide a
proof-of-concept that our novel model is capable of reproducing long-term
oscillatory behavior of many infectious diseases, and, in particular, the
periodic nature of the waves of infection. Periodic solutions are inherent to
the model, and achieved without changing parameter values in time. This may
represent an important step in the long-term modeling of COVID-19 and similar
diseases, as the natural, unforced behavior of the solution shows the
qualitative characteristics observed during the COVID-19 pandemic.


title: FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric

abstract:
  Syntax is a fundamental component of language, yet few metrics have been
employed to capture syntactic similarity or coherence at the utterance- and
document-level. The existing standard document-level syntactic similarity
metric is computationally expensive and performs inconsistently when faced with
syntactically dissimilar documents. To address these challenges, we present
FastKASSIM, a metric for utterance- and document-level syntactic similarity
which pairs and averages the most similar dependency parse trees between a pair
of documents based on tree kernels. FastKASSIM is more robust to syntactic
dissimilarities and differences in length, and runs up to to 5.2 times faster
than our baseline method over the documents in the r/ChangeMyView corpus.


title: Adaptive Kernel Kalman Filter

abstract:
  Sequential Bayesian filters in non-linear dynamic systems require the
recursive estimation of the predictive and posterior distributions. This paper
introduces a Bayesian filter called the adaptive kernel Kalman filter (AKKF).
With this filter, the arbitrary predictive and posterior distributions of
hidden states are approximated using the empirical kernel mean embeddings
(KMEs) in reproducing kernel Hilbert spaces (RKHSs). In parallel with the KMEs,
some particles, in the data space, are used to capture the properties of the
dynamical system model. Specifically, particles are generated and updated in
the data space, while the corresponding kernel weight mean vector and
covariance matrix associated with the feature mappings of the particles are
predicted and updated in the RKHSs based on the kernel Kalman rule (KKR).
Simulation results are presented to confirm the improved performance of our
approach with significantly reduced particle numbers, by comparing with the
unscented Kalman filter (UKF), particle filter (PF) and Gaussian particle
filter (GPF). For example, compared with the GPF, the proposed approach
provides around 5% logarithmic mean square error (LMSE) tracking performance
improvement in the bearing-only tracking (BOT) system when using 50 particles.


title: Majorana Algebra for the Hoffman-Singleton Graph

abstract:
  Majorana theory is an axiomatic tool introduced by A. A. Ivanov in 2009 for
studying the Monster group M and its subgroups through the 196884-dimensional
Conway-Griess-Norton algebra. The group U3(5) is the socle of the centralizer
in M of a subgroup of order 25. The involutions of this U3(5)-subgroup are
2A-involutions in the Monster. Therefore, U3(5) possesses a Majorana
representation based on the embedding in the Monster. We prove that this is the
unique Majorana representation of U3(5), and calculate its dimension, which is
798.


title:
SoK: Why Have Defenses against Social Engineering Attacks Achieved
  Limited Success?

abstract:
  Social engineering attacks are a major cyber threat because they often serve
as a first step for an attacker to break into an otherwise well-defended
network, steal victims' credentials, and cause financial losses. The problem
has received due amount of attention with many publications proposing defenses
against them. Despite this, the situation has not improved. In this SoK paper,
we aim to understand and explain this phenomenon by looking into the root cause
of the problem. To this end, we examine the literature on attacks and defenses
through a unique lens we propose -- {\em psychological factors (PFs) and
techniques (PTs)}. We find that there is a big discrepancy between attacks and
defenses: Attacks have deliberately exploited PFs by leveraging PTs, but
defenses rarely take either of these into consideration, preferring technical
solutions. This explains why existing defenses have achieved limited success.
This prompts us to propose a roadmap for a more systematic approach towards
designing effective defenses against social engineering attacks.


title: Pinning Fault Mode Modeling for DWM Shifting

abstract:
  Extreme scaling for purposes of achieving higher density and lower energy
continues to increase the probability of memory faults. For domain wall (DW)
memories, misalignment faults arise when aligning domains with access points. A
previously understudied type of shifting fault, a pinning fault may occur due
to non-uniform pinning potential distribution caused by notches with
fabrication imperfections. This non-uniformity can pin a wall during
current-induced DW motion. This paper provides a model of geometric variations
varying width, depth, and curvature variations of a notch, their impacts on the
critical shift current, and a study of the resulting impact on fault rates of
DW memory systems. An increase in the effective critical shift current due to
5% variation predicts a pinning fault rate on the order of $10^{-8}$ per shift,
which results in a mean-time-to-failure of circa 2s for a DW memory system.


title: Hyperdecoders: Instance-specific decoders for multi-task NLP

abstract:
  We investigate input-conditioned hypernetworks for multi-tasking in NLP,
generating parameter-efficient adaptations for a decoder using a hypernetwork
conditioned on the output of an encoder. This approach produces a unique
decoder for every input instance, allowing the network a larger degree of
flexibility than prior work that specializes the decoder for each task. We
apply our method to sequence classification tasks, extractive QA, and
summarisation and find that it often outperforms fully finetuning the
underlying model and surpasses previous parameter efficient fine-tuning
methods. Gains are particularly large when evaluated out-of-domain on the MRQA
benchmark. In addition, as the pretrained model is frozen, our method
eliminates negative interference among unrelated tasks, a common failure mode
in fully fine-tuned approaches. An analysis of the embeddings produced by our
model suggests that a large benefit of our approach is allowing the encoder
more effective control over the decoder, allowing mapping from hidden
representations to a final text-based label without interference from other
tasks' output formats or labels.


title: Ordering Circuits of Matroids

abstract:
  The cycles of a graph give a natural cyclic ordering to their edge-sets, and
these orderings are consistent in that two edges are adjacent in one cycle if
and only if they are adjacent in every cycle in which they appear together. An
orderable matroid is one whose set of circuits admits such a consistent
ordering. In this paper, we consider the question of determining which matroids
are orderable. Although we are able to answer this question for non-binary
matroids, it remains open for binary matroids. We give examples to provide
insight into the potential difficulty of this question in general. We also show
that, by requiring that the ordering preserves the three arcs in every
theta-graph restriction of a binary matroid $M$, we guarantee that $M$ is
orderable if and only if $M$ is graphic.


title: Data, geometry and homology

abstract:
  Homology-based invariants can be used to characterize the geometry of
datasets and thereby gain some understanding of the processes generating those
datasets. In this work we investigate how the geometry of a dataset changes
when it is subsampled in various ways. In our framework the dataset serves as a
reference object; we then consider different points in the ambient space and
endow them with a geometry defined in relation to the reference object, for
instance by subsampling the dataset proportionally to the distance between its
elements and the point under consideration. We illustrate how this process can
be used to extract rich geometrical information, allowing for example to
classify points coming from different data distributions.


title: Improving Word Translation via Two-Stage Contrastive Learning

abstract:
  Word translation or bilingual lexicon induction (BLI) is a key cross-lingual
task, aiming to bridge the lexical gap between different languages. In this
work, we propose a robust and effective two-stage contrastive learning
framework for the BLI task. At Stage C1, we propose to refine standard
cross-lingual linear maps between static word embeddings (WEs) via a
contrastive learning objective; we also show how to integrate it into the
self-learning procedure for even more refined cross-lingual maps. In Stage C2,
we conduct BLI-oriented contrastive fine-tuning of mBERT, unlocking its word
translation capability. We also show that static WEs induced from the
`C2-tuned' mBERT complement static WEs from Stage C1. Comprehensive experiments
on standard BLI datasets for diverse languages and different experimental
setups demonstrate substantial gains achieved by our framework. While the BLI
method from Stage C1 already yields substantial gains over all state-of-the-art
BLI methods in our comparison, even stronger improvements are met with the full
two-stage framework: e.g., we report gains for 112/112 BLI setups, spanning 28
language pairs.


title:
Multilingual Generative Language Models for Zero-Shot Cross-Lingual
  Event Argument Extraction

abstract:
  We present a study on leveraging multilingual pre-trained generative language
models for zero-shot cross-lingual event argument extraction (EAE). By
formulating EAE as a language generation task, our method effectively encodes
event structures and captures the dependencies between arguments. We design
language-agnostic templates to represent the event argument structures, which
are compatible with any language, hence facilitating the cross-lingual
transfer. Our proposed model finetunes multilingual pre-trained generative
language models to generate sentences that fill in the language-agnostic
template with arguments extracted from the input passage. The model is trained
on source languages and is then directly applied to target languages for event
argument extraction. Experiments demonstrate that the proposed model
outperforms the current state-of-the-art models on zero-shot cross-lingual EAE.
Comprehensive studies and error analyses are presented to better understand the
advantages and the current limitations of using generative language models for
zero-shot cross-lingual transfer EAE.


title: Fiber-optic diagnostic system for future accelerator magnets

abstract:
  The next generation high energy physics accelerators will require magnetic
fields at ~20 T. HTS coils will be an essential component of future accelerator
magnets and several efforts are currently dedicated on designing 20 T HTS- LTS
hybrid magnets. Among the existing challenges, there is the lack of a robust
quench detection system for hybrid magnet technology. Another big challenge is
represented by the high number of training quenches required by Nb3Sn magnets
to reach performance level. In this framework it is important to find a tool
that allow local real-time monitoring of magnet strain and temperature. In this
paper, we propose the use of fiber optics sensors for diagnostic and quench
detection in future accelerator superconducting magnets. Discrete and
distributed fiber optic sensors have demonstrated to be a promising tool. The
goal is to instrument hundreds of accelerator superconducting magnets and to
move beyond the proof-of-concept level. Significant developments are still
needed. Here, we are going to present the most recent results and discuss the
most urgent technical developments in order to make those sensors a robust and
reliable diagnostic tool for accelerator superconducting magnets over the next
10 year. We foresee that discrete fiber sensors will be a stable diagnostic
probe for superconducting magnets over the next 3 to 5 years. More R&D work
will be necessary for distributed fibers. The most urgent needs are the
increase of sample rate and sensitivity. Close collaboration with vendors will
be necessary to improve mechanical properties and fabrication processes in
order to produce hundreds of meters of fiber and instrument a large number of
accelerator superconducting magnets. Those R&D efforts will last up to 10 years
with a founding level that spans between 5-10 M$.


title: Future Circular Lepton Collider FCC-ee: Overview and Status

abstract:
  The worldwide High Energy Physics community widely agrees that the next
collider should be a Higgs factory. Acknowledging this priority, in 2021 CERN
has launched the international Future Circular Collider (FCC) Feasibility Study
(FS). The FCC Integrated Project foresees, in a first stage, a high-luminosity
high-energy electron-positron collider, serving as Higgs, top and electroweak
factory, and, in a second stage, an energy frontier hadron collider, with a
centre-of-mass energy of at least 100 TeV. In this paper, we address a few key
elements of the FCC-ee accelerator design, its performance reach, and
underlying technologies, as requested by the Snowmass process. The Conceptual
Design Report for the FCC, published in 2019, serves as our primary reference.
We also summarize a few recent changes and improvements.


title: The Apollonian staircase

abstract:
  A circle of curvature $n\in\mathbb{Z}^+$ is a part of finitely many primitive
integral Apollonian circle packings. Each such packing has a circle of minimal
curvature $-c\leq 0$, and we study the distribution of $c/n$ across all
primitive integral packings containing a circle of curvature $n$. As
$n\rightarrow\infty$, the distribution is shown to tend towards a picture we
name the Apollonian staircase. A consequence of the staircase is that if we
choose a random circle packing containing a circle $C$ of curvature $n$, then
the probability that $C$ is tangent to the outermost circle tends towards
$3/\pi$. These results are found by using positive semidefinite quadratic forms
to make $\mathbb{P}^1(\mathbb{C})$ a parameter space for (not necessarily
integral) circle packings. Finally, we examine an aspect of the integral theory
known as spikes. When $n$ is prime, the distribution of $c/n$ is extremely
smooth, whereas when $n$ is composite, there are certain spikes that correspond
to prime divisors of $n$ that are at most $\sqrt{n}$.


title: An explainability framework for cortical surface-based deep learning

abstract:
  The emergence of explainability methods has enabled a better comprehension of
how deep neural networks operate through concepts that are easily understood
and implemented by the end user. While most explainability methods have been
designed for traditional deep learning, some have been further developed for
geometric deep learning, in which data are predominantly represented as graphs.
These representations are regularly derived from medical imaging data,
particularly in the field of neuroimaging, in which graphs are used to
represent brain structural and functional wiring patterns (brain connectomes)
and cortical surface models are used to represent the anatomical structure of
the brain. Although explainability techniques have been developed for
identifying important vertices (brain areas) and features for graph
classification, these methods are still lacking for more complex tasks, such as
surface-based modality transfer (or vertex-wise regression). Here, we address
the need for surface-based explainability approaches by developing a framework
for cortical surface-based deep learning, providing a transparent system for
modality transfer tasks. First, we adapted a perturbation-based approach for
use with surface data. Then, we applied our perturbation-based method to
investigate the key features and vertices used by a geometric deep learning
model developed to predict brain function from anatomy directly on a cortical
surface model. We show that our explainability framework is not only able to
identify important features and their spatial location but that it is also
reliable and valid.


title: A note on the multivariate generalization of a basic simple inequality

abstract:
  We introduce the multivariate analogue of the well known inequality $1+x\leq
\mathrm{e}^x$, for an abstract non negative real number $x$. The result emerges
from the study of the blow up time of certain solutions of the Cauchy problem
for a particular ODE.


title: Cicero: A Declarative Grammar for Responsive Visualization

abstract:
  Designing responsive visualizations can be cast as applying transformations
to a source view to render it suitable for a different screen size. However,
designing responsive visualizations is often tedious as authors must manually
apply and reason about candidate transformations. We present Cicero, a
declarative grammar for concisely specifying responsive visualization
transformations which paves the way for more intelligent responsive
visualization authoring tools. Cicero's flexible specifier syntax allows
authors to select visualization elements to transform, independent of the
source view's structure. Cicero encodes a concise set of actions to encode a
diverse set of transformations in both desktop-first and mobile-first design
processes. Authors can ultimately reuse design-agnostic transformations across
different visualizations. To demonstrate the utility of Cicero, we develop a
compiler to an extended version of Vega-Lite, and provide principles for our
compiler. We further discuss the incorporation of Cicero into responsive
visualization authoring tools, such as a design recommender.


title:
Challenges and opportunities of srf theory for next generation particle
  accelerators

abstract:
  We suggest a program to establish theoretical performance limits of srf
cavities using modern theories of nonequilibrium superconductivity under a
strong electromagnetic field. These theories will be used to calculate the main
parameter of merit of srf cavities: the quality factor Q and its dependencies
on the field amplitude, temperature and frequency, which would allow us to
understand how far the srf cavity performance could be pushed from the current
state of the art. Given that the quality factor is determined by multiple
mechanisms operating on very different length scales, we will address the
interconnected problems of a nonlinear surface resistance, rf losses of
vortices trapped in the cavity, the effect of materials defects and surface
topography, and the opportunities to boost the srf performance by surface
nano-structuring, impurity management and multilayers. We suggest the following
directions of theoretical srf research to address the goals of boosting the
performance of the next generation particle accelerators: 1. Establishing the Q
limit, mechanisms of nonlinear surface resistance and the residual resistance
in a nonequilibrium superconductor under a strong RF field. 2. Establishing the
srf breakdown field limit, dynamic superheating field and its dependencies on
frequency, temperature and concentration of impurities. 3. Losses due to
trapped vortices and extreme dynamics of ultrafast vortices driven by strong rf
Meissner currents in srf cavities. 4. Optimization of srf performance due to
surface nanostructuring of the cavity surface, multilayers and impurity
management.


title:
Flexible terahertz photonic light-cage modules for in-core sensing and
  high temperature applications

abstract:
  Terahertz (THz) technology is a growing and multi-disciplinary research
field, particularly for sensing and telecommunications. A number of THz
waveguides have emerged over the past years, which are set to complement the
capabilities of existing and bulky free space setups. In most designs however,
the guiding region is physically separated from the surroundings, making
interactions between light and the environment inefficient. We present photonic
THz light cages (THzLCs) operating at THz frequencies, consisting of
free-standing dielectric strands, which guide light within a hollow core with
immediate access to the environment. We show the versatility and design
flexibility of this concept, by 3D-printing several cm-length-scale modules
using a single design and four different polymer- and ceramic- materials, which
are either rigid, flexible, or resistant to high temperatures. We characterize
propagation- and bend-losses for straight- and curved- waveguides, which are of
order ~1 dB/cm in the former, and ~2-8 dB/cm in the latter for bend radii below
10 cm, and largely independent of the material. Our transmission experiments
are complemented by near-field measurements at the waveguide output, which
reveal antiresonant guidance for straight THzLCs, and a deformed fundamental
mode in the bent waveguides, in agreement with numerical conformal mapping
simulations. We show that these THzLCs can be used either as: (i) flexible,
reconfigurable, and bendable modular assemblies; (ii) in-core sensors of
structures contained directly inside the hollow core; (iii) high-temperature
sensors, with potential applications in industrial monitoring. These THzLCs are
a novel and useful addition to the growing library of THz waveguides, marrying
the waveguide-like advantages of reconfigurable, diffractionless propagation,
with the free-space-like immediacy of direct exposure to the surrounding
environment.


title:
TAKDE: Temporal Adaptive Kernel Density Estimator for Real-Time Dynamic
  Density Estimation

abstract:
  Real-time density estimation is ubiquitous in many applications, including
computer vision and signal processing. Kernel density estimation is arguably
one of the most commonly used density estimation techniques, and the use of
"sliding window" mechanism adapts kernel density estimators to dynamic
processes. In this paper, we derive the asymptotic mean integrated squared
error (AMISE) upper bound for the "sliding window" kernel density estimator.
This upper bound provides a principled guide to devise a novel estimator, which
we name the temporal adaptive kernel density estimator (TAKDE). Compared to
heuristic approaches for "sliding window" kernel density estimator, TAKDE is
theoretically optimal in terms of the worst-case AMISE. We provide numerical
experiments using synthetic and real-world datasets, showing that TAKDE
outperforms other state-of-the-art dynamic density estimators (including those
outside of kernel family). In particular, TAKDE achieves a superior test
log-likelihood with a smaller runtime.


title:
3D models of the circumstellar environments of evolved stars: Formation
  of multiple spiral structures

abstract:
  We present 3D hydrodynamic models of the interaction between the outflows of
evolved, pulsating, Asymptotic Giant Branch (AGB) stars and nearby ($< 3$
stellar radii) sub-stellar companions ($M_{\mathrm{comp}} \lesssim 40$ M$_J$).
Our models show that due to resonances between the orbital period of the
companion and the pulsation period of the AGB star, multiple spiral structures
can form; the shocks driven by the pulsations are enhanced periodically in
different regions as they encounter the denser material created by the
sub-stellar companion's wake. We discuss the properties of these spiral
structures and the effect of the companion parameters on them. We also
demonstrate that the gravitational potential of the nearby companion enhances
the mass loss from the AGB star. For more massive ($M_{\mathrm{comp}} > 40$
M$_J$) and more distant companions ($> 4$ stellar radii), a single spiral arm
forms. We discuss the possibility of observing these structures with the new
generations of high-resolution, high-sensitivity instruments, and using them to
`find' sub-stellar companions around bright, evolved stars. Our results also
highlight possible structures that could form in our solar system when the Sun
turns into an AGB star.


title: Enhanced NeUtrino BEams from kaon Tagging (ENUBET)

abstract:
  ENUBET aims at demonstrating the feasibility of a "monitored" neutrino beam,
in which the absolute normalization of the neutrino flux can be constrained at
the 1% level. The $\nu_e$ flux is determined by monitoring large-angle $e^+$
from $K_{e3}$ decays in a 40 m long instrumented decay tunnel. The $\nu_\mu$
flux is provided by muons produced by decays of $K$ and $\pi$. Being a narrow
band beam ($p=8.5$ GeV/$c$ $\pm$ 10%), the transverse position of the
interaction at the detector can be exploited to determine a priori the neutrino
energy spectrum without relying on the final state reconstruction ("narrow band
off-axis technique"). Lepton monitoring and narrow band off-axis energy
reconstruction can be implemented in a single facility based on standard
accelerator technologies for a new generation of high precision $\nu_e$ and
$\nu_\mu$ cross section measurements at the GeV scale and for precision
searches of physics beyond the standard 3$\nu$ paradigm. In 2019-2022 ENUBET
has devised the first end-to-end simulation of the facility and demonstrated
that the precision goals can be achieved in $\sim$ 3 years of data taking
employing neutrino detectors of moderate mass (ICARUS at FNAL, ProtoDUNE at
CERN). The technology of a monitored beam has been proven to be feasible and
cost-effective, and the complexity does not exceed significantly the one of a
conventional short-baseline beam. The Snowmass 2021 DPF Community Planning
Exercise is thus timely for the consideration of monitored neutrino beams
hosting the next generation of cross section experiments. The ENUBET results
will play an important role in the systematic reduction programme of future
long baseline experiments, thus enhancing the physics reach of DUNE and
Hyper-Kamiokande. In this document, we summarize the ENUBET design, physics
performance and opportunities for its implementation in a timescale comparable
with DUNE.


title: Two Approaches to Survival Analysis of Open Source Python Projects

abstract:
  A recent study applied frequentist survival analysis methods to a subset of
the Software Heritage Graph and determined which attributes of an OSS project
contribute to its health. This paper serves as an exact replication of that
study. In addition, Bayesian survival analysis methods were applied to the same
dataset, and an additional project attribute was studied to serve as a
conceptual replication. Both analyses focus on the effects of certain
attributes on the survival of open-source software projects as measured by
their revision activity. Methods such as the Kaplan-Meier estimator, Cox
Proportional-Hazards model, and the visualization of posterior survival
functions were used for each of the project attributes. The results show that
projects which publish major releases, have repositories on multiple hosting
services, possess a large team of developers, and make frequent revisions have
a higher likelihood of survival in the long run. The findings were similar to
the original study; however, a deeper look revealed quantitative
inconsistencies.


title: ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data

abstract:
  Unsupervised domain adaptation methods aim to generalize well on unlabeled
test data that may have a different (shifted) distribution from the training
data. Such methods are typically developed on image data, and their application
to time series data is less explored. Existing works on time series domain
adaptation suffer from inconsistencies in evaluation schemes, datasets, and
backbone neural network architectures. Moreover, labeled target data are
usually employed for model selection, which violates the fundamental assumption
of unsupervised domain adaptation. To address these issues, we develop a
benchmarking evaluation suite (ADATIME) to systematically and fairly evaluate
different domain adaptation methods on time series data. Specifically, we
standardize the backbone neural network architectures and benchmarking
datasets, while also exploring more realistic model selection approaches that
can work with no labeled data or just few labeled samples. Our evaluation
includes adapting state-of-the-art visual domain adaptation methods to time
series data in addition to the recent methods specifically developed for time
series data. We conduct extensive experiments to evaluate 10 state-of-the-art
methods on four representative datasets spanning 20 cross-domain scenarios. Our
results suggest that with careful selection of hyper-parameters, visual domain
adaptation methods are competitive with methods proposed for time series domain
adaptation. In addition, we find that hyper-parameters could be selected based
on realistic model selection approaches. Our work unveils practical insights
for applying domain adaptation methods on time series data and builds a solid
foundation for future works in the field. The code is available at
\href{https://github.com/emadeldeen24/AdaTime}{github.com/emadeldeen24/AdaTime}.


title:
DarkQuest: A dark sector upgrade to SpinQuest at the 120 GeV Fermilab
  Main Injector

abstract:
  Expanding the mass range and techniques by which we search for dark matter is
an important part of the worldwide particle physics program. Accelerator-based
searches for dark matter and dark sector particles are a uniquely compelling
part of this program as a way to both create and detect dark matter in the
laboratory and explore the dark sector by searching for mediators and excited
dark matter particles. This paper focuses on developing the DarkQuest
experimental concept and gives an outlook on related enhancements collectively
referred to as LongQuest. DarkQuest is a proton fixed-target experiment with
leading sensitivity to an array of visible dark sector signatures in the
MeV-GeV mass range. Because it builds off of existing accelerator and detector
infrastructure, it offers a powerful but modest-cost experimental initiative
that can be realized on a short timescale.


title: Redis for Market Monitoring

abstract:
  This note shows how to use Redis cache (near-)real-time market data, and
utilise its publish/subscribe ("pub/sub") facility to distribute the data.


title: The Heavy Photon Search Experiment

abstract:
  The Heavy Photon Search (HPS) experiment is designed to search for a new
vector boson $A^\prime$ in the mass range of 20 MeV/$c^2$ to 220 MeV/$c^2$ that
kinetically mixes with the Standard Model photon with couplings $\epsilon^2
>10^{-10}$. In addition to the general importance of exploring light, weakly
coupled physics that is difficult to probe with high-energy colliders, a prime
motivation for this search is the possibility that sub-GeV thermal relics
constitute dark matter, a scenario that requires a new comparably light
mediator, where models with a hidden $U(1)$ gauge symmetry, a "dark", "hidden
sector", or "heavy" photon, are particularly attractive. HPS searches for
visible signatures of these heavy photons, taking advantage of their small
coupling to electric charge to produce them via a process analogous to
bremsstrahlung in a fixed target and detect their subsequent decay to
$\mathrm{e}^+ \mathrm{e}^-$ pairs in a compact spectrometer. In addition to
searching for $\mathrm{e}^+ \mathrm{e}^-$ resonances atop large QED
backgrounds, HPS has the ability to precisely measure decay lengths, resulting
in unique sensitivity to dark photons, as well as other long-lived new physics.
After completion of the experiment and operation of engineering runs in 2015
and 2016 at the JLab CEBAF, physics runs in 2019 and 2021 have provided
datasets that are now being analyzed to search for dark photons and other new
phenomena.


title:
The Geometry and Topology of Stationary Multi-Axisymmetric Vacuum Black
  Holes in Higher Dimensions

abstract:
  Extending recent work in 5 dimensions, we prove the existence and uniqueness
of solutions to the reduced Einstein equations for vacuum black holes in
$(n+3)$-dimensional spacetimes admitting the isometry group $\mathbb{R}\times
U(1)^{n}$, with Kaluza-Klein asymptotics for $n\geq3$. This is equivalent to
establishing existence and uniqueness for singular harmonic maps $\varphi:
\mathbb{R}^3\setminus\Gamma\rightarrow SL(n+1,\mathbb{R})/SO(n+1)$ with
prescribed blow-up along $\Gamma$, a subset of the $z$-axis in $\mathbb{R}^3$.
We also analyze the topology of the domain of outer communication for these
spacetimes, by developing an appropriate generalization of the plumbing
construction used in the lower dimensional case. Furthermore, we provide a
counterexample to a conjecture of Hollands-Ishibashi concerning the topological
classification of the domain of outer communication. A refined version of the
conjecture is then presented and established in spacetime dimensions less than
8.


title: Algebras of Lebesgue and KB regular operators on Banach lattices

abstract:
  The notion of the regularly P-operators is introduced. It is shown that
regularly Lebesgue and regularly KB operators in a Banach lattice form operator
algebras. Some properties of these algebras are investigated.


title: Motif Mining: Finding and Summarizing Remixed Image Content

abstract:
  On the internet, images are no longer static; they have become dynamic
content. Thanks to the availability of smartphones with cameras and easy-to-use
editing software, images can be remixed (i.e., redacted, edited, and recombined
with other content) on-the-fly and with a world-wide audience that can repeat
the process. From digital art to memes, the evolution of images through time is
now an important topic of study for digital humanists, social scientists, and
media forensics specialists. However, because typical data sets in computer
vision are composed of static content, the development of automated algorithms
to analyze remixed content has been limited. In this paper, we introduce the
idea of Motif Mining - the process of finding and summarizing remixed image
content in large collections of unlabeled and unsorted data. In this paper,
this idea is formalized and a reference implementation is introduced.
Experiments are conducted on three meme-style data sets, including a newly
collected set associated with the information war in the Russo-Ukrainian
conflict. The proposed motif mining approach is able to identify related
remixed content that, when compared to similar approaches, more closely aligns
with the preferences and expectations of human observers.