title:
Improving Quantum Simulation Efficiency of Final State Radiation with
  Dynamic Quantum Circuits

abstract:
  Reference arXiv:1904.03196 recently introduced an algorithm (QPS) for
simulating parton showers with intermediate flavor states using polynomial
resources on a digital quantum computer. We make use of a new quantum hardware
capability called dynamical quantum computing to improve the scaling of this
algorithm to significantly improve the method precision. In particular, we
modify the quantum parton shower circuit to incorporate mid-circuit qubit
measurements, resets, and quantum operations conditioned on classical
information. This reduces the computational depth from
$\mathcal{O}(N^5\log_2(N)^2)$ to $\mathcal{O}(N^3\log_2(N)^2)$ and the qubit
requirements are reduced from $\mathcal{O}(N\log_2(N))$ to $\mathcal{O}(N)$.
Using "matrix product state" statevector simulators, we demonstrate that the
improved algorithm yields expected results for 2, 3, 4, and 5-steps of the
algorithm. We compare absolute costs with the original QPS algorithm, and show
that dynamical quantum computing can significantly reduce costs in the class of
digital quantum algorithms representing quantum walks (which includes the QPS).


title:
Stabilization of nonautonomous linear parabolic-like equations: oblique
  projections versus Riccati feedbacks

abstract:
  An oblique projections based feedback stabilizability result in the
literature is extended to a larger class of reaction-convection terms. A
discussion is presented including a comparison between explicit oblique
projections base feedback controls and Riccati based feedback controls.
Advantages and limitations of each type of feedback are addressed as well as
their finite-elements implementation. Results of numerical simulations are
presented comparing their stabilizing performances for the case of
time-periodic dynamics. It is shown that the solution of the periodic Riccati
based feedback can be computed iteratively.


title: Challenges and Strategies in Cross-Cultural NLP

abstract:
  Various efforts in the Natural Language Processing (NLP) community have been
made to accommodate linguistic diversity and serve speakers of many different
languages. However, it is important to acknowledge that speakers and the
content they produce and require, vary not just by language, but also by
culture. Although language and culture are tightly linked, there are important
differences. Analogous to cross-lingual and multilingual NLP, cross-cultural
and multicultural NLP considers these differences in order to better serve
users of NLP systems. We propose a principled framework to frame these efforts,
and survey existing and potential strategies.


title:
Gr\"obner bases and critical values: The asymptotic combinatorics of
  determinantal systems

abstract:
  We consider ideals involving the maximal minors of a polynomial matrix. For
example, those arising in the computation of the critical values of a
polynomial restricted to a variety for polynomial optimisation. Gr\"obner bases
are a classical tool for solving polynomial systems. For practical
computations, this consists of two stages. First, a Gr\"obner basis is computed
with respect to a DRL (degree reverse lexicographic) ordering. Then, a change
of ordering algorithm, such as \textsf{Sparse-FGLM}, designed by Faug\`ere and
Mou, is used to find a Gr\"obner basis of the same ideal but with respect to a
lexicographic ordering. The complexity of this latter step, in terms of
arithmetic operations, is $O(mD^2)$, where $D$ is the degree of the ideal and
$m$ is the number of non-trivial columns of a certain $D \times D$ matrix.
While asymptotic estimates are known for $m$ for generic polynomial systems,
thus far, the complexity of \textsf{Sparse-FGLM} was unknown for determinantal
systems.
  By assuming Fr\"oberg's conjecture we expand the work of Moreno-Soc\'ias by
detailing the structure of the DRL staircase in the determinantal setting. Then
we study the asymptotics of the quantity $m$ by relating it to the coefficients
of these Hilbert series. Consequently, we arrive at a new bound on the
complexity of the \textsf{Sparse-FGLM} algorithm for generic determinantal
systems and for generic critical point systems. We consider the ideal in the
polynomial ring $\mathbb{K}[x_1, \dots, x_n]$, where $\mathbb{K}$ is some
infinite field, generated by $p$ generic polynomials of degree $d$ and the
maximal minors of a $p \times (n-1)$ polynomial matrix with generic entries of
degree $d-1$. Then for the case $d=2$ and for $n \gg p$ we give an exact
formula for $m$ in terms of $n$ and $p$. Moreover, for $d \geq 3$, we give an
asymptotic formula, as $n \to \infty$, for $m$ in terms of $n,p$ and $d$.


title:
Generalized Symmetries in F-theory and the Topology of Elliptic
  Fibrations

abstract:
  We realize higher-form symmetries in F-theory compactifications on
non-compact elliptically fibered Calabi-Yau manifolds. Central to this
endeavour is the topology of the boundary of the non-compact elliptic
fibration, as well as the explicit construction of relative 2-cycles in terms
of Lefschetz thimbles. We apply the analysis to a variety of elliptic
fibrations, including geometries where the discriminant of the elliptic
fibration intersects the boundary. We provide a concrete realization of the
1-form symmetry group by constructing the associated charged line operator from
the elliptic fibration. As an application we compute the symmetry topological
field theories in the case of elliptic three-folds, which correspond to mixed
anomalies in 5d and 6d theories.


title: Geophysical Evolution During Rocky Planet Formation

abstract:
  Progressive astronomical characterization of planet-forming disks and rocky
exoplanets highlight the need for increasing interdisciplinary efforts to
understand the birth and life cycle of terrestrial worlds in a unified picture.
Here, we review major geophysical and geochemical processes that shape the
evolution of rocky planets and their precursor planetesimals during planetary
formation and early evolution, and how these map onto the astrophysical
timeline and varying accretion environments of planetary growth. The evolution
of the coupled core-mantle-atmosphere system of growing protoplanets diverges
in thermal, compositional, and structural states to first order, and ultimately
shapes key planetary characteristics that can discern planets harboring clement
surface conditions from those that do not. Astronomical campaigns seeking to
investigate rocky exoplanets will require significant advances in laboratory
characterization of planetary materials and time- and spatially-resolved
theoretical models of planetary evolution, to extend planetary science beyond
the Solar System and constrain the origins and frequency of habitable worlds
like our own.

categories: astro-ph.EP astro-ph.SR physics.ao-ph physics.geo-ph


title:
Offensive Language Detection in Under-resourced Algerian Dialectal
  Arabic Language

abstract:
  This paper addresses the problem of detecting the offensive and abusive
content in Facebook comments, where we focus on the Algerian dialectal Arabic
which is one of under-resourced languages. The latter has a variety of dialects
mixed with different languages (i.e. Berber, French and English). In addition,
we deal with texts written in both Arabic and Roman scripts (i.e. Arabizi). Due
to the scarcity of works on the same language, we have built a new corpus
regrouping more than 8.7k texts manually annotated as normal, abusive and
offensive. We have conducted a series of experiments using the state-of-the-art
classifiers of text categorisation, namely: BiLSTM, CNN, FastText, SVM and NB.
The results showed acceptable performances, but the problem requires further
investigation on linguistic features to increase the identification accuracy.


title:
Sharp estimates for the hypergeometric functions related to root systems
  of type $A$ and of rank 1

abstract:
  In this article, we conjecture exact estimates for the Weyl-invariant
Opdam-Cherednik hypergeometric functions. We prove the conjecture for the root
system $A_n$ and for all rank 1 cases. We provide other evidence that the
conjecture might be true in general.


title:
Unbiased Subclass Regularization for Semi-Supervised Semantic
  Segmentation

abstract:
  Semi-supervised semantic segmentation learns from small amounts of labelled
images and large amounts of unlabelled images, which has witnessed impressive
progress with the recent advance of deep neural networks. However, it often
suffers from severe class-bias problem while exploring the unlabelled images,
largely due to the clear pixel-wise class imbalance in the labelled images.
This paper presents an unbiased subclass regularization network (USRN) that
alleviates the class imbalance issue by learning class-unbiased segmentation
from balanced subclass distributions. We build the balanced subclass
distributions by clustering pixels of each original class into multiple
subclasses of similar sizes, which provide class-balanced pseudo supervision to
regularize the class-biased segmentation. In addition, we design an
entropy-based gate mechanism to coordinate learning between the original
classes and the clustered subclasses which facilitates subclass regularization
effectively by suppressing unconfident subclass predictions. Extensive
experiments over multiple public benchmarks show that USRN achieves superior
performance as compared with the state-of-the-art.


title:
Magnetically mediated hole pairing in fermionic ladders of ultracold
  atoms

abstract:
  Pairing of mobile charge carriers in doped antiferromagnets plays a key role
in the emergence of unconventional superconductivity. In these strongly
correlated materials, the pairing mechanism is often assumed to be mediated by
magnetic correlations, in contrast to phonon-mediated interactions in
conventional superconductors. A precise understanding of the underlying
mechanism in real materials is, however, still lacking, and has been driving
experimental and theoretical research for the past 40 years. Early theoretical
studies established the emergence of binding among dopants in ladder systems,
where idealised theoretical toy models played an instrumental role in the
elucidation of pairing, despite repulsive interactions. Here, we realise this
long-standing theoretical prediction and report on the observation of hole
pairing due to magnetic correlations in a quantum gas microscope setting. By
engineering doped antiferromagnetic ladders with mixed-dimensional couplings we
suppress Pauli blocking of holes at short length scales. This results in a
drastic increase in binding energy and decrease in pair size, enabling us to
observe pairs of holes predominantly occupying the same rung of the ladder. We
find a hole-hole binding energy on the order of the superexchange energy, and,
upon increased doping, we observe spatial structures in the pair distribution,
indicating repulsion between bound hole pairs. By engineering a configuration
in which binding is strongly enhanced, we delineate a novel strategy to
increase the critical temperature for superconductivity.


title:
Efficiently Generating Independent Samples Directly from the Posterior
  Distribution for a Large Class of Bayesian Generalized Linear Mixed Effects
  Models

abstract:
  Markov chain Monte Carlo (MCMC) is an all-purpose tool that allows one to
generate dependent replicates from a posterior distribution for effectively any
Bayesian hierarchical model. As such, MCMC has become a standard in Bayesian
statistics. However, convergence issues, tuning, and the effective sample size
of the MCMC are nontrivial considerations that are often overlooked or can be
difficult to assess. Moreover, these practical issues can produce a significant
computational burden. This motivates us to consider finding closed-form
expressions of the posterior distribution that are computationally
straightforward to sample from directly. We focus on a broad class of Bayesian
generalized linear mixed-effects models (GLMM) that allows one to jointly model
data of different types (e.g., Gaussian, Poisson, and binomial distributed
observations). Exact sampling from the posterior distribution for Bayesian
GLMMs is such a difficult problem that it is now arguably overlooked as a
possible problem to solve. To solve this problem, we derive a new class of
distributions that gives one the flexibility to specify the prior on fixed and
random effects to be any conjugate multivariate distribution. We refer to this
new distribution as the generalized conjugate multivariate (GCM) distribution,
and several technical results are provided. The expression of the exact
posterior distribution is given along with the steps to obtain direct
independent simulations from the posterior distribution. These direct
simulations have an efficient projection/regression form, and hence, we refer
to our method as Exact Posterior Regression (EPR). Several theoretical results
are developed that create the foundation for EPR. Illustrative examples are
provided including a simulation study and an analysis of estimates from the
U.S. Census Bureau's American Community Survey (ACS).


title:
Scaling properties of background- and chiral-magnetically-driven charge
  separation in heavy ion collisions at $\sqrt s_{\mathrm{NN}}=200$ GeV

abstract:
  The Anomalous Viscous Fluid Dynamics model, AVFD, is used in concert with the
charge-sensitive correlator $R_{\Psi_2}(\Delta S)$ to investigate the scaling
properties of background- and chiral-magnetically-driven (CME) charge
separation ($\Delta S$), characterized by the inverse variance
$\mathrm{\sigma^{-2}_{R_{\Psi_2}}}$ of the $R_{\Psi_{2}}(\Delta S)$
distributions obtained in collisions at $\sqrt s_{\mathrm{NN}}=200$ GeV. The
$\mathrm{\sigma^{-2}_{R_{\Psi_2}}}$ values for the background are observed to
be event-shape-independent. However, they scale with the reciprocal
charged-particle multiplicity $(1/\left< N_{\rm ch} \right>)$, indicating an
essential constraint for discerning background from the signal and a robust
estimate of the difference between the backgrounds in Ru+Ru and Zr+Zr
collisions. By contrast, the $\mathrm{\sigma^{-2}_{R_{\Psi_2}}}$values for
signal + background show characteristic $1/\left< N_{\rm ch} \right>$ scaling
violations that characterize the CME-driven contributions. Corrections to
recent ${R_{\Psi_2}(\Delta S)}$ measurements \cite{STAR:2021mii} that account
for the background difference in Ru+Ru and Zr+Zr collisions indicate a charge
separation difference compatible with the CME. The results further suggest that
$\mathrm{\sigma^{-2}_{R_{\Psi_2}}}$ measurements for peripheral and central
collisions in concert with $1/\left< N_{\rm ch} \right>$ scaling, provides a
robust constraint to quantify the background and aid characterization of the
CME.


title:
Nonnegative-Constrained Joint Collaborative Representation with Union
  Dictionary for Hyperspectral Anomaly Detection

abstract:
  Recently, many collaborative representation-based (CR) algorithms have been
proposed for hyperspectral anomaly detection. CR-based detectors approximate
the image by a linear combination of background dictionaries and the
coefficient matrix, and derive the detection map by utilizing recovery
residuals. However, these CR-based detectors are often established on the
premise of precise background features and strong image representation, which
are very difficult to obtain. In addition, pursuing the coefficient matrix
reinforced by the general $l_2$-min is very time consuming. To address these
issues, a nonnegative-constrained joint collaborative representation model is
proposed in this paper for the hyperspectral anomaly detection task. To extract
reliable samples, a union dictionary consisting of background and anomaly
sub-dictionaries is designed, where the background sub-dictionary is obtained
at the superpixel level and the anomaly sub-dictionary is extracted by the
pre-detection process. And the coefficient matrix is jointly optimized by the
Frobenius norm regularization with a nonnegative constraint and a sum-to-one
constraint. After the optimization process, the abnormal information is finally
derived by calculating the residuals that exclude the assumed background
information. To conduct comparable experiments, the proposed
nonnegative-constrained joint collaborative representation (NJCR) model and its
kernel version (KNJCR) are tested in four HSI data sets and achieve superior
results compared with other state-of-the-art detectors.


title: Widths of balls and free boundary minimal submanifolds

abstract:
  We observe that the $k$-dimensional width of an $n$-ball in a space form is
given by the area of an equatorial $k$-ball. We also investigate related lower
bounds for the area of a free boundary minimal submanifold in a space form
ball.


title: Low-dimensional solenoidal manifolds

abstract:
  In this paper we survey $n$-dimensional solenoidal manifolds for $n=1,2$ and
3, and present new results about them. Solenoidal manifolds of dimension $n$
are metric spaces locally modeled on the product of a Cantor set and an open
$n$-dimensional disk. Therefore, they can be "laminated" (or "foliated") by
$n$-dimensional leaves. By a theorem of A. Clark and S. Hurder, topologically
homogeneous, compact solenoidal manifolds are McCord solenoids i.e. are
obtained as the inverse limit of an increasing tower of finite, regular covers
of a compact manifold with an infinite and residually finite fundamental group.
In this case their structure is very rich since they are principal Cantor-group
bundles over a compact manifold and they behave like "laminated" versions of
compact manifolds, thus they share many of their properties. These objects
codify the commensurability properties of manifolds.


title:
Skill-based Multi-objective Reinforcement Learning of Industrial Robot
  Tasks with Planning and Knowledge Integration

abstract:
  In modern industrial settings with small batch sizes it should be easy to set
up a robot system for a new task. Strategies exist, e.g. the use of skills, but
when it comes to handling forces and torques, these systems often fall short.
We introduce an approach that provides a combination of task-level planning
with targeted learning of scenario-specific parameters for skill-based systems.
We propose the following pipeline: (1) the user provides a task goal in the
planning language PDDL, (2) a plan (i.e., a sequence of skills) is generated
and the learnable parameters of the skills are automatically identified. An
operator then chooses (3) reward functions and hyperparameters for the learning
process. Two aspects of our methodology are critical: (a) learning is tightly
integrated with a knowledge framework to support symbolic planning and to
provide priors for learning, (b) using multi-objective optimization. This can
help to balance key performance indicators (KPIs) such as safety and task
performance since they can often affect each other. We adopt a multi-objective
Bayesian optimization approach and learn entirely in simulation. We demonstrate
the efficacy and versatility of our approach by learning skill parameters for
two different contact-rich tasks. We show their successful execution on a real
7-DOF KUKA-iiwa manipulator and outperform the manual parameterization by human
robot operators.


title: Cyclotomic and abelian points in backward orbits of rational functions

abstract:
  We prove several results on backward orbits of rational functions over number
fields. First, we show that if $K$ is a number field, $\phi\in K(x)$ and
$\alpha\in K$ then the extension of $K$ generated by the abelian points in the
backward orbit of $\alpha$ is ramified only at finitely many primes. This has
the immediate strong consequence that if all points in the backward orbit of
$\alpha$ are abelian then $\phi$ is post-critically finite. We use this result
to prove two facts: on the one hand, if $\phi\in \mathbb Q(x)$ is a quadratic
rational function not conjugate over $\mathbb Q^{\text{ab}}$ to a power or a
Chebyshev map and all preimages of $\alpha$ are abelian, we show that $\phi$ is
$\mathbb Q$-conjugate to one of two specific quadratic functions, in the spirit
of a recent conjecture of Andrews and Petsche. On the other hand we provide
conditions on a quadratic rational function in $K(x)$ for the backward orbit of
a point $\alpha$ to only contain finitely many cyclotomic preimages, extending
previous results of the second author. Finally, we give necessary and
sufficient conditions for a triple $(\phi,K,\alpha)$, where $\phi$ is a
Latt\`es map over a number field $K$ and $\alpha\in K$ for the whole backward
orbit of $\alpha$ to only contain abelian points.


title: SHREC 2021: Classification in cryo-electron tomograms

abstract:
  Cryo-electron tomography (cryo-ET) is an imaging technique that allows
three-dimensional visualization of macro-molecular assemblies under near-native
conditions. Cryo-ET comes with a number of challenges, mainly low
signal-to-noise and inability to obtain images from all angles. Computational
methods are key to analyze cryo-electron tomograms.
  To promote innovation in computational methods, we generate a novel simulated
dataset to benchmark different methods of localization and classification of
biological macromolecules in tomograms. Our publicly available dataset contains
ten tomographic reconstructions of simulated cell-like volumes. Each volume
contains twelve different types of complexes, varying in size, function and
structure.
  In this paper, we have evaluated seven different methods of finding and
classifying proteins. Seven research groups present results obtained with
learning-based methods and trained on the simulated dataset, as well as a
baseline template matching (TM), a traditional method widely used in cryo-ET
research. We show that learning-based approaches can achieve notably better
localization and classification performance than TM. We also experimentally
confirm that there is a negative relationship between particle size and
performance for all methods.


title: On the Generalization Mystery in Deep Learning

abstract:
  The generalization mystery in deep learning is the following: Why do
over-parameterized neural networks trained with gradient descent (GD)
generalize well on real datasets even though they are capable of fitting random
datasets of comparable size? Furthermore, from among all solutions that fit the
training data, how does GD find one that generalizes well (when such a
well-generalizing solution exists)? We argue that the answer to both questions
lies in the interaction of the gradients of different examples during training.
Intuitively, if the per-example gradients are well-aligned, that is, if they
are coherent, then one may expect GD to be (algorithmically) stable, and hence
generalize well. We formalize this argument with an easy to compute and
interpretable metric for coherence, and show that the metric takes on very
different values on real and random datasets for several common vision
networks. The theory also explains a number of other phenomena in deep
learning, such as why some examples are reliably learned earlier than others,
why early stopping works, and why it is possible to learn from noisy labels.
Moreover, since the theory provides a causal explanation of how GD finds a
well-generalizing solution when one exists, it motivates a class of simple
modifications to GD that attenuate memorization and improve generalization.
Generalization in deep learning is an extremely broad phenomenon, and
therefore, it requires an equally general explanation. We conclude with a
survey of alternative lines of attack on this problem, and argue that the
proposed approach is the most viable one on this basis.


title:
On resampling schemes for particle filters with weakly informative
  observations

abstract:
  We consider particle filters with weakly informative observations (or
`potentials') relative to the latent state dynamics. The particular focus of
this work is on particle filters to approximate time-discretisations of
continuous-time Feynman-Kac path integral models -- a scenario that naturally
arises when addressing filtering and smoothing problems in continuous time --
but our findings are indicative about weakly informative settings beyond this
context too. We study the performance of different resampling schemes, such as
systematic resampling, SSP (Srinivasan sampling process) and stratified
resampling, as the time-discretisation becomes finer and also identify their
continuous-time limit, which is expressed as a suitably defined `infinitesimal
generator.' By contrasting these generators, we find that (certain
modifications of) systematic and SSP resampling `dominate' stratified and
independent `killing' resampling in terms of their limiting overall resampling
rate. The reduced intensity of resampling manifests itself in lower variance in
our numerical experiment. This efficiency result, through an ordering of the
resampling rate, is new to the literature. The second major contribution of
this work concerns the analysis of the limiting behaviour of the entire
population of particles of the particle filter as the time discretisation
becomes finer. We provide the first proof, under general conditions, that the
particle approximation of the discretised continuous-time Feynman-Kac path
integral models converges to a (uniformly weighted) continuous-time particle
system.


title:
Thermoelectric transport properties of gapless pinned charge density
  waves

abstract:
  Quantum strongly correlated matter exhibits properties which are not easily
explainable in the conventional framework of Fermi liquids. Universal effective
field theory tools are applicable in these cases regardless of the microscopic
details of the quantum system, since they are based on symmetries. It is
necessary, however, to construct these effective tools in full generality,
avoiding restrictions coming from particular microscopic descriptions which may
inadequately constrain the coefficients that enter in the effective theory.
  In this work we demonstrate on explicit examples how the novel hydrodynamic
coefficients which have been recently reinstated in the effective theory of
pinned charge density waves (CDW) can affect the phenomenology of the
thermo-electric transport in strongly correlated quantum matter. Our examples,
based on two classes of holographic models with pinned CDW, have microscopics
which are conceptually different from Fermi liquids. Therefore, the above novel
transport coefficients are nonzero, contrary to the conventional approach. We
show how these coefficients allow to take into account the change of sign of
the Seebeck coefficient and the low resistivity of the CDW phase of the cuprate
high temperature superconductors, without referring to the effects of Fermi
surface reconstruction.


title:
Multi-input segmentation of damaged brain in acute ischemic stroke
  patients using slow fusion with skip connection

abstract:
  Time is a fundamental factor during stroke treatments. A fast, automatic
approach that segments the ischemic regions helps treatment decisions. In
clinical use today, a set of color-coded parametric maps generated from
computed tomography perfusion (CTP) images are investigated manually to decide
a treatment plan. We propose an automatic method based on a neural network
using a set of parametric maps to segment the two ischemic regions (core and
penumbra) in patients affected by acute ischemic stroke. Our model is based on
a convolution-deconvolution bottleneck structure with multi-input and slow
fusion. A loss function based on the focal Tversky index addresses the data
imbalance issue. The proposed architecture demonstrates effective performance
and results comparable to the ground truth annotated by neuroradiologists. A
Dice coefficient of 0.81 for penumbra and 0.52 for core over the large vessel
occlusion test set is achieved. The full implementation is available at:
https://git.io/JtFGb.


title: Addendum to "Ultraviolet behaviour of Higgs inflation models"

abstract:
  This article is an addendum to [1]. We extend our computation of the on-shell
scattering amplitudes in an arbitrary inflaton background $\bar{\phi}_1$.
Although the effective Einstein frame cutoff for $\bar{\phi}_1>>M_P/\sqrt{\xi}$
turns out to be $\bar{\phi}_1$ or $\xi\bar{\phi}_1$ for the $U(1)$ model, this
is not the case for the realistic doublet Higgs model where the effective
Einstein frame cutoff turns out to be the standard $M_P/\sqrt{\xi}$ for both
the Palatini and metric formulations. Then, as it has been pointed out in [1]
the background $\bar{\phi}_1$ is the effective Jordan frame cutoff for both the
Palatini and metric formulations.


title:
Compositional Synthesis of Signal Temporal Logic Tasks via
  Assume-Guarantee Contracts

abstract:
  In this paper, we focus on the problem of compositional synthesis of
controllers enforcing signal temporal logic (STL) tasks over a class of
continuous-time nonlinear interconnected systems. By leveraging the idea of
funnel-based control, we show that a fragment of STL specifications can be
formulated as assume-guarantee contracts. A new concept of contract
satisfaction is then defined to establish our compositionality result, which
allows us to guarantee the satisfaction of a global contract by the
interconnected system when all subsystems satisfy their local contracts. Based
on this compositional framework, we then design closed-form continuous-time
feedback controllers to enforce local contracts over subsystems in a
decentralized manner. Finally, we demonstrate the effectiveness of our results
on two numerical examples.


title: A Flat Space Analogue for the Quantum Origin of Structure

abstract:
  The analytic structure of non-Gaussian correlators in inflationary
cosmologies has recently been proposed as a test of the quantum origin of
structure in the universe. To further understand this proposal, we explore the
analogous equal-time in-in correlators in flat space and show they exhibit the
same features as their cosmological counterparts. The quantum vacuum is
uniquely identified by in-in correlators with a total energy pole and no
additional poles at physical momenta. We tie this behavior directly to the
S-matrix and show that poles at physical momenta always arise from scattering
of particles present in the initial state. We relate these flat-space in-in
correlators to the probability amplitude for exciting multiple Unruh-de Witt
detectors. Localizing the detectors in spacetime, through the uncertainty
principle, provides the energy and momentum needed to excite the vacuum and
explains the connection to cosmological particle production. In addition, the
entanglement of these detectors provides a probe of the entangled state of the
underlying field and connects the properties of the correlators to the range of
entanglement of the detectors.


title: Singular symplectic spaces and holomorphic membranes

abstract:
  We set up a topological framework for degenerations of symplectic manifolds
into singular spaces paying a special attention to the behavior of Lagrangian
manifolds and their (holomorphic) membranes. We show that degenerations into
singular toric varieties provide a source of exotic Lagrangian tori.


title:
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior
  Analysis and Tuning-free Inference

abstract:
  Recently, there is a revival of interest in low-rank matrix completion-based
unsupervised learning through the lens of dual-graph regularization, which has
significantly improved the performance of multidisciplinary machine learning
tasks such as recommendation systems, genotype imputation and image inpainting.
While the dual-graph regularization contributes a major part of the success,
computational costly hyper-parameter tunning is usually involved. To circumvent
such a drawback and improve the completion performance, we propose a novel
Bayesian learning algorithm that automatically learns the hyper-parameters
associated with dual-graph regularization, and at the same time, guarantees the
low-rankness of matrix completion. Notably, a novel prior is devised to promote
the low-rankness of the matrix and encode the dual-graph information
simultaneously, which is more challenging than the single-graph counterpart. A
nontrivial conditional conjugacy between the proposed priors and likelihood
function is then explored such that an efficient algorithm is derived under
variational inference framework. Extensive experiments using synthetic and
real-world datasets demonstrate the state-of-the-art performance of the
proposed learning algorithm for various data analysis tasks.


title:
Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning
  under Policy Uncertainty

abstract:
  In stochastic games with incomplete information, the uncertainty is evoked by
the lack of knowledge about a player's own and the other players' types, i.e.
the utility function and the policy space, and also the inherent stochasticity
of different players' interactions. In existing literature, the risk in
stochastic games has been studied in terms of the inherent uncertainty evoked
by the variability of transitions and actions. In this work, we instead focus
on the risk associated with the \textit{uncertainty over types}. We contrast
this with the multi-agent reinforcement learning framework where the other
agents have fixed stationary policies and investigate risk-sensitiveness due to
the uncertainty about the other agents' adaptive policies. We propose
risk-sensitive versions of existing algorithms proposed for risk-neutral
stochastic games, such as Iterated Best Response (IBR), Fictitious Play (FP)
and a general multi-objective gradient approach using dual ascent (DAPG). Our
experimental analysis shows that risk-sensitive DAPG performs better than
competing algorithms for both social welfare and general-sum stochastic games.


title:
Scalar-mediated dark matter model at colliders and gravitational wave
  detectors -- A White paper for Snowmass 2021

abstract:
  The weakly interacting massive particles (WIMPs) have been the most popular
particle dark matter (DM) candidate for the last several decades, and it is
well known that WIMP can be probed via the direct, indirect and collider
experiments. However, the direct and indirect signals are highly suppressed in
some scalar-mediated DM models, e.g. the lepton portal model with a Majorana DM
candidate. As a result, collider searches are considered as the only hope to
probe such models. In this white paper, we propose that the gravitational wave
(GW) astronomy also serves as a powerful tool to probe such scalar mediated
WIMP models via the potential first-order phase transition GW signals. An
example for the lepton portal dark matter is provided, showing the
complementarity between collider and GW probes.


title: High-Density Coding Scheme for SWIPT Systems

abstract:
  In this study, a novel coding scheme called highdensity coding based on
high-density codebooks using a genetic local search algorithm is proposed. The
high-density codebook maximizes the energy transfer capability by maximizing
the ratio of 1 in the codebook while satisfying the conditions of a codeword
with length n, a codebook with 2k codewords, and a minimum Hamming distance of
the codebook of d. Furthermore, the proposed high-density codebook provides a
trade-off between the throughput and harvested energy with respect to n, k, and
d. The block error rate performances of the designed highdensity codebooks are
derived theoretically and compared with the simulation results. The simulation
results indicate that as d and k decrease, the throughput decreases by a
maximum of 10% and 40%, whereas the harvested energy per time increases by a
maximum of 40% and 100%, respectively. When n increases, the throughput
decreases by a maximum of 30%, while the harvested energy per time increases by
a maximum of 110%. With the proposed high-density coding scheme, the throughput
and harvested energy at the user can be controlled adaptively according to the
system requirements.


title: The KM3NeT multi-PMT optical module

abstract:
  The optical module of the KM3NeT neutrino telescope is an innovative,
multi-faceted large area photodetection module. It contains 31 three-inch
photomultiplier tubes in a single 0.44 m diameter pressure-resistant glass
sphere. The module is a sensory device also comprising calibration instruments
and electronics for power, readout and data acquisition. It is capped with a
breakout-box with electronics for connection to an electro-optical cable for
power and long-distance communication to the onshore control station. The
design of the module was qualified for the first time in the deep sea in 2013.
Since then, the technology has been further improved to meet requirements of
scalability, cost-effectiveness and high reliability. The module features a
sub-nanosecond timing accuracy and a dynamic range allowing the measurement of
a single photon up to a cascade of thousands of photons, suited for the
measurement of the Cherenkov radiation induced in water by secondary particles
from interactions of neutrinos with energies in the range of GeV to PeV. A
distributed production model has been implemented for the delivery of more than
6000 modules in the coming few years with an average production rate of more
than 100 modules per month. In this paper a review is presented of the design
of the multi-PMT KM3NeT optical module with a proven effective background
suppression and signal recognition and sensitivity to the incoming direction of
photons.


title:
Effects of external pressure on the narrow gap semiconductor
  Ce$_{3}$Cd$_{2}$As$_{6}$

abstract:
  Here we report the magnetic and electronic properties of recently discovered
Ce$_{3}$Cd$_{2}$As$_{6}$. At ambient pressure, Ce$_{3}$Cd$_{2}$As$_{6}$
presents a semiconducting behavior with an activation gap of 74(1)~meV. At
136~K, a sudden increase of the electrical resistivity and a peak in specific
heat are consistent with a charge density wave transition. At low temperatures,
antiferromagnetic order of the Ce$^{3+}$ ions occurs below $T_{\rm N} = 4.0$~K
with a magnetic hard axis along the $c$-axis and a $\Gamma_{6} =
|\pm1/2\rangle$ doublet ground state. The application of external pressure
strongly suppresses the charge density wave order, which is completely
suppressed above 0.8(1)~GPa, and induces a metallic ground state. No evidence
for superconductivity is detected above 2~K. Conversely, the antiferromagnetic
state is favored by pressure, reaching a transition temperature of 5.3~K at
3.8(1)~GPa. Notably, the resistivity anomaly characterizing the
antiferromagnetic order changes with increasing pressure, indicating that two
different magnetic phases might be present in Ce$_{3}$Cd$_{2}$As$_{6}$ under
pressure. This change in ordering appears to be associated to the crossing of
the $T_{\rm CDW}$ and $T_{\rm N}$ lines.


title:
SURF: Semi-supervised Reward Learning with Data Augmentation for
  Feedback-efficient Preference-based Reinforcement Learning

abstract:
  Preference-based reinforcement learning (RL) has shown potential for teaching
agents to perform the target tasks without a costly, pre-defined reward
function by learning the reward with a supervisor's preference between the two
agent behaviors. However, preference-based learning often requires a large
amount of human feedback, making it difficult to apply this approach to various
applications. This data-efficiency problem, on the other hand, has been
typically addressed by using unlabeled samples or data augmentation techniques
in the context of supervised learning. Motivated by the recent success of these
approaches, we present SURF, a semi-supervised reward learning framework that
utilizes a large amount of unlabeled samples with data augmentation. In order
to leverage unlabeled samples for reward learning, we infer pseudo-labels of
the unlabeled samples based on the confidence of the preference predictor. To
further improve the label-efficiency of reward learning, we introduce a new
data augmentation that temporally crops consecutive subsequences from the
original behaviors. Our experiments demonstrate that our approach significantly
improves the feedback-efficiency of the state-of-the-art preference-based
method on a variety of locomotion and robotic manipulation tasks.


title: Martian meteorites reflectance and implications for rover missions

abstract:
  In the next decade, two rovers will characterize in situ the mineralogy of
rocks on Mars, using for the first time near-infrared reflectance
spectrometers: SuperCam onboard the Mars 2020 rover and MicrOmega onboard the
ExoMars rover, although this technique is predominantly used in orbit for
mineralogical investigations. Until successful completion of sample-return
missions from Mars, Martian meteorites are currently the only samples of the
red planet available for study in terrestrial laboratories and comparison with
in situ data. However, the current spectral database available for these
samples does not represent their diversity and consists primarily of spectra
acquired on finely crushed samples, albeit grain size is known to greatly
affect spectral features. We measured the reflected light of a broad Martian
meteorite suite as a means to catalogue and characterize their spectra between
0.4 and 3 microns. These measurements are achieved using a point spectrometer
acquiring data comparable to SuperCam, and an imaging spectrometer producing
hyperspectral cubes similarly to MicrOmega. Our results indicate that point
spectrometry is sufficient to discriminate the different Martian meteorites
families, to identify their primary petrology based on band parameters, and to
detect their low content in alteration minerals. However, significant spectral
mixing occurs in the point measurements, even at spot sizes down to a few
millimeters, and imaging spectroscopy is needed to correctly identify the
various mineral phases in the meteorites. Bidirectional spectral measurements
confirm their non-Lambertian behavior, with backward and suspected forward
scattering peaks. With changing observation geometry, the main absorption
strengths show variations up to 10-15 percents. All the spectra presented are
provided in the supplementary data for further comparison with in situ and
orbital measurements.


title: Orientation Estimation using Wireless Device Radiation Patterns

abstract:
  Wireless devices inherently have a non-uniform distribution of energy from
their antenna or antennas. The shape that this forms is commonly called a
radiation pattern or antenna pattern. We demonstrate that orientation can be
estimated without the cooperation of the target device despite only having a
small number of RSS measurements per packet. We do this by applying bounds to
the amount of rotation in the time interval between packets. Using simulations,
we show that this method can achieve a mean orientation error as low as
7.6{\deg}. We then perform a security analysis to demonstrate the method's
resistance to spoofing. This paper focuses on consumer wireless devices where
patterns are not deliberately highly directional and scenarios that cannot rely
on contrived movement patterns of the entities involved, which is unrealistic
or impractical in many settings. Our work concentrates on existing wireless
systems and infrastructure common in domestic, office, and commercial.


title: RELIC: Retrieving Evidence for Literary Claims

abstract:
  Humanities scholars commonly provide evidence for claims that they make about
a work of literature (e.g., a novel) in the form of quotations from the work.
We collect a large-scale dataset (RELiC) of 78K literary quotations and
surrounding critical analysis and use it to formulate the novel task of
literary evidence retrieval, in which models are given an excerpt of literary
analysis surrounding a masked quotation and asked to retrieve the quoted
passage from the set of all passages in the work. Solving this retrieval task
requires a deep understanding of complex literary and linguistic phenomena,
which proves challenging to methods that overwhelmingly rely on lexical and
semantic similarity matching. We implement a RoBERTa-based dense passage
retriever for this task that outperforms existing pretrained information
retrieval baselines; however, experiments and analysis by human domain experts
indicate that there is substantial room for improvement over our dense
retriever.


title:
Consonant-Vowel Transition Models Based on Deep Learning for Objective
  Evaluation of Articulation

abstract:
  Spectro-temporal dynamics of consonant-vowel (CV) transition regions are
considered to provide robust cues related to articulation. In this work, we
propose an objective measure of precise articulation, dubbed the objective
articulation measure (OAM), by analyzing the CV transitions segmented around
vowel onsets. The OAM is derived based on the posteriors of a convolutional
neural network pre-trained to classify between different consonants using CV
regions as input. We demonstrate the OAM is correlated with perceptual measures
in a variety of contexts including (a) adult dysarthric speech, (b) the speech
of children with cleft lip/palate, and (c) a database of accented English
speech from native Mandarin and Spanish speakers.


title:
Time evolution of superoscillations for the Schr\"odinger equation in
  $\mathbb{R}\setminus\{0\}$

abstract:
  In the context of quantum mechanics superoscillations, or the more general
supershifts, appear as initial conditions of the time dependent Schr\"odinger
equation. Already in \cite{ABCS21_2} a unified approach was developed, which
yields time persistence of the supershift property under certain holomorphicity
and growth assumptions on the corresponding Green's function. While that theory
considers the Schr\"odinger equation on the whole real line $\mathbb{R}$, this
paper takes the natural next step and considers $\mathbb{R}\setminus\{0\}$
instead, and allow boundary conditions at $x=0^\pm$ in addition. In particular
the singular $\frac{1}{x^2}$-potential as well as the very important $\delta$
and $\delta'$ distributional potentials are covered.


title:
Chemical Habitability: Supply and Retention of Life's Essential Elements
  During Planet Formation

abstract:
  Carbon, Hydrogen, Nitrogen, Oxygen, Phosphorus and Sulfur (CHNOPS) play key
roles in the origin and proliferation of life on Earth. Given the universality
of physics and chemistry, not least the ubiquity of water as a solvent and
carbon as a backbone of complex molecules, CHNOPS are likely crucial to most
habitable worlds. To help guide and inform the search for potentially habitable
and ultimately inhabited environments, we begin by summarizing the CHNOPS
budget of various reservoirs on Earth, their role in shaping our biosphere, and
their origins in the Solar Nebula. We then synthesize our current understanding
of how these elements behave and are distributed in diverse astrophysical
settings, tracing their journeys from synthesis in dying stars to molecular
clouds, protoplanetary settings, and ultimately temperate rocky planets around
main sequence stars. We end by identifying key branching points during this
journey, highlighting instances where a forming planets' distribution of CHNOPS
can be altered dramatically, and speculating about the consequences for the
chemical habitability of these worlds.


title: Data and Analysis Preservation, Recasting, and Reinterpretation

abstract:
  We make the case for the systematic, reliable preservation of event-wise
data, derived data products, and executable analysis code. This preservation
enables the analyses' long-term future reuse, in order to maximise the
scientific impact of publicly funded particle-physics experiments. We cover the
needs of both the experimental and theoretical particle physics communities,
and outline the goals and benefits that are uniquely enabled by analysis
recasting and reinterpretation. We also discuss technical challenges and
infrastructure needs, as well as sociological challenges and changes, and give
summary recommendations to the particle-physics community.


title: CCR and CAR algebras are connected via a path of Cuntz-Toeplitz algebras

abstract:
  For $q \in \mathbb{R}$, $|q| < 1$ we consider the universal enveloping
$C^*$-algebra of a $*$-algebra of $q$-canonical commutation relations
($q$-CCR), which is generated by $a_1, \ldots, a_n$ subject to the relations \[
a_i^* a_j = \delta_{ij} 1 + q a_j a_i^* . \] It has a distinguished
representation $\pi_F$ called the Fock representation, which is believed to be
faithful. In this article we denote the image of the universal enveloping
$C^*$-algebra of $q$-CCR in the Fock representation by $\beth_q$. The question
whether $C^*$-isomorphism $\beth_q \simeq \beth_0$ holds has been considered in
the literature and proved for $|q| < 0.44$. In this article we show that
$\beth_q \simeq \beth_0$ for $|q| < 1$.


title: Structural and Magnetic Properties of Pt/Co/Mn-Based Multilayers

abstract:
  Magnetic multilayers are a rich class of materials systems with numerous
highly tunable physical parameters that determine both their magnetic and
electronic properties. Here we present a comprehensive experimental study of a
novel system, Pt/Co/Mn, which extends the group of Pt/Co/X ($\mathrm{X}=$
metal) multilayers that have been investigated thus far. We demonstrate that an
increasing Co layer thickness changes the magnetic anisotropy from out-of-plane
to in-plane, whereas the deposition of thicker Mn layers leads to a decrease in
the saturation magnetization. Temperature-dependent magnetometry measurements
reinforce the hypothesis of antiferromagnetic coupling at the Co/Mn interfaces
being responsible for the observed Mn thickness dependence of the magnetization
reversal. Moreover, magneto-optical imaging experiments indicate systematic
changes in magnetic domain patterns as a function of the Co and Mn layer
thickness, suggesting the existence of bubble-like domains -- potentially even
magnetic skyrmions -- in the case of sufficiently thick Mn layers, which are
expected to contribute to a sizeable Dzyaloshinskii-Moriya interaction in the
multilayer stacks. We identify Pt/Co/Mn as a highly complex multilayer system
with strong potential for further fundamental studies and possible
applications.


title:
Why should the U.S. care about high energy physics in Africa and Latin
  America?

abstract:
  Research, education and training in high energy physics (HEP) often draw
international collaborations even when priorities and long term visions are
defined regionally or nationally. Yet in many developing regions, HEP
activities are limited in both human capacity and expertise, as well as in
resource mobilisation. In this paper, the benefits -- to the U.S. HEP program
-- of engagements with developing countries are identified and studied through
specific examples of Africa and Latin America; conversely, the impact of HEP
education and research for developing countries are also pointed out. In the
context of the U.S. strategic planning for high energy physics, the authors
list recommendations on investments that will benefit both developed and
developing nations.


title:
Port-Hamiltonian Fluid-Structure Interaction Modeling and
  Structure-Preserving Model Order Reduction of a Classical Guitar

abstract:
  A fluid-structure interaction model in a port-Hamiltonian representation is
derived for a classical guitar. We combine the laws of continuum mechanics for
solids and fluids within a unified port-Hamiltonian (pH) modeling approach by
adapting the discretized equations on second-order level in order to obtain a
damped multi-physics model. The high-dimensionality of the resulting system is
reduced by model order reduction. The article focuses on pH-systems in
different state transformations, a variety of basis generation techniques as
well as structure-preserving model order reduction approaches that are
independent from the projection basis. As main contribution a thorough
comparison of these method combinations is conducted. In contrast to typical
frequency-based simulations in acoustics, transient time simulations of the
system are presented. The approach is embedded into a straightforward workflow
of sophisticated commercial software modeling and flexible in-house software
for multi-physics coupling and model order reduction.


title:
Imaging-based histological features are predictive of MET alterations in
  Non-Small Cell Lung Cancer

abstract:
  MET is a proto-oncogene whose somatic activation in non-small cell lung
cancer leads to increased cell growth and tumor progression. The two major
classes of MET alterations are gene amplification and exon 14 deletion, both of
which are therapeutic targets and detectable using existing molecular assays.
However, existing tests are limited by their consumption of valuable tissue,
cost and complexity that prevent widespread use. MET alterations could have an
effect on cell morphology, and quantifying these associations could open new
avenues for research and development of morphology-based screening tools. Using
H&E-stained whole slide images (WSIs), we investigated the association of
distinct cell-morphological features with MET amplifications and MET exon 14
deletions. We found that cell shape, color, grayscale intensity and
texture-based features from both tumor infiltrating lymphocytes and tumor cells
distinguished MET wild-type from MET amplified or MET exon 14 deletion cases.
The association of individual cell features with MET alterations suggested a
predictive model could distinguish MET wild-type from MET amplification or MET
exon 14 deletion. We therefore developed an L1-penalized logistic regression
model, achieving a mean Area Under the Receiver Operating Characteristic Curve
(ROC-AUC) of 0.77 +/- 0.05sd in cross-validation and 0.77 on an independent
holdout test set. A sparse set of 43 features differentiated these classes,
which included features similar to what was found in the univariate analysis as
well as the percent of tumor cells in the tissue. Our study demonstrates that
MET alterations result in a detectable morphological signal in tumor cells and
lymphocytes. These results suggest that development of low-cost predictive
models based on H&E-stained WSIs may improve screening for MET altered tumors.


title: Higher Form Symmetries TFT in 6d

abstract:
  Symmetries and anomalies of a $d$-dimensional theory are often encoded in a
$(d+1)$-dimensional topological action, called symmetry topological field
theory (TFT). These include higher form symmetries as well as their anomalies.
We derive the symmetry TFT of the 2-form and 1-form symmetries of 6d $(1,0)$
field theories, focusing on theories with a single tensor multiplet (rank 1).
We implement this by coupling the low-energy tensor branch action to the
background fields for these symmetries and by looking at the symmetry
transformation rules on dynamical and background fields, which also imply a
mixing of the 2-form and 1-form symmetries in a 3-group structure. In some
related higher rank cases, we derive the symmetry TFT from holography as well.
Its action contains a coupling between the 2-form symmetry and the 1-form
symmetry backgrounds which does not provide an anomaly for the 6d $(1,0)$
theories studied in this paper. On the other hand, it leads to an anomaly under
circle compactification. We confirm this by a pure 5d analysis provided by the
5d effective Coulomb branch Lagrangian coupled to background fields. We also
derive the symmetry TFT for 5d $SU(p)_q$ supersymmetric gauge theories and
certain non-Lagrangian 5d theories. Finally, we discuss the fate of the 2-form
and 1-form symmetry of rank 1 6d field theories when coupled to gravity.


title: Shorter quantum circuits

abstract:
  We give a novel procedure for approximating general single-qubit unitaries
from a finite universal gate set by reducing the problem to a novel magnitude
approximation problem, achieving an immediate improvement in sequence length by
a factor of 7/9. Extending the works arXiv:1612.01011 and arXiv:1612.02689, we
show that taking probabilistic mixtures of channels to solve fallback
(arXiv:1409.3552) and magnitude approximation problems saves factor of two in
approximation costs. In particular, over the Clifford+$\sqrt{\mathrm{T}}$ gate
set we achieve an average non-Clifford gate count of
$0.23\log_2(1/\varepsilon)+2.13$ and T-count $0.56\log_2(1/\varepsilon)+5.3$
with mixed fallback approximations for diamond norm accuracy $\varepsilon$.
  This paper provides a holistic overview of gate approximation, in addition to
these new insights. We give an end-to-end procedure for gate approximation for
general gate sets related to some quaternion algebras, providing pedagogical
examples using common fault-tolerant gate sets (V, Clifford+T and
Clifford+$\sqrt{\mathrm{T}}$). We also provide detailed numerical results for
Clifford+T and Clifford+$\sqrt{\mathrm{T}}$ gate sets. In an effort to keep the
paper self-contained, we include an overview of the relevant algorithms for
integer point enumeration and relative norm equation solving. We provide a
number of further applications of the magnitude approximation problems, as well
as improved algorithms for exact synthesis, in the Appendices.


title:
Symbol quantization in interstellar communications: methods and
  observations

abstract:
  Interstellar communication transmitters, intended to be discovered and
decoded to information bits, are expected to transmit signals that contain
message symbols quantized in at least one of the degrees of freedom of the
transmitted signal. A hypothesis is proposed that signal quantization, in the
form of multiplicative values of one or more signal measurements, may be
observable during the reception of hypothetical discoverable interstellar
communication signals. In previous work, using single and multiple synchronized
radio telescopes, candidate hypothetical interstellar communication signals
comprising delta-t delta-f opposite circular polarized pulse pairs have been
reported and analyzed (ref. arXiv:2105.03727, arXiv:2106.10168,
arXiv:2202.12791). In the latter report, an apparent quantization of delta-f at
multiples of 58.575 Hz was observed. In the current work, a machine process has
been implemented to further examine anomalous delta-f and delta-t quantization,
with results reported in this paper. As in some past work, a 26 foot diameter
radio telescope with fixed azimuth and elevation pointing is used to enable a
Right Ascension filter to measure signals associated with a celestial direction
of interest, relative to other directions, over a 6.3 hour range of Right
Ascension. The 5.25 plus or minus 0.15 hour Right Ascension, -7.6 degrees plus
or minus 1 degree Declination celestial direction presents repetition and
quantization anomalies, during an experiment lasting 157 days, with the first
143 days overlapping the previous experiment.


title: The Origin and Evolution of Multiple Star Systems

abstract:
  Most stars are born in multiple stellar systems. Observational advances over
the last decade have enabled high-resolution, interferometric studies of
forming multiple systems, statistical surveys of multiplicity in star-forming
regions, and new insights into disk evolution and planetary architectures in
these systems. In this review, we compile the results of observational and
theoretical studies of stellar multiplicity. We summarize the population
statistics spanning system evolution from the protostellar phase through the
main-sequence phase and evaluate the influence of the local environment. We
describe current models for the origin of stellar multiplicity and review the
landscape of numerical simulations and assess their consistency with
observations. We review the properties of disks and discuss the impact of
multiplicity on planet formation and system architectures. Finally, we
summarize open questions and discuss the technical requirements for future
observational and theoretical progress.


title: Sampling Complexity of Path Integral Methods for Trajectory Optimization

abstract:
  The use of random sampling in decision-making and control has become popular
with the ease of access to graphic processing units that can generate and
calculate multiple random trajectories for real-time robotic applications. In
contrast to sequential optimization, the sampling-based method can take
advantage of parallel computing to maintain constant control loop frequencies.
Inspired by its wide applicability in robotic applications, we calculate a
sampling complexity result applicable to general nonlinear systems considered
in the path integral method, which is a sampling-based method. The result
determines the required number of samples to satisfy the given error bounds of
the estimated control signal from the optimal value with the predefined risk
probability. The sampling complexity result shows that the variance of the
estimated control value is upper-bounded in terms of the expectation of the
cost. Then we apply the result to a linear time-varying dynamical system with
quadratic cost and an indicator function cost to avoid constraint sets.


title:
The Role of Disk Winds in the Evolution and Dispersal of Protoplanetary
  Disks

abstract:
  The assembly and architecture of planetary systems strongly depend on the
physical processes governing the evolution and dispersal of protoplanetary
disks. Since Protostars and Planets VI, new observations and theoretical
insights favor disk winds as being one of those key processes. This chapter
provides a comprehensive review of recent observations probing outflowing gas
launched over a range of disk radii for a wide range of evolutionary stages,
enabling an empirical understanding of how winds evolve. In parallel, we review
theoretical advancements in both magnetohydrodynamic and photoevaporative disk
wind models and identify predictions that can be confronted with observations.
By linking theory and observations we critically assess the role of disk winds
in the evolution and dispersal of protoplanetary disks. Finally, we explore the
impact of disk winds on planet formation and evolution and highlight
theoretical work, observations, and critical tests for future progress.


title: Magneto-Seebeck effect in bismuth

abstract:
  Thermoelectricity was discovered almost two centuries ago in bismuth. The
large and negative Seebeck coefficient of this semimetal remains almost flat
between 300 K and 100 K. This striking feature can be understood by considering
the ratio of electron and hole mobilities and the evolution of their equal
densities with temperature. The large and anisotropic magneto-Seebeck effect in
bismuth, on the other hand, has not been understood up to the present day.
Here, we report on a systematic study of the thermopower of bismuth from room
temperature down to 20 K upon application of a magnetic field of 13.8 T in the
binary-bisectrix plane. The amplitude of the Seebeck coefficient depends on the
orientation of the magnetic field and the anisotropy changes sign with
decreasing temperature. The magneto-Seebeck effect becomes non-monotonic at low
temperatures. When the magnetic field is oriented along the binary axis, the
Seebeck coefficient is not the same for positive and negative fields. This
so-called Umkehr effect arises because the high symmetry axes of the Fermi
surface ellipsoids are neither parallel to each other nor to the high symmetry
axes of the lattice. The complex evolution of thermopower can be accounted for
in a large part of the ($T,B,\Theta$)-space by a model based on semiclassical
transport theory and incorporating Landau quantization. The employed energy
dependence of the scattering time is compatible with electron-acoustic phonon
scattering. We find that the transverse Nernst response plays an important role
in setting the amplitude of the longitudinal magneto-Seebeck effect.
Furthermore, Landau quantization significantly affects thermoelectricity up to
temperatures as high as 120 K.


title: Kernelization for Treewidth-2 Vertex Deletion

abstract:
  The Treewidth-2 Vertex Deletion problem asks whether a set of at most $t$
vertices can be removed from a graph, such that the resulting graph has
treewidth at most two. A graph has treewidth at most two if and only if it does
not contain a $K_4$ minor. Hence, this problem corresponds to the NP-hard
$\mathcal{F}$-Minor Cover problem with $\mathcal{F} = \{K_4\}$. For any variant
of the $\mathcal{F}$-Minor Cover problem where $\mathcal{F}$ contains a planar
graph, it is known that a polynomial kernel exists. I.e., a preprocessing
routine that in polynomial time outputs an equivalent instance of size
$t^{O(1)}$. However, this proof is non-constructive, meaning that this proof
does not yield an explicit bound on the kernel size. The $\{K_4\}$-Minor Cover
problem is the simplest variant of the $\mathcal{F}$-Minor Cover problem with
an unknown kernel size.
  To develop a constructive kernelization algorithm, we present a new method to
decompose graphs into near-protrusions, such that near-protrusions in this new
decomposition can be reduced using elementary reduction rules. Our method
extends the `approximation and tidying' framework by van Bevern et al.
[Algorithmica 2012] to provide guarantees stronger than those provided by both
this framework and a regular protrusion decomposition. Furthermore, we provide
extensions of the elementary reduction rules used by the $\{K_4,
K_{2,3}\}$-Minor Cover kernelization algorithm introduced by Donkers et al.
[IPEC 2021].
  Using the new decomposition method and reduction rules, we obtain a kernel
consisting of $O(t^{41})$ vertices, which is the first constructive kernel.
This kernel is a step towards more concrete kernelization bounds for the
$\mathcal{F}$-Minor Cover problem where $\mathcal{F}$ contains a planar graph,
and our decomposition provides a potential direction to achieve these new
bounds.


title: On the Nullity of Altans and Iterated Altans

abstract:
  Altanisation (formation of the altan of a parent structure) originated in the
chemical literature as a formal device for constructing generalised coronenes
from smaller structures. The altan of graph $G$, denoted $\mathfrak{a}(G, H)$,
depends on the choice of attachment set $H$ (a cyclic $h$-tuple of vertices of
$G$). From a given pair $(G, H)$, the altan construction produces a pair $(G',
H')$, where $H'$ is called the induced attachment set. Repetition of the
construction, using at each stage the attachment set induced in the previous
step, defines the iterated altan. Here, we prove sharp bounds for the nullity
of altan and iterated altan graphs based on a general parent graph: for any
attachment set with odd $h$, nullities of altan and parent are equal; for any
$h$ and all $k \geq 1$, the $k$-th altan has the same nullity as the first; for
any attachment set with even $h$, the nullity of the altan exceeds the nullity
of the parent graph by one of the three values $\{0, 1, 2\}$. The case of
excess nullity $2$ has not been noticed before; for benzenoids with the natural
attachment set consisting of the CH sites, it occurs first for a parent
structure with $5$ hexagons. On the basis of extensive computation, it is
conjectured that in fact no altan of a convex benzenoid has excess nullity $2$.


title: RoSS: Utilizing Robotic Rotation for Audio Source Separation

abstract:
  This paper considers the problem of audio source separation where the goal is
to isolate a target audio signal (say Alice's speech) from a mixture of
multiple interfering signals (e.g., when many people are talking). This problem
has gained renewed interest mainly due to the significant growth in voice
controlled devices, including robots in homes, offices, and other public
facilities. Although a rich body of work exists on the core topic of source
separation, we find that robotic motion of the microphone -- say the robot's
head -- is a complementary opportunity to past approaches. Briefly, we show
that rotating the microphone array to the correct orientation can produce
desired aliasing between two interferers, causing the two interferers to pose
as one. In other words, a mixture of K signals becomes a mixture of (K-1), a
mathematically concrete gain. We show that the gain translates well to practice
provided two mobility-related challenges can be mitigated. This paper is
focused on mitigating these challenges and demonstrating the end-to-end
performance on a fully functional prototype. We believe that our Rotational
Source Separation module RoSS could be plugged into actual robot heads, or into
other devices (like Amazon Show) that are also capable of rotation.


title: Lunar Rover Localization Using Craters as Landmarks

abstract:
  Onboard localization capabilities for planetary rovers to date have used
relative navigation, by integrating combinations of wheel odometry, visual
odometry, and inertial measurements during each drive to track position
relative to the start of each drive. At the end of each drive, a
ground-in-the-loop (GITL) interaction is used to get a position update from
human operators in a more global reference frame, by matching images or local
maps from onboard the rover to orbital reconnaissance images or maps of a large
region around the rover's current position. Autonomous rover drives are limited
in distance so that accumulated relative navigation error does not risk the
possibility of the rover driving into hazards known from orbital images.
However, several rover mission concepts have recently been studied that require
much longer drives between GITL cycles, particularly for the Moon. These
concepts require greater autonomy to minimize GITL cycles to enable such large
range; onboard global localization is a key element of such autonomy. Multiple
techniques have been studied in the past for onboard rover global localization,
but a satisfactory solution has not yet emerged. For the Moon, the ubiquitous
craters offer a new possibility, which involves mapping craters from orbit,
then recognizing crater landmarks with cameras and-or a lidar onboard the
rover. This approach is applicable everywhere on the Moon, does not require
high resolution stereo imaging from orbit as some other approaches do, and has
potential to enable position knowledge with order of 5 to 10 m accuracy at all
times. This paper describes our technical approach to crater-based lunar rover
localization and presents initial results on crater detection using 3D point
cloud data from onboard lidar or stereo cameras, as well as using shading cues
in monocular onboard imagery.


title: Advancing the Landscape of Multimessenger Science in the Next Decade

abstract:
  The last decade has brought about a profound transformation in multimessenger
science. Ten years ago, facilities had been built or were under construction
that would eventually discover the nature of objects in our universe could be
detected through multiple messengers. Nonetheless, multimessenger science was
hardly more than a dream. The rewards for our foresight were finally realized
through IceCube's discovery of the diffuse astrophysical neutrino flux, the
first observation of gravitational waves by LIGO, and the first joint
detections in gravitational waves and photons and in neutrinos and photons.
Today we live in the dawn of the multimessenger era. The successes of the
multimessenger campaigns of the last decade have pushed multimessenger science
to the forefront of priority science areas in both the particle physics and the
astrophysics communities. Multimessenger science provides new methods of
testing fundamental theories about the nature of matter and energy,
particularly in conditions that are not reproducible on Earth. This white paper
will present the science and facilities that will provide opportunities for the
particle physics community renew its commitment and maintain its leadership in
multimessenger science.


title: Correlative nanoscale imaging of strained hBN spin defects

abstract:
  Spin defects like the negatively charged boron vacancy color center ($V_B^-$)
in hexagonal boron nitride (hBN) may enable new forms of quantum sensing with
near-surface defects in layered van der Waals heterostructures. Here, we reveal
the effect of strain associated with creases in hBN flakes on $V_B^-$ and $V_B$
color centers in hBN with correlative cathodoluminescence and photoluminescence
microscopies. We observe strong localized enhancement and redshifting of the
$V_B^-$ luminescence at creases, consistent with density functional theory
calculations showing $V_B^-$ migration toward regions with moderate uniaxial
compressive strain. The ability to manipulate these spin defects with highly
localized strain offers intriguing possibilities for future 2D quantum sensors.


title: Architectures of Compact Multi-planet Systems: Diversity and Uniformity

abstract:
  One of the most important developments in exoplanet science in the past
decade is the discovery of multi-planet systems with sub-Neptune-sized planets
interior to 1~AU. This chapter explores the architectures of these planetary
systems, which often display a remarkable degree of uniformity: the planets
have nearly equal sizes, regular orbital spacing, low eccentricities, and small
mutual inclinations. This uniformity stands in sharp contrast to the diverse
nature of the exoplanet sample considered as a whole (as well as our inner
solar system). We begin with a critical review of the observations -- including
possible biases -- and find that these peas-in-a-pod planetary systems are
apparently a common outcome of the planet formation process. Modest departures
from exact uniformity suggest additional patterns, such as the planet mass
slowly increasing with semi-major axis. The star formation process naturally
produces circumstellar disks with the properties required to produce these
planetary systems, although the solid material must move inward from its
initial location. We discuss primary modes of planetary assembly, the role of
orbital migration, and post-nebular atmospheric loss. Mature planetary systems
are found to be near their minimum energy (tidal equilibrium) configurations;
this finding provides a partial explanation for their observed properties and
indicates that efficient energy dissipation must occur. Finally, we consider
population synthesis models and show that peas-in-a-pod patterns emerge with
reasonable choices for the input parameters. Nonetheless, interesting
observational and theoretical challenges remain in order to understand how
these surprisingly organized planetary systems arise from the disorder of their
formation processes.


title:
Tight Vector Bin Packing with Few Small Items via Fast Exact Matching in
  Multigraphs

abstract:
  We solve the Bin Packing problem in $O^*(2^k)$ time, where $k$ is the number
of items less or equal to one third of the bin capacity. This parameter
measures the distance from the polynomially solvable case of only large (i.e.,
greater than one third) items. Our algorithm is actually designed to work for a
more general Vector Bin Packing problem, in which items are multidimensional
vectors. We improve over the previous fastest $O^*(k! \cdot 4^k)$ time
algorithm.
  Our algorithm works by reducing the problem to finding an exact weight
perfect matching in a (multi-)graph with $O^*(2^k)$ edges, whose weights are
integers of the order of $O^*(2^k)$. To solve the matching problem in the
desired time, we give a variant of the classic Mulmuley-Vazirani-Vazirani
algorithm with only a linear dependence on the edge weights and the number of
edges, which may be of independent interest.
  Moreover, we give a tight lower bound, under the Strong Exponential Time
Hypothesis (SETH), showing that the constant $2$ in the base of the exponent
cannot be further improved for Vector Bin Packing.
  Our techniques also lead to improved algorithms for Vector Multiple Knapsack,
Vector Bin Covering, and Perfect Matching with Hitting Constraints.


title:
Bayesian Inversion for Nonlinear Imaging Models using Deep Generative
  Priors

abstract:
  Most modern imaging systems involve a computational reconstruction pipeline
to infer the image of interest from acquired measurements. The Bayesian
reconstruction framework relies on the characterization of the posterior
distribution, which depends on a model of the imaging system and prior
knowledge on the image, for solving such inverse problems. Here, the choice of
the prior distribution is critical for obtaining high-quality estimates. In
this work, we use deep generative models to represent the prior distribution.
We develop a posterior sampling scheme for the class of nonlinear inverse
problems where the forward model has a neural-network-like structure. This
class includes most existing imaging modalities. We introduce the notion of
augmented generative models in order to suitably handle quantitative image
recovery. We illustrate the advantages of our framework by applying it to two
nonlinear imaging modalities-phase retrieval and optical diffraction
tomography.


title:
Simulating Bandit Learning from User Feedback for Extractive Question
  Answering

abstract:
  We study learning from user feedback for extractive question answering by
simulating feedback using supervised data. We cast the problem as contextual
bandit learning, and analyze the characteristics of several learning scenarios
with focus on reducing data annotation. We show that systems initially trained
on a small number of examples can dramatically improve given feedback from
users on model-predicted answers, and that one can use existing datasets to
deploy systems in new domains without any annotation, but instead improving the
system on-the-fly via user feedback.


title:
Astrophysical Source Classification and Distance Estimation for PyCBC
  Live

abstract:
  During the third observing run (O3) of the Advanced LIGO and Advanced Virgo
detectors, dozens of candidate gravitational-wave (GW) events have been
catalogued. A challenge of this observing run has been the rapid identification
and public dissemination of compact binary coalescence (CBC) signals, a task
carried out by low-latency searches such as PyCBC Live. During the later part
of O3, we developed a method of classifying CBC sources, via their
probabilities of containing neutron star or black hole components, within PyCBC
Live, in order to facilitate immediate follow-up observations by
electromagnetic and neutrino observatories. This fast classification uses the
chirp mass recovered by the search as input, given the difficulty of measuring
the mass ratio with high accuracy for lower-mass binaries. We also use a
distance estimate derived from the search output to correct for the bias in
chirp mass due to the cosmological redshift. We present results for simulated
signals, and for confirmed candidate events identified in low latency over O3.


title:
Gradient estimates for the insulated conductivity problem: the
  non-umbilical case

abstract:
  We study the insulated conductivity problem with inclusions embedded in a
bounded domain in $\mathbb R^n$, for $n \ge 3$. The gradient of solutions may
blow up as $\varepsilon$, the distance between inclusions, approaches to $0$.
We established in a recent paper optimal gradient estimates for a class of
inclusions including balls. In this paper, we prove such gradient estimates for
general strictly convex inclusions. Unlike the perfect conductivity problem,
the estimates depend on the principal curvatures of the inclusions, and we show
that these estimates are characterized by the first non-zero eigenvalue of a
divergence form elliptic operator on $\mathbb S^{n-2}$.


title:
Turbulence Hierarchy and Multifractality in the Integer Quantum Hall
  Transition

abstract:
  We offer a new perspective to the problem of characterizing mesoscopic
fluctuations in the inter-plateau region of the integer quantum Hall
transition. We found that longitudinal and transverse conductance fluctuations,
generated by varying the external magnetic field within a microscopic model,
are multifractal and lead to distributions of conductance increments
(magnetoconductance) with heavy tails (intermittency) and signatures of a
hierarchical structure (a cascade) in the corresponding stochastic process,
akin to Kolmogorov's theory of fluid turbulence. We confirm this picture by
interpreting the stochastic process of the conductance increments in the
framework of H-theory, which is a continuous-time stochastic approach that
incorporates the basic features of Kolmogorov's theory. The multifractal
analysis of the conductance "time series," combined with the H-theory formalism
provides, strong support for the overall characterization of mesoscopic
fluctuations in the quantum Hall transition as a multifractal stochastic
phenomenon with multiscale hierarchy, intermittency, and cascade effects.


title: Wire metamaterial filled metallic resonators

abstract:
  In this work we study electromagnetic properties of a resonator recently
suggested for the search of axions - a hypothetical candidate to explain dark
matter. A wire medium loaded resonator (called a plasma haloscope when used to
search for dark matter) consists of a box filled with a dense array of parallel
wires electrically connected to top and bottom walls. We show that the
homogenization model of wire medium works for this resonator without mesoscopic
corrections, and that the resonator quality $Q$ at the frequency of our
interest drops versus the growth of the resonator volume $V$ until it is
dominated by resistive losses in the wires. We find that even at room
temperature metals like copper can give quality factors in the thousands, an
order of magnitude higher than originally assumed. Our theoretical results for
both loaded and unloaded resonator quality factors were confirmed by building
an experimental prototype. We discuss ways to further improve WM loaded
resonators.

categories: hep-ph hep-ex physics.ins-det physics.plasm-ph


title: Causal diamonds in 2+1 dimensional quantum gravity

abstract:
  We develop the reduced phase space quantization of causal diamonds in pure
2+1 dimensional gravity with a negative cosmological constant. The system is
defined as the domain of dependence of a topological disc with fixed boundary
metric. By solving the initial value constraints in a constant-mean-curvature
time gauge and removing all the spatial gauge redundancy, we find that the
phase space is the cotangent bundle of Diff^+(S^1)/PSL(2,R). To quantize this
phase space we apply Isham's group-theoretic quantization scheme, with respect
to a BMS_3 group, and find that the quantum theory can be realized by
wavefunctions on some coadjoint orbit of the Virasoro group, with labels in
irreducible unitary representations of the corresponding little group. We find
that the twist of the diamond boundary loop is quantized in integer or
half-integer multiples of the ratio of the Planck length to the boundary
length.


title:
I Know Therefore I Score: Label-Free Crafting of Scoring Functions using
  Constraints Based on Domain Expertise

abstract:
  Several real-life applications require crafting concise, quantitative scoring
functions (also called rating systems) from measured observations. For example,
an effectiveness score needs to be created for advertising campaigns using a
number of engagement metrics. Experts often need to create such scoring
functions in the absence of labelled data, where the scores need to reflect
business insights and rules as understood by the domain experts. Without a way
to capture these inputs systematically, this becomes a time-consuming process
involving trial and error. In this paper, we introduce a label-free practical
approach to learn a scoring function from multi-dimensional numerical data. The
approach incorporates insights and business rules from domain experts in the
form of easily observable and specifiable constraints, which are used as weak
supervision by a machine learning model. We convert such constraints into loss
functions that are optimized simultaneously while learning the scoring
function. We examine the efficacy of the approach using a synthetic dataset as
well as four real-life datasets, and also compare how it performs vis-a-vis
supervised learning models.


title: The CH$_4$ abundance in Jupiter's upper atmosphere

abstract:
  Hydrocarbon species, and in particular CH$_4$, play a key role in the
stratosphere--thermosphere boundary of Jupiter, which occurs around the
$\mu$-bar pressure level. Previous analyses of solar occultation, He and
Ly-$\alpha$ airglow, and ISO/SWS measurements of the radiance around 3.3 $\mu$m
have inferred significantly different methane concentrations. Here we aim to
accurately model the CH$_4$ radiance at 3.3 $\mu$m measured by ISO/SWS by using
a comprehensive non-local thermodynamic equilibrium model and the most recent
collisional rates measured in the laboratory for CH$_4$ to shed new light onto
the methane concentration in the upper atmosphere of Jupiter. These emission
bands have been shown to present a peak contribution precisely at the $\mu$-bar
level, hence directly probing the region of interest. We find that a high
CH$_4$ concentration is necessary to explain the data, in contrast with the
most recent analyses, and that the observations favour the lower limit of the
latest laboratory measurements of the CH$_4$ collisional relaxation rates. Our
results provide precise constraints on the composition and dynamics of the
lower atmosphere of Jupiter.


title:
But that's not why: Inference adjustment by interactive prototype
  deselection

abstract:
  Despite significant advances in machine learning, decision-making of
artificial agents is still not perfect and often requires post-hoc human
interventions. If the prediction of a model relies on unreasonable factors it
is desirable to remove their effect. Deep interactive prototype adjustment
enables the user to give hints and correct the model's reasoning. In this
paper, we demonstrate that prototypical-part models are well suited for this
task as their prediction is based on prototypical image patches that can be
interpreted semantically by the user. It shows that even correct
classifications can rely on unreasonable prototypes that result from
confounding variables in a dataset. Hence, we propose simple yet effective
interaction schemes for inference adjustment: The user is consulted
interactively to identify faulty prototypes. Non-object prototypes can be
removed by prototype masking or a custom mode of deselection training.
Interactive prototype rejection allows machine learning na\"{i}ve users to
adjust the logic of reasoning without compromising the accuracy.


title:
Theory of quantum anomalous Hall effect and electric-field-induced phase
  transition in AB-stacked MoTe2/WSe2 moire heterobilayers

abstract:
  We propose a new mechanism to explain the unexpected quantum anomalous Hall
(QAH) effect and the electric-field-induced Mott-to-QAH phase transition
without charge gap closure in AB-stacked MoTe${}_2$/WSe${}_2$ moir\'e
heterobilayers. We suggest that a hole-occupied band carrying a non-zero Chern
number can be generated by an intrinsic band inversion between two
topologically distinct Dirac bands and a Coulomb-interaction-induced gap
opening in the moir\'e band structure with broken time-reversal symmetry (TRS).
The Dirac-band dispersion is induced by a pseudo-magnetic field, which is
originated from the Berry phase of Bloch electrons in the presence of a moir\'e
potential. The TRS is broken by valley-polarized interlayer-exciton
condensation under an out-of-plane electric field. The valley polarization can
be demonstrated by using an excitonic Bose-Hubbard (BH) model and
Berezinskii-Kosterlitz-Thouless (BKT) transition to transverse ferromagnetism.
In low electric fields, the equilibrium state of the system is a Mott insulator
state. At a certain electric field, the exciton condensate combining with the
Chern band becomes a more stable state and the Mott-to-QAH transition occurs.
Since the band inversion is intrinsic, there is no charge gap closure at the
transition.


title: Snowmass2021 Theory Frontier: Theory Meets the Lab

abstract:
  We review how theorists have been instrumental in launching and developing
new experiments in the last decade, and comment on the challenges and
opportunities for this program to continue to thrive going forward. This
whitepaper is a solicited contribution to the Snowmass2021 Theory Frontier.


title: FaceMap: Towards Unsupervised Face Clustering via Map Equation

abstract:
  Face clustering is an essential task in computer vision due to the explosion
of related applications such as augmented reality or photo album management.
The main challenge of this task lies in the imperfectness of similarities among
image feature representations. Given an existing feature extraction model, it
is still an unresolved problem that how can the inherent characteristics of
similarities of unlabelled images be leveraged to improve the clustering
performance. Motivated by answering the question, we develop an effective
unsupervised method, named as FaceMap, by formulating face clustering as a
process of non-overlapping community detection, and minimizing the entropy of
information flows on a network of images. The entropy is denoted by the map
equation and its minimum represents the least description of paths among images
in expectation. Inspired by observations on the ranked transition probabilities
in the affinity graph constructed from facial images, we develop an outlier
detection strategy to adaptively adjust transition probabilities among images.
Experiments with ablation studies demonstrate that FaceMap significantly
outperforms existing methods and achieves new state-of-the-arts on three
popular large-scale datasets for face clustering, e.g., an absolute improvement
of more than $10\%$ and $4\%$ comparing with prior unsupervised and supervised
methods respectively in terms of average of Pairwise F-score. Our code is
publicly available on github.


title: Label conditioned segmentation

abstract:
  Semantic segmentation is an important task in computer vision that is often
tackled with convolutional neural networks (CNNs). A CNN learns to produce
pixel-level predictions through training on pairs of images and their
corresponding ground-truth segmentation labels. For segmentation tasks with
multiple classes, the standard approach is to use a network that computes a
multi-channel probabilistic segmentation map, with each channel representing
one class. In applications where the image grid size (e.g., when it is a 3D
volume) and/or the number of labels is relatively large, the standard
(baseline) approach can become prohibitively expensive for our computational
resources. In this paper, we propose a simple yet effective method to address
this challenge. In our approach, the segmentation network produces a
single-channel output, while being conditioned on a single class label, which
determines the output class of the network. Our method, called label
conditioned segmentation (LCS), can be used to segment images with a very large
number of classes, which might be infeasible for the baseline approach. We also
demonstrate in the experiments that label conditioning can improve the accuracy
of a given backbone architecture, likely, thanks to its parameter efficiency.
Finally, as we show in our results, an LCS model can produce previously unseen
fine-grained labels during inference time, when only coarse labels were
available during training. We provide all of our code here:
https://github.com/tym002/Label-conditioned-segmentation


title: Efficient dependency models for some distributions

abstract:
  Dependency functions of dependent variables are relevant for i) performing
uncertainty quantification and sensitivity analysis in presence of dependent
variables and/or correlated variables, and ii) simulating random dependent
variables. In this paper, we mathematically derive practical dependency
functions for classical multivariate distributions such as Dirichlet,
elliptical distributions and independent uniform (resp. gamma and Gaussian)
variables under constraints that are ready to be used. Since such dependency
models are used for sampling random values and we have many dependency models
for every joint cumulative distribution function, we provide a way for choosing
the efficient sampling function using multivariate sensitivity analysis. We
illustrate our approach by means of numerical simulations.


title:
Deep Reinforcement Learning Guided Graph Neural Networks for Brain
  Network Analysis

abstract:
  Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and
functional magnetic resonance imaging (fMRI), enable us to model the human
brain as a brain network or connectome. Capturing brain networks' structural
information and hierarchical patterns is essential for understanding brain
functions and disease states. Recently, the promising network representation
learning capability of graph neural networks (GNNs) has prompted many GNN-based
methods for brain network analysis to be proposed. Specifically, these methods
apply feature aggregation and global pooling to convert brain network instances
into meaningful low-dimensional representations used for downstream brain
network analysis tasks. However, existing GNN-based methods often neglect that
brain networks of different subjects may require various aggregation iterations
and use GNN with a fixed number of layers to learn all brain networks.
Therefore, how to fully release the potential of GNNs to promote brain network
analysis is still non-trivial. To solve this problem, we propose a novel brain
network representation framework, namely BN-GNN, which searches for the optimal
GNN architecture for each brain network. Concretely, BN-GNN employs deep
reinforcement learning (DRL) to train a meta-policy to automatically determine
the optimal number of feature aggregations (reflected in the number of GNN
layers) required for a given brain network. Extensive experiments on eight
real-world brain network datasets demonstrate that our proposed BN-GNN improves
the performance of traditional GNNs on different brain network analysis tasks.


title:
Selection of entropy based features for the analysis of the Archimedes'
  spiral applied to essential tremor

abstract:
  Biomedical systems are regulated by interacting mechanisms that operate
across multiple spatial and temporal scales and produce biosignals with linear
and non-linear information inside. In this sense entropy could provide a useful
measure about disorder in the system, lack of information in time-series and/or
irregularity of the signals. Essential tremor (ET) is the most common movement
disorder, being 20 times more common than Parkinson's disease, and 50-70% of
this disease cases are estimated to be genetic in origin. Archimedes spiral
drawing is one of the most used standard tests for clinical diagnosis. This
work, on selection of nonlinear biomarkers from drawings and handwriting, is
part of a wide-ranging cross study for the diagnosis of essential tremor in
BioDonostia Health Institute. Several entropy algorithms are used to generate
nonlinear feayures. The automatic analysis system consists of several Machine
Learning paradigms.


title:
AlignTransformer: Hierarchical Alignment of Visual Regions and Disease
  Tags for Medical Report Generation

abstract:
  Recently, medical report generation, which aims to automatically generate a
long and coherent descriptive paragraph of a given medical image, has received
growing research interests. Different from the general image captioning tasks,
medical report generation is more challenging for data-driven neural models.
This is mainly due to 1) the serious data bias: the normal visual regions
dominate the dataset over the abnormal visual regions, and 2) the very long
sequence. To alleviate above two problems, we propose an AlignTransformer
framework, which includes the Align Hierarchical Attention (AHA) and the
Multi-Grained Transformer (MGT) modules: 1) AHA module first predicts the
disease tags from the input image and then learns the multi-grained visual
features by hierarchically aligning the visual regions and disease tags. The
acquired disease-grounded visual features can better represent the abnormal
regions of the input image, which could alleviate data bias problem; 2) MGT
module effectively uses the multi-grained features and Transformer framework to
generate the long medical report. The experiments on the public IU-Xray and
MIMIC-CXR datasets show that the AlignTransformer can achieve results
competitive with state-of-the-art methods on the two datasets. Moreover, the
human evaluation conducted by professional radiologists further proves the
effectiveness of our approach.


title:
Einstein field equation, recursion operators, Noether and master
  symmetries in conformable Poisson manifolds

abstract:
  We show that a Minkowski phase space endowed with a bracket relatively to a
conformable differential realizes a Poisson algebra, confering a bi-Hamiltonian
structure to the resulting manifold. We infer that the related Hamiltonian
vector field is an infinitesimal Noether symmetry, and compute the
corresponding deformed recursion operator. Besides, using the
Hamiltonian-Jacobi separability, we construct recursion operators for
Hamiltonian vector fields in conformable Poisson-Schwarzschild and
Friedmann-Lema\^itre-Robertson-Walker (FLRW) manifolds, and derive related
constants of motion, Christoffel symbols, components of Riemann and Ricci
tensors, Ricci constant and components of Einstein tensor. We highlight the
existence of a hierarchy of bi-Hamiltonian structures in both the manifolds,
and compute a family of recursion operators and master symmetries generating
the constants of motion.


title: 2-Group Symmetries and M-Theory

abstract:
  Quantum Field Theories engineered in M-theory can have 2-group symmetries,
mixing 0-form and 1-form symmetry backgrounds in non-trivial ways. In this
paper we develop methods for determining the 2-group structure from the
boundary geometry of the M-theory background. We illustrate these methods in
the case of 5d theories arising from M-theory on ordinary and generalised toric
Calabi-Yau cones, including cases in which the resulting theory is
non-Lagrangian. Our results confirm and elucidate previous results on 2-groups
from geometric engineering.