title:
High efficiency coherent microwave-to-optics conversion via off-resonant
  scattering

abstract:
  Quantum transducers that can convert quantum signals from the microwave to
the optical domain are a crucial optical interface for quantum information
technology. Coherent microwave-to-optics conversions have been realized with
various physical platforms, but all of them are limited to low efficiencies of
less than 50\%, the threshold of the no-cloning quantum regime. Here we report
a coherent microwave-to-optics transduction using Rydberg atoms and
off-resonant scattering technique with an efficiency of $82\pm 2\%$ and a
bandwidth of about 1 MHz. The high conversion efficiency is maintained for
microwave photons range from thousands to about 50, suggesting that our
transduction is readily applicable to the single-photon level. Without
requiring cavities or aggressive cooling to quantum ground states, our results
would push atomic transducers closer to practical applications in quantum
technologies.


title: Understanding person identification via gait

abstract:
  Gait recognition is the process of identifying humans from their bipedal
locomotion such as walking or running. As such gait data is privacy sensitive
information and should be anonymized. With the rise of more and higher quality
gait recording techniques, such as depth cameras or motion capture suits, an
increasing amount of high-quality gait data becomes available which requires
anonymization. As a first step towards developing anonymization techniques for
high-quality gait data, we study different aspects of movement data to quantify
their contribution to the gait recognition process. We first extract categories
of features from the literature on human gait perception and then design
computational experiments for each of the categories which we run against a
gait recognition system. Our results show that gait anonymization is a
challenging process as the data is highly redundant and interdependent.


title:
Tuning-free multi-coil compressed sensing MRI with Parallel Variable
  Density Approximate Message Passing (P-VDAMP)

abstract:
  Magnetic Resonance Imaging (MRI) has excellent soft tissue contrast but is
hindered by an inherently slow data acquisition process. Compressed sensing,
which reconstructs sparse signals from incoherently sampled data, has been
widely applied to accelerate MRI acquisitions. Compressed sensing MRI requires
one or more model parameters to be tuned, which is usually done by hand, giving
sub-optimal tuning in general. To address this issue, we build on previous work
by the authors on the single-coil Variable Density Approximate Message Passing
(VDAMP) algorithm, extending the framework to multiple receiver coils to
propose the Parallel VDAMP (P-VDAMP) algorithm. For Bernoulli random variable
density sampling, P-VDAMP obeys a "state evolution", where the intermediate
per-iteration image estimate is distributed according to the ground truth
corrupted by a zero-mean Gaussian vector with approximately known covariance.
To our knowledge, P-VDAMP is the first algorithm for multi-coil MRI data that
obeys a state evolution with accurately tracked parameters. We leverage state
evolution to automatically tune sparse parameters on-the-fly with Stein's
Unbiased Risk Estimate (SURE). P-VDAMP is evaluated on brain, knee and
angiogram datasets and compared with four variants of the Fast Iterative
Shrinkage-Thresholding algorithm (FISTA), including two tuning-free variants
from the literature. The proposed method is found to have a similar
reconstruction quality and time to convergence as FISTA with an optimally tuned
sparse weighting and offers substantial robustness and reconstruction quality
improvements over competing tuning-free methods.


title: Selective-Supervised Contrastive Learning with Noisy Labels

abstract:
  Deep networks have strong capacities of embedding data into latent
representations and finishing following tasks. However, the capacities largely
come from high-quality annotated labels, which are expensive to collect. Noisy
labels are more affordable, but result in corrupted representations, leading to
poor generalization performance. To learn robust representations and handle
noisy labels, we propose selective-supervised contrastive learning (Sel-CL) in
this paper. Specifically, Sel-CL extend supervised contrastive learning
(Sup-CL), which is powerful in representation learning, but is degraded when
there are noisy labels. Sel-CL tackles the direct cause of the problem of
Sup-CL. That is, as Sup-CL works in a \textit{pair-wise} manner, noisy pairs
built by noisy labels mislead representation learning. To alleviate the issue,
we select confident pairs out of noisy ones for Sup-CL without knowing noise
rates. In the selection process, by measuring the agreement between learned
representations and given labels, we first identify confident examples that are
exploited to build confident pairs. Then, the representation similarity
distribution in the built confident pairs is exploited to identify more
confident pairs out of noisy pairs. All obtained confident pairs are finally
used for Sup-CL to enhance representations. Experiments on multiple noisy
datasets demonstrate the robustness of the learned representations by our
method, following the state-of-the-art performance. Source codes are available
at https://github.com/ShikunLi/Sel-CL


title:
The number of occurrences of patterns in a random tree or forest
  permutation

abstract:
  The classes of tree permutations and forest permutations were defined by Acan
and Hitczenko (2016). We study random permutations of a given length from these
classes, and in particular the number of occurrences of a fixed pattern in one
of these random permutations. The main results show that the distributions of
these numbers are asymptotically normal.
  The proof uses representations of random tree and forest permutations that
enable us to express the number of occurrences of a pattern by a type of
$U$-statistics; we then use general limit theorems for the latter.


title:
Enhancing Mechanical Metamodels with a Generative Model-Based Augmented
  Training Dataset

abstract:
  Modeling biological soft tissue is complex in part due to material
heterogeneity. Microstructural patterns, which play a major role in defining
the mechanical behavior of these tissues, are both challenging to characterize,
and difficult to simulate. Recently, machine learning-based methods to predict
the mechanical behavior of heterogeneous materials have made it possible to
more thoroughly explore the massive input parameter space associated with
heterogeneous blocks of material. Specifically, we can train machine learning
(ML) models to closely approximate computationally expensive heterogeneous
material simulations where the ML model is trained on a dataset of simulations
that capture the range of spatial heterogeneity present in the material of
interest. However, when it comes to applying these techniques to biological
tissue more broadly, there is a major limitation: the relevant microstructural
patterns are both challenging to obtain and difficult to analyze. Consequently,
the number of useful examples available to characterize the input domain under
study is limited. In this work, we investigate the efficacy of ML-based
generative models as well as procedural methods as a tool for augmenting
limited input pattern datasets. We find that a Style-based Generative
Adversarial Network with adaptive discriminator augmentation is able to
successfully leverage just 1,000 example patterns to create the most authentic
generated patterns. In general, diverse generated patterns with adequate
resemblance to the real patterns can be used as inputs to finite element
simulations to meaningfully augment the training dataset. To enable this
methodological contribution, we have created an open access dataset of Finite
Element Analysis simulations based on Cahn-Hilliard patterns. We anticipate
that future researchers will be able to leverage this dataset and build on the
work presented here.


title:
Sun's Three Conjectures on Ap\'{e}ry-like Sums Involving Harmonic
  Numbers

abstract:
  In this paper, we will give another proof of Zhi-Wei Sun's three conjectures
on Ap\'{e}ry-like sums involving harmonic numbers by proving some identities
among special values of multiple polylogarithms.


title:
Recent Formation of a Spiral Disk Hosting Progenitor Globular Clusters
  at the center of the Perseus Brightest Cluster Galaxy: II. Progenitor
  Globular Clusters

abstract:
  We address the nature and origin of Super Star Clusters (SSCs) discovered by
Holtzman et al. (1992) within a radius of $\sim$$5\,\rm kpc$ from the center of
NGC 1275, the giant elliptical galaxy at the center of the Perseus Cluster. We
show that, in contrast with the much more numerous population of SSCs
subsequently discovered up to $\sim$$30\,\rm kpc$ from the center of this
galaxy, the central SSC population have maximal masses an order of magnitude
higher and a mass function with a shallower power-law slope. Furthermore,
whereas the outer SSC population have ages spanning a few $\rm Myr$ to at least
$\sim$$1\,\rm Gyr$, the central SSC population have ages strongly concentrated
around $\sim$$500 \rm \, Myr$ with a $1\,\sigma$ dispersion of $\sim$$100\,\rm
Myr$. These SSCs share a close spatial and temporal relationship with the
"central spiral," which also has a radius $\sim$$5\,\rm kpc$ centered on NGC
1275 and a characteristic stellar age of $\sim$$150\,\rm Myr$ (Paper I). We
argue that both the central SSC population and the central spiral formed from
gas deposited by a residual cooling flow, with the SSCs forming first followed
by the formation of the stellar body of the central spiral
$\sim$$300$-$400\,\rm Myr$ later. The ages of the central SSC population imply
that they are able to withstand very strong tidal fields near the center of NGC
1275, making them genuine progenitor globular clusters. Evidently, a spiral
disk hosting progenitor globular clusters has recently formed at the center of
a giant elliptical galaxy.


title: The Special Tree Number

abstract:
  Define the special tree number, denoted $\mathfrak{st}$, to be the least size
of a tree of height $\omega_1$ which is neither special nor has a cofinal
branch. This cardinal had previously been studied in the context of fragments
of $\mathsf{MA}$ but in this paper we look at its relation to other, more
typical, cardinal characteristics. Classical facts imply that $\aleph_1 \leq
\mathfrak{st} \leq 2^{\aleph_0}$, under Martin's Axiom $\mathfrak{st} =
2^{\aleph_0}$ and that $\mathfrak{st} = \aleph_1$ is consistent with
$\mathsf{MA}({\rm Knaster}) + 2^{\aleph_0} = \kappa$ for any regular $\kappa$
thus the value of $\mathfrak{st}$ is not decided by $\mathsf{ZFC}$ and in fact
can be strictly below essentially all well studied cardinal characteristics. We
show that conversely it is consistent that $\mathfrak{st} = 2^{\aleph_0} =
\kappa$ for any $\kappa$ of uncountable cofinality while ${\rm non}(\mathcal M)
= \mathfrak{a} = \mathfrak{s} = \mathfrak{g} = \aleph_1$. In particular
$\mathfrak{st}$ is independent of the lefthand side of Cicho\'{n}'s diagram,
amongst other things. The proof involves an in depth study of the standard ccc
forcing notion to specialize (wide) Aronszajn trees, which may be of
independent interest.


title:
MLSeg: Image and Video Segmentation as Multi-Label Classification and
  Selected-Label Pixel Classification

abstract:
  For a long period of time, research studies on segmentation have typically
formulated the task as pixel classification that predicts a class for each
pixel from a set of predefined, fixed number of semantic categories. Yet
standard architectures following this formulation will inevitably encounter
various challenges under more realistic settings where the total number of
semantic categories scales up (e.g., beyond $1\rm{k}$ classes). On the other
hand, a standard image or video usually contains only a small number of
semantic categories from the entire label set. Motivated by this intuition, in
this paper, we propose to decompose segmentation into two sub-problems: (i)
image-level or video-level multi-label classification and (ii) pixel-level
selected-label classification. Given an input image or video, our framework
first conducts multi-label classification over the large complete label set and
selects a small set of labels according to the class confidence scores. Then
the follow-up pixel-wise classification is only performed among the selected
subset of labels. Our approach is conceptually general and can be applied to
various existing segmentation frameworks by simply adding a lightweight
multi-label classification branch. We demonstrate the effectiveness of our
framework with competitive experimental results across four tasks including
image semantic segmentation, image panoptic segmentation, video instance
segmentation, and video semantic segmentation. Especially, with our MLSeg,
Mask$2$Former gains +$0.8\%$/+$0.7\%$/+$0.7\%$ on ADE$20$K panoptic
segmentation/YouTubeVIS $2019$ video instance segmentation/VSPW video semantic
segmentation benchmarks respectively. Code will be available
at:https://github.com/openseg-group/MLSeg


title:
Extrapolated Proportional-Integral Projected Gradient Method for Conic
  Optimization

abstract:
  Conic optimization is the minimization of a convex quadratic function subject
to conic constraints. We introduce a novel first-order method for conic
optimization, named \emph{extrapolated proportional-integral projected gradient
method (exPIPG)}, that automatically detects infeasibility. The iterates of
exPIPG either asymptotically satisfy a set of primal-dual optimality
conditions, or generate a proof of primal or dual infeasibility. We demonstrate
the application of exPIPG in optimal control problems. With an efficient C
implementation, exPIPG is about ten times as fast as many state-of-the-art
optimization solvers, including ECOS, OSQP, SCS, and MOSEK.


title: Radu groups acting on trees are CCR

abstract:
  We classify the irreducible unitary representations of closed simple groups
of automorphisms of trees acting $2$-transitively on the boundary and whose
local action at every vertex contains the alternating group. As an application,
we confirm Claudio Nebbia's CCR conjecture on trees for
$(d_0,d_1)$-semi-regular trees such that $d_0,d_1\in \Theta$, where $\Theta$ is
an asymptotically dense set of positive integers.


title:
Centres of centralizers of nilpotent elements in Lie superalgebras
  $\mathfrak{sl}(m|n)$ or $\mathfrak{osp}(m|2n)$

abstract:
  Let $\bar{G}$ be the simple algebraic supergroup $\mathrm{SL}(m|n)$ or
$\mathrm{OSp}(m|2n)$ over $\mathbb{C}$. Let
$\mathfrak{g}=\mathrm{Lie}(\bar{G})=\mathfrak{g}_{\bar{0}}\oplus\mathfrak{g}_{\bar{1}}$
and let $G=\bar{G}(\mathbb{C})$ where $\mathbb{C}$ is considered as a
superalgebra concentrated in even degree. Suppose $e\in\mathfrak{g}_{\bar{0}}$
is nilpotent. We describe the centralizer $\mathfrak{g}^{e}$ of $e$ in
$\mathfrak{g}$ and its centre $\mathfrak{z}(\mathfrak{g}^{e})$. In particular,
we give bases for $\mathfrak{g}^{e}$, $\mathfrak{z}(\mathfrak{g}^{e})$ and
$\left(\mathfrak{z}(\mathfrak{g}^{e})\right)^{G^{e}}$. We also determine the
labelled Dynkin diagram $\varDelta$ with respect to $e$ and subsequently
describe the relation between
$\left(\mathfrak{z}(\mathfrak{g}^{e})\right)^{G^{e}}$ and $\varDelta$.


title: H\"older--Zygmund classes on smooth curves

abstract:
  We prove that a function in several variables is in the local Zygmund class
$\mathcal Z^{m,1}$ if and only if its composite with every smooth curve is of
class $\mathcal Z^{m,1}$. This complements the well-known analogous result for
local H\"older--Lipschitz classes $\mathcal C^{m,\alpha}$ which we reprove
along the way. We demonstrate that these results generalize to mappings between
Banach spaces and use them to study the regularity of the superposition
operator $f_* : g \mapsto f \circ g$ acting on the global Zygmund space
$\Lambda_{m+1}(\mathbb R^d)$. We prove that, for all integers $m,k\ge 1$, the
map $f_* : \Lambda_{m+1}(\mathbb R^d) \to \Lambda_{m+1}(\mathbb R^d)$ is of
Lipschitz class $\mathcal C^{k-1,1}$ if and only if $f \in \mathcal
Z^{m+k,1}(\mathbb R)$.


title:
Neural Contextual Bandits via Reward-Biased Maximum Likelihood
  Estimation

abstract:
  Reward-biased maximum likelihood estimation (RBMLE) is a classic principle in
the adaptive control literature for tackling explore-exploit trade-offs. This
paper studies the stochastic contextual bandit problem with general bounded
reward functions and proposes NeuralRBMLE, which adapts the RBMLE principle by
adding a bias term to the log-likelihood to enforce exploration. NeuralRBMLE
leverages the representation power of neural networks and directly encodes
exploratory behavior in the parameter space, without constructing confidence
intervals of the estimated rewards. We propose two variants of NeuralRBMLE
algorithms: The first variant directly obtains the RBMLE estimator by gradient
ascent, and the second variant simplifies RBMLE to a simple index policy
through an approximation. We show that both algorithms achieve
$\widetilde{\mathcal{O}}(\sqrt{T})$ regret. Through extensive experiments, we
demonstrate that the NeuralRBMLE algorithms achieve comparable or better
empirical regrets than the state-of-the-art methods on real-world datasets with
non-linear reward functions.


title: Growth of a viscoplastic blister underneath an elastic sheet

abstract:
  Inspired by the formation of geological structures as earth's crust deforms
by magmatic intrusions, we investigate the elastohydrodynamic growth of a
viscoplastic blister under an elastic sheet. By combining experiments, scaling
analysis and numerical simulations we reveal a new regime for the growth of the
blister's height $\sim t^{5/9}$ and radius $\sim t^{2/9}$. A plug like flow
inside the blister dictates its dynamics, whereas the blister takes a
quasi-static self-similar shape given by a balance in the pressure gradient
induced by bending of the elastic sheet and the fluid's yield stress.


title:
Testing for Treatment Effect Twice Using Internal and External Controls
  in Clinical Trials

abstract:
  Leveraging external controls -- relevant individual patient data under
control from external trials or real-world data -- has the potential to reduce
the cost of randomized controlled trials (RCTs) while increasing the proportion
of trial patients given access to novel treatments. However, due to lack of
randomization, RCT patients and external controls may differ with respect to
covariates that may or may not have been measured. Hence, after controlling for
measured covariates, for instance by matching, testing for treatment effect
using external controls may still be subject to unmeasured biases. In this
paper, we propose a sensitivity analysis approach to quantify the magnitude of
bias from unmeasured covariates that would need to be present to alter the
study conclusion that presumed no unmeasured biases are introduced by employing
external controls. Whether leveraging external controls increases power or not
depends on the interplay between sample sizes and the magnitude of treatment
effect and unmeasured biases, which may be difficult to anticipate. This
motivates a combined testing procedure that performs two highly correlated
analyses, one with and one without external controls, correcting for multiple
testing using the joint distribution of the two test statistics. In theory,
power calculations, and a real application, it is shown that the gains from
using the combined testing procedure are often large and the losses due to
correction for multiple testing are often small.


title: A Gating Model for Bias Calibration in Generalized Zero-shot Learning

abstract:
  Generalized zero-shot learning (GZSL) aims at training a model that can
generalize to unseen class data by only using auxiliary information. One of the
main challenges in GZSL is a biased model prediction toward seen classes caused
by overfitting on only available seen class data during training. To overcome
this issue, we propose a two-stream autoencoder-based gating model for GZSL.
Our gating model predicts whether the query data is from seen classes or unseen
classes, and utilizes separate seen and unseen experts to predict the class
independently from each other. This framework avoids comparing the biased
prediction scores for seen classes with the prediction scores for unseen
classes. In particular, we measure the distance between visual and attribute
representations in the latent space and the cross-reconstruction space of the
autoencoder. These distances are utilized as complementary features to
characterize unseen classes at different levels of data abstraction. Also, the
two-stream autoencoder works as a unified framework for the gating model and
the unseen expert, which makes the proposed method computationally efficient.
We validate our proposed method in four benchmark image recognition datasets.
In comparison with other state-of-the-art methods, we achieve the best harmonic
mean accuracy in SUN and AWA2, and the second best in CUB and AWA1.
Furthermore, our base model requires at least 20% less number of model
parameters than state-of-the-art methods relying on generative models.


title:
On the elephant random walk with stops playing hide and seek with the
  Mittag-Leffler distribution

abstract:
  The aim of this paper is to investigate the asymptotic behavior of the
so-called elephant random walk with stops (ERWS). In contrast with the standard
elephant random walk, the elephant is allowed to be lazy by staying on his own
position. We prove that the number of ones of the ERWS, properly normalized,
converges almost surely to a Mittag-Leffler distribution. It allows us to carry
out a sharp analysis on the asymptotic behavior of the ERWS. In the diffusive
and critical regimes, we establish the almost sure convergence of the ERWS. We
also show that it is necessary to self-normalized the position of the ERWS by
the random number of ones in order to prove the asymptotic normality. In the
superdiffusive regime, we establish the almost sure convergence of the ERWS,
properly normalized, to a nondegenerate random variable. Moreover, we also show
that the fluctuation of the ERWS around its limiting random variable is still
Gaussian.


title: Locate This, Not That: Class-Conditioned Sound Event DOA Estimation

abstract:
  Existing systems for sound event localization and detection (SELD) typically
operate by estimating a source location for all classes at every time instant.
In this paper, we propose an alternative class-conditioned SELD model for
situations where we may not be interested in localizing all classes all of the
time. This class-conditioned SELD model takes as input the spatial and spectral
features from the sound file, and also a one-hot vector indicating the class we
are currently interested in localizing. We inject the conditioning information
at several points in our model using feature-wise linear modulation (FiLM)
layers. Through experiments on the DCASE 2020 Task 3 dataset, we show that the
proposed class-conditioned SELD model performs better in terms of common SELD
metrics than the baseline model that locates all classes simultaneously, and
also outperforms specialist models that are trained to locate only a single
class of interest. We also evaluate performance on the DCASE 2021 Task 3
dataset, which includes directional interference (sound events from classes we
are not interested in localizing) and notice especially strong improvement from
the class-conditioned model.


title:
Observation of strong and tunable light-induced dipole-dipole
  interactions between optically levitated nanoparticles

abstract:
  Arrays of optically trapped nanoparticles have emerged as a promising
platform for the study of complex non-equilibrium phenomena. Analogous to
atomic many-body systems, one of the crucial ingredients is the ability to
precisely control the interactions between particles. However, the optical
interactions studied thus far only provide conservative optical binding forces
of limited tunability. Here we demonstrate a coupling mechanism that is orders
of magnitude stronger and has new qualitative features. These effects arise
from the previously unexplored phase coherence between the optical fields that
drive the light-induced dipole-dipole interaction. In addition, polarization
control allows us to observe electrostatic coupling between charged particles
in the array. Our results pave the way for a fully programmable many-body
system of interacting nanoparticles with tunable dissipative and nonreciprocal
interactions, which are instrumental for exploring entanglement and topological
phases in arrays of levitated nanoparticles.


title: Trustable Co-label Learning from Multiple Noisy Annotators

abstract:
  Supervised deep learning depends on massive accurately annotated examples,
which is usually impractical in many real-world scenarios. A typical
alternative is learning from multiple noisy annotators. Numerous earlier works
assume that all labels are noisy, while it is usually the case that a few
trusted samples with clean labels are available. This raises the following
important question: how can we effectively use a small amount of trusted data
to facilitate robust classifier learning from multiple annotators? This paper
proposes a data-efficient approach, called \emph{Trustable Co-label Learning}
(TCL), to learn deep classifiers from multiple noisy annotators when a small
set of trusted data is available. This approach follows the coupled-view
learning manner, which jointly learns the data classifier and the label
aggregator. It effectively uses trusted data as a guide to generate trustable
soft labels (termed co-labels). A co-label learning can then be performed by
alternately reannotating the pseudo labels and refining the classifiers. In
addition, we further improve TCL for a special complete data case, where each
instance is labeled by all annotators and the label aggregator is represented
by multilayer neural networks to enhance model capacity. Extensive experiments
on synthetic and real datasets clearly demonstrate the effectiveness and
robustness of the proposed approach. Source code is available at
https://github.com/ShikunLi/TCL


title: Can a particle moves zigzag in time?

abstract:
  The amplitudes of quantum transitions containing time zigzags are considered.
The discussion is carried out within the framework of the Minkowski metric and
the standard quantum mechanics without adding new postulates. We claim that the
time zigzags are not suppressed at quantum level, but their contribution to the
amplitude is zero.


title:
Follow the Water: Finding Water, Snow and Clouds on Terrestrial
  Exoplanets with Photometry and Machine Learning

abstract:
  All life on Earth needs water. NASA's quest to follow the water links water
to the search for life in the cosmos. Telescopes like JWST and mission concepts
like HabEx, LUVOIR and Origins are designed to characterise rocky exoplanets
spectroscopically. However, spectroscopy remains time-intensive and therefore,
initial characterisation is critical to prioritisation of targets.
  Here, we study machine learning as a tool to assess water's existence through
broadband-filter reflected photometric flux on Earth-like exoplanets in three
forms: seawater, water-clouds and snow; based on 53,130 spectra of cold,
Earth-like planets with 6 major surfaces. XGBoost, a well-known machine
learning algorithm, achieves over 90\% balanced accuracy in detecting the
existence of snow or clouds for S/N$\gtrsim 20$, and 70\% for liquid seawater
for S/N $\gtrsim 30$. Finally, we perform mock Bayesian analysis with
Markov-chain Monte Carlo with five filters identified to derive exact surface
compositions to test for retrieval feasibility.
  The results show that the use of machine learning to identify water on the
surface of exoplanets from broadband-filter photometry provides a promising
initial characterisation tool of water in different forms. Planned small and
large telescope missions could use this to aid their prioritisation of targets
for time-intense follow-up observations.


title: Transformations of Stabilizer States in Quantum Networks

abstract:
  Stabilizer states and graph states find application in quantum error
correction, measurement-based quantum computation and various other concepts in
quantum information theory. In this work, we study party-local Clifford (PLC)
transformations among stabilizer states. These transformations arise as a
physically motivated extension of local operations in quantum networks with
access to bipartite entanglement between some of the nodes of the network.
First, we show that PLC transformations among graph states are equivalent to a
generalization of the well-known local complementation, which describes local
Clifford transformations among graph states. Then, we introduce a mathematical
framework to study PLC equivalence of stabilizer states, relating it to the
classification of tuples of bilinear forms. This framework allows us to study
decompositions of stabilizer states into tensor products of indecomposable
ones, that is, decompositions into states from the entanglement generating set
(EGS). While the EGS is finite up to $3$ parties [Bravyi et al., J. Math. Phys.
{\bf 47}, 062106~(2006)], we show that for $4$ and more parties it is an
infinite set, even when considering party-local unitary transformations.
Moreover, we explicitly compute the EGS for $4$ parties up to $10$ qubits.
Finally, we generalize the framework to qudit stabilizer states in prime
dimensions not equal to $2$, which allows us to show that the decomposition of
qudit stabilizer states into states from the EGS is unique.


title:
AssistQ: Affordance-centric Question-driven Task Completion for
  Egocentric Assistant

abstract:
  A long-standing goal of intelligent assistants such as AR glasses/robots has
been to assist users in affordance-centric real-world scenarios, such as "how
can I run the microwave for 1 minute?". However, there is still no clear task
definition and suitable benchmarks. In this paper, we define a new task called
Affordance-centric Question-driven Task Completion, where the AI assistant
should learn from instructional videos and scripts to guide the user
step-by-step. To support the task, we constructed AssistQ, a new dataset
comprising 531 question-answer samples derived from 100 newly filmed
first-person videos. Each question should be completed with multi-step
guidances by inferring from visual details (e.g., buttons' position) and
textural details (e.g., actions like press/turn). To address this unique task,
we developed a Question-to-Actions (Q2A) model that significantly outperforms
several baseline methods while still having large room for improvement. We
expect our task and dataset to advance Egocentric AI Assistant's development.
Our project page is available at: https://showlab.github.io/assistq


title:
Generating Realistic Two-Line Elements for Notional Space Vehicles and
  Constellations

abstract:
  As space becomes increasingly populated with new satellites and systems,
modeling and simulating existing and future systems becomes more important. The
two-line element set has been a standard format for sharing data about a
satellite's orbit since the 1960s, and well-developed algorithms can predict
the future location of satellites based on this data. In order to simulate
potential future systems, especially when mixed with existing systems, data
must be generated to represent the desired orbits. We present a means to create
two-line element sets with parameters that closely resemble real satellite
behavior, and rely on a novel approach to calculate the mean motion for even
greater accuracy.

categories: astro-ph.EP astro-ph.IM physics.app-ph physics.space-ph


title: Anyonic interference and braiding phase in a Mach-Zehnder Interferometer

abstract:
  The fractional quantum Hall states have long been predicted to be a testing
ground of fractional (anyonic) exchange statistics. These topological states
harbor quasiparticles with fractional charges of both abelian and non-abelian
characters. The quasiparticles' charge is commonly determined by shot noise
measurements (1, 2), and states' statistics can be revealed by appropriately
interfering the quasiparticles. While the multipath Fabry-Perot electronic
interferometer (FPI) is easier to fabricate, it is often plagued by Coulomb
interactions (3), its area breathes with the magnetic field (4), and its bulk's
charges tend to fluctuate (5). Recent FPI experiments employing adequate
screening allowed an observation of Aharonov-Bohm (AB) interference at bulk
filling $\nu$=1/3 (6). In the current work, we chose to employ an
interaction-free, two-path, Mach-Zehnder interferometer (MZI), tuned to bulk
filling $\nu$=2/5. Interfering the outer $\nu$=1/3 mode (with the inner
$\nu$=1/15 mode screening out the bulk), we observed a 'dressed AB'
periodicity, with a combined 'bare AB' flux periodicity of three flux-quanta
(3$\phi_0$) and the 'braiding phase' 2$\pi$/3. This unique interference
resulted with an AB periodicity of a single flux-quantum. Moreover, the
visibility of the interference, $v_{e/3}$, deviated markedly from that of the
electronic one $\it{v}_{e}$, agreeing with the theoretically expected
visibility, $\it{v}_{e/3} \sim {\it{v}_e}^3$. With the two non-equivalent
drains of the MZI, the fractional visibility peaked away from the ubiquitous
transmission-half of the MZI. We provide simple theoretical arguments that
support our results. The MZI proves to be a powerful tool that can be used to
probe further the statistics of more complex anyonic quasiparticles.


title: Lightweight Monocular Depth Estimation through Guided Decoding

abstract:
  We present a lightweight encoder-decoder architecture for monocular depth
estimation, specifically designed for embedded platforms. Our main contribution
is the Guided Upsampling Block (GUB) for building the decoder of our model.
Motivated by the concept of guided image filtering, GUB relies on the image to
guide the decoder on upsampling the feature representation and the depth map
reconstruction, achieving high resolution results with fine-grained details.
Based on multiple GUBs, our model outperforms the related methods on the NYU
Depth V2 dataset in terms of accuracy while delivering up to 35.1 fps on the
NVIDIA Jetson Nano and up to 144.5 fps on the NVIDIA Xavier NX. Similarly, on
the KITTI dataset, inference is possible with up to 23.7 fps on the Jetson Nano
and 102.9 fps on the Xavier NX. Our code and models are made publicly
available.


title:
Unveiling an old disk around a massive young leaking blueberry in
  SDSS-IV MaNGA

abstract:
  Extreme emission-line galaxies, such as blue compact dwarfs (BCDs), Green
Peas (GPs) and blueberries in the local Universe are the potential candidates
to understand the nature of galaxies that re-ionized the early Universe. Being
low-mass, metal-poor starburst systems they are understood as local analogs of
the high redshift Lyman Continuum (LyC) and Lyman-${\alpha}$ emitters (LAEs).
Even with their proximity to us, we know little about their spatially resolved
properties, most of the blueberries and GPs are indeed compact, remain
unresolved. Here, we report the detection of a disk-like lower-surface
brightness (LSB) stellar host with very old population around a blueberry LAE
system using broad i-band imaging and integral field spectroscopic data from
SDSS and SDSS-IV MaNGA surveys, respectively. The LSB stellar host is
structurally similar to that observed around15 local starburst BCDs.
Furthermore, the kinematics of the studied blueberry source bear sign of
misalignment between the gas and stellar components. Our findings establish an
intriguing thread connecting the blueberry and an LSB disk with old stellar
population, and suggest that blueberries and their high redshift counterparts
such as GPs do not represent peculiar cases of dwarf galaxy evolution. In fact,
with respect to the structural properties of their host galaxies, they are
compatible with a common evolutionary track of the main population of local
BCDs.


title:
The Picture of Dorian Gray with roles reversed: Notes for a sensible
  chronology of the life and the activity of the Neapolitan optician Francesco
  Fontana (c. 1585-1656)

abstract:
  In the last few years it has been alleged the possible identification of the
sitter in Jusepe de Ribera's Allegory of Sight with the Neapolitan optician
Francesco Fontana (c. 1585-1656), known for contributing to the diffusion of
the Keplerian telescope. The present paper aims to demonstrate the
impossibility of this identification, grounded on the erroneous assumption that
Fontana was already renowned as an optician around the mid-1615s, and, on the
basis of known sources, suggests a more reasonable chronology of his activity,
which postpones the spreading of his fame out of Naples to the end of the next
decade.


title: Effect of clustering on primordial black hole microlensing constraints

abstract:
  Stellar microlensing observations tightly constrain compact object dark
matter in the mass range $(10^{-11} - 10^{3}) M_{\odot}$. Primordial Black
Holes (PBHs) form clusters, and it has been argued that these microlensing
constraints are consequently weakened or evaded. For the most commonly studied
PBH formation mechanism, the collapse of large gaussian curvature perturbations
generated by inflation, the clusters are sufficiently extended that the PBHs
within them act as individual lenses. We find that if the typical mass of the
clusters is sufficiently large, $ \gtrsim 10^{6} M_{\odot}$, then the event
duration distribution can deviate significantly from that produced by a smooth
dark matter distribution, in particular at the shortest durations. As a
consequence of this, the probability distribution of the number of observed
events is non-Poissonian, peaking at a lower value, with an extended tail to
large numbers of events. However, for PBHs formed from the collapse of large
inflationary perturbations, the typical cluster is expected to contain $\sim
10^{3}$ PBHs. In this case the effect of clustering is negligibly small, apart
from for the most massive PBHs probed by decade-long stellar microlensing
surveys ($M_{\rm PBH} \sim 10^{3} M_{\odot}$).


title:
Complementarity of experiments in probing the non-relativistic effective
  theory of dark matter-nucleon interactions

abstract:
  The non-relativistic effective theory of dark matter-nucleon interactions
depends on 28 coupling strengths for dark matter spin up to 1/2. Due to the
vast parameter space of the effective theory, most experiments searching for
dark matter interpret the results assuming that only one of the coupling
strengths is non-zero. On the other hand, dark matter models generically lead
in the non-relativistic limit to several interactions which interfere with one
another, therefore the published limits cannot be straightforwardly applied to
model predictions. We present a method to determine a rigorous upper limit on
the dark matter-nucleon interaction strength including all possible
interferences among operators. We illustrate the method to derive model
independent upper limits on the interaction strengths from the null search
results from XENON1T, PICO-60 and IceCube. For some interactions, the limits on
the coupling strengths are relaxed by more than one order of magnitude. We also
present a method that allows to combine the results from different experiments,
thus exploiting the synergy between different targets in exploring the
parameter space of dark matter-nucleon interactions.


title:
The CAT Effect: Exploring the Impact of Casual Affective Triggers on
  Online Surveys' Response Rates

abstract:
  We explore the impact of Casual Affective Triggers (CAT) on response rates of
online surveys. As CAT, we refer to objects that can be included in survey
participation invitations and trigger participants' affect. The hypothesis is
that participants who receive CAT-enriched invitations are more likely to
respond to a survey. We conducted a study where the control condition received
invitations without affective triggers, and the experimental condition received
CAT-enriched invitations. We differentiated the triggers within the
experimental condition: one-third of the population received a personalized
invitation, one-third received a picture of the surveyor's cat, and one-third
received both. We followed up with a survey to validate our findings. Our
results suggest that CATs have a positive impact on response rates. We did not
find CATs to induce response bias.


title: Measuring the Mixing of Contextual Information in the Transformer

abstract:
  The Transformer architecture aggregates input information through the
self-attention mechanism, but there is no clear understanding of how this
information is mixed across the entire model. Additionally, recent works have
demonstrated that attention weights alone are not enough to describe the flow
of information. In this paper, we consider the whole attention block --
multi-head attention, residual connection, and layer normalization -- and
define a metric to measure token-to-token interactions within each layer,
considering the characteristics of the representation space. Then, we aggregate
layer-wise interpretations to provide input attribution scores for model
predictions. Experimentally, we show that our method, ALTI (Aggregation of
Layer-wise Token-to-token Interactions), provides faithful explanations and
outperforms similar aggregation methods.


title:
Topological Corrections and Conformal Backreaction in the Einstein
  Gauss-Bonnet/Weyl Theories of Gravity at D=4

abstract:
  We investigate the gravitational backreaction, generated by coupling a
general conformal sector to external, classical gravity, as described by a
conformal anomaly effective action. We address the issues raised by the
regularization of the topological Gauss-Bonnet and Weyl terms in these actions
and the use of dimensional regularization (DR). We discuss both their local and
nonlocal expressions, as possible IR and UV descriptions of conformal theories,
below and above the conformal breaking scale. Our discussion overlaps with
several recent studies of dilaton gravities - obtained via a certain singular
limit of the Einstein-Gauss-Bonnet (EGB) theory - originally introduced as a
way to bypass Lovelock's theorem.Nonlocal, purely gravitational realizations of
such EGB theories, quadratic in the dilaton field, beside their local quartic
forms, are possible by a finite renormalization of the Euler density. Such
nonlocal versions, which are deprived of any scale, can be expanded, at least
around flat space, in terms of the combination $R \Box^{-1}$ times multiple
variations of the anomaly functional, as pointed out in recent studies at
$d=4$. Similar conclusions can be drawn for the proposed nonlocal EGB theory.
The expansion emerges from previous investigations of the anomalous conformal
Ward identities that constrain such theories around the flat spacetime limit in
momentum space.


title:
BYOTee: Towards Building Your Own Trusted Execution Environments Using
  FPGA

abstract:
  In recent years, we have witnessed unprecedented growth in using
hardware-assisted Trusted Execution Environments (TEE) or enclaves to protect
sensitive code and data on commodity devices thanks to new hardware security
features, such as Intel SGX and Arm TrustZone. Even though the proprietary TEEs
bring many benefits, they have been criticized for lack of transparency,
vulnerabilities, and various restrictions. For example, existing TEEs only
provide a static and fixed hardware Trusted Computing Base (TCB), which cannot
be customized for different applications. Existing TEEs time-share a processor
core with the Rich Execution Environment (REE), making execution less efficient
and vulnerable to cache side-channel attacks. Moreover, TrustZone lacks
hardware support for multiple TEEs, remote attestation, and memory encryption.
  In this paper, we present BYOTee (Build Your Own Trusted Execution
Environments), which is an easy-to-use infrastructure for building multiple
equally secure enclaves by utilizing commodity Field Programmable Gate Arrays
(FPGA) devices. BYOTee creates enclaves with customized hardware TCBs, which
include softcore CPUs, block RAMs, and peripheral connections, in FPGA on
demand. Additionally, BYOTee provides mechanisms to attest the integrity of the
customized enclaves' hardware and software stacks, including bitstream,
firmware, and the Security-Sensitive Applications (SSA) along with their inputs
and outputs to remote verifiers. We implement a BYOTee system for the Xilinx
System-on-Chip (SoC) FPGA. The evaluations on the low-end Zynq-7000 system for
four SSAs and 12 benchmark applications demonstrate the usage, security,
effectiveness, and performance of the BYOTee framework.


title:
Multi-agent consensus over time-invariant and time-varying signed
  digraphs via eventual positivity

abstract:
  Laplacian dynamics on signed digraphs have a richer behavior than those on
nonnegative digraphs. In particular, for the so-called "repelling" signed
Laplacians, the marginal stability property (needed to achieve consensus) is
not guaranteed a priori and, even when it holds, it does not automatically lead
to consensus, as these signed Laplacians may loose rank even in strongly
connected digraphs. Furthermore, in the time-varying case, instability can
occur even when switching in a family of systems each of which corresponds to a
marginally stable signed Laplacian with the correct corank. In this paper we
present conditions guaranteeing consensus of these signed Laplacians based on
the property of eventual positivity, a Perron-Frobenius type of property for
signed matrices. The conditions cover both time-invariant and time-varying
cases. A particularly simple sufficient condition valid in both cases is that
the Laplacians are normal matrices. Such condition can be relaxed in several
ways. For instance in the time-invariant case it is enough that the Laplacian
has this Perron-Frobenius property on the right but not on the left side (i.e.,
on the transpose). For the time-varying case, convergence to consensus can be
guaranteed by the existence of a common Lyapunov function for all the signed
Laplacians. All conditions can be easily extended to bipartite consensus.


title: Determination of a class of permutation quadrinomials

abstract:
  We determine all permutation polynomials over F_{q^2} of the form X^r
A(X^{q-1}) where, for some Q which is a power of the characteristic of F_q, the
integer r is congruent to Q+1 (mod q+1) and all terms of A(X) have degrees in
{0, 1, Q, Q+1}. We then use this classification to resolve eight conjectures
and open problems from the literature, and we show that the simplest special
cases of our result imply 58 recent results from the literature. Our proof
makes a novel use of geometric techniques in a situation where they previously
did not seem applicable, namely to understand the arithmetic of high-degree
rational functions over small finite fields, despite the fact that in this
situation the Weil bounds do not provide useful information.


title:
Revisiting the $^{63}$Cu NMR signature of charge order in
  La$_{1.875}$Ba$_{0.125}$CuO$_{4}$

abstract:
  We use single crystal $^{63}$Cu NMR techniques to revisit the early $^{63}$Cu
NQR signature of charge order observed for La$_{1.875}$Ba$_{0.125}$CuO$_{4}$
($T_{\text{c}} =4$~K) [A. W. Hunt et al., Phys. Rev. Lett. {\bf 82}, 4300
(1999)]. We show that the growth of spin correlations is accelerated below
$\sim 80$~K, where the inverse Laplace transform (ILT) T$_{1}$ analysis of the
$^{139}$La NMR spin-lattice relaxation curve recently uncovered emergence of
the slow components in the lattice and/or charge fluctuations [P. M. Singer et
al., {\bf 101}, 174508 (2020)]. From the accurate measurements of the $^{63}$Cu
NMR signal intensity, spin echo decay $M(2\tau)$, spin-lattice relaxation rate
$^{63}1/T_1$, and its density distribution function $P(^{63}1/T_{1})$, we also
demonstrate that charge order at $T_{\text{charge}}\simeq 54$~K turns on strong
enhancement of spin fluctuations {\it within charge ordered domains}, thereby
making the CuO$_2$ planes extremely inhomogeneous. The charge ordered domains
grow quickly below $T_{\text{charge}}$, and the volume fraction $F_{\text{CA}}$
of the canonical domains unaffected by charge order gradually diminishes by
$\sim 35$~K. This finding agrees with our independent estimations of
$F_{\text{CA}}$ based entirely on the $^{139}$La ILTT$_{1}$ analyses, but is in
a stark contrast with much slower growth of charge ordered domains observed for
La$_{1.885}$Sr$_{0.115}$CuO$_{4}$ from its $T_{\text{charge}}\simeq 80$~K to
$T_{\text{c}}\simeq 30$~K.


title:
Learning Bidirectional Translation between Descriptions and Actions with
  Small Paired Data

abstract:
  This study achieved bidirectional translation between descriptions and
actions using small paired data. The ability to mutually generate descriptions
and actions is essential for robots to collaborate with humans in their daily
lives. The robot is required to associate real-world objects with linguistic
expressions, and large-scale paired data are required for machine learning
approaches. However, a paired dataset is expensive to construct and difficult
to collect. This study proposes a two-stage training method for bidirectional
translation. In the proposed method, we train recurrent autoencoders (RAEs) for
descriptions and actions with a large amount of non-paired data. Then, we
fine-tune the entire model to bind their intermediate representations using
small paired data. Because the data used for pre-training do not require
pairing, behavior-only data or a large language corpus can be used. We
experimentally evaluated our method using a paired dataset consisting of
motion-captured actions and descriptions. The results showed that our method
performed well, even when the amount of paired data to train was small. The
visualization of the intermediate representations of each RAE showed that
similar actions were encoded in a clustered position and the corresponding
feature vectors well aligned.


title:
Ghost sensing: the rise and role of exceptional points in planar
  geometry

abstract:
  We show the recently discovered ghost waves - a special class of non-uniform
electromagnetic waves in biaxial anisotropic media - can be used for optical
sensing based on exceptional points. In addition to showing high sensitivity
and precision, the proposed sensor employs simple planar geometry and is robust
against noise.


title:
Spill ripple mitigation by bunched beam extraction with high frequency
  synchrotron motion

abstract:
  Slow extraction of bunched beams is widely used for mitigating the formation
of micro structures due to ripples of the accelerator magnets power supplies on
the extracted beam which is also referred to as spill. That helps, in
particular, experiments with slow detectors or with low extraction rates.
Furthermore, this technique is widely used for avoiding long gaps in the spill
measured in ionisation chambers which in turn would trigger interlocks.
  On the other hand, the bunches create spill structures on time scales defined
by the rf frequency. In addition, macroscopic spill structures of duration up
to some hundred milliseconds can be created by the synchrotron motion for a
sufficiently large synchrotron tune.
  The aim of this report is to study the influence of the synchrotron tune
determined by amplitude and harmonic number of the rf voltage which
additionally depends on the transverse emittance and the longitudinal bunch
area of the circulating beam as well as the distribution of the particles'
transit times.


title:
Towards Universal Texture Synthesis by Combining Texton Broadcasting
  with Noise Injection in StyleGAN-2

abstract:
  We present a new approach for universal texture synthesis by incorporating a
multi-scale texton broadcasting module in the StyleGAN-2 framework. The texton
broadcasting module introduces an inductive bias, enabling generation of
broader range of textures, from those with regular structures to completely
stochastic ones. To train and evaluate the proposed approach, we construct a
comprehensive high-resolution dataset that captures the diversity of natural
textures as well as stochastic variations within each perceptually uniform
texture. Experimental results demonstrate that the proposed approach yields
significantly better quality textures than the state of the art. The ultimate
goal of this work is a comprehensive understanding of texture space.


title: Yet another star in the Albireo system -- The discovery of Albireo Ad

abstract:
  Context. Albireo is a well-known bright visual double star. It is still
unclear if the components A and B form a gravitationally bound system. The
component Albireo A is itself a binary star. The orbital parameters of the
Albireo Aa, Ac system have been determined only recently. Thus, Albireo is
still of interest for current research. Aims. We aim to present evidence for
the detection of a new member in the Albireo system. Furthermore, we aim to
determine the orbital parameters and to find further conclusions for the
Albireo system. Methods. We used spectroscopic observations of Albireo A
obtained with the TIGRE telescope and determined the radial velocities during a
period of over three years. We analyzed the radial velocity curve with RadVel
to determine the orbital parameters. In addition, we determined the stellar
parameters of Albireo Aa with iSpec. Results. We found clear evidence for yet
another star in the Albireo system orbiting Albireo Aa with a period of about
$P = 371$ days. Several alternative explanations for the periodic radial
velocity signal could be discarded. The new companion Albireo Ad is a low mass
star of about $0.085~M_\odot$. Conclusions. We conclude that Albireo is a
hierarchical multiple star system and remains an interesting object for future
observations and studies.


title: A fast implementation of the Monster group

abstract:
  Let $\mathbb{M}$ be the Monster group, which is the largest sporadic finite
simple group, and has first been constructed in 1982 by Griess. In 1985 Conway
has constructed a 196884-dimensional representation $\rho$ of $\mathbb{M}$ with
matrix coefficients in $\mathbb{Z}[\frac{1}{2}]$. We describe a new and very
fast algorithm for performing the group operation in $\mathbb{M}$.
  We assume that $\mathbb{M}$ is given by a certain set $\Gamma$ of generators
acting on the representation $\rho$ of $\mathbb{M}$. For a small odd integer $p
> 1$ let $\rho_p$ be the representation $\rho$ with coefficients taken modulo
$p$. In principle, we may represent elements of $\mathbb{M}$ by matrices acting
on $\rho_p$, but this is way too large for practical purposes. We construct a
triple $(v_1, v^+, v^-)$ of elements of the module $\rho_{15}$ such that any $g
\in \mathbb{M}$ can effectively be computed as a word in $\Gamma$ from the
images $(v_1 g, v^+ g, v^- g)$.
  An implementation of the group operation on $\mathbb{M}$ based on that idea
multiplies two random elements of $\mathbb{M}$ in a bit less than
50~milliseconds on a standard PC with an Intel i7-8750H CPU at 4 GHz. This is
about 100000 times faster than estimated by Wilson in 2013.


title: Higgs bundles and SYZ geometry

abstract:
  Using non-Abelian Hodge theory for parabolic Higgs bundles, we construct
infinitely many non-congruent hyperbolic affine spheres modeled on a
thrice-punctured sphere with monodromy in $\mathrm{SL}_3(\mathbb{Z})$. These
give rise to non-isometric semi-flat Calabi-Yau metrics on special Lagrangian
torus bundles over an open ball in $\mathbb{R}^{3}$ minus a Y-vertex, thereby
answering a question raised by Loftin, Yau, and Zaslow in [LYZ], [LYZerr].


title:
Olfaction-inspired MCs: Molecule Mixture Shift Keying and Cross-Reactive
  Receptor Arrays

abstract:
  In this paper, we propose a novel concept for engineered molecular
communication (MC) systems inspired by animal olfaction. We focus on a
multi-user scenario where several transmitters wish to communicate with a
central receiver. We assume that each transmitter employs a unique mixture of
different types of signaling molecules to represent its message and the
receiver is equipped with an array comprising $R$ different types of receptors
in order to detect the emitted molecule mixtures. The design of an MC system
based on \textit{orthogonal} molecule-receptor pairs implies that the hardware
complexity of the receiver linearly scales with the number of signaling
molecule types $Q$ (i.e., $R=Q$). Natural olfaction systems avoid such high
complexity by employing arrays of \textit{cross-reactive} receptors, where each
type of molecule activates multiple types of receptors and each type of
receptor is predominantly activated by multiple types of molecules albeit with
different activation strengths. For instance, the human olfactory system is
believed to discriminate several thousands of chemicals using only a few
hundred receptor types, i.e., $Q\gg R$. Motivated by this observation, we first
develop an end-to-end MC channel model that accounts for the key properties of
olfaction. Subsequently, we present the proposed transmitter and receiver
designs. In particular, given a set of signaling molecules, we develop
algorithms that allocate molecules to different transmitters and optimize the
mixture alphabet for communication. Moreover, we formulate the molecule mixture
recovery as a convex compressive sensing problem which can be efficiently
solved via available numerical solvers.


title:
Extending Life of Lithium-ion Battery Packs by Taming Heterogeneities
  via an Optimal Control-based Active Balancing Strategy

abstract:
  This paper develops a multi-objective fast charging-minimum degradation
optimal control problem (OCP) for lithium-ion battery modules, made of
series-connected cells, subject to heterogeneity induced by manufacturing
defects and non uniform operating conditions, realized via an active balancing
circuitry. Each cell is expressed via a coupled nonlinear electrochemical,
thermal, and aging model and the direct collocation approach is employed to
transcribe the OCP into a nonlinear programming problem (NLP). The proposed OCP
is formulated under two different schemes of charging operation: (i)
same-charging-time (OCP-SCT) and (ii) different-charging-time (OCP-DCT). The
former assumes simultaneous charging of all cells irrespective of their initial
conditions, whereas the latter allows for different charging times of the cells
to account for heterogeneous initial conditions. Simulations on an illustrative
case study -- a battery module with two series-connected cells -- are carried
out in the presence of intrinsic heterogeneity among the cells in terms of
state of charge and state of health. Results show that the OCP-DCT scheme
provides more flexibility to deal with heterogeneity, boasting of lower
temperature increase, charging current amplitudes, and degradation. Finally,
comparison with the common practice of constant current (CC) charging over a
long-term cycling operation shows that promising savings, in terms of retained
capacity, are attainable under the new control.


title:
Learning based Age of Information Minimization in UAV-relayed IoT
  Networks

abstract:
  Unmanned Aerial Vehicles (UAVs) are used as aerial base-stations to relay
time-sensitive packets from IoT devices to the nearby terrestrial base-station
(TBS). Scheduling of packets in such UAV-relayed IoT-networks to ensure fresh
(or up-to-date) IoT devices' packets at the TBS is a challenging problem as it
involves two simultaneous steps of (i) sampling of packets generated at IoT
devices by the UAVs [hop-1] and (ii) updating of sampled packets from UAVs to
the TBS [hop-2]. To address this, we propose Age-of-Information (AoI)
scheduling algorithms for two-hop UAV-relayed IoT-networks. First, we propose a
low-complexity AoI scheduler, termed, MAF-MAD that employs Maximum AoI First
(MAF) policy for sampling of IoT devices at UAV (hop-1) and Maximum AoI
Difference (MAD) policy for updating sampled packets from UAV to the TBS
(hop-2). We prove that MAF-MAD is the optimal AoI scheduler under ideal
conditions (lossless wireless channels and generate-at-will traffic-generation
at IoT devices). On the contrary, for general conditions (lossy channel
conditions and varying periodic traffic-generation at IoT devices), a deep
reinforcement learning algorithm, namely, Proximal Policy Optimization
(PPO)-based scheduler is proposed. Simulation results show that the proposed
PPO-based scheduler outperforms other schedulers like MAF-MAD, MAF, and
round-robin in all considered general scenarios.


title: Twitter Engagement with Retracted Articles: Who, When, and How?

abstract:
  Retracted research discussed on social media can spread misinformation, yet
we lack an understanding of how retracted articles are mentioned by academic
and non-academic users. This is especially relevant on Twitter due to the
platform's prominent role in science communication. Here, we analyze the pre
and post retraction differences in Twitter engagement metrics and content of
mentions for over 3,800 retracted English-language articles alongside
comparable non-retracted articles. We subset these findings according to the
five user types detected by our supervised learning classifier: members of the
public, scientists, bots, practitioners, and science communicators. We find
that retracted articles receive greater overall engagement than non-retracted
articles, especially among members of the public and bot users, the majority of
engagement happening prior to retraction. Our results highlight non-scientists'
involvement in retracted article discussions and suggest an opportunity for
Twitter to include a retraction notice feature.


title:
Neural Face Identification in a 2D Wireframe Projection of a Manifold
  Object

abstract:
  In computer-aided design (CAD) systems, 2D line drawings are commonly used to
illustrate 3D object designs. To reconstruct the 3D models depicted by a single
2D line drawing, an important key is finding the edge loops in the line drawing
which correspond to the actual faces of the 3D object. In this paper, we
approach the classical problem of face identification from a novel data-driven
point of view. We cast it as a sequence generation problem: starting from an
arbitrary edge, we adopt a variant of the popular Transformer model to predict
the edges associated with the same face in a natural order. This allows us to
avoid searching the space of all possible edge loops with various hand-crafted
rules and heuristics as most existing methods do, deal with challenging cases
such as curved surfaces and nested edge loops, and leverage additional cues
such as face types. We further discuss how possibly imperfect predictions can
be used for 3D object reconstruction.


title:
The evolution of protoplanetary discs in star formation and feedback
  simulations

abstract:
  We couple star cluster formation and feedback simulations of a Carina-like
star forming region with 1D disc evolutionary models to study the impact of
external photoevaporation on disc populations in massive star forming regions.
To investigate the effect of shielding of young stellar objects by star forming
material, we track the FUV field history at each star in the cluster with two
methods: i) Monte Carlo radiative transfer accounting for the shielding of
stars from the FUV by the star forming cloud ii) Geometric dilution of the
radiation from other stars which ignores shielding effects. We found that
significant shielding only occurs for a small fraction of discs and offers
protection from external photoevaporation for < 0.5 Myr. However, this initial
protection can prevent significant early gas/dust mass loss and disc radius
reduction due to external photoevaporation. Particularly, shielding for 0.5 Myr
is sufficient for much of the solid reservoir to evolve to larger sizes where
it will not be entrained in an external wind. Shielding is therefore
potentially significant for terrestrial planet formation in retaining the solid
mass budget, but the continued stripping of gas when shielding ends could still
impact migration and the gas reservoir for giant planet atmospheres. Our models
highlight issues with treating all discs in a cluster with a single
characteristic age, since shielded objects are typically only the youngest. Our
model predicts that the majority of discs in a 2 Myr Carina-like environment
are subject to strong external photoevaporation.


title:
Present status and future challenges of non-interferometric tests of
  collapse models

abstract:
  The superposition principle is the cornerstone of quantum mechanics, leading
to a variety of genuinely quantum effects. Whether the principle applies also
to macroscopic systems or, instead, there is a progressive breakdown when
moving to larger scales, is a fundamental and still open question. Spontaneous
wavefunction collapse models predict the latter option, thus questioning the
universality of quantum mechanics. Technological advances allow to challenge
collapse models and the quantum superposition principle more and more with a
variety of different experiments. Among them, non-interferometric experiments
proved to be the most effective in testing these models. We provide an overview
of such experiments, including cold atoms, optomechanical systems, X-rays
detection, bulk heating as well as comparisons with cosmological observations.
We also discuss avenues for future dedicated experiments, which aim at further
testing collapse models and the validity of quantum mechanics.

categories: quant-ph cond-mat.mes-hall gr-qc hep-ex physics.atom-ph


title: A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds

abstract:
  Recent works on 3D single object tracking treat the tracking as a
target-specific 3D detection task, where an off-the-shelf 3D detector is
commonly employed for tracking. However, it is non-trivial to perform accurate
target-specific detection since the point cloud of objects in raw LiDAR scans
is usually sparse and incomplete. In this paper, we address this issue by
explicitly leveraging temporal motion cues and propose DMT, a Detector-free
Motion prediction based 3D Tracking network that totally removes the usage of
complicated 3D detectors, which is lighter, faster, and more accurate than
previous trackers. Specifically, the motion prediction module is firstly
introduced to estimate a potential target center of the current frame in a
point-cloud free way. Then, an explicit voting module is proposed to directly
regress the 3D box from the estimated target center. Extensive experiments on
KITTI and NuScenes datasets demonstrate that our DMT, without applying any
complicated 3D detectors, can still achieve better performance (~10%
improvement on the NuScenes dataset) and faster tracking speed (i.e., 72 FPS)
than state-of-the-art approaches. Our codes will be released publicly.


title: CR-twistor spaces over manifolds with $G_2$- and $Spin(7)$-structures

abstract:
  In 1984 LeBrun constructed a CR-twistor space over an arbitrary conformal
Riemannian 3-manifold and proved that the CR-structure is formally integrable.
This twistor construction has been generalized by Rossi in 1985 for
$m$-dimensional Riemannian manifolds endowed with a $(m-1)$-fold vector cross
product (VCP). In 2011 Verbitsky generalized LeBrun's construction of
twistor-spaces to $7$-manifolds endowed with a $G_2$-structure. In this paper
we unify and generalize LeBrun's, Rossi's and Verbitsky's construction of a
CR-twistor space to the case where a Riemannian manifold $(M, g)$ has a VCP
structure. We show that the formal integrability of the CR-structure is
expressed in terms of a torsion tensor on the twistor space, which is a
Grassmanian bundle over $(M, g)$. If the VCP structure on $(M,g)$ is generated
by a $G_2$- or $Spin(7)$-structure, then the vertical component of the torsion
tensor vanishes if and only if $(M, g)$ has constant curvature, and the
horizontal component vanishes if $(M,g)$ is a torsion-free $G_2$ or
$Spin(7)$-manifold. Finally we discuss some open problems.


title:
Adaptative Perturbation Patterns: Realistic Adversarial Learning for
  Robust Intrusion Detection

abstract:
  Adversarial attacks pose a major threat to machine learning and to the
systems that rely on it. In the cybersecurity domain, adversarial cyber-attack
examples capable of evading detection are especially concerning. Nonetheless,
an example generated for a domain with tabular data must be realistic within
that domain. This work establishes the fundamental constraint levels required
to achieve realism and introduces the Adaptative Perturbation Pattern Method
(A2PM) to fulfill these constraints in a gray-box setting. A2PM relies on
pattern sequences that are independently adapted to the characteristics of each
class to create valid and coherent data perturbations. The proposed method was
evaluated in a cybersecurity case study with two scenarios: Enterprise and
Internet of Things (IoT) networks. Multilayer Perceptron (MLP) and Random
Forest (RF) classifiers were created with regular and adversarial training,
using the CIC-IDS2017 and IoT-23 datasets. In each scenario, targeted and
untargeted attacks were performed against the classifiers, and the generated
examples were compared with the original network traffic flows to assess their
realism. The obtained results demonstrate that A2PM provides a scalable
generation of realistic adversarial examples, which can be advantageous for
both adversarial training and attacks.


title: Physical characteristics of mixed-species swarming colonies

abstract:
  In nature, bacterial collectives typically consist of multiple species, which
are interacting both biochemically and physically. Nonetheless, past studies on
the physical properties of swarming bacteria were focused on axenic (single
species) populations. In bacterial swarming, intricate interactions between the
individuals lead to clusters, rapid jets and vortices that depend on cell
characteristics such as speed and length. In this work, we show the first
results of rapidly swarming mixed-species populations of {\sl Bacillus
subtilis} and {\sl Serratia marcescens}, two model swarm species that are known
to swarm well in axenic situations. In mixed liquid cultures, both species have
higher reproduction rates. We show that the mixed population swarms together
well and that the fraction between the species determines all dynamical scales
- from the microscopic (e.g. speed distribution), mesoscopic (vortex size) and
macroscopic (colony size structure). Understanding mixed-species swarms is
essential for a comprehensive understanding of the bacterial swarming
phenomenon and its biological and evolutionary implications.


title:
A Sharp Characterization of Linear Estimators for Offline Policy
  Evaluation

abstract:
  Offline policy evaluation is a fundamental statistical problem in
reinforcement learning that involves estimating the value function of some
decision-making policy given data collected by a potentially different policy.
In order to tackle problems with complex, high-dimensional observations, there
has been significant interest from theoreticians and practitioners alike in
understanding the possibility of function approximation in reinforcement
learning. Despite significant study, a sharp characterization of when we might
expect offline policy evaluation to be tractable, even in the simplest setting
of linear function approximation, has so far remained elusive, with a
surprising number of strong negative results recently appearing in the
literature.
  In this work, we identify simple control-theoretic and linear-algebraic
conditions that are necessary and sufficient for classical methods, in
particular Fitted Q-iteration (FQI) and least squares temporal difference
learning (LSTD), to succeed at offline policy evaluation. Using this
characterization, we establish a precise hierarchy of regimes under which these
estimators succeed. We prove that LSTD works under strictly weaker conditions
than FQI. Furthermore, we establish that if a problem is not solvable via LSTD,
then it cannot be solved by a broad class of linear estimators, even in the
limit of infinite data. Taken together, our results provide a complete picture
of the behavior of linear estimators for offline policy evaluation (OPE), unify
previously disparate analyses of canonical algorithms, and provide
significantly sharper notions of the underlying statistical complexity of OPE.


title: Projective geometry of homogeneous second order Hamiltonian operators

abstract:
  We prove the invariance of homogeneous second-order Hamiltonian operators
under the action of projective reciprocal transformations. We establish a
correspondence between such operators in dimension $n$ and $3$-forms in
dimension $n + 1$. In this way we classify second order Hamiltonian operators
using the known classification of $3$-forms in dimensions $\leq$ 9. Systems of
first-order conservation laws that are Hamiltonian with respect to such
operators are also explicitly found. The integrability of the systems is
discussed in detail.


title: On the fate of quantum black holes

abstract:
  We study the quantum dynamics of the Lema\^itre-Tolman-Bondi space-times
using a polymer quantization prescription based on loop quantum cosmology that
incorporates fundamental discreteness. By solving an effective equation derived
from this quantization, we find analytical solutions for the Oppenheimer-Snyder
and thin-shell collapse models, and numerical solutions for a variety of
asymptotically flat collapsing dust profiles. Our study (i) tracks the
formation, evolution and disappearance of dynamical horizons, (ii) shows that
matter undergoes a non-singular bounce that results in an outgoing shock wave,
(iii) determines black hole lifetime to be proportional to the square its mass,
and (iv) provides a conformal diagram that substantially modifies the standard
"information loss" picture by resolving the singularity and replacing the event
horizon by transient apparent horizons.


title: Power laws prevail in medical ultrasound

abstract:
  Major topics in medical ultrasound rest on the physics of wave propagation
through tissue. These include fundamental treatments of backscatter, speed of
sound, attenuation, and speckle formation. Each topic has developed its own
rich history, lexicography, and particular treatments. However, there is ample
evidence to suggest that power law relations are operating at a fundamental
level in all the basic phenomena related to medical ultrasound. This review
paper develops, from literature over the past 60 years, the accumulating
theoretical basis and experimental evidence that point to power law behaviors
underlying the most important tissue-wave interactions in ultrasound and in
shear waves which are now employed in elastography. The common framework of
power laws can be useful as a coherent overview of topics, and as a means for
improved tissue characterization.


title:
Sedimentation of colloidal plate-sphere mixtures and inference of
  particle characteristics from stacking sequences

abstract:
  We investigate theoretically the effect of gravity on a plate-sphere
colloidal mixture by means of an Onsager-like density functional to describe
the bulk, and sedimentation path theory to incorporate gravity. We calculate
the stacking diagram of the mixture for two sets of buoyant masses and
different values of the sample height. Several stacking sequences appear due to
the intricate interplay between gravity, the sample height, and bulk phase
separation. These include the experimentally observed floating nematic
sequence, which consists of a nematic layer sandwiched between two isotropic
layers. The values of the thicknesses of the layers in a complex stacking
sequence can be used to obtain microscopic information of the mixture. Using
the thicknesses of the layers in the floating nematic sequence we are able to
infer the values of the buoyant masses from the colloidal concentrations and
vice versa. We also predict new phenomena that can be experimentally tested,
such as a nontrivial evolution of the stacking sequence by increasing the
sample height in which new layers appear either at the top or at the bottom of
the sample.


title:
A Uniform Type Ia Supernova Distance Ladder with the Zwicky Transient
  Facility: Absolute Calibration Based on the Tip of the Red Giant Branch
  (TRGB) Method

abstract:
  The current Cepheid-calibrated distance ladder measurement of $H_0$ is
reported to be in tension with the values inferred from the cosmic microwave
background (CMB), assuming standard model cosmology. However, the tip of the
red giant branch (TRGB) reports an estimate of $H_0$ in better agreement with
the CMB. Hence, it is critical to reduce systematic uncertainties in local
measurements to understand the origin of the Hubble tension. In this paper, we
propose a uniform distance ladder, combining SNe~Ia observed by the Zwicky
Transient Facility (ZTF) with a TRGB calibration of their absolute luminosity.
A large, volume-limited, sample of both calibrator and Hubble flow SNe Ia from
the \emph{same} survey minimizes two of the largest sources of systematics:
host-galaxy bias and non-uniform photometric calibration. We present results
from a pilot study using existing TRGB distance to the host galaxy of ZTF SN Ia
SN 2021rhu (aka ZTF21abiuvdk). Combining the ZTF calibrator with a
volume-limited sample from the first data release of ZTF Hubble flow SNe Ia, we
infer $H_0 = 76.94 \pm 6.4\, {\rm km}\,{\rm s^{-1}}\,{\rm Mpc^{-1}}$, an $8.3
\%$ measurement. The error budget is dominated by the single object calibrating
the SN Ia luminosity in this pilot study. However, the ZTF sample includes
already five other SNe Ia within $\sim$ 20 Mpc for which TRGB distances can be
obtained with HST. Finally, we present the prospects of building this distance
ladder out to 80 Mpc with JWST observations of more than one hundred SNe Ia.


title: On geometry of simultaneous approximation to three real numbers

abstract:
  Considering simultaneous approximation to three numbers, we study the
geometry of the sequence of best approximations. We provide a sharper lower
bound for the ratio between ordinary and uniform exponent of Diophantine
approximation, optimal in terms of this geometry.


title: Lower Bounds for the Reachability Problem in Fixed Dimensional VASSes

abstract:
  We study the complexity of the reachability problem for Vector Addition
Systems with States (VASSes) in fixed dimensions. We provide four lower bounds
improving the currently known state-of-the-art: 1) \np-hardness for unary flat
$4$-VASSes (VASSes in dimension 4), 2) \pspace-hardness for unary $5$-VASSes,
3) \expspace-hardness for binary $6$-VASSes and 4) \tower-hardness for unary
$8$-VASSes.


title:
Heteroepitaxial control of Fermi liquid, Hund metal, and Mott insulator
  phases in the single-atomic-layer limit

abstract:
  Interfaces between dissimilar correlated oxides can offer devices with
versatile functionalities. In that respect, manipulating and measuring novel
physical properties of oxide heterointerfaces are highly desired. Yet, despite
extensive studies, obtaining direct information on their momentum-resolved
electronic structure remains a great challenge. This is because most correlated
interfacial phenomena appear within a few atomic layers from the interface,
thus limiting the application of available experimental probes. Here, we
utilize atomic-scale epitaxy and photoemission spectroscopy to demonstrate the
interface control of correlated electronic phases in atomic-scale
ruthenate--titanate heterostructures. While bulk SrRuO$_3$ is a ferromagnetic
metal, the heterointerfaces exclusively realize three distinct correlated
phases in the single-atomic-layer limit. Our theory reveals that atomic-scale
structural proximity effects lead to the emergence of Fermi liquid, Hund metal,
and Mott insulator phases in the quantum-confined SrRuO$_3$. These results
highlight the extensive interfacial tunability of electronic phases, hitherto
hidden in the atomically thin correlated heterostructure.


title:
Milliarcsecond X-ray astrometry to resolve inner regions of AGN at $z>1$
  using gravitational lensing

abstract:
  We report the localization of the X-ray emission from two strongly lensed
AGN, CLASS B0712+472 ($z=1.34$) and CLASS B1608+656 ($z=1.394$). We obtain
milliarcsecond X-ray astrometry by developing a novel method that combines
parametric lens modelling with a Bayesian analysis. We spatially locate the
X-ray sources in CLASS B0712+472 and CLASS B1608+656 within 11 mas and 9 mas
from the radio source, respectively. For CLASS B0712+472, we find that the
X-ray emission is co-spatial with the radio and optical emission. While, in
CLASS B1608+656, the X-ray emission is co-spatial with radio, but displaced
with respect to the optical emission at 1$\sigma$ level, which positions this
source as an offset AGN candidate. This high astrometric precision improves on
the limitations of existing X-ray instruments by two orders of magnitude. The
demonstrated method opens a path to search for offset and binary AGN at $z>1$,
and to directly test supermassive black hole formation models in a redshift
range that has been mostly underconstrained to date.


title:
PERCEPT: a new online change-point detection method using topological
  data analysis

abstract:
  Topological data analysis (TDA) provides a set of data analysis tools for
extracting embedded topological structures from complex high-dimensional
datasets. In recent years, TDA has been a rapidly growing field which has found
success in a wide range of applications, including signal processing,
neuroscience and network analysis. In these applications, the online detection
of changes is of crucial importance, but this can be highly challenging since
such changes often occur in a low-dimensional embedding within high-dimensional
data streams. We thus propose a new method, called PERsistence diagram-based
ChangE-PoinT detection (PERCEPT), which leverages the learned topological
structure from TDA to sequentially detect changes. PERCEPT follows two key
steps: it first learns the embedded topology as a point cloud via persistence
diagrams, then applies a non-parametric monitoring approach for detecting
changes in the resulting point cloud distributions. This yields a
non-parametric, topology-aware framework which can efficiently detect online
changes from high-dimensional data streams. We investigate the effectiveness of
PERCEPT over existing methods in a suite of numerical experiments where the
data streams have an embedded topological structure. We then demonstrate the
usefulness of PERCEPT in two applications in solar flare monitoring and human
gesture detection.


title:
Optimal parameters related with continuity properties of the multilinear
  fractional integral operator between Lebesgue and Lipschitz spaces

abstract:
  We deal with the boundedness of the multilinear fractional integral operator
$I_{\gamma,m}$ from a product of weighted Lebesgue spaces into adequate
weighted Lipschitz spaces. Our results generalize some previous estimates not
only for the linear case but also for the unweighted problem in the multilinear
context. We characterize the classes of weights for which the problem described
above holds and show the optimal range of the parameters involved. The
optimality is understood in the sense that the parameters defining the
corresponding spaces belong to a certain region. We further exhibit examples of
weights for the class which cover the mentioned area.


title: Dual Lottery Ticket Hypothesis

abstract:
  Fully exploiting the learning capacity of neural networks requires
overparameterized dense networks. On the other side, directly training sparse
neural networks typically results in unsatisfactory performance. Lottery Ticket
Hypothesis (LTH) provides a novel view to investigate sparse network training
and maintain its capacity. Concretely, it claims there exist winning tickets
from a randomly initialized network found by iterative magnitude pruning and
preserving promising trainability (or we say being in trainable condition). In
this work, we regard the winning ticket from LTH as the subnetwork which is in
trainable condition and its performance as our benchmark, then go from a
complementary direction to articulate the Dual Lottery Ticket Hypothesis
(DLTH): Randomly selected subnetworks from a randomly initialized dense network
can be transformed into a trainable condition and achieve admirable performance
compared with LTH -- random tickets in a given lottery pool can be transformed
into winning tickets. Specifically, by using uniform-randomly selected
subnetworks to represent the general cases, we propose a simple sparse network
training strategy, Random Sparse Network Transformation (RST), to substantiate
our DLTH. Concretely, we introduce a regularization term to borrow learning
capacity and realize information extrusion from the weights which will be
masked. After finishing the transformation for the randomly selected
subnetworks, we conduct the regular finetuning to evaluate the model using fair
comparisons with LTH and other strong baselines. Extensive experiments on
several public datasets and comparisons with competitive approaches validate
our DLTH as well as the effectiveness of the proposed model RST. Our work is
expected to pave a way for inspiring new research directions of sparse network
training in the future. Our code is available at
https://github.com/yueb17/DLTH.


title:
Second-life Lithium-ion batteries: A chemistry-agnostic and scalable
  health estimation algorithm

abstract:
  Battery state of health is an essential metric for diagnosing battery
degradation during testing and operation. While many unique measurements are
possible in the design phase, for practical applications often only
temperature, voltage and current sensing are accessible. This paper presents a
novel combination of machine learning techniques to produce accurate
predictions significantly faster than standard Gaussian processes. The
data-driven approach uses feature generation with simple mathematics, feature
filtering, and bagging, which is validated with publicly available aging
datasets of more than 200 cells with slow and fast charging, across different
cathode chemistries, and for various operating conditions. Based on multiple
training-test partitions, average and median state of health prediction root
mean square error (RMSE) is found to be less than 1.48% and 1.27%,
respectively, with a limited amount of input data, showing the capability of
the approach even when input data and time are limiting factors. The process
developed in this paper has direct applicability to today's incumbent open
challenge of assessing retired batteries on the basis of their residual health,
and therefore nominal remaining useful life, to allow fast classification for
second-life reutilization.


title: Edge Intersection Graphs of Paths on a Triangular Grid

abstract:
  We introduce a new class of intersection graphs, the edge intersection graphs
of paths on a triangular grid, called EPGt graphs. We show similarities and
differences from this new class to the well-known class of EPG graphs. A turn
of a path at a grid point is called a bend. An EPGt representation in which
every path has at most $k$ bends is called a B$_k$-EPGt representation and the
corresponding graphs are called B$_k$-EPGt graphs. We provide examples of
B$_{2}$-EPG graphs that are B$_{1}$-EPGt. We characterize the representation of
cliques with three vertices and chordless 4-cycles in B$_{1}$-EPGt
representations. We also prove that B$_{1}$-EPGt graphs have Strong Helly
number $3$. Furthermore, we prove that B$_{1}$-EPGt graphs are $7$-clique
colorable.


title: End-to-End Semi-Supervised Learning for Video Action Detection

abstract:
  In this work, we focus on semi-supervised learning for video action detection
which utilizes both labeled as well as unlabeled data. We propose a simple
end-to-end consistency based approach which effectively utilizes the unlabeled
data. Video action detection requires both, action class prediction as well as
a spatio-temporal localization of actions. Therefore, we investigate two types
of constraints, classification consistency, and spatio-temporal consistency.
The presence of predominant background and static regions in a video makes it
challenging to utilize spatio-temporal consistency for action detection. To
address this, we propose two novel regularization constraints for
spatio-temporal consistency; 1) temporal coherency, and 2) gradient smoothness.
Both these aspects exploit the temporal continuity of action in videos and are
found to be effective for utilizing unlabeled videos for action detection. We
demonstrate the effectiveness of the proposed approach on two different action
detection benchmark datasets, UCF101-24 and JHMDB-21. In addition, we also show
the effectiveness of the proposed approach for video object segmentation on the
Youtube-VOS which demonstrates its generalization capability The proposed
approach achieves competitive performance by using merely 20% of annotations on
UCF101-24 when compared with recent fully supervised methods. On UCF101-24, it
improves the score by +8.9% and +11% at 0.5 f-mAP and v-mAP respectively,
compared to supervised approach.


title: The Concept of Force in Population Dynamics

abstract:
  The area of population dynamics has a rich history of the development and
analysis of models of biological and social phenomena using ordinary
differential equations. This paper describes a method for understanding the
influence one variable exerts on another in such models as a force, with the
relative effects of these forces providing a narrative explanation of the
curvature in variable behaviour. Using the stock/flow form of a model, a
symbolic notation is developed that identifies the forces with the causal
pathways of the model's feedback loops. A force is measured by its impact,
defined as the ratio of acceleration to rate of change, computed by
differentiation along its associated pathway between variables. Different
phases of force dominance are determined to enhance the standard stability
analysis of the models, providing an explanation of model behaviour in
Newtonian mechanical terms. The concepts developed are applied to well-known
models from mathematical biology: the Spruce Budworm model, where force
dominance identifies scenarios that give clarity to intervention points; and
the Lotka-Volterra predator-prey model where the analysis highlights the
importance of dissipative forces in achieving stability. Conclusions are drawn
on the explanatory power of this approach, with suggestions made for future
work.


title: Oriented Diameter of Planar Triangulations

abstract:
  The diameter of an undirected or a directed graph is defined to be the
maximum shortest path distance over all pairs of vertices in the graph. Given
an undirected graph $G$, we examine the problem of assigning directions to each
edge of $G$ such that the diameter of the resulting oriented graph is
minimized. The minimum diameter over all strongly connected orientations is
called the oriented diameter of $G$. The problem of determining the oriented
diameter of a graph is known to be NP-hard, but the time-complexity question is
open for planar graphs. In this paper we compute the exact value of the
oriented diameter for triangular grid graphs. We then prove an $n/3$ lower
bound and an $n/2+O(\sqrt{n})$ upper bound on the oriented diameter of planar
triangulations. It is known that given a planar graph $G$ with bounded
treewidth and a fixed positive integer $k$, one can determine in linear time
whether the oriented diameter of $G$ is at most $k$. In contrast, we consider a
weighted version of the oriented diameter problem and show it to be is weakly
NP-complete for planar graphs with bounded pathwidth.


title:
Field-dependent Shubnikov-de Haas oscillations in ferromagnetic Weyl
  semimetal Co3Sn2S2

abstract:
  We report a study of Shubnikov-de Haas oscillations in high quality single
crystals of ferromagnetic Weyl semimetal Co$_3$Sn$_2$S$_2$. The Fermi surfaces
resolved in our experiments are three-dimensional and reflect an underlying
trigonal crystallographic symmetry. Combined with density functional
theoretical calculations, we identify that the majority of the Fermi surfaces
in the system -- of both electron and hole nature -- arise from the strong
energy dispersion of the (spin-orbit gapped) mirror-protected nodal rings. We
observe that an in-plane magnetic field induces a continuous evolution of Fermi
surfaces, in contrast to field perpendicular to the kagome lattice planes which
has little effect. Viewed alongside the easy-axis anisotropy of the system, our
observation reveals an evolution of the electronic structure of
Co$_3$Sn$_2$S$_2$ -- including the Weyl points -- with the ferromagnetic moment
orientation. Through the case study of Co$_3$Sn$_2$S$_2$, our results provide
concrete experimental evidence of an anisotropic interplay via spin-orbit
coupling between the magnetic degrees of freedom and electronic band
singularities, which has long been expected in semimetallic and metallic
magnetic topological systems.


title: Four-point correlators of light-ray operators in CCFT

abstract:
  We compute the four-point correlator of two gluon light-ray operators and two
gluon primaries from the four-gluon celestial amplitude in $(2,2)$ signature
spacetime. The correlator is non-distributional and allows us to verify that
light-ray operators appear in the OPE of two gluon primaries. We also carry out
a conformal block decomposition of the terms involving the exchange of gluon
operators.


title: Generalizing prime avoidance to arbitrary ideals

abstract:
  Recently in \cite[Theorem 2.2]{Tarizadeh-Chen} we generalized the classical
prime avoidance lemma to arbitrary ideals. In this article, two applications of
that result and further improvements of the prime avoidance lemma are provided.


title: Weyl cosmology

abstract:
  The homogeneous and isotropic cosmological model in the Weyl conformal
geometry is considered. We showed that, despite the conformal invariance, the
dust matter is allowed in such a universe. It is shown that the number of dust
particles is not conserved, i. e., they are continuously produced. The general
form of the law for their creation is found.


title:
RAPTEE: Leveraging trusted execution environments for Byzantine-tolerant
  peer sampling services

abstract:
  Peer sampling is a first-class abstraction used in distributed systems for
overlay management and information dissemination. The goal of peer sampling is
to continuously build and refresh a partial and local view of the full
membership of a dynamic, large-scale distributed system. Malicious nodes under
the control of an adversary may aim at being over-represented in the views of
correct nodes, increasing their impact on the proper operation of protocols
built over peer sampling. State-of-the-art Byzantine resilient peer sampling
protocols reduce this bias as long as Byzantines are not overly present. This
paper studies the benefits brought to the resilience of peer sampling services
when considering that a small portion of trusted nodes can run code whose
authenticity and integrity can be assessed within a trusted execution
environment, and specifically Intel's software guard extensions technology
(SGX). We present RAPTEE, a protocol that builds and leverages trusted
gossip-based communications to hamper an adversary's ability to increase its
system-wide representation in the views of all nodes. We apply RAPTEE to
BRAHMS, the most resilient peer sampling protocol to date. Experiments with
10,000 nodes show that with only 1% of SGX-capable devices, RAPTEE can reduce
the proportion of Byzantine IDs in the view of honest nodes by up to 17% when
the system contains 10% of Byzantine nodes. In addition, the security
guarantees of RAPTEE hold even in the presence of a powerful attacker
attempting to identify trusted nodes and injecting view-poisoned trusted nodes.


title: Bubble-driven Gas Uplift in Galaxy Clusters and its Velocity Features

abstract:
  Buoyant bubbles of relativistic plasma are essential for active galactic
nucleus feedback in galaxy clusters, stirring and heating the intracluster
medium (ICM). Observations suggest that these rising bubbles maintain their
integrity and sharp edges much longer than predicted by hydrodynamic
simulations. In this study, we assume that bubbles can be modeled as rigid
bodies and demonstrate that intact bubbles and their long-term interactions
with the ambient ICM play an important role in shaping gas kinematics, forming
thin gaseous structures (e.g., H$\alpha$ filaments), and generating internal
waves in cluster cores. We find that well-developed eddies are formed in the
wake of a buoyantly rising bubble, and it is these eddies, rather than the
Darwin drift, that are responsible for most of the gas mass uplift. The eddies
gradually elongate along the bubble's direction of motion due to the strong
density stratification of the atmosphere and eventually detach from the bubble,
quickly evolving into a high-speed jet-like stream propagating towards the
cluster center. This picture naturally explains the presence of long straight
and horseshoe-shaped H$\alpha$ filaments in the Perseus cluster, inward and
outward motions of the gas, and the X-ray-weighted gas velocity distributions
near the northwestern bubble observed by Hitomi. Our model reproduces the
observed H$\alpha$ velocity structure function of filaments, providing a simple
interpretation for its steep scaling and normalization: laminar gas flows and
large eddies within filaments driven by the intact bubbles, rather than
spatially homogeneous small-scale turbulence, are sufficient to produce a
structure function consistent with observations.