title: Precision electroweak physics at the LHeC and FCC-eh

abstract:
  The proposed electron-proton collider experiments LHeC and FCC-eh at CERN are
the highest resolution microscopes that can be realised in the present century
and they would represent a really unique research facility. We exploit
simulated neutral-current and charged-current deep-inelastic scattering data of
the LHeC and the FCC-eh and examine their sensitivity for precision physics in
the Electroweak sector of the Standard Model (SM), like the effective weak
mixing angle $\sin^2\theta_{\textrm{W},\ell}^\textrm{eff}$, or the light-quark
weak-neutral-current couplings. Unique measurements are further feasible at
high precision for the running of the weak mixing angle, as well as for
electroweak effects in charged current interactions. The sensitivity to beyond
SM effects is studied using the generic $S$, $T$ and $U$ parameterization. The
report summarizes previous studies about the LHeC and presents new prospects
for the FCC-eh.


title:
When does the Auslander-Reiten translation operate linearly on the
  Grothendieck group? -- Part I

abstract:
  For a hereditary, finite dimensional $\Bbbk$-algebra $A$ the Coxeter
transformation is a linear endomorphism $\Phi_A \colon
\mathsf{K}_0(\mathsf{mod} A) \to \mathsf{K}_0(\mathsf{mod} A)$ of the
Grothendieck group $\mathsf{K}_0(\mathsf{mod} A)$ of $\mathsf{mod} A$ such that
$\Phi_A[M] = [\tau_A M]$ for every non-projective indecomposable $A$-module
$M$, that is the Auslander-Reiten translation $\tau_A$ induces a linear
endomorphism of $\mathsf{K}_0(\mathsf{mod} A)$. It is natural to ask whether
there are other algebras $A$ having a linear endomorphism $\Phi_A \in
\operatorname{End}_\mathbb{Z}(\mathsf{K}_0(\mathsf{mod} A))$ with $\Phi_A [M] =
[\tau_A M]$ for all non-projective $M \in \mathsf{ind} A$. We will show that
this is the case for all Nakayama algebras. Conversely, we will show that if an
algebra $A = \Bbbk Q / I$, where $Q$ is a connected and non-acyclic quiver and
$I \lhd \Bbbk Q$ is an admissible ideal, admits such a linear endomorphism then
$A$ is already a cyclic Nakayama algebra.


title:
Sampling Bias Correction for Supervised Machine Learning: A Bayesian
  Inference Approach with Practical Applications

abstract:
  Given a supervised machine learning problem where the training set has been
subject to a known sampling bias, how can a model be trained to fit the
original dataset? We achieve this through the Bayesian inference framework by
altering the posterior distribution to account for the sampling function. We
then apply this solution to binary logistic regression, and discuss scenarios
where a dataset might be subject to intentional sample bias such as label
imbalance. This technique is widely applicable for statistical inference on big
data, from the medical sciences to image recognition to marketing. Familiarity
with it will give the practitioner tools to improve their inference pipeline
from data collection to model selection.


title:
Snowmass2021 Cosmic Frontier White Paper: Fundamental Physics and Beyond
  the Standard Model

abstract:
  Gravitational wave detectors are formidable tools to explore strong-field
gravity, especially black holes and neutron stars. These compact objects are
extraordinarily efficient at producing electromagnetic and gravitational
radiation. As such, they are ideal laboratories for fundamental physics and
have an immense discovery potential. The detection of black hole binaries by
third-generation Earth-based detectors, space-based detectors and pulsar timing
arrays will provide exquisite tests of general relativity. Loud "golden" events
and extreme mass-ratio inspirals can strengthen the observational evidence for
horizons by mapping the exterior spacetime geometry, inform us on possible
near-horizon modifications, and perhaps reveal a breakdown of Einstein's
gravity. Measurements of the black-hole spin distribution and continuous
gravitational-wave searches can turn black holes into efficient detectors of
ultralight bosons across ten or more orders of magnitude in mass. A precise
monitoring of the phase of inspiralling binaries can constrain the existence of
additional propagating fields and characterize the environment in which the
binaries live, bounding the local dark matter density and properties.
Gravitational waves from compact binaries will probe general relativity and
fundamental physics in previously inaccessible regimes, and allow us to address
fundamental issues in our current understanding of the cosmos.


title: Stochastic epidemic model on a simplicial complex

abstract:
  Complex networks with pairwise connections have been vastly used for the
modeling of interactions within systems. Although these type of models are
capable to capture rich structures and different phases within a great variety
of situations, their lack of explicit higher order interactions might result,
in some contexts, limited. In this work a stochastic epidemic model on a
simplicial complex is defined, generalizing the known Markovian SIR epidemic
process on networks. By direct simulations and mean field analysis is shown
that already by the introduction of third order interactions through a simplex,
the SIR model can display important differences in its dynamics and stationary
states with respect to the pairwise version of the model.


title: On Abel-Jacobi Maps of Lagrangian Families

abstract:
  We study in this article the cohomological properties of Lagrangian families
on projective hyper-K\"ahler manifolds. First, we give a criterion for the
vanishing of Abel-Jacobi maps of Lagrangian families. Using this criterion, we
show that under a natural condition, if the variation of Hodge structures on
the degree $1$ cohomomology of the fibers of the Lagrangian family is maximal,
its Abel-Jacobi map is trivial. We also construct Lagrangian families on
generalized Kummer varieties whose Abel-Jacobi map is not trivial, showing that
our criterion is optimal.


title:
QSDsan: An Integrated Platform for Quantitative Sustainable Design of
  Sanitation and Resource Recovery Systems

abstract:
  Sustainable sanitation and resource recovery technologies are needed to
address rapid environmental and socioeconomic changes. Research prioritization
is critical to expedite the development and deployment of such technologies
across their vast system space (e.g., technology choices, design and operating
decisions). In this study, we introduce QSDsan - an open-source tool written in
Python (under the object-oriented programming paradigm) and developed for the
quantitative sustainable design (QSD) of sanitation and resource recovery
systems. As an integrated platform for system design, process modeling and
simulation, techno-economic analysis (TEA), and life cycle assessment (LCA),
QSDsan can be used to enumerate and investigate the opportunity space for
emerging technologies under uncertainty, while considering contextual
parameters that are critical to technology deployment. We illustrate the core
capabilities of QSDsan through two distinct examples: (i) evaluation of a
complete sanitation value chain that compares three alternative systems; and
(ii) dynamic simulation of the wastewater treatment plant described in the
benchmark simulation model no. 1 (BSM1). Through these examples, we show the
utility of QSDsan to automate design, enable flexible process modeling, achieve
rapid and reproducible simulations, and to perform advanced statistical
analyses with integrated visualization. We strive to make QSDsan a
community-led platform with online documentation, tutorials (explanatory notes,
executable scripts, and video demonstrations), and a growing ecosystem of
supporting packages (e.g., DMsan for decision-making). This platform can be
freely accessed, used, and expanded by researchers, practitioners, and the
public alike, ultimately contributing to the advancement of safe and affordable
sanitation technologies around the globe.


title: AI agents for facilitating social interactions and wellbeing

abstract:
  Wellbeing AI has been becoming a new trend in individuals' mental health,
organizational health, and flourishing our societies. Various applications of
wellbeing AI have been introduced to our daily lives. While social
relationships within groups are a critical factor for wellbeing, the
development of wellbeing AI for social interactions remains relatively scarce.
In this paper, we provide an overview of the mediative role of AI-augmented
agents for social interactions. First, we discuss the two-dimensional framework
for classifying wellbeing AI: individual/group and analysis/intervention.
Furthermore, wellbeing AI touches on intervening social relationships between
human-human interactions since positive social relationships are key to human
wellbeing. This intervention may raise technical and ethical challenges. We
discuss opportunities and challenges of the relational approach with wellbeing
AI to promote wellbeing in our societies.


title:
Predatory Medicine: Exploring and Measuring the Vulnerability of Medical
  AI to Predatory Science

abstract:
  Medical Artificial Intelligence (MedAI) for diagnosis, treatment options, and
drug development represents the new age of healthcare. The security, integrity,
and credibility of MedAI tools are paramount issues because human lives are at
stake. MedAI solutions are often heavily dependent on scientific medical
research literature as a primary data source that draws the attacker's
attention as a potential target. We present a first study of how the output of
MedAI can be polluted with Predatory Publications Presence (PPP). We study two
MedAI systems: mediKanren (disease independent) and CancerMine
(Disease-specific), which use research literature as primary data input from
the research repository PubMed, PubMed derived database SemMedDB, and NIH
translational Knowledge Graphs (KGs). Our study has a three-pronged focus: (1)
identifying the PPP in PubMed; (2) verifying the PPP in SemMedDB and the KGs;
(3) demonstrating the existing vulnerability of PPP traversing to the MedAI
output. Our contribution lies in identifying the existing PPP in the MedAI
inputs and demonstrating how predatory science can jeopardize the credibility
of MedAI solutions, making their real-life deployment questionable.


title: An Uncommon Task: Participatory Design in Legal AI

abstract:
  Despite growing calls for participation in AI design, there are to date few
empirical studies of what these processes look like and how they can be
structured for meaningful engagement with domain experts. In this paper, we
examine a notable yet understudied AI design process in the legal domain that
took place over a decade ago, the impact of which still informs legal
automation efforts today. Specifically, we examine the design and evaluation
activities that took place from 2006 to 2011 within the TeXT Retrieval
Conference's (TREC) Legal Track, a computational research venue hosted by the
National Institute of Standards and Technologies. The Legal Track of TREC is
notable in the history of AI research and practice because it relied on a range
of participatory approaches to facilitate the design and evaluation of new
computational techniques--in this case, for automating attorney document review
for civil litigation matters. Drawing on archival research and interviews with
coordinators of the Legal Track of TREC, our analysis reveals how an
interactive simulation methodology allowed computer scientists and lawyers to
become co-designers and helped bridge the chasm between computational research
and real-world, high-stakes litigation practice. In analyzing this case from
the recent past, our aim is to empirically ground contemporary critiques of AI
development and evaluation and the calls for greater participation as a means
to address them.


title:
Variational inequalities on unbounded domains for zero-sum
  singular-controller vs. stopper games

abstract:
  We study a class of zero-sum games between a singular-controller and a
stopper over finite-time horizon. The underlying process is a multi-dimensional
(locally non-degenerate) controlled stochastic differential equation (SDE)
evolving in an unbounded domain. We prove that such games admit a value and
provide an optimal strategy for the stopper. The value of the game is shown to
be the maximal solution, in a suitable Sobolev class, of a variational
inequality of `min-max' type with obstacle constraint and gradient constraint.
Although the variational inequality and the game are solved on an unbounded
domain we do not require boundedness of either the coefficients of the
controlled SDE or of the cost functions in the game.


title:
Pressure Ulcer Categorisation using Deep Learning: A Clinical Trial to
  Evaluate Model Performance

abstract:
  Pressure ulcers are a challenge for patients and healthcare professionals. In
the UK, 700,000 people are affected by pressure ulcers each year. Treating them
costs the National Health Service {\pounds}3.8 million every day. Their
etiology is complex and multifactorial. However, evidence has shown a strong
link between old age, disease-related sedentary lifestyles and unhealthy eating
habits. Pressure ulcers are caused by direct skin contact with a bed or chair
without frequent position changes. Urinary and faecal incontinence, diabetes,
and injuries that restrict body position and nutrition are also known risk
factors. Guidelines and treatments exist but their implementation and success
vary across different healthcare settings. This is primarily because healthcare
practitioners have a) minimal experience in dealing with pressure ulcers, and
b) a general lack of understanding of pressure ulcer treatments. Poorly
managed, pressure ulcers lead to severe pain, poor quality of life, and
significant healthcare costs. In this paper, we report the findings of a
clinical trial conducted by Mersey Care NHS Foundation Trust that evaluated the
performance of a faster region-based convolutional neural network and mobile
platform that categorised and documented pressure ulcers. The neural network
classifies category I, II, III, and IV pressure ulcers, deep tissue injuries,
and unstageable pressure ulcers. Photographs of pressure ulcers taken by
district nurses are transmitted over 4/5G communications to an inferencing
server for classification. Classified images are stored and reviewed to assess
the model's predictions and relevance as a tool for clinical decision making
and standardised reporting. The results from the study generated a mean average
Precision=0.6796, Recall=0.6997, F1-Score=0.6786 with 45 false positives using
an @.75 confidence score threshold.


title: Information temperature as a measure of DNA's and texts complexity

abstract:
  C. Shannon introduced the notion of entropy for random sequences. What about
their temperature? After discussing some methods for introducing information
temperature (IT) for binary random stationary ergodic sequence, we suggest
using IT as a characteristic of complexity and intelligence of an agent capable
of writing or generating meaningful texts. In particular, we discuss the
question of whether the temperature can characterize the academic level of the
text, or serve as an indicator of the quality of brain activity of the text
author.


title:
Learning from humans: combining imitation and deep reinforcement
  learning to accomplish human-level performance on a virtual foraging task

abstract:
  We develop a method to learn bio-inspired foraging policies using human data.
We conduct an experiment where humans are virtually immersed in an open field
foraging environment and are trained to collect the highest amount of rewards.
A Markov Decision Process (MDP) framework is introduced to model the human
decision dynamics. Then, Imitation Learning (IL) based on maximum likelihood
estimation is used to train Neural Networks (NN) that map human decisions to
observed states. The results show that passive imitation substantially
underperforms humans. We further refine the human-inspired policies via
Reinforcement Learning (RL), using on-policy algorithms that are more suitable
to learn from pre-trained networks. We show that the combination of IL and RL
can match human results and that good performance strongly depends on an
egocentric representation of the environment. The developed methodology can be
used to efficiently learn policies for unmanned vehicles which have to solve
missions in an open field environment.


title:
Explicit lower bound of blow-up time in a fully parabolic
  attraction-repulsion chemotaxis system with nonlinear terms

abstract:
  It is known that for the parabolic-elliptic Keller-Segel type system in a
smooth bounded domain in 3-dimensional space, the lower bound of a blow-up time
of unbounded solution is given. This paper extends the previous works to deal
with the fully parabolic Keller-Segel type system in any spatial dimension
larger than 3. Firstly, we prove that any blow-up time of an energy function is
also the classical blow-up time when its energy level is sufficiently large.
Secondly, we give an explicit estimation for the lower bound of blow-up time
for the fully parabolic attraction-repulsion chemotaxis system with nonlinear
terms, under homogeneous Neumann boundary conditions, in a smooth bounded
domain.


title: Quantum Telescopy Games

abstract:
  We consider the Clock Game - task formulated in the framework of quantum
information theory - that can be used to improve the existing schemes of
quantum-enhanced telescopy. The problem of learning when a stellar photon
reaches a telescope is translated into an abstract game, which we call the
Clock Game. A winning strategy is provided that involves performing a quantum
non-demolition measurement that verifies which stellar spatio-temporal modes
are occupied by a photon without disturbing the phase information. We prove
tight lower bounds on the entanglement cost needed to win the Clock Game, with
the amount of necessary entangled bits equaling the number of time-bins being
distinguished. This lower bound on the entanglement cost applies to any
telescopy protocol protocol that aims to non-destructively extract the time-bin
information of an incident photon through local measurements, and our result
implies that the protocol of [Khabiboulline et al. Phys. Rev. Lett. 123, 70504
(2019)] is optimal in terms of entanglement consumption. The full task of the
phase extraction is also considered, and we show that the quantum Fisher
Information of the stellar phase can be achieved by local measurements and
shared entanglement without the necessity of non-linear optical operations. The
optimal phase measurement is achieved asymptotically with increasing number of
ancilla qubits, whereas a single qubit pair is required if non-linear
operations are allowed.


title:
Fast cycling HTS based superconducting accelerator magnets: Feasibility
  study and readiness demonstration program driven by neutrino physics and muon
  collider needs

abstract:
  Development of energy-efficient fast cycling accelerator magnets is critical
for the next generation of proton rapid cycling synchrotrons (RCS) for neutrino
research and booster accelerators of future muon colliders. We see a unique
opportunity for having such magnets to be built on base of High Temperature
Superconductors (HTS). Besides being superconducting at relatively high
temperatures, rare-earth HTS tapes have shown very small AC losses compared to
low-temperature NbTi superconductor cables. Recent tests of the HTS-based 0.5 m
long two-bore superconducting accelerator magnet have shown record high dB/dt
ramping rates of about 300 T/s at 10 Hz repetition rate and 0.5 T B-field span.
No temperature rise in 6 K cooling He was observed within the 0.003 K error
setting the upper limit on the cryogenic power loss in the magnet conductor
coil to be less than 0.2 W/m. Based on this result we outline a possible
upgrade of this test magnet design to 2 T B-field in the 10 mm beam gap with
the dB/dt ramping rates up to 1000 T/s. The power test results of this short
sample magnet will be used to project both cryogenic and electrical power
losses as a function of the magnet B-field and the dB/dt ramping rates. Then
these projections will be scaled to the range of expected accelerator magnet
beam gaps and B-fields for the proton and muon RCS accelerators. We invite
collaborators to join these studies and call for support of the R&D program
aimed at comprehensive demonstration of this approach that includes design,
construction, and power tests of a long prototype of the HTS-based fast-cycling
accelerator magnet by 2028.


title: Perception Over Time: Temporal Dynamics for Robust Image Understanding

abstract:
  While deep learning surpasses human-level performance in narrow and specific
vision tasks, it is fragile and over-confident in classification. For example,
minor transformations in perspective, illumination, or object deformation in
the image space can result in drastically different labeling, which is
especially transparent via adversarial perturbations. On the other hand, human
visual perception is orders of magnitude more robust to changes in the input
stimulus. But unfortunately, we are far from fully understanding and
integrating the underlying mechanisms that result in such robust perception. In
this work, we introduce a novel method of incorporating temporal dynamics into
static image understanding. We describe a neuro-inspired method that decomposes
a single image into a series of coarse-to-fine images that simulates how
biological vision integrates information over time. Next, we demonstrate how
our novel visual perception framework can utilize this information "over time"
using a biologically plausible algorithm with recurrent units, and as a result,
significantly improving its accuracy and robustness over standard CNNs. We also
compare our proposed approach with state-of-the-art models and explicitly
quantify our adversarial robustness properties through multiple ablation
studies. Our quantitative and qualitative results convincingly demonstrate
exciting and transformative improvements over the standard computer vision and
deep learning architectures used today.


title: Enabling U.S. participation in Future Higgs Factories

abstract:
  Exciting proposals for a new Higgs factory collider, aimed at the search for
new physics and precision studies of particles and forces, especially
measurement of the Higgs boson couplings at the loop level, will be evaluated
as part of the Snowmass process. Potential facilities include (among others)
ILC, FCC-ee, C3, CEPC, CLIC, muon collider and advanced accelerator concepts
being investigated by Snowmass topical group AF6, potentially located in Asia,
Europe, or the United States. The European Strategy has endorsed an
electron-positron Higgs factory as its highest priority after HL-LHC. Much of
the detector, software, and physics preparative studies needed for these
machines is in common, and is currently being implemented by physicists
world-wide. In this white paper for the 2021 Snowmass process we look at
current global activity on future Higgs factories and give examples of
investments that could be made in these common areas over the next five years
to establish a leadership role for the U.S. in a future Higgs factory, wherever
it is built. The U.S. high energy physics program confronts a number of
challenges that a strong role in the study of the Higgs boson can address.
These include, in addition to the scientific results, maintaining leading roles
in international partnerships, nurturing and advancing world-leading
capabilities and expert resources, and maintaining and attracting talent. The
international effort would benefit from increased U.S. participation, and the
U.S., in turn, would maintain stature through the partnership.


title:
Fast and flexible inference approach for joint models of multivariate
  longitudinal and survival data using Integrated Nested Laplace Approximations

abstract:
  Joint modeling longitudinal and survival data offers many advantages such as
addressing measurement error and missing data in the longitudinal processes,
understanding and quantifying the association between the longitudinal markers
and the survival events and predicting the risk of events based on the
longitudinal markers. A joint model involves multiple submodels (one for each
longitudinal/survival outcome) usually linked together through correlated or
shared random effects. Their estimation is computationally expensive
(particularly due to a multidimensional integration of the likelihood over the
random effects distribution) so that inference methods become rapidly
intractable, and restricts applications of joint models to a small number of
longitudinal markers and/or random effects. We introduce a Bayesian
approximation based on the Integrated Nested Laplace Approximation algorithm
implemented in the R package R-INLA to alleviate the computational burden and
allow the estimation of multivariate joint models with less restrictions. Our
simulation studies show that R-INLA substantially reduces the computation time
and the variability of the parameter estimates compared to alternative
estimation strategies. We further apply the methodology to analyze 5
longitudinal markers (3 continuous, 1 count, 1 binary, and 16 random effects)
and competing risks of death and transplantation in a clinical trial on primary
biliary cholangitis. R-INLA provides a fast and reliable inference technique
for applying joint models to the complex multivariate data encountered in
health research.


title:
Electrically Tunable Localized versus Delocalized Intralayer Moir\'e
  Excitons and Trions in a Twisted MoS$_2$ Bilayer

abstract:
  Moir\'e superlattice-induced sub-bands in twisted van der Waals homo- and
hetero-structures govern their optical and electrical properties, rendering
additional degrees of freedom such as twist angle. Here, we demonstrate the
moir\'e superlattice effects on the intralayer excitons and trions in a twisted
bilayer of MoS$_2$ of H-type stacking at marginal twist angles. We identify the
emission from localized and multiple delocalized sub-bands of intralayer
moir\'e excitons and show their electrical modulation by the corresponding
trion formation. The electrical control of the oscillator strength of the
moir\'e excitons also results in a strong tunability of resonant Raman
scattering. We find that the gate-induced doping significantly modulates the
electronic moir\'e potential, however leaves the excitonic moir\'e confinement
unaltered. This effect, coupled with variable moir\'e trap filling by tuning
the optical excitation density, allows us to delineate the different phases of
localized and delocalized moir\'e trions. We demonstrate that the moir\'e
excitons exhibit strong valley coherence that changes in a striking
non-monotonic W-shape with gating due to motional narrowing. These observations
from the simultaneous electrostatic control of quasiparticle-dependent moir\'e
potential will lead to exciting effects of tunable many-body phenomena in
moir\'e superlattices.

categories: cond-mat.mes-hall cond-mat.mtrl-sci physics.app-ph physics.optics


title: The Potential of a TeV-Scale Muon-Ion Collider

abstract:
  We propose the development of a novel muon-proton and muon-nucleus collider
facility at the TeV scale that is capable of performing precision deep
inelastic scattering measurements in new regimes and providing a rich program
in nuclear and particle physics. Such a facility could seed, or leverage, the
development of a muon-antimuon collider and make use of the existing hadron
accelerator infrastructure when sited at a facility such as Brookhaven National
Laboratory, Fermilab, or CERN. We discuss the possible energy and luminosity
design parameters for several collider configurations, and illustrate the
science potential with several studies on deep inelastic scattering kinematics,
Higgs and vector boson production, top quark production, and beyond Standard
Model leptoquark production. Detector design considerations and a possible road
map toward development are also given.


title:
Continuous Habitable Zones: Using Bayesian Methods to Prioritize
  Characterization of Potentially Habitable Worlds

abstract:
  The number of potentially habitable planets continues to increase, but we
lack the time and resources to characterize all of them. With $\sim$30 known
potentially habitable planets and an ever-growing number of candidate and
confirmed planets, a robust statistical framework for prioritizing
characterization of these planets is desirable. Using the $\sim$2 Gy it took
life on Earth to make a detectable impact on the atmosphere as a benchmark, we
use a Bayesian statistical method to determine the probability that a given
radius around a star has been continuously habitable for 2 Gy. We perform this
analysis on 9 potentially habitable exoplanets with planetary radii $<$1.8
R$_\oplus$ and/or planetary masses $<$10 M$_\oplus$ around 9 low-mass host
stars ($\sim$0.5-1.1 M$_\odot$) with measured stellar mass and metallicity, as
well as Venus, Earth, and Mars. Ages for the host stars are generated by the
analysis. The technique is also used to provide age estimates for 2768 low-mass
stars (0.5-1.3 M$_\odot$) in the TESS Continuous Viewing Zones.


title:
Manipulating the symmetry of transverse momentum entangled biphoton
  states

abstract:
  Bell states are a fundamental resource in photonic quantum information
processing. These states have been generated successfully in many photonic
degrees of freedom. Their manipulation, however, in the momentum space remains
challenging. Here, we present a scheme for engineering the symmetry of
two-photon states entangled in the transverse momentum degree of freedom
through the use of a spatially variable phase object. We demonstrate how a
Hong-Ou-Mandel interferometer must be constructed to verify the symmetry in
momentum entanglement via photon "bunching"/"anti-bunching" observation. We
also show how this approach allows generating states that acquire an arbitrary
phase under the exchange operation.


title: Generalized Interference of Fermions and Bosons

abstract:
  Using tools from representation theory, we derive expressions for the
coincidence rate of partially-distinguishable particles in an interferometry
experiment. Our expressions are valid for either bosons or fermions, and for
any number of particles. In an experiment with $n$ particles the expressions we
derive contain a term for each partition of the integer $n$; Gamas's theorem is
used to determine which of these terms are automatically zero based on the
pairwise level of distinguishability between particles. Most sampling schemes
(such as Boson Sampling) are limited to completely indistinguishable particles;
our work aids in the understanding of systems where an arbitrary level of
distinguishability is permitted. As an application of our work we introduce a
sampling scheme with partially-distinguishable fermions, which we call
Generalized Fermion Sampling.


title:
High-pressure TPCs in pressurized caverns: opportunities in dark matter
  and neutrino physics

abstract:
  The natural gas and hydrogen storage industries have experience creating
huge, pressurized underground spaces. The most common of these is "solution
mining", a method for making brine-filled cavities in salt formations. Unlike
conventionally-mined underground spaces, these spaces are (a) inexpensive to
construct and operate, (b) naturally serve as pressure vessels, at size scales
impossible to construct in a conventional lab, and (b) permit safe use of
flammable and/or toxic materials. If various engineering challenges could be
met, solution-mined caverns would allow unprecedentedly-large high pressure gas
TPCs. Lined rock caverns (LRC) may permit high pressure TPCs of considerable
size in more conventional spaces. In this whitepaper, we review some of the new
physics opportunities available in these caverns and suggest an R&D program
needed to realize them.


title:
Determining clinically relevant features in cytometry data using
  persistent homology

abstract:
  Cytometry experiments yield high-dimensional point cloud data that is
difficult to interpret manually. Boolean gating techniques coupled with
comparisons of relative abundances of cellular subsets is the current standard
for cytometry data analysis. However, this approach is unable to capture more
subtle topological features hidden in data, especially if those features are
further masked by data transforms or significant batch effects or
donor-to-donor variations in clinical data. Analysis of publicly available
cytometry data describing non-na\"ive CD8+ T cells in COVID-19 patients and
healthy controls shows that systematic structural differences exist between
single cell protein expressions in COVID-19 patients and healthy controls. We
identify proteins of interest by a decision-tree based classifier, sample
points randomly and compute persistence diagrams from these sampled points. The
resulting persistence diagrams identify regions in cytometry datasets of
varying density and identify protruded structures such as `elbows'. We compute
Wasserstein distances between these persistence diagrams for random pairs of
healthy controls and COVID-19 patients and find that systematic structural
differences exist between COVID-19 patients and healthy controls in the
expression data for T-bet, Eomes, and Ki-67. Further analysis shows that
expression of T-bet and Eomes are significantly downregulated in COVID-19
patient non-na\"ive CD8+ T cells compared to healthy controls. This
counter-intuitive finding may indicate that canonical effector CD8+ T cells are
less prevalent in COVID-19 patients than healthy controls. This method is
applicable to any cytometry dataset for discovering novel insights through
topological data analysis which may be difficult to ascertain otherwise with a
standard gating strategy or existing bioinformatic tools.


title: Cross-lingual Inference with A Chinese Entailment Graph

abstract:
  Predicate entailment detection is a crucial task for question-answering from
text, where previous work has explored unsupervised learning of entailment
graphs from typed open relation triples. In this paper, we present the first
pipeline for building Chinese entailment graphs, which involves a novel
high-recall open relation extraction (ORE) method and the first Chinese
fine-grained entity typing dataset under the FIGER type ontology. Through
experiments on the Levy-Holt dataset, we verify the strength of our Chinese
entailment graph, and reveal the cross-lingual complementarity: on the parallel
Levy-Holt dataset, an ensemble of Chinese and English entailment graphs
outperforms both monolingual graphs, and raises unsupervised SOTA by 4.7 AUC
points.


title:
Preliminary experiments on automatic gender recognition based on online
  capital letters

abstract:
  In this paper we present some experiments to automatically classify online
handwritten text based on capital letters. Although handwritten text is not as
discriminative as face or voice, we still found some chance for gender
classification based on handwritten text. Accuracies are up to 74%, even in the
most challenging case of capital letters.


title:
On the $\sigma$-Pair Correlation Density of Quadratic Sequences Modulo
  One

abstract:
  In this note we study the $\sigma$-pair correlation density
\begin{equation*}R_2^\sigma([a,b], \{ \theta_n \}_n, N)= \frac{1}{N^{2-\sigma}}
\# \big \{ 1 \leq j \neq k \leq N \, \big| \, \theta_{j} - \theta_{k} \in \big
[ \frac{a}{N^\sigma},\frac{b}{N^\sigma} \big ]+ \mathbb Z \big \}
\end{equation*} of a sequence $\{ \theta_n\}_n$ that is equidistributed modulo
one for $0 \leq \sigma <2$.
  The case $\sigma=1$ is commonly referred to as the pair correlation density
and the sequence $\{ n^2 \alpha \}_n$ has been of special interest due to its
connection to a conjecture of Berry and Tabor on the energy levels of generic
completely integrable systems.
  We prove that if $\alpha$ is Diophantine of type $3-\epsilon$ for every
$\epsilon>0$, then for any $0 \leq \sigma <1$ \begin{align*} \mathrm
R_2^\sigma([a,b], \{ \alpha n^2 \}_n, N) \to b-a, \text{ as } N \to \infty.
\end{align*} In this case, we say that the sequence exhibits $\sigma$-pair
correlation.
  In addition to this, we show that for any $0 \leq \sigma < \frac{1}{4}(9
-\sqrt{17})=1.21922...$ there is a set of full Lebesgue measure such that the
sequence $\{ \alpha n^2 \}_n$ exhibits $\sigma$-pair correlation.


title: MPGDs for TPCs at future lepton colliders

abstract:
  This submission will focus on advancements and advantages of Micro Pattern
Gas Detector (MPGD) technologies and their applications to the construction of
a dedicated Time Projection Chamber (TPC) that can serve as an excellent main
tracker for any multipurpose detector that can be foreseen to operate at a
future lepton collider. The first portion of the report will be the executive
summary. It will be followed by sections detailing the applications of MPGDs
specifically to the construction of the LCTPC for the ILD at ILC, for a
possible upgrade of the Belle II detector at SuperKEKB and for the design of a
TPC for a detector at CEPC. MPGD technologies offer synergies with other
detector R&D and several application domains; a few examples will be provided
in the context of the ongoing Snowmass long range planning exercise in the USA.
Links to industrial partnership and work with institutions in the USA will be
highlighted when appropriate.


title: Enumerating coprime permutations

abstract:
  Define a permutation $\sigma$ to be coprime if $\gcd(m,\sigma(m)) = 1$ for
$m\in[n]$. In this note, proving a recent conjecture of Pomerance, we prove
that the number of coprime permutations on $[n]$ is $n!\cdot (c+o(1))^n$ where
\[c = \prod_{p\text{ prime }}\frac{(p-1)^{2(1-1/p)}}{p\cdot (p-2)^{(1-2/p)}}.\]
The techniques involve entropy maximization for the upper bound, and a mixture
of number-theoretic bounds, permanent estimates, and the absorbing method for
the lower bound.


title:
Parameter Inference of Time Series by Delay Embeddings and Learning
  Differentiable Operators

abstract:
  A common issue in dealing with real-world dynamical systems is identifying
system parameters responsible for its behavior. A frequent scenario is that one
has time series data, along with corresponding parameter labels, but there
exists new time series with unknown parameter labels, which one seeks to
identify. We tackle this problem by first delay-embedding the time series into
a higher dimension to obtain a proper ordinary differential equation (ODE), and
then having a neural network learn to predict future time-steps of the
trajectory given the present time-step. We then use the learned neural network
to backpropagate prediction errors through the parameter inputs of the neural
network in order to obtain a gradient in parameter space. Using this gradient,
we can approximately identify parameters of time series. We demonstrate the
viability of our approach on the chaotic Lorenz system, as well as real-world
data with the Hall-effect Thruster (HET).


title: Hopf type theorems in Riemannian manifolds

abstract:
  In 1951, H. Hopf proved that the only surfaces, homeomorphic to the sphere,
with constant mean curvature in the Euclidean space are the round (geometrical)
spheres. In this paper we survey some contributions of Renato Tribuzy to
generalize the result of Hopf as well as some recent results of the authors
using these techniques for shrinking solitons of curvature flows and for
surfaces in three-dimensional warped product manifolds, specially the de
Sitter-Schwarzschild and Reissner-Nordstrom manifolds.


title: Bit-Metric Decoding Rate in Multi-User MIMO Systems: Theory

abstract:
  Link-adaptation (LA) is one of the most important aspects of wireless
communications where the modulation and coding scheme (MCS) used by the
transmitter is adapted to the channel conditions in order to meet a certain
target error-rate. In a single-user SISO (SU-SISO) system, LA is performed by
computing the post-equalization signal-to-interference-noise ratio (SINR) at
the receiver. The same technique can be employed in multi-user MIMO (MU-MIMO)
receivers that use linear detectors. Another important use of post-equalization
SINR is for physical layer (PHY) abstraction, where several PHY blocks like the
channel encoder, the detector, and the channel decoder are replaced by an
abstraction model in order to speed up system-level simulations. This is
achieved by mapping the post-equalization SINR to a codeword error rate (CER)
or a block error rate (BLER). However, for MU-MIMO systems with non-linear
receivers, like those that use variants of the sphere-decoder algorithm, there
is no known equivalent of post-equalization SINR which makes both LA and PHY
abstraction extremely challenging. This important issue is addressed in this
two-part paper. A metric called the bit-metric decoding rate (BMDR) of a
detector for a set of channel realizations is presented in this part. BMDR is
the proposed equivalent of post-equalization SINR for arbitrary detectors.
Since BMDR does not have a closed form expression that would enable its
instantaneous calculation, a machine-learning approach to predict it is
presented. The second part describes the algorithms to perform LA, detector
selection, and PHY abstraction using BMDR for MU-MIMO systems with arbitrary
detectors. Extensive simulation results corroborating the claims are presented.


title:
Ambipolar diffusion: Self-similar solutions and MHD code testing.
  Cylindrical symmetry

abstract:
  Ambipolar diffusion is a process occurring in partially ionised astrophysical
systems that imparts a complicated mathematical and physical nature to Ohm's
law. The numerical codes that solve the magnetohydrodynamic (MHD) equations
have to be able to deal with the singularities that are naturally created in
the system by the ambipolar diffusion term. The global aim is to calculate a
set of theoretical self-similar solutions to the nonlinear diffusion equation
with cylindrical symmetry that can be used as tests for MHD codes which include
the ambipolar diffusion term. First, following the general methods developed in
the applied mathematics literature, we obtained the theoretical solutions as
eigenfunctions of a nonlinear ordinary differential equation. Phase-plane
techniques were used to integrate through the singularities at the locations of
the nulls, which correspond to infinitely sharp current sheets. In the second
half of the paper, we consider the use of these solutions as tests for MHD
codes. To that end, we used the Bifrost code, thereby testing the capabilities
of these solutions as tests as well as (inversely) the accuracy of Bifrost's
recently developed ambipolar diffusion module. The obtained solutions are shown
to constitute a demanding, but nonetheless viable, test for MHD codes that
incorporate ambipolar diffusion. The Bifrost code is able to reproduce the
theoretical solutions with sufficient accuracy up to very advanced diffusive
times. Using the code, we also explored the asymptotic properties of our
theoretical solutions in time when initially perturbed with either small or
finite perturbations. The functions obtained in this paper are relevant as
physical solutions and also as tests for general MHD codes. They provide a more
stringent and general test than the simple
Zeldovich-Kompaneets-Barenblatt-Pattle solution.

categories: astro-ph.SR astro-ph.GA astro-ph.HE astro-ph.IM


title: Bit-Metric Decoding Rate in Multi-User MIMO Systems: Applications

abstract:
  This is the second part of a two-part paper that focuses on link-adaptation
(LA) and physical layer (PHY) abstraction for multi-user MIMO (MU-MIMO) systems
with non-linear receivers. The first part proposes a new metric, called
bit-metric decoding rate (BMDR) for a detector, as being the equivalent of
post-equalization signal-to-interference-noise ratio (SINR) for non-linear
receivers. Since this BMDR does not have a closed form expression, a
machine-learning based approach to estimate it effectively is presented. In
this part, the concepts developed in the first part are utilized to develop
novel algorithms for LA, dynamic detector selection from a list of available
detectors, and PHY abstraction in MU-MIMO systems with arbitrary receivers.
Extensive simulation results that substantiate the efficacy of the proposed
algorithms are presented.


title: Improved Tail Estimates for the Distribution of Quadratic Weyl Sums

abstract:
  We consider quadratic Weyl sums $S_N(x;c,\alpha)=\sum_{n=1}^N\exp\{2\pi
i((\frac{1}{2}n^2+cn)x+\alpha n)\}$ for $c=\alpha=0$ (the rational case) or
$(c,\alpha)\notin\mathbb{Q}^2$ (the irrational case), where $x$ is randomly
distributed according to a probability measure absolutely continuous with
respect to the Lebesgue measure. The limiting distribution in the complex plane
of $\frac{1}{\sqrt{N}}S_N(x;c,\alpha)$ as $N\to\infty$ was described by Marklof
[13] (respectively Cellarosi and Marklof [5]) in the rational (resp.
irrational) case. According to the limiting distribution, the probability of
landing outside a ball of radius $R$ is known to be asymptotic to $\frac{4\log
2}{\pi^2}R^{-4}(1+o(1))$ in the rational case and to
$\frac{6}{\pi^2}R^{-6}(1+O(R^{-12/31}))$ in the irrational case, as
$R\to\infty$. In this work we refine the technique of Cellarosi and Marklof [5]
to improve the known tail estimates to $\frac{4\log
2}{\pi^2}R^{-4}(1+O_\varepsilon(R^{-2+\varepsilon}))$ and
$\frac{6}{\pi^2}R^{-6}(1+O_\varepsilon(R^{-2+\varepsilon}))$ for every
$\varepsilon>0$. In the rational case, we rely on the equidistribution of a
rational horocycle lift to a torus bundle over the unit tangent bundle to the
classical modular surface. All the constants implied by the
$O_\varepsilon$-notations are made explicit


title:
A prototype optical link board with redundancy design for the ATLAS
  liquid argon calorimeter Phase-2 upgrade

abstract:
  A prototype optical-link board has been developed for the ATLAS Liquid Argon
Calorimeter Phase-2 upgrade. The board consists of 24 lpGBT chips and 8 VTRx+
modules and demonstrates the full optical link design of the future front-end
board. The board has 22 simplex optical links to transmit detector data, which
are emulated in FPGAs and injected through 6 FMC connectors, to the
off-detector electronics. The board implements 2 duplex optical links for
clocks, control, and monitoring with redundant design to improve the system
reliability. Data transmission and all redundant designs have been verified. A
fiber routing scheme, in which 2 or 3 VTRx+ modules are re-connected to the
front panel with an MPO connector, has been prototyped.


title: Randomized quasi-optimal local approximation spaces in time

abstract:
  We target time-dependent partial differential equations (PDEs) with
coefficients that are arbitrarily rough in both space and time. To tackle these
problems, we construct reduced basis/ multiscale ansatz functions defined in
space that can be combined with time stepping schemes within model order
reduction or multiscale methods. To that end, we propose to perform several
simulations of the PDE for few time steps in parallel starting at different,
randomly drawn start points, prescribing random initial conditions; applying a
singular value decomposition to a subset of the so obtained snapshots yields
the reduced basis/ multiscale ansatz functions. This facilitates constructing
the reduced basis/ multiscale ansatz functions in an embarrassingly parallel
manner. In detail, we suggest using a data-dependent probability distribution
based on the data functions of the PDE to select the start points. Each local
in time simulation of the PDE with random initial conditions approximates a
local approximation space in one time point that is optimal in the sense of
Kolmogorov. The derivation of these optimal local approximation spaces which
are spanned by the left singular vectors of a compact transfer operator that
maps arbitrary initial conditions to the solution of the PDE in a later point
of time, is one other main contribution of this paper. By solving the PDE
locally in time with random initial conditions, we construct local ansatz
spaces in time that converge provably at a quasi-optimal rate and allow for
local error control. Numerical experiments demonstrate that the proposed method
can outperform existing methods like the proper orthogonal decomposition even
in a sequential setting.


title: A 40 Gbps Optical Transceiver for Particle Physics Experiments

abstract:
  We present the design and the test results of a quad-channel optical
transceiver module (QTRx) possibly for future particle physics experiments. The
transmitters of QTRx, each at 10 Gbps, are based on a Quad-channel VCSEL Diode
array Driver (QLDD) and 1 x 4 VCSEL array. The receivers of QTRx, with data
rates of 2.56 Gbps or 10 Gbps per channel, are based on a Quad-channel
Trans-Impedance and limiting Amplifier (QTIA) and 1 x 4 photodiode array of
GaAs or InGaAs. QTRx is 20 mm x 10 mm x 5 mm and couples to an MT fiber
connector. Test results indicate that QTRx achieves the design goals with a
power consumption of 124 mW per transmitter channel at 10 Gbps and 120 mW at
2.56 Gbps for the receiver channel with an on-chip charge pump. The
sensitivities of QTIA are -17 dBm at 2.56 Gbps and -8 dBm at 10 Gbps,
respectively. Further improvements with a gold-finger interface and a more
compact optical lens are being designed.


title: Distributed Dual Quaternion Based Localization of Visual Sensor Networks

abstract:
  In this paper we consider the localization problem for a visual sensor
network. Inspired by the alternate attitude and position distributed
optimization framework discussed in [1], we propose an estimation scheme that
exploits the unit dual quaternion algebra to describe the sensors pose. This
representation is beneficial in the formulation of the optimization scheme
allowing to solve the localization problem without designing two interlaced
position and orientation estimators, thus improving the estimation error
distribution over the two pose components and the overall localization
performance. Furthermore, the numerical experimentation asserts the robustness
of the proposed algorithm w.r.t. the initial conditions.


title: Synthetic Controls in Action

abstract:
  In this article we propose a set of simple principles to guide empirical
practice in synthetic control studies. The proposed principles follow from
formal properties of synthetic control estimators, and pertain to the nature,
implications, and prevention of over-fitting biases within a synthetic control
framework, to the interpretability of the results, and to the availability of
validation exercises. We discuss and visually demonstrate the relevance of the
proposed principles under a variety of data configurations.


title:
Correlations between cascaded photons from spatially localized
  biexcitons in ZnSe

abstract:
  Radiative cascades emit correlated photon pairs, providing a pathway for the
generation of entangled photons. The realization of a radiative cascade with
impurity atoms in semiconductors, a leading platform for the generation of
quantum light, would therefore provide a new avenue for the development of
entangled photon pair sources. Here we demonstrate a radiative cascade from the
decay of a biexciton at an impurity-atom complex in a ZnSe quantum well. The
emitted photons show clear temporal correlations revealing the time--ordering
of the cascade. Our result establishes impurity atoms in ZnSe as a potential
platform for photonic quantum technologies using radiative cascades.


title:
A Gaseous Argon-Based Near Detector to Enhance the Physics Capabilities
  of DUNE

abstract:
  This document presents the concept and physics case for a magnetized gaseous
argon-based detector system (ND-GAr) for the Deep Underground Neutrino
Experiment (DUNE) Near Detector. This detector system is required in order for
DUNE to reach its full physics potential in the measurement of CP violation and
in delivering precision measurements of oscillation parameters. In addition to
its critical role in the long-baseline oscillation program, ND-GAr will extend
the overall physics program of DUNE. The LBNF high-intensity proton beam will
provide a large flux of neutrinos that is sampled by ND-GAr, enabling DUNE to
discover new particles and search for new interactions and symmetries beyond
those predicted in the Standard Model.


title: Matroids in toric topology

abstract:
  In this paper we study general torus actions on manifolds with isolated fixed
points from combinatorial point of view. The main object of study is the poset
of face submanifolds of such actions. We introduce the notion of a locally
geometric poset -- the graded poset locally modelled by geometric lattices, and
prove that for any torus action, the poset of its faces is locally geometric.
Next we discuss the relations between posets of faces and GKM-theory. In
particular, we define the face poset of an abstract GKM-graph and show how to
reconstruct the face poset of a manifold from its GKM-graph.


title:
Prediction of stable Li-Sn compounds: boosting ab initio searches with
  neural network potentials

abstract:
  The Li-Sn binary system has been the focus of extensive research because it
features Li-rich alloys with potential applications as battery anodes. Our
present re-examination of the binary system with a combination of machine
learning and ab initio methods has allowed us to screen a vast configuration
space and uncover a number of overlooked thermodynamically stable alloys. At
ambient pressure, our evolutionary searches identified a new stable Li$_3$Sn
phase with a large BCC-based hR48 structure and a possible high-T LiSn$_4$
ground state. By building a simple model for the observed and predicted Li-Sn
BCC alloys we constructed an even larger viable hR75 structure at an exotic
19:6 stoichiometry. At 20 GPa, new 11:2, 5:1, and 9:2 phases found with our
global searches destabilize previously proposed phases with high Li content.
The findings showcase the appreciable promise machine learning interatomic
potentials hold for accelerating ab initio prediction of complex materials.


title: Inward Swirling Flamelet Model

abstract:
  A new rotational flamelet model with inward swirling flow through a stretched
vortex tube is developed for sub-grid modeling to be coupled with the resolved
flow for turbulent combustion. The model has critical new features compared to
existing models. (i) Non-premixed flames, premixed flames, or multi-branched
flame structures are determined rather than prescribed. (ii) The effects of
vorticity and the related centifugal acceleration are determined. (iii) The
strain rates and vorticity applied at the sub-grid level can be directly
determined from the resolved-scale strain rates and vorticity without a
contrived progress variable. (iv) The flamelet model is three-dimensional. (v)
The effect of variable density is addressed. (vi) The inward swirl is created
by vorticity combined with two compressive normal strain components; this
feature distinguishes the model from counterflow flamelet models. Solutions to
the multicomponent Navier-Stokes equations governing the flamelet model are
obtained. By coordinate transformation, a similar solution is found for the
model, through a system of ordinary differential equations. Vorticity creates a
centrifugal force on the sub-grid counterflow that modifies the molecular
transport rates, burning rates, and flammability limits. Sample computations of
the inward swirling rotational flamelet model without coupling to the resolved
flow are presented to demonstrate the importance of the new features. Premixed,
nonpremixed, and multi-branched flame structures are examined. Parameter
surveys are made with rate of normal strain, vorticity, Damk\"{o}hler number,
and Prandtl number. The centrifugal effect has interesting consequences when
combined with the variable-density field. Flow direction can reverse; burning
rates can be modified; flammability limits can be extended.


title:
Categorization model of moving small-scale intensity enhancements in
  solar active regions

abstract:
  The small-scale moving intensity enhancements remotely observed in the
extreme ultraviolet images of the solar active regions, which we refer to as
active region moving campfires (ARMCs), are related to local plasma temperature
and/or density enhancements. Their dynamics is driven by the physical processes
in the entire coronal plasma. Our previous study of ARMCs indicates that they
have characteristic velocities at around the background sound speed. The main
goal of our work is to carry out a simultaneous analysis of EUV images from two
observational missions, SDO/AIA and Hi-C 2.1. The aims of the performed
cross-validating analysis of both SDO/AIA and Hi-C 2.1 data were to reveal how
the observed moving features are distributed over the studied active region,
AR12712, test the existence of different groups of ARMCs with distinct physical
characteristics. We use the statistical model of intensity centroid convergence
and tracking that was developed in our previous paper. Furthermore, a Gaussian
mixture model fit of the observed complex of moving ARMCs is elaborated to
reveal the existence of distinct ARMC groups and to study the physical
characteristics of these different groups. In data from the 171\AA, 193\AA\ and
211\AA\ channels of SDO/AIA, we identified several groups of ARMCs with respect
to both blob intensity and velocity profiles. The existence of such groups is
confirmed by the cross-validation of the 172\AA\ data sets from Hi-C 2.1. The
ARMCs studied in this paper have characteristic velocities in the range of the
typical sound speeds in coronal loops. Hence, these moving objects differ from
the well-known rapid Alfv\'enic velocity jets from magnetic reconnection sites.
This is also proven by the fact that ARMCs propagate along the active region
magnetic structure (strands). The nature of the discovered statistical grouping
of the ARMC events is not known.


title:
Phonon-Mediated Attractive Interactions between Excitons in
  Lead-Halide-Perovskites

abstract:
  Understanding the origin of electron-phonon coupling in lead-halide
perovskites (LHP) is key to interpreting and leveraging their optical and
electronic properties. Here we perform femtosecond-resolved, optical-pump,
electron-diffraction-probe measurements to quantify the lattice reorganization
occurring as a result of photoexcitation in LHP nanocrystals. Photoexcitation
is found to drive a reduction in lead-halide octahedra tilts and distortions in
the lattice, a result of deformation potential coupling to low energy optical
phonons. Our results indicate particularly strong coupling in FAPbBr3, and far
weaker coupling in CsPbBr3, highlighting differences in the dominant machanisms
governing electron-phonon coupling in LHPs. We attribute the enhanced coupling
in FAPbBr3 to its disordered crystal structure, which persists down to
cryogenic temperatures. We find the reorganizations induced by each exciton in
a multiexcitonic state constructively interfere, giving rise to a coupling
strength which scales quadratically with the exciton number. This superlinear
scaling induces phonon-mediated attractive interactions between excitations in
LHPs.


title:
Resilient UAV Formation for Coverage and Connectivity of Spatially
  Dispersed Users

abstract:
  Unmanned aerial vehicles (UAVs) are a convenient choice for carrying mobile
base stations to rapidly setup communication services for ground users. Unlike
terrestrial networks, UAVs do not have fiber optic back-haul connectivity
except when they are tethered to the ground, which restricts their mobility. In
the absence of back-haul, e.g., in remote areas, emergency situations, or in
battlefields, there is a need to ensure connectivity among UAVs in addition to
coverage of ground users for creating local area networks. This paper provides
a distributed and dynamic approach for UAV formation-based control for coverage
and connectivity of spatially dispersed users. We use flocking dynamics as a
guide to constructing tailored formations of UAVs on the fly. Simulation
results demonstrate that if sufficient aerial base stations are available, the
proposed approach results in a strongly connected network of UAVs that is able
to provide both a backhaul and fronthaul network. The approach can be further
extended to create multi-tier extra-terrestrial networks to cater for
large-scale applications.


title:
Estimating Cluster Masses from SDSS Multi-band Images with Transfer
  Learning

abstract:
  The total masses of galaxy clusters characterize many aspects of astrophysics
and the underlying cosmology. It is crucial to obtain reliable and accurate
mass estimates for numerous galaxy clusters over a wide range of redshifts and
mass scales. We present a transfer-learning approach to estimate cluster masses
using the ugriz-band images in the SDSS Data Release 12. The target masses are
derived from X-ray or SZ measurements that are only available for a small
subset of the clusters. We designed a semi-supervised deep learning model
consisting of two convolutional neural networks. In the first network, a
feature extractor is trained to classify the SDSS photometric bands. The second
network takes the previously trained features as inputs to estimate their total
masses. The training and testing processes in this work depend purely on real
observational data. Our algorithm reaches a mean absolute error (MAE) of 0.232
dex on average and 0.214 dex for the best fold. The performance is comparable
to that given by redMaPPer, 0.192 dex. We have further applied a joint
integrated gradient and class activation mapping method to interpret such a
two-step neural network. The performance of our algorithm is likely to improve
as the size of training dataset increases. This proof-of-concept experiment
demonstrates the potential of deep learning in maximizing the scientific return
of the current and future large cluster surveys.


title: Peel $\mid$ Pile? Cross-Framework Portability of Quantum Software

abstract:
  In recent years, various vendors have made quantum software frameworks
available. Yet with vendor-specific frameworks, code portability seems at risk,
especially in a field where hardware and software libraries have not yet
reached a consolidated state, and even foundational aspects of the technologies
are still in flux. Accordingly, the development of vendor-independent quantum
programming languages and frameworks is often suggested. This follows the
established architectural pattern of introducing additional levels of
abstraction into software stacks, thereby piling on layers of abstraction. Yet
software architecture also provides seemingly less abstract alternatives,
namely to focus on hardware-specific formulations of problems that peel off
unnecessary layers. In this article, we quantitatively and experimentally
explore these strategic alternatives, and compare popular quantum frameworks
from the software implementation perspective. We find that for several
specific, yet generalisable problems, the mathematical formulation of the
problem to be solved is not just sufficiently abstract and serves as precise
description, but is likewise concrete enough to allow for deriving
framework-specific implementations with little effort. Additionally, we argue,
based on analysing dozens of existing quantum codes, that porting between
frameworks is actually low-effort, since the quantum- and framework-specific
portions are very manageable in terms of size, commonly in the order of mere
hundreds of lines of code. Given the current state-of-the-art in quantum
programming practice, this leads us to argue in favour of peeling off
unnecessary abstraction levels.


title:
SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using
  Kernel Methods

abstract:
  We present SOCKS, a data-driven stochastic optimal control toolbox based in
kernel methods. SOCKS is a collection of data-driven algorithms that compute
approximate solutions to stochastic optimal control problems with arbitrary
cost and constraint functions, including stochastic reachability, which seeks
to determine the likelihood that a system will reach a desired target set while
respecting a set of pre-defined safety constraints. Our approach relies upon a
class of machine learning algorithms based in kernel methods, a nonparametric
technique which can be used to represent probability distributions in a
high-dimensional space of functions known as a reproducing kernel Hilbert
space. As a nonparametric technique, kernel methods are inherently data-driven,
meaning that they do not place prior assumptions on the system dynamics or the
structure of the uncertainty. This makes the toolbox amenable to a wide variety
of systems, including those with nonlinear dynamics, black-box elements, and
poorly characterized stochastic disturbances. We present the main features of
SOCKS and demonstrate its capabilities on several benchmarks.


title: Moments, Sums of Squares, and Tropicalization

abstract:
  We use tropicalization to study the duals to cones of nonnegative polynomials
and sums of squares on a semialgebraic set $S$. The truncated cones of moments
of measures supported on the set $S$ is dual to nonnegative polynomials on $S$,
while "pseudo-moments" are dual to sums of squares approximations to
nonnegative polynomials. We provide explicit combinatorial descriptions of
tropicalizations of the moment and pseudo-moment cones, and demonstrate their
usefulness in distinguishing between nonnegative polynomials and sums of
squares. We give examples that show new limitations of sums of squares
approximations of nonnegative polynomials. When the semialgebraic set is
defined by binomial inequalites, its moment and pseuo-moment cones are closed
under Hadamard product. In this case, their tropicalizations are polyhedral
cones that encode all binomial inequalities on the moment and pseudo-moment
cones.


title:
Remote control of spin-polarized topological corner states by a local
  electric potential

abstract:
  In higher-order topological insulators, the lower-dimensional boundary states
are separated by a non-negligible distance. Interestingly, the crystalline
symmetries protect the robustness of their higher-order corner or hinge states
with long-range entanglement. Here, we demonstrate the possibility of direct
control of the topological corner states by introducing the spin degree of
freedom in a two-dimensional rhombus-shaped Kekul\'{e} nanostructure with local
magnetization and electric potential. By applying a local electric potential on
one corner, the spin polarization can be reversed on both corners, along with a
phase transition from the antiferromagnetic to ferromagnetic order. We also
propose a material realization using a $\gamma$-graphyne nanostructure with the
first-principles calculations. Our studies give a showcase of the remote
correlation of quantum states in higher-order topological materials and open a
door for the development of spintronic and quantum applications.


title: Weighing Dirac fermions by nonlinear Hall effect

abstract:
  Breaking the time-reversal symmetry on the surface of a topological insulator
can open a gap for the linear dispersion and make the Dirac fermions massive.
This can be achieved by either doping a topological insulator with magnetic
elements or proximity-coupling it to magnetic insulators. While the exchange
gap can be directly imaged in the former case, measuring it at the buried
magnetic insulator/topological insulator interface remains to be challenging.
Here, we report the observation of a large nonlinear Hall effect in iron
garnet/Bi2Se3 heterostructures. Besides illuminating its magnetic origin, we
also show that this nonlinear Hall effect can be utilized to measure the size
of the exchange gap and the magnetic-proximity onset temperature. Our results
demonstrate the nonlinear Hall effect as a spectroscopic tool to probe the
modified band structure at magnetic insulator/topological insulator interfaces.


title:
Statistical properties of the off-diagonal matrix elements of
  observables in eigenstates of integrable systems

abstract:
  We study the statistical properties of the off-diagonal matrix elements of
observables in the energy eigenstates of integrable quantum systems. We discuss
the difference between such matrix elements in the presence of interactions
(which are dense) and those of noninteracting systems (which are sparse). We
focus on the quasi-momentum occupation of hard-core bosons in one dimension,
and show that the distributions of the off-diagonal matrix elements are well
described by generalized Gamma distributions, both in the presence and absence
of translational invariance but not in the presence of localization. We also
show that earlier results obtained for the off-diagonal matrix elements of
observables in the spin-1/2 XXZ model are well described by a generalized Gamma
distribution.


title: Ultra-sharp lateral $p\text{-}n$ junctions in modulation-doped graphene

abstract:
  We demonstrate ultra-sharp (${\lesssim}\,10\text{ nm}$) lateral $p\text{-}n$
junctions in graphene using a combination of electronic transport, scanning
tunneling microscopy, and first principles calculations. The $p\text{-}n$
junction lies at the boundary between two differentially-doped regions of a
monolayer graphene sheet, where one side is intrinsic and the other is
charge-doped by close proximity to a flake of $\alpha$-RuCl$_3$ across a thin
insulating barrier. We extract the $p\text{-}n$ junction contribution to the
device resistance, and use it to place bounds on the junction width. We achieve
an ultra-sharp junction when the boundary between the intrinsic and doped
regions is defined by a cleaved crystalline edge of a $\alpha$-RuCl$_3$ flake
located 2 nm from the graphene. Scanning tunneling spectroscopy in
heterostructures of graphene, hexagonal boron nitride, and $\alpha$-RuCl$_3$
shows potential variations on a sub-10 nm length scale. Density functional
theory calculations reveal the charge-doping of graphene decays sharply over
just nanometers away from the edge of the $\alpha$-RuCl$_3$ flake.


title: Beyond Conic Section Mainlobe Coverage for Unmanned Aerial Vehicle

abstract:
  The cellular-connected drone market is one of the most promising markets of
5G. However, it still accounts for a small share of overall telecommunication
market, and it is unlikely to increase significantly for the foreseeable
future. Deploying dedicated network with up-tilted antennas can be an option,
but the monetary cost of dedicated network directly impacts the acceptance of
mobile operators. Therefore, cost-efficient aerial coverage solutions must be
developed. Reusing network for terrestrial coverage is a cost-efficient
approach for aerial coverage, but several critical challenges caused by antenna
sidelobe should be solved. In this paper, a novel method for aerial coverage is
proposed. By tweaking the measurement report handling mechanism, signals from
sidelobes reported by drones above the predefined height can be identified and
ignored. Simulation results show that the conventional cellular network with
the proposed method can provide wide and continuous aerial coverage with
satisfactory quality.


title: Instance-Dependent Regret Analysis of Kernelized Bandits

abstract:
  We study the kernelized bandit problem, that involves designing an adaptive
strategy for querying a noisy zeroth-order-oracle to efficiently learn about
the optimizer of an unknown function $f$ with a norm bounded by $M<\infty$ in a
Reproducing Kernel Hilbert Space~(RKHS) associated with a positive definite
kernel $K$. Prior results, working in a \emph{minimax framework}, have
characterized the worst-case~(over all functions in the problem class) limits
on regret achievable by \emph{any} algorithm, and have constructed algorithms
with matching~(modulo polylogarithmic factors) worst-case performance for the
\matern family of kernels. These results suffer from two drawbacks. First, the
minimax lower bound gives no information about the limits of regret achievable
by the commonly used algorithms on specific problem instances. Second, due to
their worst-case nature, the existing upper bound analysis fails to adapt to
easier problem instances within the function class. Our work takes steps to
address both these issues. First, we derive \emph{instance-dependent} regret
lower bounds for algorithms with uniformly~(over the function class) vanishing
normalized cumulative regret. Our result, valid for all the practically
relevant kernelized bandits algorithms, such as, GP-UCB, GP-TS and
SupKernelUCB, identifies a fundamental complexity measure associated with every
problem instance. We then address the second issue, by proposing a new minimax
near-optimal algorithm which also adapts to easier problem instances.


abstract:
  The emerging neural topic models make topic modeling more easily adaptable
and extendable in unsupervised text mining. However, the existing neural topic
models is difficult to retain representative information of the documents
within the learnt topic representation. In this paper, we propose a neural
topic model which incorporates deep mutual information estimation, i.e., Neural
Topic Modeling with Deep Mutual Information Estimation(NTM-DMIE). NTM-DMIE is a
neural network method for topic learning which maximizes the mutual information
between the input documents and their latent topic representation. To learn
robust topic representation, we incorporate the discriminator to discriminate
negative examples and positive examples via adversarial learning. Moreover, we
use both global and local mutual information to preserve the rich information
of the input documents in the topic representation. We evaluate NTM-DMIE on
several metrics, including accuracy of text clustering, with topic
representation, topic uniqueness and topic coherence. Compared to the existing
methods, the experimental results show that NTM-DMIE can outperform in all the
metrics on the four datasets.


title:
Transverse Vector Decomposition Method for Analytical Inversion of
  Exoplanet Transit Spectra

abstract:
  We develop a new method for analytical inversion of binned exoplanet transit
spectra and for retrieval of planet parameters. The method has a geometrical
interpretation and treats each observed spectrum as a single vector $\vec r$ in
the multidimensional spectral space of observed bin values. We decompose the
observed $\vec{r}$ into a wavelength-independent component $\vec{r}_\parallel$
corresponding to the spectral mean across all observed bins, and a transverse
component $\vec{r}_\perp$ which is wavelength-dependent and contains the
relevant information about the atmospheric chemistry. The method allows us to
extract, without any prior assumptions or additional information, the relative
mass (or volume) mixing ratios of the absorbers in the atmosphere, the scale
height to stellar radius ratio, $H/R_S$, and the atmospheric temperature. The
method is illustrated and validated with several examples of increasing
complexity.


title:
Parameterization of hydrodynamic friction in a model for sheared
  suspensions of rough particles

abstract:
  We propose a method to parameterize a coarse grained model for the
hydrodynamic friction between nearly touching rough spheres in suspension
flows. The frictional resistance due to surface roughness primarily alters the
sliding and rolling modes of motion of rough particles. Stokesian dynamics
simulations incorporating a near-field pairwise resistance model accounting for
these enhanced frictional modes were employed to compute particle trajectories
in shear flow. In this model, the resistance to sliding and rolling modes of
motion are augmented from a weakly diverging log$(1/h)$ form for smooth spheres
to a strongly diverging $1/h$ form for rough spheres to account for the
additional resistance due to squeezing flows between surface asperities, where
$h$ is the mean surface separation between particles. We determine new bounds
on the relative magnitude of the augmentations to the resistance to different
modes of motion using inequality constraints reflecting the positive
definiteness of the Stokes resistance tensor for a pair of rough particles.
Using the simulations of a particle pair in a shear flow, a simple model for
angular rotation rate of the pair centerline is computed as a function of its
orientation in the shear flow and the free parameters of the hydrodynamic
resistance model: the friction coupling strength, $\alpha$, and friction
coupling range, $h_0$. Values of $\alpha$ and $h_0$ for real-world rough
particles can then be inferred by matching the pair rotation rate in the model
to experimental observations when a dilute rough particle suspension is
subjected to a linear shear flow. The same model is used to calculate the
hydrodynamic contribution to the high frequency viscosity of rough particle
suspensions. For different $\alpha$ and $h_0$, we observe that the viscosity
diverges differently depending on $h_0$.


title:
Thermal entanglement and quantum coherence of a single electron in a
  double quantum dot with Rashba Interaction

abstract:
  In this work, we study the thermal quantum coherence in a semiconductor
double quantum dot. The device consists of a single electron in a double
quantum dot with Rashba spin-orbit coupling in the presence of an external
magnetic field. In our scenario, the thermal entanglement of the single
electron is driven by the charge and spin qubits, the latter controlled by
Rashba coupling. Analytical expressions are obtained for thermal concurrence
and correlated coherence using the density matrix formalism. The main goal of
this work is to provide a good understanding of the effects of temperature and
several parameters in quantum coherence. In addition, our findings show that we
can use the Rashba coupling to tune in the thermal entanglement and quantum
coherence of the system. Moreover, we focus on the role played by thermal
entanglement and correlated coherence responsible for quantum correlations. We
observe that the correlated coherence is more robust than the thermal
entanglement in all cases, so quantum algorithms based only on correlated
coherence may be stronger than those based on entanglement.


title: Mass loss by atmospheric escape from extremely close-in planets

abstract:
  We explore atmospheric escape from close-in exoplanets with the highest mass
loss rates. First, we locate the transition from stellar X-ray and UV-driven
escape to rapid Roche lobe overflow, which occurs once the 10-100 nbar pressure
level in the atmosphere reaches the Roche lobe. Planets enter this regime when
the ratio of the substellar radius to the polar radius along the visible
surface pressure level, that aligns with a surface of constant Roche potential,
is X/Z~$\gtrsim$~1.2 for Jovian planets (Mp~$\gtrsim$~100 M$_{\Earth}$) and
X/Z~$\gtrsim$~1.02 for sub-Jovian planets ($M_p \approx$~10--100 M$_{\Earth}$).
Around a sun-like star, this regime applies to orbital periods of less than two
days for planets with radii of about 3--14 R$_{\Earth}$. Our results agree with
the properties of known transiting planets and can explain parts of the
sub-Jovian desert in the population of known exoplanets. Second, we present
detailed numerical simulations of atmospheric escape from a planet like Uranus
or Neptune orbiting close to a sun-like star that support the results above and
point to interesting qualitative differences between hot Jupiters and
sub-Jovian planets. We find that hot Neptunes with solar metallicity hydrogen
and helium envelopes have relatively more extended upper atmospheres than
typical hot Jupiters, with a lower ionization fraction and higher abundances of
escaping molecules. This is consistent with existing ultraviolet transit
observations of warm Neptunes and it might provide a way to use future
observations and models to distinguish solar metallicity atmospheres from
higher metallicity atmospheres.


title:
Effects of Gravitational Wave Radiation of Eccentric Neutron Star-White
  Dwarf Binaries on the Periodic Activity of Fast Radio Burst Sources

abstract:
  We revisit the eccentric neutron star (NS)-white dwarf (WD) binary model for
the periodic activity of fast radio burst (FRB) sources, by including the
effects of gravitational wave (GW) radiation. In this model, the WD fills its
Roche lobe at the periastron and mass transfer occurs from the WD to the NS.
The accreted materials can be fragmented and arrive at the NS episodically,
resulting in multiple bursts through curvature radiation. Consequently, the WD
may be kicked away owing to the conservation of angular momentum. To initiate
the next mass transfer, the WD has to refill its Roche lobe through GW
radiation. In this scenario, whether the periodic activity can show up relies
on three timescales, i.e., the orbital period $P_{\rm orb}$, the timescale
$T_{\rm GW}$ for the Roche lobe to be refilled, and the time span $T_{\rm
frag}$ for all the episodic events corresponding to each mass transfer process.
Only when the two conditions $T_{\rm GW} \gtrsim P_{\rm orb}$ and $T_{\rm frag}
< P_{\rm orb}$ are both satisfied, the periodic activity will manifest itself
and the period should be equal to $P_{\rm orb}$. In this spirit, the periodic
activity is more likely to show up for relatively long periods ($P_{\rm orb}
\gtrsim$ several days). Thus, it is reasonable that FRBs 180916 and 121102, the
only two sources having been claimed to manifest periodic activity, both
correspond to relatively long periods.


title:
MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image
  Inpainting

abstract:
  Although achieving significant progress, existing deep generative inpainting
methods are far from real-world applications due to the low generalization
across different scenes. As a result, the generated images usually contain
artifacts or the filled pixels differ greatly from the ground truth.
Image-level predictive filtering is a widely used image restoration technique,
predicting suitable kernels adaptively according to different input scenes.
Inspired by this inherent advantage, we explore the possibility of addressing
image inpainting as a filtering task. To this end, we first study the
advantages and challenges of image-level predictive filtering for image
inpainting: the method can preserve local structures and avoid artifacts but
fails to fill large missing areas. Then, we propose semantic filtering by
conducting filtering on the deep feature level, which fills the missing
semantic information but fails to recover the details. To address the issues
while adopting the respective advantages, we propose a novel filtering
technique, i.e., Multilevel Interactive Siamese Filtering (MISF), which
contains two branches: kernel prediction branch (KPB) and semantic & image
filtering branch (SIFB). These two branches are interactively linked: SIFB
provides multi-level features for KPB while KPB predicts dynamic kernels for
SIFB. As a result, the final method takes the advantage of effective semantic &
image-level filling for high-fidelity inpainting. We validate our method on
three challenging datasets, i.e., Dunhuang, Places2, and CelebA. Our method
outperforms state-of-the-art baselines on four metrics, i.e., L1, PSNR, SSIM,
and LPIPS. Please try the released code and model at
https://github.com/tsingqguo/misf.


title: Min-max free boundary minimal surface with genus at least one

abstract:
  In this paper, we build up a min-max theory for minimal surfaces using
sweepouts of surfaces of genus $g\geq 1$ and $m\geq 1$ ideal boundary
components. We show that the width for the area functional can be achieved by a
bubble tree limit consisting of branched genus $g$ free boundary minimal
surfaces with nodes, and possibly finitely many branched minimal spheres and
free boundary minimal disks.


title: DURRNet: Deep Unfolded Single Image Reflection Removal Network

abstract:
  Single image reflection removal problem aims to divide a
reflection-contaminated image into a transmission image and a reflection image.
It is a canonical blind source separation problem and is highly ill-posed. In
this paper, we present a novel deep architecture called deep unfolded single
image reflection removal network (DURRNet) which makes an attempt to combine
the best features from model-based and learning-based paradigms and therefore
leads to a more interpretable deep architecture. Specifically, we first propose
a model-based optimization with transform-based exclusion prior and then design
an iterative algorithm with simple closed-form solutions for solving each
sub-problems. With the deep unrolling technique, we build the DURRNet with
ProxNets to model natural image priors and ProxInvNets which are constructed
with invertible networks to impose the exclusion prior. Comprehensive
experimental results on commonly used datasets demonstrate that the proposed
DURRNet achieves state-of-the-art results both visually and quantitatively.


title: Mean field information Hessian matrices on graphs

abstract:
  We derive mean-field information Hessian matrices on finite graphs. The
"information" refers to entropy functions on the probability simplex. And the
"mean-field" means nonlinear weight functions of probabilities supported on
graphs. These two concepts define a mean-field optimal transport type metric.
In this metric space, we first derive Hessian matrices of energies on graphs,
including linear, interaction energies, entropies. We name their smallest
eigenvalues as mean-field Ricci curvature bounds on graphs. We next provide
examples on two-point spaces and graph products. We last present several
applications of the proposed matrices. E.g., we prove discrete Costa's entropy
power inequalities on a two-point space.


title: Ensemble Semi-supervised Entity Alignment via Cycle-teaching

abstract:
  Entity alignment is to find identical entities in different knowledge graphs.
Although embedding-based entity alignment has recently achieved remarkable
progress, training data insufficiency remains a critical challenge.
Conventional semi-supervised methods also suffer from the incorrect entity
alignment in newly proposed training data. To resolve these issues, we design
an iterative cycle-teaching framework for semi-supervised entity alignment. The
key idea is to train multiple entity alignment models (called aligners)
simultaneously and let each aligner iteratively teach its successor the
proposed new entity alignment. We propose a diversity-aware alignment selection
method to choose reliable entity alignment for each aligner. We also design a
conflict resolution mechanism to resolve the alignment conflict when combining
the new alignment of an aligner and that from its teacher. Besides, considering
the influence of cycle-teaching order, we elaborately design a strategy to
arrange the optimal order that can maximize the overall performance of multiple
aligners. The cycle-teaching process can break the limitations of each model's
learning capability and reduce the noise in new training data, leading to
improved performance. Extensive experiments on benchmark datasets demonstrate
the effectiveness of the proposed cycle-teaching framework, which significantly
outperforms the state-of-the-art models when the training data is insufficient
and the new entity alignment has much noise.


title:
Snowmass 2021 Instrumentation Frontier (IF5 - MPGDs) -- White Paper 2:
  Micro Pattern Gaseous Detectors for Nuclear Physics

abstract:
  Many current and future nuclear physics (NP) experiments across the United
States have and are implementing micro-pattern gas detectors (MPGDs) to be used
for tracking and PID purposes. MPGDs are capable of operating in high rate
environments and providing excellent spatial resolution over a large-area with
a low material budget. Summarized in this white paper is the role that MPGDs
are playing in NP experiments and the R&D which is needed to meet the
requirements of future NP experiments.


title: Island and Page curve for one-sided asymptotically flat black hole

abstract:
  Great breakthrough in solving black hole information paradox took place when
semiclassical island rule for entanglement entropy of Hawking radiation was
proposed in recent years. Up to now, most papers which discussed island rule of
asymptotic flat black hole with $D \ge 4$ focus on eternal black hole. In this
paper, we take one more step further by discussing island of "in" vacuum state
which describes one-sided asymptotically flat black hole formed by
gravitational collapse in $D \ge 4$. We find that island $I$ emerges at late
time and saves entropy bound. And boundary of island $\partial I$ depends on
the position of cutoff surface. When cutoff surface is far from horizon,
$\partial I$ is inside and near horizon. When cutoff surface is set to be near
horizon, $\partial I$ is outside and near horizon. This is different from the
case of eternal black hole in which $\partial I$ is always outside horizon no
matter cutoff surface is far from or near horizon. We will see that different
states will manifestly affect $S_{\text{ent}}$ in island formula when cutoff
surface is far from horizon and thus have different result for Page time.


title: ELLE: Efficient Lifelong Pre-training for Emerging Data

abstract:
  Current pre-trained language models (PLM) are typically trained with static
data, ignoring that in real-world scenarios, streaming data of various sources
may continuously grow. This requires PLMs to integrate the information from all
the sources in a lifelong manner. Although this goal could be achieved by
exhaustive pre-training on all the existing data, such a process is known to be
computationally expensive. To this end, we propose ELLE, aiming at efficient
lifelong pre-training for emerging data. Specifically, ELLE consists of (1)
function preserved model expansion, which flexibly expands an existing PLM's
width and depth to improve the efficiency of knowledge acquisition; and (2)
pre-trained domain prompts, which disentangle the versatile knowledge learned
during pre-training and stimulate the proper knowledge for downstream tasks. We
experiment ELLE with streaming data from 5 domains on BERT and GPT. The results
show the superiority of ELLE over various lifelong learning baselines in both
pre-training efficiency and downstream performances. The codes are publicly
available at https://github.com/thunlp/ELLE.


title: Stability for nonlinear wave motions damped by time-dependent frictions

abstract:
  We are concerned with the dynamical behavior of solutions to semilinear wave
systems with time-varying damping and nonconvex force potential. Our result
shows that the dynamical behavior of solution is asymptotically stable without
any bifurcation and chaos. And it is a sharp condition on the damping
coefficient for the solution to converge to some equilibrium. To illustrate our
theoretical results, we provide some numerical simulations for dissipative
sine-Gordon equation and dissipative Klein-Gordon equation.


title:
Performance Analysis of Intelligent Reflecting Surface Assisted
  Opportunistic Communications

abstract:
  Intelligent reflecting surfaces (IRSs) are a promising technology for
enhancing coverage and spectral efficiency, especially in the millimeter wave
(mmWave) bands. Existing approaches to leverage the benefits of IRS involve the
use of a resource-intensive channel estimation step followed by a
computationally expensive algorithm to optimize the reflection coefficients at
the IRS. In this work, we present and analyze several alternative schemes,
where the phase configuration of the IRS is randomized and multi-user diversity
is exploited to opportunistically select the best user at each point in time
for data transmission. We show that the throughput of an IRS assisted
opportunistic communication (OC) system asymptotically converges to the optimal
beamforming-based throughput under fair allocation of resources, as the number
of users gets large. We also introduce schemes that enhance the rate of
convergence of the OC rate to the beamforming rate with the number of users.
For all the proposed schemes, we derive the scaling law of the throughput in
terms of the system parameters, as the number of users gets large. Following
this, we extend the setup to wideband channels via an orthogonal frequency
division multiplexing (OFDM) system and discuss two OC schemes in an IRS
assisted setting that clearly elucidate the superior performance that IRS aided
OC systems can offer over conventional systems, at very low implementation cost
and complexity.


title:
Tensor Radiomics: Paradigm for Systematic Incorporation of
  Multi-Flavoured Radiomics Features

abstract:
  Radiomics features extract quantitative information from medical images,
towards the derivation of biomarkers for clinical tasks, such as diagnosis,
prognosis, or treatment response assessment. Different image discretization
parameters (e.g. bin number or size), convolutional filters, segmentation
perturbation, or multi-modality fusion levels can be used to generate radiomics
features and ultimately signatures. Commonly, only one set of parameters is
used; resulting in only one value or flavour for a given RF. We propose tensor
radiomics (TR) where tensors of features calculated with multiple combinations
of parameters (i.e. flavours) are utilized to optimize the construction of
radiomics signatures. We present examples of TR as applied to PET/CT, MRI, and
CT imaging invoking machine learning or deep learning solutions, and
reproducibility analyses: (1) TR via varying bin sizes on CT images of lung
cancer and PET-CT images of head & neck cancer (HNC) for overall survival
prediction. A hybrid deep neural network, referred to as TR-Net, along with two
ML-based flavour fusion methods showed improved accuracy compared to regular
rediomics features. (2) TR built from different segmentation perturbations and
different bin sizes for classification of late-stage lung cancer response to
first-line immunotherapy using CT images. TR improved predicted patient
responses. (3) TR via multi-flavour generated radiomics features in MR imaging
showed improved reproducibility when compared to many single-flavour features.
(4) TR via multiple PET/CT fusions in HNC. Flavours were built from different
fusions using methods, such as Laplacian pyramids and wavelet transforms. TR
improved overall survival prediction. Our results suggest that the proposed TR
paradigm has the potential to improve performance capabilities in different
medical imaging tasks.


title:
Geometry of infinite dimensional unitary groups: convexity and fixed
  points

abstract:
  In this article we study convexity properties of distance functions in
infinite dimensional Finsler unitary groups, such as the full unitary group,
the unitary Schatten perturbations of the identity and unitary groups of finite
von Neumann algebras. The Finsler structures are defined by translation of
different norms on the tangent space at the identity. We first prove a
convexity result for the metric derived from the operator norm on the full
unitary group. We also prove strong convexity results for the squared metrics
in Hilbert-Schmidt unitary groups and unitary groups of finite von Neumann
algebras, in both cases the tangent spaces are endowed with an inner product
defined with a trace. These results are applied to fixed point properties and
to quantitative metric bounds in certain rigidity problems. Radius bounds for
all convexity and fixed point results are shown to be optimal.


title:
FIG-OP: Exploring Large-Scale Unknown Environments on a Fixed Time
  Budget

abstract:
  We present a method for autonomous exploration of large-scale unknown
environments under mission time constraints. We start by proposing the
Frontloaded Information Gain Orienteering Problem (FIG-OP) -- a generalization
of the traditional orienteering problem where the assumption of a reliable
environmental model no longer holds. The FIG-OP addresses model uncertainty by
frontloading expected information gain through the addition of a greedy
incentive, effectively expediting the moment in which new area is uncovered. In
order to reason across multi-kilometre environments, we solve FIG-OP over an
information-efficient world representation, constructed through the aggregation
of information from a topological and metric map. Our method was extensively
tested and field-hardened across various complex environments, ranging from
subway systems to mines. In comparative simulations, we observe that the FIG-OP
solution exhibits improved coverage efficiency over solutions generated by
greedy and traditional orienteering-based approaches (i.e. severe and minimal
model uncertainty assumptions, respectively).


title: Towards Equal Opportunity Fairness through Adversarial Learning

abstract:
  Adversarial training is a common approach for bias mitigation in natural
language processing. Although most work on debiasing is motivated by equal
opportunity, it is not explicitly captured in standard adversarial training. In
this paper, we propose an augmented discriminator for adversarial training,
which takes the target class as input to create richer features and more
explicitly model equal opportunity. Experimental results over two datasets show
that our method substantially improves over standard adversarial debiasing
methods, in terms of the performance--fairness trade-off.


title:
Deformable VisTR: Spatio temporal deformable attention for video
  instance segmentation

abstract:
  Video instance segmentation (VIS) task requires classifying, segmenting, and
tracking object instances over all frames in a video clip. Recently, VisTR has
been proposed as end-to-end transformer-based VIS framework, while
demonstrating state-of-the-art performance. However, VisTR is slow to converge
during training, requiring around 1000 GPU hours due to the high computational
cost of its transformer attention module. To improve the training efficiency,
we propose Deformable VisTR, leveraging spatio-temporal deformable attention
module that only attends to a small fixed set of key spatio-temporal sampling
points around a reference point. This enables Deformable VisTR to achieve
linear computation in the size of spatio-temporal feature maps. Moreover, it
can achieve on par performance as the original VisTR with 10$\times$ less GPU
training hours. We validate the effectiveness of our method on the Youtube-VIS
benchmark. Code is available at https://github.com/skrya/DefVIS.