title: Unsupervised Prompt Learning for Vision-Language Models

abstract:
  Contrastive vision-language models like CLIP have shown great progress in
zero-shot transfer learning. This new paradigm uses large-scale image-text
pairs for training and aligns images and texts in a common embedding space. In
the inference stage, the proper text description, known as prompt, needs to be
carefully designed for zero-shot transfer. To avoid laborious prompt
engineering and simultaneously improve transfer performance, recent works such
as CoOp, CLIP-Adapter and Tip-Adapter propose to adapt vision-language models
for downstream image recognition tasks by either optimizing the continuous
prompt representations or training an additional adapter network on top of the
pre-trained vision-language models on a small set of labeled data. Though
promising improvements are achieved, using labeled images from target datasets
may violate the intention of zero-shot transfer of pre-trained vision-language
models. In this paper, we propose an unsupervised prompt learning (UPL)
framework, which does not require any annotations of the target dataset, to
improve the zero-shot transfer of CLIP-like vision-language models.
Experimentally, for zero-shot transfer, our UPL outperforms original CLIP with
prompt engineering and on ImageNet as well as other 10 datasets. An enhanced
version of UPL is even on par with the 8-shot CoOp and the 8-shot TIP-Adapter
on most datasets while our method does not need any labeled images for
training. Code and models are available at
https://github.com/tonyhuang2022/UPL.


title:
Numerical simulation of the EM forced flow during Sn-Pb alloy
  directional horizontal solidification

abstract:
  This article presents a study of the influence of electromagnetically (EM)
forced convection on segregation formation. As a reference case the
solidification of Sn-5 wt pct Pb alloy from the Hebditch and Hunt experiment is
taken. Applied forcing of convective flow is determined by the dimensionless
electromagnetic forcing parameter $F$. The study was carried out in the range
$-3.5\times 10^6<F<3.5\times 10^6$. Velocity profiles and flow patterns in the
liquid phase are obtained for different applied EM forcing conditions. As a
result of parametric analysis, the dependence of the Reynolds number on the
electromagnetic forcing parameter was obtained. Electromagnetic forces can
significantly affect the flow in the liquid bulk, increasing and slowing down
the velocity, as well as changing the circulating flow direction. Solute
concentration distribution analysis has shown that EM-forced convection does
not affect global solute segregation formation. For the two cases, segregation
channels were obtained. However, the analysis of the global segregation index
showed that an increase in the Reynolds number provokes a slight decrease in
this parameter. The main mechanism of segregation formation is transport of
solute rich liquid into the mushy zone. In considered numerical experiment
configuration, the penetration into the mushy zone is not large, and even a
change in the direction of convection in the liquid bulk does not significantly
affect the mushy zone flow. The calculations were made by means of open source
code in OpenFOAM and Elmer.


title: An Introduction to Scattering Theory

abstract:
  The purpose of these lectures is to give an accessible and self contained
introduction to quantum scattering theory in one dimension. Part A defines the
theoretical playground, and develops basic concepts of scattering theory in the
time domain (Asymptotic Condition, in- and out- states, scattering operator
$\hat{S}$). The aim of Part B is then to build up, in a step-by-step fashion,
the time independent scattering theory in energy domain. This amounts to
introduce the Lippmann-Schwinger equation for the stationary scattering states
(denoted as $| \psi_{E(\pm 1)}^\pm \rangle$), to discuss fundamental properties
of $| \psi_{E(\pm 1)}^\pm \rangle$, and subsequently to construct $\hat{S}$ and
$\hat{T}$ operators in terms of $| \psi_{E(\pm 1)}^\pm \rangle$. Physical
contents of the $\hat{S}$ and $\hat{T}$ operators is then illuminated by
deriving explicit formulas for the probability of transmission/reflection of
our quantum particle through/from the interaction region of the potential. An
illustrative numerical example is given, which also highlights an existence of
scattering resonances. Finally, Part C elaborates the nonhermitian scattering
theory (Siegert pseudostate formalism), which offers an extremely powerful tool
suitable for clear cut understanding of the resonance phenomena.


title: PlutoNet: An Efficient Polyp Segmentation Network

abstract:
  Polyps in the colon can turn into cancerous cells if not removed with early
intervention. Deep learning models are used to minimize the number of polyps
that goes unnoticed by the experts, and to accurately segment the detected
polyps during these interventions. Although these models perform well on these
tasks, they require too many parameters, which can pose a problem with
real-time applications. To address this problem, we propose a novel
segmentation model called PlutoNet which requires only 2,626,337 parameters
while outperforming state-of-the-art models on multiple medical image
segmentation tasks. We use EfficientNetB0 architecture as a backbone and
propose the novel \emph{modified partial decoder}, which is a combination of
partial decoder and full scale connections, which further reduces the number of
parameters required, as well as captures semantic details. We use asymmetric
convolutions to handle varying polyp sizes. Finally, we weight each feature map
to improve segmentation by using a squeeze and excitation block. In addition to
polyp segmentation in colonoscopy, we tested our model on segmentation of
nuclei and surgical instruments to demonstrate its generalizability to
different medical image segmentation tasks. Our model outperformed the
state-of-the-art models with a Dice score of \%92.3 in CVC-ClinicDB dataset and
\%89.3 in EndoScene dataset, a Dice score of \%91.93 on the 2018 Data Science
Bowl Challenge dataset, and a Dice score of \%94.8 on Kvasir-Instrument
dataset. Our experiments and ablation studies show that our model is superior
in terms of accuracy, and it is able generalize well to multiple medical
segmentation tasks.


title: Embedding of Functional Human Brain Networks on a Sphere

abstract:
  Human brain activity is often measured using the blood-oxygen-level dependent
(BOLD) signals obtained through functional magnetic resonance imaging (fMRI).
The strength of connectivity between brain regions is then measured and
represented as Pearson correlation matrices. As the number of brain regions
increases, the dimension of matrix increases. It becomes extremely cumbersome
to even visualize and quantify such weighted complete networks. To remedy the
problem, we propose to embedded brain networks onto a hypersphere, which is a
Riemannian manifold with constant positive curvature.


title:
Identification of Autism spectrum disorder based on a novel feature
  selection method and Variational Autoencoder

abstract:
  The development of noninvasive brain imaging such as resting-state functional
magnetic resonance imaging (rs-fMRI) and its combination with AI algorithm
provides a promising solution for the early diagnosis of Autism spectrum
disorder (ASD). However, the performance of the current ASD classification
based on rs-fMRI still needs to be improved. This paper introduces a
classification framework to aid ASD diagnosis based on rs-fMRI. In the
framework, we proposed a novel filter feature selection method based on the
difference between step distribution curves (DSDC) to select remarkable
functional connectivities (FCs) and utilized a multilayer perceptron (MLP)
which was pretrained by a simplified Variational Autoencoder (VAE) for
classification. We also designed a pipeline consisting of a normalization
procedure and a modified hyperbolic tangent (tanh) activation function to
replace the original tanh function, further improving the model accuracy. Our
model was evaluated by 10 times 10-fold cross-validation and achieved an
average accuracy of 78.12%, outperforming the state-of-the-art methods reported
on the same dataset. Given the importance of sensitivity and specificity in
disease diagnosis, two constraints were designed in our model which can improve
the model's sensitivity and specificity by up to 9.32% and 10.21%,
respectively. The added constraints allow our model to handle different
application scenarios and can be used broadly.


title: Learning to Walk Autonomously via Reset-Free Quality-Diversity

abstract:
  Quality-Diversity (QD) algorithms can discover large and complex behavioural
repertoires consisting of both diverse and high-performing skills. However, the
generation of behavioural repertoires has mainly been limited to simulation
environments instead of real-world learning. This is because existing QD
algorithms need large numbers of evaluations as well as episodic resets, which
require manual human supervision and interventions. This paper proposes
Reset-Free Quality-Diversity optimization (RF-QD) as a step towards autonomous
learning for robotics in open-ended environments. We build on Dynamics-Aware
Quality-Diversity (DA-QD) and introduce a behaviour selection policy that
leverages the diversity of the imagined repertoire and environmental
information to intelligently select of behaviours that can act as automatic
resets. We demonstrate this through a task of learning to walk within defined
training zones with obstacles. Our experiments show that we can learn full
repertoires of legged locomotion controllers autonomously without manual resets
with high sample efficiency in spite of harsh safety constraints. Finally,
using an ablation of different target objectives, we show that it is important
for RF-QD to have diverse types solutions available for the behaviour selection
policy over solutions optimised with a specific objective. Videos and code
available at https://sites.google.com/view/rf-qd.


title:
Automatic Parameter Optimization Using Genetic Algorithm in Deep
  Reinforcement Learning for Robotic Manipulation Tasks

abstract:
  Learning agents can make use of Reinforcement Learning (RL) to decide their
actions by using a reward function. However, the learning process is greatly
influenced by the elect of values of the parameters used in the learning
algorithm. This work proposed a Deep Deterministic Policy Gradient (DDPG) and
Hindsight Experience Replay (HER) based method, which makes use of the Genetic
Algorithm (GA) to fine-tune the parameters' values. This method (GA-DRL)
experimented on six robotic manipulation tasks: fetch-reach; fetch-slide;
fetch-push; fetch-pick and place; door-opening; and aubo-reach. Analysis of
these results demonstrated a significant increase in performance and a decrease
in learning time. Also, we compare and provide evidence that GA-DRL is better
than the existing methods.


title: Qade: Solving Differential Equations on Quantum Annealers

abstract:
  We present a general method, called Qade, for solving differential equations
using a quantum annealer. The solution is obtained as a linear combination of a
set of basis functions. On current devices, Qade can solve systems of coupled
partial differential equations that depend linearly on the solution and its
derivatives, with non-linear variable coefficients and arbitrary inhomogeneous
terms. We test the method with several examples and find that state-of-the-art
quantum annealers can find the solution accurately for problems requiring a
small enough function basis. We provide a Python package implementing the
method at gitlab.com/jccriado/qade.


title: Enhanced star formation in $z\sim6$ quasar companions

abstract:
  Quasars powered by supermassive black holes (MBH, $>10^8~M_{\odot}$) at
$z\sim 6$ are predicted to reside in cosmic over-dense regions. However,
observations so far could not confirm this expectation due to limited
statistics. The picture is further complicated by the possible effects of
quasar outflows (i.e. feedback) that could either suppress or stimulate the
star formation rate (SFR) of companion galaxies, thus modifying the expected
bias. Here we quantify feedback effects on the properties and detectability of
companions by comparing cosmological zoom-in simulations of a quasar in which
feedback is either included or turned-off. With respect to the no-feedback
case, companions (a) directly impacted by the outflow have their SFR increased
by a factor $2-3$, and (b) tend to be more massive. Both effects shift the
[CII]158$\mu$m and UV luminosity functions toward brighter magnitudes. This
leads us to conclude that quasar feedback slightly increases the effective
quasar bias, boosting the number density of observable quasar companions, in
agreement with what has been found around the brightest quasars of recent ALMA
[CII] surveys. Deeper observations performed with JWST and/or ALMA will improve
the statistical significance of this result by detecting a larger number of
fainter quasar companions.


title:
Emergent XY* transition driven by symmetry fractionalization and anyon
  condensation

abstract:
  Anyons in a topologically ordered phase can carry fractional quantum numbers
with respect to the symmetry group of the considered system, one example being
the fractional charge of the quasiparticles in the fractional quantum Hall
effect. When such symmetry-fractionalized anyons condense, the resulting phase
must spontaneously break the symmetry and display a local order parameter. In
this paper, we study the phase diagram and anyon condensation transitions of a
$\mathbb{Z}_2$ topological order perturbed by Ising interactions in the Toric
Code. The interplay between the global Ising symmetry and the lattice space
group symmetries results in a non-trivial symmetry fractionalization class for
the anyons, and is shown to lead to two characteristically different
symmetry-broken phases. To understand the anyon condensation transitions, we
use the recently introduced critical torus energy spectrum technique to
identify a line of emergent 2+1D XY* transitions ending at a fine-tuned
(Ising$^2$)* critical point. We provide numerical evidence for the occurrence
of two symmetry breaking patterns predicted by the specific symmetry
fractionalization class of the anyons in the explored phase diagram. In
combination with quantum Monte Carlo simulations we measure unusually large
critical exponents for the scaling of the correlation function at the anyon
condensation transitions, and we identify lines of (weakly) first order
transitions in the phase diagram. As an important result, we discuss the phase
diagram of a resulting 2+1D Ashkin-Teller model, where we demonstrate that
torus spectroscopy is capable of identifying emergent XY/O(2) critical
behaviour, thereby solving some longstanding open questions in the domain of
the 3D Ashkin-Teller models. To establish the generality of our results, we
propose a field theoretical description which is in excellent agreement with
the numerical results.


title: The Bottom-Up EFT: Complete UV Resonances of the SMEFT Operators

abstract:
  The standard model effective field theory (SMEFT) provides systematic
parameterization of all possible new physics above the electroweak scale.
According to the amplitude-operator correspondence, an effective operator can
be decomposed into a linear combination of several j-basis operators, which
correspond to local amplitudes carrying certain spin and gauge quantum numbers
in a particular scattering channel. Based on the Poincare and gauge symmetries
of scattering amplitude, we construct the j-basis using the Casimir method for
both the Lorentz and gauge sectors. The quantum numbers of the j-basis
operators fix the quantum numbers of any intermediate state in the
corresponding amplitudes, such as a UV resonance. This can be re-interpreted as
the j-basis/UV correspondence, thus obtaining the j-bases in all partitions of
fields for an operator amounts to finding all of its UV origins at tree level,
constituting the central part of the bottom-up EFT framework. Applying the
j-basis analysis to SMEFT, we obtain a complete list of possible tree-level UV
origins of the effective operators at the dimension 5, 6, 7, and all the
bosonic operators at the dimension 8.


title:
Binary Natal Kicks in the Galactic Center: X-ray Binaries, Hypervelocity
  Stars, and Gravitational Waves

abstract:
  Theoretical and observational studies suggest that stellar binaries exist in
large numbers in galactic nuclei like our own Galactic Center. Neutron stars
(NSs), and debatedly, black holes (BHs) and white dwarfs (WDs), receive natal
kicks at birth. In this work we study the effect of two successive natal kicks
on a population of stellar binaries orbiting the massive black hole (MBH) in
our Galactic Center. These natal kicks can significantly alter the binary orbit
in a variety of ways, and also the orbit of the binary around the MBH. We found
a variety of dynamical outcomes resulting from these kicks, including a steeper
cusp of single NSs relative to the initial binary distribution. Furthermore,
hypervelocity star and binary candidates, including hypervelocity X-ray
binaries, are a common outcome of natal kicks. In addition, we show that the
population of X-ray binaries in the Galactic Center can be used as a diagnostic
for the BH natal kick distribution. Finally, we estimate the rate of
gravitational wave (GW) events triggered by natal kicks, including binary
mergers and EMRIs.


title:
Evidence of ram pressure stripping of WLM, a dwarf galaxy far away from
  any large host galaxy

abstract:
  Dwarf galaxies are affected by all the evolutionary processes normally at
work in galaxies of any mass. As fainter and less massive galaxies, however,
dwarf galaxies are particularly susceptible to environmental mechanisms that
can more easily perturb these systems. Importantly, the presence of nearby
large galaxies are expected to have a profound effect on dwarf galaxies.
Gravitational (especially tidally-induced) effects from the large galaxy can
cause mass to be lost from the dwarf, and the passage of the dwarf through the
gaseous medium surrounding the large galaxy can additionally cause the dwarf to
lose its own gas through a process called ram pressure stripping. Such effects
are considered to be the main sources of difference between "satellite" and
"field" dwarf galaxy populations. Here, we report on new observations of the
gaseous content of Wolf-Lundmark-Melotte (WLM), an archetype of isolated,
gas-rich field dwarf galaxies in the Local Universe, which shows a much more
complex situation. Previous studies of its gaseous disk suggest it has
perturbed kinematics; here, we identify four trailing, extended gas clouds in
the opposite direction to WLM's spatial motion, as well as a spatial offset
between the WLM gas and stars. Overall, the morphology and kinematics of this
gas shows that WLM is undergoing ram pressure stripping, despite being 930 and
830 kpc from the Milky Way and M31, respectively. This finding indicates the
presence of an inter-galactic, gaseous reservoir far from large galaxies whose
evolutionary role on galaxies, both large and small, may not be fully
appreciated.


title:
High energy particles from young supernovae: gamma-ray and neutrino
  connections

abstract:
  Up to about one year after explosion, core-collapse supernovae ("young
supernovae," YSNe) are factories of high-energy neutrinos and gamma-rays as the
shock accelerated protons efficiently interact with the protons in the dense
circumstellar medium. We explore the detection prospects of secondary particles
from YSNe of Type IIn, II-P, II-L, and Ibc. Type IIn YSNe are found to produce
the largest flux of neutrinos and gamma-rays, followed by II-P YSNe. Fermi-LAT
and the Cherenkov Telescope Array (IceCube-Gen2) have the potential to detect
Type IIn YSNe up to $10$~Mpc ($4$~Mpc), with the remaining YSNe Types being
detectable closer to Earth. We also find that YSNe may dominate the diffuse
neutrino background, especially between $10$~TeV and $10^3$~TeV, while they do
not constitute a dominant component to the isotropic gamma-ray background
observed by Fermi-LAT. At the same time, the IceCube high-energy starting
events and Fermi-LAT data already allow us to exclude a large fraction of the
model parameter space of YSNe otherwise inferred from multi-wavelength
electromagnetic observations of these transients.


title: Deviation to the Tri-Bi-Maximal flavor pattern and equivalent classes

abstract:
  In the model-independent context, where the neutrino mass matrix is assumed
to be diagonalized by means of a unitary matrix that possess the Tri-Bi-Maximal
(TBM) flavor mixing pattern. We present an analysis where the TBM deviation is
explored by considering different forms, with texture zeros, for the charged
lepton mass matrix. These last mass matrices are classified into equivalent
classes. We are interested in the charged lepton mass matrices with the minimum
free parameter number, $i.e.$ the maximum number of texture zeros, that allows
us to correctly reproduce the reactor mixing angle value. We show a deviation
from the TBM pattern in terms of the charged lepton masses as well as the
theoretical expressions and their parameter space for the mixing angles.
Finally, we present the phenomenological implications of numerical values of
the "Majorana-like" phase factors on the neutrinoless double-beta decay.


title:
A note on the quasiperiodic many-body localization transition in
  dimension $d>1$

abstract:
  The nature of the many-body localization (MBL) transition and even the
existence of the MBL phase in random many-body quantum systems have been
actively debated in recent years. In spatial dimension $d>1$, there is some
consensus that the MBL phase is unstable to rare thermal inclusions that can
lead to an avalanche that thermalizes the whole system. In this note, we
explore the possibility of MBL in quasiperiodic systems in dimension $d>1$. We
argue that (i) the MBL phase is stable at strong enough quasiperiodic
modulations for $d = 2$, and (ii) the possibility of an avalanche strongly
constrains the finite-size scaling behavior of the MBL transition. We present a
suggestive construction that MBL is unstable for $d \geq 3$.


title: Gas temperature structure across transition disk cavities

abstract:
  [Abridged] Most disks observed at high angular resolution show substructures.
Knowledge about the gas surface density and temperature is essential to
understand these. The aim of this work is to constrain the gas temperature and
surface density in two transition disks: LkCa15 and HD 169142. We use new ALMA
observations of the $^{13}$CO $J=6-5$ transition together with archival $J=2-1$
data of $^{12}$CO, $^{13}$CO and C$^{18}$O to observationally constrain the gas
temperature and surface density. Furthermore, we use the thermochemical code
DALI to model the temperature and density structure of a typical transition
disk. The $6-5/2-1$ line ratio in LkCa15 constrains the gas temperature in the
emitting layers inside the dust cavity to be up to 65 K, warmer than in the
outer disk at 20-30 K. For the HD 169142, the peak brightness temperature
constrains the gas in the dust cavity of HD 169142 to be 170 K, whereas that in
the outer disk is only 100 K. Models also show that a more luminous central
star, a lower abundance of PAHs and the absence of a dusty inner disk increase
the temperature of the emitting layers and hence the line ratio in the gas
cavity. The gas column density in the LkCa15 dust cavity drops by a factor >2
compared to the outer disk, with an additional drop of an order of magnitude
inside the gas cavity at 10 AU. In the case of HD 169142, the gas column
density drops by a factor of 200$-$500 inside the gas cavity, which could be
due to a massive companion of several M$_{\mathrm{J}}$. The broad dust-depleted
gas region from 10-68 AU for LkCa15 may imply several lower mass planets. This
work demonstrates that knowledge of the gas temperature is important to
determine the gas surface density and thus whether planets, and if so what kind
of planets, are the most likely carving the dust cavities.


title: Discovery prospects for long-lived multiply charged particles at the LHC

abstract:
  In this work, we aim to provide a comprehensive and largely model independent
investigation on prospects to detect long-lived multiply charged particles at
the LHC. We consider particles with spin 0 and $\frac{1}{2}$, with electric
charges in range $1 \le |Q/e| \le 8$, which are singlet or triplet under
$SU(3)_C$. Such particles might be produced as particle-antiparticle pairs and
propagate through detectors, or form a positronium(quarkonium)-like bound
state. We consider both possibilities and estimate lower mass bounds on new
particles, that can be provided by ATLAS, CMS and MoEDAL experiments at the end
of Run 3 and HL-LHC data taking periods. We find out that the sensitivities of
ATLAS and CMS are generally stronger than those of MoEDAL at Run 3, while they
may be competitive at HL-LHC for $3 \lesssim |Q/e| \lesssim 7$ for all types of
long-lived particles we consider.


title:
Energizing Star Formation: The Cosmic Ray Ionization Rate in NGC 253
  Derived From ALCHEMI Measurements of H$_3$O$^+$ and SO

abstract:
  The cosmic ray ionization rate (CRIR) is a key parameter in understanding the
physical and chemical processes in the interstellar medium. Cosmic rays are a
significant source of energy in star formation regions, which impacts the
physical and chemical processes which drive the formation of stars. Previous
studies of the circum-molecular zone (CMZ) of the starburst galaxy NGC 253 have
found evidence for a high CRIR value; $10^3-10^6$ times the average cosmic ray
ionization rate within the Milky Way. This is a broad constraint and one goal
of this study is to determine this value with much higher precision. We exploit
ALMA observations towards the central molecular zone of NGC 253 to measure the
CRIR. We first demonstrate that the abundance ratio of H$_3$O$^+$ and SO is
strongly sensitive to the CRIR. We then combine chemical and radiative transfer
models with nested sampling to infer the gas properties and CRIR of several
star-forming regions in NGC 253 due to emission from their transitions. We find
that each of the four regions modelled has a CRIR in the range
$(1-80)\times10^{-14}$ s$^{-1}$ and that this result adequately fits the
abundances of other species that are believed to be sensitive to cosmic rays
including C$_2$H, HCO$^+$, HOC$^+$, and CO. From shock and PDR/XDR models, we
further find that neither UV/X-ray driven nor shock dominated chemistry are a
viable single alternative as none of these processes can adequately fit the
abundances of all of these species.


title: Islands and Light Gravitons in type IIB String Theory

abstract:
  We consider the setup of an evaporating black hole in AdS$_{4}$ coupled to an
external bath, embedded in type IIB string theory. We study quantum extremal
islands in these backgrounds, in relation to the existence of a massive
graviton. Using explicit results of the microscopic embedding of AdS$_{4}$
massive gravity in string theory, we investigate whether it is possible to
achieve backgrounds with extremal islands, in which the lowest lying graviton
is only slightly massive. For certain regions of the microscopic parameters,
the graviton mass can be computed explicitly, and we explain how it directly
affects the existence and the properties of the islands. We also show that
islands can in principle exist within the regime of validity of the massive
gravity effective field theory. However we see via numerical computations that
the existence of quantum extremal islands at zero temperature is highly
constrained, also when the dilaton is allowed to vary, so that the mass of the
graviton cannot be made arbitrarily light. At finite temperature, we also
identify a critical parameter, above and below which islands still exist but
exhibit a different behavior. Our work supports recent proposals that the
unitary evolution of black holes in higher dimensions, and more precisely their
Page curve, strongly relies on the presence of a massive graviton in the
effective theory.


title: Fingerprints of freeze-in dark matter in an early matter-dominated era

abstract:
  We study the impact of an alternate cosmological history with an early
matter-dominated epoch on the freeze-in production of dark matter. Such early
matter domination is triggered by a meta-stable matter field dissipating into
radiation. In general, the dissipation rate has a non-trivial temperature and
scale factor dependence. Compared to the usual case of dark matter production
via the freeze-in mechanism in a radiation-dominated universe, in this
scenario, orders of magnitude larger coupling between the visible and the dark
sector can be accommodated. Finally, as a proof of principle, we consider a
specific model where the dark matter is produced by a sub-GeV dark photon
having a kinetic mixing with the Standard Model photon. We point out that the
parameter space of this model can be probed by the experiments in the presence
of an early matter-dominated era.


title:
TemporalUV: Capturing Loose Clothing with Temporally Coherent UV
  Coordinates

abstract:
  We propose a novel approach to generate temporally coherent UV coordinates
for loose clothing. Our method is not constrained by human body outlines and
can capture loose garments and hair. We implemented a differentiable pipeline
to learn UV mapping between a sequence of RGB inputs and textures via UV
coordinates. Instead of treating the UV coordinates of each frame separately,
our data generation approach connects all UV coordinates via feature matching
for temporal stability. Subsequently, a generative model is trained to balance
the spatial quality and temporal stability. It is driven by supervised and
unsupervised losses in both UV and image spaces. Our experiments show that the
trained models output high-quality UV coordinates and generalize to new poses.
Once a sequence of UV coordinates has been inferred by our model, it can be
used to flexibly synthesize new looks and modified visual styles. Compared to
existing methods, our approach reduces the computational workload to animate
new outfits by several orders of magnitude.


title:
MOA-2020-BLG-135Lb: A New Neptune-class Planet for the Extended MOA-II
  Exoplanet Microlens Statistical Analysis

abstract:
  We report the light-curve analysis for the event MOA-2020-BLG-135, which
leads to the discovery of a new Neptune-class planet, MOA-2020-BLG-135Lb. With
a derived mass ratio of $q=1.52_{-0.31}^{+0.39} \times 10^{-4}$ and separation
$s\approx1$, the planet lies exactly at the break and likely peak of the
exoplanet mass-ratio function derived by the MOA collaboration (Suzuki et al.
2016). We estimate the properties of the lens system based on a Galactic model
and considering two different Bayesian priors: one assuming that all stars have
an equal planet-hosting probability and the other that planets are more likely
to orbit more massive stars. With a uniform host mass prior, we predict that
the lens system is likely to be a planet of mass
$m_\mathrm{planet}=11.3_{-6.9}^{+19.2} M_\oplus$ and a host star of mass
$M_\mathrm{host}=0.23_{-0.14}^{+0.39} M_\odot$, located at a distance
$D_L=7.9_{-1.0}^{+1.0}\;\mathrm{kpc}$. With a prior that holds that planet
occurrence scales in proportion to the host star mass, the estimated lens
system properties are $m_\mathrm{planet}=25_{-15}^{+22} M_\oplus$,
$M_\mathrm{host}=0.53_{-0.32}^{+0.42} M_\odot$, and $D_L=8.3_{-1.0}^{+0.9}\;
\mathrm{kpc}$. This planet qualifies for inclusion in the extended MOA-II
exoplanet microlens sample.


title: The fuzzy BTZ

abstract:
  We introduce a model of a noncommutative BTZ black hole, obtained by
quantisation of Poincar\'e coordinates together with a moving frame. The fuzzy
BTZ black hole carries a covariant differential calculus, satisfies Einstein's
equations and has a constant negative curvature. The construction passes
through a larger space, the fuzzy anti-de Sitter, and implements discrete BTZ
identifications as conjugations by a unitary operator. We derive the spectrum
of the suitably regularised radial coordinate: it consists of a continuum of
scattering states outside the horizon $r_+$ and an infinite discrete set of
bound states inside.


title: On Primitivity and Vanishing of Dirichlet Series

abstract:
  For a rational valued periodic function, we associate a Dirichlet series and
provide a new necessary and sufficient condition for the vanishing of this
Dirichlet series specialized at positive integers. This question was initiated
by Chowla, and carried out by Okada for a particular infinite sum. Our approach
relies on the decomposition of the Dirichlet characters in terms of primitive
characters. Using this, we find some new family of natural numbers for which a
conjecture of Erd\"os holds. We also characterize rational valued periodic
functions for which the associated Dirichlet series vanishes at two different
positive integers under some additional conditions.


title: Dynamically Induced Topological Inflation

abstract:
  We propose an inflation model in which the inflationary era is driven by the
strong dynamics of $Sp(2)$ gauge theory. The quark condensation in the confined
phase of $Sp(2)$ gauge theory generates the inflaton potential comparable to
the energy of the thermal bath at the time of phase transition. Afterwards,
with super-Planckian global minimum, the inflation commences at a false vacuum
region lying between true vacuum regions and hence the name "topological
inflation". Featured by the huge separation between the scale of the false
vacuum ($V(0)^{1/4}\sim10^{15}{\rm GeV}$) and the global minimum
($\langle\phi\rangle\sim M_{P}$), the model can be consistent with CMB
observables without suffering from the initial condition problem. Crucially,
this is achieved without any fine-tuning of parameters in $V(\phi)$. In
addition to $Sp(2)$, this model is based on an anomaly free $Z_{6R}$ discrete
$R$ symmetry. Remarkably, while all parameters are fixed by CMB observations,
the model predicts a hierarchy of energy scales including the inflation scale,
SUSY-breaking scale, R-symmetry breaking scale, Higgsino mass and the
right-handed neutrino mass given in terms of the dynamical scale of $Sp(2)$.


title:
cyberaCTIve: a STIX-based Tool for Cyber Threat Intelligence in Complex
  Models

abstract:
  Cyber threat intelligence (CTI) is practical real-world information that is
collected with the purpose of assessing threats in cyber-physical systems
(CPS). A practical notation for sharing CTI is STIX. STIX offers facilities to
create, visualise and share models; however, even a moderately simple project
can be represented in STIX as a quite complex graph, suggesting to spread CTI
across multiple simpler sub-projects. Our tool aims to enhance the STIX-based
modelling task in contexts when such simplifications are infeasible. Examples
can be the microgrid and, more in general, the smart grid.


title: Bispectrality of $AG_2$ Calogero-Moser-Sutherland system

abstract:
  We consider the generalised Calogero-Moser-Sutherland quantum integrable
system associated to the configuration of vectors $AG_2$, which is a union of
the root systems $A_2$ and $G_2$. We establish the existence of and construct a
suitably defined Baker-Akhiezer function for the system, and we show that it
satisfies bispectrality. We also find two corresponding dual difference
operators of rational Macdonald-Ruijsenaars type in an explicit form.


title: Integrating out beyond tree level and relativistic superfluids

abstract:
  We revisit certain subtleties of renormalization that arise when one derives
a low-energy effective action by integrating out the heavy fields of a more
complete theory. Usually these subtleties are circumvented by matching some
physical observables, such as scattering amplitudes, but a more involved
procedure is required if one is interested in deriving the effective theory to
all orders in the light fields (but still to fixed order in the derivative
expansion). As a concrete example, we study the $U(1)$ Goldstone low-energy
effective theory that describes the spontaneously broken phase of a $\phi^4$
theory for a complex scalar. Working to lowest order in the derivative
expansion, but to all orders in the Goldstones, we integrate out the radial
mode at one loop and express the low-energy effective action in terms of the
renormalized couplings of the UV completion. This yields the one-loop equation
of state for the superfluid phase of (complex) $\phi^4$. We perform the same
analysis for a renormalizable scalar $SO(N)$ theory at finite chemical
potential, integrating out the gapped Goldstones as well, and confirm that the
effective theory for the gapless Goldstone exhibits no obvious sign of the
original $SO(N)$ symmetry.


title:
N-body Interactions will be Detectable in the HR-8799 System within 5
  years with VLTI-GRAVITY

abstract:
  While Keplerian orbits account for the majority of the astrometric motion of
directly-imaged planets, perturbations due to N-body interactions allow us to
directly constrain exoplanet masses in multiplanet systems. This has the
potential to improve our understanding of massive directly-imaged planets,
which nearly all currently have only model-dependent masses. The VLTI-GRAVITY
instrument has demonstrated that interferometry can achieve 100x better
astrometric precision (Gravity Collaboration et al. 2019) than existing
methods, a level of precision that makes detection of planet-planet
interactions possible. In this study, we show that in the HR-8799 system,
planet-planet deviations from currently used Keplerian approximations (Lacour
et al. 2021) are expected to be up to one-quarter of a milliarc-second within
five years, which will make them detectable with VLTI-GRAVITY. Modeling of this
system to directly constrain exoplanet masses will be crucial in order to make
precise predictions.


title: Orientation Controlled Anisotropy in Single Crystals of Quasi-1D BaTiS3

abstract:
  Low-dimensional materials with chain-like (one-dimensional) or layered
(twodimensional) structures are of significant interest due to their
anisotropic electrical, optical, thermal properties. One material with
chain-like structure, BaTiS3 (BTS), was recently shown to possess giant
in-plane optical anisotropy and glass-like thermal conductivity. To understand
the origin of these effects, it is necessary to fully characterize the optical,
thermal, and electronic anisotropy of BTS. To this end, BTS crystals with
different orientations (aand c-axis orientations) were grown by chemical vapor
transport. X-ray absorption spectroscopy (XAS) was used to characterize the
local structure and electronic anisotropy of BTS. Fourier transform infrared
(FTIR) reflection/transmission spectra show a large inplane optical anisotropy
in the a-oriented crystals, while the c-axis oriented crystals were nearly
isotropic in-plane. BTS platelet crystals are promising uniaxial materials for
IR optics with their optic axis parallel to the c-axis. The thermal
conductivity measurements revealed a thermal anisotropy of ~4.5 between the c-
and a-axis. Time-domain Brillouin scattering showed that the longitudinal sound
speed along the two axes is nearly the same suggesting that the thermal
anisotropy is a result of different phonon scattering rates.


title: Magnetic fields and young stellar objects in cometary cloud LDN 1616

abstract:
  LDN 1615/1616 and CB 28 (hereafter, L1616) together form a cometary globule
located at an angular distance of about 8 degrees west of the Orion OB1
association, aligned roughly along the east-west direction, and showing a
distinct head-tail structure. The presence of massive stars in the Orion belt
has been considered to be responsible for the radiation driven implosion mode
of star formation in L1616. Based on the latest Gaia EDR3 measurements of the
previously known young stellar objects (YSOs) associated with L1616, we find
the distance to this cloud of 384$\pm$5 pc. We present optical polarimetry
towards L1616 that maps the plane-of-sky component of the ambient magnetic
field (B$_{POS}$) geometry. Based on the proper motion of the YSOs associated
with L1616, we investigate their plane-of-sky motion relative to the exciting
star $\epsilon$ Ori. Using the Gaia EDR3 measurements of the distances and
proper motions of the YSOs, we find two additional sources comoving with the
known YSOs. One comoving source is HD33056, a B9 star and the other might be a
young pre-main sequence star not reported in previous studies. The mean
direction of B$_{POS}$ is found to follow the cloud structure. This could be
the effect of dragging of the magnetic field lines by the impact of the
ionizing radiation from $\epsilon$ Ori. Based on the pressure exerted on L1616,
and the ages of the associated YSOs, we show that it could possibly be the main
source of ionization in L1616, and thus the star formation in it.


title:
An Eulerian-Lagrangian Runge-Kutta finite volume (EL-RK-FV) method for
  solving convection and convection-diffusion equations

abstract:
  We propose a new Eulerian-Lagrangian Runge-Kutta finite volume method for
numerically solving convection and convection-diffusion equations.
Eulerian-Lagrangian and semi-Lagrangian methods have grown in popularity mostly
due to their ability to allow large time steps. Our proposed scheme is
formulated by integrating the PDE on a space-time region partitioned by
approximations of the characteristics determined from the Rankine-Hugoniot jump
condition; and then rewriting the time-integral form into a time differential
form to allow application of Runge-Kutta (RK) methods via the method-of-lines
approach. The scheme can be viewed as a generalization of the standard
Runge-Kutta finite volume (RK-FV) scheme for which the space-time region is
partitioned by approximate characteristics with zero velocity. The high-order
spatial reconstruction is achieved using the recently developed weighted
essentially non-oscillatory schemes with adaptive order (WENO-AO); and the
high-order temporal accuracy is achieved by explicit RK methods for convection
equations and implicit-explicit (IMEX) RK methods for convection-diffusion
equations. Our algorithm extends to higher dimensions via dimensional
splitting. Numerical experiments demonstrate our algorithm's robustness,
high-order accuracy, and ability to handle extra large time steps.


title:
Can we detect coronal mass ejections through asymmetries of
  Sun-as-a-star extreme-ultraviolet spectral line profiles?

abstract:
  Coronal mass ejections (CMEs) are the largest-scale eruptive phenomena in the
solar system. Associated with enormous plasma ejections and energy release,
CMEs have an important impact on the solar-terrestrial environment. Accurate
predictions of the arrival times of CMEs at the Earth depend on the precise
measurements on their three-dimensional velocities, which can be achieved using
simultaneous line-of-sight (LOS) and plane-of-sky (POS) observations. Besides
the POS information from routine coronagraph and extreme ultraviolet (EUV)
imaging observations, spectroscopic observations could unveil the physical
properties of CMEs including their LOS velocities. We propose that spectral
line asymmetries measured by Sun-as-a-star spectrographs can be used for
routine detections of CMEs and estimations of their LOS velocities during their
early propagation phases. Such observations can also provide important clues
for the detection of CMEs on other solar-like stars. However, few studies have
concentrated on whether we can detect CME signals and accurately diagnose CME
properties through Sun-as-a-star spectral observations. In this work, we
constructed a geometric CME model and derived the analytical expressions for
full-disk integrated EUV line profiles during CMEs. For different CME
properties and instrumental configurations, full disk-integrated line profiles
were synthesized. We further evaluated the detectability and diagnostic
potential of CMEs from the synthetic line profiles. Our investigations provide
important constraints on the future design of Sun-as-a-star spectrographs for
CME detections through EUV line asymmetries.


title:
Probing nucleon spin structure in deep-inelastic scattering,
  proton-proton collisions and Drell-Yan processes

abstract:
  A pedagogical summary of current and past experimental results of
spin-dependent nucleon structure prior to the arrival of the Electron-Ion
Collider is attempted. After an introduction, results from fixed-target
experiments at SLAC, Fermilab, Jefferson Lab, CERN and DESY and collider
experiments from RHIC are presented, starting with the longitudinal spin
structure of the nucleon, followed by generalized parton distributions (GPDs),
which map the proton in transverse position space. The final part discusses
transverse proton or parton spin and transverse parton momenta (TMDs), and
their their (spin-orbit) correlations, which are addressed by a multitude of
recent experimental results. The GPDs and TMDs provide complementary pathways
to mapping multi-dimensional nucleon structure.


title:
Read, Revise, Repeat: A System Demonstration for Human-in-the-loop
  Iterative Text Revision

abstract:
  Revision is an essential part of the human writing process. It tends to be
strategic, adaptive, and, more importantly, iterative in nature. Despite the
success of large language models on text revision tasks, they are limited to
non-iterative, one-shot revisions. Examining and evaluating the capability of
large language models for making continuous revisions and collaborating with
human writers is a critical step towards building effective writing assistants.
In this work, we present a human-in-the-loop iterative text revision system,
Read, Revise, Repeat (R3), which aims at achieving high quality text revisions
with minimal human efforts by reading model-generated revisions and user
feedbacks, revising documents, and repeating human-machine interactions. In R3,
a text revision model provides text editing suggestions for human writers, who
can accept or reject the suggested edits. The accepted edits are then
incorporated into the model for the next iteration of document revision.
Writers can therefore revise documents iteratively by interacting with the
system and simply accepting/rejecting its suggested edits until the text
revision model stops making further revisions or reaches a predefined maximum
number of revisions. Empirical experiments show that R3 can generate revisions
with comparable acceptance rate to human writers at early revision depths, and
the human-machine interaction can get higher quality revisions with fewer
iterations and edits. The collected human-model interaction dataset and system
code are available at \url{https://github.com/vipulraheja/IteraTeR}. Our system
demonstration is available at \url{https://youtu.be/lK08tIpEoaE}.


title: Standard model anomalies: Lepton flavour non-universality and lepton g-2

abstract:
  We critically analyze the body of results that hints to the existence of New
Physics from possible violations of lepton universality observed by the LHCb
experiment in the $\mu/e$ ratios $R_{K}$ and $R_{K^*}$ to the $g-2$ lepton
anomalies. The analysis begins with a theoretical, in depth, study of the
$\mu/e$ ratios $R_{K}$ and $R_{K^*}$ as well as the process $B_s \rightarrow
\mu^+ \mu^-$. Here we consider the impact of complex Wilson coefficients and
derive constraints on their imaginary and real parts. We then move to a
comprehensive comparison with experimental results. We show that, by fitting a
single Wilson coefficient, the deviations from the Standard Model are at the
$4.7\sigma$ level when including only the hadronic insensitive observables
while it increases to $6.1\sigma$ when including also the hadronic sensitive
ones. When switching on all relevant Wilson coefficients and combining both
hadronic sensitive and insensitive data into the fit, the deviation from the
Standard Model peaks at 7.2$\sigma$ and decreases at the $4.9\sigma$ level if
we assume that the central values of $R_K$ and $R_K^{\ast}$ are taken to be
unity. We further estimate the non-perturbative long distance hadronic
contributions and show that their inclusion still requires New Physics to fit
the data. We then introduce the $g-2$ lepton anomalies results. Different
theoretical models are considered that can explain the discrepancies from the
Standard Model. In the final part of our work we estimate the impact of the
forthcoming data from LHCb (coming from LHC Run3) and Belle II, when it will
have accumulated about $5~ab^{-1}$.


title:
Statistical QoS Analysis of Reconfigurable Intelligent Surface-assisted
  D2D Communication

abstract:
  This work performs the statistical QoS analysis of a Rician block-fading
reconfigurable intelligent surface (RIS)-assisted D2D link in which the
transmit node operates under delay QoS constraints. First, we perform mode
selection for the D2D link, in which the D2D pair can either communicate
directly by relaying data from RISs or through a base station (BS). Next, we
provide closed-form expressions for the effective capacity (EC) of the
RIS-assisted D2D link. When channel state information at the transmitter (CSIT)
is available, the transmit D2D node communicates with the variable rate
$r_t(n)$ (adjustable according to the channel conditions); otherwise, it uses a
fixed rate $r_t$. It allows us to model the RIS-assisted D2D link as a Markov
system in both cases. We also extend our analysis to overlay and underlay D2D
settings. To improve the throughput of the RIS-assisted D2D link when CSIT is
unknown, we use the HARQ retransmission scheme and provide the EC analysis of
the HARQ-enabled RIS-assisted D2D link. Finally, simulation results demonstrate
that: i) the EC increases with an increase in RIS elements, ii) the EC
decreases when strict QoS constraints are imposed at the transmit node, iii)
the EC decreases with an increase in the variance of the path loss estimation
error, iv) the EC increases with an increase in the probability of ON states,
v) EC increases by using HARQ when CSIT is unknown, and it can reach up to
$5\times$ the usual EC (with no HARQ and without CSIT) by using the optimal
number of retransmissions.


title:
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D
  Head Alignment from a Single Image

abstract:
  We present DAD-3DHeads, a dense and diverse large-scale dataset, and a robust
model for 3D Dense Head Alignment in the wild. It contains annotations of over
3.5K landmarks that accurately represent 3D head shape compared to the
ground-truth scans. The data-driven model, DAD-3DNet, trained on our dataset,
learns shape, expression, and pose parameters, and performs 3D reconstruction
of a FLAME mesh. The model also incorporates a landmark prediction branch to
take advantage of rich supervision and co-training of multiple related tasks.
Experimentally, DAD-3DNet outperforms or is comparable to the state-of-the-art
models in (i) 3D Head Pose Estimation on AFLW2000-3D and BIWI, (ii) 3D Face
Shape Reconstruction on NoW and Feng, and (iii) 3D Dense Head Alignment and 3D
Landmarks Estimation on DAD-3DHeads dataset. Finally, the diversity of
DAD-3DHeads in camera angles, facial expressions, and occlusions enables a
benchmark to study in-the-wild generalization and robustness to distribution
shifts. The dataset webpage is https://p.farm/research/dad-3dheads.


title: Symmetric cooperative motion in one dimension

abstract:
  We explore the relationship between recursive distributional equations and
convergence results for finite difference schemes of parabolic partial
differential equations (PDEs). We focus on a family of random processes called
symmetric cooperative motions, which generalize the symmetric simple random
walk and the symmetric hipster random walk introduced in [Addario-Berry,
Cairns, Devroye, Kerriou and Mitchell, arXiv:1909.07367]. We obtain a
distributional convergence result for symmetric cooperative motions and, along
the way, obtain a novel proof of the Bernoulli central limit theorem. In
addition, we prove a PDE result relating distributional solutions and viscosity
solutions of the porous medium equation and the parabolic $p$-Laplace equation,
respectively, in one dimension.


title:
Empirical Tight-Binding Parameters for Wurtzite group III-V(non-Nitride)
  and IV Materials

abstract:
  Many commonly used nearest neighbor tight binding models for cubic
semiconductors often result in inaccurate band structures when transferred to
hexagonal polytypes. The resulting bandgaps are systematically too small, and
in some cases calculations may erroneously predict a metal for the wurtzite
polytype. We have calculated a set of $\rm spds^*$ tight binding parameters for
the hexagonal phases of non-nitride III-V and group V semiconductors fit to
experimental data and empirical pseudopotential calculations. Our fitting
procedure constrains the parameters to be as close as possible to those for the
cubic polytype so as to make the the parameters maximally transferable. Our
parameters, when combined with the existing cubic parameters, provide a model
suitable for modeling the electronic structure of polytypic heterostructures
containing both the wurtzite and zincblende crystal phases.


title:
A Message Passing Based Average Consensus Algorithm for Decentralized
  Frequency and Phase Synchronization in Distributed Phased Arrays

abstract:
  We consider the problem of decentralized frequency and phase synchronization
in distributed phased arrays via local broadcast of the node electrical states.
Frequency and phase synchronization between nodes in a distributed array is
necessary to support beamforming, but due to the operational dynamics of the
local oscillators of the nodes, the frequencies and phases of their output
signals undergo the random drift and jitter in between the update intervals.
Furthermore, frequency and phase estimation errors contribute to the total
phase errors, leading to a residual phase error in the array that degrades
coherent operation. Recently, a classical decentralized frequency and phase
synchronization algorithm based on consensus averaging was proposed with which
the standard deviation of the residual phase errors upon convergence was
reduced to $10^{-4}$ degrees for internode update intervals of $0.1$ ms,
however this was obtained for arrays with at least $400$ nodes and a high
connectivity ratio of $0.9$. In this paper, we propose a message passing based
average consensus (MPAC) algorithm to improve the synchronization of the
electrical states of the nodes in distributed arrays. Simulation results show
that the proposed MPAC algorithm significantly reduces the residual phase
errors to about $10^{-11}$ degrees, requiring only $20$ moderately connected
nodes in an array. Furthermore, MPAC converges faster than the DFPC-based
algorithms, particularly for the larger arrays with a moderate connectivity.


title: T cell morphodynamics reveal periodic shape oscillations in 3D migration

abstract:
  T cells use sophisticated shape dynamics (morphodynamics) to migrate towards
and neutralise infected and cancerous cells. However, there is limited
quantitative understanding of the migration process in 3D extracellular
matrices (ECMs) and across timescales. Here, we leveraged recent advances in
lattice light-sheet microscopy to quantitatively explore the 3D morphodynamics
of migrating T cells at high spatiotemporal resolution. We first developed a
new shape descriptor based on spherical harmonics, incorporating key
polarisation information of the uropod. We found that the shape space of T
cells is low-dimensional. At the behavioural level, run-and-stop migration
modes emerge at ~150 s, and we mapped the morphodynamic composition of each
mode using multiscale wavelet analysis, finding 'stereotyped' motifs. Focusing
on the run mode, we found morphodynamics oscillating periodically (every ~100
s) that can be broken down into a biphasic process: front-widening with
retraction of the uropod, followed by a rearward surface motion and forward
extension, where intercalation with the ECM in both of these steps likely
facilitates forward motion. Further application of these methods may enable the
comparison of T cell migration across different conditions (e.g.
differentiation, activation, tissues, and drug treatments), and improve the
precision of immunotherapeutic development.


title: Inert Higgs Dark Matter for New CDF W-boson Mass and Detection Prospects

abstract:
  The $W$-boson mass, which was recently measured at FermiLab, suggests the
presence of new multiplets beyond the Standard Model (SM). One of the minimal
extensions of the SM is to introduce an additional scalar doublet, in which the
non-SM scalars can enhance $W$-boson mass via the loop corrections. On the
other hand, with a proper discrete symmetry, the lightest new scalar in the
doublet can be stable and play the role of dark matter particle. We show that
the inert two Higgs doublet model can naturally handle the new $W$-boson mass
without violating other constraints, and the preferred dark matter mass is
between $54$ and $74$ GeV. We identify three feasible parameter regions for the
thermal relic density: the $SA$ co-annihilation, the Higgs resonance, and the
$SS \to WW^*$ annihilation. We find that the first region can be fully tested
by the HL-LHC, the second region will be tightly constrained by direct
detection experiments, and the third region could yield detectable GeV
gamma-ray and antiproton signals in the Galaxy that may have been observed by
Fermi-LAT and AMS-02.


title: Adaptive-Gravity: A Defense Against Adversarial Samples

abstract:
  This paper presents a novel model training solution, denoted as
Adaptive-Gravity, for enhancing the robustness of deep neural network
classifiers against adversarial examples. We conceptualize the model
parameters/features associated with each class as a mass characterized by its
centroid location and the spread (standard deviation of the distance) of
features around the centroid. We use the centroid associated with each cluster
to derive an anti-gravity force that pushes the centroids of different classes
away from one another during network training. Then we customized an objective
function that aims to concentrate each class's features toward their
corresponding new centroid, which has been obtained by anti-gravity force. This
methodology results in a larger separation between different masses and reduces
the spread of features around each centroid. As a result, the samples are
pushed away from the space that adversarial examples could be mapped to,
effectively increasing the degree of perturbation needed for making an
adversarial example. We have implemented this training solution as an iterative
method consisting of four steps at each iteration: 1) centroid extraction, 2)
anti-gravity force calculation, 3) centroid relocation, and 4) gravity
training. Gravity's efficiency is evaluated by measuring the corresponding
fooling rates against various attack models, including FGSM, MIM, BIM, and PGD
using LeNet and ResNet110 networks, benchmarked against MNIST and CIFAR10
classification problems. Test results show that Gravity not only functions as a
powerful instrument to robustify a model against state-of-the-art adversarial
attacks but also effectively improves the model training accuracy.


title: A Shuttle-Efficient Qubit Mapper for Trapped-Ion Quantum Computers

abstract:
  Trapped-ion (TI) quantum computer is one of the forerunner quantum
technologies. However, TI systems can have a limited number of qubits in a
single trap. Execution of meaningful quantum algorithms requires a multiple
trap system. In such systems, the computation may frequently involve ions from
two different traps for which the qubits must be co-located in the same trap,
hence one of the ions needs to be shuttled (moved) between traps, increasing
the vibrational energy, degrading fidelity, and increasing the program
execution time. The choice of initial mapping influences the number of
shuttles. The existing Greedy policy counts the number of gates occurring
between each pair of qubits and assigns edge weight. The qubits with high edge
weights are placed close to each other. However, it neglects the stage of the
program at which the gate is occurring. Intuitively, the contribution of the
late-occurring gates to the initial mapping reduces since the ions might have
already shuttled to a different trap to satisfy other gate operations. In this
paper, we target this gap and propose a new policy especially for programs with
considerable depth and high number of qubits (valid for practical-scale quantum
programs). Our policy is program adaptive and prioritizes the gates
re-occurring at the initial stages of the program over late occurring gates.
Our technique achieves an average reduction of 9% shuttles/program (with 21.3%
at best) for 120 random circuits and enhances the program fidelity up to 3.3X
(1.41X on average).


title:
Flat Folding an Unassigned Single-Vertex Complex (Combinatorially
  Embedded Planar Graph with Specified Edge Lengths) without Flat Angles

abstract:
  A foundational result in origami mathematics is Kawasaki and Justin's simple,
efficient characterization of flat foldability for unassigned single-vertex
crease patterns (where each crease can fold mountain or valley) on flat
material. This result was later generalized to cones of material, where the
angles glued at the single vertex may not sum to $360^\circ$. Here we
generalize these results to when the material forms a complex (instead of a
manifold), and thus the angles are glued at the single vertex in the structure
of an arbitrary planar graph (instead of a cycle). Like the earlier
characterizations, we require all creases to fold mountain or valley, not
remain unfolded flat; otherwise, the problem is known to be NP-complete (weakly
for flat material and strongly for complexes). Equivalently, we efficiently
characterize which combinatorially embedded planar graphs with prescribed edge
lengths can fold flat, when all angles must be mountain or valley (not unfolded
flat). Our algorithm runs in $O(n \log^3 n)$ time, improving on the previous
best algorithm of $O(n^2 \log n)$.


title:
Large-eddy simulation of Rayleigh-B\'enard convection for extreme
  Rayleigh numbers

abstract:
  We adopt the stretched spiral vortex sub-grid model for large-eddy simulation
(LES) of turbulent convection at extreme Rayleigh numbers. We simulate
Rayleigh-B\'enard convection (RBC) for Rayleigh numbers ranging from $10^6$ to
$10^13$ and for Prandtl numbers 0.768 and 1. We choose a box of dimensions
1:1:10 to reduce computational cost. Our LES yields Nusselt and Reynolds
numbers that are in good agreement with the direct-numerical simulation (DNS)
results of Iyer et al. (Proc. Natl. Acad. Sci., vol 117 (14), 2020, pp.
7594-7598), albeit with a smaller grid size and at significantly reduced
computational expense. For example, in our simulations at $Ra = 10^13$, we use
grids that are 1/120 times the grid-resolution as that of the DNS (Iyer et al.,
Proc. Natl. Acad. Sci., vol 117 (14), 2020, pp. 7594-7598). The Reynolds
numbers in our simulations range from 1,000 to 200,000. Consistent with the
literature, we obtain scaling relations for Nusselt and Reynolds numbers as $Nu
\sim Ra^{0.322}$ and $Re \sim Ra^{0.502}$. We also perform LES of RBC with
periodic side-walls, for which we obtain the corresponding scaling exponents as
0.343 and 0.477 respectively. Our LES is a promising tool to push simulations
of thermal convection to extreme Rayleigh numbers, and hence enable us to test
the transition to ultimate convection regime.


title:
Learning Purely Tactile In-Hand Manipulation with a Torque-Controlled
  Hand

abstract:
  We show that a purely tactile dextrous in-hand manipulation task with
continuous regrasping, requiring permanent force closure, can be learned from
scratch and executed robustly on a torque-controlled humanoid robotic hand. The
task is rotating a cube without dropping it, but in contrast to OpenAI's
seminal cube manipulation task, the palm faces downwards and no cameras but
only the hand's position and torque sensing are used. Although the task seems
simple, it combines for the first time all the challenges in execution as well
as learning that are important for using in-hand manipulation in real-world
applications. We efficiently train in a precisely modeled and identified rigid
body simulation with off-policy deep reinforcement learning, significantly sped
up by a domain adapted curriculum, leading to a moderate 600 CPU hours of
training time. The resulting policy is robustly transferred to the real
humanoid DLR Hand-II, e.g., reaching more than 46 full 2${\pi}$ rotations of
the cube in a single run and allowing for disturbances like different cube
sizes, hand orientation, or pulling a finger.


title:
Momentum domain polarization probing of forward and inverse spin Hall
  effect of leaky modes in plasmonic crystals

abstract:
  Simultaneous manifestation of both forward and inverse photonic Spin Hall
effect in geometrically tailored anisotropic waveguided plasmonic crystal
system is observed through the excitation of the leaky hybridized quasiguided
modes. The quasiguided modes of the plasmonic crystal manifested in the
far-field momentum (Fourier) domain as circular ring-like intensity
distributions, and the different spin orbit interaction (SOI) effects revealed
their exclusive signature as polarization-dependent azimuthal intensity lobes
on top of it. Using a darkfield Fourier domain polarization Mueller matrix
platform, we have observed input spin (circular polarization)- dependent
trajectory of the leaky quasiguided modes in the momentum domain (forward spin
Hall effect) and its reciprocal effect as the wave vector-controlled spin
selection of quasiguided modes (inverse spin Hall effect). These effects are
separately manifested in characteristic Mueller matrix elements enabling their
interpretation as geometrical circular anisotropy effects. Resonance-enabled
enhancement and control of these effects are also demonstrated by exploiting
the spectral Fano-type resonance. The far-field manifestation of
spin-directional excitation of leaky quasiguided modes, their unique
interpretation through momentum domain Mueller matrix, regulation and control
of the SOI effects in plasmonic-photonic crystal systems, opens up exciting
avenues in spin-orbit photonic research.


title: A Self-Consistent Model for Dust-Gas Coupling in Protoplanetary Disks

abstract:
  Various physical processes that ensue within protoplanetary disks --
including vertical settling of icy/rocky grains, radial drift of solids,
planetesimal formation, as well as planetary accretion itself -- are
facilitated by hydrodynamic interactions between H/He gas and high-$Z$ dust.
The Stokes number, which quantifies the strength of dust-gas coupling, thus
plays a central role in protoplanetary disk evolution, and its poor
determination constitutes an important source of uncertainty within the theory
of planet formation. In this work, we present a simple model for dust-gas
coupling, and demonstrate that for a specified combination of the nebular
accretion rate, $\dot{M}$, and turbulence parameter, $\alpha$, the radial
profile of the Stokes number can be calculated uniquely. Our model indicates
that the Stokes number grows sub-linearly with orbital radius, but increases
dramatically across the water-ice line. For fiducial protoplanetary disk
parameters of $\dot{M}=10^{-8}\,M_{\odot}/$year and $\alpha=10^{-3}$, our
theory yields characteristic values of the Stokes number on the order of
$\mathrm{St}\sim10^{-4}$ (corresponding to $\sim$mm-sized silicate dust) in the
inner nebula and $\mathrm{St}\sim10^{-1}$ (corresponding to $\sim$few-cm-sized
icy grains), in the outer regions of the disk. Accordingly, solids are expected
to settle into a thin sub-disk at large stellocentric distances, while
remaining vertically well-mixed inside the ice line.


title:
Dynamic simulation of aortic valve stenosis using a lumped parameter
  cardiovascular system model with flow regime dependent valve pressure loss
  characteristics

abstract:
  Valvular heart diseases are growing concern in impoverished parts of the
world, such as Southern-Africa, claiming more than 31 % of total deaths related
to cardiovascular diseases. The ability to model the effects of regurgitant and
obstructive lesions on the valve body can assist clinicians in preparing
personalised treatments. In the present work, a multi-compartment lumped
parameter model of the human cardiovascular system is developed, with a newly
proposed valve modelling approach which accounts for geometry and flow regime
dependent pressure drops along with the valve cusp motion. The model is applied
to study various degrees of aortic stenosis using typical human cardiovascular
parameters. The results generated with the proposed model, are compared to
predictions using previously published valve modelling approaches and both sets
of results are compared to typical local and global physiological parameters
found in literature such left-ventricular systolic pressures, peak and mean
aortic valve pressure drops and vena contracta velocities. The results show
that the previously published valve models under predicts expected severely
stenosed peak and mean transvalvular pressure drops by approximately 47% and
30% respectively, whereas the newly proposed model under predicts the peak
pressure drop by 20% and over predicts mean pressure drop by 7%.


title: Framed $\mathbb E_n$-Algebras from Quantum Field Theory

abstract:
  This paper addresses the following question: given a topological quantum
field theory on $\mathbb R^n$ built from an action functional, when is it
possible to globalize the theory so that it makes sense on an arbitrary smooth
oriented $n$-manifold? We study a broad class of topological field theories --
those of AKSZ type -- and obtain an explicit condition for the vanishing of the
framing anomaly, i.e., the obstruction to performing this globalization
procedure. We also interpret our results in terms of identifying the
observables as an algebra over the framed little $n$-disks operad. Our analysis
uses the BV formalism for perturbative field theory and the notion of
factorization homology.


title: Physics-assisted Generative Adversarial Network for X-Ray Tomography

abstract:
  X-ray tomography is capable of imaging the interior of objects in three
dimensions non-invasively, with applications in biomedical imaging, materials
science, electronic inspection, and other fields. The reconstruction process
can be an ill-conditioned inverse problem, requiring regularization to obtain
satisfactory reconstructions. Recently, deep learning has been adopted for
tomographic reconstruction. Unlike iterative algorithms which require a
distribution that is known a priori, deep reconstruction networks can learn a
prior distribution through sampling the training distributions. In this work,
we develop a Physics-assisted Generative Adversarial Network (PGAN), a two-step
algorithm for tomographic reconstruction. In contrast to previous efforts, our
PGAN utilizes maximum-likelihood estimates derived from the measurements to
regularize the reconstruction with both known physics and the learned prior.
Synthetic objects with spatial correlations are integrated circuits (IC) from a
proposed model CircuitFaker. Compared with maximum-likelihood estimation, PGAN
can reduce the photon requirement with limited projection angles to achieve a
given error rate. We further attribute the improvement to the learned prior by
reconstructing objects created without spatial correlations. The advantages of
using a prior from deep learning in X-ray tomography may further enable
low-photon nanoscale imaging.


title: Extremum Seeking with Intermittent Measurements: A Lie-brackets Approach

abstract:
  Extremum seeking systems are powerful methods able to steer the input of a
(dynamical) cost function towards an optimizer, without any prior knowledge of
the cost function. To achieve their objective, they typically combine
time-periodic signals with the on-line measurement of the cost. However, in
some practical applications, the cost can only be measured during some regular
time-intervals, and not continuously, contravening the classical extremum
seeking framework. In this paper, we first analyze how existing Lie-bracket
based extremum seeking systems behave when being fed with intermittent
measurements, instead of continuous ones. We then propose two modifications of
those schemes to improve both the convergence time and the steady-state
accuracy in presence of intermittent measurements. The performances of the
different schemes are compared on a case study.


title: Comparison of two multiplexed portable cold-atom vacuum standards

abstract:
  We compare the vacuum measured by two portable cold-atom vacuum standards
(pCAVS) based on ultracold $^7$Li atoms. The pCAVS are quantum-based standards
that use a priori scattering calculations to convert a measured loss rate of
cold atoms from a conservative trap into a background gas pressure. Our pCAVS
devices share the same laser system and measure the vacuum concurrently. The
two pCAVS together detected a leak with a rate on the order of $10^{-6}$ Pa
L/s. After fixing the leak, the pCAVS measured a pressure of about 40 nPa with
2.6 % uncertainty. The two pCAVS agree within their uncertainties, even when
swapping some of their component parts. Operation of the pCAVS was found to
cause some additional outgassing, on the order of $10^{-8}$ Pa L/s, which can
be mitigated in the future by better thermal management.


title:
Introducing a Framework and a Decision Protocol to Calibrate Recommender
  Systems

abstract:
  Recommender Systems use the user's profile to generate a recommendation list
with unknown items to a target user. Although the primary goal of traditional
recommendation systems is to deliver the most relevant items, such an effort
unintentionally can cause collateral effects including low diversity and
unbalanced genres or categories, benefiting particular groups of categories.
This paper proposes an approach to create recommendation lists with a
calibrated balance of genres, avoiding disproportion between the user's profile
interests and the recommendation list. The calibrated recommendations consider
concomitantly the relevance and the divergence between the genres distributions
extracted from the user's preference and the recommendation list. The main
claim is that calibration can contribute positively to generate fairer
recommendations. In particular, we propose a new trade-off equation, which
considers the users' bias to provide a recommendation list that seeks for the
users' tendencies. Moreover, we propose a conceptual framework and a decision
protocol to generate more than one thousand combinations of calibrated systems
in order to find the best combination. We compare our approach against
state-of-the-art approaches using multiple domain datasets, which are analyzed
by rank and calibration metrics. The results indicate that the trade-off, which
considers the users' bias, produces positive effects on the precision and to
the fairness, thus generating recommendation lists that respect the genre
distribution and, through the decision protocol, we also found the best system
for each dataset.


title: Hub Location with Protection under Link Failures

abstract:
  This paper introduces the Hub Location Problem under Link Failures, a hub
location problem in which activated inter-hub links may fail with a given
probability. Two different optimization models are studied, which construct hub
backbone networks protected under hub links disruptions by imposing that for
each commodity an additional routing path exists besides its original routing
path. Both models consider the minimization of the set-up costs of the
activated hubs and inter-hub links plus the expected value of the routing costs
of the original and alternative paths. The first model builds explicitly the
alternative routing paths, while the second model guarantees that for each
commodity at least one alternative path exists using a large set of
connectivity constraints, although the alternative paths are not built
explicitly. The results of extensive computational testing allow to analyze the
performance of the two proposed models and to evaluate the extra cost required
to design a robust backbone network under hub links failures. The obtained
results support the validity of the proposal.


title: Accounting for Misclassification in Multispecies Distribution Models

abstract:
  1. Species identification errors may have severe implications for the
inference of species distributions. Accounting for misclassification in species
distributions is an important topic of biodiversity research. With an
increasing amount of biodiversity that comes from Citizen Science projects,
where identification is not verified by preserved specimens, this issue is
becoming more important. This has often been dealt with by accounting for false
positives in species distribution models. However, the problem should account
for misclassifications in general.
  2. Here we present a flexible framework that accounts for misclassification
in the distribution models and provides estimates of uncertainty around these
estimates. The model was applied to data on viceroy, queen and monarch
butterflies in the United States. The data were obtained from the iNaturalist
database in the period 2019 to 2020.
  3. Simulations and analysis of butterfly data showed that the proposed model
was able to correct the reported abundance distribution for misclassification
and also predict the true state for misclassified state.


title: Cumulant mapping as the basis of multi-dimensional spectrometry

abstract:
  Cumulant mapping employs a statistical reconstruction of the whole by
sampling its parts. The theory developed in this work formalises and extends ad
hoc methods of `multi-fold' or `multi-dimensional' covariance mapping. Explicit
formulae have been derived for the expected values of up to the 6th cumulant
and the variance has been calculated for up to the 4th cumulant. A method of
extending these formulae to higher cumulants has been described. The formulae
take into account reduced fragment detection efficiency and a background from
uncorrelated events. Number of samples needed for suppressing the statistical
noise to a required level can be estimated using a Matlab code included in
Supplemental Material. The theory can be used to assess the experimental
feasibility of studying molecular fragmentations induced by femtosecond or
x-ray free-electron lasers. It is also relevant for extending the conventional
mass spectrometry of biomolecules to multiple dimensions.


title:
Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active
  Region Data

abstract:
  We consider the flare prediction problem that distinguishes flare-imminent
active regions that produce an M- or X-class flare in the future 24 hours, from
quiet active regions that do not produce any flare within $\pm 24$ hours. Using
line-of-sight magnetograms and parameters of active regions in two data
products covering Solar Cycle 23 and 24, we train and evaluate two deep
learning algorithms -- CNN and LSTM -- and their stacking ensembles. The
decisions of CNN are explained using visual attribution methods. We have the
following three main findings. (1) LSTM trained on data from two solar cycles
achieves significantly higher True Skill Scores (TSS) than that trained on data
from a single solar cycle with a confidence level of at least 0.95. (2) On data
from Solar Cycle 23, a stacking ensemble that combines predictions from LSTM
and CNN using the TSS criterion achieves significantly higher TSS than the
"select-best" strategy with a confidence level of at least 0.95. (3) A visual
attribution method called Integrated Gradients is able to attribute the CNN's
predictions of flares to the emerging magnetic flux in the active region. It
also reveals a limitation of CNN as a flare prediction method using
line-of-sight magnetograms: it treats the polarity artifact of line-of-sight
magnetograms as positive evidence of flares.


title:
Deconvolution of the Functional Ultrasound Response in the Mouse Visual
  Pathway Using Block-Term Decomposition

abstract:
  Functional ultrasound (fUS) indirectly measures brain activity by recording
changes in cerebral blood volume and flow in response to neural activation.
Conventional approaches model such functional neuroimaging data as the
convolution between an impulse response, known as the hemodynamic response
function (HRF), and a binarized representation of the input (i.e., source)
signal based on the stimulus onsets, the so-called experimental paradigm (EP).
However, the EP may not be enough to characterize the whole complexity of the
underlying source signals that evoke the hemodynamic changes, such as in the
case of spontaneous resting state activity. Furthermore, the HRF varies across
brain areas and stimuli. To achieve an adaptable framework that can capture
such dynamics and unknowns of the brain function, we propose a deconvolution
method for multivariate fUS time-series that reveals both the region-specific
HRFs, and the source signals that induce the hemodynamic responses in the
studied regions. We start by modeling the fUS time-series as convolutive
mixtures and use a tensor-based approach for deconvolution based on two
assumptions: (1) HRFs are parametrizable, and (2) source signals are
uncorrelated. We test our approach on fUS data acquired during a visual
experiment on a mouse subject, focusing on three regions within the mouse
brain's colliculo-cortical, image-forming pathway: the lateral geniculate
nucleus, superior colliculus and visual cortex. The estimated HRFs in each
region are in agreement with prior works, whereas the estimated source signal
is observed to closely follow the EP. Yet, we note a few deviations from the EP
in the estimated source signal that most likely arise due to the trial-by-trial
variability of the neural response across different repetitions of the stimulus
observed in the selected regions.


title:
Comment on "Measuring the enhancement factor of the hyperpolarized Xe in
  nuclear magnetic resonance gyroscopes"

abstract:
  Z. Xu, et al. [Phys. Rev. A 103, 023114 (2021)], have reported a measurement
for the enhancement factor $\kappa$, which characterizes the overlap of the
electron wavefunction with the noble-gas nucleus in Cs-$^{129}$Xe spin-exchange
optical pumping (SEOP), that is more than an order of magnitude higher than
previous experimental results and theoretical estimates. There is no plausible
proposed physical basis for this discrepancy. As the authors repeatedly
mischaracterize the nature and properties of the enhancement factor and do not
completely describe their experimental technique, there is little reason to
accept or consider this result in its current form in the context of future
relevant work on Cs-Xe SEOP and its applications.


title: Climate of the Field: Snowmass 2021

abstract:
  How are formal policies put in place to create an inclusive, equitable, safe
environment? How do these differ between different communities of practice
(institutions, labs, collaborations, working groups)? What policies towards a
more equitable community are working? For those that aren't working, what
external support is needed in order to make them more effective? We present a
discussion of the current climate of the field in high energy particle physics
and astrophysics (HEPA), as well as current efforts toward making the community
a more diverse, inclusive, and equitable environment. We also present issues
facing both institutions and HEPA collaborations, with a set of interviews with
a selection of HEPA collaboration DEI leaders. We encourage the HEPA community
and the institutions & agencies that support it to think critically about the
prioritization of people in HEPA over the coming decade, and what resources and
policies need to be in place in order to protect and elevate minoritized
populations within the HEPA community.


title: Using Multiple Self-Supervised Tasks Improves Model Robustness

abstract:
  Deep networks achieve state-of-the-art performance on computer vision tasks,
yet they fail under adversarial attacks that are imperceptible to humans. In
this paper, we propose a novel defense that can dynamically adapt the input
using the intrinsic structure from multiple self-supervised tasks. By
simultaneously using many self-supervised tasks, our defense avoids
over-fitting the adapted image to one specific self-supervised task and
restores more intrinsic structure in the image compared to a single
self-supervised task approach. Our approach further improves robustness and
clean accuracy significantly compared to the state-of-the-art single task
self-supervised defense. Our work is the first to connect multiple
self-supervised tasks to robustness, and suggests that we can achieve better
robustness with more intrinsic signal from visual data.


title: Gravitationally Lensed Black Hole Emission Tomography

abstract:
  Measurements from the Event Horizon Telescope enabled the visualization of
light emission around a black hole for the first time. So far, these
measurements have been used to recover a 2D image under the assumption that the
emission field is static over the period of acquisition. In this work, we
propose BH-NeRF, a novel tomography approach that leverages gravitational
lensing to recover the continuous 3D emission field near a black hole. Compared
to other 3D reconstruction or tomography settings, this task poses two
significant challenges: first, rays near black holes follow curved paths
dictated by general relativity, and second, we only observe measurements from a
single viewpoint. Our method captures the unknown emission field using a
continuous volumetric function parameterized by a coordinate-based neural
network, and uses knowledge of Keplerian orbital dynamics to establish
correspondence between 3D points over time. Together, these enable BH-NeRF to
recover accurate 3D emission fields, even in challenging situations with sparse
measurements and uncertain orbital dynamics. This work takes the first steps in
showing how future measurements from the Event Horizon Telescope could be used
to recover evolving 3D emission around the supermassive black hole in our
Galactic center.


title:
Ultraviolet Spectroscopy and TARDIS Models of the Broad-lined Type-Ic
  Supernova 2014ad

abstract:
  Few published ultraviolet (UV) spectra exist for stripped-envelope
supernovae, and none to date for broad-lined Type Ic supernovae (SN Ic-bl).
These objects have extremely high ejecta velocities and are the only supernova
type directly linked to gamma-ray bursts (GRBs). Here we present two epochs of
HST/STIS spectra of the SN Ic-bl 2014ad, the first UV spectra for this class.
We supplement this with 26 new epochs of ground-based optical spectra,
augmenting a rich spectral time series. The UV spectra do not show strong
features, likely due to high opacity, and are consistent with broadened
versions of other SN Ic spectra observed in the UV. We measure Fe II 5169
Angstrom velocities and show that SN 2014ad has even higher ejecta velocities
than most SNe Ic both with and without observed GRBs. We construct models of
the SN 2014ad UV+optical spectra using TARDIS, a 1D Monte-Carlo
radiative-transfer spectral synthesis code. The models fit the data well at
multiple epochs in the optical but underestimate the flux in the UV. We find
that high densities at high velocities are needed to reproduce the spectra,
with $\sim$3 M$_\odot$ of material at $v >$ 22,000 km s$^{-1}$, assuming
spherical symmetry. Our nebular line fits suggest a steep density profile at
low velocities. Together, these results imply a higher total ejecta mass than
estimated from previous light curve analysis and expected from theory. This may
be reconciled by a flattening of the density profile at low velocity and extra
emission near the center of the ejecta.


title:
Quantitative Evaluation of Common Cause Failures in High
  Safety-significant Safety-related Digital Instrumentation and Control Systems
  in Nuclear Power Plants

abstract:
  Digital instrumentation and control (DIC) systems at nuclear power plants
(NPPs) have many advantages over analog systems. They are proven to be more
reliable, cheaper, and easier to maintain given obsolescence of analog
components. However, they also pose new engineering and technical challenges,
such as possibility of common cause failures (CCFs) unique to digital systems.
This paper proposes a Platform for Risk Assessment of DIC (PRADIC) that is
developed by Idaho National Laboratory (INL). A methodology for evaluation of
software CCFs in high safety-significant safety-related DIC systems of NPPs was
developed as part of the framework. The framework integrates three stages of a
typical risk assessment, qualitative hazard analysis and quantitative
reliability and consequence analyses. The quantified risks compared with
respective acceptance criteria provide valuable insights for system
architecture alternatives allowing design optimization in terms of risk
reduction and cost savings. A comprehensive case study performed to demonstrate
the framework capabilities is documented in this paper. Results show that the
PRADIC is a powerful tool capable to identify potential digital-based CCFs,
estimate their probabilities, and evaluate their impacts on system and plant
safety.


title:
First-passage times in complex energy landscapes: a case study with
  nonmuscle myosin II assembly

abstract:
  Complex energy landscapes often arise in biological systems, e.g. for protein
folding, biochemical reactions or intracellular transport processes. Their
physical effects are often reflected in the first-passage times arising from
these energy landscapes. However, their calculation is notoriously challenging
and it is often difficult to identify the most relevant features of a given
energy landscape. Here we show how this can be achieved by coarse-graining the
Fokker-Planck equation to a master equation and decomposing its first-passage
times in an iterative process. We apply this method to the electrostatic
interaction between two rods of nonmuscle myosin II (NM2), which is the main
molecular motor for force generation in nonmuscle cells. Energy landscapes are
computed directly from the amino acid sequences of the three different
isoforms. Our approach allows us to identify their most likely arrangements
during self-assembly into nonmuscle myosin II minifilaments and how they change
under force. In particular, we find that antiparallel configurations are more
stable than parallel ones, but also show more changes under mechanical loading.
Our work demonstrates the rich dynamics that can be expected for NM2-assemblies
under mechanical load and in general shows how one can identify the most
relevant energy barriers in complex energy landscapes.

categories: q-bio.SC cond-mat.soft cond-mat.stat-mech q-bio.BM


title:
A survey on learning from imbalanced data streams: taxonomy, challenges,
  empirical study, and reproducible experimental framework

abstract:
  Class imbalance poses new challenges when it comes to classifying data
streams. Many algorithms recently proposed in the literature tackle this
problem using a variety of data-level, algorithm-level, and ensemble
approaches. However, there is a lack of standardized and agreed-upon procedures
on how to evaluate these algorithms. This work presents a taxonomy of
algorithms for imbalanced data streams and proposes a standardized, exhaustive,
and informative experimental testbed to evaluate algorithms in a collection of
diverse and challenging imbalanced data stream scenarios. The experimental
study evaluates 24 state-of-the-art data streams algorithms on 515 imbalanced
data streams that combine static and dynamic class imbalance ratios,
instance-level difficulties, concept drift, real-world and semi-synthetic
datasets in binary and multi-class scenarios. This leads to the largest
experimental study conducted so far in the data stream mining domain. We
discuss the advantages and disadvantages of state-of-the-art classifiers in
each of these scenarios and we provide general recommendations to end-users for
selecting the best algorithms for imbalanced data streams. Additionally, we
formulate open challenges and future directions for this domain. Our
experimental testbed is fully reproducible and easy to extend with new methods.
This way we propose the first standardized approach to conducting experiments
in imbalanced data streams that can be used by other researchers to create
trustworthy and fair evaluation of newly proposed methods. Our experimental
framework can be downloaded from
https://github.com/canoalberto/imbalanced-streams.


title: Gaps in Topological Magnon Spectra: Intrinsic vs. Extrinsic Effects

abstract:
  For topological magnon spectra, determining and explaining the presence of a
gap at a magnon crossing point is a key to characterize the topological
properties of the system. An inelastic neutron scattering study of a single
crystal is a powerful experimental technique that is widely employed to probe
the magnetic excitation spectra of topological materials. Here, we show that
when the scattering intensity rapidly disperses in the vicinity of a crossing
point, such as a Dirac point, the apparent topological gap size is extremely
sensitive to experimental conditions including sample mosaic, resolution, and
momentum integration range. We demonstrate these effects using comprehensive
neutron-scattering measurements of CrCl$_3$. Our measurements confirm the
gapless nature of the Dirac magnon in CrCl$_3$, but also reveal an artificial,
i.e. extrinsic, magnon gap unless the momentum integration range is carefully
controlled. Our study provides an explanation of the apparent discrepancies
between spectroscopic and first-principles estimates of Dirac magnon gap sizes,
and provides guidelines for accurate spectroscopic measurement of topological
magnon gaps.


title: Encapsulated bacteria deform lipid vesicles into flagellated swimmers

abstract:
  We study a synthetic system of motile Escherichia coli bacteria encapsulated
inside giant lipid vesicles. Forces exerted by the bacteria on the inner side
of the membrane are sufficient to extrude membrane tubes filled with one or
several bacteria. We show that a physical coupling between the membrane tube
and the flagella of the enclosed cells transforms the tube into an effective
helical flagellum propelling the vesicle. We develop a simple theoretical model
to estimate the propulsive force from the speed of the vesicles, and
demonstrate the good efficiency of this coupling mechanism. Together, these
results point to novel design principles for conferring motility to synthetic
cells.


title:
Automated Design of Salient Object Detection Algorithms with Brain
  Programming

abstract:
  Despite recent improvements in computer vision, artificial visual systems'
design is still daunting since an explanation of visual computing algorithms
remains elusive. Salient object detection is one problem that is still open due
to the difficulty of understanding the brain's inner workings. Progress on this
research area follows the traditional path of hand-made designs using
neuroscience knowledge. In recent years two different approaches based on
genetic programming appear to enhance their technique. One follows the idea of
combining previous hand-made methods through genetic programming and fuzzy
logic. The other approach consists of improving the inner computational
structures of basic hand-made models through artificial evolution. This
research work proposes expanding the artificial dorsal stream using a recent
proposal to solve salient object detection problems. This approach uses the
benefits of the two main aspects of this research area: fixation prediction and
detection of salient objects. We decided to apply the fusion of visual saliency
and image segmentation algorithms as a template. The proposed methodology
discovers several critical structures in the template through artificial
evolution. We present results on a benchmark designed by experts with
outstanding results in comparison with the state-of-the-art.


title: Galaxy clustering in the VVV Near-IR Galaxy Catalogue

abstract:
  Mapping galaxies at low Galactic latitudes and determining their clustering
status are fundamental steps in defining the large-scale structure in the
nearby Universe. The VVV Near-IR Galaxy Catalogue (VVV NIRGC) allows us to
explore this region in great detail. Our goal is to identify galaxy
overdensities and characterize galaxy clustering in the Zone of Avoidance. We
use different clustering algorithms to identify galaxy overdensities: the
Voronoi tessellations, the Minimum Spanning Tree and the Ordering Points To
Identify the Clustering Structure. We studied the membership, isolation,
compactness, and flux limits to identify compact groups of galaxies. Each
method identified a variety of galaxy systems across the Galactic Plane that
are publicly available.We also explore the probability that these systems are
formed by concordant galaxies using mock catalogues. Nineteen galaxy systems
were identified in all of the four methods. They have the highest probability
to be real overdensities. We stress the need for spectroscopic follow-up
observations to confirm and characterize these new structures.