title: The Road Ahead for CODEX-b

abstract:
  In this Snowmass contribution we present a comprehensive status update on the
progress and plans for the proposed CODEX-b detector, intended to search for
long-lived particles beyond the Standard Model. We review the physics case for
the proposal and present recent progress on optimization strategies for the
detector and shielding design, as well as the development of new fast and full
simulation frameworks. A summary of the technical design for a smaller
demonstrator detector (CODEX-$\beta$) for the upcoming Run~3 of the LHC is also
discussed, alongside the road towards realization of the full experiment at the
High-Luminosity LHC.


title: Spectral gap and edge universality of dense random regular graphs

abstract:
  Let $\mathcal A$ be the adjacency matrix of a random $d$-regular graph on $N$
vertices, and we denote its eigenvalues by $\lambda_1\geq \lambda_2\cdots \geq
\lambda_{N}$. For $N^{2/3}\ll d\leq N/2$, we prove optimal rigidity estimates
of the extreme eigenvalues of $\mathcal A$, which in particular imply that \[
\max\{|\lambda_N|,\lambda_2\} <2\sqrt{d-1} \] with overwhelming probability. In
the same regime of $d$, we also show that \[
N^{2/3}\bigg(\frac{\lambda_2}{\sqrt{d(N-d)/N}}-2\bigg)
\overset{d}{\longrightarrow} \mathrm{TW}_1\,, \]where $\mathrm{TW}_1$ is the
Tracy-Widom distribution for GOE; analogues results also hold for other
non-trivial extreme eigenvalues.


title: Gradient Methods with Memory for Minimizing Composite Functions

abstract:
  The recently introduced Gradient Methods with Memory use a subset of the past
oracle information to create a model of the objective function, whose accuracy
enables them to surpass the traditional Gradient Methods in practical
performance. The model introduces an overhead that is substantial, unless
dealing with smooth unconstrained problems. In this work, we introduce several
Gradient Methods with Memory that can solve composite problems efficiently,
including unconstrained problems with non-smooth objectives. The auxiliary
problem at each iteration still cannot be solved exactly but we show how to
alter the model and how to initialize the auxiliary problem solver to ensure
that this inexactness does not degrade the convergence guarantees. Moreover, we
dynamically increase the convergence guarantees as to provably surpass those of
their memory-less counterparts. These properties are preserved when applying
acceleration and the containment of inexactness further prevents error
accumulation. Our methods are able to estimate key geometry parameters to
attain state-of-the art worst-case rates on many important subclasses of
composite problems, where the objective smooth part satisfies a strong
convexity condition or a relaxation thereof. In particular, we formulate a
restart strategy applicable to optimization methods with sublinear convergence
guarantees of any order. We support the theoretical results with simulations.


title:
GCFSR: a Generative and Controllable Face Super Resolution Method
  Without Facial and GAN Priors

abstract:
  Face image super resolution (face hallucination) usually relies on facial
priors to restore realistic details and preserve identity information. Recent
advances can achieve impressive results with the help of GAN prior. They either
design complicated modules to modify the fixed GAN prior or adopt complex
training strategies to finetune the generator. In this work, we propose a
generative and controllable face SR framework, called GCFSR, which can
reconstruct images with faithful identity information without any additional
priors. Generally, GCFSR has an encoder-generator architecture. Two modules
called style modulation and feature modulation are designed for the
multi-factor SR task. The style modulation aims to generate realistic face
details and the feature modulation dynamically fuses the multi-level encoded
features and the generated ones conditioned on the upscaling factor. The simple
and elegant architecture can be trained from scratch in an end-to-end manner.
For small upscaling factors (<=8), GCFSR can produce surprisingly good results
with only adversarial loss. After adding L1 and perceptual losses, GCFSR can
outperform state-of-the-art methods for large upscaling factors (16, 32, 64).
During the test phase, we can modulate the generative strength via feature
modulation by changing the conditional upscaling factor continuously to achieve
various generative effects.


title:
The Right to be Forgotten in Federated Learning: An Efficient
  Realization with Rapid Retraining

abstract:
  In Machine Learning, the emergence of \textit{the right to be forgotten} gave
birth to a paradigm named \textit{machine unlearning}, which enables data
holders to proactively erase their data from a trained model. Existing machine
unlearning techniques focus on centralized training, where access to all
holders' training data is a must for the server to conduct the unlearning
process. It remains largely underexplored about how to achieve unlearning when
full access to all training data becomes unavailable. One noteworthy example is
Federated Learning (FL), where each participating data holder trains locally,
without sharing their training data to the central server. In this paper, we
investigate the problem of machine unlearning in FL systems. We start with a
formal definition of the unlearning problem in FL and propose a rapid
retraining approach to fully erase data samples from a trained FL model. The
resulting design allows data holders to jointly conduct the unlearning process
efficiently while keeping their training data locally. Our formal convergence
and complexity analysis demonstrate that our design can preserve model utility
with high efficiency. Extensive evaluations on four real-world datasets
illustrate the effectiveness and performance of our proposed realization.


title: Possible chiral spin liquid state in the $S=1/2$ kagome Heisenberg model

abstract:
  The nature of the ground state for the $S = 1/2$ kagome Heisenberg
antiferromagnet (KHAF) has been elusive. We revisit this challenging problem
and provide numerical evidence that its ground state might be a chiral spin
liquid. Combining the density matrix renormalization group method and
analytical analyses, we demonstrate that the previously observed chiral spin
liquid phase in the KHAF with longer-range couplings is stable in a broader
region. We characterize the nature of the ground state by computing energy
derivatives, revealing ground-state degeneracy arising from spontaneous
breaking of time-reversal symmetry, and targeting the semion sector. We further
investigate the phase diagram in the vicinity of the KHAF and observe a
$\sqrt{3}\times\sqrt{3}$ magnetically ordered phase and two valence-bond
crystal phases.


title:
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic
  Equilibrium Computation

abstract:
  We consider model-based multi-agent reinforcement learning, where the
environment transition model is unknown and can only be learned via expensive
interactions with the environment. We propose H-MARL (Hallucinated Multi-Agent
Reinforcement Learning), a novel sample-efficient algorithm that can
efficiently balance exploration, i.e., learning about the environment, and
exploitation, i.e., achieve good equilibrium performance in the underlying
general-sum Markov game. H-MARL builds high-probability confidence intervals
around the unknown transition model and sequentially updates them based on
newly observed data. Using these, it constructs an optimistic hallucinated game
for the agents for which equilibrium policies are computed at each round. We
consider general statistical models (e.g., Gaussian processes, deep ensembles,
etc.) and policy classes (e.g., deep neural networks), and theoretically
analyze our approach by bounding the agents' dynamic regret. Moreover, we
provide a convergence rate to the equilibria of the underlying Markov game. We
demonstrate our approach experimentally on an autonomous driving simulation
benchmark. H-MARL learns successful equilibrium policies after a few
interactions with the environment and can significantly improve the performance
compared to non-exploratory methods.


title: White Paper on Light Sterile Neutrino Searches and Related Phenomenology

abstract:
  This white paper provides a comprehensive review of our present understanding
of experimental neutrino anomalies that remain unresolved, charting the
progress achieved over the last decade at the experimental and phenomenological
level, and sets the stage for future programmatic prospects in addressing those
anomalies. It is purposed to serve as a guiding and motivational "encyclopedic"
reference, with emphasis on needs and options for future exploration that may
lead to the ultimate resolution of the anomalies. We see the main experimental,
analysis, and theory-driven thrusts that will be essential to achieving this
goal being: 1) Cover all anomaly sectors -- given the unresolved nature of all
four canonical anomalies, it is imperative to support all pillars of a diverse
experimental portfolio, source, reactor, decay-at-rest, decay-in-flight, and
other methods/sources, to provide complementary probes of and increased
precision for new physics explanations; 2) Pursue diverse signatures -- it is
imperative that experiments make design and analysis choices that maximize
sensitivity to as broad an array of these potential new physics signatures as
possible; 3) Deepen theoretical engagement -- priority in the theory community
should be placed on development of standard and beyond standard models relevant
to all four short-baseline anomalies and the development of tools for efficient
tests of these models with existing and future experimental datasets; 4) Openly
share data -- Fluid communication between the experimental and theory
communities will be required, which implies that both experimental data
releases and theoretical calculations should be publicly available; and 5)
Apply robust analysis techniques -- Appropriate statistical treatment is
crucial to assess the compatibility of data sets within the context of any
given model.

categories: hep-ex astro-ph.CO hep-ph physics.ins-det


title:
Intend-Wait-Cross: Towards Modeling Realistic Pedestrian Crossing
  Behavior

abstract:
  In this paper, we present a microscopic agent-based pedestrian behavior model
Intend-Wait-Cross. The model is comprised of rules representing behaviors of
pedestrians as a series of decisions that depend on their individual
characteristics (e.g. demographics, walking speed, law obedience) and
environmental conditions (e.g. traffic flow, road structure). The model's main
focus is on generating realistic crossing decision-model, which incorporates an
improved formulation of time-to-collision (TTC) computation accounting for
context, vehicle dynamics, and perceptual noise.
  Our model generates a diverse population of agents acting in a highly
configurable environment. All model components, including individual
characteristics of pedestrians, types of decisions they make, and environmental
factors, are motivated by studies on pedestrian traffic behavior. Model
parameters are calibrated using a combination of naturalistic driving data and
estimates from the literature to maximize the realism of the simulated
behaviors. A number of experiments validate various aspects of the model, such
as pedestrian crossing patterns, and individual characteristics of pedestrians.


title: Minimum-Error Triangulations for Sea Surface Reconstruction

abstract:
  We apply state-of-the-art computational geometry methods to the problem of
reconstructing a time-varying sea surface from tide gauge records. Our work
builds on a recent article by Nitzke et al.~(Computers \& Geosciences,
157:104920, 2021) who have suggested to learn a triangulation $D$ of a given
set of tide gauge stations. The objective is to minimize the misfit of the
piecewise linear surface induced by $D$ to a reference surface that has been
acquired with satellite altimetry. The authors restricted their search to
k-order Delaunay ($k$-OD) triangulations and used an integer linear program in
order to solve the resulting optimization problem.
  In geometric terms, the input to our problem consists of two sets of points
in $\mathbb{R}^2$ with elevations: a set $\mathcal{S}$ that is to be
triangulated, and a set $\mathcal{R}$ of reference points. Intuitively, we
define the error of a triangulation as the average vertical distance of a point
in $\mathcal{R}$ to the triangulated surface that is obtained by interpolating
elevations of $\mathcal{S}$ linearly in each triangle. Our goal is to find the
triangulation of $\mathcal{S}$ that has minimum error with respect to
$\mathcal{R}$.
  In our work, we prove that the minimum-error triangulation problem is NP-hard
and cannot be approximated within any multiplicative factor in polynomial time
unless $P=NP$. At the same time we show that the problem instances that occur
in our application (considering sea level data from several hundreds of tide
gauge stations worldwide) can be solved relatively fast using dynamic
programming when restricted to $k$-OD triangulations for $k\le 7$. In
particular, instances for which the number of connected components of the
so-called $k$-OD fixed-edge graph is small can be solved within few seconds.


title:
On the strong convergence of the Faedo-Galerkin approximations to a
  strong T-periodic solution of the torso-coupled bi-domain model

abstract:
  In this paper, we investigate the convergence of the Faedo-Galerkin
approximations, in a strong sense, to a strong T-periodic solution of the
torso-coupled bidomain model where $T$ is the period of activation of the inner
wall of heart. First, we define the torso-coupled bi-domain operator and prove
some of its more important properties for our work. After, we define the
abstract evolution system of equations associated with torso-coupled bidomain
model and give the definition of strong solution. We prove that the
Faedo-Galerkin's approximations have the regularity of a strong solution, and
we find that some restrictions can be imposed over the initial conditions, so
that this sequence of Faedo-Galerkin fully converge to a global strong solution
of the Cauchy problem. Finally, this results are used for showing the existence
a strong $T$-periodic solution.


title:
Improved reproducibility of diffusion kurtosis imaging using regularized
  non-linear optimization informed by artificial neural networks

abstract:
  Diffusion kurtosis imaging is an extension of diffusion tensor imaging that
provides scientifically and clinically valuable information about brain tissue
microstructure but suffers from poor robustness to noise, especially in voxels
containing tightly packed aligned axons. We present a new algorithm for
estimating diffusion and kurtosis tensors using regularized non-linear
optimization and make it publicly available in an easy-to-use open-source
Python software package. Our approach uses fully-connected feed-forward neural
networks to predict kurtosis values in voxels where the standard non-linear
least squares fit fails. The predicted values are then used in the objective
function to avoid implausible kurtosis values. We show that our algorithm is
more robust than standard non-linear least squares and a previously proposed
regularized non-linear optimization method. The algorithm was then applied on a
multi-site scan-rescan dataset acquired using a clinical scan protocol to
assess the reproducibility of diffusion kurtosis parameter estimation in human
white matter using the proposed algorithm. Our results show that the
reproducibility of diffusion kurtosis parameters is similar to diffusion tensor
parameters.


title: Snowmass 2021 Community Survey Report

abstract:
  The Snowmass Community Survey was designed by the Snowmass Early Career (SEC)
Survey Core Initiative team between April 2020 and June 2021, and released to
the community on June 28, 2021. It aims to be a comprehensive assessment of the
state of the high-energy particle and astrophysics (HEPA) community, if not the
field, though the Snowmass process is largely based within the United States.
Among other topics, some of the central foci of the Survey were to gather
demographic, career, physics outlook, and workplace culture data on a large
segment of the Snowmass community. With nearly $1500$ total interactions with
the Survey, the SEC Survey team hopes the findings and discussions within this
report will be of service to the community over the next decade. Some
conclusions should reinforce the aspects of HEPA which are already functional
and productive, while others should strengthen arguments for cultural and
policy changes within the field.

categories: hep-ex astro-ph.IM hep-ph physics.soc-ph


title:
Randomized algorithms for Tikhonov regularization in linear least
  squares

abstract:
  We describe two algorithms to efficiently solve regularized linear least
squares systems based on sketching. The algorithms compute preconditioners for
$\min \|Ax-b\|^2_2 + \lambda \|x\|^2_2$, where $A\in\mathbb{R}^{m\times n}$ and
$\lambda>0$ is a regularization parameter, such that LSQR converges in
$\mathcal{O}(\log(1/\epsilon))$ iterations for $\epsilon$ accuracy. We focus on
the context where the optimal regularization parameter is unknown, and the
system must be solved for a number of parameters $\lambda$. Our algorithms are
applicable in both the underdetermined $m\ll n$ and the overdetermined $m\gg n$
setting. Firstly, we propose a Cholesky-based sketch-to-precondition algorithm
that uses a `partly exact' sketch, and only requires one sketch for a set of
$N$ regularization parameters $\lambda$. The complexity of solving for $N$
parameters is $\mathcal{O}(mn\log(\max(m,n)) +N(\min(m,n)^3 +
mn\log(1/\epsilon)))$. Secondly, we introduce an algorithm that uses a sketch
of size $\mathcal{O}(\text{sd}_{\lambda}(A))$ for the case where the
statistical dimension $\text{sd}_{\lambda}(A)\ll\min(m,n)$. The scheme we
propose does not require the computation of the Gram matrix, resulting in a
more stable scheme than existing algorithms in this context. We can solve for
$N$ values of $\lambda_i$ in $\mathcal{O}(mn\log(\max(m,n)) +
\min(m,n)\,\text{sd}_{\min\lambda_i}(A)^2 + Nmn\log(1/\epsilon))$ operations.


title: Convergence analysis of the intrinsic surface finite element method

abstract:
  The Intrinsic Surface Finite Element Method (ISFEM) was recently proposed to
solve Partial Differential Equations (PDEs) on surfaces. ISFEM proceeds by
writing the PDE with respect to a local coordinate system anchored to the
surface and makes direct use of the resulting covariant basis. Starting from a
shape-regular triangulation of the surface, existence of a local
parametrization for each triangle is exploited to approximate relevant
quantities on the local chart. Standard two-dimensional FEM techniques in
combination with surface quadrature rules complete the ISFEM formulation thus
achieving a method that is fully intrinsic to the surface and makes limited use
of the surface embedding only for the definition of linear basis functions.
However, theoretical properties have not yet been proved. In this work, we
complement the original derivation of ISFEM with its complete convergence
theory and propose the analysis of the stability and error estimates by
carefully tracking the role of the geometric quantities in the constants of the
error inequalities. Numerical experiments are included to support the
theoretical results.


title: Effective non-local parity-dependent couplings in qubit chains

abstract:
  For the efficient implementation of quantum algorithms, practical ways to
generate many-body entanglement are a basic requirement. Specifically, coupling
multiple qubit pairs at once can be advantageous and can lead to multi-qubit
operations useful in the construction of hardware-tailored algorithms. Here we
harness the simultaneous coupling of qubits on a chain and engineer a set of
non-local parity-dependent quantum operations suitable for a wide range of
applications. The resulting effective long-range couplings directly implement a
parametrizable Trotter-step for Jordan-Wigner fermions and can be used for
simulations of quantum dynamics, efficient state generation in variational
quantum eigensolvers, parity measurements for error-correction schemes, and the
generation of efficient multi-qubit gates. Moreover, we present numerical
simulations of the gate operation in a superconducting quantum circuit
architecture, which show a high gate fidelity of $>99.9\%$ for realistic
experimental parameters.


title:
Single-Pixel Imaging in Space and Time with Optically-Modulated Free
  Electrons

abstract:
  Single-pixel imaging, originally developed in light optics, facilitates fast
three-dimensional sample reconstruction, as well as probing with light
wavelengths undetectable by conventional multi-pixel detectors. However, the
spatial resolution of optics-based single-pixel microscopy is limited by
diffraction to hundreds of nanometers. Here, we propose an implementation of
single-pixel imaging relying on attainable modifications of currently available
ultrafast electron microscopes in which optically-modulated electrons are used
instead of photons to achieve sub-nanometer spatially- and temporally-resolved
single-pixel imaging. We simulate electron beam profiles generated by
interaction with the optical field produced by an externally programable
spatial light modulator and demonstrate the feasibility of the method by
showing that the sample image and its temporal evolution can be reconstructed
using realistic imperfect illumination patterns. Electron single-pixel imaging
holds strong potential for application in low-dose probing of beam-sensitive
biological and molecular samples, including rapid screening during in-situ
experiments.


title: Nuclear effects in the deuteron and global QCD analyses

abstract:
  We report the results of a new global QCD analysis which includes
deep-inelastic $e/\mu$ scattering (DIS) data off proton and deuterium, as well
as Drell-Yan lepton pair production in proton-proton and proton-deuterium
collisions and $W^\pm/Z$ boson production data from $pp$ and $p \bar p$
collisions at the LHC and Tevatron. Nuclear effects in the deuteron are treated
in terms of bound (off-shell) nucleons within a nuclear convolution approach
including an off-shell correction. This latter is controlled by a universal
off-shell function which describes the modification of parton distributions in
bound nucleons and is determined in our analysis along with the parton
distribution functions (PDFs). A number of systematic studies are performed to
estimate the uncertainties arising from the use of various deuterium data sets,
from the modeling of higher twist contributions to the structure functions,
from the treatment of target mass corrections, as well as from the nuclear
corrections in the deuteron. We obtain predictions for the ratios
$F_2^n/F_2^p$, and $d/u$, focusing on the region of large Bjorken $x$. We also
compare our results with the ones obtained by other QCD analyses, as well as
with the recent data from the MARATHON experiment.


title:
Toward the limits of complexity of interstellar chemistry: Rotational
  spectroscopy and astronomical search for n- and i-butanal

abstract:
  In recent times, large organic molecules of exceptional complexity have been
found in diverse regions of the interstellar medium. In this context, we aim to
provide accurate frequencies of the ground vibrational state of two key
aliphatic aldehydes, n-butanal and its branched-chain isomer, i-butanal, to
enable their eventual detection in the interstellar medium. We employ a
frequency modulation millimeter-wave absorption spectrometer to measure the
rotational features of n- and i-butanal. We use the spectral line survey ReMoCA
performed with the Atacama Large Millimeter/submillimeter Array to search for
n- and i-butanal toward the star-forming region Sgr B2(N). We also search for
both aldehydes toward the molecular cloud G+0.693-0.027 with IRAM 30 m and
Yebes 40 m observations. Several thousand rotational transitions belonging to
the lowest-energy conformers have been assigned in the laboratory spectra up to
325 GHz. A precise set of the relevant rotational spectroscopic constants has
been determined for each structure. We report non-detections of n- and
i-butanal toward both sources, Sgr B2(N1S) and G+0.693-0.027. We find that n-
and i-butanal are at least 2-6 and 6-18 times less abundant than acetaldehyde
toward Sgr B2(N1S), respectively, and that n-butanal is at least 63 times less
abundant than acetaldehyde toward G+0.693-0.027. Comparison with astrochemical
models indicates good agreement between observed and simulated abundances
(where available). Grain-surface chemistry appears sufficient to reproduce
aldehyde ratios in G+0.693-0.027; gas-phase production may play a more active
role in Sgr B2(N1S). Our astronomical results indicate that the family of
interstellar aldehydes in the Galactic center region is characterized by a drop
of one order of magnitude in abundance at each incrementation in the level of
molecular complexity.


title:
Remarks on the vertex and the edge metric dimension of 2-connected
  graphs

abstract:
  The vertex (resp. edge) metric dimension of a graph G is the size of a
smallest vertex set in G which distinguishes all pairs of vertices (resp.
edges) in G and it is denoted by dim(G) (resp. edim(G)). The upper bounds
dim(G) <= 2c(G) - 1 and edim(G) <= 2c(G)-1; where c(G) denotes the cyclomatic
number of G, were established to hold for cacti without leaves distinct from
cycles, and moreover all leafless cacti which attain the bounds were
characterized. It was further conjectured that the same bounds hold for general
connected graphs without leaves and this conjecture was supported by showing
that the problem reduces to 2-connected graphs. In this paper we focus on Theta
graphs, as the most simple 2-connected graphs distinct from cycle, and show
that the the upper bound 2c(G) - 1 holds for both metric dimensions of Theta
graphs and we characterize all Theta graphs for which the bound is attained. We
conclude by conjecturing that there are no other extremal graphs for the bound
2c(G) - 1 in the class of leafless graphs besides already known extremal cacti
and extremal Theta graphs mentioned here.


title: Snowmass White Paper: Effective Field Theory Matching and Applications

abstract:
  Mapping UV theories onto low energy effective descriptions is a procedure
known as matching. The last decade has seen tremendous progress in the
development of new tools for efficiently performing matching calculations, by
relying on so-called functional methods. This white paper summarizes the status
of functional matching. Specifically, matching for relativistic theories is a
fully solved problem up to one-loop order in perturbation theory, and to
arbitrary order in the effective field theory expansion. A streamlined
prescription that has been partially automated facilitates the application of
functional matching to phenomenological studies in the Standard Model EFT
framework.


title: Phenomenology of Double Descent in Finite-Width Neural Networks

abstract:
  `Double descent' delineates the generalization behaviour of models depending
on the regime they belong to: under- or over-parameterized. The current
theoretical understanding behind the occurrence of this phenomenon is primarily
based on linear and kernel regression models -- with informal parallels to
neural networks via the Neural Tangent Kernel. Therefore such analyses do not
adequately capture the mechanisms behind double descent in finite-width neural
networks, as well as, disregard crucial components -- such as the choice of the
loss function. We address these shortcomings by leveraging influence functions
in order to derive suitable expressions of the population loss and its lower
bound, while imposing minimal assumptions on the form of the parametric model.
Our derived bounds bear an intimate connection with the spectrum of the Hessian
at the optimum, and importantly, exhibit a double descent behaviour at the
interpolation threshold. Building on our analysis, we further investigate how
the loss function affects double descent -- and thus uncover interesting
properties of neural networks and their Hessian spectra near the interpolation
threshold.


title:
Inverse Online Learning: Understanding Non-Stationary and Reactionary
  Policies

abstract:
  Human decision making is well known to be imperfect and the ability to
analyse such processes individually is crucial when attempting to aid or
improve a decision-maker's ability to perform a task, e.g. to alert them to
potential biases or oversights on their part. To do so, it is necessary to
develop interpretable representations of how agents make decisions and how this
process changes over time as the agent learns online in reaction to the accrued
experience. To then understand the decision-making processes underlying a set
of observed trajectories, we cast the policy inference problem as the inverse
to this online learning problem. By interpreting actions within a potential
outcomes framework, we introduce a meaningful mapping based on agents choosing
an action they believe to have the greatest treatment effect. We introduce a
practical algorithm for retrospectively estimating such perceived effects,
alongside the process through which agents update them, using a novel
architecture built upon an expressive family of deep state-space models.
Through application to the analysis of UNOS organ donation acceptance
decisions, we demonstrate that our approach can bring valuable insights into
the factors that govern decision processes and how they change over time.


title: FNAL PIP-II Accumulator Ring

abstract:
  The FNAL accelerator complex is poised to reach MW neutrino beams on target
for the exploration of the dark sector physics and rare physics program spaces.
Future operations of the complex will include CW linac operations at beam
intensities that have not been seen before \cite{PIP2,RCS_LOI}. The ambitious
beam program relies on multi-turn H$^{-}$ injection into the FNAL Booster and
then extracted into delivery rings or the Booster Neutrino Beam (BNB) 8 GeV HEP
program. A new rapid-cycling synchrotron (RCS) will be required to reach the
LBNF goal of 2.4 MW because of intense space-charge limitations. There are many
accelerator engineering challenges that are already known and many that will be
discovered. This proposal calls for an intermediate step that will both
facilitate the operation of Booster in the PIP-II era and gain operational
experience associated with high power injection rings. This step includes the
design, construction and installation of a 0.8 GeV accumulator ring
(upgradeable to 1+ GeV) to be located in the PIP-II Booster Transfer Line
(BTL). The PIP-II accumulator ring (PAR) may be primarily designed around
permanent magnets or use standard iron core magnet technology with an aperture
selected to accommodate the desired high intensity protons at 0.8 GeV.


title:
An improved approach to manufacture CNT reinforced magnesium AZ91
  composites with increased strength and ductility

abstract:
  Multiwalled carbon nanotubes (MWCNTs) are decorated with Pt nanoparticles by
a "layer-by-layer" approach using poly (sodium 4-styrene sulfonate) (PSS) and
poly (diallyl dimethylammonium chloride) (PDDA). Transmission electron
microscopy (TEM) images and Energy Dispersive X-Ray (EDX) analysis of the
samples confirm Pt deposition on surfaces of CNTs. Dispersibility and
dispersion stability of MWCNTs in the solvents are enhanced when MWCNTs are
coated with Pt nanoparticles. Mg AZ91 composites reinforced with MWCNTs are
then produced by a melt stirring process. Compression tests of the composites
show that adding 0.05\% wt Pt-coated MWCNTs in AZ91 improves the composite's
mechanical properties compared to the pure AZ91 and pristine MWCNT/AZ91.
Fracture surface analysis of the composite using a scanning electron microscope
(SEM) shows individuals pulled out MWCNTs in the case of the Pt-coated
MWCNT/AZ91 composites. We attribute this finding to the uniform dispersion of
Pt-coated MWCNTs in Mg due to the improved wettability of Pt-coated MWCNTs in
Mg melts. Molecular dynamics (MD) simulations of the interaction between
Pt-coated MWCNTs and Mg support this interpretation.


title:
Defending From Physically-Realizable Adversarial Attacks Through
  Internal Over-Activation Analysis

abstract:
  This work presents Z-Mask, a robust and effective strategy to improve the
adversarial robustness of convolutional networks against physically-realizable
adversarial attacks. The presented defense relies on specific Z-score analysis
performed on the internal network features to detect and mask the pixels
corresponding to adversarial objects in the input image. To this end, spatially
contiguous activations are examined in shallow and deep layers to suggest
potential adversarial regions. Such proposals are then aggregated through a
multi-thresholding mechanism. The effectiveness of Z-Mask is evaluated with an
extensive set of experiments carried out on models for both semantic
segmentation and object detection. The evaluation is performed with both
digital patches added to the input images and printed patches positioned in the
real world. The obtained results confirm that Z-Mask outperforms the
state-of-the-art methods in terms of both detection accuracy and overall
performance of the networks under attack. Additional experiments showed that
Z-Mask is also robust against possible defense-aware attacks.


title:
A Bayesian Nonparametric Approach to Species Sampling Problems with
  Ordering

abstract:
  Species-sampling problems (SSPs) refer to a vast class of statistical
problems that, given an observable sample from an unknown population of
individuals belonging to some species, call for estimating (discrete)
functionals of the unknown species composition of additional unobservable
samples. A common feature of SSPs is the invariance with respect to species
labelling, i.e. species' labels are immaterial in defining the functional of
interest, which is at the core of the development of the Bayesian nonparametric
(BNP) approach to SSPs under the popular Pitman-Yor process (PYP) prior. In
this paper, we consider SSPs that are not invariant to species labelling, in
the sense that an ordering or ranking is assigned to species' labels, and we
develop a BNP approach to such problems. In particular, inspired by the
population genetics literature on age-ordered alleles' compositions, with a
renowned interest in the frequency of the oldest allele, we study the following
SSP with ordering: given an observable sample from unknown population of
individuals belonging to some species (alleles), with species' labels being
ordered according to weights (ages), estimate the frequencies of the first r
order species' labels in an enlarged sample obtained by including additional
unobservable samples. Our BNP approach relies on an ordered version of the PYP
prior, which leads to an explicit posterior distribution of the first r order
frequencies, with corresponding estimates being simple and computationally
efficient. We apply our approach to the analysis of genetic variation, showing
its effectiveness in the estimation of the frequency of the oldest allele, and
then discuss other applications in the contexts of citations to academic
articles and online purchases of items.


title:
Tile Based Modeling of DNA Self-Assembly for Two Graph Families with
  Appended Paths

abstract:
  Branched molecules of deoxyribonucleic acid (DNA) can self-assemble into
nanostructures through complementary cohesive strand base pairing. The
production of DNA nanostructures is valuable in targeted drug delivery and
biomolecular computing. With theoretical efficiency of laboratory processes in
mind, we use a flexible tile model for DNA assembly. We aim to minimize the
number of different types of branched junction molecules necessary to assemble
certain target structures. We represent target structures as discrete graphs
and branched DNA molecules as vertices with half-edges. We present the minimum
numbers of required branched molecule and cohesive-end types under three levels
of restrictive conditions for the tadpole and lollipop graph families. These
families represent cycle and complete graphs with a path appended via a single
cut-vertex. We include three general lemmas regarding such vertex-induced path
subgraphs. Through proofs and examples, we demonstrate the challenges that can
arise in determining optimal construction strategies.


title:
Top-quark mass extraction from $t\bar{t}j +X$ events at the LHC: theory
  predictions

abstract:
  Past work has proven the possibility of extracting the top-quark mass, one of
the fundamental parameters of the Standard Model, from the comparison of theory
predictions and experimental measurements of differential cross-sections for
$t\bar{t} j~+~X$ hadroproduction. Various experimental analyses in this respect
have already been performed, and new ones are in preparation on the basis of
the latest data from $pp$ collisions collected at the Large Hadron Collider. We
have produced and made public a comprehensive set of theoretical predictions
for the relevant differential distributions, ready to be used for presently
ongoing and forthcoming experimental analyses. We investigate the role of
different theoretical inputs, in particular the factorization and
renormalization scales, PDFs and top-quark mass renormalization schemes, and we
quantify the uncertainties related to different choices for these inputs,
providing recommendations.


title:
Federated Cycling (FedCy): Semi-supervised Federated Learning of
  Surgical Phases

abstract:
  Recent advancements in deep learning methods bring computer-assistance a step
closer to fulfilling promises of safer surgical procedures. However, the
generalizability of such methods is often dependent on training on diverse
datasets from multiple medical institutions, which is a restrictive requirement
considering the sensitive nature of medical data. Recently proposed
collaborative learning methods such as Federated Learning (FL) allow for
training on remote datasets without the need to explicitly share data. Even so,
data annotation still represents a bottleneck, particularly in medicine and
surgery where clinical expertise is often required. With these constraints in
mind, we propose FedCy, a federated semi-supervised learning (FSSL) method that
combines FL and self-supervised learning to exploit a decentralized dataset of
both labeled and unlabeled videos, thereby improving performance on the task of
surgical phase recognition. By leveraging temporal patterns in the labeled
data, FedCy helps guide unsupervised training on unlabeled data towards
learning task-specific features for phase recognition. We demonstrate
significant performance gains over state-of-the-art FSSL methods on the task of
automatic recognition of surgical phases using a newly collected
multi-institutional dataset of laparoscopic cholecystectomy videos.
Furthermore, we demonstrate that our approach also learns more generalizable
features when tested on data from an unseen domain.


title:
Sparse random hypergraphs: Non-backtracking spectra and community
  detection

abstract:
  We consider the community detection problem in a sparse $q$-uniform
hypergraph $G$, assuming that $G$ is generated according to the so-called
Hypergraph Stochastic Block Model (HSBM). We prove that a spectral method based
on the non-backtracking operator for hypergraphs works with high probability
down to the generalized Kesten-Stigum detection threshold conjectured by
Angelini et al. We characterize the spectrum of the non-backtracking operator
for the sparse HSBM, and provide an efficient dimension reduction procedure
using the Ihara-Bass formula for hypergraphs. As a result, community detection
for the sparse HSBM on $n$ vertices can be reduced to an eigenvector problem of
a $2n\times 2n$ non-normal matrix constructed from the adjacency matrix and the
degree matrix of the hypergraph. To the best of our knowledge, this is the
first provable and efficient spectral algorithm that achieves the conjectured
threshold for HSBMs with $k$ blocks generated according to a general symmetric
probability tensor.


title:
Snowmass2021 Computational Frontier White Paper: Cosmological
  Simulations and Modeling

abstract:
  Powerful new observational facilities will come online over the next decade,
enabling a number of discovery opportunities in the "Cosmic Frontier", which
targets understanding of the physics of the early universe, dark matter and
dark energy, and cosmological probes of fundamental physics, such as neutrino
masses and modifications of Einstein gravity. Synergies between different
experiments will be leveraged to present new classes of cosmic probes as well
as to minimize systematic biases present in individual surveys. Success of this
observational program requires actively pairing it with a well-matched
state-of-the-art simulation and modeling effort. Next-generation cosmological
modeling will increasingly focus on physically rich simulations able to model
outputs of sky surveys spanning multiple wavebands. These simulations will have
unprecedented resolution, volume coverage, and must deliver guaranteed
high-fidelity results for individual surveys as well as for the
cross-correlations across different surveys. The needed advances are as
follows: (1) Development of scientifically rich and broadly-scoped simulations,
which capture the relevant physics and correlations between probes (2) Accurate
translation of simulation results into realistic image or spectral data to be
directly compared with observations (3) Improved emulators and/or data-driven
methods serving as surrogates for expensive simulations, constructed from a
finite set of full-physics simulations (4) Detailed and transparent
verification and validation programs for both simulations and analysis tools.
(Abridged)


title:
Emergent soft-gap Anderson models at quantum criticality in a lattice
  Hamiltonian within dynamical mean field theory

abstract:
  Local quantum criticality in itinerant fermion systems has been extensively
investigated through the soft-gap Anderson impurity model, wherein a localized,
correlated impurity, hybridizes with a broad conduction band with a singular,
$|\omega|^r$, density of states. However, lattice models hosting quantum
critical points (QCPs), do not appear to have such a spectrum emerging at the
QCP. In this work, we report the emergence of such a singular form of the
density of states in a three-orbital lattice model, within dynamical mean field
theory, precisely at a quantum critical point, separating a gapless, Fermi
liquid, metallic phase from a gapped, Mott insulating phase. The corresponding
Matsubara self-energy is observed to have a form, $-{\rm Im}\Sigma(i\omega_n\to
0)\sim |\omega_n|^{\alpha}$, with the temperature-dependent exponent $\alpha$
varying from $+1$ deep in the FL regime, to $-1$ in the Mott insulator regime.
Interestingly, we find that $\alpha$ becomes temperature independent, and hence
isosbestic, precisely at the QCP. The isosbestic exponent is shown to be a
consequence of an emergent soft-gap spectrum, $|\omega|^r$ at the QCP, where $r
= -\alpha_{\rm iso}$. We discuss the implications of our findings for non-Fermi
liquid behaviour in the quantum critical region of the phase diagram.


title: The Project 8 Neutrino Mass Experiment

abstract:
  Measurements of the $\beta^-$ spectrum of tritium give the most precise
direct limits on neutrino mass. Project 8 will investigate neutrino mass using
Cyclotron Radiation Emission Spectroscopy (CRES) with an atomic tritium source.
CRES is a new experimental technique that has the potential to surmount the
systematic and statistical limitations of current-generation direct measurement
methods. Atomic tritium avoids an irreducible systematic uncertainty associated
with the final states populated by the decay of molecular tritium. Project 8
will proceed in a phased approach toward a goal of 40 meV/c$^2$ neutrino-mass
sensitivity.


title: Spectra of self-similar ergodic actions

abstract:
  This note is devoted to infinite self-similar rank 1 constructions and their
applications to the spectral theory of dynamical systems. The properties of
orthogonal operators induced by self-similar constructions provide some unusual
spectral properties of Gaussian and Poisson suspensions.


title:
WIMP Dark Matter at High Energy Muon Colliders $-$A White Paper for
  Snowmass 2021

abstract:
  In a previous publication, we showed that a high energy muon collider can
make decisive statements about the electroweak (WIMP) Dark Matter (DM),
reaching a DM mass which could give the observed thermal relic abundance. In
this document, we report new studies of the spin-$0$ minimal WIMP DM at high
energy muon colliders, and update our results on the fermionic spin-$1/2$ case.
We find that, by combining multiple inclusive missing mass search channels, it
is possible to fully cover the thermal targets of fermionic and scalar
doublets, and Dirac triplet, with a 10 TeV muon collider. Higher energies, 14
TeV$-$30 TeV, would be able to cover the thermal targets of Majorana and scalar
triplet. For direct discovery of the higher EW multiplets with $n\geq5$, one
may need to go beyond a 30 TeV muon collider to fully cover their thermal mass
expectation.


title:
Development and demonstration of next generation technology for Nb_3Sn
  accelerator magnets with lower cost, improved performance uniformity, and
  higher operating point in the 12-14 T range

abstract:
  The scope of the proposal outlined in this white paper is the development and
demonstration of the technology needed for next generation of Nb_3Sn
accelerator magnets in the 12-14 T range. The main goal is to cut magnet
cold-mass cost by a factor 2 or higher with respect to the Nb_3Sn magnets
produced by the US Accelerator Upgrade Project (AUP) for the High-Luminosity
Large Hadron Collider (HL-LHC). This goal will be achieved by significant
reduction of labor hours, higher operating point, and improved performance
uniformity. A key factor will be automation that will be achieved through
industry involvement and benefitting from the experience gained in US national
laboratories through the production of the AUP magnets. This partnership will
enable the development of a technology that will be easily transferable to
industry for mid- and large-scale production of Nb_3Sn accelerator magnets in
the 12-14 T range. This step is essential to enable next generation of
colliders such as the FNAL-proposed Muon Collider, FCC and other HEP hadron
colliders.
  This is a Directed R&D where direction is given by the field range and
industry involvement for high-automation and industry-ready technology. The
plan includes ten milestones, to be achieved in 6-8 years at the cost of 5-7
$M/year.


title:
Improving Di-Higgs Sensitivity at Future Colliders in Hadronic Final
  States with Machine Learning

abstract:
  One of the central goals of the physics program at the future colliders is to
elucidate the origin of electroweak symmetry breaking, including precision
measurements of the Higgs sector. This includes a detailed study of Higgs boson
(H) pair production, which can reveal the H self-coupling. Since the discovery
of the Higgs boson, a large campaign of measurements of the properties of the
Higgs boson has begun and many new ideas have emerged during the completion of
this program. One such idea is the use of highly boosted and merged hadronic
decays of the Higgs boson ($\mathrm{H}\to\mathrm{b}\bar{\mathrm{b}}$,
$\mathrm{H}\to\mathrm{W}\mathrm{W}\to\mathrm{q}\bar{\mathrm{q}}\mathrm{q}\bar{\mathrm{q}}$)
with machine learning methods to improve the signal-to-background
discrimination. In this white paper, we champion the use of these modes to
boost the sensitivity of future collider physics programs to Higgs boson pair
production, the Higgs self-coupling, and Higgs-vector boson couplings. We
demonstrate the potential improvement possible at the Future Circular Collider
in hadron mode, especially with the use of graph neural networks.


title:
Snowmass2021 Cosmic Frontier White Paper: Dark Matter Physics from Halo
  Measurements

abstract:
  The non-linear process of cosmic structure formation produces gravitationally
bound overdensities of dark matter known as halos. The abundances, density
profiles, ellipticities, and spins of these halos can be tied to the underlying
fundamental particle physics that governs dark matter at microscopic scales.
Thus, macroscopic measurements of dark matter halos offer a unique opportunity
to determine the underlying properties of dark matter across the vast landscape
of dark matter theories. This white paper summarizes the ongoing rapid
development of theoretical and experimental methods, as well as new
opportunities, to use dark matter halo measurements as a pillar of dark matter
physics.


title: Information-Theoretic Secure and Private Voting System

abstract:
  In this paper, we present a private voting system that consists of N
authorized voters who may vote to one of the K candidates or vote abstain. Each
voter wants to compute the final tally while staying private and robust against
malicious voters, who try to gain information about the vote of the other
voters beyond the final result, or send incorrect information to affect the
final tally. We design an information-theoretic private voting system based on
Shamir secret sharing, which is secure and robust as long as there are up to
(N-1)/3 malicious voters.


title:
Unified Theory of the Anomalous and Topological Hall Effects with Phase
  Space Berry Curvatures

abstract:
  Hall experiments in chiral magnets are often analyzed as the sum of an
anomalous Hall effect, dominated by momentum-space Berry curvature, and a
topological Hall effect, arising from the real-space Berry curvature in the
presence of skyrmions, in addition to the ordinary Hall resistivity. This
raises the questions of how one can incorporate, on an equal footing, the
effects of the anomalous velocity and the real space winding of the
magnetization, and when such a decomposition of the resistivity is justified.
We provide definitive answers to these questions by including the effects of
all phase-space Berry curvatures in a semi-classical approach and by solving
the Boltzmann equation in a weak spin-orbit coupling regime when the
magnetization texture varies slowly on the scale of the mean free path. We show
that the Hall resistivity is then just the sum of the anomalous and topological
contributions, with negligible corrections from Berry curvature-independent and
mixed curvature terms. We also use an exact Kubo formalism to numerically
investigate the opposite limit of infinite mean path, and show that the results
are similar to the semi-classical results.


title: The COS Legacy Archive Spectroscopy SurveY (CLASSY) Treasury Atlas

abstract:
  Far-ultraviolet (FUV; ~1200-2000 angstroms) spectra are fundamental to our
understanding of star-forming galaxies, providing a unique window on massive
stellar populations, chemical evolution, feedback processes, and reionization.
The launch of JWST will soon usher in a new era, pushing the UV spectroscopic
frontier to higher redshifts than ever before, however, its success hinges on a
comprehensive understanding of the massive star populations and gas conditions
that power the observed UV spectral features. This requires a level of detail
that is only possible with a combination of ample wavelength coverage,
signal-to-noise, spectral-resolution, and sample diversity that has not yet
been achieved by any FUV spectral database.
  We present the COS Legacy Spectroscopic SurveY (CLASSY) treasury and its
first high level science product, the CLASSY atlas. CLASSY builds on the HST
archive to construct the first high-quality (S/N_1500 >~ 5/resel),
high-resolution (R~15,000) FUV spectral database of 45 nearby (0.002 < z <
0.182) star-forming galaxies. The CLASSY atlas, available to the public via the
CLASSY website, is the result of optimally extracting and coadding 170
archival+new spectra from 312 orbits of HST observations.
  The CLASSY sample covers a broad range of properties including stellar mass
(6.2 < logM_star(M_sol) < 10.1), star formation rate (-2.0 < log SFR (M_sol/yr)
< +1.6), direct gas-phase metallicity (7.0 < 12+log(O/H) < 8.8), ionization
(0.5 < O_32 < 38.0), reddening (0.02 < E(B-V < 0.67), and nebular density (10 <
n_e (cm^-3) < 1120). CLASSY is biased to UV-bright star-forming galaxies,
resulting in a sample that is consistent with z~0 mass-metallicity
relationship, but is offset to higher SFRs by roughly 2 dex, similar to z >~2
galaxies. This unique set of properties makes the CLASSY atlas the benchmark
training set for star-forming galaxies across cosmic time.


title: CERC -- Circular $e^+e^-$ Collider using Energy-Recovery Linac

abstract:
  We present a Circular Energy Recovery Collider (CERC) as an alternative
approach for a high-energy high-luminosity electron-positron collider to
current designs for high-energy electron-positron colliders either based on two
storage rings with 100 km circumference or two large linear accelerators. Using
Energy Recovery Linacs (ERL) located in the same-size 100 km tunnel would allow
a large reduction of the beam energy losses, and therefore a reduction of the
power consumption, while providing higher luminosity. It also opens a path for
extending the center-of-mass (CM) energy to 500 GeV, which would enable
double-Higgs production, and even to 600 GeV for production and measurements of
the top Yukawa coupling. Furthermore, this approach would allow recycling of
not only the energy but also of the particles. This feature opens the
possibility for colliding fully polarized electron and positron beams.


title: Stubborn: A Strong Baseline for Indoor Object Navigation

abstract:
  We present a strong baseline that surpasses the performance of previously
published methods on the Habitat Challenge task of navigating to a target
object in indoor environments. Our method is motivated from primary failure
modes of prior state-of-the-art: poor exploration, inaccurate object
identification, and agent getting trapped due to imprecise map construction. We
make three contributions to mitigate these issues: (i) First, we show that
existing map-based methods fail to effectively use semantic clues for
exploration. We present a semantic-agnostic exploration strategy (called
Stubborn) without any learning that surprisingly outperforms prior work. (ii)
We propose a strategy for integrating temporal information to improve object
identification. (iii) Lastly, due to inaccurate depth observation the agent
often gets trapped in small regions. We develop a multi-scale collision map for
obstacle identification that mitigates this issue.


title: The Future of Gamma-Ray Experiments in the MeV-EeV Range

abstract:
  Gamma-rays, the most energetic photons, carry information from the far
reaches of extragalactic space with minimal interaction or loss of information.
They bring messages about particle acceleration in environments so extreme they
cannot be reproduced on earth for a closer look. Gamma-ray astrophysics is so
complementary with collider work that particle physicists and astroparticle
physicists are often one in the same. Gamma-ray instruments, especially the
Fermi Gamma-ray Space Telescope, have been pivotal in major multi-messenger
discoveries over the past decade. There is presently a great deal of interest
and scientific expertise available to push forward new technologies, to plan
and build space- and ground-based gamma-ray facilities, and to build
multi-messenger networks with gamma rays at their core. It is therefore
concerning that before the community comes together for planning exercises
again, much of that infrastructure could be lost to a lack of long-term
planning for support of gamma-ray astrophysics. Gamma-rays with energies from
the MeV to the EeV band are therefore central to multiwavelength and
multi-messenger studies to everything from astroparticle physics with compact
objects, to dark matter studies with diffuse large scale structure. These goals
and new discoveries have generated a wave of new gamma-ray facility proposals
and programs. This paper highlights new and proposed gamma-ray technologies and
facilities that have each been designed to address specific needs in the
measurement of extreme astrophysical sources that probe some of the most
pressing questions in fundamental physics for the next decade. The proposed
instrumentation would also address the priorities laid out in the recent
Astro2020 Decadal Survey, a complementary study by the astrophysics community
that provides opportunities also relevant to Snowmass.


title:
Coherent elastic neutrino-nucleus scattering: Terrestrial and
  astrophysical applications

abstract:
  Coherent elastic neutrino-nucleus scattering (CE$\nu$NS) is a process in
which neutrinos scatter on a nucleus which acts as a single particle. Though
the total cross section is large by neutrino standards, CE$\nu$NS has long
proven difficult to detect, since the deposited energy into the nucleus is
$\sim$ keV. In 2017, the COHERENT collaboration announced the detection of
CE$\nu$NS using a stopped-pion source with CsI detectors, followed up the
detection of CE$\nu$NS using an Ar target. The detection of CE$\nu$NS has
spawned a flurry of activities in high-energy physics, inspiring new
constraints on beyond the Standard Model (BSM) physics, and new experimental
methods. The CE$\nu$NS process has important implications for not only
high-energy physics, but also astrophysics, nuclear physics, and beyond. This
whitepaper discusses the scientific importance of CE$\nu$NS, highlighting how
present experiments such as COHERENT are informing theory, and also how future
experiments will provide a wealth of information across the aforementioned
fields of physics.


title:
CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and
  Argumentation Detection

abstract:
  We present CoNTACT: a Dutch language model adapted to the domain of COVID-19
tweets. The model was developed by continuing the pre-training phase of RobBERT
(Delobelle, 2020) by using 2.8M Dutch COVID-19 related tweets posted in 2021.
In order to test the performance of the model and compare it to RobBERT, the
two models were tested on two tasks: (1) binary vaccine hesitancy detection and
(2) detection of arguments for vaccine hesitancy. For both tasks, not only
Twitter but also Facebook data was used to show cross-genre performance. In our
experiments, CoNTACT showed statistically significant gains over RobBERT in all
experiments for task 1. For task 2, we observed substantial improvements in
virtually all classes in all experiments. An error analysis indicated that the
domain adaptation yielded better representations of domain-specific
terminology, causing CoNTACT to make more accurate classification decisions.


title: Implicit Motion Handling for Video Camouflaged Object Detection

abstract:
  We propose a new video camouflaged object detection (VCOD) framework that can
exploit both short-term dynamics and long-term temporal consistency to detect
camouflaged objects from video frames. An essential property of camouflaged
objects is that they usually exhibit patterns similar to the background and
thus make them hard to identify from still images. Therefore, effectively
handling temporal dynamics in videos becomes the key for the VCOD task as the
camouflaged objects will be noticeable when they move. However, current VCOD
methods often leverage homography or optical flows to represent motions, where
the detection error may accumulate from both the motion estimation error and
the segmentation error. On the other hand, our method unifies motion estimation
and object segmentation within a single optimization framework. Specifically,
we build a dense correlation volume to implicitly capture motions between
neighbouring frames and utilize the final segmentation supervision to optimize
the implicit motion estimation and segmentation jointly. Furthermore, to
enforce temporal consistency within a video sequence, we jointly utilize a
spatio-temporal transformer to refine the short-term predictions. Extensive
experiments on VCOD benchmarks demonstrate the architectural effectiveness of
our approach. We also provide a large-scale VCOD dataset named MoCA-Mask with
pixel-level handcrafted ground-truth masks and construct a comprehensive VCOD
benchmark with previous methods to facilitate research in this direction.
Dataset Link: https://xueliancheng.github.io/SLT-Net-project.


title: A Supervised Learning Approach to Rankability

abstract:
  The rankability of data is a recently proposed problem that considers the
ability of a dataset, represented as a graph, to produce a meaningful ranking
of the items it contains. To study this concept, a number of rankability
measures have recently been proposed, based on comparisons to a complete
dominance graph via combinatorial and linear algebraic methods. In this paper,
we review these measures and highlight some questions to which they give rise
before going on to propose new methods to assess rankability, which are
amenable to efficient estimation. Finally, we compare these measures by
applying them to both synthetic and real-life sports data.


title: Intertwining orbital current order and superconductivity in Kagome metal

abstract:
  The nature of superconductivity in newly discovered Kagome materials,
$\text{AV}_3\text{Sb}_5$ (A=K, Rb, Cs), has been a subject of intense debate.
Recent experiments suggest the presence of orbital current order on top of the
charge density wave (CDW) and superconductivity. Since the orbital current
order breaks time-reversal symmetry, it may fundamentally affect possible
superconducting states. In this work, we investigate the mutual influence
between the orbital current order and superconductivity in Kagome metal with
characteristic van Hove singularity (vHS). By explicitly deriving the
Landau-Ginzburg theory, we classify possible orbital current order and
superconductivity. It turns out that distinct unconventional
superconductivities are expected, depending on the orbital current ordering
types. Thus, this information can be used to infer the superconducting order
parameter when the orbital current order is identified and vice versa. We also
discuss possible experiments that may distinguish such superconducting states
coexisting with the orbital current order.


title:
Schrieffer-Wolff Transformations for Experiments: Dynamically
  Suppressing Virtual Doublon-Hole Excitations in a Fermi-Hubbard Simulator

abstract:
  In strongly interacting systems with a separation of energy scales,
low-energy effective Hamiltonians help provide insights into the relevant
physics at low temperatures. The emergent interactions in the effective model
are mediated by virtual excitations of high-energy states: For example, virtual
doublon-hole excitations in the Fermi-Hubbard model mediate antiferromagnetic
spin-exchange interactions in the derived effective model, known as the
$t-J-3s$ model. Formally this procedure is described by performing a unitary
Schrieffer-Wolff basis transformation. In the context of quantum simulation, it
can be advantageous to consider the effective model to interpret experimental
results. However, virtual excitations such as doublon-hole pairs can obfuscate
the measurement of physical observables. Here we show that quantum simulators
allow one to access the effective model even more directly by performing
measurements in a rotated basis. We propose a protocol to perform a
Schrieffer-Wolff transformation on Fermi-Hubbard low-energy eigenstates (or
thermal states) to dynamically prepare approximate $t-J-3s$ model states using
fermionic atoms in an optical lattice. Our protocol involves performing a
linear ramp of the optical lattice depth, which is slow enough to eliminate the
virtual doublon-hole fluctuations but fast enough to freeze out the dynamics in
the effective model. We perform a numerical study using exact diagonalization
and find an optimal ramp speed for which the state after the lattice ramp has
maximal overlap with the $t-J-3s$ model state. We compare our numerics to
experimental data from our Lithium-6 fermionic quantum gas microscope and show
a proof-of-principle demonstration of this protocol. More generally, this
protocol can be beneficial to studies of effective models by enabling the
suppression of virtual excitations in a wide range of quantum simulation
experiments.


title: Algebraic Hastatic Order in One-Dimensional Two-Channel Kondo Lattice

abstract:
  The two-channel Kondo lattice likely hosts a rich array of phases, including
hastatic order, a channel-symmetry breaking heavy Fermi liquid. We revisit its
one-dimensional phase diagram using density matrix renormalization group. In
contrast to previous work, we find algebraic hastatic orders generically for
strong coupling. These are heavy Tomonaga-Luttinger liquids with
nonanalyticities at Fermi wave-vectors captured by hastatic density waves.
Intriguingly, we find a recently predicted additional order parameter, not
present at large-$N$, arising from RKKY mediated interference between hastatic
spinors, and indications of residual repulsive interactions at strong coupling.


title: The Efficacy of Pessimism in Asynchronous Q-Learning

abstract:
  This paper is concerned with the asynchronous form of Q-learning, which
applies a stochastic approximation scheme to Markovian data samples. Motivated
by the recent advances in offline reinforcement learning, we develop an
algorithmic framework that incorporates the principle of pessimism into
asynchronous Q-learning, which penalizes infrequently-visited state-action
pairs based on suitable lower confidence bounds (LCBs). This framework leads
to, among other things, improved sample efficiency and enhanced adaptivity in
the presence of near-expert data. Our approach permits the observed data in
some important scenarios to cover only partial state-action space, which is in
stark contrast to prior theory that requires uniform coverage of all
state-action pairs. When coupled with the idea of variance reduction,
asynchronous Q-learning with LCB penalization achieves near-optimal sample
complexity, provided that the target accuracy level is small enough. In
comparison, prior works were suboptimal in terms of the dependency on the
effective horizon even when i.i.d. sampling is permitted. Our results deliver
the first theoretical support for the use of pessimism principle in the
presence of Markovian non-i.i.d. data.

categories: cs.LG cs.IT math.IT math.OC math.ST stat.ML stat.TH


title:
Ancilla-free implementation of generalized measurements for qubits
  embedded in a qudit space

abstract:
  Informationally complete (IC) positive operator-valued measures (POVMs) are
generalized quantum measurements that offer advantages over the standard
computational basis readout of qubits. For instance, IC-POVMs enable efficient
extraction of operator expectation values, a crucial step in many quantum
algorithms. POVM measurements are typically implemented by coupling one
additional ancilla qubit to each logical qubit, thus imposing high demands on
the device size and connectivity. Here, we show how to implement a general
class of IC-POVMs without ancilla qubits. We exploit the higher-dimensional
Hilbert space of a qudit in which qubits are often encoded. POVMs can then be
realized by coupling each qubit to two of the available qudit states, followed
by a projective measurement. We develop the required control pulse sequences
and numerically establish their feasibility for superconducting transmon qubits
through pulse-level simulations. Finally, we present an experimental
demonstration of a qudit-space POVM measurement on IBM Quantum hardware. This
paves the way to making POVM measurements broadly available to quantum
computing applications.


title:
A Nivat Theorem for Weighted Alternating Automata over Commutative
  Semirings

abstract:
  In this paper, we give a Nivat-like characterization for weighted alternating
automata over commutative semirings (WAFA). We investigate the use of trees in
the run semantics for WAFA and prove that weighted alternating automata can be
characterized as the concatenation of weighted finite tree automata (WFTA) and
a specific class of tree homomorphism. We show that the class of series
recognized by weighted alternating automata is closed under inverses of
homomorphisms, but not under homomorphisms. We give a logical characterization
of weighted alternating automata, which uses weighted MSO logic for trees.
Finally we investigate the strong connection between weighted alternating
automata and polynomial automata. Using the corresponding result for polynomial
automata, we are able to prove that the ZERONESS problem for weighted
alternating automata with the rational numbers as weights is decidable.


title:
Medium-Grain Niobium SRF Cavity Production Technology for Science
  Frontiers and Accelerator Applications

abstract:
  We propose cost-effective production of medium grain (MG) niobium (Nb) discs
directly sliced from forged and annealed billet. This production method
provides clean surface conditions and reliable mechanical characteristics with
sub-millimeter average grain size resulting in stable SRF cavity production. We
propose to apply this material to particle accelerator applications in the
science and industrial frontiers. The science applications require high field
gradients (>~40 MV/m) particularly in pulse mode. The industrial applications
require high Q0 values with moderate gradients (~30 MV/m) in CW mode operation.
This report describes the MG Nb disc production recently demonstrated and
discusses future prospects for application in advanced particle accelerators in
the science and industrial frontiers.


title:
Enhancing crowd flow prediction in various spatial and temporal
  granularities

abstract:
  Thanks to the diffusion of the Internet of Things, nowadays it is possible to
sense human mobility almost in real time using unconventional methods (e.g.,
number of bikes in a bike station). Due to the diffusion of such technologies,
the last years have witnessed a significant growth of human mobility studies,
motivated by their importance in a wide range of applications, from traffic
management to public security and computational epidemiology. A mobility task
that is becoming prominent is crowd flow prediction, i.e., forecasting
aggregated incoming and outgoing flows in the locations of a geographic region.
Although several deep learning approaches have been proposed to solve this
problem, their usage is limited to specific types of spatial tessellations and
cannot provide sufficient explanations of their predictions. We propose
CrowdNet, a solution to crowd flow prediction based on graph convolutional
networks. Compared with state-of-the-art solutions, CrowdNet can be used with
regions of irregular shapes and provide meaningful explanations of the
predicted crowd flows. We conduct experiments on public data varying the
spatio-temporal granularity of crowd flows to show the superiority of our model
with respect to existing methods, and we investigate CrowdNet's reliability to
missing or noisy input data. Our model is a step forward in the design of
reliable deep learning models to predict and explain human displacements in
urban environments.


title: SATr: Slice Attention with Transformer for Universal Lesion Detection

abstract:
  Universal Lesion Detection (ULD) in computed tomography plays an essential
role in computer-aided diagnosis. Promising ULD results have been reported by
multi-slice-input detection approaches which model 3D context from multiple
adjacent CT slices, but such methods still experience difficulty in obtaining a
global representation among different slices and within each individual slice
since they only use convolution-based fusion operations. In this paper, we
propose a novel Slice Attention Transformer (SATr) block which can be easily
plugged into convolution-based ULD backbones to form hybrid network structures.
Such newly formed hybrid backbones can better model long-distance feature
dependency via the cascaded self-attention modules in the Transformer block
while still holding a strong power of modeling local features with the
convolutional operations in the original backbone. Experiments with five
state-of-the-art methods show that the proposed SATr block can provide an
almost free boost to lesion detection accuracy without extra hyperparameters or
special network designs.


title:
Automated fault tree learning from continuous-valued sensor data: a case
  study on domestic heaters

abstract:
  Many industrial sectors have been collecting big sensor data. With recent
technologies for processing big data, companies can exploit this for automatic
failure detection and prevention. We propose the first completely automated
method for failure analysis, machine-learning fault trees from raw
observational data with continuous variables. Our method scales well and is
tested on a real-world, five-year dataset of domestic heater operations in The
Netherlands, with 31 million unique heater-day readings, each containing 27
sensor and 11 failure variables. Our method builds on two previous procedures:
the C4.5 decision-tree learning algorithm, and the LIFT fault tree learning
algorithm from Boolean data. C4.5 pre-processes each continuous variable: it
learns an optimal numerical threshold which distinguishes between faulty and
normal operation of the top-level system. These thresholds discretise the
variables, thus allowing LIFT to learn fault trees which model the root failure
mechanisms of the system and are explainable. We obtain fault trees for the 11
failure variables, and evaluate them in two ways: quantitatively, with a
significance score, and qualitatively, with domain specialists. Some of the
fault trees learnt have almost maximum significance (above 0.95), while others
have medium-to-low significance (around 0.30), reflecting the difficulty of
learning from big, noisy, real-world sensor data. The domain specialists
confirm that the fault trees model meaningful relationships among the
variables.


title: From Big to Small: Adaptive Learning to Partial-Set Domains

abstract:
  Domain adaptation targets at knowledge acquisition and dissemination from a
labeled source domain to an unlabeled target domain under distribution shift.
Still, the common requirement of identical class space shared across domains
hinders applications of domain adaptation to partial-set domains. Recent
advances show that deep pre-trained models of large scale endow rich knowledge
to tackle diverse downstream tasks of small scale. Thus, there is a strong
incentive to adapt models from large-scale domains to small-scale domains. This
paper introduces Partial Domain Adaptation (PDA), a learning paradigm that
relaxes the identical class space assumption to that the source class space
subsumes the target class space. First, we present a theoretical analysis of
partial domain adaptation, which uncovers the importance of estimating the
transferable probability of each class and each instance across domains. Then,
we propose Selective Adversarial Network (SAN and SAN++) with a bi-level
selection strategy and an adversarial adaptation mechanism. The bi-level
selection strategy up-weighs each class and each instance simultaneously for
source supervised training, target self-training, and source-target adversarial
adaptation through the transferable probability estimated alternately by the
model. Experiments on standard partial-set datasets and more challenging tasks
with superclasses show that SAN++ outperforms several domain adaptation
methods.


title:
HIE-SQL: History Information Enhanced Network for Context-Dependent
  Text-to-SQL Semantic Parsing

abstract:
  Recently, context-dependent text-to-SQL semantic parsing which translates
natural language into SQL in an interaction process has attracted a lot of
attention. Previous works leverage context-dependence information either from
interaction history utterances or the previous predicted SQL queries but fail
in taking advantage of both since of the mismatch between natural language and
logic-form SQL. In this work, we propose a History Information Enhanced
text-to-SQL model (HIE-SQL) to exploit context-dependence information from both
history utterances and the last predicted SQL query. In view of the mismatch,
we treat natural language and SQL as two modalities and propose a bimodal
pre-trained model to bridge the gap between them. Besides, we design a
schema-linking graph to enhance connections from utterances and the SQL query
to the database schema. We show our history information enhanced methods
improve the performance of HIE-SQL by a significant margin, which achieves new
state-of-the-art results on the two context-dependent text-to-SQL benchmarks,
the SparC and CoSQL datasets, at the writing time.


title:
Synergy between cosmological and laboratory searches in neutrino
  physics: a white paper

abstract:
  The intersection of the cosmic and neutrino frontiers is a rich field where
much discovery space still remains. Neutrinos play a pivotal role in the hot
big bang cosmology, influencing the dynamics of the universe over numerous
decades in cosmological history. Recent studies have made tremendous progress
in understanding some properties of cosmological neutrinos, primarily their
energy density. Upcoming cosmological probes will give higher precision on the
energy density, but could also start probing other properties of the neutrino
spectra. When convolved with results from terrestrial experiments, cosmology
can become even more acute at probing new physics related to neutrinos or even
Beyond the Standard Model (BSM). Any discordance between laboratory and
cosmological data sets may reveal new BSM physics or suggest alternative models
of cosmology. We give examples of the intersection between terrestrial and
cosmological probes in the neutrino sector, and briefly discuss the
possibilities of what different experiments may see in conjunction with
cosmological observatories.


title:
Dawn of the transformer era in speech emotion recognition: closing the
  valence gap

abstract:
  Recent advances in transformer-based architectures which are pre-trained in
self-supervised manner have shown great promise in several machine learning
tasks. In the audio domain, such architectures have also been successfully
utilised in the field of speech emotion recognition (SER). However, existing
works have not evaluated the influence of model size and pre-training data on
downstream performance, and have shown limited attention to generalisation,
robustness, fairness, and efficiency. The present contribution conducts a
thorough analysis of these aspects on several pre-trained variants of wav2vec
2.0 and HuBERT that we fine-tuned on the dimensions arousal, dominance, and
valence of MSP-Podcast, while additionally using IEMOCAP and MOSI to test
cross-corpus generalisation. To the best of our knowledge, we obtain the top
performance for valence prediction without use of explicit linguistic
information, with a concordance correlation coefficient (CCC) of .638 on
MSP-Podcast. Furthermore, our investigations reveal that transformer-based
architectures are more robust to small perturbations compared to a CNN-based
baseline and fair with respect to biological sex groups, but not towards
individual speakers. Finally, we are the first to show that their extraordinary
success on valence is based on implicit linguistic information learnt during
fine-tuning of the transformer layers, which explains why they perform on-par
with recent multimodal approaches that explicitly utilise textual information.
Our findings collectively paint the following picture: transformer-based
architectures constitute the new state-of-the-art in SER, but further advances
are needed to mitigate remaining robustness and individual speaker issues. To
make our findings reproducible, we release the best performing model to the
community.


title:
Quantitative Gaussian Approximation of Randomly Initialized Deep Neural
  Networks

abstract:
  Given any deep fully connected neural network, initialized with random
Gaussian parameters, we bound from above the quadratic Wasserstein distance
between its output distribution and a suitable Gaussian process. Our explicit
inequalities indicate how the hidden and output layers sizes affect the
Gaussian behaviour of the network and quantitatively recover the distributional
convergence results in the wide limit, i.e., if all the hidden layers sizes
become large.


title: Bipolaronic high-temperature superconductivity

abstract:
  Electron-lattice interactions play a prominent role in quantum materials,
making a deeper understanding of direct routes to phonon-mediated
high-transition-temperature ($T_{\mathrm{c}}$) superconductivity desirable.
However, it has been known for decades that weak electron-phonon coupling gives
rise to low values of $T_{\mathrm{c}}$, while strong electron-phonon coupling
leads to lattice instability or formation of bipolarons, generally assumed to
be detrimental to superconductivity. Thus, the route to high-$T_{\mathrm{c}}$
materials from phonon-mediated mechanisms has heretofore appeared to be limited
to raising the phonon frequency as in the hydrogen sulfides. Here we present a
simple model for phonon-mediated high-$T_{\mathrm{c}}$ superconductivity based
on superfluidity of light bipolarons. In contrast to the greatly simplified
Holstein model where lattice distortions modulate the electron's potential
energy, we investigate the situation where lattice distortions modulate the
electron hopping. This physics gives rise to small-size, yet light bipolarons,
which we study using a sign-problem-free quantum Monte Carlo approach,
demonstrating a new route to phonon-mediated high-$T_\mathrm{c}$
superconductivity. We find that $T_\mathrm{c}$ in our model generically and
significantly exceeds typical upper bounds based on Migdal-Eliashberg theory or
superfluidity of Holstein bipolarons. Our work establishes principles towards
the design of high-$T_{\mathrm{c}}$ superconductors via functional material
engineering.


title: Flavor Anomalies and Quark-Lepton Unification

abstract:
  We show that one can explain the neutral and charged anomalies in B-meson
decays in the minimal theory for quark-lepton unification.The implications for
flavor violating processes are discussed in detail. Strikingly, experimental
observations suggest that the unification of quarks and leptons could be
realized below the ${\cal O}(10^2)$ TeV scale.


title:
The Effect of Molecular Cloud Properties on the Kinematics of Stars
  Formed in the Trifid Region

abstract:
  The dynamical states of molecular clouds may affect the properties of the
stars they form. In the vicinity of the Trifid Nebula ($d=1180\pm25$ pc), the
main star cluster (Trifid Main) lies within an expanding section of the
molecular cloud; however, ~0.3 deg to the north (Trifid North), the cloud's
velocity structure is more tranquil. We acquired a Chandra X-ray observation to
identify pre-main-sequence stars in Trifid North, complementing a previous
observation of Trifid Main. In Trifid North, we identified 51 candidate
pre-main-sequence stars, of which 13 are high-confidence Trifid members based
on Gaia EDR3 parallaxes and proper motions. We also re-analyzed membership of
Trifid Main and separated out multiple background stellar associations. Trifid
North represents a stellar population ~10% as rich as Trifid Main that formed
in a separate part of the cloud. The 1D stellar velocity dispersion in Trifid
North ($0.6\pm0.2$ km/s) is three times lower than in Trifid Main ($1.9\pm0.2$
km/s). Furthermore, in Trifid Main, proper motions indicate that the portion of
the star cluster superimposed on the optical nebula is expanding. Expansion of
the HII region around the O-star HD 164492A, and the resulting gas expulsion,
can explain both the motions of the stars and gas in Trifid Main. Contrary to
previous studies, we find no evidence that a cloud-cloud collision triggered
star formation in the region.


title: Formation and fate of low metallicity stars in IllustrisTNG50

abstract:
  Low metallicity stars give rise to unique spectacular transients and are of
immense interest for understanding stellar evolution. Their importance has only
grown further with the recent detections of mergers of stellar mass black holes
that likely originate mainly from low metallicity progenitor systems. Moreover,
the formation of low metallicity stars is intricately linked to galaxy
evolution, in particular to early enrichment and to later accretion and mixing
of lower metallicity gas. Because low metallicity stars are difficult to
observe directly, cosmological simulations are crucial for understanding their
formation. Here we quantify the rates and locations of low metallicity star
formation using the high-resolution TNG50 magnetohydrodynamical cosmological
simulation, and we examine where low metallicity stars end up at $z=0$. We find
that $20\%$ of stars with $Z_*<0.1\,\mathrm{Z_\odot}$ form after $z=2$, and
that such stars are still forming in galaxies of all masses at $z=0$ today.
Moreover, most low-metallicity stars at $z=0$ reside in massive galaxies. We
analyse the radial distribution of low metallicity star formation, and discuss
the curious case of seven galaxies in TNG50 that form stars from primordial gas
even at $z=0$.


title:
Alpha states demystified: Towards microscopic models of AdS$_2$
  holography

abstract:
  We continue our study of factorizing theories of dilaton gravity,
characterized by a universal bilocal interaction. All such factorizing theories
can be shown to have discrete spectra, distinguished only by their local
dilaton potentials. We show how such theories can be used to construct all
alpha-states in the Hilbert space of baby universes of ordinary JT gravity.
Large classes of these theories with different local potentials are found to be
non-perturbatively equivalent and have identical discrete spectra. This is a
concrete example of how different bulk descriptions can give rise to the same
boundary theory. Such equivalences manifest themselves as null states, which
have to be quotiented out in order to construct a proper baby universe Hilbert
space. Our results also allow us to revisit the mechanism discussed by Coleman,
Giddings, and Strominger, and concretely link ensemble averaging to the
appearance or disappearance of spacetime wormholes. We then investigate JT
gravity deformed only by the universal bilocal interaction. In this theory, the
only terms that do not cancel in a topological expansion are disks, which
capture perturbative fluctuations around a two-dimensional black hole saddle.
We find that this theory of black holes has an evenly spaced spectrum, instead
of a quantum chaotic one. We present a dual quantum mechanical system with
exactly the same discrete spectrum, and propose that this is an example of a
new holographic duality between a two-dimensional theory of quantum gravity and
a conventional quantum mechanics.


title:
Extinguishing the FIRE: environmental quenching of satellite galaxies
  around Milky Way-mass hosts in simulations

abstract:
  The star formation and gas content of satellite galaxies around the Milky Way
(MW) and Andromeda (M31) are depleted relative to more isolated galaxies in the
Local Group (LG) at fixed stellar mass. We explore the environmental regulation
of gas content and quenching of star formation in $z=0$ galaxies at
$M_{*}=10^{5-10}$ $\rm{M}_{\odot}$ around 14 MW-mass hosts from the FIRE-2
simulations. Lower-mass satellites ($M_{*}\lesssim10^7$ $\rm{M}_{\odot}$) are
mostly quiescent and higher-mass satellites ($M_{*}\gtrsim10^8$
$\rm{M}_{\odot}$) are mostly star-forming, with intermediate-mass satellites
($M_{*}\approx10^{7-8}$ $\rm{M}_{\odot}$) split roughly equally between
quiescent and star-forming. Hosts with more gas in their circumgalactic medium
have a higher quiescent fraction of massive satellites ($M_{*}=10^{8-9}$
$\rm{M}_{\odot}$). We find no significant dependence on isolated versus paired
(LG-like) host environments, and the quiescent fractions of satellites around
MW-mass and LMC-mass hosts from the FIRE-2 simulations are remarkably similar.
Environmental effects that lead to quenching can also occur as preprocessing in
low-mass groups prior to MW infall. Lower-mass satellites typically quenched
before MW infall as central galaxies or rapidly during infall into a low-mass
group or a MW-mass galaxy. Intermediate- to high-mass satellites usually
require $\geq1-2$ pericentre passages ($\approx2.5-5$ Gyr) within a MW-mass
halo to quench. Most galaxies with $M_{*}\gtrsim10^{6.5}$ $\rm{M}_{\odot}$ did
not quench before falling into a host, indicating a possible upper mass limit
for isolated quenching. The simulations are broadly consistent with the
quiescent fractions of satellites observed in the LG and the SAGA survey,
because the simulation average lies between them and the host-to-host scatter
is large.


title: Coset space actions for nonrelativistic strings

abstract:
  We formulate the stringy nonrelativistic limits of the flat space and
AdS$_5\times$S$^5$ string as coset models, based on the string Bargmann and
extended string Newton-Hooke algebras respectively. Our construction mimics the
typical relativistic one, but differs in several interesting ways. Using our
coset formulation we give a Lax representation of the equations of motion of
both models.


title:
Quantifying Feedback from Narrow Line Region Outflows in Nearby Active
  Galaxies. IV. The Effects of Different Density Estimates on the Ionized Gas
  Masses and Outflow Rates

abstract:
  Active galactic nuclei (AGN) can launch outflows of ionized gas that may
influence galaxy evolution, and quantifying their full impact requires
spatially resolved measurements of the gas masses, velocities, and radial
extents. We previously reported these quantities for the ionized narrow-line
region (NLR) outflows in six low-redshift AGN, where the gas velocities and
extents were determined from Hubble Space Telescope long-slit spectroscopy.
However, calculating the gas masses required multi-component photoionization
models to account for radial variations in the gas densities, which span
$\sim$6 orders of magnitude. In order to simplify this method for larger
samples with less spectral coverage, we compare these gas masses with those
calculated from techniques in the literature. First, we use a recombination
equation with three different estimates for the radial density profiles. These
include constant densities, those derived from [S II], and power-law profiles
based on constant values of the ionization parameter ($U$). Second, we use
single-component photoionization models with power-law density profiles based
on constant $U$, and allow $U$ to vary with radius based on the [O
III]/H$\beta$ ratios. We find that assuming a constant density of $n_\mathrm{H}
=$ 10$^2$ cm$^{-3}$ overestimates the gas masses for all six outflows,
particularly at small radii where the outflow rates peak. The use of [S II]
marginally matches the total gas masses, but also overestimates at small radii.
Overall, single-component photoionization models where $U$ varies with radius
are able to best match the gas mass and outflow rate profiles when there are
insufficient emission lines to construct detailed physical models.


title:
Radio Analysis of SN 2004C Reveals an Unusual CSM Density Profile as a
  Harbinger of Core Collapse

abstract:
  We present extensive multi-frequency VLA and VLBA observations of the
radio-bright supernova (SN) IIb SN 2004C that span $\sim(40-2793)$ days
post-explosion. We interpret the temporal evolution of the radio spectral
energy distribution (SED) in the context of synchrotron self-absorbed (SSA)
emission from the explosion's forward shock as it expands in the circumstellar
medium (CSM) previously sculpted by the mass-loss history of the stellar
progenitor. VLBA observations and modeling of the VLA data point to a blastwave
with average velocity $\sim0.06c$ that carries an energy of $\sim 10^{49}$ erg.
Our modeling further reveals a flat CSM density profile $\rho_{\rm{CSM}}
\propto R^{-0.03 \pm0.22}$ up to a break radius $R_{br} \approx (1.96 \pm 0.10)
\times 10^{16}$ cm, with a steep density gradient following $\rho_{\rm{CSM}}
\propto R^{-2.3 \pm 0.5}$ at larger radii. We infer that the flat part of the
density profile corresponds to a CSM shell with mass $\sim0.021 M_{\odot}$, and
that the progenitor's effective mass-loss rate varied with time over the range
$(50-500) \times 10^{-5} M_{\odot} \rm{yr}^{-1}$ for an adopted wind velocity
$v_w =1000$ km $s^{-1}$ and shock microphysical parameters ${\epsilon}_e = 0.1,
{\epsilon}_B = 0.01$. These results add to the mounting observational evidence
for departures from the traditional single-wind mass-loss scenarios in evolved,
massive stars in the centuries leading up to core collapse. Potentially viable
scenarios include mass loss powered by gravity waves and/or interaction with a
binary companion.


title:
The Metallicity Distribution Function in Outer Halo Fields of Simulated
  Elliptical Galaxies Compared to Observations of NGC 5128

abstract:
  Stellar metallicity distribution functions (MDF) have been measured for
resolved stellar populations in the outer halos of many galaxies in nearby
groups. Among them, the MDF of NGC 5128, the central giant elliptical in the
Centaurus group, provides essential constraints for theories of massive galaxy
formation and hierarchical assembly. To investigate the formation and chemical
evolution history of the outer halo of giant elliptical galaxies, we examine
the chemical properties of three zoom-in high resolution cosmological
hydrodynamical simulations of an NGC 5128-like giant elliptical galaxy and
compare their outer halo MDFs to the observed one of NGC 5128. Even though the
simulated galaxies have different merging histories and age distributions, all
predicted MDFs are in good qualitative agreement with the observed one. The
median metallicity of the simulated galaxies is on average $\rm [M/H]=-0.41 \pm
0.06$ compared to the observed value of $\rm [M/H]=-0.38 \pm 0.02$ for NGC
5128, and the dispersion in metallicity is $\sim 0.77$ dex for both observed
and simulated galaxies. We investigate the origin of the stars ending up in the
outer halo field of simulated galaxies and show that most have an `accreted'
origin, formed in other small galaxies and later accreted in mergers. Only
$\sim 15$ percent of the stars are formed `in situ' within the main progenitor
of galaxy and radially migrate outwards. We show that the contribution of
metal-rich in situ stars is sub-dominant in the outer halos of our simulated
galaxies, but can be prominent in the inner regions.


title:
There's no difference: Convolutional Neural Networks for transient
  detection without template subtraction

abstract:
  We present a Convolutional Neural Network (CNN) model for the separation of
astrophysical transients from image artifacts, a task known as "real-bogus"
classification, that does not rely on Difference Image Analysis (DIA) which is
a computationally expensive process involving image matching on small spatial
scales in large volumes of data. We explore the use of CNNs to (1) automate the
"real-bogus" classification, (2) reduce the computational costs of transient
discovery. We compare the efficiency of two CNNs with similar architectures,
one that uses "image triplets" (templates, search, and the corresponding
difference image) and one that adopts a similar architecture but takes as input
the template and search only. Without substantially changing the model
architecture or retuning the hyperparameters to the new input, we observe only
a small decrease in model efficiency (97% to 92% accuracy). We further
investigate how the model that does not receive the difference image learns the
required information from the template and search by exploring the saliency
maps. Our work demonstrates that (1) CNNs are excellent models for "real-bogus"
classification that rely exclusively on the imaging data and require no feature
engineering task; (2) high-accuracy models can be built without the need to
construct difference images. Since once trained, neural networks can generate
predictions at minimal computational costs, we argue that future
implementations of this methodology could dramatically reduce the computational
costs in the detection of genuine transients in synoptic surveys like Rubin
Observatory's Legacy Survey of Space and Time by bypassing the DIA step
entirely.


title:
Accelerated Bayesian SED Modeling using Amortized Neural Posterior
  Estimation

abstract:
  State-of-the-art spectral energy distribution (SED) analyses use a Bayesian
framework to infer the physical properties of galaxies from observed photometry
or spectra. They require sampling from a high-dimensional space of SED model
parameters and take $>10-100$ CPU hours per galaxy, which renders them
practically infeasible for analyzing the $billions$ of galaxies that will be
observed by upcoming galaxy surveys ($e.g.$ DESI, PFS, Rubin, Webb, and Roman).
In this work, we present an alternative scalable approach to rigorous Bayesian
inference using Amortized Neural Posterior Estimation (ANPE). ANPE is a
simulation-based inference method that employs neural networks to estimate the
posterior probability distribution over the full range of observations. Once
trained, it requires no additional model evaluations to estimate the posterior.
We present, and publicly release, ${\rm SED}{flow}$, an ANPE method to produce
posteriors of the recent Hahn et al. (2022) SED model from optical photometry.
${\rm SED}{flow}$ takes ${\sim}1$ $second~per~galaxy$ to obtain the posterior
distributions of 12 model parameters, all of which are in excellent agreement
with traditional Markov Chain Monte Carlo sampling results. We also apply ${\rm
SED}{flow}$ to 33,884 galaxies in the NASA-Sloan Atlas and publicly release
their posteriors: see https://changhoonhahn.github.io/SEDflow.