title:
Small counts in nested Karlin's occupancy scheme generated by discrete
  Weibull-like distributions

abstract:
  A nested Karlin's occupancy scheme is a symbiosis of classical Karlin's
balls-in-boxes scheme and a weighted branching process. To define it, imagine a
deterministic weighted branching process in which weights of the first
generation individuals are given by the elements of a discrete probability
distribution. For each positive integer $j$, identify the $j$th generation
individuals with the $j$th generation boxes. The collection of balls is one and
the same for all generations, and each ball starts at the root of the weighted
branching process tree and moves along the tree according to the following
rule: transition from a mother box to a daughter box occurs with probability
given by the ratio of the daughter and mother weights.
  Assume that there are $n$ balls and that the discrete probability
distribution responsible for the first generation is Weibull-like. Denote by
$\mathcal{K}_n^{(j)}(l)$ and $\mathcal{K}_n^{*(j)}(l)$ the number of the $j$th
generation boxes which contain at least $l$ balls and exactly $l$ balls,
respectively. We prove functional limit theorems (FLTs) for the matrix-valued
processes $\big(\mathcal{K}_{[{\rm
e}^{T+\cdot}]}^{(j)}(l)\big)_{j,l\in\mathbb{N}}$ and $\big(\mathcal{ K}_{[{\rm
e}^{T+\cdot}]}^{*(j)}(l)\big)_{j,l\in\mathbb{N}}$, properly normalized and
centered, as $T\to \infty$. The present FLTs are an extension of a FLT proved
by Iksanov, Kabluchko and Kotelnikova (2022) for the vector-valued process
$\big(\mathcal{K}_{[{\rm e}^{T+\cdot}]}^{(j)}(1)\big)_{j\in\mathbb{N}}$. While
the rows of each of the limit matrix-valued processes are independent and
identically distributed, the entries within each row are stationary Gaussian
processes with explicitly given covariances and cross-covariances. We provide
an integral representation for each row. The results obtained are new even for
Karlin's occupancy scheme.


title:
Strategies in Education, Outreach, and Inclusion to Enhance the US
  Workforce in Accelerator Science and Engineering

abstract:
  We summarize the community-based consensus for improvements concerning
education, public outreach, and inclusion in Accelerator Science and
Engineering that will enhance the workforce in the USA. The improvements
identified reflect the product of discussions held within the 2021-2022
Snowmass community planning process by topical group AF1: Beam Physics and
Accelerator Education within the Accelerator Frontier. Although the Snowmass
process centers on high-energy physics, this document outlines required
improvements for the entire U.S. accelerator science and engineering enterprise
because education of those entering and in the field, outreach to the public,
and inclusion are inextricably linked.


title:
Further Evidence of Modified Spin-down in Sun-like Stars: Pile-ups in
  the Temperature-Period Distribution

abstract:
  We combine stellar surface rotation periods determined from NASA's Kepler
mission with spectroscopic temperatures to demonstrate the existence of
pile-ups at the long-period and short-period edges of the temperature-period
distribution for main-sequence stars with temperatures exceeding $\sim 5500~K$.
The long-period pile-up is well-described by a curve of constant Rossby number,
with a critical value of $\mathrm{Ro_{crit}}~\lesssim~2$. The long-period
pile-up was predicted by van Saders et al. (2019) as a consequence of weakened
magnetic braking, in which wind-driven angular momentum losses cease once stars
reach a critical Rossby number. Stars in the long-period pile-up are found to
have a wide range of ages ($\sim 2-6~$Gyr), meaning that, along the pile-up,
rotation period is strongly predictive of a star's surface temperature but
weakly predictive of its age. The short-period pile-up, which is also
well-described by a curve of constant Rossby number, is not a prediction of the
weakened magnetic braking hypothesis but may instead be related to a phase of
slowed surface spin-down due to core-envelope coupling. The same mechanism was
proposed by Curtis et al. (2020) to explain the overlapping rotation sequences
of low-mass members of differently aged open clusters. The relative dearth of
stars with intermediate rotation periods between the short- and long-period
pile-ups is also well-described by a curve of constant Rossby number, which
aligns with the period gap initially discovered by McQuillan et al. (2013a) in
M-type stars. These observations provide further support for the hypothesis
that the period gap is due to stellar astrophysics, rather than a non-uniform
star-formation history in the Kepler field.


title: Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution

abstract:
  Convolutional neural network (CNN) has achieved great success on image
super-resolution (SR). However, most deep CNN-based SR models take massive
computations to obtain high performance. Downsampling features for
multi-resolution fusion is an efficient and effective way to improve the
performance of visual recognition. Still, it is counter-intuitive in the SR
task, which needs to project a low-resolution input to high-resolution. In this
paper, we propose a novel Hybrid Pixel-Unshuffled Network (HPUN) by introducing
an efficient and effective downsampling module into the SR task. The network
contains pixel-unshuffled downsampling and Self-Residual Depthwise Separable
Convolutions. Specifically, we utilize pixel-unshuffle operation to downsample
the input features and use grouped convolution to reduce the channels. Besides,
we enhance the depthwise convolution's performance by adding the input feature
to its output. Experiments on benchmark datasets show that our HPUN achieves
and surpasses the state-of-the-art reconstruction performance with fewer
parameters and computation costs.


title: Chaos and thermalization in a trapped boson system I: thermal states

abstract:
  A detailed study of chaos and thermalization is performed for a system of
bosonic atoms trapped in a linear chain with disorder. For large values of the
disorder parameter, the system is regular and localized. For weak disorder, the
presence of chaos and the validity of the Eigenstate Thermalization Hypothesis
are exhibited for most eigenstates, while those in the extrema of the energy
spectrum are regular and do not thermalize. We study the temporal evolution of
the experimentally accessible states of the occupation basis. The whole set of
states of this basis are classified according to their maximum occupation and a
crowding parameter, which has a linear relation with their average energy.
Those states whose energies lie in the chaotic region have larger participation
ratios in the eigenbasis, while those in the regular region have participation
ratios noticeably smaller. The states with the largest participation ratio in
the chaotic region exhibit the characteristic random matrix correlation hole in
their survival probability, while those in the regular region do not, and have
revivals for a long time.


title:
Towards True Detail Restoration for Super-Resolution: A Benchmark and a
  Quality Metric

abstract:
  Super-resolution (SR) has become a widely researched topic in recent years.
SR methods can improve overall image and video quality and create new
possibilities for further content analysis. But the SR mainstream focuses
primarily on increasing the naturalness of the resulting image despite
potentially losing context accuracy. Such methods may produce an incorrect
digit, character, face, or other structural object even though they otherwise
yield good visual quality. Incorrect detail restoration can cause errors when
detecting and identifying objects both manually and automatically. To analyze
the detail-restoration capabilities of image and video SR models, we developed
a benchmark based on our own video dataset, which contains complex patterns
that SR models generally fail to correctly restore. We assessed 32 recent SR
models using our benchmark and compared their ability to preserve scene
context. We also conducted a crowd-sourced comparison of restored details and
developed an objective assessment metric that outperforms other quality metrics
by correlation with subjective scores for this task. In conclusion, we provide
a deep analysis of benchmark results that yields insights for future SR-based
work.


title:
Traffic-Aware UAV Placement Using a Generalizable Deep Reinforcement
  Learning Methodology

abstract:
  Unmanned Aerial Vehicles (UAVs) acting as Flying Access Points (FAPs) are
being used to provide on-demand wireless connectivity in extreme scenarios.
Despite ongoing research, the optimization of UAVs' positions according to
dynamic users' traffic demands remains challenging. We propose the
Traffic-aware UAV Placement Algorithm (TUPA), which positions a UAV acting as
FAP according to the users' traffic demands, in order to maximize the network
utility. Using a DRL approach enables the FAP to autonomously learn and adapt
to dynamic conditions and requirements of networking scenarios. Moreover, the
proposed DRL methodology allows TUPA to generalize knowledge acquired during
training to unknown combinations of users' positions and traffic demands, with
no additional training. TUPA is trained and evaluated using network simulator
ns-3 and ns3-gym framework. The results demonstrate that TUPA increases the
network utility, compared to baseline solutions, increasing the average network
utility up to 4x in scenarios with heterogeneous traffic demands.


title:
Any Way You Look At It: Semantic Crossview Localization and Mapping with
  LiDAR

abstract:
  Currently, GPS is by far the most popular global localization method.
However, it is not always reliable or accurate in all environments. SLAM
methods enable local state estimation but provide no means of registering the
local map to a global one, which can be important for inter-robot collaboration
or human interaction. In this work, we present a real-time method for utilizing
semantics to globally localize a robot using only egocentric 3D semantically
labelled LiDAR and IMU as well as top-down RGB images obtained from satellites
or aerial robots. Additionally, as it runs, our method builds a globally
registered, semantic map of the environment. We validate our method on KITTI as
well as our own challenging datasets, and show better than 10 meter accuracy, a
high degree of robustness, and the ability to estimate the scale of a top-down
map on the fly if it is initially unknown.


title:
Synthetic Question Value Estimation for Domain Adaptation of Question
  Answering

abstract:
  Synthesizing QA pairs with a question generator (QG) on the target domain has
become a popular approach for domain adaptation of question answering (QA)
models. Since synthetic questions are often noisy in practice, existing work
adapts scores from a pretrained QA (or QG) model as criteria to select
high-quality questions. However, these scores do not directly serve the
ultimate goal of improving QA performance on the target domain. In this paper,
we introduce a novel idea of training a question value estimator (QVE) that
directly estimates the usefulness of synthetic questions for improving the
target-domain QA performance. By conducting comprehensive experiments, we show
that the synthetic questions selected by QVE can help achieve better
target-domain QA performance, in comparison with existing techniques. We
additionally show that by using such questions and only around 15% of the human
annotations on the target domain, we can achieve comparable performance to the
fully-supervised baselines.


title: Correlation functions and pressure of non-isothermal plasma

abstract:
  Expressions for correlation functions of classical non-isothermal
two-component plasma are derived. In the limiting case of $\Theta_e\gg\Theta_i$
strong correlations arise due to the existence of weakly damping waves (ionic
sound), whose phase velocity is between the thermal velocities of particles,
$u_e\gg\omega/k\gg u_i$. Owing to these correlations, the additional term in
the expression for pressure due to the Coulomb interaction changes its sign as
compared to the case of thermodynamic equilibrium where such waves do not
exist.


title:
C-MORE: Pretraining to Answer Open-Domain Questions by Consulting
  Millions of References

abstract:
  We consider the problem of pretraining a two-stage open-domain question
answering (QA) system (retriever + reader) with strong transfer capabilities.
The key challenge is how to construct a large amount of high-quality
question-answer-context triplets without task-specific annotations.
Specifically, the triplets should align well with downstream tasks by: (i)
covering a wide range of domains (for open-domain applications), (ii) linking a
question to its semantically relevant context with supporting evidence (for
training the retriever), and (iii) identifying the correct answer in the
context (for training the reader). Previous pretraining approaches generally
fall short of one or more of these requirements. In this work, we automatically
construct a large-scale corpus that meets all three criteria by consulting
millions of references cited within Wikipedia. The well-aligned pretraining
signals benefit both the retriever and the reader significantly. Our pretrained
retriever leads to 2%-10% absolute gains in top-20 accuracy. And with our
pretrained reader, the entire system improves by up to 4% in exact match.


title: An Analysis of Negation in Natural Language Understanding Corpora

abstract:
  This paper analyzes negation in eight popular corpora spanning six natural
language understanding tasks. We show that these corpora have few negations
compared to general-purpose English, and that the few negations in them are
often unimportant. Indeed, one can often ignore negations and still make the
right predictions. Additionally, experimental results show that
state-of-the-art transformers trained with these corpora obtain substantially
worse results with instances that contain negation, especially if the negations
are important. We conclude that new corpora accounting for negation are needed
to solve natural language understanding tasks when negation is present.


title: Some sufficient conditions for transitivity of Anosov diffeomorphisms

abstract:
  Given a $C^2$- Anosov diffemorphism $f: M \rightarrow M,$ we prove that the
jacobian condition $Jf^n(p) = 1,$ for every point $p$ such that $f^n(p) = p,$
implies transitivity. As application in the celebrated theory of
Sinai-Ruelle-Bowen, this result allows us to state a classical theorem of
Livsic-Sinai without directly assuming transitivity as a general hypothesis. A
special consequence of our result is that every $C^2$-Anosov diffeomorphism,
for which every point is regular, is indeed transitive.


title: Creating Multimedia Summaries Using Tweets and Videos

abstract:
  While popular televised events such as presidential debates or TV shows are
airing, people provide commentary on them in real-time. In this paper, we
propose a simple yet effective approach to combine social media commentary and
videos to create a multimedia summary of televised events. Our approach
identifies scenes from these events based on spikes of mentions of people
involved in the event and automatically selects tweets and frames from the
videos that occur during the time period of the spike that talk about and show
the people being discussed.


title: $\ell_p$ Slack Norm Support Vector Data Description

abstract:
  The support vector data description (SVDD) approach serves as a de facto
standard for one-class classification where the learning task entails inferring
the smallest hyper-sphere to enclose target objects while linearly penalising
any errors/slacks via an $\ell_1$-norm penalty term. In this study, we
generalise this modelling formalism to a general $\ell_p$-norm ($p\geq1$) slack
penalty function. By virtue of an $\ell_p$ slack norm, the proposed approach
enables formulating a non-linear cost function with respect to slacks. From a
dual problem perspective, the proposed method introduces a sparsity-inducing
dual norm into the objective function, and thus, possesses a higher capacity to
tune into the inherent sparsity of the problem for enhanced descriptive
capability. A theoretical analysis based on Rademacher complexities
characterises the generalisation performance of the proposed approach in terms
of parameter $p$ while the experimental results on several datasets confirm the
merits of the proposed method compared to other alternatives.


title:
The Digital Divide in Canada and the Role of LEO Satellites in Bridging
  the Gap

abstract:
  Overcoming the digital divide in rural and remote areas has always been a big
challenge for Canada with its huge geographical area. In 2016, the Canadian
Radio-television and Telecommunications Commission announced broadband Internet
as a basic service available for all Canadians. However, approximately one
million Canadians still do not have access to broadband services as of 2020.
The COVID-19 pandemic has made the situation more challenging, as social,
economic, and educational activities have increasingly been transferred online.
The condition is more unfavorable for Indigenous communities. A key challenge
in deploying rural and remote broadband Internet is to plan and implement
high-capacity backbones, which are now available only in denser urban areas.
For any Internet provider, it is almost impossible to make a viable business
proposal in these areas. For example, the vast land of the Northwest
Territories, Yukon, and Nunavuts diverse geographical features present
obstacles for broadband infrastructure. In this paper, we investigate the
digital divide in Canada with a focus on rural and remote areas. In so doing,
we highlight two potential solutions using low Earth orbit (LEO) constellations
to deliver broadband Internet in rural and remote areas to address the access
inequality and the digital divide. The first solution involves integrating LEO
constellations as a backbone for the existing 4G/5G telecommunications network.
This solution uses satellites in a LEO constellation to provide a backhaul
network connecting the 4G/5G access network to its core network. The 3rd
Generation Partnership Project already specifies how to integrate LEO satellite
networks into the 4G/5G network, and the Canadian satellite operator Telesat
has already showcased this solution with one terrestrial operator, TIM Brasil,
in their 4G network.


title: A relation between entropy and transitivity of Anosov diffeomorphisms

abstract:
  It is known that transitive Anosov diffeomorphisms have a unique measure of
maximal entropy (MME). Here we discuss the converse question. Under suitable
hypothesis on Lyapunov exponents on the set of periodic points and the
structure of the MME we get transitivity of $C^1-$Anosov diffeomorphisms.


title: Understanding Privacy Switching Behaviour on Twitter

abstract:
  Changing a Twitter account's privacy setting between public and protected
changes the visibility of past tweets. By inspecting the privacy setting of
over 100K Twitter users over 3 months, we noticed that over 40% of those users
change their privacy setting at least once with around 16% changing it over 5
times. This motivated us to explore the reasons why people switch their privacy
setting. We studied these switching phenomena quantitatively by comparing the
tweeting behaviour of users when public vs protected, and qualitatively using
two follow-up surveys (n=100, n=324) to understand potential reasoning behind
the observed behaviours. Our quantitative analysis shows that users who switch
privacy settings mention others and share hashtags more when their setting is
public. Our surveys highlighted that users turn protected to share personal
content and regulate boundaries while they turn public to interact with others
in ways prevented by being protected.


title: Painting the Landscape of Automotive Software in GitHub

abstract:
  The automotive industry has transitioned from being an electro-mechanical to
a software-intensive industry. A current high-end production vehicle contains
100 million+ lines of code surpassing modern airplanes, the Large Hadron
Collider, the Android OS, and Facebook's front-end software, in code size by a
huge margin. Today, software companies worldwide, including Apple, Google,
Huawei, Baidu, and Sony are reportedly working to bring their vehicles to the
road. This paper ventures into the automotive software landscape in open
source, providing the first glimpse into this multi-disciplinary industry with
a long history of closed source development. We paint the landscape of
automotive software on GitHub by describing its characteristics and development
styles.
  The landscape is defined by 15,000+ users contributing to ~600
actively-developed automotive software projects created in a span of 12 years
from 2010 until 2021. These projects range from vehicle dynamics-related
software; firmware and drivers for sensors like LiDAR and camera; algorithms
for perception and motion control; to complete operating systems integrating
the above. Developments in the field are spearheaded by industry and academia
alike, with one in three actively developed automotive software repositories
owned by an organization. We observe shifts along multiple dimensions,
including preferred language from MATLAB to Python and prevalence of perception
and decision-related software over traditional automotive software. This study
witnesses the open-source automotive software boom in its infancy with many
implications for future research and practice.


title:
Backpropagation through Time and Space: Learning Numerical Methods with
  Multi-Agent Reinforcement Learning

abstract:
  We introduce Backpropagation Through Time and Space (BPTTS), a method for
training a recurrent spatio-temporal neural network, that is used in a
homogeneous multi-agent reinforcement learning (MARL) setting to learn
numerical methods for hyperbolic conservation laws. We treat the numerical
schemes underlying partial differential equations (PDEs) as a Partially
Observable Markov Game (POMG) in Reinforcement Learning (RL). Similar to
numerical solvers, our agent acts at each discrete location of a computational
space for efficient and generalizable learning. To learn higher-order spatial
methods by acting on local states, the agent must discern how its actions at a
given spatiotemporal location affect the future evolution of the state. The
manifestation of this non-stationarity is addressed by BPTTS, which allows for
the flow of gradients across both space and time. The learned numerical
policies are comparable to the SOTA numerics in two settings, the Burgers'
Equation and the Euler Equations, and generalize well to other simulation
set-ups.


title:
Relative oscillation theory and essential spectra of Sturm--Liouville
  operators

abstract:
  We develop relative oscillation theory for general Sturm-Liouville
differential expressions of the form \[
  \frac{1}{r}\left(-\frac{\mathrm d}{\mathrm dx} p \frac{\mathrm d}{\mathrm dx}
+ q\right) \] and prove perturbation results and invariance of essential
spectra in terms of the real coefficients $p$, $q$, $r$. The novelty here is
that we also allow perturbations of the weight function $r$ in which case the
unperturbed and the perturbed operator act in different Hilbert spaces.


title:
Improved Analysis of Current-Steering DACs Using Equivalent Timing
  Errors

abstract:
  Current-steering (CS) digital-to-analog converters (DACs) generate analog
signals by combining weighted current sources. Ideally, the current sources are
combined at each switching instant simultaneously. However, this is not true in
practice due to timing mismatch, resulting in nonlinear distortion. This work
uses the equivalent timing error model, introduced by previous work, to analyze
the signal-to-distortion ratio (SDR) resulting from these timing errors. Using
a behavioral simulation model we demonstrate that our analysis is significantly
more accurate than the previous methods. We also use our simulation model to
investigate the effect of timing mismatch in partially-segmented CS-DACs, i.e.,
those comprised of both equally-weighted and binary-weighted current sources.


title: Overdetermined problems and relative Cheeger sets in unbounded domains

abstract:
  In this paper we study a partially overdetermined mixed boundary value
problem for domains $\Omega$ contained in an unbounded set $\mathcal C$. We
introduce the notion of Cheeger set relative to $\mathcal C$ and show that if a
domain $\Omega \subset \mathcal C$ admits a solution of the overdetermined
problem, then it coincides with its relative Cheeger set. We also study the
related problem of characterizing constant mean curvature surfaces $\Gamma$
inside $\mathcal C$. In the case when $\mathcal C$ is a cylinder we obtain
further results whenever the relative boundary of $\Omega$ or the surface
$\Gamma$ is a graph on the base of the cylinder.


title: Translating Canonical SQL to Imperative Code in Coq

abstract:
  SQL is by far the most widely used and implemented query language. Yet, on
some key features, such as correlated queries and NULL value semantics, many
implementations diverge or contain bugs. We leverage recent advances in the
formalization of SQL and query compilers to develop DBCert, the first
mechanically verified compiler from SQL queries written in a canonical form to
imperative code. Building DBCert required several new contributions which are
described in this paper. First, we specify and mechanize a complete translation
from SQL to the Nested Relational Algebra which can be used for query
optimization. Second, we define Imp, a small imperative language sufficient to
express SQL and which can target several execution languages including
JavaScript. Finally, we develop a mechanized translation from the nested
relational algebra to Imp, using the nested relational calculus as an
intermediate step.


title:
ABN: Agent-Aware Boundary Networks for Temporal Action Proposal
  Generation

abstract:
  Temporal action proposal generation (TAPG) aims to estimate temporal
intervals of actions in untrimmed videos, which is a challenging yet plays an
important role in many tasks of video analysis and understanding. Despite the
great achievement in TAPG, most existing works ignore the human perception of
interaction between agents and the surrounding environment by applying a deep
learning model as a black-box to the untrimmed videos to extract video visual
representation. Therefore, it is beneficial and potentially improve the
performance of TAPG if we can capture these interactions between agents and the
environment. In this paper, we propose a novel framework named Agent-Aware
Boundary Network (ABN), which consists of two sub-networks (i) an Agent-Aware
Representation Network to obtain both agent-agent and agents-environment
relationships in the video representation, and (ii) a Boundary Generation
Network to estimate the confidence score of temporal intervals. In the
Agent-Aware Representation Network, the interactions between agents are
expressed through local pathway, which operates at a local level to focus on
the motions of agents whereas the overall perception of the surroundings are
expressed through global pathway, which operates at a global level to perceive
the effects of agents-environment. Comprehensive evaluations on 20-action
THUMOS-14 and 200-action ActivityNet-1.3 datasets with different backbone
networks (i.e C3D, SlowFast and Two-Stream) show that our proposed ABN robustly
outperforms state-of-the-art methods regardless of the employed backbone
network on TAPG. We further examine the proposal quality by leveraging
proposals generated by our method onto temporal action detection (TAD)
frameworks and evaluate their detection performances. The source code can be
found in this URL https://github.com/vhvkhoa/TAPG-AgentEnvNetwork.git.


title: CachePerf: A Unified Cache Miss Classifier via Hybrid Hardware Sampling

abstract:
  The cache plays a key role in determining the performance of applications, no
matter for sequential or concurrent programs on homogeneous and heterogeneous
architecture. Fixing cache misses requires to understand the origin and the
type of cache misses. However, this remains to be an unresolved issue even
after decades of research. This paper proposes a unified profiling
tool--CachePerf--that could correctly identify different types of cache misses,
differentiate allocator-induced issues from those of applications, and exclude
minor issues without much performance impact. The core idea behind CachePerf is
a hybrid sampling scheme: it employs the PMU-based coarse-grained sampling to
select very few susceptible instructions (with frequent cache misses) and then
employs the breakpoint-based fine-grained sampling to collect the memory access
pattern of these instructions. Based on our evaluation, CachePerf only imposes
14% performance overhead and 19% memory overhead (for applications with large
footprints), while identifying the types of cache misses correctly. CachePerf
detected 9 previous-unknown bugs. Fixing the reported bugs achieves from 3% to
3788% performance speedup. CachePerf will be an indispensable complementary to
existing profilers due to its effectiveness and low overhead.


title: Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPC

abstract:
  In this paper, we present a systematic approach for high-performance and
efficient trajectory tracking control of autonomous wheel loaders. With the
nonlinear dynamic model of a wheel loader, nonlinear model predictive control
(MPC) is used in offline trajectory planning to obtain a high-performance
state-control trajectory while satisfying the state and control constraints. In
tracking control, the nonlinear model is embedded into a Linear Parameter
Varying (LPV) model and the LPV-MPC strategy is used to achieve fast online
computation and good tracking performance. To demonstrate the effectiveness and
the advantages of the LPV-MPC, we test and compare three model predictive
control strategies in the high-fidelity simulation environment. With the
planned trajectory, three tracking control strategies LPV-MPC, nonlinear MPC,
and LTI-MPC are simulated and compared in the perspectives of computational
burden and tracking performance. The LPV-MPC can achieve better performance
than conventional LTI-MPC because more accurate nominal system dynamics are
captured in the LPV model. In addition, LPV-MPC achieves slightly worse
tracking performance but tremendously improved computational efficiency than
nonlinear MPC. A video with loading cycles completed by our autonomous wheel
loader in the simulation environment can be found here:
https://youtu.be/QbNfS_wZKKA.


title: Provable Adversarial Robustness for Fractional Lp Threat Models

abstract:
  In recent years, researchers have extensively studied adversarial robustness
in a variety of threat models, including L_0, L_1, L_2, and L_infinity-norm
bounded adversarial attacks. However, attacks bounded by fractional L_p "norms"
(quasi-norms defined by the L_p distance with 0<p<1) have yet to be thoroughly
considered. We proactively propose a defense with several desirable properties:
it provides provable (certified) robustness, scales to ImageNet, and yields
deterministic (rather than high-probability) certified guarantees when applied
to quantized data (e.g., images). Our technique for fractional L_p robustness
constructs expressive, deep classifiers that are globally Lipschitz with
respect to the L_p^p metric, for any 0<p<1. However, our method is even more
general: we can construct classifiers which are globally Lipschitz with respect
to any metric defined as the sum of concave functions of components. Our
approach builds on a recent work, Levine and Feizi (2021), which provides a
provable defense against L_1 attacks. However, we demonstrate that our proposed
guarantees are highly non-vacuous, compared to the trivial solution of using
(Levine and Feizi, 2021) directly and applying norm inequalities. Code is
available at https://github.com/alevine0/fractionalLpRobustness.


title: One Bad Apple Can Spoil Your IPv6 Privacy

abstract:
  IPv6 is being more and more adopted, in part to facilitate the millions of
smart devices that have already been installed at home. Unfortunately, we find
that the privacy of a substantial fraction of end-users is still at risk,
despite the efforts by ISPs and electronic vendors to improve end-user
security, e.g., by adopting prefix rotation and IPv6 privacy extensions. By
analyzing passive data from a large ISP, we find that around 19% of end-users'
privacy can be at risk. When we investigate the root causes, we notice that a
single device at home that encodes its MAC address into the IPv6 address can be
utilized as a tracking identifier for the entire end-user prefix -- even if
other devices use IPv6 privacy extensions. Our results show that IoT devices
contribute the most to this privacy leakage and, to a lesser extent, personal
computers and mobile devices. To our surprise, some of the most popular IoT
manufacturers have not yet adopted privacy extensions that could otherwise
mitigate this privacy risk. Finally, we show that third-party providers, e.g.,
hypergiants, can track up to 17% of subscriber lines in our study.


title: Status of QUBIC, the Q&U Bolometric Interferometer for Cosmology

abstract:
  The Q&U Bolometric Interferometer for Cosmology (QUBIC) is a novel kind of
polarimeter optimized for the measurement of the $B$-mode polarization of the
Cosmic Microwave Background (CMB), which is one of the major challenges of
observational cosmology. The signal is expected to be of the order of a few
tens of nK, prone to instrumental systematic effects and polluted by various
astrophysical foregrounds which can only be controlled through multichroic
observations. QUBIC is designed to address these observational issues with a
novel approach that combines the advantages of interferometry in terms of
control of instrumental systematics with those of bolometric detectors in terms
of wide-band, background-limited sensitivity.


title: CapsNet for Medical Image Segmentation

abstract:
  Convolutional Neural Networks (CNNs) have been successful in solving tasks in
computer vision including medical image segmentation due to their ability to
automatically extract features from unstructured data. However, CNNs are
sensitive to rotation and affine transformation and their success relies on
huge-scale labeled datasets capturing various input variations. This network
paradigm has posed challenges at scale because acquiring annotated data for
medical segmentation is expensive, and strict privacy regulations. Furthermore,
visual representation learning with CNNs has its own flaws, e.g., it is
arguable that the pooling layer in traditional CNNs tends to discard positional
information and CNNs tend to fail on input images that differ in orientations
and sizes. Capsule network (CapsNet) is a recent new architecture that has
achieved better robustness in representation learning by replacing pooling
layers with dynamic routing and convolutional strides, which has shown
potential results on popular tasks such as classification, recognition,
segmentation, and natural language processing. Different from CNNs, which
result in scalar outputs, CapsNet returns vector outputs, which aim to preserve
the part-whole relationships. In this work, we first introduce the limitations
of CNNs and fundamentals of CapsNet. We then provide recent developments of
CapsNet for the task of medical image segmentation. We finally discuss various
effective network architectures to implement a CapsNet for both 2D images and
3D volumetric medical image segmentation.


title: Latent-Variable Advantage-Weighted Policy Optimization for Offline RL

abstract:
  Offline reinforcement learning methods hold the promise of learning policies
from pre-collected datasets without the need to query the environment for new
transitions. This setting is particularly well-suited for continuous control
robotic applications for which online data collection based on trial-and-error
is costly and potentially unsafe. In practice, offline datasets are often
heterogeneous, i.e., collected in a variety of scenarios, such as data from
several human demonstrators or from policies that act with different purposes.
Unfortunately, such datasets can exacerbate the distribution shift between the
behavior policy underlying the data and the optimal policy to be learned,
leading to poor performance. To address this challenge, we propose to leverage
latent-variable policies that can represent a broader class of policy
distributions, leading to better adherence to the training data distribution
while maximizing reward via a policy over the latent variable. As we
empirically show on a range of simulated locomotion, navigation, and
manipulation tasks, our method referred to as latent-variable
advantage-weighted policy optimization (LAPO), improves the average performance
of the next best-performing offline reinforcement learning methods by 49% on
heterogeneous datasets, and by 8% on datasets with narrow and biased
distributions.


title: Automorphism groups of affine varieties without non-algebraic elements

abstract:
  Given an affine algebraic variety $X$, we prove that if the neutral component
$\mathrm{Aut}^\circ(X)$ of the automorphism group consists of algebraic
elements, then it is nested, i.e., is a direct limit of algebraic subgroups.
This improves our earlier result. To prove it, we obtain the following fact. If
a connected ind-group $G$ contains a closed connected ind-subgroup $H\subset G$
with a geometrically smooth point, and for any $g\in G$ some power of $g$
belongs to $H$, then $G=H.$


title: Meta-Learning of NAS for Few-shot Learning in Medical Image Applications

abstract:
  Deep learning methods have been successful in solving tasks in machine
learning and have made breakthroughs in many sectors owing to their ability to
automatically extract features from unstructured data. However, their
performance relies on manual trial-and-error processes for selecting an
appropriate network architecture, hyperparameters for training, and
pre-/post-procedures. Even though it has been shown that network architecture
plays a critical role in learning feature representation feature from data and
the final performance, searching for the best network architecture is
computationally intensive and heavily relies on researchers' experience.
Automated machine learning (AutoML) and its advanced techniques i.e. Neural
Architecture Search (NAS) have been promoted to address those limitations. Not
only in general computer vision tasks, but NAS has also motivated various
applications in multiple areas including medical imaging. In medical imaging,
NAS has significant progress in improving the accuracy of image classification,
segmentation, reconstruction, and more. However, NAS requires the availability
of large annotated data, considerable computation resources, and pre-defined
tasks. To address such limitations, meta-learning has been adopted in the
scenarios of few-shot learning and multiple tasks. In this book chapter, we
first present a brief review of NAS by discussing well-known approaches in
search space, search strategy, and evaluation strategy. We then introduce
various NAS approaches in medical imaging with different applications such as
classification, segmentation, detection, reconstruction, etc. Meta-learning in
NAS for few-shot learning and multiple tasks is then explained. Finally, we
describe several open problems in NAS.


title:
Revealing Charge Carrier Dynamics and Transport in Te-Doped GaAsSb and
  GaAsSbN Nanowires by Correlating Ultrafast Terahertz Spectroscopy and
  Optoelectronic Characterization

abstract:
  Recent advances in the growth of III-V semiconductor nanowires (NWs) hold
great promise for nanoscale optoelectronic device applications. Recently, it
was found that a small amount of nitrogen (N) incorporation in III-V
semiconductor NWs can effectively red-shift their wavelength of operation and
tailor their electronic properties for specific applications. However,
understanding the impact of N incorporation on non-equilibrium charge carrier
dynamics and transport in semiconducting NWs is critical in achieving efficient
semiconducting NW devices. In this work, ultrafast optical pump-terahertz (THz)
probe spectroscopy (OPTP) and electrical characterization have been used to
study non-equilibrium carrier dynamics and equilibrium transport in Te-doped
GaAsSb and dilute nitride GaAsSb NWs, with the goal of correlating these
results with their photo-response under bias and their low-frequency noise
characteristics. Nitrogen incorporation in GaAsSb NWs led to a significant
increase in the carrier scattering rate, resulting in a severe reduction in
carrier mobility. Carrier recombination lifetimes of 33 ps and 147 ps in
GaAsSbN and GaAsSb NWs, respectively, were determined using ultrafast OPTP
measurements. The reduction in the carrier lifetime and photoinduced optical
conductivities are due to the presence of N-induced defects, leading to
deterioration in the electrical and optical characteristics of dilute nitride
NWs relative to the non-nitride NWs. Finally, we observed a very fast rise time
of ~ 2 ps for both NW materials, directly impacting their potential use as
high-speed photodetectors.


title:
Improved Band Gaps and Structural Properties from
  Wannier-Fermi-L\"{o}wdin Self-Interaction Corrections for Periodic Systems

abstract:
  The accurate prediction of band gaps and structural properties in periodic
systems continues to be one of the central goals of electronic structure
theory. However, band gaps obtained from popular exchange-correlation
functionals (such as LDA and PBE) are severely underestimated partly due to the
spurious self-interaction error (SIE) inherent to these functionals. In this
work, we present a new formulation and implementation of Wannier
function-derived Fermi-L\"{o}wdin (WFL) orbitals for correcting the SIE in
periodic systems. Since our approach utilizes a variational minimization of the
self-interaction energy with respect to the Wannier charge centers, it is
computationally more efficient than the HSE hybrid functional and other
self-interaction corrections that require a large number of transformation
matrix elements. Calculations on several (17 in total) prototypical molecular
solids, semiconductors, and wide-bandgap materials show that our WFL
self-interaction correction approach gives better band gaps and bulk moduli
compared to semilocal functionals, largely due to the partial removal of
self-interaction errors.


title:
BPE vs. Morphological Segmentation: A Case Study on Machine Translation
  of Four Polysynthetic Languages

abstract:
  Morphologically-rich polysynthetic languages present a challenge for NLP
systems due to data sparsity, and a common strategy to handle this issue is to
apply subword segmentation. We investigate a wide variety of supervised and
unsupervised morphological segmentation methods for four polysynthetic
languages: Nahuatl, Raramuri, Shipibo-Konibo, and Wixarika. Then, we compare
the morphologically inspired segmentation methods against Byte-Pair Encodings
(BPEs) as inputs for machine translation (MT) when translating to and from
Spanish. We show that for all language pairs except for Nahuatl, an
unsupervised morphological segmentation algorithm outperforms BPEs consistently
and that, although supervised methods achieve better segmentation scores, they
under-perform in MT challenges. Finally, we contribute two new morphological
segmentation datasets for Raramuri and Shipibo-Konibo, and a parallel corpus
for Raramuri--Spanish.


title:
Lateral recoil optical forces on nanoparticles near nonreciprocal
  surfaces

abstract:
  We investigate lateral recoil forces exerted on nanoparticles located near
plasmonic platforms with in-plane nonreciprocal response. We derive approximate
analytical expressions for these forces, demonstrating their explicit
dependence on the dispersion relation that characterizes the system and
unveiling the mechanisms that govern them. In particular, a dominant force
component appears due to the momentum imbalance of nonreciprocal surface
plasmons excited on the platform surface. This force can be orders of magnitude
larger than other recoil forces, acts only along or against the direction of
the external bias, and is quasi-independent of the direction, polarization, and
wavelength of the incident plane wave. Lateral recoil forces are explored using
drift-biased graphene metasurfaces, a platform that is also proposed to sort
nanoparticles as a function of their size. Nonreciprocal plasmonic systems may
enable new venues to trap, bind, and manipulate nanoparticles and to alleviate
some of the challenges of conventional optical tweezers.


title: Categoricity transfer for short AECs with amalgamation over sets

abstract:
  Let ${\bf K}$ be an $\mathrm{LS}({\bf K})$-short abstract elementary class
and assume more than the existence of a monster model (amalgamation over sets
and arbitrarily large models). Suppose ${\bf K}$ is categorical in some
$\mu>\mathrm{LS}({\bf K})$, then it is categorical in all $\mu'\geq\mu$. Our
result removes the successor requirement of $\mu$ made by Grossberg-VanDieren,
at the cost of using shortness instead of tameness; and of using amalgamation
over sets instead of over models. It also removes the primes requirement by
Vasey which assumes tameness and amalgamation over models. As a corollary, we
obtain an alternative proof of the upward categoricity transfer for first-order
theories by Morley and Shelah. In our construction, we simplify Vasey's results
to build a weakly successful frame. This allows us to use Shelah-Vasey's
argument to obtain primes for sufficiently saturated models. If we replace the
categoricity assumption by $\mathrm{LS}({\bf K})$-superstability, ${\bf K}$ is
already excellent for sufficiently saturated models. This sheds light on the
investigation of the main gap theorem for uncountable first-order theories
within ZFC.


title: Risk-Averse No-Regret Learning in Online Convex Games

abstract:
  We consider an online stochastic game with risk-averse agents whose goal is
to learn optimal decisions that minimize the risk of incurring significantly
high costs. Specifically, we use the Conditional Value at Risk (CVaR) as a risk
measure that the agents can estimate using bandit feedback in the form of the
cost values of only their selected actions. Since the distributions of the cost
functions depend on the actions of all agents that are generally unobservable,
they are themselves unknown and, therefore, the CVaR values of the costs are
difficult to compute. To address this challenge, we propose a new online
risk-averse learning algorithm that relies on one-point zeroth-order estimation
of the CVaR gradients computed using CVaR values that are estimated by
appropriately sampling the cost functions. We show that this algorithm achieves
sub-linear regret with high probability. We also propose two variants of this
algorithm that improve performance. The first variant relies on a new sampling
strategy that uses samples from the previous iteration to improve the
estimation accuracy of the CVaR values. The second variant employs residual
feedback that uses CVaR values from the previous iteration to reduce the
variance of the CVaR gradient estimates. We theoretically analyze the
convergence properties of these variants and illustrate their performance on an
online market problem that we model as a Cournot game.


title:
On the Usefulness of the Fit-on-the-Test View on Evaluating Calibration
  of Classifiers

abstract:
  Every uncalibrated classifier has a corresponding true calibration map that
calibrates its confidence. Deviations of this idealistic map from the identity
map reveal miscalibration. Such calibration errors can be reduced with many
post-hoc calibration methods which fit some family of calibration maps on a
validation dataset. In contrast, evaluation of calibration with the expected
calibration error (ECE) on the test set does not explicitly involve fitting.
However, as we demonstrate, ECE can still be viewed as if fitting a family of
functions on the test data. This motivates the fit-on-the-test view on
evaluation: first, approximate a calibration map on the test data, and second,
quantify its distance from the identity. Exploiting this view allows us to
unlock missed opportunities: (1) use the plethora of post-hoc calibration
methods for evaluating calibration; (2) tune the number of bins in ECE with
cross-validation. Furthermore, we introduce: (3) benchmarking on pseudo-real
data where the true calibration map can be estimated very precisely; and (4)
novel calibration and evaluation methods using new calibration map families PL
and PL3.


title:
Robustness through Cognitive Dissociation Mitigation in Contrastive
  Adversarial Training

abstract:
  In this paper, we introduce a novel neural network training framework that
increases model's adversarial robustness to adversarial attacks while
maintaining high clean accuracy by combining contrastive learning (CL) with
adversarial training (AT). We propose to improve model robustness to
adversarial attacks by learning feature representations that are consistent
under both data augmentations and adversarial perturbations. We leverage
contrastive learning to improve adversarial robustness by considering an
adversarial example as another positive example, and aim to maximize the
similarity between random augmentations of data samples and their adversarial
example, while constantly updating the classification head in order to avoid a
cognitive dissociation between the classification head and the embedding space.
This dissociation is caused by the fact that CL updates the network up to the
embedding space, while freezing the classification head which is used to
generate new positive adversarial examples. We validate our method, Contrastive
Learning with Adversarial Features(CLAF), on the CIFAR-10 dataset on which it
outperforms both robust accuracy and clean accuracy over alternative supervised
and self-supervised adversarial learning methods.


title:
Prospects for reconstructing the gravitational-wave signals from
  core-collapse supernovae with Advanced LIGO-Virgo and the BayesWave algorithm

abstract:
  Our current understanding of the core-collapse supernova explosion mechanism
is incomplete, with multiple viable models for how the initial shock wave might
be energized enough to lead to a successful explosion. Detection of a
gravitational-wave signal emitted in the initial few seconds after stellar
core-collapse would provide unique and crucial insight into this process. With
the Advanced LIGO and Advanced Virgo detectors expected to approach their
design sensitivities soon, we could potentially detect this signal from a
supernova within our galaxy. In anticipation of such a scenario, we study how
well the BayesWave algorithm can recover the gravitational-wave signal from
core-collapse supernova models in simulated advanced detector noise, and
optimize its ability to accurately reconstruct the signal waveforms. We find
that BayesWave can confidently reconstruct the signal from a range of supernova
explosion models in Advanced LIGO-Virgo for network signal-to-noise ratios
$\gtrsim 30$, reaching maximum reconstruction accuracies of $\sim 90\%$ at SNR
$\sim 100$. For low SNR signals that are not confidently recovered, our
optimization efforts result in gains in reconstruction accuracy of up to
$20-40\%$, with typical gains of $\sim 10\%$.


title:
On the Convergence of Certified Robust Training with Interval Bound
  Propagation

abstract:
  Interval Bound Propagation (IBP) is so far the base of state-of-the-art
methods for training neural networks with certifiable robustness guarantees
when potential adversarial perturbations present, while the convergence of IBP
training remains unknown in existing literature. In this paper, we present a
theoretical analysis on the convergence of IBP training. With an
overparameterized assumption, we analyze the convergence of IBP robust
training. We show that when using IBP training to train a randomly initialized
two-layer ReLU neural network with logistic loss, gradient descent can linearly
converge to zero robust training error with a high probability if we have
sufficiently small perturbation radius and large network width.


title:
Existence of strong solutions for the Oldroyd model with multivalued
  right-hand side

abstract:
  The initial value problem for a coupled system is studied. The system
consists of a differential inclusion and a differential equation and models the
fluid flow of a viscoelastic fluid of Oldroyd type. The set-valued right-hand
side of the differential inclusion satisfies certain measurability, continuity
and growth conditions. The local existence (and global existence for small
data) of a strong solution to the coupled system is shown using a
generalisation of Kakutani's fixed-point theorem and applying results from the
single-valued case.


title: Right-angled links in thickened surfaces

abstract:
  Traditionally, alternating links are studied with alternating diagrams on
$S^2$ in $S^3$. In this paper, we consider links which are alternating on
higher genus surfaces $S_g$ in $S_g \times I$. We define what it means for such
a link to be right-angled generalized completely realizable (RGCR) and show
that this property is equivalent to the link having two totally geodesic
checkerboard surfaces, equivalent to each checkerboard surface consisting of
one type of polygon, and equivalent to a set of restrictions on the link's
alternating projection diagram. We then use these diagram restrictions to
classify RGCR links according to the polygons in their checkerboard surfaces
and provide a bound on the number of RGCR links for a given surface of genus
$g$. Along the way, we answer a question posed by Champanerkar, Kofman, and
Purcell about links with alternating projections on the torus.


title:
Point-Unet: A Context-aware Point-based Neural Network for Volumetric
  Segmentation

abstract:
  Medical image analysis using deep learning has recently been prevalent,
showing great performance for various downstream tasks including medical image
segmentation and its sibling, volumetric image segmentation. Particularly, a
typical volumetric segmentation network strongly relies on a voxel grid
representation which treats volumetric data as a stack of individual voxel
`slices', which allows learning to segment a voxel grid to be as
straightforward as extending existing image-based segmentation networks to the
3D domain. However, using a voxel grid representation requires a large memory
footprint, expensive test-time and limiting the scalability of the solutions.
In this paper, we propose Point-Unet, a novel method that incorporates the
efficiency of deep learning with 3D point clouds into volumetric segmentation.
Our key idea is to first predict the regions of interest in the volume by
learning an attentional probability map, which is then used for sampling the
volume into a sparse point cloud that is subsequently segmented using a
point-based neural network. We have conducted the experiments on the medical
volumetric segmentation task with both a small-scale dataset Pancreas and
large-scale datasets BraTS18, BraTS19, and BraTS20 challenges. A comprehensive
benchmark on different metrics has shown that our context-aware Point-Unet
robustly outperforms the SOTA voxel-based networks at both accuracies, memory
usage during training, and time consumption during testing. Our code is
available at https://github.com/VinAIResearch/Point-Unet.


title: 3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation

abstract:
  Medical image segmentation has been so far achieving promising results with
Convolutional Neural Networks (CNNs). However, it is arguable that in
traditional CNNs, its pooling layer tends to discard important information such
as positions. Moreover, CNNs are sensitive to rotation and affine
transformation. Capsule network is a data-efficient network design proposed to
overcome such limitations by replacing pooling layers with dynamic routing and
convolutional strides, which aims to preserve the part-whole relationships.
Capsule network has shown a great performance in image recognition and natural
language processing, but applications for medical image segmentation,
particularly volumetric image segmentation, has been limited. In this work, we
propose 3D-UCaps, a 3D voxel-based Capsule network for medical volumetric image
segmentation. We build the concept of capsules into a CNN by designing a
network with two pathways: the first pathway is encoded by 3D Capsule blocks,
whereas the second pathway is decoded by 3D CNNs blocks. 3D-UCaps, therefore
inherits the merits from both Capsule network to preserve the spatial
relationship and CNNs to learn visual representation. We conducted experiments
on various datasets to demonstrate the robustness of 3D-UCaps including
iSeg-2017, LUNA16, Hippocampus, and Cardiac, where our method outperforms
previous Capsule networks and 3D-Unets.


title: On Distributed Gravitational N-Body Simulations

abstract:
  The N-body problem is a classic problem involving a system of N discrete
bodies mutually interacting in a dynamical system. At any moment in time there
are N*(N - 1)/2 such interactions occurring. This scaling as N^2 leads to
computational difficulties where simulations range from tens of thousands of
bodies to many millions. Approximation algorithms, such as the famous
Barnes-Hut algorithm, simplify the number of interactions to scale as N(log N).
Even still, this improvement in complexity is insufficient to achieve the
desired performance for very large simulations on computing clusters with many
nodes and many cores. In this work we explore a variety of algorithmic
techniques for distributed and parallel variations on the Barnes-Hut algorithm
to improve parallelism and reduce inter-process communication requirements.
Explicit algorithms and details are provided for reproducibility. Our MPI
implementation of distributed gravitational N-body simulation, freely available
on GitHub, is evaluated on a cluster of 10 nodes, each with two 6-core CPUs, to
test the effectiveness and scalability of the aforementioned techniques.


title:
Snowmass2021 Cosmic Frontier White Paper:Primordial Black Hole Dark
  Matter

abstract:
  Primordial Black Holes (PBHs) are a viable candidate to comprise some or all
of the dark matter and provide a unique window into the high-energy physics of
the early universe. This white paper discusses the scientific motivation,
current status, and future reach of observational searches for PBHs. Future
observational facilities supported by DOE, NSF, and NASA will provide
unprecedented sensitivity to PBHs. However, devoted analysis pipelines and
theoretical modeling are required to fully leverage these novel data. The
search for PBHs constitutes a low-cost, high-reward science case with
significant impact on the high energy physics community.


title: Opportunities for Technosignature Science in the Astro2020 Report

abstract:
  The Astro2020 report outlines numerous recommendations that could
significantly advance technosignature science. Technosignatures refer to any
observable manifestations of extraterrestrial technology, and the search for
technosignatures is part of the continuum of the astrobiological search for
biosignatures. The search for technosignatures is directly relevant to the
"World and Suns in Context" theme and "Pathways to Habitable Worlds" program in
the Astro2020 report. The relevance of technosignatures was explicitly
mentioned in "E1 Report of the Panel on Exoplanets, Astrobiology, and the Solar
System," which stated that "life's global impacts on a planet's atmosphere,
surface, and temporal behavior may therefore manifest as potentially detectable
exoplanet biosignatures, or technosignatures" and that potential
technosignatures, much like biosignatures, must be carefully analyzed to
mitigate false positives. The connection of technosignatures to this high-level
theme and program can be emphasized, as the report makes clear the purpose is
to address the question "Are we alone?" This question is also presented in the
Explore Science 2020-2024 plan as a driver of NASA's mission.
  This white paper summarizes the potential technosignature opportunities
within the recommendations of the Astro2020 report, should they be implemented
by funding agencies. The objective of this paper is to demonstrate the
relevance of technosignature science to a wide range of missions and urge the
scientific community to include the search for technosignatures as part of the
stated science justifications for the large and medium programs that include
the Infrared/Optical/Ultraviolet space telescope, Extremely Large Telescopes,
probe-class far-infrared and X-ray missions, and various facilities in radio
astronomy.


title:
Deep learning applications for quality control in particle detector
  construction

abstract:
  The growing complexity of particle detectors makes their construction and
quality control a new challenge. We present studies that explore the use of
deep learning-based computer vision techniques to perform quality checks of
detector components and assembly steps, which will automate procedures and
minimize the need for human interventions. This study focuses on the
construction steps of a silicon detector, which involve forming a mechanical
structure with the sensor and wire bonding individual cells to electronics for
reading out signals. Silicon detectors in high energy physics experiments today
have millions of channels. Manual quality control of these and other high
channel-density detectors requires enormous amounts of labor and can be prone
to errors. Here, we explore computer vision applications to either augment or
fully replace visual inspections done by humans. We investigated convolutional
neural networks for image classification and autoencoders for anomalies
detection. Two proof-of-concept studies will be presented.


title:
Thermodynamic formalism and large deviation principle of multiplicative
  Ising models

abstract:
  The aim of this study is tree-fold. First, we investigate the thermodynamics
of the Ising models with respect to 2-multiple Hamiltonians. This extends the
previous results of [Chazotte and Redig, Electron. J. Probably., 2014] to
$\mathbb{N}^d$. Second, we establish the large deviation principle (LDP) of the
average $\frac{1}{N} S_N^G$, where $S_N^G$ is a 2-multiple sum along a
semigroup generated by k numbers which are k co-primes. This extends the
previous results [Ban et al. Indag. Math., 2021] to a board class of the
long-range interactions. Finally, the results described above are generalized
to the multidimensional lattice $\mathbb{N}^d, d\geq1$.


title:
Improving white dwarfs as chronometers with Gaia parallaxes and
  spectroscopic metallicities

abstract:
  White dwarfs (WDs) offer unrealized potential in solving two problems in
astrophysics: stellar age accuracy and precision. WD cooling ages can be
inferred from surface temperatures and radii, which can be constrained with
precision by high quality photometry and parallaxes. Accurate and precise Gaia
parallaxes along with photometric surveys provide information to derive cooling
and total ages for vast numbers of WDs. Here we analyse 1372 WDs found in wide
binaries with MS companions, and report on the cooling and total age precision
attainable in these WD+MS systems. The total age of a WD can be further
constrained if its original metallicity is known, because the main-sequence
(MS) progenitor lifetime depends on metallicity at fixed mass, yet metallicity
is unavailable via spectroscopy of the WD. We show that incorporating
spectroscopic metallicity constraints from 38 wide binary MS companions
substantially decreases internal uncertainties in WD total ages compared to a
uniform constraint. Averaged over the 38 stars in our sample, the total
(internal) age uncertainty improves from 21.04% to 16.77% when incorporating
the spectroscopic constraint. Higher mass WDs yield better total age precision;
for 8 WDs with zero age main sequence masses $\geq$ 2.0 solar masses, the mean
uncertainty in total ages improves from 8.61% to 4.54% when incorporating
spectroscopic metallicities. We find that it is often possible to achieve 5%
total age precision for WDs with progenitor masses above 2.0 solar masses if
parallaxes with $\leq$ 1% precision and Pan-STARRS $g, r$, and $i$ photometry
with $\leq$ 0.01 mag precision are available.


title:
Extensive Threat Analysis of Vein Attack Databases and Attack Detection
  by Fusion of Comparison Scores

abstract:
  The last decade has brought forward many great contributions regarding
presentation attack detection for the domain of finger and hand vein
biometrics. Among those contributions, one is able to find a variety of
different attack databases that are either private or made publicly available
to the research community. However, it is not always shown whether the used
attack samples hold the capability to actually deceive a realistic vein
recognition system. Inspired by previous works, this study provides a
systematic threat evaluation including three publicly available finger vein
attack databases and one private dorsal hand vein database. To do so, 14
distinct vein recognition schemes are confronted with attack samples and the
percentage of wrongly accepted attack samples is then reported as the Impostor
Attack Presentation Match Rate. As a second step, comparison scores from
different recognition schemes are combined using score level fusion with the
goal of performing presentation attack detection.


title:
Big Industry Engagement to Benefit HEP: Microelectronics Support from
  Large CAD Companies

abstract:
  Microelectronics development is critical to a wide number of DOE projects and
mission space. Creating Helpful Incentives to Produce Semiconductors (CHIPS)
and manufacturing Application Specific Integrated Circuits (ASIC) are important
to DOE, so the infrastructure that allows DOE to carry out its mission needs to
exist. This paper discusses the current initiatives and recommends a business
model to build an ecosystem for microelectronics design for DOE which includes
three main building blocks: the Computer Aided Design (CAD) - Electronic Design
Automation (EDA) design tools, basic design IPs, and access to semiconductor
fabrication facilities.


title: A 5% measurement of the Hubble constant from Type II supernovae

abstract:
  The most stringent local measurement of the Hubble constant from
Cepheid-calibrated Type Ia supernovae (SNe~Ia) differs from the value inferred
via the cosmic microwave background radiation ({\it Planck}$+\Lambda$CDM) by
more than 5$\sigma$. This so-called "Hubble tension" has been confirmed by
other independent methods, and thus does not appear to be a possible
consequence of systematic errors. Here, we continue upon our prior work of
using Type II supernovae to provide another, largely-independent method to
measure the Hubble constant. From 13 SNe~II with geometric, Cepheid, or tip of
the red giant branch (TRGB) host-galaxy distance measurements, we derive H$_0=
75.4^{+3.8}_{-3.7}$ km s$^{-1}$ Mpc$^{-1}$ (statistical errors only),
consistent with the local measurement but in disagreement by $\sim 2.0\sigma$
with the Planck $+\Lambda$CDM value. Using only Cepheids ($N=7$), we find H$_0
= 77.6^{+5.2}_{-4.8}$ km s$^{-1}$ Mpc$^{-1}$, while using only TRGB ($N=5$), we
derive H$_0 = 73.1^{+5.7}_{-5.3}$ km s$^{-1}$ Mpc$^{-1}$. Via 13 variants of
our dataset, we derive a systematic uncertainty estimate of 1.5 km s$^{-1}$
Mpc$^{-1}$. The median value derived from these variants differs by just 0.3 km
s$^{-1}$ Mpc$^{-1}$ from that produced by our fiducial model. Because we only
replace SNe~Ia with SNe~II -- and we do not find tension between the Cepheid
and TRGB H$_0$ measurements -- our work reveals no indication that SNe~Ia or
Cepheids could be the sources of the "H$_0$ tension." We caution, however, that
our conclusions rest upon a modest calibrator sample; as this sample grows in
the future, our results should be verified.


title: A Survey of Multi-Agent Reinforcement Learning with Communication

abstract:
  Communication is an effective mechanism for coordinating the behavior of
multiple agents. In the field of multi-agent reinforcement learning, agents can
improve the overall learning performance and achieve their objectives by
communication. Moreover, agents can communicate various types of messages,
either to all agents or to specific agent groups, and through specific
channels. With the growing body of research work in MARL with communication
(Comm-MARL), there is lack of a systematic and structural approach to
distinguish and classify existing Comm-MARL systems. In this paper, we survey
recent works in the Comm-MARL field and consider various aspects of
communication that can play a role in the design and development of multi-agent
reinforcement learning systems. With these aspects in mind, we propose several
dimensions along which Comm-MARL systems can be analyzed, developed, and
compared.


title: Infinite rank Hermitian Lattices and Loop Groups

abstract:
  In this paper, we associate a family of infinite-rank pro-Hermitian Euclidean
lattices to elements of a formal loop group and a highest weight representation
of the underlying affine Kac--Moody algebra. In the case that the element has a
polynomial representative, we can prove our lattices are theta-finite in the
sense of Bost, allowing us to attach to each of our lattices a well-defined
theta-like function.


title: Adaptive n-ary Activation Functions for Probabilistic Boolean Logic

abstract:
  Balancing model complexity against the information contained in observed data
is the central challenge to learning. In order for complexity-efficient models
to exist and be discoverable in high dimensions, we require a computational
framework that relates a credible notion of complexity to simple parameter
representations. Further, this framework must allow excess complexity to be
gradually removed via gradient-based optimization. Our n-ary, or n-argument,
activation functions fill this gap by approximating belief functions
(probabilistic Boolean logic) using logit representations of probability. Just
as Boolean logic determines the truth of a consequent claim from relationships
among a set of antecedent propositions, probabilistic formulations generalize
predictions when antecedents, truth tables, and consequents all retain
uncertainty. Our activation functions demonstrate the ability to learn
arbitrary logic, such as the binary exclusive disjunction (p xor q) and ternary
conditioned disjunction ( c ? p : q ), in a single layer using an activation
function of matching or greater arity. Further, we represent belief tables
using a basis that directly associates the number of nonzero parameters to the
effective arity of the belief function, thus capturing a concrete relationship
between logical complexity and efficient parameter representations. This opens
optimization approaches to reduce logical complexity by inducing parameter
sparsity.


title: Flooding in weighted sparse random graphs of active and passive nodes

abstract:
  This paper discusses first passage percolation and flooding on large weighted
sparse random graphs with two types of nodes: active and passive nodes. In
mathematical physics passive nodes can be interpreted as closed gates where
fluid flow or water cannot pass through and active nodes can be interpreted as
open gates where water may keep flowing further. The model of this paper has
many applications in real life, for example, information spreading, where
passive nodes are interpreted as passive receivers who may read messages but do
not respond to them. In the epidemic context passive nodes may be interpreted
as individuals who self-isolate themselves after having a disease to stop
spreading the disease any further. When all weights on edges between active
nodes and between active and passive nodes are independent and exponentially
distributed (but not necessary identically distributed), this article provides
an approximation formula for the weighted typical flooding time.


title:
Speaker Information Can Guide Models to Better Inductive Biases: A Case
  Study On Predicting Code-Switching

abstract:
  Natural language processing (NLP) models trained on people-generated data can
be unreliable because, without any constraints, they can learn from spurious
correlations that are not relevant to the task. We hypothesize that enriching
models with speaker information in a controlled, educated way can guide them to
pick up on relevant inductive biases. For the speaker-driven task of predicting
code-switching points in English--Spanish bilingual dialogues, we show that
adding sociolinguistically-grounded speaker features as prepended prompts
significantly improves accuracy. We find that by adding influential phrases to
the input, speaker-informed models learn useful and explainable linguistic
information. To our knowledge, we are the first to incorporate speaker
characteristics in a neural model for code-switching, and more generally, take
a step towards developing transparent, personalized models that use speaker
information in a controlled way.


title:
Stochastic Simulation Uncertainty Analysis to Accelerate Flexible
  Biomanufacturing Process Development

abstract:
  Motivated by critical challenges and needs from biopharmaceuticals
manufacturing, we propose a general metamodel-assisted stochastic simulation
uncertainty analysis framework to accelerate the development of a simulation
model or digital twin with modular design for flexible production processes.
Since we often face very limited observations and complex biomanufacturing
processes with high inherent stochasticity, there exist both simulation and
model uncertainties in the system performance estimates. In biopharmaceutical
manufacturing, model uncertainty often dominates. The proposed framework can
produce a confidence interval that accounts for simulation and model
uncertainties by using a metamodel-assisted bootstrapping approach.
Furthermore, a variance decomposition is utilized to estimate the relative
contributions from each source of model uncertainty, as well as simulation
uncertainty. This information can efficiently guide digital twin development.
Asymptotic analysis provides theoretical support for our approach, while the
empirical study demonstrates that it has good finite-sample performance.


title: Laser-Driven Structure-Based Accelerators

abstract:
  Particle acceleration in microstructures driven by ultrafast solid state
lasers is a rapidly evolving area of advanced accelerator research, leading to
a variety of concepts based on planar-symmetric dielectric gratings, hollow
core fibers, photonic crystals, and plasmonic meta-surfaces. This approach
leverages well-established industrial fabrication capabilities and the
commercial availability of tabletop lasers to reduce cost, with demonstrated
axial accelerating fields in the GV/m range. Wide-ranging international efforts
have significantly improved understanding of gradient limits, structure design,
particle focusing and transport, staging, and development of compatible
low-emittance electron sources. With a near-term focus on low-current MeV-scale
applications for compact scientific and medical instruments, as well as novel
diagnostics capabilities, structure-based laser-driven accelerators have
several key benefits that warrant consideration for future high-energy physics
machines, including low beamstrahlung energy loss, modest power requirements,
stability, and readiness of supporting technologies.


title: One-Bit Phase Retrieval: More Samples Means Less Complexity?

abstract:
  The classical problem of phase retrieval has found a wide array of
applications in optics, imaging and signal processing. In this paper, we
consider the phase retrieval problem in a one-bit setting, where the signals
are sampled using one-bit analog-to-digital converters (ADCs). A significant
advantage of deploying one-bit ADCs in signal processing systems is their
superior sampling rates as compared to their high-resolution counterparts. This
leads to an enormous amount of one-bit samples gathered at the output of the
ADC in a short period of time. We demonstrate that this advantage pays
extraordinary dividends when it comes to convex phase retrieval formulations,
namely that the often encountered matrix semi-definiteness constraints as well
as rank constraints (that are computationally prohibitive to enforce), become
redundant for phase retrieval in the face of a growing sample size. Several
numerical results are presented to illustrate the effectiveness of the proposed
methodologies.


title: Gravitational Memory Effect for Near-Horizon Asymptotic Symmetries

abstract:
  Gravitational memory effect has emerged as a new window and opened up several
intriguing avenues in the field of gravitational wave astronomy together with
its inter-connection to asymptotic symmetries (AS). The recent developments in
this direction have drawn considerable attention from theoretical as well as
observational perspectives. It has been shown that such effects can also be
recovered near the horizon of black holes which might play a crucial role in
understanding the information paradox. With this motivation, we have studied
the gravitational memory effect near the horizon of black holes analogous to
the one obtained at asymptotic null infinity.
  We first study how the AS emerge near the horizon of black holes and how they
can be detected via some ideal detectors as some permanent change of
configurations (Memory) once GW passes through the setup. The emergence of AS
is studied from two different perspectives. First, how AS emerge as soldering
freedoms when two black holes are being glued along a common null surface.
Secondly, in the form of AS that preserve the asymptotic form of the near
horizon metric of a black hole. We have established an analogous form of the
displacement memory near the horizon of non-extreme and extreme black holes for
both these perspectives. We also discuss the detection prospects of the
supertranslation memory or hair. In this direction, other than the memory
effect, we have explored the possibility of detecting a supertranslated black
hole through standard tests of General Relativity. We propose a scenario in
which such a configuration may be distinguished from ordinary black holes by
examining the photon trajectories near the horizon. As we know that the
gravitational memory has not been detected yet, in this direction, our study
might serve as a useful contribution towards investigation of the signatures of
AS in the future.


title:
Koopman-based Differentiable Predictive Control for the Dynamics-Aware
  Economic Dispatch Problem

abstract:
  The dynamics-aware economic dispatch (DED) problem embeds low-level generator
dynamics and operational constraints to enable near real-time scheduling of
generation units in a power network. DED produces a more dynamic supervisory
control policy than traditional economic dispatch (T-ED) that leads to reduced
overall generation costs. However, the incorporation of differential equations
that govern the system dynamics makes DED an optimization problem that is
computationally prohibitive to solve. In this work, we present a new
data-driven approach based on differentiable programming to efficiently obtain
parametric solutions to the underlying DED problem. In particular, we employ
the recently proposed differentiable predictive control (DPC) for offline
learning of explicit neural control policies using an identified Koopman
operator (KO) model of the power system dynamics. We demonstrate the high
solution quality and five orders of magnitude computational-time savings of the
DPC method over the original online optimization-based DED approach on a 9-bus
test power grid network.


title: Label Semantics for Few Shot Named Entity Recognition

abstract:
  We study the problem of few shot learning for named entity recognition.
Specifically, we leverage the semantic information in the names of the labels
as a way of giving the model additional signal and enriched priors. We propose
a neural architecture that consists of two BERT encoders, one to encode the
document and its tokens and another one to encode each of the labels in natural
language format. Our model learns to match the representations of named
entities computed by the first encoder with label representations computed by
the second encoder. The label semantics signal is shown to support improved
state-of-the-art results in multiple few shot NER benchmarks and on-par
performance in standard benchmarks. Our model is especially effective in low
resource settings.


title:
Moduli of unstable bundles of HN-length two with fixed algebra of
  endomorphisms

abstract:
  Let X be a smooth irreducible complex projective curve of genus g > 1. In
this paper, we give necessary and sufficient conditions for an unstable bundle
of HN-lenght 2 to have a particular algebra of endomorphisms. Then, fixing the
dimension of the algebra of endomorphisms we obtain a stratification of the
moduli scheme such that each strata is a coarse moduli space. A particular case
of interest is when the unstable bundles are simple. In that case the moduli
space is fine. Topological properties of those moduli spaces are described and
will depend on the generality of the curve X. Such results differ from the
corresponding results for the moduli space of stable bundles, where
non-emptiness, dimension etc. are independent of the curve.


title: Integer partitions with large Dyson rank

abstract:
  The Dyson rank of an integer partition is the difference between its largest
part and the number of parts it contains. Using Fine-Dyson symmetry, we give
formulas for the number of partitions of n with rank larger than n/2, and we
prove identities for counts of partitions with large rank in fixed residue
classes.


title:
Global Well-posedness and Regularity of Weak Solutions to the Prandtl's
  System

abstract:
  We continue our study on the global solution to the two-dimensional Prandtl's
system for unsteady boundary layers in the class considered by Oleinik provided
that the pressure is favorable. First, by using a different method from [13],
we gave a direct proof of existence of a global weak solution by a direct BV
estimate. Then we prove the uniqueness and continuous dependence on data of
such a weak solution to the initial-boundary value problem. Finally, we show
the smoothness of the weak solutions and then the global existence of smooth
solutions.


title: Detecting silent data corruptions in the wild

abstract:
  Silent Errors within hardware devices occur when an internal defect manifests
in a part of the circuit which does not have check logic to detect the
incorrect circuit operation. The results of such a defect can range from
flipping a single bit in a single data value, up to causing the software to
execute the wrong instructions. Silent data corruptions (SDC) in hardware
impact computational integrity for large-scale applications. Manifestations of
silent errors are accelerated by datapath variations, temperature variance, and
age, among other silicon factors. These errors do not leave any record or trace
in system logs. As a result, silent errors stay undetected within workloads,
and their effects can propagate across several services, causing problems to
appear in systems far removed from the original defect. In this paper, we
describe testing strategies to detect silent data corruptions within a large
scale infrastructure. Given the challenging nature of the problem, we
experimented with different methods for detection and mitigation. We compare
and contrast two such approaches - 1. Fleetscanner (out-of-production testing)
and 2. Ripple (in-production testing).We evaluate the infrastructure tradeoffs
associated with the silicon testing funnel across 3+ years of production
experience.


title:
Elastic and elastic-gravity waves on flow past submerged obstacles with
  small bending stiffness

abstract:
  Linearized flow past a submerged obstacle with an elastic sheet resting on
the flow surface are studied in the limit of small bending stiffness, in two
and three dimensions. Gravitational effects are included in the two-dimensional
geometry, but absent in the three-dimensional geometry. In each of these
problems, the waves are exponentially small in the asymptotic limit, and can be
computed using exponential asymptotic methods. In the two-dimensional problem,
flow past a submerged step is considered. It is found that the relative
strength of the gravitational and elastic restoring forces produce two distinct
classes of elastic sheet behaviour. In one parameter regime, constant-amplitude
elastic waves and gravity waves extend indefinitely upstream and downstream
from the obstacle. In the other parameter regime, all waves decay exponentially
away from the obstacle. The equivalent nonlinear two-dimensional geometry is
then studied; this asymptotic analysis predicts the existence of a third
intermediate regime in which waves persist indefinitely only in one direction,
depending on whether the submerged step rises or falls. In the
three-dimensional geometry, it is predicted that the elastic waves extend ahead
of the submerged source, decaying algebraically in space. The form of these
elastic waves is computed, and validated by comparison with numerical
computations of the elastic sheet behaviour.


title: AdapLeR: Speeding up Inference by Adaptive Length Reduction

abstract:
  Pre-trained language models have shown stellar performance in various
downstream tasks. But, this usually comes at the cost of high latency and
computation, hindering their usage in resource-limited settings. In this work,
we propose a novel approach for reducing the computational cost of BERT with
minimal loss in downstream performance. Our method dynamically eliminates less
contributing tokens through layers, resulting in shorter lengths and
consequently lower computational cost. To determine the importance of each
token representation, we train a Contribution Predictor for each layer using a
gradient-based saliency method. Our experiments on several diverse
classification tasks show speedups up to 22x during inference time without much
sacrifice in performance. We also validate the quality of the selected tokens
in our method using human annotations in the ERASER benchmark. In comparison to
other widely used strategies for selecting important tokens, such as saliency
and attention, our proposed method has a significantly lower false positive
rate in generating rationales. Our code is freely available at
https://github.com/amodaresi/AdapLeR .


title:
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine
  Reading Comprehension

abstract:
  Recent machine reading comprehension datasets such as ReClor and LogiQA
require performing logical reasoning over text. Conventional neural models are
insufficient for logical reasoning, while symbolic reasoners cannot directly
apply to text. To meet the challenge, we present a neural-symbolic approach
which, to predict an answer, passes messages over a graph representing logical
relations between text units. It incorporates an adaptive logic graph network
(AdaLoGN) which adaptively infers logical relations to extend the graph and,
essentially, realizes mutual and iterative reinforcement between neural and
symbolic reasoning. We also implement a novel subgraph-to-node message passing
mechanism to enhance context-option interaction for answering multiple-choice
questions. Our approach shows promising results on ReClor and LogiQA.


title: Measurement-based quantum computation using two-component BECs

abstract:
  We propose measurement-based quantum computation (MBQC) using two-component
Bose-Einstein condensates (BECs). When constructing our theory, we need to
introduce creation operator and annihilation operator for MBQC. This is
realized by considering the Stokes operator (Schwinger boson). As a result, we
find that the state after a measurement depends on the number of particles.


title: AI Autonomy: Self-Initiation, Adaptation and Continual Learning

abstract:
  As more and more AI agents are used in practice, it is time to think about
how to make these agents fully autonomous so that they can (1) learn by
themselves continually in a self-motivated and self-initiated manner rather
than being retrained offline periodically on the initiation of human engineers
and (2) accommodate or adapt to unexpected or novel circumstances. As the
real-world is an open environment that is full of unknowns or novelties,
detecting novelties, characterizing them, accommodating or adapting to them,
and gathering ground-truth training data and incrementally learning the
unknowns/novelties are critical to making the AI agent more and more
knowledgeable and powerful over time. The key challenge is how to automate the
process so that it is carried out continually on the agent's own initiative and
through its own interactions with humans, other agents and the environment just
like human on-the-job learning. This paper proposes a framework (called SOLA)
for this learning paradigm to promote the research of building autonomous and
continual learning enabled AI agents. To show feasibility, an implemented agent
is also described.


title:
Spatially resolved observations of Europa's surface with Subaru/IRCS at
  1.0-1.8 $\mu m$: Upper limits to the abundances of hydrated Cl-bearing salts

abstract:
  Recent infrared observations at 1.5-4.0 $\mu m$ using large ground-based
telescopes have suggested that Cl-bearing salts are likely present on Europa's
surface as non-ice materials. The chemical compositions of those Cl-bearing
salts are key to understanding Europa's ocean chemistry and habitability. Here
we report the results of ground-based telescope observations of Europa across
two wavelength ranges, 1.0-1.5 and 1.5-1.8 $\mu m$, of which the former range
includes absorption features owing to some hydrated Cl-bearing salts. We
obtained spatially resolved reflectance spectra using the Subaru Telescope/IRCS
and the adaptive optics system AO188 with high wavelength resolutions ($\delta
\lambda$ ~ 2 nm for 1.0-1.5 $\mu m$ and $\delta \lambda$ ~ 0.9 nm for 1.5-1.8
$\mu m$) and low noise levels (1$\sigma$ ~ 1-2 $\times$ 10$^{-3}$). We found no
clear absorption features at ~1.2 $\mu$m caused by hydrated Cl-bearing salts.
We estimated that conservative upper limits to the abundances of
MgCl$_2$$\cdot$nH$_2$O, NaClO$_4$$\cdot$2H$_2$O, Mg(ClO$_3$)$_2$$\cdot$6H$_2$O,
and Mg(ClO$_4$)$_2$$\cdot$6H$_2$O on Europa are 17% (<10% for most) at the
3$\sigma$ noise level. These values are lower than the proposed abundance of
some hydrated Cl-bearing salts (> ~20%) on Europa based on previous
observations. This supports the idea that Cl-bearing salts on Europa are likely
anhydrous Na salts of NaCl and/or NaClO$_4$, or hydrated NaCl$\cdot$2H$_2$O.
The presence of Na salts suggests that Na$^+$ could be the major cation in
Europa's ocean, which would be possible if the oceanic pH is circumneutral or
alkaline.


title: Time domain methods for X-ray and gamma-ray astronomy

abstract:
  A variety of statistical methods for understanding variability in the time
domain for low count rate X-ray and gamma-ray sources are explored. Variability
can be detected using nonparametric (Anderson-Darling and overdispersion tests)
and parametric (sequential likelihood-based tests) tools. Once detected,
variability can be characterized by nonparametric (autocorrelation function,
structure function,wavelet analysis) and parametric (multiple change point
model such as Bayesian Blocks, integer autoregressive models, C-statistic and
Poisson regression) methods. New multidimensional variability detection
approaches are outlined. Software packages designed for high energy data
analysis are deficient but tools are available in the R statistical software
environment. Most of the methods presented here are not commonly used in high
energy astronomy.


title:
On the infinite dimension limit of invariant measures and solutions of
  Zeitlin's 2D Euler equations

abstract:
  In this work we consider a finite dimensional approximation for the 2D Euler
equations on the sphere, proposed by V. Zeitlin, and show their convergence
towards a solution to Euler equations with marginals distributed as the
enstrophy measure. The method relies on nontrivial computations on the
structure constants of $\mathbb{S}^2$, that appear to be new. In the last
section we discuss the problem of extending our results to Gibbsian measures
associated with higher Casimirs.


title:
Eccentric Pairs: Analytic Gravitational Waves from Binary Black Holes in
  Elliptic Orbits

abstract:
  Gravitational waves from eccentric binaries have intricate signals encoding
important features about the location, creation and evolution of the sources.
Eccentricity shortens the merger time, which will make those signals
statistically predominant in the observed data once detectors will reach the
required sensitivity. We accurately implement fully analytical GW templates
from eccentric binary black hole mergers. We start by bringing together all the
ingredients, then assemble the strain for the inspiral by employing high-order
post-Newtonian corrections. In preparation for the merger, we compile
analytical approximations available in the field for estimating the final black
hole mass and spin, essential for building the two models chosen for the
merger: Backwards one Body and Implicit Rotating Source. We calculate the spin,
mass, ringdown frequency and damping time of the remnant and implement the BoB
model for the merger, weighing it up against the IRS model and the numerical
data. We achieve overall agreement between the BoB and IRS models with
excellent match in frequency around merger, expected discrepancy in amplitude,
and a phase difference in the IRS waveform. We complete the eccentric inspiral
with the quasi-circular BoB merger model in frequency, amplitude and phase,
then we build the hybrid strain for the whole evolution of the binary. For low
eccentricity we reach coincidence in the overlap, with no ambiguity in the time
interval. For high-eccentricity we compensate for the implicit quasi-circular
assumption of the BoB approach. The applicability of our calculations stems
from their thorough and streamlined implementation, which offers researchers in
the field of gravitational waves a tool straightforward to understand,
reproduce, use and extend.


title:
The Aemulus Project V: Cosmological constraint from small-scale
  clustering of BOSS galaxies

abstract:
  We analyze clustering measurements of BOSS galaxies using a simulation-based
emulator of two-point statistics. We focus on the monopole and quadrupole of
the redshift-space correlation function, and the projected correlation
function, at scales of $0.1\sim60~h^{-1}$Mpc. Although our simulations are
based on $w$CDM with general relativity (GR), we include a scaling parameter of
the halo velocity field, $\gamma_f$, defined as the amplitude of the halo
velocity field relative to the GR prediction. We divide the BOSS data into
three redshift bins. After marginalizing over other cosmological parameters,
galaxy bias parameters, and the velocity scaling parameter, we find
$f\sigma_{8}(z=0.25) = 0.404\pm0.03$, $f\sigma_{8}(z=0.4) = 0.444\pm0.025$ and
$f\sigma_{8}(z=0.55) = 0.385\pm0.019$. Compared with Planck observations using
a flat $\Lambda$CDM model, our results are lower by $2.29\sigma$, $1.3\sigma$
and $4.58\sigma$ respectively. These results are consistent with other recent
simulation-based results at non-linear scales, including weak lensing
measurements of BOSS LOWZ galaxies, two-point clustering of eBOSS LRGs, and an
independent clustering analysis of BOSS LOWZ. All these results are generally
consistent with a combination of $\gamma_f^{1/2}\sigma_8\approx 0.75$. We note,
however, that the BOSS data is well fit assuming GR, i.e. $\gamma_f=1$. We
cannot rule out an unknown systematic error in the galaxy bias model at
non-linear scales, but near-future data and modeling will enhance our
understanding of the galaxy--halo connection, and provide a strong test of new
physics beyond the standard model.


title:
Lorenz map, inequality ordering and curves based on multidimensional
  rearrangements

abstract:
  We propose a multivariate extension of the Lorenz curve based on multivariate
rearrangements of optimal transport theory. We define a vector Lorenz map as
the integral of the vector quantile map associated to a multivariate resource
allocation. Each component of the Lorenz map is the cumulative share of each
resource, as in the traditional univariate case. The pointwise ordering of such
Lorenz maps defines a new multivariate majorization order. We define a
multi-attribute Gini index and complete ordering based on the Lorenz map. We
propose the level sets of an Inverse Lorenz Function as a practical tool to
visualize and compare inequality in two dimensions, and apply it to
income-wealth inequality in the United States between 1989 and 2019.


title: Selection and parallel trends

abstract:
  One of the perceived advantages of difference-in-differences (DiD) methods is
that they do not explicitly restrict how units select into treatment. However,
when justifying DiD, researchers often argue that the treatment is
"quasi-randomly" assigned. We investigate what selection mechanisms are
compatible with the parallel trends assumptions underlying DiD. We derive
necessary conditions for parallel trends that clarify whether and how selection
can depend on time-invariant and time-varying unobservables. Motivated by these
necessary conditions, we suggest a menu of interpretable sufficient conditions
for parallel trends, thereby providing the formal underpinnings for justifying
DiD based on contextual information about selection into treatment. We provide
results for both separable and nonseparable outcome models and show that this
distinction has implications for the use of covariates in DiD analyses.