title:
Knowledge Base Index Compression via Dimensionality and Precision
  Reduction

abstract:
  Recently neural network based approaches to knowledge-intensive NLP tasks,
such as question answering, started to rely heavily on the combination of
neural retrievers and readers. Retrieval is typically performed over a large
textual knowledge base (KB) which requires significant memory and compute
resources, especially when scaled up. On HotpotQA we systematically investigate
reducing the size of the KB index by means of dimensionality (sparse random
projections, PCA, autoencoders) and numerical precision reduction.
  Our results show that PCA is an easy solution that requires very little data
and is only slightly worse than autoencoders, which are less stable. All
methods are sensitive to pre- and post-processing and data should always be
centered and normalized both before and after dimension reduction. Finally, we
show that it is possible to combine PCA with using 1bit per dimension. Overall
we achieve (1) 100$\times$ compression with 75%, and (2) 24$\times$ compression
with 92% original retrieval performance.


title: Scalar gauge-Higgs models with discrete Abelian symmetry groups

abstract:
  We investigate the phase diagram and the nature of the phase transitions of
three-dimensional lattice gauge-Higgs models obtained by gauging the Z_N
subgroup of the global Z_q invariance group of the Z_q clock model (N is a
submultiple of q). The phase diagram is generally characterized by the presence
of three different phases, separated by three distinct transition lines. We
investigate the critical behavior along the two transition lines characterized
by the ordering of the scalar field. Along the transition line separating the
disordered-confined phase from the ordered-deconfined phase, standard arguments
within the Landau-Ginzburg-Wilson framework predict that the behavior is the
same as in a generic ferromagnetic model with Z_p global symmetry, p being the
ratio q/N. Thus, continuous transitions belong to the Ising and to the O(2)
universality class for p=2 and p>3, respectively, while for p=3 only
first-order transitions are possible. The results of Monte Carlo simulations
confirm these predictions. There is also a second transition line, which
separates two phases in which gauge fields are essentially ordered. Along this
line we observe the same critical behavior as in the Z_q clock model, as it
occurs in the absence of gauge fields.


title:
Question Generation for Reading Comprehension Assessment by Modeling How
  and What to Ask

abstract:
  Reading is integral to everyday life, and yet learning to read is a struggle
for many young learners. During lessons, teachers can use comprehension
questions to increase engagement, test reading skills, and improve retention.
Historically such questions were written by skilled teachers, but recently
language models have been used to generate comprehension questions. However,
many existing Question Generation (QG) systems focus on generating literal
questions from the text, and have no way to control the type of the generated
question. In this paper, we study QG for reading comprehension where
inferential questions are critical and extractive techniques cannot be used. We
propose a two-step model (HTA-WTA) that takes advantage of previous datasets,
and can generate questions for a specific targeted comprehension skill. We
propose a new reading comprehension dataset that contains questions annotated
with story-based reading comprehension skills (SBRCS), allowing for a more
complete reader assessment. Across several experiments, our results show that
HTA-WTA outperforms multiple strong baselines on this new dataset. We show that
the HTA-WTA model tests for strong SCRS by asking deep inferential questions.


title: A Short Tutorial on Mean-Field Spin Glass Techniques for Non-Physicists

abstract:
  This tutorial is based on lecture notes written for a class taught in the
Statistics Department at Stanford in the Winter Quarter of 2017. The objective
was to provide a working knowledge of some of the techniques developed over the
last 40 years by theoretical physicists and mathematicians to study mean field
spin glasses and their applications to high-dimenensional statistics and
statistical learning.


title: Shortened universal cycles for permutations

abstract:
  Kitaev, Potapov, and Vajnovszki [On shortening u-cycles and u-words for
permutations, Discrete Appl. Math, 2019] described how to shorten universal
words for permutations, to length $n!+n-1-i(n-1)$ for any $i \in [(n-2)!]$, by
introducing incomparable elements. They conjectured that it is also possible to
use incomparable elements to shorten universal cycles for permutations to
length $n!-i(n-1)$ for any $i \in [(n-2)!]$. In this note we prove their
conjecture. The proof is constructive, and, on the way, we also show a new
method for constructing universal cycles for permutations.


title:
First-principles density limit scaling in tokamaks based on edge
  turbulent transport and implications for ITER

abstract:
  A first-principles scaling law, based on turbulent transport considerations,
and a multi-machine database of density limit discharges from the ASDEX
Upgrade, JET and TCV tokamaks, show that the increase of the boundary turbulent
transport with the plasma collisionality sets the maximum density achievable in
tokamaks. This scaling law shows a strong dependence on the heating power,
therefore predicting for ITER a significantly larger safety margin than the
Greenwald empirical scaling (Greenwald et al, Nucl. Fusion, 28(12), 1988) in
case of unintentional H-L transition.


title: Variational Quantum Evolution Equation Solver

abstract:
  Variational quantum algorithms offer a promising new paradigm for solving
partial differential equations on near-term quantum computers. Here, we propose
a variational quantum algorithm for solving a general evolution equation
through implicit time-stepping of the Laplacian operator. The use of encoded
source states informed by preceding solution vectors results in faster
convergence compared to random re-initialization. Through statevector
simulations of the heat equation, we demonstrate how the time complexity of our
algorithm scales with the ansatz volume for gradient estimation and how the
time-to-solution scales with the diffusion parameter. Our proposed algorithm
extends economically to higher-order time-stepping schemes, such as the
Crank-Nicolson method. We present a semi-implicit scheme for solving systems of
evolution equations with non-linear terms, such as the reaction-diffusion and
the incompressible Navier-Stokes equations, and demonstrate its validity by
proof-of-concept results.


title:
Domination ratio of a family of integer distance digraphs with arbitrary
  degree

abstract:
  An integer distance digraph is the Cayley graph $\Gamma(\mathbb{Z},S)$ of the
additive group $\mathbb{Z}$ of all integers with respect to a finite subset
$S\subseteq\mathbb{Z}$. The domination ratio of $\Gamma(\mathbb{Z},S)$, defined
as the minimum density of its dominating sets, is related to some number theory
problems, such as tiling the integers and finding the maximum density of a set
of integers with missing differences. We precisely determine the domination
ratio of the integer distance graph $\Gamma(\mathbb{Z},\{1,2,\ldots,d-2,s\})$
for any integers $d$ and $s$ satisfying $d\ge2$ and $s\notin[0,d-2]$. Our
result generalizes a previous result on the domination ratio of the graph
$\Gamma(\mathbb{Z},\{1,s\})$ with $s\in\mathbb{Z}\setminus\{0,1\}$ and also
implies the domination number of certain circulant graphs
$\Gamma(\mathbb{Z}_n,S)$, where $\mathbb{Z}_n$ is the finite cyclic group of
integers modulo $n$ and $S$ is a subset of $\mathbb{Z}_n$.


title: Some perspectives on Eisenstein series

abstract:
  We review some topics in the analytic theory of Eisenstein series, including
meromorphic continuation, $L^2$-spectral expansion and Fourier coefficients. We
also discuss some open problems.


title:
Shorter Signatures from Proofs of Knowledge for the SD, MQ, PKP and RSD
  Problems

abstract:
  The MPC in the head introduced in [IKOS07] has established itself as an
important paradigm in order to design efficient digital signatures. In
particular, it has been leveraged in the Picnic scheme [CDG+ 20] that is
currently considered in the third round of NIST Post-Quantum Standardization
process. In addition, it has been used in [Beu20] to introduce the Proof of
Knowledge (PoK) with Helper paradigm. This construction permits to design
shorter signatures but induces a non negligible performance overhead. In this
paper, our contributions are twofold. Firstly, we introduce a new PoK with
Helper for the Syndrome Decoding (SD) problem. This construction relies on
ideas from [BGKM22] and [FJR21] and improve the latter using a new technique
that can be seen as performing the cut and choose with a meet in the middle
approach. Secondly and most importantly, we introduce a new paradigm to design
PoK that brings improvements over the PoK with Helper one. Indeed, we show how
one can substitute the Helper in these constructions by leveraging the
underlying structure of the considered problem. This new approach does not
suffer from the performance overhead inherent to the PoK with Helper paradigm
hence offers different trade-offs between signature sizes and performances.
Interestingly, our new approach is quite generic and can be applied to many
problems and their associated PoK. In order to demonstrate this versatility, we
provide new PoK related to the SD, MQ, PKP and RSD problems. In practice, these
PoK lead to shorter signatures for the aforementioned problems. Indeed,
considering (public key + signature), we get sizes below 12 kB for our
signature related to the SD problem, below 8 kB for our signature related to
the MQ problem, below 9 kB for our signature related to the PKP problem and
below 7 kB for our signature related to the RSD problem.


title:
Simulations of un-equal binary collisions embedded in a turbulent gas
  cloud far away of a massive gravitational center

abstract:
  We simulate the collapse of a turbulent gas cloud with the particle-based
code Gadget2. We choose two sub-clouds, formed by those particles located
around the centers $\vec{r}_L$ and $\vec{r}_R$ and within the radii $r_L$ and
$r_R$, respectively. A translational velocity $\vec{v}_L$ or $\vec{v}_R$ is
added, so that the sub-clouds move towards each other to collide. The radius
and pre-collision velocity of the sub-clouds are chosen to be un-equal, and
both head-on and oblique collisions are considered. The simulations are all
calibrated to have the same total mass and the initial energy ratio
$\alpha=0.16$, which is defined as the ratio of thermal energy to gravitational
energy. We compare low-$\beta$ models to a high-$\beta$ models, where $\beta$
is defined as the ratio of kinetic energy to gravitational energy. Finally, we
consider the turbulent cloud to be under the gravitational influence of an
object located far enough, in order to approximate the tidal effects by means
of an azimuthal velocity $V_{\rm cir}$ added to the cloud particles in addition
to the translational and turbulent velocities mentioned above. We compare a
low-$V_{\rm cir}$ model with a high-$V_{\rm cir}$ one.


title: Gabor frame bound optimizations

abstract:
  We study sharp frame bounds of Gabor systems over rectangular lattices for
different windows. In some cases we obtain optimality results for the square
lattice, while in other cases the lattices optimizing the frame bounds and the
condition number are different. Also, in some cases optimal lattices do not
exist at all and a degenerated system is optimal.


title: How SVC enables Distributed Caching in MEC?

abstract:
  With an ever increasing demand for the delivery of internet video service,
the service providers are facing a huge challenge to deliver ultra-HD (2k/4k)
video at sub-second latency. The multi-access edge computing (MEC) platform
actually helps in achieving this objective by caching popular contents at the
edge of cellular network. This not only reduces the delivery latency, but also
the load and the cost of the backhaul links. However, MEC platforms are
afflicted by constrained resources in terms of storage and processing
capabilities; and centralized caching of contents may nullify the advantage of
reduced latency by lowering the offloading probability. Distributed caching at
the edge not only improves the offloading probability, but also dynamically
adjusts the load distribution among the MEC servers. In this article, we
propose an architecture for deployment of MEC platforms by exploiting the
characteristics of a scalable video encoding technique. The layered video
coding techniques, such as the scalable video coding (SVC), is used by the
content providers to adjust to the network dynamics, by dynamically dropping
packets in order to reduce latency. We show how an SVC video easily lends
itself to distributed caching at the edge. Then we investigate the
latency-storage trade-off by storing the video layers at different parts of the
access networks.


title: Branch Decomposition-Independent Edit Distances for Merge Trees

abstract:
  Edit distances between merge trees of scalar fields have many applications in
scientific visualization, such as ensemble analysis, feature tracking or
symmetry detection. In this paper, we propose branch mappings, a novel approach
to the construction of edit mappings for merge trees. Classic edit mappings
match nodes or edges of two trees onto each other, and therefore have to either
rely on branch decompositions of both trees or have to use auxiliary node
properties to determine a matching. In contrast, branch mappings employ branch
properties instead of node similarity information, and are independent of
predetermined branch decompositions. Especially for topological features, which
are typically based on branch properties, this allows a more intuitive distance
measure which is also less susceptible to instabilities from small-scale
perturbations. We describe a quartic runtime algorithm for computing optimal
branch mappings, which is faster than the only other branch
decomposition-independent method in the literature by more than a linear
factor. Furthermore, we compare the results of our method on synthetic and
real-world examples to demonstrate its practicality and utility.


title:
Continuous Variable Entanglement in an Optical Parametric Oscillator
  Based on a Nondegenerate Four Wave Mixing Process in Hot Alkali Atoms

abstract:
  We present the measurement of entanglement between twin beams generated with
a doubly resonant optical parameter oscillator (OPO) based on four-wave mixing
in hot $^{85}$Rb vapor above threshold. This is the first measurement of
entanglement in an OPO with $\chi^{(3)}$ media above threshold. We reconstruct
the covariance matrix for several configurations and with a full picture of the
four side band mode state, we study entanglement between all possible
bi-partitions. We show a robust generation of entanglement with stronger
generation for a specific pair of modes. For this system, we show that atomic
density is a determinant factor for generation and loss of quantum
correlations. The generation of entangled fields by an atomic OPO close to
atomic resonance of alkali atoms enables natural integration into quantum
networks.


title: A survey on recently proposed activation functions for Deep Learning

abstract:
  Artificial neural networks (ANN), typically referred to as neural networks,
are a class of Machine Learning algorithms and have achieved widespread
success, having been inspired by the biological structure of the human brain.
Neural networks are inherently powerful due to their ability to learn complex
function approximations from data. This generalization ability has been able to
impact multidisciplinary areas involving image recognition, speech recognition,
natural language processing, and others. Activation functions are a crucial
sub-component of neural networks. They define the output of a node in the
network given a set of inputs. This survey discusses the main concepts of
activation functions in neural networks, including; a brief introduction to
deep neural networks, a summary of what are activation functions and how they
are used in neural networks, their most common properties, the different types
of activation functions, some of the challenges, limitations, and alternative
solutions faced by activation functions, concluding with the final remarks.


title:
Paying More Attention to Self-attention: Improving Pre-trained Language
  Models via Attention Guiding

abstract:
  Pre-trained language models (PLM) have demonstrated their effectiveness for a
broad range of information retrieval and natural language processing tasks. As
the core part of PLM, multi-head self-attention is appealing for its ability to
jointly attend to information from different positions. However, researchers
have found that PLM always exhibits fixed attention patterns regardless of the
input (e.g., excessively paying attention to [CLS] or [SEP]), which we argue
might neglect important information in the other positions. In this work, we
propose a simple yet effective attention guiding mechanism to improve the
performance of PLM by encouraging attention towards the established goals.
Specifically, we propose two kinds of attention guiding methods, i.e., map
discrimination guiding (MDG) and attention pattern decorrelation guiding (PDG).
The former definitely encourages the diversity among multiple self-attention
heads to jointly attend to information from different representation subspaces,
while the latter encourages self-attention to attend to as many different
positions of the input as possible. We conduct experiments with multiple
general pre-trained models (i.e., BERT, ALBERT, and Roberta) and
domain-specific pre-trained models (i.e., BioBERT, ClinicalBERT, BlueBert, and
SciBERT) on three benchmark datasets (i.e., MultiNLI, MedNLI, and
Cross-genre-IR). Extensive experimental results demonstrate that our proposed
MDG and PDG bring stable performance improvements on all datasets with high
efficiency and low cost.


title:
A Variational Ansatz for the Ground State of the Quantum
  Sherrington-Kirkpatrick Model

abstract:
  We present an ansatz for the ground states of the Quantum
Sherrington-Kirkpatrick model, a paradigmatic model for quantum spin glasses.
Our ansatz, based on the concept of generalized coherent states, very well
captures the fundamental aspects of the model, including the ground state
energy and the position of the spin glass phase transition. It further enables
us to study some previously unexplored features, such as the non-vanishing
longitudinal field regime and the entanglement structure of the ground states.
We find that the ground state entanglement can be captured by a simple ensemble
of weighted graph states with normally distributed phase gates, leading to a
volume law entanglement, contrasting with predictions based on entanglement
monogamy.


title:
CREIME: A Convolutional Recurrent model for Earthquake Identification
  and Magnitude Estimation

abstract:
  The detection and rapid characterisation of earthquake parameters such as
magnitude are of prime importance in seismology, particularly in applications
such as Earthquake Early Warning (EEW). Traditionally, algorithms such as
STA/LTA are used for event detection, while frequency or amplitude domain
parameters calculated from 1-3 seconds of first P-arrival data are sometimes
used to provide a first estimate of (body wave) magnitude. Owing to extensive
involvement of human experts in parameter determination, these approaches are
often found to be insufficient. Moreover, these methods are sensitive to the
signal to noise ratio and may often lead to false or missed alarms depending on
the choice of parameters. We, therefore, propose a multitasking deep learning
model the Convolutional Recurrent model for Earthquake Identification and
Magnitude Estimation (CREIME) that: (i) detects the first earthquake signal,
from background seismic noise, (ii) determines first P arrival time as well as
(iii) estimates the magnitude using the raw 3-component waveform data from a
single station as model input. Considering, speed is of essence in EEW, we use
up to two seconds of P-wave information which, to the best of our knowledge, is
a significantly smaller data window (5 second window with up to of P wave data)
compared to the previous studies. To examine the robustness of CREIME we test
it on two independent datasets and find that it achieves an average accuracy of
98 percent for event vs noise discrimination and is able to estimate first P
arrival time and local magnitude with average root mean squared errors of 0.13
seconds and 0.65 units, respectively. We also compare CREIME architecture with
architectures of other baseline models, by training them on the same data, and
also with traditional algorithms such as STA/LTA, and show that our
architecture outperforms these methods.


title:
Kinematics of molecular gas in star-forming galaxies with large-scale
  ionised outflows

abstract:
  We investigate the kinematics of the molecular gas in a sample of seven
edge-on (i>60 degrees) galaxies identified as hosting large-scale outflows of
ionised gas, using ALMA CO(1-0) observations at ~1 kpc resolution. We build on
Hogarth et al. (2021), where we find that molecular gas is more centrally
concentrated in galaxies which host winds than in control objects. We perform
full 3-dimensional kinematic modelling with multiple combinations of kinematic
components, allowing us to infer whether these objects share any similarities
in their molecular gas structure. We use modelling to pinpoint the kinematic
centre of each galaxy, in order to interpret their minor- and major-axis
position velocity diagrams (PVDs). From the PVDs, we find that the bulk of the
molecular gas in our galaxies is dynamically cold, tracing the rotation of the
stellar disc, but with minor flux asymmetries. Most notably, we find evidence
of radial gas motion in some of our minor-axis PVDs. In our highest S/N object,
we include bi-symmetric radial flow in our kinematic model, and find (via the
Bayesian Information Criterion) that the presence of radial gas motion is
strongly favoured. This may provide a mechanism by which molecular gas and star
formation are centrally concentrated, enabling the launch of massive ionised
gas winds.


title: Density Fluctuations in Stochastic Kinematic Flows

abstract:
  At the macroscopic scale, many important models of collective motion fall
into the class of kinematic flows for which both velocity and diffusion terms
depend only on particle density. When total particle numbers are fixed and
finite, simulations of corresponding microscopic dynamics exhibit stochastic
effects which can induce a variety of interesting behaviours not present in the
large system limit. In this article we undertake a systematic examination of
finite-size fluctuations in a general class of particle models whose statistics
correspond to those of stochastic kinematic flows. Doing so, we are able to
characterise phenomena including: quasi-jams in models of traffic flow;
stochastic pattern formation amongst spatially-coupled oscillators; anomalous
bulk sub-diffusion in porous media; and travelling wave fluctuations in a model
of bacterial swarming.


title: Six-Functor-Formalisms on Higher Stacks

abstract:
  In this article, it is shown that derivator six-functor-formalisms on any
(classical) site canonically extend to higher geometric stacks as defined by
To\"en-Vezzosi under some natural locality conditions. As an application, it is
shown that the six-functor-formalisms of Morel-Voevodsky-Ayoub extend to higher
(Nisnevich-)Artin stacks locally of finite type over some fixed base scheme.


title:
Snowmass2021 - White Paper, Implications of Energy Peak for Collider
  Phenomenology: Top Quark Mass Determination and Beyond

abstract:
  We first review the decade-old, broad collider physics research program
dubbed energy-peaks. We consider the energy distribution of a massless particle
in the lab frame arising from the two-body decay of a heavy particle produced
unpolarized, whose boost distribution is arbitrary. Remarkably, the location of
the peak of this child particle's energy distribution is identical to its
single-valued energy in the rest frame of the parent, which is a function of
the parent's mass and that of the other decay product. We summarize
generalizations to other types of decay and a variety of applications to BSM.
The energy-peak idea can also furnish a measurement of the top quark via the
energy of the bottom quark from its decay, which, based on the
"parent-boost-invariance," is less sensitive to details of the production
mechanism of the top quark (cf.~most other methods assume purely SM production
of the top quarks, hence are subject to uncertainties therein, including a
possible BSM contribution). The original proposal along this line was to simply
use the $b$-jet energy as a very good approximation to the bottom quark energy.
This method has been successfully implemented by the CMS collaboration.
However, the $b$-jet energy-peak method is afflicted by the jet-energy scale
(JES) uncertainty. Fortunately, this drawback can be circumvented by using the
decay length of a $B$-hadron contained in the $b$-jet as a proxy for the bottom
quark energy. An interesting proposal is to then appropriately dovetail the
above two ideas resulting in a "best of both worlds" determination of the top
quark mass, i.e., based on a measurement of the $B$-hadron decay length, but
improved by the energy-peak concept: this would be free of JES uncertainty and
largely independent of the top quark production model. We summarize here the
results of such an analysis which is to appear in a forthcoming paper.


title: Beam Search: Faster and Monotonic

abstract:
  Beam search is a popular satisficing approach to heuristic search problems
that allows one to trade increased computation time for lower solution cost by
increasing the beam width parameter. We make two contributions to the study of
beam search. First, we show how to make beam search monotonic; that is, we
provide a new variant that guarantees non-increasing solution cost as the beam
width is increased. This makes setting the beam parameter much easier. Second,
we show how using distance-to-go estimates can allow beam search to find better
solutions more quickly in domains with non-uniform costs. Together, these
results improve the practical effectiveness of beam search.


title:
Fundamental Physics with ESPRESSO, Constraining a simple parametrisation
  for varying $\alpha$

abstract:
  The spectrograph ESPRESSO obtained updated limits on the variation of the
fine-structure constant, $\alpha$, through measurements along the line of sight
of a bright quasar with unprecedented precision and accuracy. These impose new
constraints on cosmological models with a varying $\alpha$. We assume such a
model where the electromagnetic sector is coupled to a scalar field dark energy
responsible for the current acceleration of the Universe. We parametrise the
variation of $\alpha$ with two extra parameters, one defining the cosmological
evolution of the quintessence component and the other fixing the coupling with
the electromagnetic field. The objective of this work is to constrain these
parameters with both astrophysical and local probes. We also make a comparative
analysis on how each data probe may constrain our parametrisation. We perform a
Bayesian analysis by comparing the predictions of the model with observations.
The astrophysical datasets are composed of quasars spectra measurements,
including the latest ESPRESSO data point, as well as Planck observations of the
cosmic microwave background. We combine them with local results from atomic
clocks and the MICROSCOPE experiment. The constraints placed on the
quintessence parameter are consistent with a null variation of the field, i.e.
compatible with a $\Lambda$CDM cosmology. The constraints on the coupling to
the electromagnetic sector are dominated by the E\"otv\"os parameter local
bound. The ESPRESSO data point is extremely important to study the cosmological
evolution of $\alpha$ as it probes an interval of redshift not accessible to
other observations. However, for this particular model, current available data
favours a null variation of $\alpha$ resulting mostly from the strong
MICROSCOPE limits.


title:
Observation of giant surface second harmonic generation coupled to
  nematic orders in the van der Waals antiferromagnet FePS$_3$

abstract:
  Second harmonic generation has been applied to study lattice, electronic and
magnetic proprieties in atomically thin materials. However, inversion symmetry
breaking is usually required for the materials to generate a large signal. In
this work, we report a giant second-harmonic generation that arises below the
N\'eel temperature in few-layer centrosymmetric FePS$_3$. Layer-dependent study
indicates the detected signal is from the second-order nonlinearity of the
surface. The magnetism-induced surface second-harmonic response is two orders
of magnitude larger than those reported in other magnetic systems, with the
surface nonlinear susceptibility reaching 0.08--0.13 nm$^2$/V in 2 L--5 L
samples. By combing linear dichroism and second harmonic generation
experiments, we further confirm the giant second-harmonic generation is coupled
to nematic orders formed by the three possible Zigzag antiferromagnetic
domains. Our study shows that the surface second-harmonic generation is also a
sensitive tool to study antiferromagnetic states in centrosymmetric atomically
thin materials.


title: An Empirical Study of End-to-End Temporal Action Detection

abstract:
  Temporal action detection (TAD) is an important yet challenging task in video
understanding. It aims to simultaneously predict the semantic label and the
temporal interval of every action instance in an untrimmed video. Rather than
end-to-end learning, most existing methods adopt a head-only learning paradigm,
where the video encoder is pre-trained for action classification, and only the
detection head upon the encoder is optimized for TAD. The effect of end-to-end
learning is not systematically evaluated. Besides, there lacks an in-depth
study on the efficiency-accuracy trade-off in end-to-end TAD. In this paper, we
present an empirical study of end-to-end temporal action detection. We validate
the advantage of end-to-end learning over head-only learning and observe up to
11\% performance improvement. Besides, we study the effects of multiple design
choices that affect the TAD performance and speed, including detection head,
video encoder, and resolution of input videos. Based on the findings, we build
a mid-resolution baseline detector, which achieves the state-of-the-art
performance of end-to-end methods while running more than 4$\times$ faster. We
hope that this paper can serve as a guide for end-to-end learning and inspire
future research in this field. Code and models are available at
\url{https://github.com/xlliu7/E2E-TAD}.


title: Quantitative differentiation and the medial axis

abstract:
  We study the medial axis of a set $K$ in Euclidean space (the set of points
in space with more than one closest point in $K$) from a "coarse" and
"quantitative" perspective. We show that on "most" balls $B(x,r)$ in the
complement of $K$, the set of almost-closest points to $x$ in $K$ takes up a
small angle as seen from $x$. In other words, most locations and scales in the
complement of $K$ "appear" to fall outside the medial axis if one looks with
only a certain finite resolution. The word "most" involves a Carleson packing
condition, and our bounds are independent of the set $K$.


title:
Parallel, Portable Algorithms for Distance-2 Maximal Independent Set and
  Graph Coarsening

abstract:
  Given a graph, finding the distance-2 maximal independent set (MIS-2) of the
vertices is a problem that is useful in several contexts such as algebraic
multigrid coarsening or multilevel graph partitioning. Such multilevel methods
rely on finding the independent vertices so they can be used as seeds for
aggregation in a multilevel scheme. We present a parallel MIS-2 algorithm to
improve performance on modern accelerator hardware. This algorithm is
implemented using the Kokkos programming model to enable performance
portability. We demonstrate the portability of the algorithm and the
performance on a variety of architectures (x86/ARM CPUs and NVIDIA/AMD GPUs).
The resulting algorithm is also deterministic, producing an identical result
for a given input across all of these platforms. The new MIS-2 implementation
outperforms implementations in state of the art libraries like CUSP and
ViennaCL by 3-8x while producing similar quality results. We further
demonstrate the benefits of this approach by developing parallel graph
coarsening scheme for two different use cases. First, we develop an algebraic
multigrid (AMG) aggregation scheme using parallel MIS-2 and demonstrate the
benefits as opposed to previous approaches used in the MueLu multigrid package
in Trilinos. We also describe an approach for implementing a parallel
multicolor "cluster" Gauss-Seidel preconditioner using this MIS-2 coarsening,
and demonstrate better performance with an efficient, parallel, multicolor
Gauss-Seidel algorithm.


title:
Hydrogen abstraction reactions in formic and thioformic acid isomers by
  hydrogen and deuterium atoms. Insights on isomerism and deuteration

abstract:
  The isomerism of molecules in the interstellar medium and the mechanisms
behind it are essential questions in the chemistry of organic molecules in
space. In particular, for the simple formic and thioformic acids, the low
temperatures found in molecular clouds indicate that cis-trans isomerization in
the gas-phase must be impeded. Reactions happening on top of interstellar dust
grains may explain the isomer interconversion at low temperatures. We studied
the isomerization processes of formic and thioformic acid susceptible to happen
on the surface of interstellar dust grains and initiated by H abstraction
reactions. Similarly, deuterium enrichment of the acids can occur by the same
mechanism. Our objective is to shed light on both topics to increase our
understanding of key precursors of organic molecules in space.


title:
Dependence of the glass transition and jamming densities on spatial
  dimension

abstract:
  We investigate the dynamics of soft sphere liquids through computer
simulations for spatial dimensions from $d =3$ to $8$, over a wide range of
temperatures and densities. Employing a scaling of density-temperature
dependent relaxation times, we precisely identify the density $\phi_0$ which
marks the ideal glass transition in the hard sphere limit, and a crossover from
sub- to super-Arrhenius temperature dependence. The difference between $\phi_0$
and the athermal jamming density $\phi_J$, small in 3 and 4 dimensions,
increases with dimension, with $\phi_0 > \phi_J$ for $d > 4$. We compare our
results with recent theoretical calculations.


title:
Last Layer Re-Training is Sufficient for Robustness to Spurious
  Correlations

abstract:
  Neural network classifiers can largely rely on simple spurious features, such
as backgrounds, to make predictions. However, even in these cases, we show that
they still often learn core features associated with the desired attributes of
the data, contrary to recent findings. Inspired by this insight, we demonstrate
that simple last layer retraining can match or outperform state-of-the-art
approaches on spurious correlation benchmarks, but with profoundly lower
complexity and computational expenses. Moreover, we show that last layer
retraining on large ImageNet-trained models can also significantly reduce
reliance on background and texture information, improving robustness to
covariate shift, after only minutes of training on a single GPU.


title:
Mass Spectra and Semileptonic Decays of Doubly Heavy $ \Xi $ and $
  \Omega $ Baryons

abstract:
  In the framework of a non-relativistic quark model, the mass spectra of the
ground and excited states of doubly heavy $\Xi$ and $\Omega$ baryons are
calculated.
  We estimate the mass difference between the $ \Omega $ and corresponding $
\Xi $ baryons as $M_{\Omega}-M_{\Xi}\simeq178 $ MeV for all the states
containing $ cc,~bc $, or $ bb $ quarks.
  A simple form of the universal Isgur-Wise function, as the transition form
factor between the doubly heavy baryons, is introduced. Working in the
close-to-zero recoil limit, we investigate the $ b \rightarrow c $ semileptonic
decay widths and branching fractions of the doubly heavy baryons. The obtained
results are compared with other theoretical predictions.


title: S-R2F2U-Net: A single-stage model for teeth segmentation

abstract:
  Precision tooth segmentation is crucial in the oral sector because it
provides location information for orthodontic therapy, clinical diagnosis, and
surgical treatments. In this paper, we investigate residual, recurrent, and
attention networks to segment teeth from panoramic dental images. Based on our
findings, we suggest three single-stage models: Single Recurrent R2U-Net
(S-R2U-Net), Single Recurrent Filter Double R2U-Net (S-R2F2U-Net), and Single
Recurrent Attention Enabled Filter Double (S-R2F2-Attn-U-Net). Particularly,
S-R2F2U-Net outperforms state-of-the-art models in terms of accuracy and dice
score. A hybrid loss function combining the cross-entropy loss and dice loss is
used to train the model. In addition, it reduces around 45% of model parameters
compared to the R2U-Net model. Models are trained and evaluated on a benchmark
dataset containing 1500 dental panoramic X-ray images. S-R2F2U-Net achieves
97.31% of accuracy and 93.26% of dice score, showing superiority over the
state-of-the-art methods. Codes are available at
https://github.com/mrinal054/teethSeg_sr2f2u-net.git.


title:
Variability of the Sun's luminosity places constraints on the thermal
  equilibrium of the convection zone

abstract:
  Luminosity, which is the total amount of radiant energy emitted by an object,
is one of the most critical quantities in astrophysics for characterizing
stars. Equally important is the temporal evolution of a star's luminosity
because of its intimate connection with the stellar energy budget, large-scale
convective motion, and heat storage in the stellar interior. Here, we model the
solar luminosity by extending a semi-empirical total solar irradiance (TSI)
model that uses solar-surface magnetism to reconstruct solar irradiance over
the entire 4{\pi} solid angle around the Sun. This model was constrained by
comparing its output to the irradiance in the Earth's direction with the
measured TSI. Comparing the solar luminosity to the TSI on timescales from days
to for cycles 23 and 24, we find poor agreement on short timescales (< solar
rotation). On longer timescales, however, we find good agreement between the
luminosity model and the TSI, which suggests that the extrapolation of
luminosities to multi-cycle timescales based on TSI reconstructions may be
possible. We show that the solar luminosity is not constant but varies in phase
with the solar cycle. This variation has an amplitude of 0.14% from minimum to
maximum for solar cycle 23. Considering the energetics in the solar convection
zone, it is therefore obvious that a steady-state input from the radiative zone
at the solar minimum level would lead to a gradual reduction in the energy
content in the convection zone over multi-century timescales. We show that the
luminosity at the base of the convection zone should be approximately 0.032%
higher than that at the solar surface during solar minimum to maintain net
energy equilibrium through the solar cycle. These different energy-input
scenarios place constraints on the long-term evolution of the total solar
irradiance and its impact on the solar forcing of climate variability.


title:
A Note on Boundedness of Singular Integral Operators on Function Spaces
  over Local Fields

abstract:
  In this article we consider the classical singular integral operator over a
local field with rough kernels. We study the boundedness of such an operator on
different function spaces by relaxing the smoothness condition on kernels.


title:
A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural
  Networks

abstract:
  We propose a design methodology to facilitate fault tolerance of deep
learning models. First, we implement a many-core fault-tolerant neuromorphic
hardware design, where neuron and synapse circuitries in each neuromorphic core
are enclosed with astrocyte circuitries, the star-shaped glial cells of the
brain that facilitate self-repair by restoring the spike firing frequency of a
failed neuron using a closed-loop retrograde feedback signal. Next, we
introduce astrocytes in a deep learning model to achieve the required degree of
tolerance to hardware faults. Finally, we use a system software to partition
the astrocyte-enabled model into clusters and implement them on the proposed
fault-tolerant neuromorphic design. We evaluate this design methodology using
seven deep learning inference models and show that it is both area and power
efficient.


title:
Intervertebral Disc Labeling With Learning Shape Information, A Look
  Once Approach

abstract:
  Accurate and automatic segmentation of intervertebral discs from medical
images is a critical task for the assessment of spine-related diseases such as
osteoporosis, vertebral fractures, and intervertebral disc herniation. To date,
various approaches have been developed in the literature which routinely relies
on detecting the discs as the primary step. A disadvantage of many cohort
studies is that the localization algorithm also yields false-positive
detections. In this study, we aim to alleviate this problem by proposing a
novel U-Net-based structure to predict a set of candidates for intervertebral
disc locations. In our design, we integrate the image shape information (image
gradients) to encourage the model to learn rich and generic geometrical
information. This additional signal guides the model to selectively emphasize
the contextual representation and suppress the less discriminative features. On
the post-processing side, to further decrease the false positive rate, we
propose a permutation invariant 'look once' model, which accelerates the
candidate recovery procedure. In comparison with previous studies, our proposed
approach does not need to perform the selection in an iterative fashion. The
proposed method was evaluated on the spine generic public multi-center dataset
and demonstrated superior performance compared to previous work. We have
provided the implementation code in
https://github.com/rezazad68/intervertebral-lookonce


title:
"The Pedestrian next to the Lamppost" Adaptive Object Graphs for Better
  Instantaneous Mapping

abstract:
  Estimating a semantically segmented bird's-eye-view (BEV) map from a single
image has become a popular technique for autonomous control and navigation.
However, they show an increase in localization error with distance from the
camera. While such an increase in error is entirely expected - localization is
harder at distance - much of the drop in performance can be attributed to the
cues used by current texture-based models, in particular, they make heavy use
of object-ground intersections (such as shadows), which become increasingly
sparse and uncertain for distant objects. In this work, we address these
shortcomings in BEV-mapping by learning the spatial relationship between
objects in a scene. We propose a graph neural network which predicts BEV
objects from a monocular image by spatially reasoning about an object within
the context of other objects. Our approach sets a new state-of-the-art in BEV
estimation from monocular images across three large-scale datasets, including a
50% relative improvement for objects on nuScenes.


title:
Probing massive neutrinos with the Minkowski functionals of large-scale
  structure

abstract:
  Massive neutrinos suppress the growth of structure under their free-streaming
scales. The effect is most prominent on small scales where the widely-used
two-point statistics can no longer capture the full information. In this work,
we study the signatures massive neutrinos leave on large-scale structure (LSS)
as revealed by its morphological properties, which are fully described by $4$
Minkowski functionals (MFs), and quantify the constraints on the summed
neutrino mass $M_{\nu}$ from the MFs, by using publicly available N-body
simulations. We find the MFs provide important complementary information, and
give tighter constraints on $M_{\nu}$ than the power spectrum. Specifically,
depending on whether massive neutrinos are included in the density field (the
`m' field) or not (the `cb' field), we find the constraint on $M_{\nu}$ from
the MFs with a smoothing scale of $R_G=5 h^{-1}$Mpc is $48$ or $4$ times better
than that from the power spectrum. When the MFs are combined with the power
spectrum, they can improve the constraint on $M_{\nu}$ from the latter by a
factor of 63 for the `m' field and 5 for the `cb' field. Notably, when the `m'
field is used, the constraint on $M_{\nu}$ from the MFs can reach $0.0177$eV
with a volume of $1(h^{-1}\rm Gpc)^3$, while the combination of the MFs and
power spectrum can tighten this constraint to be $0.0133$eV, a $4.5\sigma$
significance on detecting the minimum sum of the neutrino masses. For the `m'
field, we also find the $\sigma_8$ and $M_{\nu}$ degeneracy is broken with the
MFs, leading to stronger constraints on all 6 cosmological parameters
considered in this work than the power spectrum.


title: Cancelling microwave crosstalk with fixed-frequency qubits

abstract:
  Scalable quantum information processing requires that modular gate operations
can be executed in parallel. The presence of crosstalk decreases the individual
addressability, causing erroneous results during simultaneous operations. For
superconducting qubits which operate in the microwave regime, electromagnetic
isolation is often limited due to design constraints, leading to signal
crosstalk that can deteriorate the quality of simultaneous gate operations.
Here, we propose and demonstrate a method based on AC Stark effect for
calibrating the microwave signal crosstalk. The method is suitable for
processors based on fixed-frequency qubits which are known for high coherence
and simple control. The optimal compensation parameters can be reliably
identified from a well-defined interference pattern. We implement the method on
an array of 7 superconducting qubits, and show its effectiveness in removing
the majority of crosstalk errors.


title: Marrying Fairness and Explainability in Supervised Learning

abstract:
  Machine learning algorithms that aid human decision-making may inadvertently
discriminate against certain protected groups. We formalize direct
discrimination as a direct causal effect of the protected attributes on the
decisions, while induced discrimination as a change in the causal influence of
non-protected features associated with the protected attributes. The
measurements of marginal direct effect (MDE) and SHapley Additive exPlanations
(SHAP) reveal that state-of-the-art fair learning methods can induce
discrimination via association or reverse discrimination in synthetic and
real-world datasets. To inhibit discrimination in algorithmic systems, we
propose to nullify the influence of the protected attribute on the output of
the system, while preserving the influence of remaining features. We introduce
and study post-processing methods achieving such objectives, finding that they
yield relatively high model accuracy, prevent direct discrimination, and
diminishes various disparity measures, e.g., demographic disparity.


title:
Guaranteed Bounds for Posterior Inference in Universal Probabilistic
  Programming

abstract:
  We propose a new method to approximate the posterior distribution of
probabilistic programs by means of computing guaranteed bounds. The starting
point of our work is an interval-based trace semantics for a recursive,
higher-order probabilistic programming language with continuous distributions.
Taking the form of (super-/subadditive) measures, these lower/upper bounds are
non-stochastic and provably correct: using the semantics, we prove that the
actual posterior of a given program is sandwiched between the lower and upper
bounds (soundness); moreover the bounds converge to the posterior
(completeness). As a practical and sound approximation, we introduce a
weight-aware interval type system, which automatically infers interval bounds
on not just the return value but also weight of program executions,
simultaneously. We have built a tool implementation, called GuBPI, which
automatically computes these posterior lower/upper bounds. Our evaluation on
examples from the literature shows that the bounds are useful, and can even be
used to recognise wrong outputs from stochastic posterior inference procedures.


title:
Search for the $Z^\prime$ boson decaying to a right-handed neutrino pair
  in leptophobic $\mathrm{U(1)}$ models

abstract:
  The $U(1)$ extensions of the Standard Model contain a heavy neutral gauge
boson $Z^\prime$. If leptophobic, the boson can evade the stringent bounds from
the dilepton resonance searches. In these extensions, right-handed neutrinos
$N_R$ are also well-motivated by the gauge-anomaly cancellation and
light-neutrino mass generation via seesaw mechanisms. The coexistence of a
leptophobic $Z^\prime$ and the right-handed neutrinos opens up a new
possibility of searching for these particles simultaneously through the
production of $Z^\prime$ at the LHC and its decay to a $N_R$ pair. We study
this process in an inverse seesaw set-up where the heavy neutrinos are
pseudo-Dirac in nature. Hence, in our case, the $N_R$ pair produce only
opposite-sign lepton pairs, as opposed to the Majorana-type neutrinos, which
can produce both the same and the opposite-sign lepton pairs. In particular,
the signature we study involves a same-flavour-opposite-sign lepton pair plus
hadronically-decaying boosted $W$ bosons. Our analysis shows that the High
Luminosity LHC can discover a TeV-scale leptophobic $Z^\prime$ decaying via
$N_R$ pair for a wide range of available parameters in our model. Considerable
parameter regions beyond the reach of the future dijet resonance searches can
be probed through our channel.


title: Cosmology from Strong Interactions

abstract:
  A wealth of theoretical and phenomenological information about Quantum
Chromodynamics at short and long distances collected so far in major collider
measurements has profound implications in cosmology. In this review, we give a
brief discussion of major implications of strongly-coupled dynamics of quarks
and gluons and effects of their collective motion in physics of early Universe
and in astrophysics.


title: Koopman-based spectral clustering of directed and time-evolving graphs

abstract:
  While spectral clustering algorithms for undirected graphs are well
established and have been successfully applied to unsupervised machine learning
problems ranging from image segmentation and genome sequencing to signal
processing and social network analysis, clustering directed graphs remains
notoriously difficult. Two of the main challenges are that the eigenvalues and
eigenvectors of graph Laplacians associated with directed graphs are in general
complex-valued and that there is no universally accepted definition of clusters
in directed graphs. We first exploit relationships between the graph Laplacian
and transfer operators and in particular between clusters in undirected graphs
and metastable sets in stochastic dynamical systems and then use a
generalization of the notion of metastability to derive clustering algorithms
for directed and time-evolving graphs. The resulting clusters can be
interpreted as coherent sets, which play an important role in the analysis of
transport and mixing processes in fluid flows.


title: Inducing Positive Perspectives with Text Reframing

abstract:
  Sentiment transfer is one popular example of a text style transfer task,
where the goal is to reverse the sentiment polarity of a text. With a sentiment
reversal comes also a reversal in meaning. We introduce a different but related
task called positive reframing in which we neutralize a negative point of view
and generate a more positive perspective for the author without contradicting
the original meaning. Our insistence on meaning preservation makes positive
reframing a challenging and semantically rich task. To facilitate rapid
progress, we introduce a large-scale benchmark, Positive Psychology Frames,
with 8,349 sentence pairs and 12,755 structured annotations to explain positive
reframing in terms of six theoretically-motivated reframing strategies. Then we
evaluate a set of state-of-the-art text style transfer models, and conclude by
discussing key challenges and directions for future work.


title: Scheduling to Minimize Age of Information with Multiple Sources

abstract:
  We consider a G/G/1 queueing system with a single server, where updates
arrive from different sources stochastically with possibly different update
inter-generation time distributions. The server can transmit/serve at most one
update at any time, with potentially different transmission/service times for
updates belonging to distinct sources. The age of information (AoI) of any
source is a function of the time difference between the departure time of
successive updates of that source. Each fully/partially transmitted update
incurs a fixed (energy) cost, and the goal of the scheduler is to minimize the
linear combination of the sum of the age of information across all sources and
the total energy cost. We propose a simple non-preemptive randomized scheduling
algorithm that randomly marks arriving updates from a source to be eligible for
transmission with a fixed probability and discards them otherwise. Every time
the server becomes free, it chooses a source for transmission randomly with
another fixed probability and begins to transmit the most recently marked
update of the chosen source. Both the respective probabilities are chosen by
solving a convex program. The competitive ratio of the proposed algorithm
(against a non-preemptive offline optimal algorithm) is shown to be 3 plus the
maximum of the ratio of the variance and the mean of the inter-arrival time
distribution of sources. For several common distributions such as exponential,
uniform and Rayleigh, the competitive ratio is at most 4. For preemptive
policies, a G/M/1 system is considered and a non-preemptive policy is shown to
have competitive ratio (against a preemptive offline optimal algorithm) at most
5 plus the maximum of the ratio of the variance and the mean of the
inter-arrival time distribution of sources.


title:
Strongly convergent homogeneous approximations to inhomogeneous Markov
  jump processes and applications

abstract:
  The study of inhomogeneous Markov jump processes is a traditional topic
within probability theory that has recently attracted substantial attention in
various applications. However, their flexibility also incurs a substantial
mathematical burden - even in simple settings - which is usually circumvented
by using well-known generic distributional approximations or simulations. This
article provides a novel approximation method that tailors the dynamics of a
homogeneous Markov jump process to meet those of its inhomogeneous counterpart
on an increasingly fine Poisson grid - a procedure broadly known as
uniformization. Strong convergence of the processes in terms of the Skorokhod
$J_1$ topology is established, and convergence rates are provided. Special
attention is devoted to the case where the target process has one absorbing
state and the remaining ones transient, for which the absorption times also
converge. Some applications are outlined, such as ruin probability calculation,
univariate hazard-rate density estimation, and multivariate phase-type density
evaluation. Finally, extensions to semi-Markovian models are presented.


title: Operational classical mechanics: Holonomic Systems

abstract:
  We construct an operational formulation of classical mechanics without
presupposing previous results from analytical mechanics. In doing so, several
concepts from analytical mechanics will be rediscovered from an entirely new
perspective. We start by expressing the basic concepts of the position and
velocity of point particles as the eigenvalues of self-adjoint operators acting
on a suitable Hilbert space. The concept of Holonomic constraint is shown to be
equivalent to a restriction to a linear subspace of the free Hilbert space. The
principal results we obtain are: (1) the Lagrange equations of motion are
derived without the use of D'Alembert or Hamilton principles, (2) the
constraining forces are obtained without the use of Lagrange multipliers, (3)
the passage from a position-velocity to a position-momentum description of the
movement is done without the use of a Legendre transformation, (4) the
Koopman-von Neumann theory is obtained as a result of our ab initio operational
approach, (5) previous work on the Schwinger action principle for classical
systems is generalized to include holonomic constraints.


title:
Testing Lorentz invariance of electrons with LHAASO observations of PeV
  gamma-rays from the Crab Nebula

abstract:
  The Large High Altitude Air Shower Observatory~(LHAASO) recently reported the
detection of gamma-ray emissions with energies up to $1.1~\textrm{PeV}$ from
the Crab Nebula. Using the absence of vacuum Cherenkov effect by
inverse-Compton electrons, we improve previous bounds to linear-order Lorentz
invariance violation (LV) in the dispersion relations of electrons by $10^{4}$
times. We show that the LV effect on electrons is severely constrained,
compatible with certain type of LV as expected by some models of quantum
gravity~(QG), such as the string/D-brane inspired space-time foam. We argue
that such models are supported by the Crab Nebula constraints from the LHAASO
observations, as well as various LV phenomenologies for photons to date.


title: Video Demoireing with Relation-Based Temporal Consistency

abstract:
  Moire patterns, appearing as color distortions, severely degrade image and
video qualities when filming a screen with digital cameras. Considering the
increasing demands for capturing videos, we study how to remove such
undesirable moire patterns in videos, namely video demoireing. To this end, we
introduce the first hand-held video demoireing dataset with a dedicated data
collection pipeline to ensure spatial and temporal alignments of captured data.
Further, a baseline video demoireing model with implicit feature space
alignment and selective feature aggregation is developed to leverage
complementary information from nearby frames to improve frame-level video
demoireing. More importantly, we propose a relation-based temporal consistency
loss to encourage the model to learn temporal consistency priors directly from
ground-truth reference videos, which facilitates producing temporally
consistent predictions and effectively maintains frame-level qualities.
Extensive experiments manifest the superiority of our model. Code is available
at \url{https://daipengwa.github.io/VDmoire_ProjectPage/}.


title:
LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of
  Feature Similarity

abstract:
  In this work, we introduce LEAD, an approach to discover landmarks from an
unannotated collection of category-specific images. Existing works in
self-supervised landmark detection are based on learning dense (pixel-level)
feature representations from an image, which are further used to learn
landmarks in a semi-supervised manner. While there have been advances in
self-supervised learning of image features for instance-level tasks like
classification, these methods do not ensure dense equivariant representations.
The property of equivariance is of interest for dense prediction tasks like
landmark estimation. In this work, we introduce an approach to enhance the
learning of dense equivariant representations in a self-supervised fashion. We
follow a two-stage training approach: first, we train a network using the BYOL
objective which operates at an instance level. The correspondences obtained
through this network are further used to train a dense and compact
representation of the image using a lightweight network. We show that having
such a prior in the feature extractor helps in landmark detection, even under
drastically limited number of annotations while also improving generalization
across scale variations.


title:
Computation of the Time-Dependent Dirac Equation with Physics-Informed
  Neural Networks

abstract:
  We propose to compute the time-dependent Dirac equation using
physics-informed neural networks (PINNs), a new powerful tool in scientific
machine learning avoiding the use of approximate derivatives of differential
operators. PINNs search solutions in the form of parameterized (deep) neural
networks, whose derivatives (in time and space) are performed by automatic
differentiation. The computational cost comes from the need to solve
high-dimensional optimization problems using stochastic gradient methods and
train the network with a large number of points. Specifically, we derive
PINNs-based algorithms and present some key fundamental properties of these
algorithms when applied to the Dirac equations in different physical
frameworks.


title: Simple and Effective Synthesis of Indoor 3D Scenes

abstract:
  We study the problem of synthesizing immersive 3D indoor scenes from one or
more images. Our aim is to generate high-resolution images and videos from
novel viewpoints, including viewpoints that extrapolate far beyond the input
images while maintaining 3D consistency. Existing approaches are highly
complex, with many separately trained stages and components. We propose a
simple alternative: an image-to-image GAN that maps directly from reprojections
of incomplete point clouds to full high-resolution RGB-D images. On the
Matterport3D and RealEstate10K datasets, our approach significantly outperforms
prior work when evaluated by humans, as well as on FID scores. Further, we show
that our model is useful for generative data augmentation. A
vision-and-language navigation (VLN) agent trained with trajectories
spatially-perturbed by our model improves success rate by up to 1.5% over a
state of the art baseline on the R2R benchmark. Our code will be made available
to facilitate generative data augmentation and applications to downstream
robotics and embodied AI tasks.


title:
SMU-Net: Style matching U-Net for brain tumor segmentation with missing
  modalities

abstract:
  Gliomas are one of the most prevalent types of primary brain tumours,
accounting for more than 30\% of all cases and they develop from the glial stem
or progenitor cells. In theory, the majority of brain tumours could well be
identified exclusively by the use of Magnetic Resonance Imaging (MRI). Each MRI
modality delivers distinct information on the soft tissue of the human brain
and integrating all of them would provide comprehensive data for the accurate
segmentation of the glioma, which is crucial for the patient's prognosis,
diagnosis, and determining the best follow-up treatment. Unfortunately, MRI is
prone to artifacts for a variety of reasons, which might result in missing one
or more MRI modalities. Various strategies have been proposed over the years to
synthesize the missing modality or compensate for the influence it has on
automated segmentation models. However, these methods usually fail to model the
underlying missing information. In this paper, we propose a style matching
U-Net (SMU-Net) for brain tumour segmentation on MRI images. Our co-training
approach utilizes a content and style-matching mechanism to distill the
informative features from the full-modality network into a missing modality
network. To do so, we encode both full-modality and missing-modality data into
a latent space, then we decompose the representation space into a style and
content representation. Our style matching module adaptively recalibrates the
representation space by learning a matching function to transfer the
informative and textural features from a full-modality path into a
missing-modality path. Moreover, by modelling the mutual information, our
content module surpasses the less informative features and re-calibrates the
representation space based on discriminative semantic features. The evaluation
process on the BraTS 2018 dataset shows a significant results.


title:
Intrinsic Roughening and Phase Condensation in Strongly Interacting
  Systems

abstract:
  Most of our current understanding of phase separation is based on ideas that
disregard correlations between constituents. Here we illuminate unexpected
effects of correlations on the structure and thermodynamics of interfaces and
in turn phase separation, which are decisive in systems with strong
interactions. Evaluating the continuum limit of the Ising model on the
Bethe-Guggenheim level we develop a Cahn-Hilliard theory that takes into
account pair correlations. These are shown to cause an intrinsic roughening of
interfaces at interaction strengths near and above the thermal energy, which is
corroborated in the Ising model. Roughening is a result of correlations
stabilizing large-scale inhomogeneities at sufficiently strong coupling, and is
shown to have profound implications for nucleation phenomena. The widely
adopted Flory-Huggins theory in turn predicts an unphysical behavior already at
moderate interactions. We discuss the biological relevance of our results in
the context of phase condensation of strongly interacting methyl-accepting
chemotaxis proteins in bacterial membranes. Our results identify pair
correlations as an essential determinant of interfaces and condensates in the
strong interaction limit that so far seems to have been overlooked.

categories: cond-mat.stat-mech cond-mat.soft physics.bio-ph physics.chem-ph


title: Induced Stinespring factorization and the Wittstock support theorem

abstract:
  Given a pair of self-adjoint-preserving completely bounded maps on the same
$C^*$-algebra, say that $\varphi \leq \psi$ if the kernel of $\varphi$ is a
subset of the kernel of $\psi$ and $\psi \circ \varphi^{-1}$ is completely
positive. The \emph{Agler class} of a map $\varphi$ is the class of $\psi \geq
\varphi.$ Such maps admit colligation formulae, and, in Lyapunov type
situations, transfer function type realizations on the Stinespring coefficients
of their Wittstock decompositions. As an application, we prove that the support
of an extremal Wittstock decomposition is unique.


title:
Unleashing Vanilla Vision Transformer with Masked Image Modeling for
  Object Detection

abstract:
  We present an approach to efficiently and effectively adapt a masked image
modeling (MIM) pre-trained vanilla Vision Transformer (ViT) for object
detection, which is based on our two novel observations: (i) A MIM pre-trained
vanilla ViT can work surprisingly well in the challenging object-level
recognition scenario even with random sampled partial observations, e.g., only
25% ~ 50% of the input sequence. (ii) In order to construct multi-scale
representations for object detection, a random initialized compact
convolutional stem supplants the pre-trained large kernel patchify stem, and
its intermediate features can naturally serve as the higher resolution inputs
of a feature pyramid without upsampling. While the pre-trained ViT is only
regarded as the third-stage of our detector's backbone instead of the whole
feature extractor, resulting in a ConvNet-ViT hybrid architecture. The proposed
detector, named MIMDet, enables a MIM pre-trained vanilla ViT to outperform
hierarchical Swin Transformer by 2.3 box AP and 2.5 mask AP on COCO, and
achieve even better results compared with other adapted vanilla ViT using a
more modest fine-tuning recipe while converging 2.8x faster. Code and
pre-trained models are available at \url{https://github.com/hustvl/MIMDet}.


title:
LilNetX: Lightweight Networks with EXtreme Model Compression and
  Structured Sparsification

abstract:
  We introduce LilNetX, an end-to-end trainable technique for neural networks
that enables learning models with specified accuracy-rate-computation
trade-off. Prior works approach these problems one at a time and often require
post-processing or multistage training which become less practical and do not
scale very well for large datasets or architectures. Our method constructs a
joint training objective that penalizes the self-information of network
parameters in a reparameterized latent space to encourage small model size
while also introducing priors to increase structured sparsity in the parameter
space to reduce computation. We achieve up to 50% smaller model size and 98%
model sparsity on ResNet-20 while retaining the same accuracy on the CIFAR-10
dataset as well as 35% smaller model size and 42% structured sparsity on
ResNet-50 trained on ImageNet, when compared to existing state-of-the-art model
compression methods. Code is available at
https://github.com/Sharath-girish/LilNetX.


title: Gauge Quantum Thermodynamics of Time-local non-Markovian Evolutions

abstract:
  Dealing with a generic time-local non-Markovian master equation, currents and
power are defined to be process-dependent as in classical thermodynamics. Each
process is characterized by a symmetry transformation, a gauge of the quantum
master equation, and is associated to different amounts of heat and/or work.
Once the thermodynamical quantities are fixed by the symmetry requirement, a
consistent interpretation of the laws of thermodynamics emerges. We also
provide necessary and sufficient conditions for a system to have a
gauge-independent thermodynamical description.


title:
Enhanced Direct Speech-to-Speech Translation Using Self-supervised
  Pre-training and Data Augmentation

abstract:
  Direct speech-to-speech translation (S2ST) models suffer from data scarcity
issues as there exists little parallel S2ST data, compared to the amount of
data available for conventional cascaded systems that consist of automatic
speech recognition (ASR), machine translation (MT), and text-to-speech (TTS)
synthesis. In this work, we explore self-supervised pre-training with unlabeled
speech data and data augmentation to tackle this issue. We take advantage of a
recently proposed speech-to-unit translation (S2UT) framework that encodes
target speech into discrete representations, and transfer pre-training and
efficient partial finetuning techniques that work well for speech-to-text
translation (S2T) to the S2UT domain by studying both speech encoder and
discrete unit decoder pre-training. Our experiments show that self-supervised
pre-training consistently improves model performance compared with multitask
learning with a BLEU gain of 4.3-12.0 under various data setups, and it can be
further combined with data augmentation techniques that apply MT to create
weakly supervised training data. Audio samples are available at:
https://facebookresearch.github.io/speech_translation/enhanced_direct_s2st_units/index.html .


title: Temporal Alignment Networks for Long-term Video

abstract:
  The objective of this paper is a temporal alignment network that ingests long
term video sequences, and associated text sentences, in order to: (1) determine
if a sentence is alignable with the video; and (2) if it is alignable, then
determine its alignment. The challenge is to train such networks from
large-scale datasets, such as HowTo100M, where the associated text sentences
have significant noise, and are only weakly aligned when relevant. Apart from
proposing the alignment network, we also make four contributions: (i) we
describe a novel co-training method that enables to denoise and train on raw
instructional videos without using manual annotation, despite the considerable
noise; (ii) to benchmark the alignment performance, we manually curate a
10-hour subset of HowTo100M, totalling 80 videos, with sparse temporal
descriptions. Our proposed model, trained on HowTo100M, outperforms strong
baselines (CLIP, MIL-NCE) on this alignment dataset by a significant margin;
(iii) we apply the trained model in the zero-shot settings to multiple
downstream video understanding tasks and achieve state-of-the-art results,
including text-video retrieval on YouCook2, and weakly supervised video action
segmentation on Breakfast-Action; (iv) we use the automatically aligned
HowTo100M annotations for end-to-end finetuning of the backbone model, and
obtain improved performance on downstream action recognition tasks.


title:
Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce,
  Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health
  Management (PHM)

abstract:
  In the current Industrial 4.0 revolution, Prognostics and Health Management
(PHM) is an emerging field of research. The difficulty of obtaining data from
electromechanical systems in an industrial setting increases proportionally
with the scale and accessibility of the automated industry, resulting in a less
interpolated PHM system. To put it another way, the development of an accurate
PHM system for each industrial system necessitates a unique dataset acquired
under specified conditions. In most circumstances, obtaining this one-of-a-kind
dataset is difficult, and the resulting dataset has a significant imbalance, a
lack of certain useful information, and multi-domain knowledge. To address
this, this paper provides a fault detection and diagnosis system that evaluates
and pre-processes Imbalanced, Scarce, Multi-Domain (ISMD) data acquired from an
industrial robot utilizing Signal Processing (SP) techniques and Deep
Learning-based (DL) domain knowledge transfer. The domain knowledge transfer is
used to produce a synthetic dataset with a high interpolation rate that
contains all the useful information about each domain. For domain knowledge
transfer and data generation, Continuous Wavelet Transform (CWT) with
Generative Adversarial Network (GAN) was used, as well as Convolutional Neural
Network (CNN) to test the suggested methodology using transfer learning and
categorize several faults. The proposed methodology was tested on a real
experimental bench that included an industrial robot created by Hyundai
Robotics Co. This development resulted in a satisfactory resolution with 99.7%
(highest) classification accuracy achieved by transfer learning on several CNN
benchmark models.


title:
Evolutionary Programmer: Autonomously Creating Path Planning Programs
  based on Evolutionary Algorithms

abstract:
  Evolutionary algorithms are wildly used in unmanned aerial vehicle path
planning for their flexibility and effectiveness. Nevertheless, they are so
sensitive to the change of environment that can't adapt to all scenarios. Due
to this drawback, the previously successful planner frequently fail in a new
scene. In this paper, a first-of-its-kind machine learning method named
Evolutionary Programmer is proposed to solve this problem. Concretely, the most
commonly used Evolutionary Algorithms are decomposed into a series of
operators, which constitute the operator library of the system. The new method
recompose the operators to a integrated planner, thus, the most suitable
operators can be selected for adapting to the changing circumstances. Different
from normal machine programmers, this method focuses on a specific task with
high-level integrated instructions and thus alleviate the problem of huge
search space caused by the briefness of instructions. On this basis, a 64-bit
sequence is presented to represent path planner and then evolved with the
modified Genetic Algorithm. Finally, the most suitable planner is created by
utilizing the information of the previous planner and various randomly
generated ones.


title: On The Geometry Of Elliptic Pairs

abstract:
  An elliptic pair $(X, C)$ is a projective rational surface $X$ with log
terminal singularities, and an irreducible curve $C$ contained in the smooth
locus of $X$, with arithmetic genus one and self-intersection zero. They are a
useful tool for determining whether the pseudo-effective cone of $X$ is
polyhedral, and interesting algebraic and geometric objects in their own right.
Especially of interest are toric elliptic pairs, where $X$ is the blow-up of a
projective toric surface at the identity element of the torus. In this paper,
we classify all toric elliptic pairs of Picard number two. Strikingly, it turns
out that there are only three of these. Furthermore, we study a class of
non-toric elliptic pairs coming from the blow-up of $\mathbb{P}^2$ at nine
points on a nodal cubic, in characteristic $p$. This construction gives us
examples of surfaces where the pseudo-effective cone is non-polyhedral for a
set of primes $p$ of positive density, and, assuming the generalized Riemann
hypothesis, polyhedral for a set of primes $p$ of positive density.


title: Multi-task nonparallel support vector machine for classification

abstract:
  Direct multi-task twin support vector machine (DMTSVM) explores the shared
information between multiple correlated tasks, then it produces better
generalization performance. However, it contains matrix inversion operation
when solving the dual problems, so it costs much running time. Moreover, kernel
trick cannot be directly utilized in the nonlinear case. To effectively avoid
above problems, a novel multi-task nonparallel support vector machine (MTNPSVM)
including linear and nonlinear cases is proposed in this paper. By introducing
epsilon-insensitive loss instead of square loss in DMTSVM, MTNPSVM effectively
avoids matrix inversion operation and takes full advantage of the kernel trick.
Theoretical implication of the model is further discussed. To further improve
the computational efficiency, the alternating direction method of multipliers
(ADMM) is employed when solving the dual problem. The computational complexity
and convergence of the algorithm are provided. In addition, the property and
sensitivity of the parameter in model are further explored. The experimental
results on fifteen benchmark datasets and twelve image datasets demonstrate the
validity of MTNPSVM in comparison with the state-of-the-art algorithms.
Finally, it is applied to real Chinese Wine dataset, and also verifies its
effectiveness.


title:
Incremental Unsupervised Feature Selection for Dynamic Incomplete
  Multi-view Data

abstract:
  Multi-view unsupervised feature selection has been proven to be efficient in
reducing the dimensionality of multi-view unlabeled data with high dimensions.
The previous methods assume all of the views are complete. However, in real
applications, the multi-view data are often incomplete, i.e., some views of
instances are missing, which will result in the failure of these methods.
Besides, while the data arrive in form of streams, these existing methods will
suffer the issues of high storage cost and expensive computation time. To
address these issues, we propose an Incremental Incomplete Multi-view
Unsupervised Feature Selection method (I$^2$MUFS) on incomplete multi-view
streaming data. By jointly considering the consistent and complementary
information across different views, I$^2$MUFS embeds the unsupervised feature
selection into an extended weighted non-negative matrix factorization model,
which can learn a consensus clustering indicator matrix and fuse different
latent feature matrices with adaptive view weights. Furthermore, we introduce
the incremental leaning mechanisms to develop an alternative iterative
algorithm, where the feature selection matrix is incrementally updated, rather
than recomputing on the entire updated data from scratch. A series of
experiments are conducted to verify the effectiveness of the proposed method by
comparing with several state-of-the-art methods. The experimental results
demonstrate the effectiveness and efficiency of the proposed method in terms of
the clustering metrics and the computational cost.


title:
An Intelligent Framework for Oversubscription Management in CPU-GPU
  Unified Memory

abstract:
  This paper proposes a novel intelligent framework for oversubscription
management in CPU-GPU UVM. We analyze the current rule-based methods of GPU
memory oversubscription with unified memory, and the current learning-based
methods for other computer architectural components. We then identify the
performance gap between the existing rule-based methods and the theoretical
upper bound. We also identify the advantages of applying machine intelligence
and the limitations of the existing learning-based methods. This paper proposes
a novel intelligent framework for oversubscription management in CPU-GPU UVM.
It consists of an access pattern classifier followed by a pattern-specific
Transformer-based model using a novel loss function aiming for reducing page
thrashing. A policy engine is designed to leverage the model's result to
perform accurate page prefetching and pre-eviction. We evaluate our intelligent
framework on a set of 11 memory-intensive benchmarks from popular benchmark
suites. Our solution outperforms the state-of-the-art (SOTA) methods for
oversubscription management, reducing the number of pages thrashed by 64.4\%
under 125\% memory oversubscription compared to the baseline, while the SOTA
method reduces the number of pages thrashed by 17.3\%. Our solution achieves an
average IPC improvement of 1.52X under 125\% memory oversubscription, and our
solution achieves an average IPC improvement of 3.66X under 150\% memory
oversubscription. Our solution outperforms the existing learning-based methods
for page address prediction, improving top-1 accuracy by 6.45\% (up to 41.2\%)
on average for a single GPGPU workload, improving top-1 accuracy by 10.2\% (up
to 30.2\%) on average for multiple concurrent GPGPU workloads.


title: On order isomorphisms intertwining semigroups for Dirichlet forms

abstract:
  This paper is devoted to characterizing the so-called order isomorphisms
intertwining the $L^2$-semigroups of two Dirichlet forms. We first show that
every unitary order isomorphism intertwining semigroups is the composition of
$h$-transformation and quasi-homeomorphism. In addition, under the absolute
continuity condition on Dirichlet forms, every (not necessarily unitary) order
isomorphism intertwining semigroups is the composition of $h$-transformation,
quasi-homeomorphism, and multiplication by a certain step function.


title: Follow My Eye: Using Gaze to Supervise Computer-Aided Diagnosis

abstract:
  When deep neural network (DNN) was first introduced to the medical image
analysis community, researchers were impressed by its performance. However, it
is evident now that a large number of manually labeled data is often a must to
train a properly functioning DNN. This demand for supervision data and labels
is a major bottleneck in current medical image analysis, since collecting a
large number of annotations from experienced experts can be time-consuming and
expensive. In this paper, we demonstrate that the eye movement of radiologists
reading medical images can be a new form of supervision to train the DNN-based
computer-aided diagnosis (CAD) system. Particularly, we record the tracks of
the radiologists' gaze when they are reading images. The gaze information is
processed and then used to supervise the DNN's attention via an Attention
Consistency module. To the best of our knowledge, the above pipeline is among
the earliest efforts to leverage expert eye movement for deep-learning-based
CAD. We have conducted extensive experiments on knee X-ray images for
osteoarthritis assessment. The results show that our method can achieve
considerable improvement in diagnosis performance, with the help of gaze
supervision.


title: Multi-Scale Memory-Based Video Deblurring

abstract:
  Video deblurring has achieved remarkable progress thanks to the success of
deep neural networks. Most methods solve for the deblurring end-to-end with
limited information propagation from the video sequence. However, different
frame regions exhibit different characteristics and should be provided with
corresponding relevant information. To achieve fine-grained deblurring, we
designed a memory branch to memorize the blurry-sharp feature pairs in the
memory bank, thus providing useful information for the blurry query input. To
enrich the memory of our memory bank, we further designed a bidirectional
recurrency and multi-scale strategy based on the memory bank. Experimental
results demonstrate that our model outperforms other state-of-the-art methods
while keeping the model complexity and inference time low. The code is
available at https://github.com/jibo27/MemDeblur.


title:
End-To-End Optimization of Online Neural Network-supported Two-Stage
  Dereverberation for Hearing Devices

abstract:
  A two-stage online dereverberation algorithm for hearing devices is presented
in this paper. The approach combines a multi-channel multi-frame linear
filtering approach with a single-channel single-frame post-filter. Both
components rely on power spectral density (PSD) estimates provided by deep
neural networks (DNNs). This contribution extends our prior work, which shows
that directly optimizing for a criterion at the output of the multi-channel
linear filtering stage results in a more efficient dereverberation, as compared
to placing the criterion at the output of the DNN to optimize the PSD
estimation. In the present work, we show that the dereverberation performance
of the proposed first stage particularly improves the early-to-mid
reverberation ratio if trained end-to-end. We thus argue that it can be
combined with a post-filtering stage which benefits from the early-to-mid ratio
improvement and is consequently able to efficiently suppress the residual late
reverberation. This proposed two stage procedure is shown to be both very
effective in terms of dereverberation performance and computational demands.
Furthermore, the proposed system can be adapted to the needs of different types
of hearing-device users by controlling the amount of reduction of early
reflections. The proposed system outperforms the previously proposed end-to-end
DNN-supported linear filtering algorithm, as well as other traditional
approaches, based on an evaluation using the noise-free version of the WHAMR!
dataset.


title:
Magnetic charge effects on thermodynamic phase transition of modified
  Anti de Sitter Ay\'{o}n-Beato-Garc\'{i}a Black Holes with five parameters

abstract:
  In this letter we choose generalized Ayon Beato Gracia (ABG) magnetic charged
black hole with five parameters to investigate possibility of thermodynamic
phase transition and coexistence of different gass/liquid/solid phases of this
black hole. In fact this work is an extension of our recently work where ABG
black hole with three parameters was used to seek the phase transition. In this
work we obtain other physical values on the parameters with respect to our
previous work where the phase transition is happened together with coexistence
point of different phases in the phase space.


title: Analysis of Different Losses for Deep Learning Image Colorization

abstract:
  Image colorization aims to add color information to a grayscale image in a
realistic way. Recent methods mostly rely on deep learning strategies. While
learning to automatically colorize an image, one can define well-suited
objective functions related to the desired color output. Some of them are based
on a specific type of error between the predicted image and ground truth one,
while other losses rely on the comparison of perceptual properties. But, is the
choice of the objective function that crucial, i.e., does it play an important
role in the results? In this chapter, we aim to answer this question by
analyzing the impact of the loss function on the estimated colorization
results. To that goal, we review the different losses and evaluation metrics
that are used in the literature. We then train a baseline network with several
of the reviewed objective functions: classic L1 and L2 losses, as well as more
complex combinations such as Wasserstein GAN and VGG-based LPIPS loss.
Quantitative results show that the models trained with VGG-based LPIPS provide
overall slightly better results for most evaluation metrics. Qualitative
results exhibit more vivid colors when with Wasserstein GAN plus the L2 loss or
again with the VGG-based LPIPS. Finally, the convenience of quantitative user
studies is also discussed to overcome the difficulty of properly assessing on
colorized images, notably for the case of old archive photographs where no
ground truth is available.


title: D-instanton Induced Superpotential

abstract:
  We use string field theory to fix the normalization of the D-instanton
corrections to the superpotential involving the moduli fields of type II string
theory compactified on an orientifold of a Calabi-Yau threefold in the absence
of fluxes. We focus on $O(1)$ instantons whose only zero modes are the four
bosonic modes associated with translation of the instanton in non-compact
directions and a pair of fermionic zero modes associated with the two
supercharges broken by the instanton. We work with a generic superconformal
field theory and express our answer in terms of the spectrum of open strings on
the instanton. We analyse the contribution of multi-instantons of this kind to
the superpotential and argue that it vanishes when background fluxes are
absent.


title: Planckian properties of 2D semiconductor systems

abstract:
  We describe and discuss the low-temperature resistivity (and the
temperature-dependent inelastic scattering rate) of several different doped 2D
semiconductor systems from the perspective of the Planckian hypothesis
asserting that $\hbar/\tau =k_\mathrm{B}T$ provides a scattering bound, where
$\tau$ is the appropriate relaxation time. The regime of transport considered
here is well-below the Bloch-Gruneisen regime so that phonon scattering is
negligible. The temperature-dependent part of the resistivity is almost
linear-in-$T$ down to arbitrarily low temperatures, with the linearity arising
from an interplay between screening and disorder, connected with carrier
scattering from impurity-induced Friedel oscillations. The temperature
dependence disappears if the Coulomb interaction between electrons is
suppressed. The temperature coefficient of the resistivity is enhanced at lower
densities, enabling a detailed study of the Planckian behavior both as a
function of the materials system and carrier density. Although the precise
Planckian bound never holds, we find somewhat surprisingly that the bound seems
to apply approximately with the scattering rate never exceeding $k_\mathrm{B}
T$ by more than an order of magnitude either in the experiment or in the
theory. In addition, we calculate the temperature-dependent electron-electron
inelastic scattering rate by obtaining the temperature-dependent self-energy
arising from Coulomb interaction, also finding it to obey the Planckian bound
within an order of magnitude at all densities and temperatures. We introduce
the concept of a generalized Planckian bound where $\hbar/\tau$ is bounded by
$\alpha k_\mathrm{B} T$ with $\alpha\sim 10$ or so in the super-Planckian
regime with the strict Planckian bound of $\alpha$=1 being a nongeneric
finetuned situation.


title: Instant Extraction of Non-Perturbative Tripartite Entanglement

abstract:
  We consider the problem of extracting tripartite entanglement through single
local instantaneous interactions of a separable target system A-B-C with a
scalar field. We find, non-perturbatively, that tripartite entanglement is
easily extracted in this scenario, in strong contrast to bipartite extraction,
which is not possible due to a no-go theorem. The tripartite entanglement is of
the GHZ-type, and an optimal value of the coupling exists that admits maximal
extraction.


title:
Determining the Hubble Constant without the Sound Horizon: A $3.6\%$
  Constraint on $H_0$ from Galaxy Surveys, CMB Lensing and Supernovae

abstract:
  Many theoretical resolutions to the so-called "Hubble tension" rely on
modifying the sound horizon at recombination, $r_s$, and thus the acoustic
scale used as a standard ruler in the cosmic microwave background (CMB) and
large scale structure (LSS) datasets. As shown in a number of recent works,
these observables can also be used to compute $r_s$-independent constraints on
$H_0$ by making use of the horizon scale at matter-radiation equality, $k_{\rm
eq}$, which has different sensitivity to high redshift physics than $r_s$. In
this work, we present the tightest $k_{\rm eq}$-based constraints on the
expansion rate from current data, finding $H_0=64.8^{+2.2}_{-2.5}$ at 68$\%$ CL
from a combination of BOSS galaxy power spectra, Planck CMB lensing, and the
newly released Pantheon+ supernova constraints, as well as physical priors on
the baryon density, neutrino mass, and spectral index (in
$\mathrm{km}\,\mathrm{s}^{-1}\mathrm{Mpc}^{-1}$ units). The BOSS and Planck
measurements have different degeneracy directions, leading to the improved
combined constraints, with a bound of $H_0 = 67.1^{+2.5}_{-2.9}$
($63.6^{+2.9}_{-3.6}$) from BOSS (Planck) alone. The results show some
dependence on the neutrino mass bounds, with the constraint broadening to $H_0
= 68.0^{+2.9}_{-3.2}$ if we instead impose a weak prior on $\sum m_\nu$ from
terrestrial experiments rather than assuming $\sum m_\nu<0.26\,\mathrm{eV}$, or
shifting to $H_0 = 64.6\pm2.4$ if the neutrino mass is fixed to its minimal
value. Even without any dependence on the sound horizon, our results are in
$\approx 3\sigma$ tension with those obtained from the Cepheid-calibrated
distance ladder, providing evidence against new physics models that vary $H_0$
by changing acoustic physics or the expansion history immediately prior to
recombination.


title: More Evidence for Variable Helium Absorption from HD 189733b

abstract:
  We present a new Keck/NIRSPEC observation of metastable helium absorption
from the upper atmosphere of HD 189733b, a hot Jupiter orbiting a nearby
moderately active star. We measure an average helium transit depth of $0.420
\pm 0.013$% integrated over the [-20, 20] km/s velocity range. Comparing this
measurement to eight previously published transit observations with different
instruments, we find that our depth is 32% (9$\sigma$) lower than the average
of the three CARMENES transits, but only 16% (4.4$\sigma$) lower than the
average of the five GIANO transits. We perform 1D hydrodynamical simulations of
the outflow, and find that XUV variability on the order of 33%--common for this
star--can change the helium absorption depth by 60%. We conclude that changes
in stellar XUV flux can explain the observational variability in helium
absorption. 3D models are necessary to explore other sources of variability,
such as shear instability and changing stellar wind conditions.


title:
High Equivalent Width of H{\alpha}+[N II] Emission in z~8 Lyman-break
  Galaxies from IRAC 5.8{\mu}m Observations: Evidence for Efficient
  Lyman-continuum Photon production in the Epoch of Re-ionization

abstract:
  We measure, for the first time, the median equivalent width (EW) of
H$\alpha$+[N II] in star-forming galaxies at $z\sim8$. Our estimate leverages
the unique photometric depth of the Spitzer/IRAC $5.8\mu$m-band mosaics
(probing $\approx 5500 - 7100$ A at $z\sim8$) of the GOODS Reionization Era
Wide Area Treasury from Spitzer (GREATS) program. We median stacked the stamps
of $102$ Lyman-break galaxies in the $3.6, 4.5, 5.8$ and $8.0\mu$m bands, after
carefully removing potential contamination from neighbouring sources. We infer
an extreme rest-frame EW$_0$(H$\alpha$+[N II])$=2328^{+1326}_{-1127}$ A from
the measured red $[3.6]-[5.8]=0.82\pm0.27$ mag, consistent with young
($\lesssim10^7$ yr) average stellar population ages at $z\sim8$. This implies
an ionizing photon production efficiency of
$\log(\xi_{\mathrm{ion},0}/\mathrm{erg\ Hz}^{-1})=25.97^{+0.18}_{-0.28}$. Such
a high value for photo production, similar to the highest values found at
$z\lesssim4$, indicates that only modest escape fractions
$f_\mathrm{esc}\lesssim0.3$ (at $2\sigma$) are sufficient for galaxies brighter
than $M_\mathrm{UV}<-18$ mag to re-ionize the neutral Hydrogen at $z\sim8$.
This requirement is relaxed even more to $f_\mathrm{esc}\le 0.1$ when
considering galaxies brighter than $M_\mathrm{UV}\approx -13$ mag, consistent
with recent luminosity functions and as typically assumed in studies addressing
re-ionization. These exceptional results clearly indicate that galaxies can be
the dominant source of reionizing photons, and provide us with an exciting
glimpse into what we might soon learn about the early universe, and
particularly about the Reionization Epoch, from forthcoming JWST/MIRI and
NIRCam programs.


title: The analytic two-loop soft function for leading-jet $p_T$

abstract:
  We present the calculation of the two-loop soft function for the transverse
momentum distribution of the leading jet produced in association with any
colour-singlet system (e.g.~a Higgs or a $Z$ boson). This constitutes a central
ingredient for the resummation of the above distribution as well as the
jet-vetoed cross section at the next-to-next-to-next-to-leading logarithmic
order, both of which play an important role in the precision physics programme
at the Large Hadron Collider. The calculation is performed in soft-collinear
effective theory with an appropriate regularisation of the rapidity divergences
that occur in the phase-space integrals. We obtain analytic results by
employing an exponential regulator and by taking a Laurent expansion in the jet
radius $R$. All expressions are presented as ancillary files with this article.