title: Risk-based path planning for autonomous vehicles

abstract:
  In this paper, a risk map-based path planning algorithm is introduced for
autonomous vehicles. Multivariate B-splines are implemented to generate a risk
map, which measures the risk of colliding with different objects. In the
following step, a two-level optimal control problem is designed. At the first
level, an overall lowest risk trajectory is found. Then in the second level,
among the paths whose risk value is the lowest, the one with the minimum
steering effort is determined. Finally, numerical simulations are carried out
with the optimal control software OCPID-DAE1. Results show that this method is
useful and meaningful to find a path of minimum risk for an autonomous vehicle.


title:
Monocular Robot Navigation with Self-Supervised Pretrained Vision
  Transformers

abstract:
  In this work, we consider the problem of learning a perception model for
monocular robot navigation using few annotated images. Using a Vision
Transformer (ViT) pretrained with a label-free self-supervised method, we
successfully train a coarse image segmentation model for the Duckietown
environment using 70 training images. Our model performs coarse image
segmentation at the 8x8 patch level, and the inference resolution can be
adjusted to balance prediction granularity and real-time perception
constraints. We study how best to adapt a ViT to our task and environment, and
find that some lightweight architectures can yield good single-image
segmentation at a usable frame rate, even on CPU. The resulting perception
model is used as the backbone for a simple yet robust visual servoing agent,
which we deploy on a differential drive mobile robot to perform two tasks: lane
following and obstacle avoidance.


title:
3D structure of jet-induced diffusion wake in an expanding quark-gluon
  plasma

abstract:
  Diffusion wake accompanying the jet-induced Mach-cone provides a unique probe
of the properties of quark-gluon plasma in high-energy heavy-ion collisions. It
can be characterized by a depletion of soft hadrons in the opposite direction
of the propagating jet. We explore the 3D structure of the diffusion wake
induced by $\gamma$-triggered jets in Pb+Pb collisions at the LHC energy within
the coupled linear Boltzmann transport and hydro model. We identify a valley
structure caused by the diffusion wake on top of a ridge from the initial
multiple parton interaction (MPI) in jet-hadron correlation as a function of
rapidity and azimuthal angle. This leads to a double-peak structure in the
rapidity distribution of soft hadrons in the opposite direction of the jets as
an unambiguous signal of the diffusion wake without subtraction of MPI
background. Using a two-Gaussian fit, we extract the diffusion wake and MPI
contributions to the double peak. The depth of the diffusion wake valley is
found to increase with the jet energy loss as characterized by the $\gamma$-jet
asymmetry. Its sensitivity to the equation of state and shear viscosity is also
studied.


title:
Learn to Match with No Regret: Reinforcement Learning in Markov Matching
  Markets

abstract:
  We study a Markov matching market involving a planner and a set of strategic
agents on the two sides of the market. At each step, the agents are presented
with a dynamical context, where the contexts determine the utilities. The
planner controls the transition of the contexts to maximize the cumulative
social welfare, while the agents aim to find a myopic stable matching at each
step. Such a setting captures a range of applications including ridesharing
platforms. We formalize the problem by proposing a reinforcement learning
framework that integrates optimistic value iteration with maximum weight
matching. The proposed algorithm addresses the coupled challenges of sequential
exploration, matching stability, and function approximation. We prove that the
algorithm achieves sublinear regret.


title:
A Multi-Instrument Investigation of the Frequency Stability of
  Oscillations Above the Acoustic Cut-Off Frequency with Solar Activity

abstract:
  Below the acoustic cut-off frequency, oscillations are trapped within the
solar interior and become resonant. However, signatures of oscillations persist
above the acoustic cut-off frequency, and these travelling waves are known as
pseudomodes. Acoustic oscillation frequencies are known to be correlated with
the solar cycle, but the pseudomode frequencies are predicted to vary in
anti-phase. We have studied the variation in pseudomode frequencies with time
systematically through the solar cycle. We analyzed Sun-as-a-star data from
Variability of Solar Irradiance and Gravity Oscillations (VIRGO), and Global
Oscillations at Low Frequencies (GOLF), as well as the decomposed data from
Global Oscillation Network (GONG) for harmonic degrees $0\le l \le 200$. The
data cover over two solar cycles (1996--2021, depending on instrument). We
split them into overlapping 100-day long segments and focused on two frequency
ranges, namely $5600$--$6800\,\rm\mu Hz$ and $5600$--$7800\,\rm\mu Hz$. The
frequency shifts between segments were then obtained by fitting the
cross-correlation function between the segments' periodograms. For VIRGO and
GOLF, we found no significant variation of pseudomode frequencies with solar
activity. However, in agreement with previous studies, we found that the
pseudomode frequency variations are in anti-phase with the solar cycle for GONG
data. Furthermore, the pseudomode frequency shifts showed a double-peak feature
at their maximum, which corresponds to solar activity minimum, and is not seen
in solar activity proxies. An, as yet unexplained, pseudo-periodicity in the
amplitude of the variation with harmonic degree $l$ is also observed in the
GONG data.


title:
Observed cosmic evolution of galaxy dust properties with metallicity and
  tensions with models

abstract:
  The dust abundance of the interstellar medium plays an important role in
galaxy physics, the chemical evolution of matter and the absorption and
re-emission of stellar light. The last years have seen a surge in observational
and theoretical studies constraining the dust-abundance of galaxies up to
$z\sim5$. In this work we gather the latest observational measurements (with a
focus on absorption studies covering metallicities in the range $6.8 < 12 +
\log{(O/H)}<9$) and theoretical predictions (from six different galaxy
formation models) for the dust-to-gas (DTG) and dust-to-metal (DTM) ratio of
galaxies. The observed trend between DTG and DTM and gas-phase metallicity can
be described by a linear relation and shows no evolution from $0<z<5$.
Importantly, the fit to the DTG-metallicity relation provides a refined tool
for robust dust-based gas mass estimates inferred from millimeter
dust-continuum observations. The lack of evolution in the observed relations
are indicative of a quickly reached balance (already when the Universe was 1.2
Gyr old) between the formation and destruction of dust and a constant timescale
for star-formation at fixed metallicities over cosmic time. None of the models
is able to reproduce the observed trends over the entire range in metallicity
and redshift probed. The comparison between models and simulations furthermore
rules out some of the current implementations for the growth and destruction of
dust in galaxy formation models and places tight constraints on the predicted
timescale for star-formation.


title: Hamming sandwiches

abstract:
  We describe primitive association schemes $\mathfrak{X}$ of degree $n$ such
that $\mathrm{Aut}(\mathfrak{X})$ is imprimitive and
$|\mathrm{Aut}(\mathfrak{X})| \geq \exp(n^{1/8})$, contradicting a conjecture
of Babai. This and other examples we give are the first known examples of
nonschurian primitive coherent configurations (PCC) with more than a
quasipolynomial number of automorphisms.
  Our constructions are "Hamming sandwiches", association schemes sandwiched
between the $d$th tensor power of the trivial scheme and the $d$-dimensional
Hamming scheme. We study Hamming sandwiches in general, and exhaustively for $d
\leq 8$.
  We revise Babai's conjecture by suggesting that any PCC with more than a
quasipolynomial number of automorphisms must be an association scheme
sandwiched between a tensor power of a Johnson scheme and the corresponding
full Cameron scheme. If true, it follows that any nonschurian PCC has at most
$\exp O(n^{1/8} \log n)$ automorphisms.


title: Uniqueness of solutions in thermopiezoelectricity of nonsimple materials

abstract:
  In this paper, using the entropy production inequality of Green and Laws, the
theory of thermopiezoelectricity is presented, in which the second gradient of
displacement and the second gradient of electric potential are included in the
set of independent constitutive variables. At first, the appropriate
thermodynamic restrictions and constitutive equations are obtained. Then, the
basic equations of linear theory are derived and a uniqueness result for the
mixed boundary-initial value problem is presented.


title: WaveMix: Resource-efficient Token Mixing for Images

abstract:
  Although certain vision transformer (ViT) and CNN architectures generalize
well on vision tasks, it is often impractical to use them on green, edge, or
desktop computing due to their computational requirements for training and even
testing. We present WaveMix as an alternative neural architecture that uses a
multi-scale 2D discrete wavelet transform (DWT) for spatial token mixing.
Unlike ViTs, WaveMix neither unrolls the image nor requires self-attention of
quadratic complexity. Additionally, DWT introduces another inductive bias --
besides convolutional filtering -- to utilize the 2D structure of an image to
improve generalization. The multi-scale nature of the DWT also reduces the
requirement for a deeper architecture compared to the CNNs, as the latter
relies on pooling for partial spatial mixing. WaveMix models show
generalization that is competitive with ViTs, CNNs, and token mixers on several
datasets while requiring lower GPU RAM (training and testing), number of
computations, and storage. WaveMix have achieved State-of-the-art (SOTA)
results in EMNIST Byclass and EMNIST Balanced datasets.


title:
Robust Design of Rate-Splitting Multiple Access With Imperfect CSI for
  Cell-Free MIMO Systems

abstract:
  Rate-Splitting Multiple Access (RSMA) for multi-user downlink operates by
splitting the message for each user equipment (UE) into a private message and a
set of common messages, which are simultaneously transmitted by means of
superposition coding. The RSMA scheme can enhance throughput and connectivity
as compared to conventional multiple access techniques by optimizing the
rate-splitting ratios along with the corresponding downlink beamforming
vectors. This work examines the impact of erroneous channel state information
(CSI) on the performance of RSMA in cell-free multiple-input multiple-output
(MIMO) systems. An efficient robust optimization algorithm is proposed by using
closed-form lower bound expressions on the expected data rates. Extensive
numerical results show the importance of robust design in the presence of CSI
errors and how the performance gain of RSMA over conventional schemes is
affected by CSI imperfection.


title: HyperMixer: An MLP-based Green AI Alternative to Transformers

abstract:
  Transformer-based architectures are the model of choice for natural language
understanding, but they come at a significant cost, as they have quadratic
complexity in the input length and can be difficult to tune. In the pursuit of
Green AI, we investigate simple MLP-based architectures. We find that existing
architectures such as MLPMixer, which achieves token mixing through a static
MLP applied to each feature independently, are too detached from the inductive
biases required for natural language understanding. In this paper, we propose a
simple variant, HyperMixer, which forms the token mixing MLP dynamically using
hypernetworks. Empirically, we demonstrate that our model performs better than
alternative MLP-based models, and on par with Transformers. In contrast to
Transformers, HyperMixer achieves these results at substantially lower costs in
terms of processing time, training data, and hyperparameter tuning.


title:
Low-Loss Subspace Compression for Clean Gains against Multi-Agent
  Backdoor Attacks

abstract:
  Recent exploration of the multi-agent backdoor attack demonstrated the
backfiring effect, a natural defense against backdoor attacks where backdoored
inputs are randomly classified. This yields a side-effect of low accuracy
w.r.t. clean labels, which motivates this paper's work on the construction of
multi-agent backdoor defenses that maximize accuracy w.r.t. clean labels and
minimize that of poison labels. Founded upon agent dynamics and low-loss
subspace construction, we contribute three defenses that yield improved
multi-agent backdoor robustness.


title:
GL-equivariance in positive characteristic I: The infinite exterior
  algebra

abstract:
  We study the category of GL-equivariant modules over the infinite exterior
algebra in positive characteristic. Our main structural result is a shift
theorem a la Nagpal. Using this, we obtain a Church--Ellenberg type bound for
the Castelnuovo--Mumford regularity. We also prove finiteness results for local
cohomology.


title: Multiscale linearization of nonautonomous systems

abstract:
  We present sufficient conditions under which a given linear nonautonomous
system and its nonlinear perturbation are topologically conjugated. Our
conditions are of a very general form and provided that the nonlinear
perturbations are well-behaved, we do not assume any asymptotic behaviour of
the linear system. Moreover, the control on the nonlinear perturbations may
differ along finitely many mutually complementary directions. We consider both
the cases of one-sided discrete and continuous dynamics.


title: Learning to Bound: A Generative Cram\'er-Rao Bound

abstract:
  The Cram\'er-Rao bound (CRB), a well-known lower bound on the performance of
any unbiased parameter estimator, has been used to study a wide variety of
problems. However, to obtain the CRB, requires an analytical expression for the
likelihood of the measurements given the parameters, or equivalently a precise
and explicit statistical model for the data. In many applications, such a model
is not available. Instead, this work introduces a novel approach to approximate
the CRB using data-driven methods, which removes the requirement for an
analytical statistical model. This approach is based on the recent success of
deep generative models in modeling complex, high-dimensional distributions.
Using a learned normalizing flow model, we model the distribution of the
measurements and obtain an approximation of the CRB, which we call Generative
Cram\'er-Rao Bound (GCRB). Numerical experiments on simple problems validate
this approach, and experiments on two image processing tasks of image denoising
and edge detection with a learned camera noise model demonstrate its power and
benefits.


title:
Gap Labelling for Discrete One-Dimensional Ergodic Schr\"odinger
  Operators

abstract:
  In this survey, we give an introduction to and proof of the gap labelling
theorem for discrete one-dimensional ergodic Schr\"odinger operators via the
Schwartzman homomorphism. To keep the paper relatively self-contained, we
include background on the integrated density of states, the oscillation theorem
for 1D operators, and the construction of the Schwartzman homomorphism. We
illustrate the result with some examples. In particular, we show how to use the
Schwartzman formalism to recover the classical gap-labelling theorem for
almost-periodic potentials. We also consider operators generated by subshifts
and operators generated by affine homeomorphisms of finite-dimensional tori. In
the latter case, one can use the gap-labelling theorem to show that the
spectrum associated with potentials generated by suitable transformations (such
as Arnold's cat map) is an interval.


title: Unit Perturbations in Budgeted Spanning Tree Problems

abstract:
  The minimum spanning tree of a graph is a well-studied structure that is the
basis of countless graph theoretic and optimization problem. We study the
minimum spanning tree (MST) perturbation problem where the goal is to spend a
fixed budget to increase the weight of edges in order to increase the weight of
the MST as much as possible. Two popular models of perturbation are bulk and
continuous. In the bulk model, the weight of any edge can be increased exactly
once to some predetermined weight. In the continuous model, one can pay a
fractional amount of cost to increase the weight of any edge by a proportional
amount. Frederickson and Solis-Oba \cite{FS96} have studied these two models
and showed that bulk perturbation for MST is as hard as the $k$-cut problem
while the continuous perturbation model is solvable in poly-time. In this
paper, we study an intermediate unit perturbation variation of this problem
where the weight of each edge can be increased many times but at an integral
unit amount every time. We provide an $(opt/2 -1)$-approximation in polynomial
time where $opt$ is the optimal increase in the weight. We also study the
associated dual targeted version of the problem where the goal is to increase
the weight of the MST by a target amount while minimizing the cost of
perturbation. We provide a $2$-approximation for this variation. Furthermore we
show that assuming the Small Set Expansion Hypothesis, both problems are hard
to approximate. We also point out an error in the proof provided by
Frederickson and Solis-Oba in \cite{FS96} with regard to their solution to the
continuous perturbation model. Although their algorithm is correct, their
analysis is flawed. We provide a correct proof here.


title: Higher-order corrections for $t{\bar t}$ production at high energies

abstract:
  I present theoretical calculations of $t{\bar t}$ production cross sections
through aN$^3$LO in proton-proton collisions. I show that soft-gluon
corrections dominate the perturbative expansion not only for LHC energies but
also for future collider energies through 100 TeV. Detailed results are
presented for various collision energies. The $K$-factors are large and
increase slowly with increasing energy. The scale uncertainties decrease
greatly as we move to higher perturbative orders.


title: Direct Statistical Simulation of the Busse Annulus

abstract:
  We consider direct statistical simulation (DSS) of a paradigm system of
convection interacting with mean flows. In the Busse Annulus model zonal jets
are generated through the interaction of convectively driven turbulence and
rotation; non-trivial dynamics including the emergence of multiple jets and
bursting `predator-prey' type dynamics can be found. We formulate the DSS by
expanding around the mean flow in terms of equal-time cumulants and arrive at a
closed set of equations of motion for the cumulants. Here, we present results
using an expansion terminated at the second cumulant (CE2); it is fundamentally
a quasi-linear theory. We focus on particular cases including bursting and
bistable multiple jets and demonstrate that CE2 can reproduce the results of
direct numerical simulation if particular attention is given to symmetry
considerations.


title:
Angular cuts to reduce the tautaubar+jet background to the higgsino
  signal at the LHC

abstract:
  We re-examine higgsino pair production in association with a hard QCD jet at
the LHC. We focus on \ell^+\ell^- +MET +jet events from the production and
subsequent decay, \tchi_2^0\to\tchi_1^0\ell^+\ell^-, of the heavier neutral
higgsino. The novel feature of our analysis is that we propose angular cuts to
reduce the irreducible background from Z(-> \tau\bar{\tau})+jet events more
efficiently than the m_{\tau\tau}^2<0 cut that has been used by the ATLAS and
CMS collaborations. Additional cuts, needed to reduce backgrounds from
t\bar{t}, WWj and W/Z+\ell\bar{\ell} production, are also delineated. We
evaluate the reach of LHC14 for 300 and 3000~fb^{-1} and stress that the
dilepton mass distribution would serve to characterize the higgsino signal.


title: Local constancy of pro-unipotent Kummer maps

abstract:
  It is a theorem of Kim-Tamagawa that the $\mathbb Q_\ell$-pro-unipotent
Kummer map associated to a smooth projective curve $Y$ over a finite extension
of $\mathbb Q_p$ is locally constant when $\ell\neq p$. The present paper
establishes two generalisations of this result. Firstly, we extend the
Kim-Tamagawa Theorem to the case that $Y$ is a smooth variety of any dimension.
Secondly, we formulate and prove the analogue of the Kim-Tamagawa Theorem in
the case $\ell = p$, again in arbitrary dimension. In the course of proving the
latter, we give a proof of an \'etale-de Rham comparison theorem for
pro-unipotent fundamental groupoids using methods of Scholze and
Diao-Lan-Liu-Zhu. This extends the comparison theorem proved by Vologodsky for
certain truncations of the fundamental groupoids.


title: On a Continuous-Time Version of Willems' Lemma

abstract:
  In this paper, a method to represent every input-output trajectory of a
continuous-time linear system in terms of previously collected data is
presented. This corresponds to a continuous-time version of the well-known
Willems' lemma. The result is obtained by sampling the continuous signals at
regular intervals, and constructing Hankel-like structures that closely
resemble their discrete-time counterparts. Then, it is shown how to use
measured persistently excited data to design a time-varying vector of
parameters that allows the generation of arbitrary piecewise differentiable
trajectories. A class of input signals that satisfies the conditions for
persistence of excitation is also provided.


title:
On the dimension of the "cohits" space $\mathbb Z_2\otimes_{\mathcal
  A_2} H^{*}((\mathbb RP(\infty))^{\times t}, \mathbb Z_2)$ and some
  applications

abstract:
  We denote by $\mathbb Z_2$ the prime field of two elements and by $P_t =
\mathbb Z_2[x_1, \ldots, x_t]$ the polynomial algebra of $t$ generators $x_1,
\ldots, x_t$ with the degree of each $x_i$ being one. Let $\mathcal A_2$ be the
Steenrod algebra over $\mathbb Z_2.$ A central problem of homotopy theory is to
determine a minimal set of generators for the "cohits" space $\mathbb
Z_2\otimes_{\mathcal A_2} P_t.$ This problem, which is called the "hit" problem
for Steenrod algebra, has been systematically studied for $t\leq 4.$ The
present paper is devoted to the investigation of the structure of $\mathbb
Z_2\otimes_{\mathcal A_2} P_t$ in some certain "generic" degrees. More
specifically, we explicitly determine a monomial basis of $\mathbb
Z_2\otimes_{\mathcal A_2} P_5$ in degree $n_s=5(2^{s}-1) + 42.2^{s}$ for every
non-negative integer $s.$ As a result, it confirms Sum's conjecture [14] for a
relation between the minimal sets of $\mathcal A_2$-generators of the algebras
$P_{t-1}$ and $P_{t}$ in the case $t=5$ and degree $n_s$. As applications, we
obtain the dimension of $\mathbb Z_2\otimes_{\mathcal A_2} P_6$ in the generic
degree $5(2^{s+5}-1) + n_0.2^{s+5}$ for all $s\geq 0,$ and show that the
Singer's cohomological transfer [11] is an isomorphism in bidegree $(5,
5+n_s)$.


title: Mid-Air Helicopter Delivery at Mars Using a Jetpack

abstract:
  Mid-Air Helicopter Delivery (MAHD) is a new Entry, Descent and Landing (EDL)
architecture to enable in situ mobility for Mars science at lower cost than
previous missions. It uses a jetpack to slow down a Mars Science Helicopter
(MSH) after separation from the backshell, and reach aerodynamic conditions
suitable for helicopter take-off in mid air. For given aeroshell dimensions,
only MAHD's lander-free approach leaves enough room in the aeroshell to
accommodate the largest rotor option for MSH. This drastically improves flight
performance, notably allowing +150\% increased science payload mass. Compared
to heritage EDL approaches, the simpler MAHD architecture is also likely to
reduce cost, and enables access to more hazardous and higher-elevation terrains
on Mars. This paper introduces a design for the MAHD system architecture and
operations. We present a mechanical configuration that fits both MSH and the
jetpack within the 2.65-m Mars heritage aeroshell, and a jetpack control
architecture which fully leverages the available helicopter avionics. We
discuss preliminary numerical models of the flow dynamics resulting from the
interaction between the jets, the rotors and the side winds. We define a
force-torque sensing architecture capable of handling the wind and trimming the
rotors to prepare for safe take-off. Finally, we analyze the dynamic
environment and closed-loop control simulation results to demonstrate the
preliminary feasibility of MAHD.


title: High-Dimensional Expanders from Chevalley Groups

abstract:
  Let $\Phi$ be an irreducible root system (other than $G_2$) of rank at least
$2$, let $\mathbb{F}$ be a finite field with $p = \operatorname{char}
\mathbb{F} > 3$, and let $\mathrm{G}(\Phi,\mathbb{F})$ be the corresponding
Chevalley group. We describe a strongly explicit high-dimensional expander
(HDX) family of dimension $\mathrm{rank}(\Phi)$, where
$\mathrm{G}(\Phi,\mathbb{F})$ acts simply transitively on the top-dimensional
faces; these are $\lambda$-spectral HDXs with $\lambda \to 0$ as $p \to
\infty$. This generalizes a construction of Kaufman and Oppenheim (STOC 2018),
which corresponds to the case $\Phi = A_d$. Our work gives three new families
of spectral HDXs of any dimension $\ge 2$, and four exceptional constructions
of dimension $4$, $6$, $7$, and $8$.


title: Detection of AI Synthesized Hindi Speech

abstract:
  The recent advancements in generative artificial speech models have made
possible the generation of highly realistic speech signals. At first, it seems
exciting to obtain these artificially synthesized signals such as speech clones
or deep fakes but if left unchecked, it may lead us to digital dystopia. One of
the primary focus in audio forensics is validating the authenticity of a
speech. Though some solutions are proposed for English speeches but the
detection of synthetic Hindi speeches have not gained much attention. Here, we
propose an approach for discrimination of AI synthesized Hindi speech from an
actual human speech. We have exploited the Bicoherence Phase, Bicoherence
Magnitude, Mel Frequency Cepstral Coefficient (MFCC), Delta Cepstral, and Delta
Square Cepstral as the discriminating features for machine learning models.
Also, we extend the study to using deep neural networks for extensive
experiments, specifically VGG16 and homemade CNN as the architecture models. We
obtained an accuracy of 99.83% with VGG16 and 99.99% with homemade CNN models.


title:
All one needs to know about shared micromobility simulation: a complete
  survey

abstract:
  As the shared micromobility becomes a part of our daily life and environment,
we expect the number of low-speed modes for first-and-last mile trips to grow
rapidly. The shared micomobility is expected to serve billions of humans,
bringing us considerable advantages. With this growth, shared micromobility
simulation such as docked stations based shared bikes, dockless shared bikes
and e-scooters, are regarded as promising solutions to deal with a large number
of first-and-last mile trips. In this paper, we first provide a comprehensive
overview of shared micromobility simulation and its related validation metrics.
Next, we classify the research topics of shared micromobility simulation,
summarize, and classify the existing works. Finally, challenges and future
directions are provided for further research.


title:
Biological, Family and Cultural Predictors of Personality Structure
  analysis based on personality prediction models constructed by open data
  source

abstract:
  Objective: This study takes further step on understanding personality
structure in order to cope with the mental health during the COVID-19 global
pandemic situation. Methods: Categorized the independent variables into
biological, family and cultural predictors according to the datasets of the
Big-5 personality survey online. And established multiple regression prediction
models and exhaustive CHAID decision tree model of each personality trait.
Results: Females are different from males in personality. The personality
changes when growing. One-handed dominants are less agreeable and open than
those who use both hands. Different sexual orientation does have variety
personality. Native language used and education attainment is significantly
related to personality accordingly. Marriage did help shaping personality to be
more extroverted, less neurotic or agreeable and more conscientious and open.
People raised in urban are more agreeable and open. Neurotic and open people
often come from small families. person participated in voting are more
extroverted, conscientious and open but less neurotic and agreeable. Different
religions and races have different characteristics in each dimension of
personality and there is no clear pattern have been found. Conclusion:
Personality traits are indeed affected by multiple confounding factors. but the
exploration on multiple cultures predictors still needed more details


title:
Impact of Critical and Auto Ticket: Analysis for Management and Workers
  Productivity in using a Ticketing System

abstract:
  Ticketing system is common in Technical Support in Information Technology
Industry. At present time, even management is using it. It serves as a way to
connect the company and the client, end to end. The researchers conducted
research where it aims to come up with a solution on how we are going to
prevent, troubleshoot, and give insight for possible business impact to those
everyday issues. Researchers used data collection to gather data from
management, support workers, and Service Now open-source ticketing system to
visualize the ticketing system application. Critical ticket gives a lot of
pressure to the resources as they needed to resolve the incident in accordance
with the service level agreement. Having knowledge management helps resource to
find references on how to deal with the incident. It helps them to execute
workaround quickly and think of a way on how to resolve it permanently. It is
concluded that critical and auto ticket affects the everyday productivity of
the worker especially teaching new employees despite ongoing critical
incidents. Researchers provided solutions such as knowledge Management and
Dashboard to document all the solutions encountered and monitor the SLA and
incoming tickets. It is recommended to have further research on how critical
and auto ticket affects the mental health of resources and its direct impact to
businesses. It is also recommended to have a study on how knowledge management
work and help resources to identify correct workaround despite of having a lot
of troubleshooting guides.


title: The Composite Higgs Signal at the Next Big Collider

abstract:
  The Gildener-Weinberg (GW) mechanism produces a Higgs boson $H$ that is a
dilaton. That is, $H$ is both naturally light and naturally aligned. It also
predicts additional singly-charged and neutral Higgs bosons all of whose masses
are $< 500\,{\rm GeV}$ and, therefore, within reach of the LHC now. I argue
that the GW Higgs is composite -- a bound state of fermions whose strong
interactions are at some high, unknown scale $\Lambda_H > 1\,{\rm TeV}$. The
lone harbingers of $H$ compositeness, ones that may be accessible at the next
multi-TeV collider, are isovector vector $\rho_H$ and axial vector $a_H$ bound
states whose masses are $\cal{O}(\Lambda_H)$. They decay into the only
fermion-antifermion composites lighter than they are, the Higgs boson and
longitudinally-polarized weak bosons: $\rho_H^{\pm,0} \to W^\pm_L Z_L$, $W^+_L
W^-_L$ and $a_H^{\pm,0} \to W^\pm_L H$, $Z_L H$. Observing these resonant,
highly-boosted weak-scale bosons would establish their composite nature.


title: Algorithmic audits of algorithms, and the law

abstract:
  Algorithmic decision making is now widespread, ranging from health care
allocation to more common actions such as recommendation or information
ranking. The aim to audit these algorithms has grown alongside. In this paper,
we focus on external audits that are conducted by interacting with the user
side of the target algorithm, hence considered as a black box. Yet, the legal
framework in which these audits take place is mostly ambiguous to researchers
developing them: on the one hand, the legal value of the audit outcome is
uncertain; on the other hand the auditors' rights and obligations are unclear.
The contribution of this paper is to articulate two canonical audit forms to
law, to shed light on these aspects: 1) the first audit form (we coin the Bobby
audit form) checks a predicate against the algorithm, while the second
(Sherlock) is more loose and opens up to multiple investigations. We find that:
Bobby audits are more amenable to prosecution, yet are delicate as operating on
real user data. This can lead to reject by a court (notion of admissibility).
Sherlock audits craft data for their operation, most notably to build
surrogates of the audited algorithm. It is mostly used for acts for
whistleblowing, as even if accepted as a proof, the evidential value will be
low in practice. 2) these two forms require the prior respect of a proper right
to audit, granted by law or by the platform being audited; otherwise the
auditor will be also prone to prosecutions regardless of the audit outcome.
This article thus highlights the relation of current audits with law, in order
to structure the growing field of algorithm auditing.


title: Trusted Data Forever: Is AI the Answer?

abstract:
  Archival institutions and programs worldwide work to ensure that the records
of governments, organizations, communities, and individuals are preserved for
future generations as cultural heritage, as sources of rights, and as vehicles
for holding the past accountable and to inform the future. This commitment is
guaranteed through the adoption of strategic and technical measures for the
long-term preservation of digital assets in any medium and form - textual,
visual, or aural. Public and private archives are the largest providers of data
big and small in the world and collectively host yottabytes of trusted data, to
be preserved forever. Several aspects of retention and preservation,
arrangement and description, management and administrations, and access and use
are still open to improvement. In particular, recent advances in Artificial
Intelligence (AI) open the discussion as to whether AI can support the ongoing
availability and accessibility of trustworthy public records. This paper
presents preliminary results of the InterPARES Trust AI (I Trust AI)
international research partnership, which aims to (1) identify and develop
specific AI technologies to address critical records and archives challenges;
(2) determine the benefits and risks of employing AI technologies on records
and archives; (3) ensure that archival concepts and principles inform the
development of responsible AI; and (4) validate outcomes through a conglomerate
of case studies and demonstrations.


title:
A Predictive Model for Student Performance in Classrooms Using Student
  Interactions With an eTextbook

abstract:
  With the rise of online eTextbooks and Massive Open Online Courses (MOOCs), a
huge amount of data has been collected related to students' learning. With the
careful analysis of this data, educators can gain useful insights into the
performance of their students and their behavior in learning a particular
topic. This paper proposes a new model for predicting student performance based
on an analysis of how students interact with an interactive online eTextbook.
By being able to predict students' performance early in the course, educators
can easily identify students at risk and provide a suitable intervention. We
considered two main issues the prediction of good/bad performance and the
prediction of the final exam grade. To build the proposed model, we evaluated
the most popular classification and regression algorithms on data from a data
structures and algorithms course (CS2) offered in a large public research
university. Random Forest Regression and Multiple Linear Regression have been
applied in Regression. While Logistic Regression, decision tree, Random Forest
Classifier, K Nearest Neighbors, and Support Vector Machine have been applied
in classification.


title:
Cloud Computing-based Higher Education Platforms during the COVID-19
  Pandemic

abstract:
  Cloud computing has become the infrastructure that supports people's daily
activities, business operations, and education delivery around the world. Cloud
computing-based education platforms have been widely applied to assist online
teaching during the COVID-19 pandemic. This paper examines the impact and
importance of cloud computing in remote learning and education. This study
conducted multiple-case analyses of 22 online platforms of higher education in
Chinese universities during the epidemic. A comparative analysis of the 22
platforms revealed that they applied different cloud computing models and tools
based on their unique requirements and needs. The study results provide
strategic insights to higher education institutions regarding effective
approaches to applying cloud computing-based platforms for remote education,
especially during crisis situations.


title: Needs and Artificial Intelligence

abstract:
  Throughout their history, homo sapiens have used technologies to better
satisfy their needs. The relation between needs and technology is so
fundamental that the US National Research Council defined the distinguishing
characteristic of technology as its goal "to make modifications in the world to
meet human needs". Artificial intelligence (AI) is one of the most promising
emerging technologies of our time. Similar to other technologies, AI is
expected "to meet [human] needs". In this article, we reflect on the
relationship between needs and AI, and call for the realisation of needs-aware
AI systems. We argue that re-thinking needs for, through, and by AI can be a
very useful means towards the development of realistic approaches for
Sustainable, Human-centric, Accountable, Lawful, and Ethical (HALE) AI systems.
We discuss some of the most critical gaps, barriers, enablers, and drivers of
co-creating future AI-based socio-technical systems in which [human] needs are
well considered and met. Finally, we provide an overview of potential threats
and HALE considerations that should be carefully taken into account, and call
for joint, immediate, and interdisciplinary efforts and collaborations.


title: Open-Ended Knowledge Tracing

abstract:
  Knowledge tracing refers to the problem of estimating each student's
knowledge component/skill mastery level from their past responses to questions
in educational applications. One direct benefit knowledge tracing methods
provide is the ability to predict each student's performance on the future
questions. However, one key limitation of most existing knowledge tracing
methods is that they treat student responses to questions as binary-valued,
i.e., whether the responses are correct or incorrect. Response correctness
analysis/prediction is easy to navigate but loses important information,
especially for open-ended questions: the exact student responses can
potentially provide much more information about their knowledge states than
only response correctness. In this paper, we present our first exploration into
open-ended knowledge tracing, i.e., the analysis and prediction of students'
open-ended responses to questions in the knowledge tracing setup. We first lay
out a generic framework for open-ended knowledge tracing before detailing its
application to the domain of computer science education with programming
questions. We define a series of evaluation metrics in this domain and conduct
a series of quantitative and qualitative experiments to test the boundaries of
open-ended knowledge tracing methods on a real-world student code dataset.


title:
An Analysis of Energy Consumption and Carbon Footprints of
  Cryptocurrencies and Possible Solutions

abstract:
  There is an urgent need to control global warming caused by humans to achieve
a sustainable future. $CO_2$ levels are rising steadily and while countries
worldwide are actively moving toward the sustainability goals proposed during
the Paris Agreement in 2015, we are still a long way to go from achieving a
sustainable mode of global operation. The increased popularity of
cryptocurrencies since the introduction of Bitcoin in 2009 has been accompanied
by an increasing trend in greenhouse gas emissions and high electrical energy
consumption. Popular energy tracking studies (e.g., Digiconomist and the
Cambridge Bitcoin Energy Consumption Index (CBECI)) have estimated energy
consumption ranges of 29.96 TWh to 135.12 TWh and 26.41 TWh to 176.98 TWh
respectively for Bitcoin as of July 2021, which are equivalent to the energy
consumption of countries such as Sweden and Thailand. The latest estimate by
Digiconomist on carbon footprints shows a 64.18 Mt$CO_2$ emission by Bitcoin as
of July 2021, close to the emissions by Greece and Oman. This review compiles
estimates made by various studies from 2018 to 2021. We compare with the energy
consumption and carbon footprints of these cryptocurrencies with countries
around the world, and centralized transaction methods such as Visa. We identify
the problems associated with cryptocurrencies, and propose solutions that can
help reduce their energy usage and carbon footprints. Finally, we present case
studies on cryptocurrency networks namely, Ethereum 2.0 and Pi Network, with a
discussion on how they solve some of the challenges we have identified.


title: Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace

abstract:
  AI plays a key role in current cyberspace and future immersive ecosystems
that pinpoint user experiences. Thus, the trustworthiness of such AI systems is
vital as failures in these systems can cause serious user harm. Although there
are related works on exploring trustworthy AI (TAI) metrics in the current
cyberspace, ecosystems towards user-centered services, such as the metaverse,
are much more complicated in terms of system performance and user experience
assessment, thus posing challenges for the applicability of existing
approaches. Thus, we give an overlook on fairness, privacy and robustness,
across the historical path from existing approaches. Eventually, we propose a
research agenda towards systematic yet user-centered TAI in immersive
ecosystems.


title: Biometric recognition: why not massively adopted yet?

abstract:
  Although there has been a dramatically reduction on the prices of capturing
devices and an increase on computing power in the last decade, it seems that
biometric systems are still far from massive adoption for civilian
applications. This paper deals with the causes of this phenomenon, as well as
some misconceptions regarding biometric identification.


title:
Exploring Fairness in District-based Multi-party Elections under
  different Voting Rules using Stochastic Simulations

abstract:
  Many democratic societies use district-based elections, where the region
under consideration is geographically divided into districts and a
representative is chosen for each district based on the preferences of the
electors who reside there. These representatives belong to political parties,
and the executive powers are acquired by that party which has a majority of the
elected district representatives. In most systems, each elector can express
preference for one candidate, though they may have a complete or partial
ranking of the candidates/parties. We show that this can lead to situations
where many electors are dissatisfied with the election results, which is not
desirable in a democracy. The results may be biased towards the supporters of a
particular party, and against others. Inspired by current literature on
fairness of Machine Learning algorithms, we define measures of fairness to
quantify the satisfaction of electors, irrespective of their political choices.
We also consider alternative election policies using concepts of voting rules
and rank aggregation, to enable voters to express their detailed preferences
without making the electoral process cumbersome or opaque. We then evaluate
these policies using the aforementioned fairness measures with the help of
Monte Carlo simulations. Such simulations are obtained using a proposed
stochastic model for election simulation, that takes into account community
identities of electors and its role in influencing their residence and
political preferences. We show that this model can simulate actual multi-party
elections in India. Through extensive simulations, we find that allowing voters
to provide 2 preferences reduces the disparity between supporters of different
parties in terms of the election result.


title:
Mobius fluid dynamics on the unitary groups]{M\"{o}bius fluid dynamics
  on the unitary groups

abstract:
  We study the nonrigid dynamics induced by the standard birational actions of
the split unitary groups $G=O_{o}\left( n,n\right) $, $SU\left( n,n\right) $
and $Sp\left( n,n\right) $ on the compact classical Lie groups $M=SO_{n}$, $%
U_{n}$ and $Sp_{n}$, respectively. More precisely, we study the geometry of $%
G$ endowed with the kinetic energy metric associated with the action of $G$ on
$M,$ assuming that $M$ carries its canonical bi-invariant Riemannian metric and
has initially a homogeneous distribution of mass. By the least action
principle, force free motions (thought of as curves in $G$) correspond to
geodesics of $G$. The geodesic equation may be understood as an inviscid
Burgers equation with M\"{o}bius constraints. We prove that the kinetic energy
metric on $G$ is not complete and in particular not invariant, find symmetries
and totally geodesic submanifolds of $G$ and address the question under which
conditions geodesics of rigid motions are geodesics of $G$. Besides, we study
equivalences with the dynamics of conformal and projective motions of the
sphere in low dimensions.


title:
Cognitive Diagnosis with Explicit Student Vector Estimation and
  Unsupervised Question Matrix Learning

abstract:
  Cognitive diagnosis is an essential task in many educational applications.
Many solutions have been designed in the literature. The deterministic input,
noisy "and" gate (DINA) model is a classical cognitive diagnosis model and can
provide interpretable cognitive parameters, e.g., student vectors. However, the
assumption of the probabilistic part of DINA is too strong, because it assumes
that the slip and guess rates of questions are student-independent. Besides,
the question matrix (i.e., Q-matrix) recording the skill distribution of the
questions in the cognitive diagnosis domain often requires precise labels given
by domain experts. Thus, we propose an explicit student vector estimation
(ESVE) method to estimate the student vectors of DINA with a local
self-consistent test, which does not rely on any assumptions for the
probabilistic part of DINA. Then, based on the estimated student vectors, the
probabilistic part of DINA can be modified to a student dependent model that
the slip and guess rates are related to student vectors. Furthermore, we
propose an unsupervised method called heuristic bidirectional calibration
algorithm (HBCA) to label the Q-matrix automatically, which connects the
question difficulty relation and the answer results for initialization and uses
the fault tolerance of ESVE-DINA for calibration. The experimental results on
two real-world datasets show that ESVE-DINA outperforms the DINA model on
accuracy and that the Q-matrix labeled automatically by HBCA can achieve
performance comparable to that obtained with the manually labeled Q-matrix when
using the same model structure.


title:
Judging the algorithm: A case study on the risk assessment tool for
  gender-based violence implemented in the Basque country

abstract:
  Since 2010, the output of a risk assessment tool that predicts how likely an
individual is to commit severe violence against their partner has been
integrated within the Basque country courtrooms. The EPV-R, the tool developed
to assist police officers during the assessment of gender-based violence cases,
was also incorporated to assist the decision-making of judges. With
insufficient training, judges are exposed to an algorithmic output that
influences the human decision of adopting measures in cases of gender-based
violence.
  In this paper, we examine the risks, harms and limits of algorithmic
governance within the context of gender-based violence. Through the lens of an
Spanish judge exposed to this tool, we analyse how the EPV-R is impacting on
the justice system. Moving beyond the risks of unfair and biased algorithmic
outputs, we examine legal, social and technical pitfalls such as opaque
implementation, efficiency's paradox and feedback loop, that could led to
unintended consequences on women who suffer gender-based violence. Our
interdisciplinary framework highlights the importance of understanding the
impact and influence of risk assessment tools within judicial decision-making
and increase awareness about its implementation in this context.


title:
A New Era: Intelligent Tutoring Systems Will Transform Online Learning
  for Millions

abstract:
  Despite artificial intelligence (AI) having transformed major aspects of our
society, less than a fraction of its potential has been explored, let alone
deployed, for education. AI-powered learning can provide millions of learners
with a highly personalized, active and practical learning experience, which is
key to successful learning. This is especially relevant in the context of
online learning platforms. In this paper, we present the results of a
comparative head-to-head study on learning outcomes for two popular online
learning platforms (n=199 participants): A MOOC platform following a
traditional model delivering content using lecture videos and multiple-choice
quizzes, and the Korbit learning platform providing a highly personalized,
active and practical learning experience. We observe a huge and statistically
significant increase in the learning outcomes, with students on the Korbit
platform providing full feedback resulting in higher course completion rates
and achieving learning gains 2 to 2.5 times higher than both students on the
MOOC platform and students in a control group who don't receive personalized
feedback on the Korbit platform. The results demonstrate the tremendous impact
that can be achieved with a personalized, active learning AI-powered system.
Making this technology and learning experience available to millions of
learners around the world will represent a significant leap forward towards the
democratization of education.


title:
A new benchmark of soft X-ray transition energies of Ne, CO$_2$, and
  SF$_6$: paving a pathway towards ppm accuracy

abstract:
  A key requirement for the correct interpretation of high-resolution X-ray
spectra is that transition energies are known with high accuracy and precision.
We investigate the K-shell features of Ne, CO$_2$, and SF$_6$ gases, by
measuring their photo ion-yield spectra at the BESSY II synchrotron facility
simultaneously with the 1s-np fluorescence emission of He-like ions produced in
the Polar-X EBIT. Accurate ab initio calculations of transitions in these ions
provide the basis of the calibration. While the CO$_2$ result agrees well with
previous measurements, the SF$_6$ spectrum appears shifted by ~0.5 eV, about
twice the uncertainty of the earlier results. Our result for Ne shows a large
departure from earlier results, but may suffer from larger systematic effects
than our other measurements. The molecular spectra agree well with our results
of time-dependent density functional theory. We find that the statistical
uncertainty allows calibrations in the desired range of 1-10 meV, however,
systematic contributions still limit the uncertainty to ~40-100 meV, mainly due
to the temporal stability of the monochromator energy scale. Combining our
absolute calibration technique with a relative energy calibration technique
such as photoelectron energy spectroscopy will be necessary to realize its full
potential of achieving uncertainties as low as 1-10 meV.


title: The Braess Paradox in Dynamic Traffic

abstract:
  The Braess's Paradox (BP) is the observation that adding one or more roads to
the existing road network will counter-intuitively increase traffic congestion
and slow down the overall traffic flow. Previously, the existence of the BP is
modeled using the static traffic assignment model, which solves for the user
equilibrium subject to network flow conservation to find the equilibrium state
and distributes all vehicles instantaneously. Such approach neglects the
dynamic nature of real-world traffic, including vehicle behaviors and the
interaction between vehicles and the infrastructure. As such, this article
proposes a dynamic traffic network model and empirically validates the
existence of the BP under dynamic traffic. In particular, we use
microsimulation environment to study the impacts of an added path on a grid
network. We explore how the network flow, vehicle travel time, and network
capacity respond, as well as when the BP will occur.


title: Barlow constrained optimization for Visual Question Answering

abstract:
  Visual question answering is a vision-and-language multimodal task, that aims
at predicting answers given samples from the question and image modalities.
Most recent methods focus on learning a good joint embedding space of images
and questions, either by improving the interaction between these two
modalities, or by making it a more discriminant space. However, how informative
this joint space is, has not been well explored. In this paper, we propose a
novel regularization for VQA models, Constrained Optimization using Barlow's
theory (COB), that improves the information content of the joint space by
minimizing the redundancy. It reduces the correlation between the learned
feature components and thereby disentangles semantic concepts. Our model also
aligns the joint space with the answer embedding space, where we consider the
answer and image+question as two different `views' of what in essence is the
same semantic information. We propose a constrained optimization policy to
balance the categorical and redundancy minimization forces. When built on the
state-of-the-art GGE model, the resulting model improves VQA accuracy by 1.4%
and 4% on the VQA-CP v2 and VQA v2 datasets respectively. The model also
exhibits better interpretability.


title:
Self-organized explosive synchronization in complex networks: Emergence
  of synchronization bombs

abstract:
  We introduce the concept of synchronization bombs as large networks of
coupled heterogeneous oscillators that operate in a bistable regime and
abruptly transit from incoherence to phase-locking (or vice-versa) by adding
(or removing) one or a few links. Here we build a self-organized and stochastic
version of these bombs, by optimizing global synchrony with decentralized
information in a competitive link-percolation process driven by a local rule.
We find explosive fingerprints on the emerging network structure, including
frequency-degree correlations, disassortative patterns and a delayed
percolation threshold. We show that these bomb-like transitions can be designed
both in systems of Kuramoto -- periodic -- and R\"ossler -- chaotic --
oscillators and in a model of cardiac pacemaker cells. We analytically
characterize the transitions in the Kuramoto case by combining a precise
collective coordinates approach and the Ott-Antonsen ansatz. Furthermore, we
study the robustness of the phenomena under changes in the main parameters and
the unexpected effect of optimal noise in our model. Our results propose a
minimal self-organized mechanism of network growth to understand and control
explosive synchronization in adaptive biological systems like the brain and
engineered ones like power-grids or electronic circuits. From a theoretical
standpoint, the emergence of synchronization explosions and bistability induced
by localized structural perturbations -- without any fine-tuning of global
parameters -- joins explosive synchronization and percolation under the same
mechanistic framework.


title: Robustness and Usefulness in AI Explanation Methods

abstract:
  Explainability in machine learning has become incredibly important as machine
learning-powered systems become ubiquitous and both regulation and public
sentiment begin to demand an understanding of how these systems make decisions.
As a result, a number of explanation methods have begun to receive widespread
adoption. This work summarizes, compares, and contrasts three popular
explanation methods: LIME, SmoothGrad, and SHAP. We evaluate these methods with
respect to: robustness, in the sense of sample complexity and stability;
understandability, in the sense that provided explanations are consistent with
user expectations; and usability, in the sense that the explanations allow for
the model to be modified based on the output. This work concludes that current
explanation methods are insufficient; that putting faith in and adopting these
methods may actually be worse than simply not using them.


title: Provably Accurate and Scalable Linear Classifiers in Hyperbolic Spaces

abstract:
  Many high-dimensional practical data sets have hierarchical structures
induced by graphs or time series. Such data sets are hard to process in
Euclidean spaces and one often seeks low-dimensional embeddings in other space
forms to perform the required learning tasks. For hierarchical data, the space
of choice is a hyperbolic space because it guarantees low-distortion embeddings
for tree-like structures. The geometry of hyperbolic spaces has properties not
encountered in Euclidean spaces that pose challenges when trying to rigorously
analyze algorithmic solutions. We propose a unified framework for learning
scalable and simple hyperbolic linear classifiers with provable performance
guarantees. The gist of our approach is to focus on Poincar\'e ball models and
formulate the classification problems using tangent space formalisms. Our
results include a new hyperbolic perceptron algorithm as well as an efficient
and highly accurate convex optimization setup for hyperbolic support vector
machine classifiers. Furthermore, we adapt our approach to accommodate
second-order perceptrons, where data is preprocessed based on second-order
information (correlation) to accelerate convergence, and strategic perceptrons,
where potentially manipulated data arrives in an online manner and decisions
are made sequentially. The excellent performance of the Poincar\'e second-order
and strategic perceptrons shows that the proposed framework can be extended to
general machine learning problems in hyperbolic spaces. Our experimental
results, pertaining to synthetic, single-cell RNA-seq expression measurements,
CIFAR10, Fashion-MNIST and mini-ImageNet, establish that all algorithms
provably converge and have complexity comparable to those of their Euclidean
counterparts. Accompanying codes can be found at:
https://github.com/thupchnsky/PoincareLinearClassification.


title:
One-dimensional symmetry of positive bounded solutions to the subcubic
  and cubic nonlinear Schr\"{o}dinger equation in the half-space in dimensions
  $N=4,5$

abstract:
  We are concerned with the half-space Dirichlet problem
\[\left\{\begin{array}{ll}
  -\Delta v+v=|v|^{p-1}v & \textrm{in}\ \mathbb{R}^N_+,
  v=c\ \textrm{on}\ \partial\mathbb{R}^N_+, &\lim_{x_N\to \infty}v(x',x_N)=0\
\textrm{uniformly in}\ x'\in\mathbb{R}^{N-1}, \end{array}\right. \] where
$\mathbb{R}^N_+=\{x\in \mathbb{R}^N \ : \ x_N>0\}$ for some $N\geq 2$, and
$p>1$, $c>0$ are constants. It was shown recently by Fernandez and Weth [Math.
Ann. (2021)] that there exists an explicit number $c_p\in (1,\sqrt{e})$,
depending only on $p$, such that for $0<c<c_p$ there are infinitely many
bounded positive solutions, whereas, for $c>c_p$ there are no bounded positive
solutions. They also posed as an interesting open question whether the
one-dimensional solution is the unique bounded positive solution in the case
where $c = c_p$. If $N=2, 3$, we recently showed this one-dimensional symmetry
property in [Partial Differ. Equ. Appl. (2021)] by adapting some ideas from the
proof of De Giorgi's conjecture in low dimensions. Here, we first focus on the
case $1<p<3$ and prove this uniqueness property in dimensions $2\leq N\leq 5$.
Then, for the cubic NLS, where $p = 3$, we establish this for $2 \leq N \leq
4$. Our approach is completely different and relies on showing that a suitable
auxiliary function, inspired by a Lyapunov-Schmidt type decomposition of the
solution, is a nonnegative super-solution to a Lane-Emden-Fowler equation in
$\mathbb{R}^{N-1}$, for which an optimal Liouville type result is available.


title: A Push-Relabel Based Additive Approximation for Optimal Transport

abstract:
  Optimal Transport is a popular distance metric for measuring similarity
between distributions. Exact algorithms for computing Optimal Transport can be
slow, which has motivated the development of approximate numerical solvers
(e.g. Sinkhorn method). We introduce a new and very simple combinatorial
approach to find an $\varepsilon$-approximation of the OT distance. Our
algorithm achieves a near-optimal execution time of $O(n^2/\varepsilon^2)$ for
computing OT distance and, for the special case of the assignment problem, the
execution time improves to $O(n^2/\varepsilon)$. Our algorithm is based on the
push-relabel framework for min-cost flow problems.
  Unlike the other combinatorial approach (Lahn, Mulchandani and Raghvendra,
NeurIPS 2019) which does not have a fast parallel implementation, our algorithm
has a parallel execution time of $O(\log n/\varepsilon^2)$. Interestingly,
unlike the Sinkhorn algorithm, our method also readily provides a compact
transport plan as well as a solution to an approximate version of the dual
formulation of the OT problem, both of which have numerous applications in
Machine Learning. For the assignment problem, we provide both a CPU
implementation as well as an implementation that exploits GPU parallelism.
Experiments suggest that our algorithm is faster than the Sinkhorn algorithm,
both in terms of CPU and GPU implementations, especially while computing
matchings with a high accuracy.


title: Another look at the Bal\'azs-Quastel-Sepp\"al\"ainen theorem

abstract:
  We study the KPZ equation with a $1+1$ spacetime white noise, started at
equilibrium, and give a different proof of the main result of \cite{bqs}, i.e.,
the variance of the solution at time $t$ is of order $t^{2/3}$. Instead of
using a discrete approximation through the exclusion process and the second
class particle, we utilize the connection to directed polymers in random
environment. Along the way, we show the annealed density of the stationary
continuum directed polymer equals to the two-point covariance function of the
stationary stochastic Burgers equation, confirming the physics prediction in
\cite{MT}.


title: On the LS-category of homomorphisms

abstract:
  We prove the equality $\cat(\phi)=\cd(\phi)$ for homomorphisms
$\phi:\Gamma\to \Lambda$ of a torsion free finitely generated nilpotent groups
$\Gamma$ to an arbitrary group $\Lambda$. We construct an epimorphism
$\psi:G\to H$ between geometrically finite groups with $\cat(\psi)> \cd(\psi)$.


title:
Predicting 2D Normal Grain Growth using a Physics-Regularized
  Interpretable Machine Learning Model

abstract:
  The objective of this work is to demonstrate the application of physics-based
deep learning in spatio-temporal predictions of normal polycrystalline grain
growth in two dimensions (2D). The Physics-Regularized Interpretable Machine
Learning Microstructure Evolution (PRIMME) model is a multi-layer neural
network trained on synthesized grain growth data created using a Monte Carlo
Potts model from the Stochastic Parallel PARticle Kinetic Simulator (SPPARKS).
It observes the interfacial features of grain boundaries and takes pixel-wise
actions to evolve them autonomously. A physical regularization mechanism was
introduced to encourage local energy minimization. PRIMME predicts grain growth
over nondimensional time that can be dimensionalized using an analytical model
of a shrinking circular grain. PRIMME predicts normal grain growth for any size
2D rectangular domain and any number or configuration of initial grains, and
provides data structures to interpret the predicted growth. It predicts an
accurate steady state grain size distribution and quadratic change in the
average grain size with time. These results show that machine learning methods
are capable of capturing physical laws that govern grain growth. This opens new
avenues for discovering more complicated physical phenomena in the future, such
as abnormal grain growth.


title:
Global Constraints on Yukawa Operators in the Standard Model Effective
  Theory

abstract:
  CP-violating contributions to Higgs--fermion couplings are absent in the
standard model of particle physics (SM), but are motivated by models of
electroweak baryogenesis. Here, we employ the framework of the SM effective
theory (SMEFT) to parameterise deviations from SM Yukawa couplings. We present
the leading contributions of the relevant operators to the fermionic electric
dipole moments (EDMs). We obtain constraints on the SMEFT Wilson coefficients
from the combination of LHC data and experimental bounds on the electron,
neutron, and mercury EDMs, and for the first time, we perform a combined fit to
LHC and EDM data allowing the presence of CP-violating contributions from
several fermion species simultaneously. Among other results, we find
non-trivial correlations between EDM and LHC constraints even in the
multi-parameter scans, for instance, when floating the CP-even and CP-odd
couplings to all third-generation fermions.


title: Battery Cloud with Advanced Algorithms

abstract:
  A Battery Cloud or cloud battery management system leverages the cloud
computational power and data storage to improve battery safety, performance,
and economy. This work will present the Battery Cloud that collects measured
battery data from electric vehicles and energy storage systems. Advanced
algorithms are applied to improve battery performance. Using remote vehicle
data, we train and validate an artificial neural network to estimate pack SOC
during vehicle charging. The strategy is then tested on vehicles. Furthermore,
high accuracy and onboard battery state of health estimation methods for
electric vehicles are developed based on the differential voltage (DVA) and
incremental capacity analysis (ICA). Using cycling data from battery cells at
various temperatures, we extract the charging cycles and calculate the DVA and
ICA curves, from which multiple features are extracted, analyzed, and
eventually used to estimate the state of health. For battery safety, a
data-driven thermal anomaly detection method is developed. The method can
detect unforeseen anomalies such as thermal runaways at the very early stage.
With the further development of the internet of things, more and more battery
data will be available. Potential applications of battery cloud also include
areas such as battery manufacture, recycling, and electric vehicle battery
swap.


title:
On the wave equation on moving domains: regularity, energy balance and
  application to dynamic debonding

abstract:
  We revisit some issues about existence and regularity for the wave equation
in noncylindrical domains. Using a method of diffeomorphisms, we show how,
through increasing regularity assumptions, the existence of weak solutions,
their improved regularity and an energy balance can be derived. As an
application, we give a rigorous definition of dynamic energy release rate
density for some problems of debonding, and we formulate a proper notion of
solution for such problems. We discuss the consistence of such formulation with
previous ones, given in literature for particular cases.


title:
Stable and Solution-Processable Cumulenic sp-Carbon Wires: A New
  Paradigm for Organic Electronics

abstract:
  Solution-processed, large-area, and flexible electronics largely relies on
the excellent electronic properties of sp$^2$-hybridized carbon molecules,
either in the form of $\pi$-conjugated small molecules and polymers or graphene
and carbon nanotubes. Carbon with sp-hybridization, the foundation of the
elusive allotrope carbyne, offers vast opportunities for functionalized
molecules in the form of linear carbon atomic wires (CAWs), with intriguing and
even superior predicted electronic properties. While CAWs represent a vibrant
field of research, to date, they have only been applied sparingly to molecular
devices. The recent observation of the field-effect in microcrystalline
cumulenes suggests their potential applications in solution-processed thin-film
transistors but concerns surrounding the stability and electronic performance
have precluded developments in this direction. In the present study, ideal
field-effect characteristics are demonstrated for solution-processed thin films
of tetraphenyl[3]cumulene, the shortest semiconducting CAW. Films are deposited
through a scalable, large-area, meniscus-coating technique, providing
transistors with hole mobilities in excess of 0.1 cm$^2$ V$^{-1}$ s$^{-1}$, as
well as promising operational stability under dark conditions. These results
offer a solid foundation for the exploitation of a vast class of molecular
semiconductors for organic electronics based on sp-hybridized carbon systems
and create a previously unexplored paradigm.


title: Extracting Forces from Noisy Dynamics in Dusty Plasmas

abstract:
  Extracting environmental forces from noisy data is a common yet challenging
task in complex physical systems. Machine learning represents a robust approach
to this problem, yet is mostly tested on simulated data with known parameters.
Here we use supervised machine learning to extract the electrostatic,
hydrodynamic, and stochastic forces acting on micron-sized charged particles
levitated in an argon plasma. Trained on simulated particle trajectories using
more than 100 dynamical and statistical features, the model predicts system
parameters with 50\% better accuracy than conventional methods, and provides
non-contact measurements of the particle charge and Debye length.


title: Bending waves excited by irregular gas inflow along warps

abstract:
  Gaia has revealed clear evidence of bending waves in the vertical kinematics
of stars in the Solar Neighbourhood. We study bending waves in two simulations,
one warped, with the warp due to misaligned gas inflow, and the other unwarped.
We find slow, retrograde bending waves in both models, with the ones in the
warped model having larger amplitudes. We also find fast, prograde bending
waves. Prograde bending waves in the unwarped model are very weak, in agreement
with the expectation that these waves should decay on short, ~ crossing,
timescales, due to strong winding. However, prograde bending waves are much
stronger for the duration of the warped model, pointing to irregular gas inflow
along the warp as a continuous source of excitation. We demonstrate that large
amplitude bending waves that propagate through the Solar Neighbourhood give
rise to a correlation between the mean vertical velocity and the angular
momentum, with a slope consistent with that found by Gaia. The bending waves
affect populations of all ages, but the sharpest features are found in the
young populations, hinting that short wavelength waves are not supported by the
older, kinematically hotter, populations. Our results demonstrate the
importance of misaligned gas accretion as a recurrent source of vertical
perturbations of disc galaxies, including in the Milky Way.


title: Optical angular momentum in atomic transitions: a paradox

abstract:
  Stated simply the paradox is as follows: it is clear that the orbital angular
momentum of a light beam in its direction of propagation is an intrinsic
quantity, and therefore has the same value everywhere in the beam. How then can
a Gaussian beam, with precisely zero orbital angular momentum, drive a
(single-photon) quadrupole transition which requires the transfer of angular
momentum 2$\hbar$ to an absorbing atom?


title:
The genus of curves in $\mathbb P^4$ and $\mathbb P^5$ not contained in
  quadrics

abstract:
  A classical problem in the theory of projective curves is the classification
of all their possible genera in terms of the degree and the dimension of the
space where they are embedded. Fixed integers $r,d,s$, Castelnuovo-Halphen's
theory states a sharp upper bound for the genus of a non-degenerate, reduced
and irreducible curve of degree $d$ in $\mathbb P^r$, under the condition of
being not contained in a surface of degree $<s$. This theory can be generalized
in several ways. For instance, fixed integers $r,d,k$, one may ask for the
maximal genus of a curve of degree $d$ in $\mathbb P^r$, not contained in a
hypersurface of a degree $<k$. In the present paper we examine the genus of
curves $C$ of degree $d$ in $\mathbb P^r$ not contained in quadrics (i.e.
$h^0(\mathbb P^r, \mathcal I_C(2))=0$). When $r=4$ and $r=5$, and $d\gg0$, we
exhibit a sharp upper bound for the genus. For certain values of $r\geq 7$, we
are able to determine a sharp bound except for a constant term, and the
argument applies also to curves not contained in cubics.


title: Identifying the Deviator

abstract:
  A group of players are supposed to follow a prescribed profile of strategies.
If they follow this profile, they will reach a given target. We show that if
the target is not reached because some player deviates, then an outside
observer can identify the deviator. We also construct identification methods in
two nontrivial cases.


title: Self-restricting Noise and Quantum Relative Entropy Decay

abstract:
  Open quantum systems as modeled by quantum channels and quantum Markov
semigroups usually decay to subspaces that are invariant under environmental
interactions. It is known that finite-dimensional semigroups with detailed
balance decay exponentially under modified logarithmic-Sobolev inequalities
(MLSIs). Here we analyze discrete and continuous processes that include unitary
components, breaking the detailed balance assumption. We find counter-examples
to analogs of MLSIs for these systems. The generalized quantum Zeno effect
appears for many Lindbladians that combine a decay process with unitary drift.
As incompatible long-time and Zeno limits compete, strong noise often protects
subsystems and subspaces from its own spread. We observe this interplay between
decay and Zeno-like effects experimentally on superconducting qubits using IBM
Q devices. Nonetheless, by combining MLSIs for effective self-adjoint decay
processes across different times, we obtain eventual exponential decay. We
similarly obtain decay rate lower bounds for discrete compositions of quantum
channels.


title:
Calculation of neutron electric dipole moment due to the QCD topological
  term, Weinberg three-gluon operator and the quark chromoelectric moment

abstract:
  We present results for the neutron electric dipole moment due to the
dimension 4 and dimension 6 gluonic CP violation, and the isovector quark
chromoelectric dipole moment using clover valence quarks on HISQ dynamical
ensembles generated by the MILC Collaboration. For the gluonic operators, we
use the gradient flow scheme to obtain divergence-free continuum results. For
the chromoelectric dipole moment operator, we use the unflowed local operator
but discuss how the quadratically divergent mixing with the pseudoscalar
operator can be controlled nonperturbatively.


title: On Lowest Lying $\frac{1}{2}^-$ Octet Baryons

abstract:
  The recently proposed $N^*(890)$ $1/2^-$ baryon is studied in a flavor
$SU(3)$ scheme with $K$ matrix unitarization, by fitting to low energy cross
section and phase shift data. It is found that $N^*(890)$ co-exists with low
lying poles in other channels, which have been extensively discussed in the
literature, though they belong to different octets, in the $SU(3)$ limit. Hence
the existence of $N^*(890)$ is further verified.


title: Gray-categories model algebraic tricategories

abstract:
  Lack described a Quillen model structure on the category GrayCat of
Gray-categories and Gray-functors, for which the weak equivalences are the weak
3-equivalences. In this note, we adapt the technique of Gurski, Johnson, and
Osorno to show the localization of GrayCat at the weak equivalences is
equivalent to the category of algebraic tricategories and pseudo-natural
equivalence classes of weak 3-functors.


title: Direct LiDAR-Inertial Odometry

abstract:
  This paper proposes a new LiDAR-inertial odometry framework that generates
accurate state estimates and detailed maps in real-time on resource-constrained
mobile robots. Our Direct LiDAR-Inertial Odometry (DLIO) algorithm utilizes a
hybrid architecture that combines the benefits of loosely-coupled and
tightly-coupled IMU integration to enhance reliability and real-time
performance while improving accuracy. The proposed architecture has two key
elements. The first is a fast keyframe-based LiDAR scan-matcher that builds an
internal map by registering dense point clouds to a local submap with a
translational and rotational prior generated by a nonlinear motion model. The
second is a factor graph and high-rate propagator that fuses the output of the
scan-matcher with preintegrated IMU measurements for up-to-date pose, velocity,
and bias estimates. These estimates enable us to accurately deskew the next
point cloud using a nonlinear kinematic model for precise motion correction, in
addition to initializing the next scan-to-map optimization prior. We
demonstrate DLIO's superior localization accuracy, map quality, and lower
computational overhead by comparing it to the state-of-the-art using multiple
benchmark, public, and self-collected datasets on both consumer and hobby-grade
hardware.


title:
Comparison of CYGNSS and Jason-3 Wind Speed Measurements via Gaussian
  Processes

abstract:
  Wind is a critical component of the Earth system and has unmistakable impacts
on everyday life. The CYGNSS satellite mission improves observational coverage
of ocean winds via a fleet of eight micro-satellites that use reflected GNSS
signals to infer surface wind speed. We present analyses characterizing
variability in wind speed measurements among the eight CYGNSS satellites and
between antennas. In particular, we use a carefully constructed Gaussian
process model that leverages comparisons between CYGNSS and Jason-3 during a
one-year period from September 2019 to September 2020. The CYGNSS sensors
exhibit a range of biases, most of them between -1.0 m/s and +0.2 m/s with
respect to Jason-3, indicating that some CYGNSS sensors are biased with respect
to one another and with respect to Jason-3. The biases between the starboard
and port antennas within a CYGNSS satellite are smaller. Our results are
consistent with, yet sharper than, a more traditional paired comparison
analysis. We also explore the possibility that the bias depends on wind speed,
finding some evidence that CYGNSS satellites have positive biases with respect
to Jason-3 at low wind speeds. However, we argue that there are subtle issues
associated with estimating wind speed-dependent biases, so additional careful
statistical modeling and analysis is warranted.


title: Class Fairness in Online Matching

abstract:
  In the classical version of online bipartite matching, there is a given set
of offline vertices (aka agents) and another set of vertices (aka items) that
arrive online. When each item arrives, its incident edges -- the agents who
like the item -- are revealed and the algorithm must irrevocably match the item
to such agents. We initiate the study of class fairness in this setting, where
agents are partitioned into a set of classes and the matching is required to be
fair with respect to the classes. We adopt popular fairness notions from the
fair division literature such as envy-freeness (up to one item),
proportionality, and maximin share fairness to our setting. Our class versions
of these notions demand that all classes, regardless of their sizes, receive a
fair treatment. We study deterministic and randomized algorithms for matching
indivisible items (leading to integral matchings) and for matching divisible
items (leading to fractional matchings). We design and analyze three novel
algorithms. For matching indivisible items, we propose an
adaptive-priority-based algorithm, MATCH-AND-SHIFT, prove that it achieves
1/2-approximation of both class envy-freeness up to one item and class maximin
share fairness, and show that each guarantee is tight. For matching divisible
items, we design a water-filling-based algorithm, EQUAL-FILLING, that achieves
(1-1/e)-approximation of class envy-freeness and class proportionality; we
prove (1-1/e) to be tight for class proportionality and establish a 3/4 upper
bound on class envy-freeness. Finally, we build upon EQUAL-FILLING to design a
randomized algorithm for matching indivisible items, EQAUL-FILLING-OCS, which
achieves 0.593-approximation of class proportionality. The algorithm and its
analysis crucially leverage the recently introduced technique of online
correlated selection (OCS) [Fahrbach et al., 2020].


title:
Equilibrium and non-equilibrium molecular dynamics approaches for the
  linear viscoelasticity of polymer melts

abstract:
  Viscoelastic properties of polymer melts are particularly challenging to
compute due to the intrinsic stress fluctuations in molecular dynamics (MD). We
compared equilibrium and non-equilibrium MD approaches for extracting the
storage (G') and loss moduli (G") over a wide frequency range from a
bead-spring chain model, in both unentangled and entangled regimes. We found
that, with properly chosen data processing and noise reduction procedures,
different methods render quantitatively equivalent results. In equilibrium MD
(EMD), applying the Green-Kubo relation with a multi-tau correlator method for
noise filtering generates smooth stress relaxation modulus profiles, from which
accurate G' and G" can be obtained. For unentangled chains, combining the Rouse
model with a short-time correction provides a convenient option that
circumvents the stress fluctuation challenge altogether. For non-equilibrium MD
(NEMD), we found that combining a stress pre-averaging treatment with discrete
Fourier transform analysis reliably computes G' and G" with much shorter
simulation length than previously reported. Comparing the efficiency and
statistical accuracy of these methods, we concluded that EMD is both reliable
and efficient, and is suitable when the whole spectrum of linear viscoelastic
properties is desired, whereas NEMD offers flexibility when only some frequency
ranges are of interest.


title:
Direct computation of magnetic surfaces in Boozer coordinates and coil
  optimization for quasi-symmetry

abstract:
  We propose a new method to compute magnetic surfaces that are parametrized in
Boozer coordinates for vacuum magnetic fields. We also propose a measure for
quasi-symmetry on the computed surfaces and use it to design coils that
generate a magnetic field that is quasi-symmetric on those surfaces. The
rotational transform of the field and complexity measures for the coils are
also controlled in the design problem.
  Using an adjoint approach, we are able to obtain analytic derivatives for
this optimization problem, yielding an efficient gradient-based algorithm.
Starting from an initial coil set that presents nested magnetic surfaces for a
large fraction of the volume, our method converges rapidly to coil systems
generating fields with excellent quasi-symmetry and low particle losses. In
particular for low complexity coils, we are able to significantly improve the
performance compared to coils obtained from the standard two-stage approach,
e.g.~reduce losses of fusion-produced alpha particles born at half-radius from
$17.7\%$ to $6.6\%$. We also demonstrate 16-coil configurations with alpha loss
< $1\%$ and neoclassical transport magnitude $\epsilon_{\mathrm{eff}}^{3/2}$
less than approximately $5\times 10^{-9}.$


title: Nonlinear spin Hall effect in $\mathcal{PT}$-symmetric collinear magnets

abstract:
  We theoretically investigate a nonlinear spin Hall effect in
$\mathcal{PT}$-symmetric antiferromagnetic metals, which serve as an efficient
spin current generator. We elucidate that an emergent spin-dependent Berry
curvature dipole is a microscopic origin of the nonlinear spin Hall effect,
which becomes nonzero with neither relativistic spin-orbit coupling, uniform
magnetization, nor spin-split band structure. By analyzing a microscopic
antiferromagnetic model without spin-orbit coupling for an intuitive
understanding of the phenomena, we elucidate essential hopping processes and a
condition to enhance the nonlinear spin Hall conductivity. We also provide a
complete table to include useful correspondence among the N\'eel vector,
odd-parity multipoles, nonlinear spin conductivity tensor, and candidate
materials in all the $\mathcal{PT}$-symmetric black-and-white magnetic point
groups.


title:
Seiberg-Witten geometry, modular rational elliptic surfaces and BPS
  quivers

abstract:
  We study the Coulomb branches of the rank-one 4d $\mathcal{N} = 2$ quantum
field theories, including the KK theories obtained from the circle
compactification of the 5d $\mathcal{N}= 1$ $E_n$ Seiberg theories. The focus
is set on the relation between their Seiberg-Witten geometries and rational
elliptic surfaces, with more attention being given to the modular surfaces,
which we completely classify using the classification of subgroups of the
modular group ${\rm PSL}(2,\mathbb{Z})$. We derive closed-form expressions for
the modular functions for all congruence and some of the non-congruence
subgroups associated with these geometries. Moreover, we show how the BPS
quivers of these theories can be determined directly from the fundamental
domains of the monodromy groups and study how changes of these domains can be
interpreted as quiver mutations. This approach can also be generalized to
theories whose Coulomb branches contain `undeformable' singularities, leading
to known quivers of such theories.


title: Flat minima generalize for low-rank matrix recovery

abstract:
  Empirical evidence suggests that for a variety of overparameterized nonlinear
models, most notably in neural network training, the growth of the loss around
a minimizer strongly impacts its performance. Flat minima -- those around which
the loss grows slowly -- appear to generalize well. This work takes a step
towards understanding this phenomenon by focusing on the simplest class of
overparameterized nonlinear models: those arising in low-rank matrix recovery.
We analyze overparameterized matrix and bilinear sensing, robust PCA,
covariance matrix estimation, and single hidden layer neural networks with
quadratic activation functions. In all cases, we show that flat minima,
measured by the trace of the Hessian, exactly recover the ground truth under
standard statistical assumptions. For matrix completion, we establish weak
recovery, although empirical evidence suggests exact recovery holds here as
well. We complete the paper with synthetic experiments that illustrate our
findings.


title: Direct Imaging of Contacts and Forces in Colloidal Gels

abstract:
  Colloidal dispersions are prized as model systems to understand basic
properties of materials, and are central to a wide range of industries from
cosmetics to foods to agrichemicals. Among the key developments in using
colloids to address challenges in condensed matter is to resolve the particle
coordinates in 3D, allowing a level of analysis usually only possible in
computer simulation. However in amorphous materials, relating mechanical
properties to microscopic structure remains problematic. This makes it rather
hard to understand, for example, mechanical failure. Here we address this
challenge by studying the contacts and the forces between particles, as well as
their positions. To do so, we use a colloidal model system (an emulsion) in
which the interparticle forces and local stress can be linked to the
microscopic structure. We demonstrate the potential of our method to reveal
insights into the failure mechanisms of soft amorphous solids by determining
local stress in a colloidal gel. In particular, we identify `force chains' of
load-bearing droplets, and local stress anisotropy, and investigate their
connection with locally rigid packings of the droplets.


title: Sediment load determines the shape of rivers

abstract:
  Understanding how rivers adjust to the sediment load they carry is critical
to predicting the evolution of landscapes. Presently, however, no physically
based model reliably captures the dependence of basic river properties, such as
its shape or slope, on the discharge of sediment, even in the simple case of
laboratory rivers. Here, we show how the balance between fluid stress and
gravity acting on the sediment grains, along with cross-stream diffusion of
sediment, determines the shape and sediment flux profile of laminar laboratory
rivers which carry sediment as bedload. Using this model, which reliably
reproduces the experiments without any tuning, we confirm the hypothesis,
originally proposed by Parker (1978), that rivers are restricted to exist close
to the threshold of sediment motion (within about 20%). This limit is set by
the fluid-sediment interaction and is independent of the water and sediment
load carried by the river. Thus, as the total sediment discharge increases, the
intensity of sediment flux (sediment discharge per unit width) in a river
saturates, and the river can only transport more sediment by widening. In this
large discharge regime, the cross-stream diffusion of momentum in the flow
permits sediment transport. Conversely, in the weak transport regime, the
transported sediment concentrates around the river center without significantly
altering the river shape. If this theory holds for natural rivers, the aspect
ratio of a river could become a proxy for sediment discharge - a quantity
notoriously difficult to measure in the field.


title:
IT5: Large-scale Text-to-text Pretraining for Italian Language
  Understanding and Generation

abstract:
  The T5 model and its unified text-to-text paradigm contributed in advancing
the state-of-the-art for many natural language processing tasks. While some
multilingual variants of the T5 model have recently been introduced, their
performances were found to provide suboptimal performances for languages other
than English if compared to monolingual variants. We are motivated by these
findings to introduce IT5, the first family of encoder-decoder transformer
models pretrained specifically on Italian. We perform a thorough cleaning of a
web-crawled Italian corpus including more than 40 billion words and use it to
pretrain three IT5 models of different sizes. The performance of IT5 models and
their multilingual counterparts is then evaluated on a broad range of natural
language understanding and generation benchmarks for Italian. We find the
monolingual IT5 models to provide the best scale-to-performance ratio across
tested models, consistently outperforming their multilingual counterparts and
setting a new state-of-the-art for most Italian conditional language generation
tasks.


title:
Snow topography on undeformed Arctic sea ice captured by an idealized
  "snow dune" model

abstract:
  Our ability to predict the future of Arctic sea ice is limited by ice's
sensitivity to detailed surface conditions such as the distribution of snow and
melt ponds. Snow on top of the ice decreases ice's thermal conductivity,
increases its reflectivity (albedo), and provides a source of meltwater for
melt ponds during summer that decrease the ice's albedo. In this paper, we
develop a simple model of pre-melt snow topography that accurately describes
snow cover of flat, undeformed Arctic sea ice on several study sites for which
data was available. The model considers a surface that is a sum of randomly
sized and placed "snow dunes" represented as Gaussian mounds. This model
generalizes the "void model" of Popovi\'c et al. (2018) and, as such,
accurately describes the statistics of melt pond geometry. We test this model
against detailed LiDAR measurements of the pre-melt snow topography. We show
that the model snow-depth distribution is statistically indistinguishable from
the measurements on flat ice, while small disagreement exists if the ice is
deformed. We then use this model to determine analytic expressions for the
conductive heat flux through the ice and for melt pond coverage evolution
during an early stage of pond formation. We also formulate a criterion for ice
to remain pond-free throughout the summer. Results from our model could be
directly included in large-scale models, thereby improving our understanding of
energy balance on sea ice and allowing for more reliable predictions of Arctic
sea ice in a future climate.


title:
The Fundamental Price of Secure Aggregation in Differentially Private
  Federated Learning

abstract:
  We consider the problem of training a $d$ dimensional model with distributed
differential privacy (DP) where secure aggregation (SecAgg) is used to ensure
that the server only sees the noisy sum of $n$ model updates in every training
round. Taking into account the constraints imposed by SecAgg, we characterize
the fundamental communication cost required to obtain the best accuracy
achievable under $\varepsilon$ central DP (i.e. under a fully trusted server
and no communication constraints). Our results show that $\tilde{O}\left(
\min(n^2\varepsilon^2, d) \right)$ bits per client are both sufficient and
necessary, and this fundamental limit can be achieved by a linear scheme based
on sparse random projections. This provides a significant improvement relative
to state-of-the-art SecAgg distributed DP schemes which use
$\tilde{O}(d\log(d/\varepsilon^2))$ bits per client.
  Empirically, we evaluate our proposed scheme on real-world federated learning
tasks. We find that our theoretical analysis is well matched in practice. In
particular, we show that we can reduce the communication cost significantly to
under $1.2$ bits per parameter in realistic privacy settings without decreasing
test-time performance. Our work hence theoretically and empirically specifies
the fundamental price of using SecAgg.


title:
Defending Graph Convolutional Networks against Dynamic Graph
  Perturbations via Bayesian Self-supervision

abstract:
  In recent years, plentiful evidence illustrates that Graph Convolutional
Networks (GCNs) achieve extraordinary accomplishments on the node
classification task. However, GCNs may be vulnerable to adversarial attacks on
label-scarce dynamic graphs. Many existing works aim to strengthen the
robustness of GCNs; for instance, adversarial training is used to shield GCNs
against malicious perturbations. However, these works fail on dynamic graphs
for which label scarcity is a pressing issue. To overcome label scarcity,
self-training attempts to iteratively assign pseudo-labels to highly confident
unlabeled nodes but such attempts may suffer serious degradation under dynamic
graph perturbations. In this paper, we generalize noisy supervision as a kind
of self-supervised learning method and then propose a novel Bayesian
self-supervision model, namely GraphSS, to address the issue. Extensive
experiments demonstrate that GraphSS can not only affirmatively alert the
perturbations on dynamic graphs but also effectively recover the prediction of
a node classifier when the graph is under such perturbations. These two
advantages prove to be generalized over three classic GCNs across five public
graph datasets.


title: Periodic orbits in Ho\v{r}ava-Lifshitz cosmologies

abstract:
  We consider spatially homogeneous Ho\v{r}ava-Lifshitz (HL) models that
perturb General Relativity (GR) by a parameter $v\in (0,1)$ such that GR occurs
at $v=1/2$. We describe the dynamics for the extremal case $v=0$, which possess
the usual Bianchi hierarchy: type I (Kasner circle of equilibria), type II
(heteroclinics that induce the Kasner map) and type
$\mathrm{VI_0},\mathrm{VII_0}$ (further heteroclinics). For type VIII and IX,
we prove the existence of periodic orbits which are far from the Mixmaster
attractor, and thereby yield a new behaviour which is not described by the BKL
picture.


title: Towards Flexible Anonymous Networks

abstract:
  Anonymous Communication designs such as Tor build their security upon
distributing the trust in many volunteers running relays in many locations
globally. These volunteers run the Tor code upon various operating systems,
each potentially having different software packaging policies. In practice, it
leads to a heterogeneous network in which many versions of the same Tor
software exist, with a different set of protocol features. Because of the
heterogeneous aspect of the network, the maintainers had to come up with
forward-compatible protocol design strategies. Their role is to guarantee that
different versions of the Tor software interact without unrecoverable errors.
In this work, we cast the protocol tolerance enabled with forward-compatible
protocol considerations as a double-edged sword. Despite being beneficial for
the developers, we argue that protocol tolerance is the systemic cause behind
many strong attacks against Tor in the past fifteen years.
  To address this issue, we propose FAN for Flexible Anonymous Network, a new
software architecture for volunteer-based distributed networks that shifts the
dependence away from protocol tolerance without losing the ability for the
developers to ensure the continuous evolution of their software. We realize an
implementation, evaluate the overheads and, experiment with several of FAN's
benefits to defend against a severe attack still applicable to Tor today.