title:
Privacy-Preserving Speech Representation Learning using Vector
  Quantization

abstract:
  With the popularity of virtual assistants (e.g., Siri, Alexa), the use of
speech recognition is now becoming more and more widespread.However, speech
signals contain a lot of sensitive information, such as the speaker's identity,
which raises privacy concerns.The presented experiments show that the
representations extracted by the deep layers of speech recognition networks
contain speaker information.This paper aims to produce an anonymous
representation while preserving speech recognition performance.To this end, we
propose to use vector quantization to constrain the representation space and
induce the network to suppress the speaker identity.The choice of the
quantization dictionary size allows to configure the trade-off between utility
(speech recognition) and privacy (speaker identity concealment).


title: On convolution powers of 1/x

abstract:
  Convolution powers of $1/x$ are transformed into functions $f_n$, which
satisfy a simple recurrence relation. Solutions are characterized and analyzed.


title: The IAU Working Definition of an Exoplanet

abstract:
  In antiquity, all of the enduring celestial bodies that were seen to move
relative to the background sky of stars were considered planets. During the
Copernican revolution, this definition was altered to objects orbiting around
the Sun, removing the Sun and Moon but adding the Earth to the list of known
planets. The concept of planet is thus not simply a question of nature, origin,
composition, mass or size, but historically a concept related to the motion of
one body around the other, in a hierarchical configuration.
  After discussion within the IAU Commission F2 "Exoplanets and the Solar
System", the criterion of the star-planet mass ratio has been introduced in the
definition of the term "exoplanet", thereby requiring the hierarchical
structure seen in our Solar System for an object to be referred to as an
exoplanet. Additionally, the planetary mass objects orbiting brown dwarfs,
provided they follow the mass ratio criterion, are now considered as
exoplanets. Therefore, the current working definition of an exoplanet, as
amended in August 2018 by IAU Commission F2 "Exoplanets and the Solar System",
reads as follows:
  - Objects with true masses below the limiting mass for thermonuclear fusion
of deuterium (currently calculated to be 13 Jupiter masses for objects of solar
metallicity) that orbit stars, brown dwarfs or stellar remnants and that have a
mass ratio with the central object below the $L_4$/$L_5$ instability ($M/M_{\rm
central}$$<$$2/(25+\sqrt{621}$)$\approx$1/25) are "planets", no matter how they
formed.
  - The minimum mass/size required for an extrasolar object to be considered a
planet should be the same as that used in our Solar System, which is a mass
sufficient both for self-gravity to overcome rigid body forces and for clearing
the neighborhood around the object's orbit.
  Here we discuss the history and the rationale behind this definition.


title:
SemTUI: a Framework for the Interactive Semantic Enrichment of Tabular
  Data

abstract:
  The large availability of datasets fosters the use of \acrshort{ml} and
\acrshort{ai} technologies to gather insights, study trends, and predict unseen
behaviours out of the world of data. Today, gathering and integrating data from
different sources is mainly a manual activity that requires the knowledge of
expert users at an high cost in terms of both time and money. It is, therefore,
necessary to make the process of gathering and linking data from many different
sources affordable to make datasets ready to perform the desired analysis. In
this work, we propose the development of a comprehensive framework, named
SemTUI, to make the enrichment process flexible, complete, and effective
through the use of semantics. The approach is to promote fast integration of
external services to perform enrichment tasks such as reconciliation and
extension; and to provide users with a graphical interface to support
additional tasks, such as refinement to correct ambiguous results provided by
automatic enrichment algorithms. A task-driven user evaluation proved SemTUI to
be understandable, usable, and capable of achieving table enrichment with
little effort and time with user tests that involved people with different
skills and experiences.


title:
Constraining Dark Matter Inside Stars Using Spectroscopic Binaries and a
  Modified Mass-Luminosity Relation

abstract:
  The presence of a dissipative dark matter (DM) sector may allow for the
trapping of a significant DM mass inside stars, either during structure
formation or by accretion over their lifetime, influencing stellar behavior
well into the Main Sequence stage. Motivated by this scenario, we place an
upper bound on the fractional DM mass within current-day Main Sequence stars.
Using double-lined spectroscopic binaries (SB2 stars), gravitational masses are
extracted and contrasted with luminous masses, derived using a modified
mass-luminosity relation which accounts for the effect of DM. High-accuracy
mass and luminosity data from a sample of 486 binary stars in the $0.18 <
M/M_\odot < 31$ mass range are partitioned into appropriate mass domains and
analyzed. A 95% C.L. upper limit of sub-5% is found for the subset of 263 stars
in the $1 < M/M_\odot < 2.4$ regime.


title: The synchronised dance of the Magellanic Clouds' star formation history

abstract:
  We use the SMASH survey to obtain unprecedented deep photometry reaching down
to the oldest main sequence turn-offs in the colour-magnitude diagrams (CMDs)
of the Small Magellanic Cloud (SMC) and quantitatively derive its star
formation history (SFH) using CMD fitting techniques. We identify five
distinctive peaks of star formation in the last 3.5 Gyr, at $\sim $3, $\sim$2,
$\sim$1.1, $\sim $0.45 Gyr ago, and one presently. We compare these to the SFH
of the Large Magellanic Cloud (LMC) finding unequivocal synchronicity, with
both galaxies displaying similar periods of enhanced star formation over the
past $\sim$3.5 Gyr. The parallelism between their SFHs indicates that tidal
interactions between the MCs have recurrently played an important role in their
evolution for at least the last $\sim$3.5 Gyr, tidally truncating the SMC and
shaping the LMC's spiral arm. We show, for the first time, an SMC-LMC
correlated SFH at recent times in which enhancements of star formation are
localised in the northern spiral arm of the LMC, and globally across the SMC.
These novel findings should be used to constrain not only the orbital history
of the MCs but also how star formation should be treated in simulations.


title: On the Importance of Rare Kaon Decays: A Snowmass 2021 White Paper

abstract:
  We stress the importance of precise measurements of rare decays
$K^+\rightarrow\pi^+\nu\bar\nu$, $K_L\rightarrow\pi^0\nu\bar\nu$,
$K_{L,S}\to\mu^+\mu^-$ and $K_{L,S}\to\pi^0\ell^+\ell^-$ for the search of new
physics (NP). This includes both branching ratios and the distributions in
$q^2$, the invariant mass-squared of the neutrino system in the case of
$K^+\rightarrow\pi^+\nu\bar\nu$ and $K_L\rightarrow\pi^0\nu\bar\nu$ and of the
$\ell^+\ell^-$ system in the case of the remaining decays. In particular the
correlations between these observables and their correlations with the ratio
$\varepsilon'/\varepsilon$ in $K_L\to\pi\pi$ decays, the CP-violating parameter
$\varepsilon_K$ and the $K^0-\bar K^0$ mass difference $\Delta M_K$, should
help to disentangle the nature of possible NP. We stress the strong sensitivity
of all observables with the exception of $\Delta M_K$ to the CKM parameter
$|V_{cb}|$ and list a number of $|V_{cb}|$-independent ratios within the SM
which exhibit rather different dependences on the angles $\beta$ and $\gamma$
of the unitarity triangle. The particular role of these decays in probing very
short distance scales far beyond the ones explored at the LHC is emphasized. In
this context the role of the Standard Model Effective Field Theory (SMEFT) is
very important. We also address briefly the issue of the footprints of Majorana
neutrinos in $K^+\rightarrow\pi^+\nu\bar\nu$ and
$K_L\rightarrow\pi^0\nu\bar\nu$.


title: Convective Penetration in Early-Type Stars

abstract:
  Observations indicate that the convective cores of stars must ingest a
substantial amount of material from the overlying radiative zone, but the
extent of this mixing and the mechanism which causes it remain uncertain.
Recently, Anders et al. (2021) developed a theory of convective penetration and
calibrated it with 3D numerical hydrodynamics simulations. Here we employ that
theory to predict the extent of convective boundary mixing in early-type
main-sequence stars. We find that convective penetration produces enough mixing
to explain core masses inferred from asteroseismology and eclipsing binary
studies, and matches observed trends in mass and age. While there are remaining
uncertainties in the theory, this agreement suggests that most convective
boundary mixing in early-type main-sequence stars arises from convective
penetration. Finally, we provide a fitting formula for the extent of core
convective penetration for main-sequence stars in the mass range from $1.1-60
M_\odot$.


title: Theory of anomalous full counting statistics in anisotropic spin chains

abstract:
  We present an analytic theory of the full counting statistics of spin
transport in the anisotropic one-dimensional XXZ spin chain. We consider a
setup in which the left and right half of the chain are initially created at
different magnetization densities, and consider the probability distribution of
the magnetization transferred between the two half-chains. We derive a
closed-form expression for the probability distribution of the magnetization
transfer, in terms of random walks on the half-line. Although the magnetization
transfer is diffusive on average, its distribution has a universal non-gaussian
form at late times. We show that this distribution strongly violates the
large-deviation principle, and explain the physical origin of this violation.
We discuss the crossovers that occur as the initial state is brought closer to
global equilibrium. Our predictions can directly be tested in experiments using
quantum gas microscopes or superconducting qubit arrays.

categories: cond-mat.stat-mech cond-mat.mes-hall cond-mat.quant-gas cond-mat.str-el quant-ph


title: Blazar nature of high-$z$ radio-loud quasars

abstract:
  We report on the Swift/XRT observation and classification of eleven blazar
candidates at $z>4$. These sources were selected as part of a sample of
extremely radio-loud quasars, in order to focus on quasars with jets oriented
roughly close to our line-of-sight. Deriving their viewing angles and their
jets bulk Lorentz factors was crucial for a strict blazar classification, and
it was possible only thanks to X-ray observations. Out of eleven sources, five
show strong and hard X-ray fluxes, that allow their blazar classification, two
are uncertain, three host relativistic jets that we observe just outside their
beaming cone (i.e. are not strictly blazars), while one went undetected by
Swift/XRT. Following this approach, we were able to trace the $>10^9M_\odot$
active supermassive black hole population hosted in jetted active galactic
nuclei. At $z\geq4$ the massive jetted sources are likely predominant in the
overall quasar population: this calls for a deep review of our understanding of
the first supermassive black holes formation and evolution. Jets are indeed key
actors in fast accretion, and must be searched for across the whole high
redshift quasar population. A note of caution must be added: radio-loudness and
in general radio features at high redshifts seem not to perfectly reflect
high-energy properties. A strong effect due to interaction with CMB radiation
is surely in place, that quenches the radio emission with respect to the
X-rays, but also more frequent occasions for the jet to be bent seem to play a
relevant role in this matter. Classifications and population studies thus must
be carefully performed, in order not to be confused by these inconsistencies.


title: Kinematic Structures in Planet-Forming Disks

abstract:
  The past 5 years have dramatically changed our view of the disks of gas and
dust around young stars. Observations with the Atacama Large
Millimeter/submillimeter Array (ALMA) and extreme adaptive optics systems have
revealed that disks are dynamical systems. Most disks contain resolved
structures, both in gas and dust, including rings, gaps, spirals, azimuthal
dust concentrations, shadows cast by misaligned inner disks, as well as
deviations from Keplerian rotation. The origin of these structures and how they
relate to the planet formation process remain poorly understood. Spatially
resolved kinematic studies offer a new and necessary window to understand and
quantify the physical processes (turbulence, winds, radial and meridional
flows, stellar multiplicity, instabilities) at play during planet formation and
disk evolution. Recent progress, driven mainly by resolved ALMA observations,
includes the detection and mass determination of embedded planets, the mapping
of the gas flow around the accreting planets, the confirmation of tidal
interactions and warped disk geometries, and stringent limits on the turbulent
velocities. In this chapter, we will review our current understanding of these
dynamical processes and highlight how kinematic mapping provides new ways to
observe planet formation in action.


title:
Asymptotic measurement schemes for every observable of a quantum field
  theory

abstract:
  In quantum measurement theory, a measurement scheme describes how an
observable of a given system can be measured indirectly using a probe. The
measurement scheme involves the specification of a probe theory, an initial
probe state, a probe observable and a coupling between the system and the
probe, so that a measurement of the probe observable after the coupling has
ceased reproduces (in expectation) the result of measuring the system
observable in the system state. Recent work has shown how local and causal
measurement schemes may be described in the context of model-independent
quantum field theory (QFT), but has not addressed the question of whether such
measurement schemes exist for all system observables. Here, we present two
treatments of this question. The first is a proof of principle which provides a
measurement scheme for every local observable of the quantized real linear
scalar field if one relaxes one of the conditions on a QFT measurement scheme
by allowing a non-compact coupling region. Secondly, restricting to compact
coupling regions, we explicitly construct asymptotic measurement schemes for
every local observable of the quantized theory. More precisely, we show that
for every local system observable $A$ there is an associated collection of
measurement schemes for system observables that converge to $A$. The
convergence holds in particular in GNS representations of suitable states on
the field algebra as well as the Weyl algebra. In this way, we show that every
observable can be asymptotically measured using locally coupled probe theories.


title: Quantum sine-Gordon dynamics in coupled spin chains

abstract:
  The sine-Gordon field theory emerges as the low-energy description in a
wealth of quantum many-body systems. Recent efforts have been directed towards
realizing quantum simulators of the model, by interfering two weakly coupled
one-dimensional cold atomic gases. The weak interactions within the atomic
clouds provide a sine-Gordon realization in the semiclassical regime.
Furthermore, the complex microscopic dynamics prevents a quantitative
understanding of the effective sine-Gordon validity realm. In this work, we
focus on a spin ladder realization and observe the emergent sine-Gordon
dynamics deep in the quantum regime. We use matrix-product state techniques to
numerically characterize the low-energy sector of the system and compare it
with the exact field theory predictions. From this comparison, we obtain
quantitative boundaries for the validity of the sine-Gordon description. We
provide encompassing evidence for the emergent field theory by probing its rich
spectrum and by observing the signatures of integrable dynamics in scattering
events.

categories: cond-mat.str-el cond-mat.quant-gas cond-mat.stat-mech quant-ph


title:
Daily-activity-dependency of effective reproduction number in COVID-19
  pandemic: direct modelling from GPS data

abstract:
  During the COVID-19 pandemic, governments faced difficulties in implementing
mobility restriction measures, as no clear quantitative relationship between
human mobility and infection spread in large cities is known. We developed a
model that enables quantitative estimations of the infection risk for
individual places and activities by using smartphone GPS data for the Tokyo
metropolitan area. The effective reproduction number is directly calculated
from the number of infectious social contacts defined by the square of the
population density at each location. The difference in the infection rate of
daily activities is considered, where the `stay-out' activity, staying at
someplace neither home nor workplace, is more than 28 times larger than other
activities. Also, the contribution to the infection strongly depends on
location. We imply that the effective reproduction number is sufficiently
suppressed if the highest-risk locations or activities are restricted. We also
discuss the effects of the Delta variant and vaccination.


title: On Type IIA AdS$_3$ solutions and massive GK geometries

abstract:
  We give necessary and sufficient conditions for warped AdS$_3$ (and Mink$_3$)
solutions of Type II supergravities to preserve ${\cal N}=(2,0)$ supersymmetry,
in terms of geometric conditions on their internal space M$_7$. Such solutions
possess a canonical $d=10$ Killing vector that can be either time-like or null.
In this work we classify the null case in massive Type IIA supergravity which
necessitates that M$_7$ decomposes as a circle fibration over a six-dimensional
base with orthogonal SU(2)-structure containing a complex four-manifold. We
narrow our focus to solutions for which M$_7$ becomes $\mathbb{T}^2$ fibred
over a foliation of a K\"{a}hler manifold over an interval. We find a class of
solutions which are the massive Type IIA version of GK geometries and present
an extremal problem which computes the central charge of the solution using
just topology. Finally, we present geometric conditions for AdS$_3$ solutions
to preserve arbitrary extended chiral supersymmetry.


title: Graded Generalized Geometry

abstract:
  Generalized geometry finds many applications in the mathematical description
of some aspects of string theory. In a nutshell, it explores various structures
on a generalized tangent bundle associated to a given manifold. In particular,
several integrability conditions can be formulated in terms of a canonical
Dorfman bracket, an example of Courant algebroid. On the other hand, smooth
manifolds can be generalized to involve functions of $\mathbb{Z}$-graded
variables which do not necessarily commute. This leads to a mathematical theory
of graded manifolds. It is only natural to combine the two theories by
exploring the structures on a generalized tangent bundle associated to a given
graded manifold.
  After recalling elementary graded geometry, graded Courant algebroids on
graded vector bundles are introduced. We show that there is a canonical bracket
on a generalized tangent bundle associated to a graded manifold. Graded
analogues of Dirac structures and generalized complex structures are explored.
We introduce differential graded Courant algebroids which can be viewed as a
generalization of Q-manifolds. A definition and examples of graded Lie
bialgebroids are given.


title: Field redefinitions, perturbative unitarity and Higgs inflation

abstract:
  For inflation driven by the Higgs field coupled non-minimally to gravity, we
study the cutoff energy scale above which perturbation theory breaks down.
Employing the metric formulation, we first give an overview of known results
and then provide a new way to calculate a lower bound on the cutoff. Our
approach neither relies on a gauge choice nor does it require any calculation
of amplitudes. Instead, it exploits the fact that the S-matrix is invariant
under field redefinitions. In agreement with previous findings, we demonstrate
that the cutoff is significantly higher during inflation than in vacuum, which
ensures the robustness of semi-classical predictions. Along the way, we
generalize our findings to the Palatini formulation and comment on a useful
parametrization of the Higgs doublet in both scenarios.


title:
Quantum Oscillations in the Magnetisation and Density of States of
  Insulators

abstract:
  The observation of $1/B$-periodic behavior in Kondo insulators SmB$_6$ and
YbB$_{12}$ challenges the conventional wisdom that quantum oscillations (QO)
necessarily arise from Fermi surfaces in metals. We revisit recently proposed
theories for this phenomena, focusing on a minimal model of an insulator with a
hybridization gap between two opposite-parity light and heavy mass bands with
an inverted band structure. We show that there are characteristic differences
between the QO frequencies in the magnetization and the low-energy density of
states (LE-DOS) of these insulators, in marked contrast with metals where all
observables exhibit oscillations at the same frequency. The magnetization
oscillations are shown to arise from all occupied Landau levels and exhibit the
same frequency as the unhybridized case. The LE-DOS oscillations arise from
gap-edge states in a disorder-free system and exhibit a beat pattern between
two distinct frequencies at low temperature. Disorder induced in-gap states
lead to an additional contribution to the DOS at the unhybridized frequency.
The temperature dependence of the magnetization and DOS oscillations are
qualitatively different and both show marked deviations from the
Lifshitz-Kosevich form. We also compute transport to ensure that we are probing
a regime with insulating upturns in the dc resistivity.


title:
Developing a Victorious Strategy to the Second Strong Gravitational
  Lensing Data Challenge

abstract:
  Strong Lensing is a powerful probe of the matter distribution in galaxies and
clusters and a relevant tool for cosmography. Analyses of strong gravitational
lenses with Deep Learning have become a popular approach due to these
astronomical objects' rarity and image complexity. Next-generation surveys will
provide more opportunities to derive science from these objects and an
increasing data volume to be analyzed. However, finding strong lenses is
challenging, as their number densities are orders of magnitude below those of
galaxies. Therefore, specific Strong Lensing search algorithms are required to
discover the highest number of systems possible with high purity and low false
alarm rate. The need for better algorithms has prompted the development of an
open community data science competition named Strong Gravitational Lensing
Challenge (SGLC). This work presents the Deep Learning strategies and
methodology used to design the highest-scoring algorithm in the II SGLC. We
discuss the approach used for this dataset, the choice for a suitable
architecture, particularly the use of a network with two branches to work with
images in different resolutions, and its optimization. We also discuss the
detectability limit, the lessons learned, and prospects for defining a
tailor-made architecture in a survey in contrast to a general one. Finally, we
release the models and discuss the best choice to easily adapt the model to a
dataset representing a survey with a different instrument. This work helps to
take a step towards efficient, adaptable and accurate analyses of strong lenses
with deep learning frameworks.


title: Factorization and Global Symmetries in Holography

abstract:
  We consider toy models of holography arising from 3d Chern-Simons theory. In
this context a duality to an ensemble average over 2d CFTs has been recently
proposed. We put forward an alternative approach in which, rather than summing
over bulk geometries, one gauges a one-form global symmetry of the bulk theory.
This accomplishes two tasks: it ensures that the bulk theory has no global
symmetries, as expected for a theory of quantum gravity, and it makes the
partition function on spacetimes with boundaries coincide with that of a
modular-invariant 2d CFT on the boundary. In particular, on wormhole geometries
one finds a factorized answer for the partition function. In the case of
non-Abelian Chern-Simons theories, the relevant one-form symmetry is
non-invertible, and its gauging corresponds to the condensation of a Lagrangian
anyon.


title:
Constraining ultra-high-energy cosmic ray composition through
  cross-correlations

abstract:
  The chemical composition of the highest end of the ultra-high-energy cosmic
ray spectrum is very hard to measure experimentally, and to this day it remains
mostly unknown. Since the trajectories of ultra-high-energy cosmic rays are
deflected in the magnetic field of the Galaxy by an angle that depends on their
atomic number $Z$, it could be possible to indirectly measure $Z$ by
quantifying the amount of such magnetic deflections. In this paper we show
that, using the angular harmonic cross-correlation between ultra-high-energy
cosmic rays and galaxies, we could effectively distinguish different atomic
numbers with current data. As an example, we show how, if $Z=1$, the
cross-correlation can exclude a $39\%$ fraction of Fe56 nuclei at $2\sigma$ for
rays above $100\text{EeV}$.


title:
The MOSDEF-LRIS Survey: Connection between Galactic-scale Outflows and
  the Properties of $z$~2 star-forming Galaxies

abstract:
  We investigate the conditions that facilitate galactic-scale outflows using a
sample of 155 typical star-forming galaxies at $z$~2 drawn from the MOSFIRE
Deep Evolution Field (MOSDEF) survey. The sample includes deep rest-frame UV
spectroscopy from the Keck Low-Resolution Imaging Spectrometer (LRIS), which
provides spectral coverage of several low-ionisation interstellar (LIS) metal
absorption lines and Ly$\alpha$ emission. Outflow velocities are calculated
from the centroids of the LIS absorption and/or Ly$\alpha$ emission, as well as
the highest-velocity component of the outflow from the blue wings of the LIS
absorption lines. Outflow velocities are found to be marginally correlated or
independent of galaxy properties, such as star-formation rate (SFR) and
star-formation rate surface density ($\Sigma_{\rm SFR}$). Outflow velocity
scales with SFR as a power-law with index 0.24, which suggests that the
outflows may be primarily driven by mechanical energy generated by supernovae
explosions, as opposed to radiation pressure acting on dusty material. On the
other hand, outflow velocity and $\Sigma_{\rm SFR}$ are not significantly
correlated, which may be due to the limited dynamic range of $\Sigma_{\rm SFR}$
probed by our sample. The relationship between outflow velocity and
$\Sigma_{\rm SFR}$ normalised by stellar mass ($\Sigma_{\rm sSFR}$), as a proxy
for gravitational potential, suggests that strong outflows (e.g., > 200 km
s$^{-1}$) appear ubiquitous above a threshold of log($\Sigma_{\rm
sSFR}/\rm{yr}^{-1}\ \rm{kpc}^{-2}$) ~ -11.3, and that above this threshold,
outflow velocity uncouples from $\Sigma_{\rm sSFR}$. These results highlight
the need for higher resolution spectroscopic data and spatially resolved
imaging to test the driving mechanisms of outflows predicted by theory.


title:
LSST Cadence Strategy Evaluations for AGN Time-series Data in
  Wide-Fast-Deep Field

abstract:
  Machine learning is a promising tool to reconstruct time-series phenomena,
such as variability of active galactic nuclei (AGN), from sparsely-sampled
data. Here we use three Continuous Auto-Regressive Moving Average (CARMA)
representations of AGN variability -- the Damped Random Walk (DRW) and
(over/under-)Damped Harmonic Oscillator (DHO) -- to simulate 10-year AGN light
curves as they would appear in the upcoming Vera Rubin Observatory Legacy
Survey of Space and Time (LSST), and provide a public tool to generate these
for any survey cadence. We investigate the impact on AGN science of five
proposed cadence strategies for LSST's primary Wide-Fast-Deep (WFD) survey. We
apply for the first time in astronomy a novel Stochastic Recurrent Neural
Network (SRNN) algorithm to reconstruct input light curves from the simulated
LSST data, and provide a metric to evaluate how well SRNN can help recover the
underlying CARMA parameters. We find that the light curve reconstruction is
most sensitive to the duration of gaps between observing season, and that of
the proposed cadences, those that change the balance between filters, or avoid
having long gaps in the {g}-band perform better. Overall, SRNN is a promising
means to reconstruct densely sampled AGN light curves and recover the long-term
Structure Function of the DRW process (SF$_\infty$) reasonably well. However,
we find that for all cadences, CARMA/SRNN models struggle to recover the
decorrelation timescale ($\tau$) due to the long gaps in survey observations.
This may indicate a major limitation in using LSST WFD data for AGN variability
science.

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


title:
Multiparameter quantum metrology in the Heisenberg Limit regime: many
  repetition scenario vs. full optimization

abstract:
  We discuss the Heisenberg limit in the multiparameter metrology within two
different paradigms -- the one, where the measurement is repeated many times
(so the Cram\'er-Rao bound is guaranteed to be asymptotically saturable) and
the second one, where all the resources are allocated into one experimental
realization (analyzed with the mimimax approach). We investigate the potential
advantage of measuring all the parameter simultaneously compared to estimating
them individually, while spending the same total amount of resources. We show
that in general the existence of such an advantage, its magnitude and
conditions under which it occurs depends on which of the two paradigms has been
chosen. In particular, for the problem of magnetic field sensing using $N$
entangled spin-$1/2$, we show that the predictions based purely on the
Cram\'er-Rao formalism may be overly pessimistic in this matter -- the minimax
approach reveals the superiority of measuring all the parameters jointly
whereas the Cram\'er-Rao approach indicates lack of such an advantage.


title:
Quasi-isogeny groups of supersingular abelian surfaces via pro-\'etale
  fundamental groups

abstract:
  We consider a $J_b(\mathbb{Q}_p)$-torsor on the supersingular locus of the
Siegel threefold constructed by Caraiani-Scholze, and show that it induces an
isomorphism between a free group on a finite number of generators, and the
group of self-quasi-isogenies of a supersingular abelian surface, respecting a
principal polarization and a prime-to-$p$ level structure. Along the way, we
classify certain pro-\'etale torsors in terms of the pro-\'etale fundamental
group, describe the category of geometric covers of non-normal schemes, and use
this to compute pro-\'etale fundamental groups of curves.


title:
An alternative, dynamic density functional-like theory for
  time-dependent density fluctuations in glass-forming fluids

abstract:
  We propose an alternative theory for the relaxation of density fluctuations
in glass-forming fluids. We derive an equation of motion for the density
correlation function which is local in time and is similar in spirit to the
equation of motion for the average non-uniform density profile derived within
the dynamic density functional theory. We identify the Franz-Parisi free energy
functional as the non-equilibrium free energy for the evolution of the density
correlation function. An appearance of a local minimum of this functional leads
to a dynamic arrest. Thus, the ergodicity breaking transition predicted by our
theory coincides with the dynamic transition of the static approach based on
the same non-equilibrium free energy functional.


title:
Revisiting the Solar Research Cyberinfrastructure Needs: A White Paper
  of Findings and Recommendations

abstract:
  Solar and Heliosphere physics are areas of remarkable data-driven
discoveries. Recent advances in high-cadence, high-resolution multiwavelength
observations, growing amounts of data from realistic modeling, and operational
needs for uninterrupted science-quality data coverage generate the demand for a
solar metadata standardization and overall healthy data infrastructure. This
white paper is prepared as an effort of the working group "Uniform Semantics
and Syntax of Solar Observations and Events" created within the "Towards
Integration of Heliophysics Data, Modeling, and Analysis Tools" EarthCube
Research Coordination Network (@HDMIEC RCN), with primary objectives to discuss
current advances and identify future needs for the solar research
cyberinfrastructure. The white paper summarizes presentations and discussions
held during the special working group session at the EarthCube Annual Meeting
on June 19th, 2020, as well as community contribution gathered during a series
of preceding workshops and subsequent RCN working group sessions. The authors
provide examples of the current standing of the solar research
cyberinfrastructure, and describe the problems related to current data handling
approaches. The list of the top-level recommendations agreed by the authors of
the current white paper is presented at the beginning of the paper.


title: Third law of thermodynamics and the scaling of quantum computers

abstract:
  The third law of thermodynamics, also known as the Nernst unattainability
principle, puts a fundamental bound on how close a system, whether classical or
quantum, can be cooled to a temperature near to absolute zero. On the other
hand, a fundamental assumption of quantum computing is to start each
computation from a register of qubits initialized in a pure state, i.e. at zero
temperature. These conflicting aspects, at the interface between quantum
computing and thermodynamics, are often overlooked or, at best, addressed only
at a single-qubit level. In this work, we argue how the existence of a small,
but finite, effective temperature, which makes the initial state a mixed state,
poses a real challenge to the fidelity constraints required for the scaling of
quantum computers. Our theoretical results, carried out for a generic quantum
circuit with $N$-qubit input states, are validated by experiments performed on
a real quantum processor.


title: Parity-time symmetric optical neural networks

abstract:
  Optical neural networks (ONNs), implemented on an array of cascaded
Mach-Zehnder interferometers (MZIs), have recently been proposed as a possible
replacement for conventional deep learning hardware. They potentially offer
higher energy efficiency and computational speed when compared to their
electronic counterparts. By utilizing tunable phase shifters, one can adjust
the output of each of MZIs in order to enable emulation of arbitrary
matrix-vector multiplication. These phase shifters are central to the
programmability of ONNs, but they require large footprint and are relatively
slow. Here we propose an ONN architecture that utilizes parity-time (PT)
symmetric couplers as its building blocks. Instead of modulating phase,
gain/loss contrasts across the array are adjusted as a means to train the
network. We demonstrate that PT symmetric optical neural networks (PT-ONN) are
adequately expressive by performing the digit-recognition task on the modified
national institute of standard and technology (MNIST) dataset. Compared to
conventional ONNs, the PT-ONN achieves a comparable accuracy (67% vs. 71%)
while circumventing the problems associated with changing phase. Our approach
may lead to new and alternative avenues for fast training in chip-scale optical
neural networks.


title:
Next-to-leading order scalar contributions to $\mu\rightarrow e$
  conversion

abstract:
  Within a class of models in which lepton flavor violation is induced
dominantly by scalar particle exchanges, we estimate the $\mu \to e$ conversion
rate in several nuclei. We include next-to-leading order (NLO) terms in the
one- and two-nucleon interactions in chiral effective theory, rectifying some
incorrect results in the previous literature. We provide an uncertainty budget
for the conversion rates and we find that NLO contributions affect the
amplitudes at the level of $10\%$, which could be larger than the uncertainty
on the leading order couplings, dominated by the strange and non-strange
nucleon sigma terms. We study the implications of our results for testing
Higgs-mediated CLFV in the future by combining results from various
experimental searches, such as $\mu \to e$ conversion in multiple target nuclei
and $\mu \to e \gamma$.


title:
Maintenance Problem of Insufficiently Financed Pension Funds -- A
  Stochastic Approach

abstract:
  The generic case of pensions fund that it is not sufficiently auto financed
and it is thoroughly maintained with an external financing effort is considered
in this chapter. To represent the unrestricted reserves value process of this
kind of funds, a time homogeneous diffusion stochastic process with finite
expected time to ruin is proposed. Then it is projected a financial tool that
regenerates the diffusion at some level with positive value every time the
diffusion hits a barrier placed at the origin. So, the financing effort can be
modeled as a renewal-reward process if the regeneration level is preserved
constant. The perpetual maintenance cost expected values and the finite time
maintenance cost evaluations are studied. An application of this approach when
the unrestricted reserves value process behaves as a generalized Brownian
motion process is presented.


title: Human Gait Analysis using Gait Energy Image

abstract:
  Gait recognition is one of the most recent emerging techniques of human
biometric which can be used for security based purposes having unobtrusive
learning method. In comparison with other bio-metrics gait analysis has some
special security features. Most of the biometric technique uses sequential
template based component analysis for recognition. Comparing with those
methods, we proposed a developed technique for gait identification using the
feature Gait Energy Image (GEI). GEI representation of gait contains all
information of each image in one gait cycle and requires less storage and low
processing speed. As only one image is enough to store the necessary
information in GEI feature recognition process is very easier than any other
feature for gait recognition. Gait recognition has some limitations in
recognition process like viewing angle variation, walking speed, clothes,
carrying load etc. Our proposed method in the paper compares the recognition
performance with template based feature extraction which needs to process each
frame in the cycle. We use GEI which gives relatively all information about all
the frames in the cycle and results in better performance than other feature of
gait analysis.


title: Multi-similarity based Hyperrelation Network for few-shot segmentation

abstract:
  Few-shot semantic segmentation aims at recognizing the object regions of
unseen categories with only a few annotated examples as supervision. The key to
few-shot segmentation is to establish a robust semantic relationship between
the support and query images and to prevent overfitting. In this paper, we
propose an effective Multi-similarity Hyperrelation Network (MSHNet) to tackle
the few-shot semantic segmentation problem. In MSHNet, we propose a new
Generative Prototype Similarity (GPS), which together with cosine similarity
can establish a strong semantic relation between the support and query images.
The locally generated prototype similarity based on global feature is logically
complementary to the global cosine similarity based on local feature, and the
relationship between the query image and the supported image can be expressed
more comprehensively by using the two similarities simultaneously. In addition,
we propose a Symmetric Merging Block (SMB) in MSHNet to efficiently merge
multi-layer, multi-shot and multi-similarity hyperrelational features. MSHNet
is built on the basis of similarity rather than specific category features,
which can achieve more general unity and effectively reduce overfitting. On two
benchmark semantic segmentation datasets Pascal-5i and COCO-20i, MSHNet
achieves new state-of-the-art performances on 1-shot and 5-shot semantic
segmentation tasks.


title:
Reconstruction of small and extended regions in EIT with a Robin
  transmission condition

abstract:
  We consider an inverse shape problem coming from electrical impedance
tomography with a Robin transmission condition. In general, a boundary
condition of Robin type models corrosion. In this paper, we study two methods
for recovering an interior corroded region from electrostatic data. We consider
the case where we have small volume and extended regions. For the case where
the region has small volume, we will derive an asymptotic expansion of the
current gap operator and prove that a MUSIC-type algorithm can be used to
recover the region. In the case where one has an extended region, we will show
that the regularized factorization method can be used to recover said region.
Numerical examples will be presented for both cases in two dimensions in the
unit circle.


title:
Extremal Event Graphs: A (Stable) Tool for Analyzing Noisy Time Series
  Data

abstract:
  Local maxima and minima, or extremal events, in experimental time series can
be used as a coarse summary to characterize data. However, the discrete
sampling in recording experimental measurements suggests uncertainty on the
true timing of extrema during the experiment. This in turn gives uncertainty in
the timing order of extrema within the time series. Motivated by applications
in genomic time series and biological network analysis, we construct a weighted
directed acyclic graph (DAG) called an extremal event DAG using techniques from
persistent homology that is robust to measurement noise. Furthermore, we define
a distance between extremal event DAGs based on the edit distance between
strings. We prove several properties including local stability for the extremal
event DAG distance with respect to pairwise $L_{\infty}$ distances between
functions in the time series data. Lastly, we provide algorithms, publicly free
software, and implementations on extremal event DAG construction and
comparison.


title:
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving
  Relation Embedding Aggregation

abstract:
  Federated Learning (FL) on knowledge graphs (KGs) has yet to be as well
studied as other domains, such as computer vision and natural language
processing. A recent study FedE first proposes an FL framework that shares
entity embeddings of KGs across all clients. However, compared with model
sharing in vanilla FL, entity embedding sharing from FedE would incur severe
privacy leakage. Specifically, the known entity embedding can be used to infer
whether a specific relation between two entities exists in a private client. In
this paper, we first develop a novel attack that aims to recover the original
data based on embedding information, which is further used to evaluate the
vulnerabilities of FedE. Furthermore, we propose a Federated learning paradigm
with privacy-preserving Relation embedding aggregation (FedR) to tackle the
privacy issue in FedE. Compared to entity embedding sharing, relation embedding
sharing policy can significantly reduce the communication cost due to its
smaller size of queries. We conduct extensive experiments to evaluate FedR with
five different embedding learning models and three benchmark KG datasets.
Compared to FedE, FedR achieves similar utility and significant (nearly 2X)
improvements in both privacy and efficiency on link prediction task.


title: CoGS: Controllable Generation and Search from Sketch and Style

abstract:
  We present CoGS, a novel method for the style-conditioned, sketch-driven
synthesis of images. CoGS enables exploration of diverse appearance
possibilities for a given sketched object, enabling decoupled control over the
structure and the appearance of the output. Coarse-grained control over object
structure and appearance are enabled via an input sketch and an exemplar
"style" conditioning image to a transformer-based sketch and style encoder to
generate a discrete codebook representation. We map the codebook representation
into a metric space, enabling fine-grained control over selection and
interpolation between multiple synthesis options for a given image before
generating the image via a vector quantized GAN (VQGAN) decoder. Our framework
thereby unifies search and synthesis tasks, in that a sketch and style pair may
be used to run an initial synthesis which may be refined via combination with
similar results in a search corpus to produce an image more closely matching
the user's intent. We show that our model, trained on the 125 object classes of
our newly created Pseudosketches dataset, is capable of producing a diverse
gamut of semantic content and appearance styles.


title:
On the expressive power of message-passing neural networks as global
  feature map transformers

abstract:
  We investigate the power of message-passing neural networks (MPNNs) in their
capacity to transform the numerical features stored in the nodes of their input
graphs. Our focus is on global expressive power, uniformly over all input
graphs, or over graphs of bounded degree with features from a bounded domain.
Accordingly, we introduce the notion of a global feature map transformer
(GFMT). As a yardstick for expressiveness, we use a basic language for GFMTs,
which we call MPLang. Every MPNN can be expressed in MPLang, and our results
clarify to which extent the converse inclusion holds. We consider exact versus
approximate expressiveness; the use of arbitrary activation functions; and the
case where only the ReLU activation function is allowed.


title: Bootstrability in Defect CFT: Integrated Correlators and Sharper Bounds

abstract:
  We continue to develop Bootstrability -- a method merging Integrability and
Conformal Bootstrap to extract CFT data in integrable conformal gauge theories
such as $\mathcal{N}$=4 SYM. In this paper, we consider the 1D defect CFT
defined on a $\frac{1}{2}$-BPS Wilson line in the theory, whose
non-perturbative spectrum is governed by the Quantum Spectral Curve (QSC). In
addition, we use that the deformed setup of a cusped Wilson line is also
controlled by the QSC. In terms of the defect CFT, this translates into two
nontrivial relations connecting integrated 4-point correlators to cusp spectral
data, such as the Bremsstrahlung and Curvature functions -- known analytically
from the QSC. Combining these new constraints and the spectrum of the $10$
lowest-lying states with the Numerical Conformal Bootstrap, we obtain very
sharp rigorous numerical bounds for the structure constant of the first
non-protected state, giving this observable with seven digits precision for the
't Hooft coupling in the intermediate coupling region
$\frac{\sqrt{\lambda}}{4\pi}\sim 1$, with the error decreasing quickly at large
't Hooft coupling. Furthermore, for the same structure constant we obtain a
$4$-loop analytic result at weak coupling. We also present results for excited
states.


title: Outcome Assumptions and Duality Theory for Balancing Weights

abstract:
  We study balancing weight estimators, which reweight outcomes from a source
population to estimate missing outcomes in a target population. These
estimators minimize the worst-case error by making an assumption about the
outcome model. In this paper, we show that this outcome assumption has two
immediate implications. First, we can replace the minimax optimization problem
for balancing weights with a simple convex loss over the assumed outcome
function class. Second, we can replace the commonly-made overlap assumption
with a more appropriate quantitative measure, the minimum worst-case bias.
Finally, we show conditions under which the weights remain robust when our
assumptions on the outcomes are wrong.


title:
Investigating finite-size effects in molecular dynamics simulations of
  ion diffusion, heat transport, and thermal motion in superionic materials

abstract:
  The effects of the finite size of the simulation box in equilibrium molecular
dynamics simulations are investigated for prototypical superionic conductors of
different types, namely the fluorite-structure materials PbF2, CaF2, and UO2
(type-II), and the {\alpha} phase of AgI (type I). Largely validated empirical
force-fields are employed to run ns-long simulations and extract general trends
for several properties, at increasing size and in a wide temperature range.
This work shows that, for the considered type-II superionic conductors, the
diffusivity dramatically depends on the system size and that the superionic
regime is shifted to larger temperatures in smaller cells. Furthermore, only
simulations of several hundred atoms are able to capture the
experimentally-observed, characteristic change in the activation energy of the
diffusion process, occurring at the order-disorder transition to the superionic
regime. Finite-size effects on ion diffusion are instead much weaker in
{\alpha}-AgI. The thermal conductivity is found generally smaller for smaller
cells, where the temperature-independent (Allen-Feldman) regime is also reached
at significantly lower temperatures. The finite-size effects on the thermal
motion of the non-mobile ions composing the solid matrix follow the simple law
which holds for solids.


title: Picard rank jumps for K3 surfaces with bad reduction

abstract:
  Let $X$ be a K3 surface over a number field. We prove that $X$ has infinitely
many specializations where its Picard rank jumps, hence extending our previous
work with Shankar--Shankar--Tang to the case where $X$ might have potentially
bad reduction. We prove a similar result for generically ordinary
non-isotrivial families of K3 surfaces over curves over
$\overline{\mathbb{F}}_p$ which extends previous work of Maulik--Shankar--Tang.
As a consequence, we give a new proof of the ordinary Hecke orbit conjecture
for orthogonal and unitary Shimura varieties.


title: Quantum Annealing with Special Drivers for Circuit Fault Diagnostics

abstract:
  We present a very general construction for quantum annealing protocols to
solve Combinational Circuit Fault Diagnosis (CCFD) problems that restricts the
evolution to the space of valid diagnoses. This is accomplished by using
special local drivers that induce a transition graph on the space of feasible
configurations that is regular and instance independent for each given circuit
topology. Analysis of small instances shows that the energy gap has a generic
form, and that the minimum gap occurs in the last third of the evolution. We
used these features to construct an improved annealing schedule and benchmarked
its performance through closed system simulations. We found that degeneracy can
help the performance of quantum annealing, especially for instances with a
higher number of faults in their minimum fault diagnosis. This contrasts with
the performance of classical approaches based on brute force search that are
used in industry for large scale circuits.


title: Adding Flavor to the SMEFT

abstract:
  We study the flavor structure of the lepton and baryon number--conserving
dimension-6 operators in the Standard Model effective field theory (SMEFT).
Building on the work of [1], we define several well-motivated flavor symmetries
and symmetry breaking patterns that serve as competing hypotheses about the
ultraviolet (UV) dynamics beyond the SM, not far above the TeV scale. In
particular, we consider four different structures in the quark sector and seven
in the charged lepton sector. The set of flavor-breaking spurions is (almost)
always taken to be the minimal one needed to reproduce the observed charged
fermion masses and mixings. For each case, we explicitly construct and count
the operators to the first few orders in the spurion expansion, providing
ready-for-use setups for phenomenological studies and global fits. We provide a
Mathematica package SMEFTflavor to facilitate similar analyses for flavor
symmetries not covered in this work.


title:
Initial Conditions for Star Formation: A Physical Description of the
  Filamentary ISM

abstract:
  The interstellar medium contains filamentary structure over a wide range of
scales. Understanding the role of this structure, both as a conduit of gas
across the scales and a diagnostic tool of local physics, is a major focus of
star formation studies. We review recent progress in studying filamentary
structure in the ISM, interpreting its properties in terms of physical
processes, and exploring formation and evolution scenarios. We include
structures from galactic-scale filaments to tenth-of-a-parsec scale filaments,
comprising both molecular and atomic structures, from both observational and
theoretical perspectives. In addition to the literature overview, we assemble a
large amount of catalogue data from different surveys and provide the most
comprehensive census of filamentary structures to date. Our census consists of
22 803 filamentary structures, facilitating a holistic perspective and new
insights. We use our census to conduct a meta-analysis, leading to a
description of filament properties over four orders of magnitudes in length and
eight in mass. Our analysis emphasises the hierarchical and dynamical nature of
filamentary structures. Filaments do not live in isolation, nor they generally
resemble static structures close to equilibrium. We propose that accretion
during filament formation and evolution sets some of the key scaling properties
of filaments. This highlights the role of accretion during filament formation
and evolution and also in setting the initial conditions for star formation.
Overall, the study of filamentary structures during the past decade has been
observationally driven. While great progress has been made on measuring the
basic properties of filaments, our understanding of their formation and
evolution is clearly lacking. In this context, we identify a number of
directions and questions we consider most pressing for the field.


title: Ulam floating functions

abstract:
  We extend the notion of Ulam floating sets from convex bodies to Ulam
floating functions. We use the Ulam floating functions to derive a new
variational formula for the affine surface area of log-concave functions.


title:
Raman and Infrared Spectroscopy For Phase Identification and Strain
  Calibration of HfO$_{2}$

abstract:
  Using density functional perturbation theory (DFPT) we computed the phonon
frequencies, Raman and IR activities of hafnia polymorphs (P4$_{2}$nmc,
Pca2$_{1}$, Pmn2$_{1}$, Pbca OI, brookite, and baddeleyite) for phase
identification. We investigated the evolution of Raman and IR activities with
respect to epitaxial strain and provide plots of frequency differences as a
function of strain for experimental calibration and identification of the
strain state of the sample. We found Raman signatures of different hafnia
polymorphs: $\omega(A_{1g})=300$ cm$^{-1}$ for P4$_{2}$nmc, $\omega(A_{1})=343$
cm$^{-1}$ for Pca2$_{1}$, $\omega(B_{2})=693$ cm$^{-1}$ for Pmn2$_{1}$,
$\omega(A_{g})=513$ cm$^{-1}$ for Pbca (OI), $\omega(A_{g})=384$ cm$^{-1}$ for
brookite, and $\omega (A_{g}) = 496$ cm$^{-1}$ for baddeleyite. We also
identified the Raman peak $B_{1g}$ mode, an anti-phase vibration of dipole
moments, ( $\omega(B_{1g}) = 758$ cm$^{-1}$ for OI, $\omega (B_{1g})= 784$
cm$^{-1}$ for brookite) as the Raman signature of antiferroelectric Pbca
structures. We calculated a large splitting between longitudinal optical (LO)
and transverse optical (TO) modes
($\Delta{\omega_{\text{LO-TO}}(A^{z}_{1})}=255$ cm$^{-1}$ in Pca2$_{1}$, and
$\Delta{\omega_{\text{LO-TO}}(A_{1})}=263$ cm$^{-1}$ in Pmn2$_{1}$) to the same
order as those observed in perovskite ferroelectrics, and related them to the
anomalously large Born effective charges of Hf atoms ($Z^{*}(\text{Hf}) =
5.54$).


title:
Strategic Maneuver and Disruption with Reinforcement Learning Approaches
  for Multi-Agent Coordination

abstract:
  Reinforcement learning (RL) approaches can illuminate emergent behaviors that
facilitate coordination across teams of agents as part of a multi-agent system
(MAS), which can provide windows of opportunity in various military tasks.
Technologically advancing adversaries pose substantial risks to a friendly
nation's interests and resources. Superior resources alone are not enough to
defeat adversaries in modern complex environments because adversaries create
standoff in multiple domains against predictable military doctrine-based
maneuvers. Therefore, as part of a defense strategy, friendly forces must use
strategic maneuvers and disruption to gain superiority in complex multi-faceted
domains such as multi-domain operations (MDO). One promising avenue for
implementing strategic maneuver and disruption to gain superiority over
adversaries is through coordination of MAS in future military operations. In
this paper, we present overviews of prominent works in the RL domain with their
strengths and weaknesses for overcoming the challenges associated with
performing autonomous strategic maneuver and disruption in military contexts.


title:
Leveraging Adversarial Examples to Quantify Membership Information
  Leakage

abstract:
  The use of personal data for training machine learning systems comes with a
privacy threat and measuring the level of privacy of a model is one of the
major challenges in machine learning today. Identifying training data based on
a trained model is a standard way of measuring the privacy risks induced by the
model. We develop a novel approach to address the problem of membership
inference in pattern recognition models, relying on information provided by
adversarial examples. The strategy we propose consists of measuring the
magnitude of a perturbation necessary to build an adversarial example. Indeed,
we argue that this quantity reflects the likelihood of belonging to the
training data. Extensive numerical experiments on multivariate data and an
array of state-of-the-art target models show that our method performs
comparable or even outperforms state-of-the-art strategies, but without
requiring any additional training samples.


title:
Distributed Estimation in Large Scale Wireless Sensor Networks via a Two
  Step Group-based Approach

abstract:
  We consider the problem of collaborative distributed estimation in a large
scale sensor network with statistically dependent sensor observations. In
collaborative setup, the aim is to maximize the overall estimation performance
by modeling the underlying statistical dependence and efficiently utilizing the
deployed sensors. To achieve greater sensor transmission and estimation
efficiency, we propose a two step group-based collaborative distributed
estimation scheme, where in the first step, sensors form dependence driven
groups such that sensors in the same group are highly dependent, while sensors
from different groups are independent, and perform a copula-based maximum a
posteriori probability (MAP) estimation via intragroup collaboration. In the
second step, the estimates generated in the first step are shared via
inter-group collaboration to reach an average consensus. A merge based K-medoid
dependence driven grouping algorithm is proposed. Moreover, we further propose
a group-based sensor selection scheme using mutual information prior to the
estimation. The aim is to select sensors with maximum relevance and minimum
redundancy regarding the parameter of interest under certain pre-specified
energy constraint. Also, the proposed group-based sensor selection scheme is
shown to be equivalent to the global/non-group based selection scheme with high
probability, but computationally more efficient. Numerical experiments are
conducted to demonstrate the effectiveness of our approach.


title: Phase separation and aging dynamics of binary liquid in porous media

abstract:
  We employ the state-of-the-art molecular dynamics simulations to study the
kinetics of phase separation and aging phenomena of segregating binary fluid
mixtures imbibed in porous materials. Different random porous structures are
considered to understand the effect of pore morphology on coarsening dynamics.
We find the effect of complex geometrical confinement resulting in the dramatic
slowing down in the phase separation dynamics. The domain growth follows the
power law with an exponent dependent on the porous host structure. After the
transient period, a crossover to a slower domain growth is observed when the
domain size becomes comparable to the pore size. Due to the geometric
confinement, the correlation function and structure factor modify to a
non-Porod behavior and violate the superuniversality hypothesis. The role of
porous host structure on the nonequilibrium aging dynamics is studied
qualitatively by computing the two-time order-parameter autocorrelation
function. This quantity exhibits scaling laws with respect to the ratio of the
domain length at the observation time and the age of the system. We find the
scaling laws hold good for such confined segregating fluid mixtures.


title: Impact of Electron Transport Models on Capillary Discharge Plasmas

abstract:
  Magnetohydrodynamics (MHD) can be used to model capillary discharge
waveguides in laser-wakefield accelerators. However, the predictive capability
of MHD can suffer due to poor microscopic closure models. Here, we study the
impact of electron heating and thermal conduction on capillary waveguide
performance as part of an effort to understand and quantify uncertainties in
modeling and designing next-generation plasma accelerators. To do so, we
perform two-dimensional high-resolution MHD simulations using an argon-filled
capillary discharge waveguide with three different electron transport
coefficients models. The models tested include (i) Davies et al. (ii) Spitzer,
and (iii) Epperlein-Haines (EH). We found that the EH model overestimates the
electron temperature inside the channel by over $20\%$ while predicting a lower
azimuthal magnetic field. Moreover, the Spitzer model, often used in MHD
simulations for plasma-based accelerators, predicts a significantly higher
electron temperature than the other models suggest.


title: The Life and Times of Giant Molecular Clouds

abstract:
  Giant molecular clouds (GMCs) are the sites of star formation and stellar
feedback in galaxies. Their properties set the initial conditions for star
formation and their lifecycles determine how feedback regulates galaxy
evolution. In recent years, the advent of high-resolution telescopes has
enabled systematic GMC-scale studies of the molecular interstellar medium in
nearby galaxies, now covering a wide range of physical conditions and allowing
for the first studies of how GMC properties depend on galactic environment.
These observational developments have been accompanied by numerical simulations
of improving resolution that are increasingly accurately accounting for the
effects of the galactic-scale environment on GMCs, while simultaneously
improving the treatment of the small-scale processes of star-formation and
stellar feedback within them. The combination of these recent developments has
greatly improved our understanding of the formation, evolution, and destruction
of GMCs. We review the current state of the field, highlight current open
questions, and discuss promising avenues for future studies.


title:
Transmit Precoder Design Approaches for Dual-Function
  Radar-Communication Systems

abstract:
  As radio-frequency (RF) antenna, component and processing capabilities
increase, the ability to perform multiple RF system functions from a common
aperture is being realized. Conducting both radar and communications from the
same system is potentially useful in vehicular, health monitoring, and
surveillance settings. This paper considers multiple-input-multiple-output
(MIMO) dual-function radar-communication (DFRC) systems in which the radar and
communication modes use distinct baseband waveforms. A transmit precoder
provides spatial multiplexing and power allocation among the radar and
communication modes. Multiple precoder design approaches are introduced for a
radar detection mode in which a total search volume is divided into dwells to
be searched sequentially. The approaches are designed to enforce a reliance on
radar waveforms for sensing purposes, yielding improved approximation of
desired ambiguity functions over prior methods found in the literature. The
methods are also shown via simulation to enable design flexibility, allowing
for prioritization of either subsystem and specification of a desired level of
radar or communication performance.


title: Triangle and Four Cycle Counting with Predictions in Graph Streams

abstract:
  We propose data-driven one-pass streaming algorithms for estimating the
number of triangles and four cycles, two fundamental problems in graph
analytics that are widely studied in the graph data stream literature.
Recently, (Hsu 2018) and (Jiang 2020) applied machine learning techniques in
other data stream problems, using a trained oracle that can predict certain
properties of the stream elements to improve on prior "classical" algorithms
that did not use oracles. In this paper, we explore the power of a "heavy edge"
oracle in multiple graph edge streaming models. In the adjacency list model, we
present a one-pass triangle counting algorithm improving upon the previous
space upper bounds without such an oracle. In the arbitrary order model, we
present algorithms for both triangle and four cycle estimation with fewer
passes and the same space complexity as in previous algorithms, and we show
several of these bounds are optimal. We analyze our algorithms under several
noise models, showing that the algorithms perform well even when the oracle
errs. Our methodology expands upon prior work on "classical" streaming
algorithms, as previous multi-pass and random order streaming algorithms can be
seen as special cases of our algorithms, where the first pass or random order
was used to implement the heavy edge oracle. Lastly, our experiments
demonstrate advantages of the proposed method compared to state-of-the-art
streaming algorithms.


title: Two-sided Poisson control of linear diffusions

abstract:
  We study a class of two-sided optimal control problems of general linear
diffusions under a so-called Poisson constraint: the controlling is only
allowed at the arrival times of an independent Poisson signal processes. We
give a weak and easily verifiable set of sufficient conditions under which we
derive a quasi-explicit unique solution to the problem in terms of the minimal
r-excessive mappings of the diffusion. We also investigate limiting properties
of the solutions with respect to the signal intensity of the Poisson process.
Lastly, we illustrate our results with an explicit example.


title:
Prediction on Raman Spectra of Intrinsic Two-Dimensional Ga$_2$O$_3$
  Monolayer

abstract:
  We investigate the vibrational properties and Raman spectra of
two-dimensional Ga$_2$O$_3$ monolayer, using density functional theory. Two
ferroelectric (FE) phases of Ga$_2$O$_3$ monolayer with wurtzite(WZ) and
zincblende(ZB) structures( FE-WZ and FE-ZB, respectively ) are considered. The
Raman tensor and angle-dependent Raman intensities of two major Raman peak
($A^1_1$ and $A^2_1$) in both FE-WZ (497, and 779 cm$^{-1}$) and FE-ZB (481,
and 772 cm$^{-1}$) Ga$_2$O$_3$ monolayers are calculated for the polarizations
of scattered light parallel and perpendicular to that of the incident light.
The characteristics of angle-dependent Raman intensities are analyzed. The
averaged non-resonant Raman spectra of minor peaks in FE-WZ($E^1$) and
FE-BZ($E^1$ and $E^2$) are compared with that of major peaks $A^1_1$ and
$A^2_1$ . These predictions on Raman spectra of Ga$_2$O$_3$ monolayer may guide
rational design of two-dimensional optical devices.


title:
Interfacial strength dominates fold formation in microscale, soft static
  friction

abstract:
  Utilizing colloidal probe, lateral force microscopy and simultaneous confocal
microscopy, combined with finite element analysis, we investigate the mechanism
of static friction for a microparticle on a soft, adhesive surface. We find
that the surface can form a self-contacting fold at the leading front, which
results from a buildup of compressive stress. A sufficiently high lateral
resistance is required for folding to occur, although the folds themselves do
not increase the peak force. Experimentally, folds are observed on substrates
that exhibit both high and low normal adhesion, motivating the use of
simulations to consider the role of adhesion energy and interfacial strength.
Our simulations illustrate that the interfacial strength plays a dominating
role in the formation of folds, rather than the overall adhesion energy. These
results reveal that adhesion energy alone is not sufficient to predict the
nucleation of folds or the lateral force, but instead the specific parameters
that define the adhesion energy must be considered.


title:
Strong solutions to McKean-Vlasov SDEs with coefficients of
  Nemytskii-type: the time-dependent case

abstract:
  We consider a large class of nonlinear FPKEs with coefficients of
Nemytskii-type depending \textit{explicitly} on time and space, for which it is
known that there exists a sufficiently Sobolev-regular distributional solution
$u\in L^1\cap L^\infty$. We show that there exists a unique strong solution to
the associated McKean-Vlasov SDE with time marginal law densities $u$. In
particular, every weak solution of this equation with time marginal law
densities $u$ can be written as a functional of the driving Brownian motion.
Moreover, plugging any Brownian motion into this very functional produces a
weak solution with time marginal law densities $u$.


title:
MolecuSense: Using Force-Feedback Gloves for Creating and Interacting
  with Ball-and-Stick Molecules in VR

abstract:
  We contribute MolecuSense, a virtual version of a physical molecule
construction kit, based on visualization in Virtual Reality (VR) and
interaction with force-feedback gloves. Targeting at chemistry education, our
goal is to make virtual molecule structures more tangible. Results of an
initial user study indicate that the VR molecular construction kit was
positively received. Compared to a physical construction kit, the VR molecular
construction kit is on the same level in terms of natural interaction. Besides,
it fosters the typical digital advantages though, such as saving, exporting,
and sharing of molecules. Feedback from the study participants has also
revealed potential future avenues for tangible molecule visualizations.


title:
GAC: A Deep Reinforcement Learning Model Toward User Incentivization in
  Unknown Social Networks

abstract:
  In recent years, providing incentives to human users for attracting their
attention and engagement has been widely adopted in many applications. To
effectively incentivize users, most incentive mechanisms determine incentive
values based on users' individual attributes, such as preferences. These
approaches could be ineffective when such information is unavailable.
Meanwhile, due to the budget limitation, the number of users who can be
incentivized is also restricted. In this light, we intend to utilize social
influence among users to maximize the incentivization. By directly
incentivizing influential users in the social network, their followers and
friends could be indirectly incentivized with fewer incentives or no incentive.
However, it is difficult to identify influential users beforehand in the social
network, as the influence strength between each pair of users is typically
unknown. In this work, we propose an end-to-end reinforcement learning-based
framework, named Geometric Actor-Critic (GAC), to discover effective incentive
allocation policies under limited budgets. More specifically, the proposed
approach can extract information from a high-level network representation for
learning effective incentive allocation policies. The proposed GAC only
requires the topology of the social network and does not rely on any prior
information about users' attributes. We use three real-world social network
datasets to evaluate the performance of the proposed GAC. The experimental
results demonstrate the effectiveness of the proposed approach.


title: Regularised unfolding with a discrete-valued penalty function

abstract:
  Regularisation schemes play a key role in solving ill-posed inverse problems.
Here we focus on discrete unfolding problems from counting experiments, even
though the formalism presented in the paper has wider applicability. The
properties of the results are quantified by the consistency between
measurements and unfolding result and by the posterior response matrix. After a
recap of the basic concepts we present a novel regularisation scheme based on a
discrete-valued penalty function and compare its performance to that of a
simple cutoff-regularisation. The discrete-valued penalty function stabilises
the result without introducing a regularisation parameter that needs to be
adjusted on a case-by-case basis. In toy studies it is shown to give very
satisfactory results. Uncertainties are handled by a bootstrap approach, since
the discontinuities in the penalty function render error propagation based on
derivatives not applicable.


title:
Surface Defect Detection and Evaluation for Marine Vessels using
  Multi-Stage Deep Learning

abstract:
  Detecting and evaluating surface coating defects is important for marine
vessel maintenance. Currently, the assessment is carried out manually by
qualified inspectors using international standards and their own experience.
Automating the processes is highly challenging because of the high level of
variation in vessel type, paint surface, coatings, lighting condition, weather
condition, paint colors, areas of the vessel, and time in service. We present a
novel deep learning-based pipeline to detect and evaluate the percentage of
corrosion, fouling, and delamination on the vessel surface from normal
photographs. We propose a multi-stage image processing framework, including
ship section segmentation, defect segmentation, and defect classification, to
automatically recognize different types of defects and measure the coverage
percentage on the ship surface. Experimental results demonstrate that our
proposed pipeline can objectively perform a similar assessment as a qualified
inspector.


title: SepTr: Separable Transformer for Audio Spectrogram Processing

abstract:
  Following the successful application of vision transformers in multiple
computer vision tasks, these models have drawn the attention of the signal
processing community. This is because signals are often represented as
spectrograms (e.g. through Discrete Fourier Transform) which can be directly
provided as input to vision transformers. However, naively applying
transformers to spectrograms is suboptimal. Since the axes represent distinct
dimensions, i.e. frequency and time, we argue that a better approach is to
separate the attention dedicated to each axis. To this end, we propose the
Separable Transformer (SepTr), an architecture that employs two transformer
blocks in a sequential manner, the first attending to tokens within the same
frequency bin, and the second attending to tokens within the same time
interval. We conduct experiments on three benchmark data sets, showing that our
separable architecture outperforms conventional vision transformers and other
state-of-the-art methods. Unlike standard transformers, SepTr linearly scales
the number of trainable parameters with the input size, thus having a lower
memory footprint. Our code is available as open source at
https://github.com/ristea/septr.


title:
A three-stage magnetic phase transition revealed in ultrahigh-quality
  van der Waals magnet CrSBr

abstract:
  van der Waals (vdW) magnets are receiving ever-growing attention nowadays due
to their significance in both fundamental research on low-dimensional magnetism
and potential applications in spintronic devices. High crystalline quality of
vdW magnets is key for maintaining intrinsic magnetic and electronic
properties, especially when exfoliated down to the 2D limit. Here,
ultrahigh-quality air-stable vdW CrSBr crystals are synthesized using the
direct vapor-solid synthesis method. The high single crystallinity and spatial
homogeneity have been thoroughly evidenced at length scales from sub-mm to
atomic resolution by X-ray diffraction, second harmonic generation, and
scanning transmission electron microscopy. More importantly, specific heat
measurements of these ultrahigh quality CrSBr crystals show three thermodynamic
anomalies at 185K, 156K, and 132K, revealing a stage-by-stage development of
the magnetic order upon cooling, which is also corroborated with the
magnetization and transport results. Our ultrahigh-quality CrSBr can further be
exfoliated down to monolayers and bilayers easily, paving the way to integrate
them into heterostructures for spintronic and magneto-optoelectronic
applications.


title:
Observation of emergent Dirac physics at the surfaces of acoustic
  higher-order topological insulators

abstract:
  Using three-dimensional (3D) sonic crystals as acoustic higher-order
topological insulators (HOTIs), we discover two-dimensional (2D) surface states
described by spin-1 Dirac equations at the interfaces between the two sonic
crystals with distinct topology but the same crystalline symmetry. We find that
the Dirac mass can be tuned by the geometry of the two sonic crystals. The sign
reversal of the Dirac mass reveals a surface topological transition where the
surface states exhibit zero refractive index behavior. When the surface states
are gapped, one-dimensional (1D) hinge states emerge due to the topology of the
gapped surface states. We confirm experimentally the zero refractive index
behavior and the emergent topological hinge states. Our study reveals a
multidimensional Wannier orbital control that leads to extraordinary properties
of surface states and unveils an interesting topological mechanism for the
control of surface waves.


title: Flavor Diagonal Nucleon Charges

abstract:
  This talk provides an update on the calculation of matrix elements of flavor
diagonal axial, scalar and tensor quark bilinear operators between the nucleon
ground state. The simulations are done using Wilson-clover fermions on a sea of
eight 2+1+1-flavor HISQ ensembles generated by the MILC collaboration. We
discuss the signal in the connected and disconnected contributions, calculation
of the renormalization constants and mixing in the RI-sMOM scheme, and control
over the simultaneous chiral-continuum-finite-volume fit used to extract the
final charges.


title: Particle Physics Outreach at Non-traditional Venues

abstract:
  Since 2016 the group known as `The Big Bang Collective' has brought High
Energy Physics outreach to music and culture festivals across Europe,
successfully engaging with and inspiring audiences who may never have sought
this out themselves through activities in their `Physics Pavilions'. The US has
a very long, glorious tradition of music festivals, and an incredible community
of sci-comm engaged physicists, from students to senior staff. With the
experience already gained in Europe, The Big Bang Collective believes the time
is now ripe to bring their Physics Pavilions to US music festivals.


title: Ideal spaces: An extension of structure spaces of rings

abstract:
  Can there be a structure space-type theory for an arbitrary class of ideals
of a ring? The ideal spaces introduced in this paper allows such a study and
our theory includes (but not restricted to) prime, maximal, minimal prime,
strongly irreducible, irreducible, completely irreducible, proper, minimal,
primary, nil, nilpotent, regular, radical, principal, finitely generated
ideals. We characterise ideal spaces that are sober. We introduce the notion of
a strongly disconnected spaces and show that for a ring with zero Jacobson
radical, strongly disconnected ideal spaces containing all maximal ideals of
the ring imply existence of non-trivial idempotent elements in the ring. We
also give a sufficient condition for a spectrum to be connected.


title: On the Combinatorial Diameters of Parallel and Series Connections

abstract:
  Combinatorial diameters of polyhedra are a classical topic in linear
programming due to its connection to the possibility of an efficient pivot rule
for the simplex method. We are interested in the diameters of polyhedra formed
from the so-called parallel or series connection of oriented matroids: oriented
matroids are the natural way to connect representable matroid theory with
linear programming, and these connections are fundamental operations for the
construction of more-complicated matroids from elementary matroid blocks.
  In this paper, we prove that, for polyhedra whose combinatorial diameter
satisfies the Hirsch-conjecture bound regardless of the right-hand sides in a
standard-form description, the diameters of their parallel or series
connections remain small in the Hirsch-conjecture bound. These results are a
substantial step toward devising a diameter bound for all polyhedra defined
through totally-unimodular matrices, based on Seymour's famous decomposition
theorem.
  Our proof techniques and results exhibit a number of interesting features.
While the parallel connection leads to a bound that adds just a constant, for
the series connection one has to linearly take into account the maximal value
in a specific coordinate of any vertex. Our proofs also require a careful
treatment of non-revisiting edge walks in degenerate polyhedra, as well as the
construction of edge walks that may take a `detour' to facets that satisfy the
non-revisiting conjecture when the underlying polyhedron may not.


title:
The Dwarf Galaxy Population at $z\sim 0.7$: A Catalog of Emission Lines
  and Redshifts from Deep Keck Observations

abstract:
  We present a catalog of spectroscopically measured redshifts over $0 < z < 2$
and emission line fluxes for 1440 galaxies, primarily ($\sim$65\%) from the
HALO7D survey, with other spectra from the DEEPwinds program. This catalog
includes redshifts for 646 dwarf galaxies with $\log(M_{\star}/M_{\odot}) <
9.5$. 810 of these galaxies did not have existing spectroscopic redshifts in
our catalogs, including 454 dwarf galaxies. Spectroscopic observations using
the DEIMOS spectrograph on the Keck II telescope provide very deep (up to 32
hours exposure, with a median of $\sim$7 hours) optical spectroscopy for this
sample of galaxies in the COSMOS, EGS, GOODS-North, and GOODS-South CANDELS
fields, and in some areas outside CANDELS. We compare our results to existing
spectroscopic and photometric redshifts in these fields, finding only a 1\%
rate of discrepancy with other spectroscopic redshifts. We measure a small
increase in median photometric redshift error (from 1.0\% to 1.3\%) and
catastrophic outlier rate (from 3.5\% to 8\%) with decreasing stellar mass. We
obtained successful redshift fits for 75\% of massive galaxies, and demonstrate
a similar 70-75\% successful redshift measurement rate in $8.5 <
\log(M_{\star}/M_{\odot}) < 9.5$ galaxies, suggesting similar survey
sensitivity in this low-mass range. We explore the redshift, mass, and
color-magnitude distributions of the catalog galaxies, finding that HALO7D
galaxies are representative of CANDELS galaxies in these distributions up to
\textit{i}-band magnitudes of 25. The catalogs presented will enable studies of
star formation, the mass-metallicity relation, star formation and morphology
relations, and other properties of the $z\sim0.7$ dwarf galaxy population.


title:
Video-based Formative and Summative Assessment of Surgical Tasks using
  Deep Learning

abstract:
  To ensure satisfactory clinical outcomes, surgical skill assessment must be
objective, time-efficient, and preferentially automated - none of which is
currently achievable. Video-based assessment (VBA) is being deployed in
intraoperative and simulation settings to evaluate technical skill execution.
However, VBA remains manually- and time-intensive and prone to subjective
interpretation and poor inter-rater reliability. Herein, we propose a deep
learning (DL) model that can automatically and objectively provide a
high-stakes summative assessment of surgical skill execution based on video
feeds and low-stakes formative assessment to guide surgical skill acquisition.
Formative assessment is generated using heatmaps of visual features that
correlate with surgical performance. Hence, the DL model paves the way to the
quantitative and reproducible evaluation of surgical tasks from videos with the
potential for broad dissemination in surgical training, certification, and
credentialing.


title:
Enhanced Temporal Knowledge Embeddings with Contextualized Language
  Representations

abstract:
  With the emerging research effort to integrate structured and unstructured
knowledge, many approaches incorporate factual knowledge into pre-trained
language models (PLMs) and apply the knowledge-enhanced PLMs on downstream NLP
tasks. However, (1) they only consider static factual knowledge, but knowledge
graphs (KGs) also contain temporal facts or events indicating evolutionary
relationships among entities at different timestamps. (2) PLMs cannot be
directly applied to many KG tasks, such as temporal KG completion.
  In this paper, we focus on \textbf{e}nhancing temporal knowledge embeddings
with \textbf{co}ntextualized \textbf{la}nguage representations (ECOLA). We
align structured knowledge contained in temporal knowledge graphs with their
textual descriptions extracted from news articles and propose a novel
knowledge-text prediction task to inject the abundant information from
descriptions into temporal knowledge embeddings. ECOLA jointly optimizes the
knowledge-text prediction objective and the temporal knowledge embeddings,
which can simultaneously take full advantage of textual and knowledge
information. For training ECOLA, we introduce three temporal KG datasets with
aligned textual descriptions. Experimental results on the temporal knowledge
graph completion task show that ECOLA outperforms state-of-the-art temporal KG
models by a large margin. The proposed datasets can serve as new temporal KG
benchmarks and facilitate future research on structured and unstructured
knowledge integration.


title:
Advances in Shannon-Based Communications and Computations Approaches to
  Understanding Information Processing in the Brain

abstract:
  This article serves as a supplement to the recently published call for
participation in a Research Topic [1] that is timed to commemorate the 75th
anniversary of Shannon's pioneering 1948 paper [2]. Here, we include some
citations of key and relevant literature, which reflect our
opinions/perspectives on the proposed topic, and serve as guidance to potential
submissions.


title:
Compact superconducting microwave resonators based on Al-AlOx-Al
  capacitor

abstract:
  We address the scaling-up problem for superconducting quantum circuits by
using lumped-element resonators based on a new fabrication method of aluminum
-- aluminum oxide -- aluminum ($\mathrm{Al/AlO_x/Al}$) parallel-plate
capacitors. The size of the resonators is only 0.04$~\mathrm{mm^2}$, which is
more than one order smaller than the typical size of coplanar resonators
(1$~\mathrm{mm^2}$). The fabrication method we developed easily fits into the
standard superconducting qubits fabrication process. We have obtained
capacitance per area 14 fF/$\mathrm{\mu m^2}$ and the internal quality factor
$\mathrm{1\times 10^3 - 8\times 10^3}$ at the single-photon level. Our results
show that such devices based on $\mathrm{Al/AlO_x/Al}$ capacitors could be
further applied to the qubit readout scheme, including resonators, filters,
amplifiers, as well as microwave metamaterials and novel types of qubits, such
as $0-\pi$ qubit.


title: Field-tuned chiral transport in charge-ordered CsV$_3$Sb$_5$

abstract:
  When conductors differ from their mirror image, their chirality is reflected
in the electron states and their dynamics. Conventionally, mirror symmetries
are broken by the atomic structure of crystals and imprint their low symmetry
onto the itinerant electron states. Unusual transport properties are a hallmark
of chiral crystals, such as a non-linear electric response known as electronic
magneto-chiral ansiotropy (eMChA). Here we report such a transport signature in
the centro-symmetric layered Kagome metal CsV$_3$Sb$_5$, observed via second
harmonic generation under magnetic field. We observe the signal in a
charge-ordered phase of the material, which has been proposed to be a
loop-current phase with spontaneously broken mirror symmetries. While charge
ordering sets in at $T_{\mathrm{CDW}}\sim$ 94 K, the eMChA transport becomes
significant only at temperatures below $T'\sim$ 34 K, indicative of further
evolutions of this ordered phase upon lowering the temperature. Crucially, we
demonstrate that the nonlinear magnetotransport can be switched by small
out-of-plane fields, in accordance with recent tunneling microscopy
experiments. Unlike structurally chiral materials, our results demonstrate how
the electronic chirality of a metal can be tuned and even abruptly switched by
small changes in magnetic fields alone -- a prerequisite for their applications
in chiral electronics.


title: Delta Distillation for Efficient Video Processing

abstract:
  This paper aims to accelerate video stream processing, such as object
detection and semantic segmentation, by leveraging the temporal redundancies
that exist between video frames. Instead of propagating and warping features
using motion alignment, such as optical flow, we propose a novel knowledge
distillation schema coined as Delta Distillation. In our proposal, the student
learns the variations in the teacher's intermediate features over time. We
demonstrate that these temporal variations can be effectively distilled due to
the temporal redundancies within video frames. During inference, both teacher
and student cooperate for providing predictions: the former by providing
initial representations extracted only on the key-frame, and the latter by
iteratively estimating and applying deltas for the successive frames. Moreover,
we consider various design choices to learn optimal student architectures
including an end-to-end learnable architecture search. By extensive experiments
on a wide range of architectures, including the most efficient ones, we
demonstrate that delta distillation sets a new state of the art in terms of
accuracy vs. efficiency trade-off for semantic segmentation and object
detection in videos. Finally, we show that, as a by-product, delta distillation
improves the temporal consistency of the teacher model.


title: Planet-Disk Interactions

abstract:
  Planet-disk interactions, where an embedded massive body interacts
gravitationally with the protoplanetary disk it was formed in, can play an
important role in reshaping both the disk and the orbit of the planet. Spiral
density waves are launched into the disk by the planet, which, if they are
strong enough, can lead to the formation of a gap. Both effects are observable
with current instruments. The back-reaction of perturbations induced in the
disk, both wave-like and non-wavelike, is a change in orbital elements of the
planet. The efficiency of orbital migration is a long-standing problem in
planet formation theory. We discuss recent progress in planet-disk interactions
for different planet masses and disk parameters, in particular the level of
turbulence, and progress in modeling observational signatures of embedded
planets.


title:
Prioritized Variable-length Test Cases Generation for Finite State
  Machines

abstract:
  Model-based Testing (MBT) is an effective approach for testing when parts of
a system-under-test have the characteristics of a finite state machine (FSM).
Despite various strategies in the literature on this topic, little work exists
to handle special testing situations. More specifically, when concurrently: (1)
the test paths can start and end only in defined states of the FSM, (2) a
prioritization mechanism that requires only defined states and transitions of
the FSM to be visited by test cases is required, and (3) the test paths must be
in a given length range, not necessarily of explicit uniform length. This paper
presents a test generation strategy that satisfies all these requirements. A
concurrent combination of these requirements is highly practical for real
industrial testing. Six variants of possible algorithms to implement this
strategy are described. Using a mixture of 180 problem instances from real
automotive and defense projects and artificially generated FSMs, all variants
are compared with a baseline strategy based on an established N-switch coverage
concept modification. Various properties of the generated test paths and their
potential to activate fictional defects defined in FSMs are evaluated. The
presented strategy outperforms the baseline in most problem configurations. Out
of the six analyzed variants, three give the best results even though a
universal best performer is hard to identify. Depending on the application of
the FSM, the strategy and evaluation presented in this paper are applicable
both in testing functional and non-functional software requirements.


title:
Towards Responsible Natural Language Annotation for the Varieties of
  Arabic

abstract:
  When building NLP models, there is a tendency to aim for broader coverage,
often overlooking cultural and (socio)linguistic nuance. In this position
paper, we make the case for care and attention to such nuances, particularly in
dataset annotation, as well as the inclusion of cultural and linguistic
expertise in the process. We present a playbook for responsible dataset
creation for polyglossic, multidialectal languages. This work is informed by a
study on Arabic annotation of social media content.


title:
DP-KB: Data Programming with Knowledge Bases Improves Transformer Fine
  Tuning for Answer Sentence Selection

abstract:
  While transformers demonstrate impressive performance on many knowledge
intensive (KI) tasks, their ability to serve as implicit knowledge bases (KBs)
remains limited, as shown on several slot-filling, question-answering (QA),
fact verification, and entity-linking tasks. In this paper, we implement an
efficient, data-programming technique that enriches training data with
KB-derived context and improves transformer utilization of encoded knowledge
when fine-tuning for a particular QA task, namely answer sentence selection
(AS2). Our method outperforms state of the art transformer approach on WikiQA
and TrecQA, two widely studied AS2 benchmarks, increasing by 2.0% p@1, 1.3%
MAP, 1.1% MRR, and 4.4% p@1, 0.9% MAP, 2.4% MRR, respectively. To demonstrate
our improvements in an industry setting, we additionally evaluate our approach
on a proprietary dataset of Alexa QA pairs, and show increase of 2.3% F1 and
2.0% MAP. We additionally find that these improvements remain even when KB
context is omitted at inference time, allowing for the use of our models within
existing transformer workflows without additional latency or deployment costs.


title:
Robotic Speech Synthesis: Perspectives on Interactions, Scenarios, and
  Ethics

abstract:
  In recent years, many works have investigated the feasibility of
conversational robots for performing specific tasks, such as healthcare and
interview. Along with this development comes a practical issue: how should we
synthesize robotic voices to meet the needs of different situations? In this
paper, we discuss this issue from three perspectives: 1) the difficulties of
synthesizing non-verbal and interaction-oriented speech signals, particularly
backchannels; 2) the scenario classification for robotic voice synthesis; 3)
the ethical issues regarding the design of robot voice for its emotion and
identity. We present the findings of relevant literature and our prior work,
trying to bring the attention of human-robot interaction researchers to design
better conversational robots in the future.


title: Invertible Optical Nonlinearity in Epsilon-near-zero Materials

abstract:
  Epsilon-near-zero (ENZ) materials such as indium tin oxide (ITO), have
recently emerged as a new platform to enhance optical nonlinearities. Here we
report a theoretical and experimental study on the origin of nonlinearities in
ITO thin films that are dominated by two mechanisms based on intraband and
interband transitions. We show that there are two competing factors that
jointly contribute to a spectrally-invertible nonlinearity of ITO near its ENZ
region i.e. the non-parabolicity of the band structure that results in a larger
effective mass in the intraband transition and the Fermi energy shift, which
determines the free carrier density. Our work reveals the relationship between
the large nonlinearity and the intrinsic material properties of the ITO films.


title: Simulation-based Anomaly Detection for Multileptons at the LHC

abstract:
  Decays of Higgs boson-like particles into multileptons is a well-motivated
process for investigating physics beyond the Standard Model (SM). A unique
feature of this final state is the precision with which the SM is known. As a
result, simulations are used directly to estimate the background. Current
searches consider specific models and typically focus on those with a single
free parameter to simplify the analysis and interpretation. In this paper, we
explore recent proposals for signal model agnostic searches using machine
learning in the multilepton final state. These tools can be used to
simultaneously search for many models, some of which have no dedicated search
at the Large Hadron Collider. We find that the machine learning methods offer
broad coverage across parameter space beyond where current searches are
sensitive, with a necessary loss of performance compared to dedicated searches
by only about one order of magnitude.