title: Constriction of actin rings by passive crosslinkers

abstract:
  In many organisms, cell division is driven by the constriction of a
cytokinetic ring, which consists of actin filaments and crosslinking proteins.
While it has long been believed that the constriction is driven by motor
proteins, it has recently been discovered that passive crosslinkers that do not
turn over fuel are able to generate enough force to constrict actin filament
rings. We develop an analytical model that considers both this passive
crosslinker force, here modeled as a force based on condensation of the
crosslinkers from solution, and the opposing elastic bending force, which we
use to make predictions about the maximum constriction forces that could be
generated by this system for varying ring topologies and crosslinker
concentrations. Our predictions indicate that such a mechanism could play an
important role in deforming the plasma membrane during cell division, or as a
component in a minimal synthetic cell.


title:
Spin excitations of a proximate Kitaev quantum spin liquid realized in
  Cu$_2$IrO$_3$

abstract:
  Magnetic moments arranged at the corners of a honeycomb lattice are predicted
to form a novel state of matter, Kitaev quantum spin liquid, under the
influence of frustration effects between bond-dependent Ising interactions.
Some layered honeycomb iridates and related materials, such as
Na$_{2}$IrO$_{3}$ and $\alpha$-RuCl$_{3}$, are proximate to Kitaev quantum spin
liquid, but bosonic spin-wave excitations associated with undesirable
antiferromagnetic long-range order mask the inherent properties of Kitaev
Hamiltonian. Here, we use $^{63}$Cu nuclear quadrupole resonance to uncover the
low energy spin excitations in the nearly ideal honeycomb lattice of effective
spin $S = 1/2$ at the Ir$^{4+}$ sites in Cu$_{2}$IrO$_{3}$. We demonstrate
that, unlike Na$_{2}$IrO$_{3}$, Ir spin fluctuations exhibit no evidence for
critical slowing down toward magnetic long range order in zero external
magnetic field. Moreover, the low energy spin excitation spectrum is dominated
by a mode that has a large excitation gap comparable to the Ising interactions,
a signature expected for Majorana fermions of Kitaev quantum spin liquid.


title:
Diagonal distance of quantum codes and hardness of the minimum distance
  problem

abstract:
  The diagonal distance or graph distance is an important parameter of a
quantum error-correcting code that characterizes whether the code is degenerate
or not. Degeneracy is a property unique to quantum codes, which allows quantum
codes, unlike their classical counterparts, to correct more errors than they
can uniquely identify. In the CWS framework introduced by Cross, Smith, Smolin
and Zeng (2009), a quantum code is constructed using a classical code and a
graph. It is known that the diagonal distance of such a code is upper bounded
by $\delta+1$, where $\delta$ is the minimum degree of the associated graph. In
this paper, we give sufficient conditions on a graph such that a CWS code
constructed from it has diagonal distance at least $\delta$, and in fact most
of the graphs in our sufficient class achieve the upper bound of $\delta+1$.
  Using this result, first we give necessary conditions for a CWS code to be
degenerate. Secondly, we prove hardness results for the problem of finding the
distance of a CWS code. We construct a CWS code from a given classical code,
with the distance of the CWS code being equal to the distance of the classical
code. This allows us to translate well-known hardness results for computing the
minimum distance in classical codes to quantum codes. Specifically, we show
that exactly computing the distance of a CWS code is NP-complete, and
multiplicatively or additively approximating it is NP-hard under
polynomial-time randomized reductions. Our reduction from the classical
problems to the quantum problems results in a non-degenerate quantum code,
hence our result implies that the quantum problems remain NP-hard even with the
promise that the code is non-degenerate. Moreover, using a mapping from
stabilizer codes to CSS codes due to Bravyi, Terhal and Leemhuis (2010), we are
able to show that the hardness results hold for CSS codes as well.


title:
Fast and selective super-resolution ultrasound in vivo with
  sono-switchable nanodroplets

abstract:
  Perfusion by the microcirculation is key to the development, maintenance and
pathology of tissue. Its measurement with high spatiotemporal resolution is
consequently valuable but remains a challenge in deep tissue. Ultrasound
Localization Microscopy (ULM) provides very high spatiotemporal resolution but
the use of microbubbles requires low contrast agent concentrations, a long
acquisition time, and gives little control over the spatial and temporal
distribution of the bubbles. The present study is the first to demonstrate
Acoustic Wave Sparsely-Activated Localization Microscopy (AWSALM) and
fast-AWSALM for in vivo super-resolution ultrasound imaging, offering contrast
on demand and vascular selectivity. Three different formulations of
sono-switchable contrast agents were tested. We demonstrate their use with
ultrasound mechanical indices well within recommended safety limits to enable
fast on-demand sparse switching at very high agent concentrations. We produce
super-localization maps of the rabbit renal vasculature with acquisition times
between 5.5 s and 0.25 s, and an 4-fold improvement in spatial resolution. We
present the unique selectivity of AWSALM in visualizing specific vascular
branches and downstream microvasculature, and we show super-localized kidney
structures in systole and diastole with fast-AWSALM. In conclusion we
demonstrate the feasibility of fast and selective measurement of microvascular
dynamics in vivo with subwavelength resolution using ultrasound and
sono-switchable nanodroplets.


title:
A Survey on Privacy for B5G/6G: New Privacy Goals, Challenges, and
  Research Directions

abstract:
  Massive progress of mobile wireless telecommunication networks was achieved
in the previous decades, with privacy enhancement in each. At present, mobile
users are getting familiar with the latest 5G networks, and the discussion for
the next generation of Beyond 5G (B5G)/6G networks has already been initiated.
It is expected that B5G/6G will push the existing network capabilities to the
next level, with higher speeds, enhanced reliability, and seamless
connectivity. To make these expectations a reality, research is progressing on
new technologies, architectures, and intelligence-based decision-making
processes related to B5G/6G. Privacy considerations are a crucial aspect that
needs further attention in such developments, as billions of people and devices
will be transmitting their data through the upcoming network. This paper
provides a comprehensive survey on privacy-related aspects for B5G/6G networks.
First, it discusses a taxonomy of different privacy perspectives. Based on the
taxonomy, the paper then conceptualizes a set of privacy goals for the B5G/6G
and the challenges that appear as barriers to reaching these goals. Next, this
work provides a set of solutions applicable to the proposed architecture of
B5G/6G networks to mitigate the challenges. Additionally, this paper discusses
the emerging field of non-personal data privacy. It also provides an overview
of standardization initiatives for privacy preservation. Finally, it concludes
with a roadmap of future directions and upcoming trends containing
privacy-related topics, which will be an arena for new research towards
privacy-enhanced B5G/6G networks. This work provides a basis for privacy
aspects that will significantly impact peoples' daily lives with future
networks.


title:
$^{139}$La NMR investigation of the interplay between the lattice,
  charge, and spin dynamics in charge ordered high $T_c$ cuprate
  La$_{1.875}$Ba$_{0.125}$CuO$_{4}$

abstract:
  We investigate the interplay between the lattice, charge, and spin dynamics
in charge ordered high $T_c$ cuprate La$_{1.875}$Ba$_{0.125}$CuO$_{4}$ ($T_{c}
=4$~K) based on the inverse Laplace transform (ILT) analysis of the $^{139}$La
nuclear spin-lattice relaxation rate $1/T_1$ (dubbed ILTT$_{1}$ analysis here
after). A major thrust of the ILTT$_{1}$ analysis is that one can deduce the
probability density function $P(1/T_1)$ of distributed $1/T_1$. We demonstrate
that $1/T_{1}^{lm}$, defined as the log-mean (i.e. the center of gravity on a
logarithmic scale) of $P(1/T_1)$, can be well approximated by $1/T_{1}^{str}$
deduced from the phenomenological stretched fit, however, $P(1/T_1)$ can
provide much richer insight into how the lattice, charge, and spin fluctuations
and their distribution develop near and below the long range charge order at
$T_{charge} \sim 54$~K. Upon entering the charge ordered state, a divergent
increase of $1/T_{1}^{lm}$ toward the spin ordering at $T_{spin}^{\mu SR}
\simeq 35$~K is accompanied by an asymmetric broadening of $P(1/T_1)$. Even
deep inside the charge ordered state, $1/T_{1}$ at a gradually diminishing
fraction of $^{139}$La sites continues to slow down as temperature is lowered,
as expected for canonical superconducting CuO$_2$ planes without enhanced spin
fluctuations. The fraction of such canonical $^{139}$La sites almost disappears
by $\simeq 40$~K. In contrast, nearly a half of the $^{139}$La sites in
La$_{1.885}$Sr$_{0.115}$CuO$_{4}$ ($T_{charge} \simeq 80$~K) still exhibits the
canonical behavior without enhanced spin fluctuations even near its $T_{c} =
31$~K. These contrasting behaviors explain why superconductivity in
La$_{1.875}$Ba$_{0.125}$CuO$_{4}$ is more strongly suppressed than in
La$_{1.885}$Sr$_{0.115}$CuO$_{4}$ despite the lower onset temperature of the
charge order.


title: On the nilpotent orbit theorem of complex variation of Hodge structures

abstract:
  We prove some results on the nilpotent orbit theorem for complex variation of
Hodge structures.


title:
A bijective proof and generalization of the non-negative crank--odd mex
  identity

abstract:
  Recent works of Andrews--Newman and Hopkins--Sellers unveil an interesting
relation between two partition statistics, the crank and the mex. They state
that, for a positive integer $n$, there are as many partitions of $n$ with
non-negative crank as partitions of $n$ with odd mex. In this paper, we provide
a generalization of this identity and prove it bijectively. Our method uses an
alternative definition of the Durfee decomposition, whose combinatorial link to
the crank was recently studied by Hopkins, Sellers, and Yee.


title:
Incoherent control of two-photon induced optical measurements in open
  quantum systems: quantum heat engine perspective

abstract:
  We present a consistent optimization procedure for the optical measurements
in open quantum systems using recently developed incoherent control protocol.
Assigning an effective hot bath for the two-entangled-photon pump we recast the
transmission of classical probe as a work in a quantum heat engine framework.
We demonstrate that maximum work in such a heat engine can exceed that for the
classical two-photon and one-photon pumps, while efficiency at maximum power
can be attributed to conventional boundaries obtained for three-level maser
heat engine. Our results pave the way for incoherent control and optimization
of optical measurements in open quantum systems that involve two-photon
processes with quantum light.


title:
Ans\"atze for Scattering Amplitudes from $p$-adic Numbers and Algebraic
  Geometry

abstract:
  Rational coefficients of special functions in scattering amplitudes are known
to simplify on singular surfaces, often diverging less strongly than the
na\"ive expectation. To systematically study these surfaces and rational
functions on them, we employ tools from algebraic geometry. We show how the
divergences of a rational function constrain its numerator to belong to
symbolic powers of ideals associated to the singular surfaces. To study the
divergences of the coefficients, we make use of $p$-adic numbers, closely
related to finite fields. These allow us to perform numerical evaluations close
to the singular surfaces in a stable manner and thereby characterize the
divergences of the coefficients. We then use this information to construct
low-dimensional Ans\"atze for the rational coefficients. As a proof-of-concept
application of our algorithm, we reconstruct the two-loop $0 \rightarrow q\bar
q\gamma\gamma\gamma$ pentagon-function coefficients with fewer than 1000
numerical evaluations.


title: The action of HRT-areas as operators in semiclassical gravity

abstract:
  We study the action of Hubeny-Rangamani-Takayanagi (HRT) area operators on
the covariant phase space of classical solutions. It has been previously
proposed that this action generates a transformation which, roughly speaking,
boosts the entanglement wedge on one side of the HRT surface relative to the
entanglement wedge on the other side. We give a sharp argument for a precise
result of this form in a general theory of Einstein-Hilbert gravity minimally
coupled to matter, taking appropriate care with asymptotically Anti-de Sitter
(AdS) boundary conditions. The result agrees with direct computations of
commutators involving HRT areas in pure 2+1 dimensional Einstein-Hilbert
gravity on spacetimes asymptotic to planar AdS. We also clarify the sense in
which this transformation is singular in the deep UV when the HRT-surface is
anchored to an asymptotically AdS boundary.


title: Gradient Ascent Pulse Engineering with Feedback

abstract:
  Efficient approaches to quantum control and feedback are essential for
quantum technologies, from sensing to quantum computation. Pure control tasks
have been successfully solved using optimization techniques, including methods
like gradient-ascent pulse engineering (GRAPE) , relying on a differentiable
model of the quantum dynamics. For feedback tasks, such methods are not
directly applicable, since the aim is to discover strategies conditioned on
measurement outcomes. There, model-free reinforcement learning (RL) has
recently proven a powerful new ansatz. What is missing is a way to combine the
best of both approaches for scenarios that go beyond weak measurements. In this
work, we introduce feedback-GRAPE, which borrows concepts from model-free RL to
incorporate the response to strong stochastic (discrete or continuous)
measurements, while still performing direct gradient ascent through the quantum
dynamics. We illustrate its power on a Jaynes-Cummings model with feedback,
where it yields interpretable feedback strategies for state preparation and
stabilization in the presence of noise. This approach could be employed for
discovering strategies in a wide range of feedback tasks, from calibration of
multi-qubit devices to linear-optics quantum computation strategies,
quantum-enhanced sensing with adaptive measurements, and quantum error
correction.


title:
Policy-Based Bayesian Experimental Design for Non-Differentiable
  Implicit Models

abstract:
  For applications in healthcare, physics, energy, robotics, and many other
fields, designing maximally informative experiments is valuable, particularly
when experiments are expensive, time-consuming, or pose safety hazards. While
existing approaches can sequentially design experiments based on prior
observation history, many of these methods do not extend to implicit models,
where simulation is possible but computing the likelihood is intractable.
Furthermore, they often require either significant online computation during
deployment or a differentiable simulation system. We introduce Reinforcement
Learning for Deep Adaptive Design (RL-DAD), a method for simulation-based
optimal experimental design for non-differentiable implicit models. RL-DAD
extends prior work in policy-based Bayesian Optimal Experimental Design (BOED)
by reformulating it as a Markov Decision Process with a reward function based
on likelihood-free information lower bounds, which is used to learn a policy
via deep reinforcement learning. The learned design policy maps prior histories
to experiment designs offline and can be quickly deployed during online
execution. We evaluate RL-DAD and find that it performs competitively with
baselines on three benchmarks.


title:
Probing Cosmology with Baryon Acoustic Oscillations using Gravitational
  Waves

abstract:
  The third-generation (3G) GW detectors such as the Einstein telescope (ET) or
Cosmic Explorer (CE) are expected to play an important role in cosmology. With
the help of 3G detectors, we will be able to probe the large-scale structure
(LSS) features such as baryon acoustic oscillations (BAO), galaxy bias, etc. We
explore the possibility to do precision cosmology, with the 3G GW detectors by
measuring the angular BAO scale using localization volumes of compact binary
merger events. The BAO are the imprints of the early Universe on the
distribution of matter and it provides a standard scale which can be treated as
a cosmic ruler. Through simulations, we show that with a 3G detector network,
by probing the angular BAO scale using purely GW observations, we can constrain
the Hubble constant for the standard model of cosmology ($\Lambda$CDM) with
$90\%$ credible regions as $H_0 = 64.01^{+ 15.25}_{-14.47}$. When combined with
BAO measurements from galaxy surveys, we show that it can be used to constrain
various models of cosmology such as parametrized models for dark energy
equations of state.


title: Leveraging Initial Hints for Free in Stochastic Linear Bandits

abstract:
  We study the setting of optimizing with bandit feedback with additional prior
knowledge provided to the learner in the form of an initial hint of the optimal
action. We present a novel algorithm for stochastic linear bandits that uses
this hint to improve its regret to $\tilde O(\sqrt{T})$ when the hint is
accurate, while maintaining a minimax-optimal $\tilde O(d\sqrt{T})$ regret
independent of the quality of the hint. Furthermore, we provide a Pareto
frontier of tight tradeoffs between best-case and worst-case regret, with
matching lower bounds. Perhaps surprisingly, our work shows that leveraging a
hint shows provable gains without sacrificing worst-case performance, implying
that our algorithm adapts to the quality of the hint for free. We also provide
an extension of our algorithm to the case of $m$ initial hints, showing that we
can achieve a $\tilde O(m^{2/3}\sqrt{T})$ regret.


title:
Robust Multi-Task Learning and Online Refinement for Spacecraft Pose
  Estimation across Domain Gap

abstract:
  This work presents Spacecraft Pose Network v2 (SPNv2), a Convolutional Neural
Network (CNN) for pose estimation of noncooperative spacecraft across domain
gap. SPNv2 is a multi-scale, multi-task CNN which consists of a shared
multi-scale feature encoder and multiple prediction heads that perform
different tasks on a shared feature output. These tasks are all related to
detection and pose estimation of a target spacecraft from an image, such as
prediction of pre-defined satellite keypoints, direct pose regression, and
binary segmentation of the satellite foreground. It is shown that by jointly
training on different yet related tasks with extensive data augmentations on
synthetic images only, the shared encoder learns features that are common
across image domains that have fundamentally different visual characteristics
compared to synthetic images. This work also introduces Online Domain
Refinement (ODR) which refines the parameters of the normalization layers of
SPNv2 on the target domain images online at deployment. Specifically, ODR
performs self-supervised entropy minimization of the predicted satellite
foreground, thereby improving the CNN's performance on the target domain images
without their pose labels and with minimal computational efforts. The GitHub
repository for SPNv2 is available at \url{https://github.com/tpark94/spnv2}.


title:
Freeze-In of radiative keV-scale neutrino dark matter from a new
  $\text{U}(1)_\text{B-L}$

abstract:
  We extend the Dirac Scotogenic model with the aim of realizing neutrino
masses together with the mass of a keV-scale dark matter (DM) candidate via the
same one-loop topology. Two of the Standard Model (SM) neutrinos become massive
Dirac fermions while the third one remains massless. Our particle content is
motivated by an anomaly free $\text{U}(1)_\text{B-L}$ gauge symmetry with
exotic irrational charges and we need to enforce an additional $\mathcal{Z}_5$
symmetry. The dark matter candidate does not mix with the active neutrinos and
does not have any decay modes to SM particles. DM is produced together with
dark radiation in the form of right handed neutrinos via out of equilibrium
annihilations of the SM fermions mediated by the heavy B-L gauge boson. In
order to avoid DM over-production from Higgs decays and to comply with
Lyman-$\alpha$ bounds we work in a low temperature reheating scenario with
$4\;\text{MeV}\lesssim T_\text{RH}\lesssim 5\;\text{GeV}$. Our setup predicts a
contribution to $\Delta N_\text{eff.}$ that decreases for larger DM masses and
is below the sensitivity of upcoming precision measurements such as CMB-S4. A
future observation of a signal with $\Delta N_\text{eff.}\gtrsim 0.012$ would
exclude our scenario. We further sketch how inflation, reheating and
Affleck-Dine baryogenesis can also be potentially realized in this unified
framework.


title:
You Cannot Always Win the Race: Analyzing the LFENCE/JMP Mitigation for
  Branch Target Injection

abstract:
  LFENCE/JMP is an existing software mitigation option for Branch Target
Injection (BTI) and similar transient execution attacks stemming from indirect
branch predictions, which is commonly used on AMD processors. However, the
effectiveness of this mitigation can be compromised by the inherent race
condition between the speculative execution of the predicted target and the
architectural resolution of the intended target, since this can create a window
in which code can still be transiently executed.
  This work investigates the potential sources of latency that may contribute
to such a speculation window. We show that an attacker can "win the race", and
thus that this window can still be sufficient to allow exploitation of
BTI-style attacks on a variety of different x86 CPUs, despite the presence of
the LFENCE/JMP mitigation.


title: Unfocused laser ignition of high-pressure He-H2-O2 combustible mixtures

abstract:
  We report consistent ignition of high-pressure (p > 20-30 bar)
hydrogen-oxygen mixtures diluted with helium, using an unfocused Nd:YAG laser.
This corresponds to laser irradiances several orders of magnitude below the
minimum ignition energies reported in the literature. By placing a mirror
inside a cylindrical vessel and filling it up to 100 bar with H2-He or O2-He
non-combustible mixtures, we obtain the pressure-dependent absorptivity of the
combustible He-H2-O2 mixture. We find no measurable absorption of the laser
signal by the medium, for the overall pressure range, to the experimental
apparatus sensitivity (about 1% of the laser irradiance). This leads credence
to the theory that ignition stems from seed electrons created by autofocusing
ionization of dust/impurities in the gas.


title:
Probabilistic Warp Consistency for Weakly-Supervised Semantic
  Correspondences

abstract:
  We propose Probabilistic Warp Consistency, a weakly-supervised learning
objective for semantic matching. Our approach directly supervises the dense
matching scores predicted by the network, encoded as a conditional probability
distribution. We first construct an image triplet by applying a known warp to
one of the images in a pair depicting different instances of the same object
class. Our probabilistic learning objectives are then derived using the
constraints arising from the resulting image triplet. We further account for
occlusion and background clutter present in real image pairs by extending our
probabilistic output space with a learnable unmatched state. To supervise it,
we design an objective between image pairs depicting different object classes.
We validate our method by applying it to four recent semantic matching
architectures. Our weakly-supervised approach sets a new state-of-the-art on
four challenging semantic matching benchmarks. Lastly, we demonstrate that our
objective also brings substantial improvements in the strongly-supervised
regime, when combined with keypoint annotations.


title: Coupled Stochastic Allen-Cahn equations: Existence and Uniqueness

abstract:
  In this paper, we concern a system of stochastic PDE's. Our system consists
of two components. Each component evolves according to the sotchastic
Allen-Cahn equation with a symmetric double well potential and with addtional
small space-time white noise. The two components interact with each other by an
attractive linear force. We aim to give a global existence and uniqueness
result in a space of continuous functions on $\mathbb{R} \times \mathbb{R}^+$.
We apply methods proposed by Doering \cite{Doering1987}.


title:
Magneto-transport signatures in periodically-driven Weyl and multi-Weyl
  semimetals

abstract:
  We investigate the influence of a time-periodic driving (for example, by
shining circularly polarized light) on three-dimensional Weyl and multi-Weyl
semimetals, in the planar Hall and planar thermal Hall set-ups. We incorporate
the effects of the drive by using the Floquet formalism in the large frequency
limit. We evaluate the longitudinal magneto conductivity, planar Hall
conductivity, longitudinal thermo-electric coefficient, and transverse
thermo-electric coefficient, using the semi-classical Boltzmann transport
equations. We demonstrate the explicit expressions of these transport
coefficients in certain limits of the parameters, where it is possible to
perform the integrals analytically. We cross-check our analytical
approximations by comparing the physical values with the numerical results,
obtained directly from the numerical integration of the integrals. The answers
obtained show that the topological charges of the corresponding semimetals have
profound signatures in these transport properties, which can be observed in
experiments.


title:
From the Lab to People's Home: Lessons from Accessing Blind
  Participants' Interactions via Smart Glasses in Remote Studies

abstract:
  Researchers have adopted remote methods, such as online surveys and video
conferencing, to overcome challenges in conducting in-person usability testing,
such as participation, user representation, and safety. However, remote user
evaluation on hardware testbeds is limited, especially for blind participants,
as such methods restrict access to observations of user interactions. We employ
smart glasses in usability testing with blind people and share our lessons from
a case study conducted in blind participants' homes (N=12), where the
experimenter can access participants' activities via dual video conferencing: a
third-person view via a laptop camera and a first-person view via smart glasses
worn by the participant. We show that smart glasses hold potential for
observing participants' interactions with smartphone testbeds remotely; on
average 58.7% of the interactions were fully captured via the first-person view
compared to 3.7% via the third-person. However, this gain is not uniform across
participants as it is susceptible to head movements orienting the ear towards a
sound source, which highlights the need for a more inclusive camera form
factor. We also share our lessons learned when it comes to dealing with lack of
screen reader support in smart glasses, a rapidly draining battery, and
Internet connectivity in remote studies with blind participants.


title:
Radiated Angular Momentum and Dissipative Effects in Classical
  Scattering

abstract:
  We present a new formula for the angular momentum $J^{\mu\nu}$ carried away
by gravitational radiation in classical scattering. This formula, combined with
the known expression for the radiated linear momentum $P^\mu$, completes the
set of radiated Poincare charges due to scattering. We parametrize $P^\mu$ and
$J^{\mu\nu}$ by non-perturbative form factors and derive exact relations using
the Poincare algebra. There is a contribution to $J^{\mu\nu}$ due to static
(zero-frequency) modes, which can be derived from Weinberg's soft theorem.
Using tools from scattering amplitudes and effective field theory, we calculate
the radiated $J^{\mu\nu}$ due to the scattering of two spinless particles to
third order in Newton's constant $G$, but to all orders in velocity. Our
form-factor analysis elucidates a novel relation found by Bini, Damour, and
Geralico between energy and angular momentum loss at $\mathcal{O}(G^3)$. Our
new results have several nontrivial implications for binary scattering at
$\mathcal{O}(G^4)$. We give a procedure to bootstrap an effective radiation
reaction force from the loss of Poincare charges due to scattering.


title:
Combining thermal resummation and gauge invariance for electroweak phase
  transition

abstract:
  For computing thermodynamics of the electroweak phase transition, we discuss
a minimal approach that reconciles both gauge invariance and thermal
resummation. Such a minimal setup consists of a two-loop dimensional reduction
to three-dimensional effective theory, a one-loop computation of the effective
potential and its expansion around the leading-order minima within the
effective theory. This approach is tractable and provides formulae for
resummation that are arguably no more complicated than those that appear in
standard techniques ubiquitous in the literature. In particular, we implement
renormalisation group improvement related to the hard thermal scale. Despite
its generic nature, we present this approach for the complex singlet extension
of the Standard Model which has interesting prospects for high energy collider
phenomenology and dark matter predictions. The presented expressions can be
used in future studies of phase transition thermodynamics and gravitational
wave production.


title: Bayesian Persuasion with Mediators

abstract:
  A sender communicates with a receiver through a sequence of mediators. The
sender is the only informed agent and the receiver is the only one taking an
action. All the agents have their own utility functions, which depend on the
receiver's action and the state.
  For any number of mediators, the sender's optimal value is characterized. For
one mediator, the characterization has a clear geometric meaning of constrained
concavification of the sender's utility, optimal persuasion requires the same
number of signals as without mediators, and the presence of the mediator is
never profitable for the sender. Surprisingly, the second mediator may improve
the sender's utility; however, optimal persuasion with several mediators may
require more signals.


title: Proximal PanNet: A Model-Based Deep Network for Pansharpening

abstract:
  Recently, deep learning techniques have been extensively studied for
pansharpening, which aims to generate a high resolution multispectral (HRMS)
image by fusing a low resolution multispectral (LRMS) image with a high
resolution panchromatic (PAN) image. However, existing deep learning-based
pansharpening methods directly learn the mapping from LRMS and PAN to HRMS.
These network architectures always lack sufficient interpretability, which
limits further performance improvements. To alleviate this issue, we propose a
novel deep network for pansharpening by combining the model-based methodology
with the deep learning method. Firstly, we build an observation model for
pansharpening using the convolutional sparse coding (CSC) technique and design
a proximal gradient algorithm to solve this model. Secondly, we unfold the
iterative algorithm into a deep network, dubbed as Proximal PanNet, by learning
the proximal operators using convolutional neural networks. Finally, all the
learnable modules can be automatically learned in an end-to-end manner.
Experimental results on some benchmark datasets show that our network performs
better than other advanced methods both quantitatively and qualitatively.


title:
A Simple Multi-Modality Transfer Learning Baseline for Sign Language
  Translation

abstract:
  This paper proposes a simple transfer learning baseline for sign language
translation. Existing sign language datasets (e.g. PHOENIX-2014T, CSL-Daily)
contain only about 10K-20K pairs of sign videos, gloss annotations and texts,
which are an order of magnitude smaller than typical parallel data for training
spoken language translation models. Data is thus a bottleneck for training
effective sign language translation models. To mitigate this problem, we
propose to progressively pretrain the model from general-domain datasets that
include a large amount of external supervision to within-domain datasets.
Concretely, we pretrain the sign-to-gloss visual network on the general domain
of human actions and the within-domain of a sign-to-gloss dataset, and pretrain
the gloss-to-text translation network on the general domain of a multilingual
corpus and the within-domain of a gloss-to-text corpus. The joint model is
fine-tuned with an additional module named the visual-language mapper that
connects the two networks. This simple baseline surpasses the previous
state-of-the-art results on two sign language translation benchmarks,
demonstrating the effectiveness of transfer learning. With its simplicity and
strong performance, this approach can serve as a solid baseline for future
research.


title:
A Beacon in the Galaxy: Updated Arecibo Message for Potential FAST and
  SETI Projects

abstract:
  An updated, binary-coded message has been developed for transmission to
extraterrestrial intelligences in the Milky Way galaxy. The proposed message
includes basic mathematical and physical concepts to establish a universal
means of communication followed by information on the biochemical composition
of life on Earth, the Solar System's time-stamped position in the Milky Way
relative to known globular clusters, as well as digitized depictions of the
Solar System, and Earth's surface. The message concludes with digitized images
of the human form, along with an invitation for any receiving intelligences to
respond. Calculation of the optimal timing during a given calendar year is
specified for potential future transmission from both the Five-hundred-meter
Aperture Spherical radio Telescope in China and the SETI Institute's Allen
Telescope Array in northern California to a selected region of the Milky Way
which has been proposed as the most likely for life to have developed. These
powerful new beacons, the successors to the Arecibo radio telescope which
transmitted the 1974 message upon which this expanded communication is in part
based, can carry forward Arecibo's legacy into the 21st century with this
equally well-constructed communication from Earth's technological civilization.


title:
Self-supervised learning for analysis of temporal and morphological drug
  effects in cancer cell imaging data

abstract:
  In this work, we propose two novel methodologies to study temporal and
morphological phenotypic effects caused by different experimental conditions
using imaging data. As a proof of concept, we apply them to analyze drug
effects in 2D cancer cell cultures. We train a convolutional autoencoder on 1M
images dataset with random augmentations and multi-crops to use as feature
extractor. We systematically compare it to the pretrained state-of-the-art
models. We further use the feature extractor in two ways. First, we apply
distance-based analysis and dynamic time warping to cluster temporal patterns
of 31 drugs. We identify clusters allowing annotation of drugs as having
cytotoxic, cytostatic, mixed or no effect. Second, we implement an
adversarial/regularized learning setup to improve classification of 31 drugs
and visualize image regions that contribute to the improvement. We increase
top-3 classification accuracy by 8% on average and mine examples of
morphological feature importance maps. We provide the feature extractor and the
weights to foster transfer learning applications in biology. We also discuss
utility of other pretrained models and applicability of our methods to other
types of biomedical data.


title: Residual Aligner Network

abstract:
  Image registration is important for medical imaging, the estimation of the
spatial transformation between different images. Many previous studies have
used learning-based methods for coarse-to-fine registration to efficiently
perform 3D image registration. The coarse-to-fine approach, however, is limited
when dealing with the different motions of nearby objects. Here we propose a
novel Motion-Aware (MA) structure that captures the different motions in a
region. The MA structure incorporates a novel Residual Aligner (RA) module
which predicts the multi-head displacement field used to disentangle the
different motions of multiple neighbouring objects. Compared with other deep
learning methods, the network based on the MA structure and RA module achieve
one of the most accurate unsupervised inter-subject registration on the 9
organs of assorted sizes in abdominal CT scans, with the highest-ranked
registration of the veins (Dice Similarity Coefficient / Average surface
distance: 62\%/4.9mm for the vena cava and 34\%/7.9mm for the portal and
splenic vein), with a half-sized structure and more efficient computation.
Applied to the segmentation of lungs in chest CT scans, the new network
achieves results which were indistinguishable from the best-ranked networks
(94\%/3.0mm). Additionally, the theorem on predicted motion pattern and the
design of MA structure are validated by further analysis.


title: Learning from Few Examples: A Summary of Approaches to Few-Shot Learning

abstract:
  Few-Shot Learning refers to the problem of learning the underlying pattern in
the data just from a few training samples. Requiring a large number of data
samples, many deep learning solutions suffer from data hunger and extensively
high computation time and resources. Furthermore, data is often not available
due to not only the nature of the problem or privacy concerns but also the cost
of data preparation. Data collection, preprocessing, and labeling are strenuous
human tasks. Therefore, few-shot learning that could drastically reduce the
turnaround time of building machine learning applications emerges as a low-cost
solution. This survey paper comprises a representative list of recently
proposed few-shot learning algorithms. Given the learning dynamics and
characteristics, the approaches to few-shot learning problems are discussed in
the perspectives of meta-learning, transfer learning, and hybrid approaches
(i.e., different variations of the few-shot learning problem).


title:
Towards performant and reliable undersampled MR reconstruction via
  diffusion model sampling

abstract:
  Magnetic Resonance (MR) image reconstruction from under-sampled acquisition
promises faster scanning time. To this end, current State-of-The-Art (SoTA)
approaches leverage deep neural networks and supervised training to learn a
recovery model. While these approaches achieve impressive performances, the
learned model can be fragile on unseen degradation, e.g. when given a different
acceleration factor. These methods are also generally deterministic and provide
a single solution to an ill-posed problem; as such, it can be difficult for
practitioners to understand the reliability of the reconstruction. We introduce
DiffuseRecon, a novel diffusion model-based MR reconstruction method.
DiffuseRecon guides the generation process based on the observed signals and a
pre-trained diffusion model, and does not require additional training on
specific acceleration factors. DiffuseRecon is stochastic in nature and
generates results from a distribution of fully-sampled MR images; as such, it
allows us to explicitly visualize different potential reconstruction solutions.
Lastly, DiffuseRecon proposes an accelerated, coarse-to-fine Monte-Carlo
sampling scheme to approximate the most likely reconstruction candidate. The
proposed DiffuseRecon achieves SoTA performances reconstructing from raw
acquisition signals in fastMRI and SKM-TEA. Code will be open-sourced at
www.github.com/cpeng93/DiffuseRecon.


title: Generalized Riesz Representation Theorem in n-Hilbert space

abstract:
  In respect of b-linear functional, Riesz representation theorem in n-Hilbert
space have been proved. We define b-sesquilinear functional in n-Hilbert space
and establish the polarization identities. A generalized form of the Schwarz
inequality in n-Hilbert space is being discussed. Finally, a generalized
version of Riesz representation theorem with respect to b-sesquilinear
functional in n-Hilbert space have been developed.


title:
NaviAirway: a bronchiole-sensitive deep learning-based airway
  segmentation pipeline for planning of navigation bronchoscopy

abstract:
  Navigation bronchoscopy is a minimally invasive procedure in which doctors
pass a bronchoscope into a subject's airways to sample the target pulmonary
lesion. A three-dimensional (3D) airway roadmap reconstructed from Computer
Tomography (CT) scans is a prerequisite for this procedure, especially when the
target is distally located. Therefore, an accurate and efficient airway
segmentation algorithm is essential to reduce bronchoscopists' burden of
pre-procedural airway identification as well as patients' discomfort during the
prolonged procedure. However, airway segmentation remains a challenging task
because of the intrinsic complex tree-like structure, imbalanced sizes of
airway branches, potential domain shifts of CT scans, and few available labeled
images. To address these problems, we present a deep learning-based pipeline,
denoted as NaviAirway, which finds finer bronchioles through four major novel
components - feature extractor modules in model architecture design, a
bronchiole-sensitive loss function, a human-vision-inspired iterative training
strategy, and a semi-supervised learning framework to utilize unlabeled CT
images. Experimental results showed that NaviAirway outperformed existing
methods, particularly in identification of higher generation bronchioles and
robustness to new CT scans. On average, NaviAirway takes five minutes to
segment the CT scans of one patient on a GPU-embedded computer. Moreover, we
propose two new metrics to complement conventional ones for a more
comprehensive and fairer evaluation of deep learning-based airway segmentation
approaches. The code is publicly available on
https://github.com/AntonotnaWang/NaviAirway.


title: Region Specific Optimization (RSO)-based Deep Interactive Registration

abstract:
  Medical image registration is a fundamental and vital task which will affect
the efficacy of many downstream clinical tasks. Deep learning (DL)-based
deformable image registration (DIR) methods have been investigated, showing
state-of-the-art performance. A test time optimization (TTO) technique was
proposed to further improve the DL models' performance. Despite the substantial
accuracy improvement with this TTO technique, there still remained some regions
that exhibited large registration errors even after many TTO iterations. To
mitigate this challenge, we firstly identified the reason why the TTO technique
was slow, or even failed, to improve those regions' registration results. We
then proposed a two-levels TTO technique, i.e., image-specific optimization
(ISO) and region-specific optimization (RSO), where the region can be
interactively indicated by the clinician during the registration result
reviewing process. For both efficiency and accuracy, we further envisioned a
three-step DL-based image registration workflow. Experimental results showed
that our proposed method outperformed the conventional method qualitatively and
quantitatively.


title: Spatial heterogeneity of air pollution statistics

abstract:
  Air pollution is one of the leading causes of death globally, and continues
to have a detrimental effect on our health. In light of these impacts, an
extensive range of statistical modelling approaches has been devised in order
to better understand air pollution statistics. However, the time-varying
statistics of different types of air pollutants are far from being fully
understood. The observed probability density functions (PDFs) of concentrations
depend very much on the spatial location and on the pollutant substance. In
this paper, we analyse a large variety of data from 3544 different European
monitoring sites and show that the PDFs of nitric oxide ($NO$), nitrogen
dioxide ($NO2$) and particulate matter ($PM10$ and $PM2.5$) concentrations
generically exhibit heavy tails and are asymptotically well approximated by
$q$-exponential distributions with a given width parameter $\lambda$. We
observe that the power-law parameter $q$ and the width parameter $\lambda$ vary
widely for the different spatial locations. For each substance, we find
different patterns of parameter clouds in the $(q, \lambda)$ plane. These
depend on the type of pollutants and on the environmental characteristics
(urban/suburban/rural/traffic/industrial/background). This means the effective
statistical physics description of air pollution exhibits a strong degree of
spatial heterogeneity.


title:
R\'enyi State Entropy for Exploration Acceleration in Reinforcement
  Learning

abstract:
  One of the most critical challenges in deep reinforcement learning is to
maintain the long-term exploration capability of the agent. To tackle this
problem, it has been recently proposed to provide intrinsic rewards for the
agent to encourage exploration. However, most existing intrinsic reward-based
methods proposed in the literature fail to provide sustainable exploration
incentives, a problem known as vanishing rewards. In addition, these
conventional methods incur complex models and additional memory in their
learning procedures, resulting in high computational complexity and low
robustness. In this work, a novel intrinsic reward module based on the R\'enyi
entropy is proposed to provide high-quality intrinsic rewards. It is shown that
the proposed method actually generalizes the existing state entropy
maximization methods. In particular, a $k$-nearest neighbor estimator is
introduced for entropy estimation while a $k$-value search method is designed
to guarantee the estimation accuracy. Extensive simulation results demonstrate
that the proposed R\'enyi entropy-based method can achieve higher performance
as compared to existing schemes.


title:
CaSS: A Channel-aware Self-supervised Representation Learning Framework
  for Multivariate Time Series Classification

abstract:
  Self-supervised representation learning of Multivariate Time Series (MTS) is
a challenging task and attracts increasing research interests in recent years.
Many previous works focus on the pretext task of self-supervised learning and
usually neglect the complex problem of MTS encoding, leading to unpromising
results. In this paper, we tackle this challenge from two aspects: encoder and
pretext task, and propose a unified channel-aware self-supervised learning
framework CaSS. Specifically, we first design a new Transformer-based encoder
Channel-aware Transformer (CaT) to capture the complex relationships between
different time channels of MTS. Second, we combine two novel pretext tasks Next
Trend Prediction (NTP) and Contextual Similarity (CS) for the self-supervised
representation learning with our proposed encoder. Extensive experiments are
conducted on several commonly used benchmark datasets. The experimental results
show that our framework achieves new state-of-the-art comparing with previous
self-supervised MTS representation learning methods (up to +7.70\% improvement
on LSST dataset) and can be well applied to the downstream MTS classification.


title:
Source-free Domain Adaptation for Multi-site and Lifespan Brain Skull
  Stripping

abstract:
  Skull stripping is a crucial prerequisite step in the analysis of brain
magnetic resonance (MR) images. Although many excellent works or tools have
been proposed, they suffer from low generalization capability. For instance,
the model trained on a dataset with specific imaging parameters (source domain)
cannot be well applied to other datasets with different imaging parameters
(target domain). Especially, for the lifespan datasets, the model trained on an
adult dataset is not applicable to an infant dataset due to the large domain
difference. To address this issue, numerous domain adaptation (DA) methods have
been proposed to align the extracted features between the source and target
domains, requiring concurrent access to the input images of both domains.
Unfortunately, it is problematic to share the images due to privacy. In this
paper, we design a source-free domain adaptation framework (SDAF) for
multi-site and lifespan skull stripping that can accomplish domain adaptation
without access to source domain images. Our method only needs to share the
source labels as shape dictionaries and the weights trained on the source data,
without disclosing private information from source domain subjects. To deal
with the domain shift between multi-site lifespan datasets, we take advantage
of the brain shape prior which is invariant to imaging parameters and ages.
Experiments demonstrate that our framework can significantly outperform the
state-of-the-art methods on multi-site lifespan datasets.


title: UENAS: A Unified Evolution-based NAS Framework

abstract:
  Neural architecture search (NAS) has gained significant attention for
automatic network design in recent years. Previous NAS methods suffer from
limited search spaces, which may lead to sub-optimal results. In this paper, we
propose UENAS, an evolution-based NAS framework with a broader search space
that supports optimizing network architectures, pruning strategies, and
hyperparameters simultaneously. To alleviate the huge search cost caused by the
expanded search space, three strategies are adopted: First, an adaptive pruning
strategy that iteratively trims the average model size in the population
without compromising performance. Second, child networks share weights of
overlapping layers with pre-trained parent networks to reduce the training
epochs. Third, an online predictor scores the joint representations of
architecture, pruning strategy, and hyperparameters to filter out inferior
combos. By the proposed three strategies, the search efficiency is
significantly improved and more well-performed compact networks with tailored
hyper-parameters are derived. In experiments, UENAS achieves error rates of
2.81% on CIFAR-10, 20.24% on CIFAR-100, and 33% on Tiny-ImageNet, which shows
the effectiveness of our method.


title: Live Laparoscopic Video Retrieval with Compressed Uncertainty

abstract:
  Searching through large volumes of medical data to retrieve relevant
information is a challenging yet crucial task for clinical care. However the
primitive and most common approach to retrieval, involving text in the form of
keywords, is severely limited when dealing with complex media formats.
Content-based retrieval offers a way to overcome this limitation, by using rich
media as the query itself. Surgical video-to-video retrieval in particular is a
new and largely unexplored research problem with high clinical value,
especially in the real-time case: using real-time video hashing, search can be
achieved directly inside of the operating room. Indeed, the process of hashing
converts large data entries into compact binary arrays or hashes, enabling
large-scale search operations at a very fast rate. However, due to fluctuations
over the course of a video, not all bits in a given hash are equally reliable.
In this work, we propose a method capable of mitigating this uncertainty while
maintaining a light computational footprint. We present superior retrieval
results (3-4 % top 10 mean average precision) on a multi-task evaluation
protocol for surgery, using cholecystectomy phases, bypass phases, and coming
from an entirely new dataset introduced here, critical events across six
different surgery types. Success on this multi-task benchmark shows the
generalizability of our approach for surgical video retrieval.


title: SuperPoint features in endoscopy

abstract:
  There is often a significant gap between research results and applicability
in routine medical practice. This work studies the performance of well-known
local features on a medical dataset captured during routine colonoscopy
procedures. Local feature extraction and matching is a key step for many
computer vision applications, specially regarding 3D modelling. In the medical
domain, handcrafted local features such as SIFT, with public pipelines such as
COLMAP, are still a predominant tool for this kind of tasks. We explore the
potential of the well known self-supervised approach SuperPoint, present an
adapted variation for the endoscopic domain and propose a challenging
evaluation framework. SuperPoint based models achieve significantly higher
matching quality than commonly used local features in this domain. Our adapted
model avoids features within specularity regions, a frequent and problematic
artifact in endoscopic images, with consequent benefits for matching and
reconstruction results.


title: Policy Regularization for Legible Behavior

abstract:
  In Reinforcement Learning interpretability generally means to provide insight
into the agent's mechanisms such that its decisions are understandable by an
expert upon inspection. This definition, with the resulting methods from the
literature, may however fall short for online settings where the fluency of
interactions prohibits deep inspections of the decision-making algorithm. To
support interpretability in online settings it is useful to borrow from the
Explainable Planning literature methods that focus on the legibility of the
agent, by making its intention easily discernable in an observer model. As we
propose in this paper, injecting legible behavior inside an agent's policy
doesn't require modify components of its learning algorithm. Rather, the
agent's optimal policy can be regularized for legibility by evaluating how the
policy may produce observations that would make an observer infer an incorrect
policy. In our formulation, the decision boundary introduced by legibility
impacts the states in which the agent's policy returns an action that has high
likelihood also in other policies. In these cases, a trade-off between such
action, and legible/sub-optimal action is made.


title: Dynamic Dual-Output Diffusion Models

abstract:
  Iterative denoising-based generation, also known as denoising diffusion
models, has recently been shown to be comparable in quality to other classes of
generative models, and even surpass them. Including, in particular, Generative
Adversarial Networks, which are currently the state of the art in many
sub-tasks of image generation. However, a major drawback of this method is that
it requires hundreds of iterations to produce a competitive result. Recent
works have proposed solutions that allow for faster generation with fewer
iterations, but the image quality gradually deteriorates with increasingly
fewer iterations being applied during generation. In this paper, we reveal some
of the causes that affect the generation quality of diffusion models,
especially when sampling with few iterations, and come up with a simple, yet
effective, solution to mitigate them. We consider two opposite equations for
the iterative denoising, the first predicts the applied noise, and the second
predicts the image directly. Our solution takes the two options and learns to
dynamically alternate between them through the denoising process. Our proposed
solution is general and can be applied to any existing diffusion model. As we
show, when applied to various SOTA architectures, our solution immediately
improves their generation quality, with negligible added complexity and
parameters. We experiment on multiple datasets and configurations and run an
extensive ablation study to support these findings.


title:
LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series
  Data

abstract:
  Federated learning (FL) and split learning (SL) are the two popular
distributed machine learning (ML) approaches that provide some data privacy
protection mechanisms. In the time-series classification problem, many
researchers typically use 1D convolutional neural networks (1DCNNs) based on
the SL approach with a single client to reduce the computational overhead at
the client-side while still preserving data privacy. Another method, recurrent
neural network (RNN), is utilized on sequentially partitioned data where
segments of multiple-segment sequential data are distributed across various
clients. However, to the best of our knowledge, it is still not much work done
in SL with long short-term memory (LSTM) network, even the LSTM network is
practically effective in processing time-series data. In this work, we propose
a new approach, LSTMSPLIT, that uses SL architecture with an LSTM network to
classify time-series data with multiple clients. The differential privacy (DP)
is applied to solve the data privacy leakage. The proposed method, LSTMSPLIT,
has achieved better or reasonable accuracy compared to the Split-1DCNN method
using the electrocardiogram dataset and the human activity recognition dataset.
Furthermore, the proposed method, LSTMSPLIT, can also achieve good accuracy
after applying differential privacy to preserve the user privacy of the cut
layer of the LSTMSPLIT.


title: Diffusion Models for Medical Anomaly Detection

abstract:
  In medical applications, weakly supervised anomaly detection methods are of
great interest, as only image-level annotations are required for training.
Current anomaly detection methods mainly rely on generative adversarial
networks or autoencoder models. Those models are often complicated to train or
have difficulties to preserve fine details in the image. We present a novel
weakly supervised anomaly detection method based on denoising diffusion
implicit models. We combine the deterministic iterative noising and denoising
scheme with classifier guidance for image-to-image translation between diseased
and healthy subjects. Our method generates very detailed anomaly maps without
the need for a complex training procedure. We evaluate our method on the
BRATS2020 dataset for brain tumor detection and the CheXpert dataset for
detecting pleural effusions.


title: A Machine Learning Approach to Digital Contact Tracing: TC4TL Challenge

abstract:
  Contact tracing is a method used by public health organisations to try
prevent the spread of infectious diseases in the community. Traditionally
performed by manual contact tracers, more recently the use of apps have been
considered utilising phone sensor data to determine the distance between two
phones. In this paper, we investigate the development of machine learning
approaches to determine the distance between two mobile phone devices using
Bluetooth Low Energy, sensory data and meta data. We use TableNet architecture
and feature engineering to improve on the existing state of the art (total nDCF
0.21 vs 2.08), significantly outperforming existing models.


title:
Breast cancer detection using artificial intelligence techniques: A
  systematic literature review

abstract:
  Cancer is one of the most dangerous diseases to humans, and yet no permanent
cure has been developed for it. Breast cancer is one of the most common cancer
types. According to the National Breast Cancer foundation, in 2020 alone, more
than 276,000 new cases of invasive breast cancer and more than 48,000
non-invasive cases were diagnosed in the US. To put these figures in
perspective, 64% of these cases are diagnosed early in the disease's cycle,
giving patients a 99% chance of survival. Artificial intelligence and machine
learning have been used effectively in detection and treatment of several
dangerous diseases, helping in early diagnosis and treatment, and thus
increasing the patient's chance of survival. Deep learning has been designed to
analyze the most important features affecting detection and treatment of
serious diseases. For example, breast cancer can be detected using genes or
histopathological imaging. Analysis at the genetic level is very expensive, so
histopathological imaging is the most common approach used to detect breast
cancer. In this research work, we systematically reviewed previous work done on
detection and treatment of breast cancer using genetic sequencing or
histopathological imaging with the help of deep learning and machine learning.
We also provide recommendations to researchers who will work in this field


title: Speed-up and slow-down of a quantum particle

abstract:
  We study non-relativistic propagation of Gaussian wave packets in
one-dimensional Eckart potential, a barrier, or a well. In the picture used,
the transmitted wave packet results from interference between the copies of the
freely propagating state with different spatial shifts (delays), x', induced by
the scattering potential. The Uncertainty Principle precludes relating the
particle's final position to the delay experienced in the potential, except in
the classical limit. Beyond this limit, even defining an effective range of the
delay is shown to be an impracticable task, owing to the oscillatory nature of
the corresponding amplitude distribution. Our examples include the classically
allowed case, semiclassical tunnelling, delays induced in the presence of a
virtual state, and scattering by a low barrier. The properties of the amplitude
distribution of the delays, and its pole representation are studied in detail.


title:
Multi-Agent Broad Reinforcement Learning for Intelligent Traffic Light
  Control

abstract:
  Intelligent Traffic Light Control System (ITLCS) is a typical Multi-Agent
System (MAS), which comprises multiple roads and traffic lights.Constructing a
model of MAS for ITLCS is the basis to alleviate traffic congestion. Existing
approaches of MAS are largely based on Multi-Agent Deep Reinforcement Learning
(MADRL). Although the Deep Neural Network (DNN) of MABRL is effective, the
training time is long, and the parameters are difficult to trace. Recently,
Broad Learning Systems (BLS) provided a selective way for learning in the deep
neural networks by a flat network. Moreover, Broad Reinforcement Learning (BRL)
extends BLS in Single Agent Deep Reinforcement Learning (SADRL) problem with
promising results. However, BRL does not focus on the intricate structures and
interaction of agents. Motivated by the feature of MADRL and the issue of BRL,
we propose a Multi-Agent Broad Reinforcement Learning (MABRL) framework to
explore the function of BLS in MAS. Firstly, unlike most existing MADRL
approaches, which use a series of deep neural networks structures, we model
each agent with broad networks. Then, we introduce a dynamic self-cycling
interaction mechanism to confirm the "3W" information: When to interact, Which
agents need to consider, What information to transmit. Finally, we do the
experiments based on the intelligent traffic light control scenario. We compare
the MABRL approach with six different approaches, and experimental results on
three datasets verify the effectiveness of MABRL.


title:
Cluster Head Detection for Hierarchical UAV Swarm With Graph
  Self-supervised Learning

abstract:
  In this paper, we study the cluster head detection problem of a two-level
unmanned aerial vehicle (UAV) swarm network (USNET) with multiple UAV clusters,
where the inherent follow strategy (IFS) of low-level follower UAVs (FUAVs)
with respect to high-level cluster head UAVs (HUAVs) is unknown. We first
propose a graph attention self-supervised learning algorithm (GASSL) to detect
the HUAVs of a single UAV cluster, where the GASSL can fit the IFS at the same
time. Then, to detect the HUAVs in the USNET with multiple UAV clusters, we
develop a multi-cluster graph attention self-supervised learning algorithm
(MC-GASSL) based on the GASSL. The MC-GASSL clusters the USNET with a gated
recurrent unit (GRU)-based metric learning scheme and finds the HUAVs in each
cluster with GASSL. Numerical results show that the GASSL can detect the HUAVs
in single UAV clusters obeying various kinds of IFSs with over 98% average
accuracy. The simulation results also show that the clustering purity of the
USNET with MC-GASSL exceeds that with traditional clustering algorithms by at
least 10% average. Furthermore, the MC-GASSL can efficiently detect all the
HUAVs in USNETs with various IFSs and cluster numbers with low detection
redundancies.


title:
Dual-Readout Calorimetry for Future Experiments Probing Fundamental
  Physics

abstract:
  In this White Paper for the 2021 Snowmass process, we detail the status and
prospects for dual-readout calorimetry. While all calorimeters allow estimation
of energy depositions in their active material, dual-readout calorimeters aim
to provide additional information on the light produced in the sensitive media
via, for example, wavelength and polarization, and/or a precision timing
measurements, allowing an estimation of the shower-by-shower particle content.
Utilizing this knowledge of the shower particle content may allow unprecedented
energy resolution for hadronic particles and jets and new types of particle
flow algorithms. We also discuss the impact continued development of this kind
of calorimetry could have on precision on Higgs boson property measurements at
future colliders.


title: Multi-Scale Adaptive Network for Single Image Denoising

abstract:
  Multi-scale architectures have shown effectiveness in a variety of tasks
including single image denoising, thanks to appealing cross-scale
complementarity. However, existing methods treat different scale features
equally without considering their scale-specific characteristics, i.e., the
within-scale characteristics are ignored. In this paper, we reveal this missing
piece for multi-scale architecture design and accordingly propose a novel
Multi-Scale Adaptive Network (MSANet) for single image denoising. To be
specific, MSANet simultaneously embraces the within-scale characteristics and
the cross-scale complementarity thanks to three novel neural blocks, i.e.,
adaptive feature block (AFeB), adaptive multi-scale block (AMB), and adaptive
fusion block (AFuB). In brief, AFeB is designed to adaptively select details
and filter noises, which is highly expected for fine-grained features. AMB
could enlarge the receptive field and aggregate the multi-scale information,
which is designed to satisfy the demands of both fine- and coarse-grained
features. AFuB devotes to adaptively sampling and transferring the features
from one scale to another scale, which is used to fuse the features with
varying characteristics from coarse to fine. Extensive experiments on both
three real and six synthetic noisy image datasets show the superiority of
MSANet compared with 12 methods.


title:
PyNET-QxQ: A Distilled PyNET for QxQ Bayer Pattern Demosaicing in CMOS
  Image Sensor

abstract:
  The deep learning-based ISP models for mobile cameras produce high-quality
images comparable to the professional DSLR camera. However, many of them are
computationally expensive, which may not be appropriate for mobile
environments. Also, the recent mobile cameras adopt non-Bayer CFAs (e.g., Quad
Bayer, Nona Bayer, and QxQ Bayer) to improve image quality; however, most deep
learning-based ISP models mainly focus on standard Bayer CFA. In this work, we
propose PyNET-QxQ based on PyNET, a light-weighted ISP explicitly designed for
the QxQ CFA pattern. The number of parameters of PyNET-QxQ is less than 2.5% of
PyNET. We also introduce a novel knowledge distillation technique, progressive
distillation, to train the compressed network effectively. Finally, experiments
with QxQ images (obtained by an actual QxQ camera sensor, under development)
demonstrate the outstanding performance of PyNET-QxQ despite significant
parameter reductions.


title:
A Note on Probability Quantification for Protective System Efficacy
  Analysis: Stochastic Dynamics, Information Flow, and Initiating Event Arrival
  Times

abstract:
  Probability Quantification (PQ) predictions of the efficacy of
safety-critical protective systems is challenging. Yet, the popularity of PQ
methodologies (e.g., Probabilistic Risk Assessment (PRA), Quantitative Risk
Analysis (QRA) and Probabilistic Safety Analysis (PSA)) is growing and can now
be found written into regulatory rules. PQ in predictive modeling is attractive
because of its grounding in probability theory. But, certain important safety
related events are not probability-measurable which is problematic for
risk-analytic methodologies that rely on PQ computations. Herein, we identify
why the dynamics of available information play an essential role in governing
the fidelity of PQ, and why PQ in the analysis of safety-critical protective
systems is limited by the un-measurability of certain critical events. We
provide an historical example that provides a practical context for our
observations. Finally we discuss the implications of measurability for
regulatory decision-making governed by recent nuclear industry legislation
advocating increased use of risk informed, performance-based regulation for
advanced reactor licensing.


title:
Time-domain feature extraction for target-specificity in Photoacoustic
  Remote Sensing Microscopy

abstract:
  Photoacoustic Remote Sensing (PARS) microscopy is an emerging label-free
optical absorption imaging modality. PARS operates by capturing
nanosecond-scale optical perturbations generated by photoacoustic pressures.
These time-domain (TD) modulations are usually projected by amplitude to
determine absorption magnitude. However, significant information on the
target's material properties is contained within the TD signals. This work
proposes a novel clustering method to learn TD features which relate to
underlying biomolecule characteristics. This technique identifies features
related to constituent biomolecules, enabling single-acquisition virtual tissue
labelling. Colorized visualizations of tissue are produced, highlighting
specific tissue components. This is demonstrated on freshly resected murine
brain tissue, clearly discerning structures including myelinated and
unmyelinated neurons (white and gray matter) and nuclear structures.


title:
MICDIR: Multi-scale Inverse-consistent Deformable Image Registration
  using UNetMSS with Self-Constructing Graph Latent

abstract:
  Image registration is the process of bringing different images into a common
coordinate system - a technique widely used in various applications of computer
vision, such as remote sensing, image retrieval, and most commonly in medical
imaging. Deep Learning based techniques have been applied successfully to
tackle various complex medical image processing problems, including medical
image registration. Over the years, several image registration techniques have
been proposed using deep learning. Deformable image registration techniques
such as Voxelmorph have been successful in capturing finer changes and
providing smoother deformations. However, Voxelmorph, as well as ICNet and
FIRE, do not explicitly encode global dependencies (i.e. the overall anatomical
view of the supplied image) and therefore can not track large deformations. In
order to tackle the aforementioned problems, this paper extends the Voxelmorph
approach in three different ways. To improve the performance in case of small
as well as large deformations, supervision of the model at different
resolutions have been integrated using a multi-scale UNet. To support the
network to learn and encode the minute structural co-relations of the given
image-pairs, a self-constructing graph network (SCGNet) has been used as the
latent of the multi-scale UNet - which can improve the learning process of the
model and help the model to generalise better. And finally, to make the
deformations inverse-consistent, cycle consistency loss has been employed. On
the task of registration of brain MRIs, the proposed method achieved
significant improvements over ANTs and VoxelMorph, obtaining a Dice score of
0.8013$\pm$0.0243 for intramodal and 0.6211$\pm$0.0309 for intermodal, while
VoxelMorph achieved 0.7747$\pm$0.0260 and 0.6071$\pm$0.0510, respectively.

categories: eess.IV cs.AI cs.CV cs.LG physics.med-ph


title: The low energy excitation spectrum of magic-angle semimetals

abstract:
  We theoretically study the excitation spectrum of a two-dimensional Dirac
semimetal in the presence of an incommensurate potential. Such models have been
shown to possess magic-angle critical points in the single particle
wavefunctions, signalled by a momentum space delocalization of plane wave
eigenstates and flat bands due to a vanishing Dirac cone velocity. Using the
kernel polynomial method, we compute the single particle Green's function to
extract the nature of the single particle excitation energy, damping rate, and
quasiparticle residue. As a result, we are able to clearly demonstrate the
redistribution of spectral weight due to quasiperiodicity-induced downfolding
of the Brillouin zone creating minibands with effective mini Brillouin zones
that correspond to emergent superlattices. By computing the damping rate we
show that the vanishing of the velocity and generation of finite density of
states at the magic-angle transition coincides with the development of an
imaginary part in the self energy and a suppression of the quasiparticle
residue that vanishes in a power law like fashion. Observing these effects with
ultracold atoms using momentum resolved radiofrequency spectroscopy is
discussed.


title:
Upper limit on the axion-photon coupling from magnetic white dwarf
  polarization

abstract:
  Polarization measurements of thermal radiation from magnetic white dwarf
(MWD) stars have been proposed as a probe of axion-photon mixing. The radiation
leaving the surface of the MWD is unpolarized, but if low-mass axions exist
then photons polarized parallel to the direction of the MWD's magnetic field
may convert into axions, which induces a linear polarization dependent on the
strength of the axion-photon coupling $g_{a\gamma\gamma}$. We model this
process by using the formalism of axion-photon mixing in the presence of
strong-field vacuum birefringence to show that of all stellar types MWDs are
the most promising targets for axion-induced polarization searches. We then
consider linear polarization data from multiple MWDs, including SDSS J135141
and Grw+70$^\circ$8247, to show that after rigorously accounting for
astrophysical uncertainties the axion-photon coupling is constrained to
$|g_{a\gamma\gamma}| \lesssim 5.4 \times 10^{-12}$ GeV$^{-1}$ at 95% confidence
for axion masses $m_a \lesssim 3 \times 10^{-7}$ eV. This upper limit puts in
tension the previously-suggested explanation of the anomalous transparency of
the Universe to TeV gamma-rays in terms of axions. We identify MWD targets for
which future data and modeling efforts could further improve the sensitivity to
axions.


title:
Electromagnetic fireworks: Fast radio bursts from rapid reconnection in
  the compressed magnetar wind

abstract:
  One scenario for the generation of fast radio bursts is magnetic reconnection
in a current sheet of the magnetar wind. Compressed by a strong magnetic pulse
induced by a magnetar flare, the current sheet fragments into a self-similar
chain of magnetic islands. Time-dependent plasma currents at their interfaces
produce coherent radiation during their hierarchical coalescence. We
investigate this scenario using 2D radiative relativistic particle-in-cell
simulations to compute the efficiency of the coherent emission and to obtain
frequency scalings. Consistent with expectations, a fraction of the reconnected
magnetic field energy, $f\sim 0.002$, is converted to packets of high-frequency
fast magnetosonic waves which can escape from the magnetar wind as radio
emission. In agreement with analytical estimates, we find that magnetic pulses
of $10^{47}\text{erg}/\text{s}$ can trigger relatively narrow-band GHz emission
with luminosities of approximately $10^{43}\text{erg}/\text{s}$, sufficient to
explain bright extragalactic fast radio bursts. The mechanism provides a
natural explanation for a downward frequency drift of burst signals, as well as
the $\sim 100$ nanosecond sub-structure recently detected in FRB 20200120E.


title:
Rapid disc settling and the transition from bursty to steady star
  formation in Milky Way-mass galaxies

abstract:
  Recent observations and simulations indicate substantial evolution in the
properties of galaxies with time, wherein rotationally-supported and steady
thin discs (like those frequently observed in the local universe) emerge from
galaxies that are clumpy, irregular, and have bursty star formation rates
(SFRs). To better understand the progenitors of local disc galaxies we carry
out an analysis of three FIRE-2 simulated galaxies with a mass similar to the
Milky Way at redshift z=0. We show that all three galaxies transition from
bursty to steady SFRs at a redshift between z=0.5 and z=0.8, and that this
transition coincides with a rapid (< ~1 Gyr) emergence of a
rotationally-supported interstellar medium (ISM).In the late phase with steady
SFR, the rotational energy comprises > ~90% of the total kinetic + thermal
energy in the ISM, and is roughly half the gravitational energy. By contrast,
during the early phase with bursty star formation, the ISM has a
quasi-spheroidal morphology and its energy budget is dominated by
quasi-isotropic flows including turbulence and coherent inflows/outflows. This
result, that rotational support is subdominant at early times, challenges the
common application of equilibrium disc models to the high-redshift progenitors
of Milky Way-like galaxies. We further find that the formation of a
rotation-supported ISM coincides with the formation of a thermal
energy-supported inner circumgalactic medium (CGM). Before this transition, the
inner CGM is also supported by turbulence and coherent flows, indicating that
at early times there is no clear boundary between the ISM and inner CGM.


title:
COSMOS2020: Ubiquitous AGN Activity of Massive Quiescent Galaxies at
  $0<z<5$ Revealed by X-ray and Radio Stacking

abstract:
  We characterize the average X-ray and radio properties of quiescent galaxies
(QGs) with $\log{(M_\star/M_\odot)}>10$ at $0<z<5$. QGs are photometrically
selected from the latest COSMOS2020 catalog. We conduct the stacking analysis
of X-ray images of the Chandra COSMOS Legacy Survey for individually undetected
QGs. Thanks to the large sample and deep images, the stacked X-ray signal is
significantly detected up to $z\sim5$. The average X-ray luminosity can not be
explained by the X-ray luminosity of X-ray binaries, suggesting that the
low-luminosity active galactic nuclei (AGNs) ubiquitously exist in QGs.
Moreover, the X-ray AGN luminosity of QGs at $z>1.5$ is higher than that of
star-forming galaxies (SFGs), derived in the same manner as QGs. The stacking
analysis of the VLA-COSMOS images is conducted for the identical sample, and
the radio signal for QGs is also detected up to $z\sim5$. We find that the
radio AGN luminosity of QGs at $z>1.5$ is also higher than SFGs, which is in
good agreement with the X-ray analysis. The enhanced AGN activity in QGs
suggested by the individual analysis in the X-ray and radio wavelength supports
its important role for quenching at high redshift. Their enhanced AGN activity
is less obvious at $z<1.5$, which can be interpreted as an increasing role of
others at lower redshifts, such as environmental quenching.


title: Entangling transmons with low-frequency protected superconducting qubits

abstract:
  Novel qubits with intrinsic noise protection constitute a promising route for
improving the coherence of quantum information in superconducting circuits.
However, many protected superconducting qubits exhibit relatively low
transition frequencies, which could make their integration with conventional
transmon circuits challenging. In this work, we propose and study a scheme for
entangling a tunable transmon with a Cooper-pair parity-protected qubit, a
paradigmatic example of a low-frequency protected qubit that stores quantum
information in opposite Cooper-pair parity states on a superconducting island.
By tuning the external flux on the transmon, we show that non-computational
states can mediate a two-qubit entangling gate that preserves the Cooper-pair
parity independent of the detailed pulse sequence. Interestingly, the
entangling gate bears similarities to a controlled-phase gate in conventional
transmon devices. Hence, our results suggest that standard high-precision gate
calibration protocols could be repurposed for operating hybrid qubit devices.


title: Higgs Precision at a 125 GeV Muon Collider

abstract:
  The $s$-channel resonant production of the Higgs boson at a 125 GeV muon
collider enables a unique way to determine the Higgs properties. However, a
clear picture of the achievable Higgs precision has not yet been established.
We perform a phenomenological study of the Higgs measurements at such resonant
muon collider Higgs factory and present a systematic, detailed, and consistent
extraction of Higgs precision measurements. Many new aspects about the
lineshape scan, including the scaling with luminosity, optimal scan range,
minimal scan steps, correlations with exclusive measurement, effective
cross-section modeling, etc., are quantitatively studied in this work. All
major exclusive Higgs channels are simulated and analyzed with Standard Model
background, detection efficiencies, acceptance, angular distributions, and
cross-channel correlations. Global analyses of the Higgs couplings are
performed in the $\kappa$ framework and the effective-field-theory one. The
results suggest that the 125 GeV muon-collider Higgs factory provides
significant improvement to the Higgs coupling reach of the HL-LHC and provides
independent and distinct Higgs precision information concerning future $e^+e^-$
colliders. We report results for both $5~{\rm fb}^{-1}$ and $20~{\rm fb}^{-1}$
integrated luminosity. These results provide comprehensive and quantitative
physics understandings helpful in planning for the muon collider roadmap and
global high-energy physics programs.


title: A universal feature of charged entanglement entropy

abstract:
  R\'enyi entropies, $S_n$, admit a natural generalization in the presence of
global symmetries. These "charged R\'enyi entropies" are functions of the
chemical potential $\mu$ conjugate to the charge contained in the entangling
region and reduce to the usual notions as $\mu\rightarrow 0$. For $n=1$, this
provides a notion of charged entanglement entropy. In this letter we prove that
for a general $d (\geq 3)$-dimensional CFT, the leading correction to the
uncharged entanglement entropy across a spherical entangling surface is
quadratic in the chemical potential, positive definite, and universally
controlled (up to fixed $d$-dependent constants) by the coefficients $C_J$ and
$a_2$. These fully characterize, for a given theory, the current correlators
$\langle JJ\rangle $ and $\langle TJJ \rangle$, as well as the energy flux
measured at infinity produced by the insertion of the current operator. Our
result is motivated by analytic holographic calculations for a special class of
higher-curvature gravities coupled to a $(d-2)$-form in general dimensions as
well as for free-fields in $d=4$. A proof for general theories and dimensions
follows from previously known universal identities involving the magnetic
response of twist operators introduced in arXiv:1310.4180 and basic
thermodynamic relations.


title:
Impact of Sommerfeld Effect and Bound State Formation in Simplified
  $t$-Channel Dark Matter Models

abstract:
  The existence of a dark matter model with a rich dark sector could be the
reason why WIMP dark matter has evaded its detection so far. For instance,
colored co-annihilation naturally leads to the prediction of heavier dark
matter masses. Importantly, in such a scenario the Sommerfeld effect and bound
state formation must be considered in order to accurately predict the relic
abundance. Based on the example of the currently widely studied $t$-channel
simplified model with a colored mediator, we demonstrate the importance of
considering these non-perturbative effects for correctly inferring the viable
model parameters. We emphasize that a flat correction factor on the relic
abundance is not sufficient in this context. Moreover, we find that parameter
space thought to be excluded by direct detection experiments and LHC searches
remains still viable. Additionally, we illustrate that long-lived particle
searches and bound-state searches at the LHC can play a crucial role in probing
such a model. We demonstrate how future direct detection experiments will be
able to close almost all of the remaining windows for freeze-out production,
making it a highly testable scenario.


title:
The phenomenology of the exotic hybrid nonet with $\pi_1(1600)$ and
  $\eta_1(1855)$

abstract:
  We study the decays of the $J^{PC}=1^{-+}$ hybrid nonet using a Lagrangian
invariant under the flavor symmetry, parity reversal, and charge conjugation.
We use the available experimental data, the lattice predictions, and the flavor
constraints to evaluate the coupling strengths of the $\pi_1(1600)$ to various
two-body mesonic states. Using these coupling constants, we estimate the
partial widths of the two-body decays of the hybrid pion, kaon and the
isoscalars. We find that the hybrid kaon can be nearly as broad as the
$\pi_1(1600)$. Quite remarkably, we find also that the light isoscalar must be
significantly narrow while the width of the heavy isoscalar can be matched to
the recently observed $\eta_1(1855)$.


title: Electroweak ALP Searches at a Muon Collider

abstract:
  A high-energy muon collider with center-of-mass energy around and above 10
TeV is also a vector boson fusion (VBF) machine, due to the significant virtual
electroweak (EW) gauge boson content of high-energy muon beams. This feature,
together with the clean environment, makes it an ideal collider to search for
TeV-scale axion-like particles (ALP) coupling to Standard Model EW gauge
bosons, which current and other future colliders have limited sensitivities to.
We present detailed analyses of heavy ALP searches in both the VBF and
associated production channels at a muon collider with different running
benchmarks. We also show projected constraints on the ALP couplings in the
effective field theory, including an operator with its coefficient not
determined by the mixed Peccei-Quinn anomaly. We demonstrate that a muon
collider could probe new ALP parameter space and push the sensitivities of the
couplings between the ALP and EW gauge bosons by one order of magnitude
compared to HL-LHC. The projected limits and search strategies for ALPs could
also be applied to other types of resonances coupling to EW gauge bosons.


title:
Detecting topological order from modular transformations of ground
  states on the torus

abstract:
  The ground states encode the information of the topological phases of a
2-dimensional system, which makes them crucial in determining the associated
topological quantum field theory (TQFT). Most numerical methods for detecting
the TQFT relied on the use of minimum entanglement states (MESs), extracting
the anyon mutual statistics and self statistics via overlaps and/or the
entanglement spectra. The MESs are the eigenstates of the Wilson loop
operators, and are labeled by the anyons corresponding to their eigenvalues.
Here we revisit the definition of the Wilson loop operators and MESs. We derive
the modular transformation of the ground states purely from the Wilson loop
algebra, and as a result, the modular $S$- and $T$-matrices naturally show up
in the overlap of MESs. Importantly, we show that due to the phase degree of
freedom of the Wilson loop operators, the MES-anyon assignment is not unique.
This ambiguity obstructs our attempt to detect the topological order, that is,
there exist different TQFTs that cannot be distinguished solely by the overlap
of MESs. In this paper, we provide the upper limit of the information one may
obtain from the overlap of MESs without other additional structure. Finally, we
show that if the phase is enriched by rotational symmetry, there may be
additional TQFT information that can be extracted from overlap of MESs.


title: Early dynamical evolution of rotating star clusters in a tidal field

abstract:
  In order to explore how the early internal rotational properties of star
clusters are affected by the external potential of their host galaxies, we have
run a suite of $N$-body simulations following the early dynamical evolution and
violent relaxation of rotating star clusters embedded in a tidal field. Our
study focuses on models for which the cluster's rotation axis has a generic
orientation relative to the torque of the tidal field. The interaction between
the violent relaxation process, angular momentum of the cluster, and the
external torque creates a complex kinematic structure within the cluster, most
prominently a radial variation in the position of the rotation axis along both
the polar and azimuthal directions. We also examine the cluster's velocity
dispersion anisotropy and show that the projected anisotropy may be affected by
the variation of the rotation axis directions within the cluster; the
combination of projection effects and the complex kinematical features may
result in the measurement of tangential anisotropy in the cluster's inner
regions. We also characterize the structural properties of our clusters as a
function of their initial rotation and virial ratio and find that clusters may
develop a triaxial morphology and a radial variation of the minor axis not
necessarily aligned with the rotation axis. Finally, we examine the long-term
evolution of these complex kinematic features.


title:
Luminous Fast Blue Optical Transients and Type Ibn/Icn SNe from
  Wolf-Rayet/Black Hole Mergers

abstract:
  Progenitor models for the "luminous" subclass of Fast Blue Optical Transients
(LFBOTs; prototype: AT2018cow) are challenged to simultaneously explain all of
their observed properties: fast optical rise times < days; peak luminosities
>1e44 erg/s; low yields <0.1 Msun of 56Ni; aspherical ejecta with a wide
velocity range (<3000 km/s to > 0.1-0.5 c with increasing polar latitude);
presence of hydrogen-depleted-but-not-free dense circumstellar material (CSM)
on radial scales from ~1e14 cm to ~3e16 cm; embedded variable source of
non-thermal X-ray/gamma-rays, suggestive of a compact object. We show that all
of these properties are consistent with the tidal disruption and
hyper-accretion of a Wolf-Rayet (WR) star by a black hole (BH) or neutron star
(NS) binary companion. In contrast with related previous models, the merger
occurs with a long delay following the common envelope (CE) event responsible
for birthing the binary, as a result of gradual angular momentum loss to a
relic circumbinary disk. Disk-wind outflows from the merger-generated accretion
flow generate the 56Ni-poor aspherical ejecta with the requisite velocity
range. The optical light curve is powered primarily by reprocessing X-rays from
the inner accretion flow/jet, though CSM shock interaction also contributes.
Primary CSM sources include mass-loss from WR star (e.g., from the L2 point)
during the earliest stages of the merger (~<1e14 cm) and the relic CE disk and
its photoevaporation-driven wind (>~ 1e16 cm). Longer delayed mergers may
instead give rise to supernovae Type Ibn/Icn (depending on the WR evolutionary
state), potentially connecting these transient classes with LFBOTs.


title: Constraining Axion-Like Particles with HAWC Observations of TeV Blazars

abstract:
  Axion-like particles (ALPs) are a broad class of pseudo-scalar bosons that
generically arise from broken symmetries in extensions of the standard model.
In many scenarios, ALPs can mix with photons in regions with high magnetic
fields. Photons from distant sources can mix with ALPs, which then travel
unattenuated through the Universe, before they mix back to photons in the Milky
Way galactic magnetic field. Thus, photons can traverse regions where their
signals would normally be blocked or attenuated. In this paper, we study TeV
$\gamma$-ray observations from distant blazars, utilizing the significant
$\gamma$-ray attenuation expected from such signals to look for excess photon
fluxes that may be due to ALP-photon mixing. We find no such excesses among a
stacked population of seven blazars and constrain the ALP-photon coupling
constant to fall below $\sim$3$\times$10$^{-11}$ GeV$^{-1}$ for ALP masses
below 300 neV. These results are competitive with, or better than, leading
terrestrial and astrophysical constraints in this mass range.


title:
Digging into the Interior of Hot Cores with ALMA (DIHCA). II. Exploring
  the Inner Binary (Multiple) System Embedded in G335 MM1 ALMA1

abstract:
  We observed the high-mass protostellar core G335.579-0.272 ALMA1 at
${\sim}200$ au (0.05") resolution with the Atacama Large
Millimeter/submillimeter Array (ALMA) at 226 GHz (with a mass sensitivity of
$5\sigma=0.2$ M$_\odot$ at 10 K). We discovered that at least a binary system
is forming inside this region, with an additional nearby bow-like structure
(${\lesssim}1000$ au) that could add an additional member to the stellar
system. These three sources are located at the center of the gravitational
potential well of the ALMA1 region and the larger MM1 cluster. The emission
from CH$_3$OH (and many other tracers) is extended ($>1000$ au), revealing a
common envelope toward the binary system. We use CH$_2$CHCN line emission to
estimate an inclination angle of the rotation axis of $26^\circ$ with respect
to the line of sight based on geometric assumptions and derive a kinematic mass
of the primary source (protostar+disk) of 3.0 M$_\odot$ within a radius of 230
au. Using SiO emission, we find that the primary source drives the large scale
outflow revealed by previous observations. Precession of the binary system
likely produces a change in orientation between the outflow at small scales
observed here and large scales observed in previous works. The bow structure
may have originated by entrainment of matter into the envelope due to widening
or precession of the outflow, or, alternatively, an accretion streamer
dominated by the gravity of the central sources. An additional third source,
forming due to instabilities in the streamer, cannot be ruled out as a
temperature gradient is needed to produce the observed absorption spectra.


title:
TOI-1670 b and c: An Inner Sub-Neptune with an Outer Warm Jupiter
  Unlikely to have Originated from High-Eccentricity Migration

abstract:
  We report the discovery of two transiting planets around the bright ($V=9.9$
mag) main sequence F7 star TOI-1670 by the Transiting Exoplanet Survey
Satellite. TOI-1670 b is a sub-Neptune ($R_\mathrm{b} = 2.06_{-0.15}^{+0.19}$
$R_\oplus$) on a 10.9-day orbit and TOI-1670 c is a warm Jupiter ($R_\mathrm{c}
= 0.987_{-0.025}^{+0.025}$ $R_\mathrm{Jup}$) on a 40.7-day orbit. Using radial
velocity observations gathered with the Tull coud\'e Spectrograph on the Harlan
J. Smith telescope and HARPS-N on the Telescopio Nazionale Galileo, we find a
planet mass of $M_\mathrm{c} = 0.63_{-0.08}^{+0.09}$ $M_\mathrm{Jup}$ for the
outer warm Jupiter, implying a mean density of $\rho_c = 0.81_{-0.11}^{+0.13}$
g cm$^{-3}$. The inner sub-Neptune is undetected in our radial velocity data
($M_\mathrm{b} < 0.13$ $M_\mathrm{Jup}$ at the 99% confidence level).
Multi-planet systems like TOI-1670 hosting an outer warm Jupiter on a nearly
circular orbit ($e_\mathrm{c} = 0.09_{-0.04}^{+0.05}$) and one or more inner
coplanar planets are more consistent with "gentle" formation mechanisms such as
disk migration or $in$ $situ$ formation rather than high-eccentricity
migration. Of the 11 known systems with a warm Jupiter and a smaller inner
companion, 8 (73%) are near a low-order mean-motion resonance, which can be a
signature of migration. TOI-1670 joins two other systems (27% of this
subsample) with period commensurabilities greater than 3, a common feature of
$in$ $situ$ formation or halted inward migration. TOI-1670 and the handful of
similar systems support a diversity of formation pathways for warm Jupiters.


title: Optimizing Patient Transitions to Skilled Nursing Facilities

abstract:
  Hospital inpatient care costs is the largest component of health care
expenditures in the US. At the same time, the number of non-hospital
rehabilitative settings, such as skilled nursing facilities (SNFs), has
increased. Lower costs and increased availability have made SNFs and other
non-hospital rehabilitation settings a promising care alternative to
hospitalization. To maximize their benefits, transitions to SNFs require
special attention, since poorly coordinated transitions can lead to worse
outcomes and higher costs via unnecessary hospital readmissions. This study
presents a framework to improve care transitions based on the premise that
certain SNFs may provide better care for some patients. We estimate readmission
rates by SNF and patient types using observational data from a tertiary
teaching hospital and nearby SNFs. We then analyze and solve a stochastic model
optimizing patient transfer decisions to minimize readmissions. Our model
accounts for patient discharge patterns and SNF capacity availability. We
provide conditions for when an easy-to-use myopic policy, which assigns
discharged patients to the SNF with the lowest readmission rate that is
available, is optimal. We also show when an optimal policy has a threshold-like
structure. Using estimated readmission rates, we compare the performance of the
myopic policy and a proposed policy that depends on the discharge process,
readmission rates, and future SNF availability. We evaluate when the myopic
policy may be beneficial to use and when the proposed transfer heuristic
provides a better alternative. Otherwise, we contend that using a stochastic
optimization model for guiding transfer decisions may help reduce readmissions.