title:
Three-Dimensional General-Relativistic Simulations of Neutrino-Driven
  Winds from Rotating Proto-Neutron Stars

abstract:
  We explore the effects of rapid rotation on the properties of neutrino-heated
winds from proto-neutron stars (PNS) formed in core-collapse supernovae or
neutron-star mergers by means of three-dimensional general-relativistic
hydrodynamical simulations with M0 neutrino transport. We focus on conditions
characteristic of a few seconds into the PNS cooling evolution when the
neutrino luminosities obey $L_{\nu_e} + L_{\bar{\nu}_e} \approx 7\times
10^{51}$ erg s$^{-1}$, and over which most of the wind mass-loss will occur.
After an initial transient phase, all of our models reach approximately
steady-state outflow solutions with positive energies and sonic surfaces
captured on the computational grid. Our non-rotating and slower-rotating models
(angular velocity relative to Keplerian $\Omega/\Omega_{\rm K} \lesssim 0.4$;
spin period $P \gtrsim 2$ ms) generate approximately spherically symmetric
outflows with properties in good agreement with previous PNS wind studies. By
contrast, our most rapidly spinning PNS solutions ($\Omega/\Omega_{\rm K}
\gtrsim 0.75$; $P \approx 1$ ms) generate outflows focused in the rotational
equatorial plane with much higher mass-loss rates (by over an order of
magnitude), lower velocities, lower entropy, and lower asymptotic electron
fractions, than otherwise similar non-rotating wind solutions. Although such
rapidly spinning PNS are likely rare in nature, their atypical nucleosynthetic
composition and outsized mass yields could render them important contributors
of light neutron-rich nuclei compared to more common slowly rotating PNS birth.
Our calculations pave the way to including the combined effects of rotation and
a dynamically-important large-scale magnetic field on the wind properties
within a 3D GRMHD framework.


title:
Modelling the ionisation state of Type Ia supernovae in the
  nebular-phase

abstract:
  The nebular spectra of Type Ia supernovae ($\gtrapprox$ 100 days after
explosion) consist mainly of emission lines from singly- and doubly-ionised
Fe-group nuclei. However, theoretical models for many scenarios predict that
non-thermal ionisation leads to multiply-ionised species whose recombination
photons ionise and deplete Fe$^{+}$ , resulting in negligible [Fe II] emission.
We investigate a method to determine the collisional excitation conditions from
[Fe II] line ratios independently from the ionisation state and find that it
cannot be applied to highly-ionised models due to the influence of
recombination cascades on Fe$^{+}$ level populations. When the ionisation state
is artificially lowered, the line ratios (and excitation conditions) are too
similar to distinguish between explosion scenarios. We investigate changes to
the treatment of non-thermal energy deposition as a way to reconcile
over-ionised theoretical models with observations and find that a simple work
function approximation provides closer agreement with the data for sub-Mch
models than a detailed Spencer-Fano treatment with widely-used cross section
data. To quantify the magnitude of additional heating processes that would be
required to sufficiently reduce ionisation from fast leptons, we artificially
boost the rate of energy loss to free electrons. We find that the equivalent of
as much as an eight times increase to the plasma loss rate would be needed to
reconcile the sub-Mch model with observed spectra. Future studies could
distinguish between reductions in the non-thermal ionisation rates and
increased recombination rates, such as by clumping.


title: High-order time crystal phases and their fractal nature

abstract:
  Discrete Floquet time crystals (DFTC) are characterized by the spontaneous
breaking of the discrete time-translational invariance characteristic of
Floquet driven systems. In analogy with equilibrium critical points, also
time-crystalline phases display critical behaviour of different order, i.e.,
oscillations whose period is a multiple $p > 2$ of the Floquet driving period.
Here, we introduce a new order parameter which is able to unambiguously detect
crystalline phases regardless of the value of $p$ and, at the same time, is a
useful tool for chaos diagnostic. This new paradigm allows us to investigate
the phase diagram of the long-range (LR) kicked Ising model to an unprecedented
depth, unveiling a rich landscape characterized by self-similar fractal
boundaries. Our solid theoretical picture describes the emergence of DFTCs
phase both as a function of the strength and period of the Floquet drive,
capturing the emergent $\mathbf{Z}_p$ symmetry in the Floquet-Bloch waves.


title: Could fresh lava be (warm) dark matter?

abstract:
  Dark matter models can be classified according to their impact on the
properties of galaxies, including cold dark matter (CDM), warm dark matter
(WDM), self-interacting dark matter (SIDM) and fuzzy dark matter (FDM). In
celebration of April Fool's Day, and also of the 1-year anniversary of the
start of the 2022 volcanic eruption at Fagradalsfjall here in Iceland, we
explore fresh lava as a candidate for WDM specifically. We verify first hand
that lava is indeed warm (exhibits free-streaming and retains temperature for
several months after the eruption ends, is 1000K, sets fire to grass, can feel
one's eyebrows singe at a distance of 4m) and dark once sufficiently decoupled
from its source of production.


title: Spatiotemporal evolution of asymmetries in turbulent trailing vortices

abstract:
  The outward propagation of asymmetries introduced to originally axisymmetric
turbulent flows is investigated, where 3D Batchelor vortices at high Reynolds
numbers and with arbitrary swirl numbers are chosen as test cases. It is well
known that disturbances (asymmetries) added to a 2D axisymmetric flow propagate
radially outward, in order to re-axisymmetrize the vortex, but cease to travel
at a critical distance, known as the stagnation radius. We utilize direct
numerical simulations (DNS) and an inviscid model developed by linearizing the
momentum and vorticity transport equations around the base (unperturbed) flow
in helical coordinates to demonstrate that, in contrast with 2D cases, 3D
vortices enable the unbounded radial propagation of asymmetries. We further
apply the Wenzel-Kramers-Brillouin (WKB) analysis to the linear model, treating
perturbations as compact wavepackets, to transform the PDEs of the linear model
to a few ODEs. However it has been shown in the climate science community that
the WKB approach ubiquitously predicts stagnation radii and heights for
disturbances introduced to 3D cyclone-like vortices, here, we identify a narrow
range of parameters, for which the WKB analysis also supports an unrestrained
propagation for disturbances. Finally, the mechanisms governing the momentum
transport at different stages and locations, thereby promoting the outward
advection of perturbations, are elucidated using the the linear rapid
distortion theory and numerical simulations. Since the DNS of the full
Navier-Stokes equations rapidly stabilizes to a laminar configuration, the DNS
of the linearized transport equations and the nonlinear governing equations
without base-flow interactions are also conducted, in order to uncover the
primary mechanisms for the growth and radial propagation of perturbations as
well as the nonlinear processes causing growth arrest at fixed swirl numbers.


title: The DECam Local Volume Exploration Survey Data Release 2

abstract:
  We present the second public data release (DR2) from the DECam Local Volume
Exploration survey (DELVE). DELVE DR2 combines new DECam observations with
archival DECam data from the Dark Energy Survey, the DECam Legacy Survey, and
other DECam community programs. DELVE DR2 consists of ~160,000 exposures that
cover >21,000 deg^2 of the high Galactic latitude (|b| > 10 deg) sky in four
broadband optical/near-infrared filters (g, r, i, z). DELVE DR2 provides
point-source and automatic aperture photometry for ~2.5 billion astronomical
sources with a median 5{\sigma} point-source depth of g=24.3, r=23.9, i=23.5,
and z=22.8 mag. A region of ~17,000 deg^2 has been imaged in all four filters,
providing four-band photometric measurements for ~618 million astronomical
sources. DELVE DR2 covers more than four times the area of the previous DELVE
data release and contains roughly five times as many astronomical objects.
DELVE DR2 is publicly available via the NOIRLab Astro Data Lab science
platform.


title:
Elevated Hot Gas and High-Mass X-ray Binary Emission in Low Metallicity
  Galaxies: Implications for Nebular Ionization and Intergalactic Medium
  Heating in the Early Universe

abstract:
  High-energy emission associated with star formation has been proposed as a
significant source of interstellar medium (ISM) ionization in low-metallicity
starbursts and an important contributor to the heating of the intergalactic
medium (IGM) in the high-redshift ($z > 8$) Universe. Using Chandra
observations of a sample of 30 galaxies at $D \approx$~200--450 Mpc that have
high specific star-formation rates of 3--9 Gyr$^{-1}$ and metallicities near $Z
\approx 0.3 Z_\odot$, we provide new measurements of the average 0.5--8 keV
spectral shape and normalization per unit star-formation rate (SFR). We model
the sample-combined X-ray spectrum as a combination of hot gas and high-mass
X-ray binary (HMXB) populations and constrain their relative contributions. We
derive scaling relations of $\log L_{\rm 0.5-8 keV}^{\rm HMXB}$/SFR $= 40.19
\pm 0.06$ and $\log L_{\rm 0.5-2 keV}^{\rm gas}$/SFR $= 39.58^{+0.17}_{-0.28}$;
significantly elevated compared to local relations. The HMXB scaling is also
somewhat higher than $L_{\rm 0.5-8 keV}^{\rm HMXB}$-SFR-$Z$ relations presented
in the literature, potentially due to our galaxies having relatively low HMXB
obscuration and young and X-ray luminous stellar populations. The elevation of
the hot gas scaling relation is at the level expected for diminished
attenuation due to a reduction of metals; however, we cannot conclude that an
$L_{\rm 0.5-2 keV}^{\rm gas}$-SFR-$Z$ relation is driven solely by changes in
ISM metal content. Finally, we present SFR-scaled spectral models (both
emergent and intrinsic) that span the X-ray--to--IR band, providing new
benchmarks for studies of the impact of ISM ionization and IGM heating in the
early Universe.


title: Searching for axion-like time-dependent cosmic birefringence with SPT-3G

abstract:
  Ultralight axion-like particles (ALPs) are compelling dark matter candidates
because of their potential to resolve small-scale discrepancies between
$\Lambda$CDM predictions and cosmological observations. Axion-photon coupling
induces a polarization rotation in linearly polarized photons traveling through
an ALP field; thus, as the local ALP dark matter field oscillates in time,
distant static polarized sources will appear to oscillate with a frequency
proportional to the ALP mass. We use observations of the cosmic microwave
background from SPT-3G, the current receiver on the South Pole Telescope, to
set upper limits on the value of the axion-photon coupling constant
$g_{\phi\gamma}$ over the approximate mass range $10^{-22} - 10^{-19}$ eV,
corresponding to oscillation periods from 12 hours to 100 days. For periods
between 1 and 100 days ($4.7 \times 10^{-22} \text{ eV} \leq m_\phi \leq 4.7
\times 10^{-20} \text{ eV}$), where the limit is approximately constant, we set
a median 95% C.L. upper limit on the amplitude of on-sky polarization rotation
of 0.071 deg. Assuming that dark matter comprises a single ALP species with a
local dark matter density of $0.3 \text{ GeV/cm}^3$, this corresponds to
$g_{\phi\gamma} < 1.18 \times 10^{-12}\text{ GeV}^{-1} \times \left(
\frac{m_{\phi}}{1.0 \times 10^{-21} \text{ eV}} \right)$. These new limits
represent an improvement over the previous strongest limits set using the same
effect by a factor of ~3.8.


title:
Probing spectral and temporal evolution of the neutron star low-mass
  X-ray binary 4U 1724-30 with AstroSat

abstract:
  We report the broadband spectro-temporal study of the poorly studied
accreting neutron star (NS) low mass X-ray binary (LMXB) 4U 1724-30 using data
from Soft X-ray Telescope (SXT) and Large Area X-ray Proportional Counters
(LAXPC) instruments on board AstroSat. The dim persistent LMXB source was
observed with AstroSat over 4 epochs in 2017, all of which corresponded to a
low-luminosity non-thermal emission dominated (hard/island) emission state with
modest spectral evolution. All the X-ray broadband spectra can be modelled by a
combination of thermal emission from the NS boundary layer (BL) or NS surface
and a non-thermal emission component possibly originating from the inverse
Comptonization of the disc seed photons. We investigate the presence of
frequency and energy-dependent variabilities to probe the origin of the
disc/coronal fluctuations. We also report the detection of a Type-I X-ray burst
displaying a photospheric radius expansion (PRE). During the burst, a hard
X-ray shortage in the 30-80 keV energy band and the enhancement of the
persistent emission reveal the burst feedback on the overall accretion process.
Using the touch-down burst flux $\sim$ $4.25 \times 10^{-8}$ erg s$^{-1}$
cm$^{-2}$, the distance of the source is estimated as $\sim$ 8.4 kpc.


title: Generating Scientific Articles with Machine Learning

abstract:
  In recent years, the field of machine learning has seen rapid growth, with
applications in a variety of domains, including image recognition, natural
language processing, and predictive modeling. In this paper, we explore the
application of machine learning to the generation of scientific articles. We
present a method for using machine learning to generate scientific articles
based on a data set of scientific papers. The method uses a machine-learning
algorithm to learn the structure of a scientific article and a set of training
data consisting of scientific papers. The machine-learning algorithm is used to
generate a scientific article based on the data set of scientific papers. We
evaluate the performance of the method by comparing the generated article to a
set of manually written articles. The results show that the machine-generated
article is of similar quality to the manually written articles.


title:
Self-Induced Decay of Intense Laser Pulse into a Pair of Surface
  Plasmons

abstract:
  We show theoretically that an intense femtosecond optical pulse incident
normally on a metal surface tends to decay into a pair of counter-propagating
surface plasmon-polaritons (SPPs). The interference field heats the medium
periodically, which causes a periodic permittivity perturbation and resonantly
amplifies the magnitudes of SPPs. The instability growth time is only 10-50 fs
for typical metals at damaging laser fluences. This mechanism is promising for
the interpretation of laser-induced periodic surface structures formation in a
single-pulse pumping regime.


title:
Random quantum circuits are approximate unitary $t$-designs in depth
  $O\left(nt^{5+o(1)}\right)$

abstract:
  The applications of random quantum circuits range from quantum computing and
quantum many-body systems to the physics of black holes. Many of these
applications are related to the generation of quantum pseudorandomness: Random
quantum circuits are known to approximate unitary $t$-designs. Unitary
$t$-designs are probability distributions that mimic Haar randomness up to
$t$th moments. In a seminal paper, Brand\~{a}o, Harrow and Horodecki prove that
random quantum circuits in a brickwork architecture of depth $O(n t^{10.5})$
are approximate unitary $t$-design. In this work, we revisit this argument,
which lower bounds the spectral gap of moment operators for local random
quantum circuits by $\Omega(n^{-1}t^{-9.5})$. We improve this lower bound to
$\Omega(n^{-1}t^{-4-o(1)})$. A direct consequence of this scaling is that
random quantum circuits generate approximate unitary $t$-designs in depth
$O(nt^{5+o(1)})$. Our techniques involve Gao's quantum union bound and the
unreasonable effectiveness of the Clifford group. As an auxiliary result, we
prove a near optimal convergence to the Haar measure for random Clifford
unitaries interleaved with Haar random single qubit unitaries in Wasserstein
distance.


title:
Landauer formula for interacting systems: a consistent non-perturbative
  approximation

abstract:
  Transport measurements are one of the most widely used methods of
characterizing small systems in chemistry and physics. When interactions are
negligible, the current through quantum dots, nanowires, molecular junctions,
and other submicron structures can be obtained using the Landauer formula. Meir
and Wingreen derived an exact expression for the current that also applies in
the presence of interactions. This powerful theoretical tool requires knowledge
of the exact Green's function. So far, an approximation extending beyond direct
finite-order perturbation theory is missing. Here, we provide general
expressions for both the electric and thermal currents where we expand the
self-energy to the lowest order (frequently dubbed the GW approximation) but
keep contributions to all orders in this quantity. Moreover, we show that the
electric current is conserved only when the self-energy and vertex corrections
are correctly included. We demonstrate that our formulae capture important
non-perturbative features and hence, provide a powerful tool in cases where the
exact solution cannot be found.


title: Efficient Computation of Extended Surface Sources

abstract:
  Source extension is a reformulation of inverse problems in wave propagation,
that at least in some cases leads to computationally tractable iterative
solution methods. The core subproblem in all source extension methods is the
solution of a linear inverse problem for a source (right hand side in a system
of wave equations) through minimization of data error in the least squares
sense with soft imposition of physical constraints on the source via an
additive quadratic penalty. A variant of the time reversal method from
photoacoustic tomography provides an approximate solution that can be used to
precondition Krylov space iteration for rapid convergence to the solution of
this subproblem. An acoustic 2D example for sources supported on a surface,
with a soft contraint enforcing point support, illustrates the effectiveness of
this preconditioner.


title: Graph Refinement for Coreference Resolution

abstract:
  The state-of-the-art models for coreference resolution are based on
independent mention pair-wise decisions. We propose a modelling approach that
learns coreference at the document-level and takes global decisions. For this
purpose, we model coreference links in a graph structure where the nodes are
tokens in the text, and the edges represent the relationship between them. Our
model predicts the graph in a non-autoregressive manner, then iteratively
refines it based on previous predictions, allowing global dependencies between
decisions. The experimental results show improvements over various baselines,
reinforcing the hypothesis that document-level information improves conference
resolution.


title: A Structured Optimal Controller for Irrigation Networks

abstract:
  In this paper, we apply an optimal LQ controller, which has an inherent
structure that allows for a distributed implementation, to an irrigation
network. The network consists of a water reservoir and connected water canals.
The goal is to keep the levels close to the set-points when farmers take out
water. The LQ controller is designed using a first-order approximation of the
canal dynamics, while the simulation model used for evaluation uses third-order
canal dynamics. The performance is compared to a P controller and an LQ
controller designed using the third-order canal dynamics. The structured
controller outperforms the P controller and is close to the theoretical optimum
given by the third-order LQ controller for disturbance rejection.


title: Speed of Sound for Hadronic and Quark Phases in a Magnetic Field

abstract:
  It is well known that for a fermion system with an isotropic equation of
state (EOS), the square of the speed of sound (SOS)$^2$ is a measure of the
stiffness of the equation of state (EOS). It is also known that in the presence
of a magnetic field the EOS becomes anisotropic with two different pressures
arising, one directed parallel to the field direction and one perpendicular to
it. Since the SOS in a medium is created by pressure oscillations, the
anisotropy in the pressure should be transferred to the SOS. In this paper, we
derive from first principles the anisotropic wavelike equation from where the
expressions for the longitudinal and transverse SOS in the presence of a
uniform magnetic field can be obtained. We also investigate the degree to which
the magnetic field in the weak and the strong limit affects the two SOS of (i)
a system of hadrons modeled by the nonlinear Walecka model and (ii) a system of
quarks modeled by the MIT bag model. We find that for the systems considered,
the effects of the magnetic field on the SOS anisotropy are mild up to
$10^{18}$G. Links to neutrons star physics are discussed throughout the paper.


title:
Calibrating constitutive models with full-field data via physics
  informed neural networks

abstract:
  The calibration of solid constitutive models with full-field experimental
data is a long-standing challenge, especially in materials which undergo large
deformation. In this paper, we propose a physics-informed deep-learning
framework for the discovery of constitutive model parameterizations given
full-field displacement data and global force-displacement data. Contrary to
the majority of recent literature in this field, we work with the weak form of
the governing equations rather than the strong form to impose physical
constraints upon the neural network predictions. The approach presented in this
paper is computationally efficient, suitable for irregular geometric domains,
and readily ingests displacement data without the need for interpolation onto a
computational grid. A selection of canonical hyperelastic materials models
suitable for different material classes is considered including the
Neo-Hookean, Gent, and Blatz-Ko constitutive models as exemplars for general
hyperelastic behavior, polymer behavior with lock-up, and compressible foam
behavior respectively. We demonstrate that physics informed machine learning is
an enabling technology and may shift the paradigm of how full-field
experimental data is utilized to calibrate constitutive models under finite
deformations.


title: Code Switched and Code Mixed Speech Recognition for Indic languages

abstract:
  Training multilingual automatic speech recognition (ASR) systems is
challenging because acoustic and lexical information is typically language
specific. Training multilingual system for Indic languages is even more tougher
due to lack of open source datasets and results on different approaches. We
compare the performance of end to end multilingual speech recognition system to
the performance of monolingual models conditioned on language identification
(LID). The decoding information from a multilingual model is used for language
identification and then combined with monolingual models to get an improvement
of 50% WER across languages. We also propose a similar technique to solve the
Code Switched problem and achieve a WER of 21.77 and 28.27 over Hindi-English
and Bengali-English respectively. Our work talks on how transformer based ASR
especially wav2vec 2.0 can be applied in developing multilingual ASR and code
switched ASR for Indic languages.


title:
$\mathcal{O}(\alpha_s^3)$ calculations for the inclusive determination
  of $|V_{cb}|$

abstract:
  For the determination of the Cabbibo-Kobayashi-Maskawa matrix element
$|V_{cb}|$ from inclusive data a precise knowledge of the semileptonic $b \to
c$ decay rate is necessary. Since this observable has a bad convergence
behavior when the heavy quark masses are expressed in the on-shell or
$\overline{\text{MS}}$ scheme the latest determinations have been obtained in
the so called kinetic mass scheme. The relation between the different schemes
needs to be known to high precision as well. In this proceedings we present our
recent calculations which push the precision of both ingredients to
$\mathcal{O}(\alpha_s^3)$. The results can be used to improve the inclusive
determination of $|V_{cb}|$.


title:
What's for Lunch? A systematic ordering of foods in the
  Soup-Salad-Sandwich phase space

abstract:
  The statistical physics of phase transitions has been hugely successful at
describing numerous natural, physical, and technological phenomena, and now a
rigorous examination of the phase space of the culinary regime is likewise ripe
for the picking. Despite great demand for the resolution of many scientific
debates over the taxonomy of food, past attempts have failed to account for
complex phase behavior and co-existence, and have thus left the public hungry
for a more substantial theory. By applying the principles of statistical
physics and thermodynamics, we here map out the complete phase space of all
culinary dishes and find three distinct phase regimes: Soup, Salad, and
Sandwich. We consider the effect of different state variables on these phase
boundaries, as well as regions of co-existence and triple points. With this
complete 3-dimensional phase diagram of all foods, we can conclusively answer
many bitter debates, including the imperishable question "is a hotdog a
sandwich?" The answer: yes.


title: Structural mechanics of filamentous cyanobacteria

abstract:
  Filamentous cyanobacteria, forming long strands of connected cells, are one
of the earliest and most successful forms of life on Earth. They exhibit
self-organised behaviour, forming large-scale patterns in structures like
biomats and stromatolites. The mechanical properties of these rigid structures
have contributed to their biological success and are important to applications
like algae-based biofuel production. For active polymers like these
cyanobacteria, one of the most important mechanical properties is the bending
modulus, or flexural rigidity. Here, we quantify the bending stiffness of three
species of filamentous cyanobacteria using a microfluidic flow device, where
single filaments are deflected by fluid flow. This is complemented by
measurements of the Young's modulus of the cell wall, via nanoindentation, and
the cell wall thickness. We find that the stiffness of the cyanobacteria is
well-captured by a simple model of a flexible rod, with most stress carried by
a rigid outer wall. Finally, we connect these results to the curved shapes that
these cyanobacteria naturally take while gliding, and quantify the forces
generated internally to maintain this shape. The measurements can be used to
model interactions between filamentous cyanobacteria, or with their
environment, and how their collective behaviour emerges from such interactions.


title: Factored Adaptation for Non-Stationary Reinforcement Learning

abstract:
  Dealing with non-stationarity in environments (i.e., transition dynamics) and
objectives (i.e., reward functions) is a challenging problem that is crucial in
real-world applications of reinforcement learning (RL). Most existing
approaches only focus on families of stationary MDPs, in which the
non-stationarity is episodic, i.e., the change is only possible across
episodes. The few works that do consider non-stationarity without a specific
boundary, i.e., also allow for changes within an episode, model the changes
monolithically in a single shared embedding vector. In this paper, we propose
Factored Adaptation for Non-Stationary RL (FANS-RL), a factored adaption
approach that explicitly learns the individual latent change factors affecting
the transition dynamics and reward functions. FANS-RL learns jointly the
structure of a factored MDP and a factored representation of the time-varying
change factors, as well as the specific state components that they affect, via
a factored non-stationary variational autoencoder. Through this general
framework, we can consider general non-stationary scenarios with different
changing function types and changing frequency. Experimental results
demonstrate that FANS-RL outperforms existing approaches in terms of rewards,
compactness of the latent state representation and robustness to varying
degrees of non-stationarity.


title: Substructure of Multiquark Hadrons (Snowmass 2021 White Paper)

abstract:
  In recent years there has been a rapidly growing body of experimental
evidence for existence of exotic, multiquark hadrons, i.e. mesons which contain
additional quarks, beyond the usual quark-antiquark pair and baryons which
consist of more than three quarks. In all cases with robust evidence they
contain at least one heavy quark Q=c or b, the majority including two heavy
quarks. Two key theoretical questions have been triggered by these discoveries:
(a) how are quarks organized inside these multiquark states -- as compact
objects with all quarks within one confinement volume, interacting via color
forces, perhaps with an important role played by diquarks, or as deuteron-like
hadronic molecules, bound by light-meson exchange? (b) what other multiquark
states should we expect? The two questions are tightly intertwined. Each of the
interpretations provides a natural explanation of parts of the data, but
neither explains all of the data. It is quite possible that both kinds of
structures appear in Nature. It may also be the case that certain states are
superpositions of the compact and molecular configurations. This Whitepaper
brings together contributions from many leading practitioners in the field,
representing a wide spectrum of theoretical interpretations. We discuss the
importance of future experimental and phenomenological work, which will lead to
better understandingof multiquark phenomena in QCD.


title:
The GMRT ARChIve Atomic gas survey (GARCIA)-I. Survey definition,
  methodology and initial results from the pilot sample

abstract:
  Interferometric observations of HI in galaxies played a pivotal role in
studies of nearby galaxies. Compared to single-dish observation, it provides
resolved distribution of gas in galaxies with unprecedented resolution. Several
extensive HI surveys of nearby galaxies have been performed in the past;
however, most of them consist of less than 100 galaxies due to individual
efforts. On the other hand, present-day archives of the radio telescopes
include data for at least several hundred galaxies. To utilize these data sets
to their full potential, we construct a sample including all galaxies observed
by the Giant Meter wave Radio Telescope (GMRT) in HI. This results in a total
of 515 galaxies, the largest sample to date. We intend to analyze all the data
uniformly and carry out different exciting science. As a pilot project, we
analyze data from 11 galaxies and present the data products in this paper. We
further investigate the neutral ISM in these galaxies and extract cold and warm
phases using a multi-Gaussian decomposition method. This pilot project assures
the quality of the data, which will enable us to perform critical science
investigations using the full sample.


title: From well-known tensor mesons to yet unknown axial-tensor mesons

abstract:
  While the ground-state tensor ($J^{PC}=2^{++}$) mesons $a_{2}(1320)$,
$K_{2}^{\ast}(1430)$, $f_{2}(1270)$, and $f_{2}^{\prime}(1525)$ are well known
experimentally and form an almost ideal nonet of quark-antiquark states, their
chiral partners, the ground-states axial-tensor ($J^{PC}=2^{--}$) mesons are
poorly settled: only the kaonic member $K_2(1820)$ of the nonet has been
experimentally found, whereas the isovector state $\rho_2$ and two isoscalar
states $\omega_2$ and $\phi_2$ are still missing. Here, we study masses,
strong, and radiative decays of tensor and axial-tensor mesons within a chiral
model that links them: the established tensor mesons are used to test the model
and to determine its parameters, and subsequently various predictions for their
chiral partners, the axial-tensor mesons, are obtained. The results are
compared to current lattice QCD outcomes as well as to other theoretical
approaches and show that the ground-state axial-tensor mesons are expected to
be quite broad, the vector-pseudoscalar mode being the most prominent decay
mode followed by the tensor-pseudoscalar one. Nonetheless, their experimental
finding seems to be possible in ongoing and/or future experiments.


title:
Counterfactual Cycle-Consistent Learning for Instruction Following and
  Generation in Vision-Language Navigation

abstract:
  Since the rise of vision-language navigation (VLN), great progress has been
made in instruction following -- building a follower to navigate environments
under the guidance of instructions. However, far less attention has been paid
to the inverse task: instruction generation -- learning a speaker~to generate
grounded descriptions for navigation routes. Existing VLN methods train a
speaker independently and often treat it as a data augmentation tool to
strengthen the follower while ignoring rich cross-task relations. Here we
describe an approach that learns the two tasks simultaneously and exploits
their intrinsic correlations to boost the training of each: the follower judges
whether the speaker-created instruction explains the original navigation route
correctly, and vice versa. Without the need of aligned instruction-path pairs,
such cycle-consistent learning scheme is complementary to task-specific
training targets defined on labeled data, and can also be applied over
unlabeled paths (sampled without paired instructions). Another agent,
called~creator is added to generate counterfactual environments. It greatly
changes current scenes yet leaves novel items -- which are vital for the
execution of original instructions -- unchanged. Thus more informative training
scenes are synthesized and the three agents compose a powerful VLN learning
system. Extensive experiments on a standard benchmark show that our approach
improves the performance of various follower models and produces accurate
navigation instructions.


title: Spatially Adaptive Online Prediction of Piecewise Regular Functions

abstract:
  We consider the problem of estimating piecewise regular functions in an
online setting, i.e., the data arrive sequentially and at any round our task is
to predict the value of the true function at the next revealed point using the
available data from past predictions. We propose a suitably modified version of
a recently developed online learning algorithm called the sleeping experts
aggregation algorithm. We show that this estimator satisfies oracle risk bounds
simultaneously for all local regions of the domain. As concrete instantiations
of the expert aggregation algorithm proposed here, we study an online mean
aggregation and an online linear regression aggregation algorithm where experts
correspond to the set of dyadic subrectangles of the domain. The resulting
algorithms are near linear time computable in the sample size. We specifically
focus on the performance of these online algorithms in the context of
estimating piecewise polynomial and bounded variation function classes in the
fixed design setup. The simultaneous oracle risk bounds we obtain for these
estimators in this context provide new and improved (in certain aspects)
guarantees even in the batch setting and are not available for the state of the
art batch learning estimators.


title: Constrained Few-shot Class-incremental Learning

abstract:
  Continually learning new classes from fresh data without forgetting previous
knowledge of old classes is a very challenging research problem. Moreover, it
is imperative that such learning must respect certain memory and computational
constraints such as (i) training samples are limited to only a few per class,
(ii) the computational cost of learning a novel class remains constant, and
(iii) the memory footprint of the model grows at most linearly with the number
of classes observed. To meet the above constraints, we propose C-FSCIL, which
is architecturally composed of a frozen meta-learned feature extractor, a
trainable fixed-size fully connected layer, and a rewritable dynamically
growing memory that stores as many vectors as the number of encountered
classes. C-FSCIL provides three update modes that offer a trade-off between
accuracy and compute-memory cost of learning novel classes. C-FSCIL exploits
hyperdimensional embedding that allows to continually express many more classes
than the fixed dimensions in the vector space, with minimal interference. The
quality of class vector representations is further improved by aligning them
quasi-orthogonally to each other by means of novel loss functions. Experiments
on the CIFAR100, miniImageNet, and Omniglot datasets show that C-FSCIL
outperforms the baselines with remarkable accuracy and compression. It also
scales up to the largest problem size ever tried in this few-shot setting by
learning 423 novel classes on top of 1200 base classes with less than 1.6%
accuracy drop. Our code is available at
https://github.com/IBM/constrained-FSCIL.


title: Resampling schemes in population annealing -- numerical results

abstract:
  Population annealing (PA) is a population-based algorithm that is designed
for equilibrium simulations of thermodynamic systems with a rough free energy
landscape. It is known to be more efficient in doing so than standard Markov
chain Monte Carlo alone. The algorithm has a number of parameters that can be
fine-tuned to improve performance. While there is some theoretical and
numerical work regarding most of these parameters, there appears to be a gap in
the literature concerning the role of resampling in PA. Here, we present a
numerical comparison of a number of resampling schemes for PA simulations of
the 2D Ising model.


title: Electroweak monopoles and their stability

abstract:
  We apply a generalized field ansatz to describe the spherically symmetric
sector of classical solutions of the electroweak theory. This sector contains
Abelian magnetic monopoles labeled by their magnetic charge $n=\pm 1,\pm
2,\ldots$, the non-Abelian monopole for $n=\pm 2$ found previously by Cho and
Maison (CM), and also the electric oscillating solutions. All magnetic
monopoles have infinite energy. We analyze their perturbative stability and use
the method of complex spacetime tetrad to separate variables and reduce the
perturbation equations to multi-channel Schroedinger-type eigenvalue problems.
The spectra of perturbations around the CM monopole do not contain negative
modes hence this solution is stable. The $n=\pm 1$ Abelian monopole is also
stable, but all monopoles with $|n|\geq 2$ are unstable with respect to
perturbations with angular momentum $j=|n|/2-1$. The Abelian $|n|=2$ monopole
is unstable only within the $j=0$ sector whereas the CM monopole also has
$|n|=2$ and belongs to the same sector, hence it may be viewed as a stable
remnant of the decay of the Abelian monopole. One may similarly conjecture that
stable remnants exist also for monopoles with $|n|>2$, hence the CM monopole
may be just the first member of a sequence of non-Abelian monopoles with higher
magnetic charges. Only the CM monopole is spherically symmetric while all
non-Abelian monopoles with $|n|>2$ are not rotationally invariant.


title: Spectral analysis in broken sheared waveguides

abstract:
  Let $\Omega \subset \mathbb R^3$ be a broken sheared waveguide, i.e., it is
built by translating a cross-section in a constant direction along a broken
line in $\mathbb R^3$. We prove that the discrete spectrum of the Dirichlet
Laplacian operator in $\Omega$ is non-empty and finite.


title: Accessing inaccessible information via quantum indistinguishability

abstract:
  Suppose that Alice stumbles upon a box containing some confidential
information and wishes to read out the latter. The problem is that the box is
practically unbreakable and the information is so encoded as not to leave any
physical trace in the environment, thereby leaving Alice with apparently no
means of gathering the information. While the information is per assumption
inaccessible classically, we show that it can be read out almost perfectly
within quantum mechanics. Interestingly, the task can be accomplished only if
Alice employs additional boxes, which need to be identical to the initial one
in the quantum-mechanical sense. After formalizing the task we show how its
solution relies on an interplay between entanglement and quantum
indistinguishability. We also discuss the broader merit of our findings,
thereby pointing out their relevance for quantum information processing.


title:
On the physical nature of the Wilson-Bappu effect: revising the gravity
  and temperature dependence

abstract:
  We present a sample of 32 stars of spectral types G and K, and luminosity
classes I to V, with moderate activity levels, covering four orders of
magnitude of surface gravity and a representative range of effective
temperature. For each star we obtained high S/N TIGRE-HEROS spectra with a
spectral resolving power of $R\approx20,000$ and have measured the Ca II K
line-widths of interest, $W_0$ and $W_1$. The main physical parameters are
determined by means of iSpec synthesis and Gaia EDR3 parallaxes. Mass estimates
are based on matching to evolution models. Using this stellar sample, that is
highly uniform in terms of spectral quality and assessment, we derive the
best-fit relation between emission line width and gravity $g$, including a
notable dependence on effective temperature $T_{\rm eff}$, of the form $W_1
\propto g^{-0.229} T_{\rm eff}^{+2.41}$. This result confirms the physical
interpretation of the Wilson-Bappu effect as a line saturation and photon
redistribution effect in the chromospheric Ca II column density, under the
assumption of hydrostatic equilibrium at the bottom of the chromosphere. While
the column density (and so $W_1$) increases towards lower gravities, the
observed temperature dependence is then understood as a simple ionization
effect -- in cooler stars, Ca II densities decrease in favor of Ca I.


title: Generalised Onsager Algebra in Quantum Lattice Models

abstract:
  The Onsager algebra is one of the cornerstones of exactly solvable models in
statistical mechanics. Starting from the generalised Clifford algebra, we
demonstrate its relations to the graph Temperley-Lieb algebra, and a
generalisation of the Onsager algebra. We present a series of quantum lattice
models as representations of the generalised Clifford algebra, possessing the
structure of a special type of the generalised Onsager algebra. The
integrability of those models is presented, analogous to the free fermionic
eight-vertex model. We also mention further extensions of the models and
physical properties related to the generalised Onsager algebra, hinting at a
general framework that includes families of quantum lattice models possessing
the structure of the generalised Onsager algebra.

categories: math-ph cond-mat.stat-mech math.MP quant-ph


title: Improving Speech Recognition for Indic Languages using Language Model

abstract:
  We study the effect of applying a language model (LM) on the output of
Automatic Speech Recognition (ASR) systems for Indic languages. We fine-tune
wav2vec $2.0$ models for $18$ Indic languages and adjust the results with
language models trained on text derived from a variety of sources. Our findings
demonstrate that the average Character Error Rate (CER) decreases by over $28$
\% and the average Word Error Rate (WER) decreases by about $36$ \% after
decoding with LM. We show that a large LM may not provide a substantial
improvement as compared to a diverse one. We also demonstrate that high quality
transcriptions can be obtained on domain-specific data without retraining the
ASR model and show results on biomedical domain.


title:
The structure of relatively hyperbolic groups in convex real projective
  geometry

abstract:
  In this paper we prove a general structure theorem for relatively hyperbolic
groups (with arbitrary peripheral subgroups) acting naive convex co-compactly
on properly convex domains in real projective space. We also establish a
characterization of such groups in terms of the existence of an invariant
collection of closed unbounded convex subsets with good isolation properties.
This is a real projective analogue of results of Hindawi-Hruska-Kleiner for
${\rm CAT}(0)$ spaces. We also obtain an equivariant homeomorphism between the
Bowditch boundary of the group and a quotient of the ideal boundary.


title: NGSO Constellation Design for Global Connectivity

abstract:
  Non-geostationary orbit (NGSO) satellite constellations represent a
cornerstone in the NewSpace paradigm and thus have become one of the hottest
topics for the industry, academia, but also for national space agencies and
regulators. For instance, numerous companies worldwide, including Starlink,
OneWeb, Kepler, SPUTNIX, and Amazon have started or will soon start to deploy
their own NGSO constellations, which aim to provide either broadband or IoT
services. One of the major drivers for such a high interest on NGSO
constellations is that, with an appropriate design, they are capable of
providing global coverage and connectivity.


title: Taurine in Taurus. An Over-Caffeinated Search for Coffee in Space

abstract:
  Caffeination can open tired eyes and enhance focus. Over-caffeination,
furthermore, can lead to errors but also to unexpected discoveries that might
not have happened without 30 hours of sleep deprivation and 500mg of caffeine
in our bodies. This paper presents exactly such a discovery. Upon much staring
into our coffee cups, empty anew, the thought struck us: coffee in space.
Caffeine may not be the only key. HL Tau, Taurus, bull... Taurine! We grinded
some red bourbon for a new pour-over, and developed the new,
coffee-groundsbreaking Large Astrocomical Taurine Tester Experiment (LATTE) in
just 1/4 of a day. We felt bull-ish about our chances of making a great
discovery! We installed LATTE, aimed it at the well-known young star HL Tau,
and there it was: an abundance of taurine gas beautifully outlining a cup of
cosmic flat white, with the ring structure of HL Tau turning out to be latte
art performed by a skillful cosmic barista. The first Robusta discovery of
coffee in space. Speaking of coffee, we hope you have a nice hot cup with you,
and we encourage you to pun-tinue all the way to the end of this bean-grinding
paper.

categories: astro-ph.EP astro-ph.IM astro-ph.SR physics.bio-ph


title:
Autonomous Navigation of AGVs in Unknown Cluttered Environments:
  log-MPPI Control Strategy

abstract:
  Sampling-based model predictive control (MPC) optimization methods, such as
Model Predictive Path Integral (MPPI), have recently shown promising results in
various robotic tasks. However, it might produce an infeasible trajectory when
the distributions of all sampled trajectories are concentrated within high-cost
even infeasible regions. In this study, we propose a new method called log-MPPI
equipped with a more effective trajectory sampling distribution policy which
significantly improves the trajectory feasibility in terms of satisfying system
constraints. The key point is to draw the trajectory samples from the normal
log-normal (NLN) mixture distribution, rather than from Gaussian distribution.
Furthermore, this work presents a method for collision-free navigation in
unknown cluttered environments by incorporating the 2D occupancy grid map into
the optimization problem of the sampling-based MPC algorithm. We first validate
the efficiency and robustness of our proposed control strategy through
extensive simulations of 2D autonomous navigation in different types of
cluttered environments as well as the cart-pole swing-up task. We further
demonstrate, through real-world experiments, the applicability of log-MPPI for
performing a 2D grid-based collision-free navigation in an unknown cluttered
environment, showing its superiority to be utilized with the local costmap
without adding additional complexity to the optimization problem. A video
demonstrating the real-world and simulation results is available at
https://youtu.be/_uGWQEFJSN0.


title: Learning Local Displacements for Point Cloud Completion

abstract:
  We propose a novel approach aimed at object and semantic scene completion
from a partial scan represented as a 3D point cloud. Our architecture relies on
three novel layers that are used successively within an encoder-decoder
structure and specifically developed for the task at hand. The first one
carries out feature extraction by matching the point features to a set of
pre-trained local descriptors. Then, to avoid losing individual descriptors as
part of standard operations such as max-pooling, we propose an alternative
neighbor-pooling operation that relies on adopting the feature vectors with the
highest activations. Finally, up-sampling in the decoder modifies our feature
extraction in order to increase the output dimension. While this model is
already able to achieve competitive results with the state of the art, we
further propose a way to increase the versatility of our approach to process
point clouds. To this aim, we introduce a second model that assembles our
layers within a transformer architecture. We evaluate both architectures on
object and indoor scene completion tasks, achieving state-of-the-art
performance.


title:
Is Word Error Rate a good evaluation metric for Speech Recognition in
  Indic Languages?

abstract:
  We propose a new method for the calculation of error rates in Automatic
Speech Recognition (ASR). This new metric is for languages that contain half
characters and where the same character can be written in different forms. We
implement our methodology in Hindi which is one of the main languages from
Indic context and we think this approach is scalable to other similar languages
containing a large character set. We call our metrics Alternate Word Error Rate
(AWER) and Alternate Character Error Rate (ACER).
  We train our ASR models using wav2vec 2.0\cite{baevski2020wav2vec} for Indic
languages. Additionally we use language models to improve our model
performance. Our results show a significant improvement in analyzing the error
rates at word and character level and the interpretability of the ASR system is
improved upto $3$\% in AWER and $7$\% in ACER for Hindi. Our experiments
suggest that in languages which have complex pronunciation, there are multiple
ways of writing words without changing their meaning. In such cases AWER and
ACER will be more useful rather than WER and CER as metrics. Furthermore, we
open source a new benchmarking dataset of 21 hours for Hindi with the new
metric scripts.


title:
A Shared Parameter Model for Systolic Blood Pressure Accounting for Data
  Missing Not at Random in the HUNT Study

abstract:
  In this work, blood pressure eleven years ahead is modeled using data from a
longitudinal population-based health survey, the Trondelag Health (HUNT) Study,
while accounting for missing data due to dropout between consecutive surveys
(20-50 %). We propose and validate a shared parameter model (SPM) in the
Bayesian framework with age, sex, body mass index, and initial blood pressure
as explanatory variables. Further, we propose a novel evaluation scheme to
assess data missing not at random (MNAR) by comparing the predictive
performance of the fitted SPM with and without conditioning on the missing
process. The results demonstrate that the SPM is suitable for inference for a
dataset of this size (cohort of 64385 participants) and structure. The SPM
indicates data MNAR and gives different parameter estimates than a naive model
assuming data missing at random. The SPM and naive models are compared based on
predictive performance in a validation dataset. The naive model performs
slightly better than the SPM for the present participants. This is in
accordance with results from a simulation study based on the SPM where we find
that the naive model performs better for the present participants, while the
SPM performs better for the dropouts.


title:
Perfectly Perform Machine Learning Task with Imperfect Optical Hardware
  Accelerator

abstract:
  Optical architectures have been emerging as an energy-efficient and
high-throughput hardware platform to accelerate computationally intensive
general matrix-matrix multiplications (GEMMs) in modern machine learning (ML)
algorithms. However, the inevitable imperfection and non-uniformity in
large-scale optoelectronic devices prevent the scalable deployment of optical
architectures, particularly those with innovative nano-devices. Here, we report
an optical ML hardware to accelerate GEMM operations based on cascaded spatial
light modulators and present a calibration procedure that enables accurate
calculations despite the non-uniformity and imperfection in devices and system.
We further characterize the hardware calculation accuracy under different
configurations of electrical-optical interfaces. Finally, we deploy the
developed optical hardware and calibration procedure to perform a ML task of
predicting the intersubband plasmon frequency in single-wall carbon nanotubes.
The obtained prediction accuracy from the optical hardware agrees well with
that obtained using a general purpose electronic graphic process unit.


title:
Understanding the role of single-board computers in engineering and
  computer science education: A systematic literature review

abstract:
  In the last decade, Single-Board Computers (SBCs) have been employed more
frequently in engineering and computer science both to technical and
educational levels. Several factors such as the versatility, the low-cost, and
the possibility to enhance the learning process through technology have
contributed to the educators and students usually employ these devices.
However, the implications, possibilities, and constraints of these devices in
engineering and Computer Science (CS) education have not been explored in
detail. In this systematic literature review, we explore how the SBCs are
employed in engineering and computer science and what educational results are
derived from their usage in the period 2010-2020 at tertiary education. For
that, 154 studies were selected out of n=605 collected from the academic
databases Ei Compendex, ERIC, and Inspec. The analysis was carried-out in two
phases, identifying, e.g., areas of application, learning outcomes, and
students and researchers' perceptions. The results mainly indicate the
following aspects: (1) The areas of laboratories and e-learning, computing
education, robotics, Internet of Things (IoT), and persons with disabilities
gather the studies in the review. (2) Researchers highlight the importance of
the SBCs to transform the curricula in engineering and CS for the students to
learn complex topics through experimentation in hands-on activities. (3) The
typical cognitive learning outcomes reported by the authors are the improvement
of the students' grades and the technical skills regarding the topics in the
courses. Concerning the affective learning outcomes, the increase of interest,
motivation, and engagement are commonly reported by the authors.


title: Non-orientable Lagrangian fillings of Legendrian knots

abstract:
  We investigate when a Legendrian knot in standard contact $\mathbb{R}^3$ has
a non-orientable exact Lagrangian filling. We prove analogs of several results
in the orientable setting, develop new combinatorial obstructions to
fillability, and determine when several families of knots have such fillings.
In particular, we determine completely when an alternating knot (and more
generally a plus-adequate knot) is decomposably non-orientably fillable, and
classify the fillability of most torus and 3-strand pretzel knots. We also
describe rigidity phenomena of decomposable non-orientable fillings, including
finiteness of the possible normal Euler numbers of fillings, and the
minimization of crosscap numbers of fillings, obtaining results which contrast
in interesting ways with the smooth setting.


title:
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung
  Nodules Using Time-Series Deep Learning

abstract:
  Lung cancer is the leading cause of cancer-related mortality worldwide. Lung
cancer screening (LCS) using annual low-dose computed tomography (CT) scanning
has been proven to significantly reduce lung cancer mortality by detecting
cancerous lung nodules at an earlier stage. Improving risk stratification of
malignancy risk in lung nodules can be enhanced using machine/deep learning
algorithms. However most existing algorithms: a) have primarily assessed single
time-point CT data alone thereby failing to utilize the inherent advantages
contained within longitudinal imaging datasets; b) have not integrated into
computer models pertinent clinical data that might inform risk prediction; c)
have not assessed algorithm performance on the spectrum of nodules that are
most challenging for radiologists to interpret and where assistance from
analytic tools would be most beneficial.
  Here we show the performance of our time-series deep learning model
(DeepCAD-NLM-L) which integrates multi-model information across three
longitudinal data domains: nodule-specific, lung-specific, and clinical
demographic data. We compared our time-series deep learning model to a)
radiologist performance on CTs from the National Lung Screening Trial enriched
with the most challenging nodules for diagnosis; b) a nodule management
algorithm from a North London LCS study (SUMMIT). Our model demonstrated
comparable and complementary performance to radiologists when interpreting
challenging lung nodules and showed improved performance (AUC=88\%) against
models utilizing single time-point data only. The results emphasise the
importance of time-series, multi-modal analysis when interpreting malignancy
risk in LCS.


title:
A Self-Learning Neural Network Approach for RFI Detection and Removal in
  Radio Astronomy

abstract:
  We present a novel method for the detection and removal of Radio Frequency
Interference (RFI) from the raw digitized signal in the signal processing chain
of a typical radio astronomy experiment. The main advantage of our method is
that it does not require a training set. Instead, our method relies on the fact
that the true signal of interest coming from astronomical sources is thermal
and therefore described as a Gaussian random process, which cannot be
compressed. We employ a variational encoder/decoder network to find the
compressible information in the datastream that can explain the most variance
with the fewest degrees of freedom. We demonstrate it on a set of toy problems
and stored ringbuffers from the Baryon Mapping eXperiment (BMX) prototype. We
discuss advantages and limitations of this method and possible implementation
in the front-end of future radio experiments.


title: Multi-scale model predicting friction of crystalline materials

abstract:
  We present a multi-scale computational framework suitable for designing solid
lubricant interfaces fully in silico. The approach is based on stochastic
thermodynamics founded on the classical thermally activated two-dimensional
Prandtl-Tomlinson model, linked with First Principles methods to accurately
capture the properties of real materials. It allows investigating the energy
dissipation due to friction in materials as it arises directly from their
electronic structure, and naturally accessing the time-scale range of a typical
friction force microscopy. This opens new possibilities for designing a broad
class of material surfaces with atomically tailored properties. We apply the
multi-scale framework to a class of two-dimensional layered materials and
reveal a delicate interplay between the topology of the energy landscape and
dissipation that known static approaches based solely on the energy barriers
fail to capture.

categories: cond-mat.mtrl-sci cond-mat.mes-hall physics.app-ph physics.chem-ph


title:
COWS all tHE way Down (COWSHED) I: Could cow based planetoids support
  methane atmospheres?

abstract:
  More often than not a lunch time conversation will veer off into bizarre and
uncharted territories. In rare instances these frontiers of conversation can
lead to deep insights about the Universe we inhabit. This paper details the
fruits of one such conversation. In this paper we will answer the question: How
many cows do you need to form a planetoid entirely comprised of cows, which
will support a methane atmoosphere produced by the planetary herd? We will not
only present the necessary assumptions and theory underpinning the
cow-culations, but also present a thorough (and rather robust) discussion of
the viability of, and implications for accomplishing, such a feat.

categories: astro-ph.EP physics.ao-ph physics.geo-ph physics.pop-ph


title: Contemplating the fate of modified gravity

abstract:
  Possible models of modified gravity are being extensively studied now, with
most phenomenological motivations coming from puzzles and tensions in cosmology
with a natural desire to better fit the known and newly coming data. At the
same time, available experimental evidence is limited for testing gravity as a
force, beyond the regimes in which the theory of general relativity has proven
to be successful. This situation leads researchers to look for "the simplest
modification" to general relativity, in a certain class of models, that is
enough to solve one or more problems. As a result, we are lost amidst a variety
of theories with no deeper guiding principle. In this essay, we openly discuss
these problems, and propose some ideas on how to go.


title:
The role of ocean circulation in driving hemispheric symmetry breaking
  of the ice shell of Enceladus

abstract:
  The ice shell of Enceladus exhibits strong asymmetry between its hemispheres,
with all known geysers concentrated over the south pole, even though its
orbital configuration is almost perfectly symmetric. By exploring ocean
circulation across a range of ocean salinities and core/shell heating
partitions, we study the role of ice-ocean interaction in hemispheric symmetry
breaking. We find that: (i) asymmetry is enhanced by cross-equatorial ocean
heat transport when the ice shell is the major heat source and vice versa, (ii)
the magnitude of ocean heat transport is comparable to the global heat
production, significantly affecting the ice shell evolution and equilibrium
state and (iii) more than one equilibrium state can exist due to a positive
feedback between melting and ocean circulation.


title: Decentralization illusion in DeFi: Evidence from MakerDAO

abstract:
  Decentralized Autonomous Organization (DAO) is very popular in Decentralized
Finance (DeFi) applications as it provides a decentralized governance solution
through blockchain. We analyze the governance characteristics in the relevant
Maker protocol and its stablecoin Dai (DAI) and governance token Maker (MKR).
To achieve that, we establish several measurements of centralized governance.
Our empirical analysis investigates the effect of centralized governance over a
series of factors related to MKR, DAI and Ethereum, such as financial,
transaction, exchange, network and twitter sentiment indicators. Our results
show that governance centralization influences both the Maker protocol and
Ethereum blockchain. The main implication of this study is that centralized
governance in MakerDAO very much exists, while DeFi investors face a trade-off
between efficiency and decentralization. This further contributes to the
contemporary debate on whether DeFi can be truly decentralized.


title:
High thermoelectric performance in metastable phase of silicon: a
  first-principles study

abstract:
  In this work, both thermal and electrical transport properties of
diamond$-$cubic Si (Si$-$I) and metastable R8 phase of Si (Si$-$XII) are
comparatively studied by using first$-$principles calculations combined with
Boltzmann transport theory. The metastable Si$-$XII shows one magnitude lower
lattice thermal conductivity than stable Si$-$I from 300 to 500~K, attributed
from the stronger phonon scattering in three$-$phonon scattering processes of
Si$-$XII. For the electronic transport properties, although Si$-$XII with
smaller band gap (0.22 eV) shows lower Seebeck coefficient, the electrical
conductivities of anisotropic $n$$-$type Si$-$XII show considerable values
along $x$ axis due to the small effective masses of electron along this
direction. The peaks of thermoelectric figure of merit ($ZT$) in $n$$-$type
Si$-$XII are higher than that of $p$$-$type ones along the same direction.
Owing to the lower lattice thermal conductivity and optimistic electrical
conductivity, Si$-$XII exhibits larger optimal $ZT$ compared with Si$-$I in
both $p$$-$ and $n$$-$type doping. For $n$$-$type Si$-$XII, the optimal $ZT$
values at 300, 400, and 500 K can reach 0.24, 0.43, and 0.63 along $x$ axis at
carrier concentration of $2.6\times10^{19}$, $4.1\times10^{19}$, and
$4.8\times10^{19}$~cm$^{-3}$, respectively. The reported results elucidate that
the metastable Si could be integrated to the thermoelectric power generator.

categories: cond-mat.mtrl-sci cond-mat.mes-hall physics.app-ph physics.comp-ph


title:
Joint domain adaptation and speech bandwidth extension using time-domain
  GANs for speaker verification

abstract:
  Speech systems developed for a particular choice of acoustic domain and
sampling frequency do not translate easily to others. The usual practice is to
learn domain adaptation and bandwidth extension models independently. Contrary
to this, we propose to learn both tasks together. Particularly, we learn to map
narrowband conversational telephone speech to wideband microphone speech. We
developed parallel and non-parallel learning solutions which utilize both
paired and unpaired data. First, we first discuss joint and disjoint training
of multiple generative models for our tasks. Then, we propose a two-stage
learning solution where we use a pre-trained domain adaptation system for
pre-processing in bandwidth extension training. We evaluated our schemes on a
Speaker Verification downstream task. We used the JHU-MIT experimental setup
for NIST SRE21, which comprises SRE16, SRE-CTS Superset and SRE21. Our results
provide the first evidence that learning both tasks is better than learning
just one. On SRE16, our best system achieves 22% relative improvement in Equal
Error Rate w.r.t. a direct learning baseline and 8% w.r.t. a strong bandwidth
expansion system.


title:
A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and
  Nonsmooth Bi-level Optimization

abstract:
  Many important machine learning applications involve regularized nonconvex
bi-level optimization. However, the existing gradient-based bi-level
optimization algorithms cannot handle nonconvex or nonsmooth regularizers, and
they suffer from a high computation complexity in nonconvex bi-level
optimization. In this work, we study a proximal gradient-type algorithm that
adopts the approximate implicit differentiation (AID) scheme for nonconvex
bi-level optimization with possibly nonconvex and nonsmooth regularizers. In
particular, the algorithm applies the Nesterov's momentum to accelerate the
computation of the implicit gradient involved in AID. We provide a
comprehensive analysis of the global convergence properties of this algorithm
through identifying its intrinsic potential function. In particular, we
formally establish the convergence of the model parameters to a critical point
of the bi-level problem, and obtain an improved computation complexity
$\mathcal{O}(\kappa^{3.5}\epsilon^{-2})$ over the state-of-the-art result.
Moreover, we analyze the asymptotic convergence rates of this algorithm under a
class of local nonconvex geometries characterized by a {\L}ojasiewicz-type
gradient inequality. Experiment on hyper-parameter optimization demonstrates
the effectiveness of our algorithm.


title:
Knowledge-based Entity Prediction for Improved Machine Perception in
  Autonomous Systems

abstract:
  Knowledge-based entity prediction (KEP) is a novel task that aims to improve
machine perception in autonomous systems. KEP leverages relational knowledge
from heterogeneous sources in predicting potentially unrecognized entities. In
this paper, we provide a formal definition of KEP as a knowledge completion
task. Three potential solutions are then introduced, which employ several
machine learning and data mining techniques. Finally, the applicability of KEP
is demonstrated on two autonomous systems from different domains; namely,
autonomous driving and smart manufacturing. We argue that in complex real-world
systems, the use of KEP would significantly improve machine perception while
pushing the current technology one step closer to achieving the full autonomy.


title: Disjoint hypercyclicity, Sidon sets and weakly mixing operators

abstract:
  We prove that a finite set of natural numbers $J$ satisfies that $J\cup\{0\}$
is not Sidon if and only if for any operator $T$, the disjoint hypercyclicity
of $\{T^j:j\in J\}$ implies that $T$ is weakly mixing. As an application we
show the existence of a non weakly mixing operator $T$ such that $T\oplus
T^2\ldots \oplus T^n$ is hypercyclic for every $n$.


title: End-to-end Document Recognition and Understanding with Dessurt

abstract:
  We introduce Dessurt, a relatively simple document understanding transformer
capable of being fine-tuned on a greater variety of document tasks than prior
methods. It receives a document image and task string as input and generates
arbitrary text autoregressively as output. Because Dessurt is an end-to-end
architecture that performs text recognition in addition to the document
understanding, it does not require an external recognition model as prior
methods do, making it easier to fine-tune to new visual domains. We show that
this model is effective at 9 different dataset-task combinations.


title: The Gonality of Rook Graphs

abstract:
  Two dimensional rook graphs are the Cartesian product of two complete graphs.
In this paper we prove that the gonality of these graphs is the expected value
of $(n-1)m$ where $n$ is size of the smaller complete graph and $m$ is the size
of the larger. We also explore the scramble number of these graphs, which is a
new graph invariant and a lower bound on the gonality.


title: Quantifying the presence/absence of meso-scale structures in networks

abstract:
  Meso-scale structures are network features where nodes with similar
properties are grouped together instead of being treated individually. In this
work, we provide formal and mathematical definitions of three such structures:
assortative communities, disassortative communities and core-periphery. We then
leverage these definitions and a Bayesian framework to quantify the
presence/absence of each structure in a network. This allows for probabilistic
statements about the network structure as well as uncertainty estimates of the
group labels and edge probabilities. The method is applied to real-world
networks, yielding provocative results about well-known network data sets.


title: TR-MOT: Multi-Object Tracking by Reference

abstract:
  Multi-object Tracking (MOT) generally can be split into two sub-tasks, i.e.,
detection and association. Many previous methods follow the tracking by
detection paradigm, which first obtain detections at each frame and then
associate them between adjacent frames. Though with an impressive performance
by utilizing a strong detector, it will degrade their detection and association
performance under scenes with many occlusions and large motion if not using
temporal information. In this paper, we propose a novel Reference Search (RS)
module to provide a more reliable association based on the deformable
transformer structure, which is natural to learn the feature alignment for each
object among frames. RS takes previous detected results as references to
aggregate the corresponding features from the combined features of the adjacent
frames and makes a one-to-one track state prediction for each reference in
parallel. Therefore, RS can attain a reliable association coping with
unexpected motions by leveraging visual temporal features while maintaining the
strong detection performance by decoupling from the detector. Our RS module can
also be compatible with the structure of the other tracking by detection
frameworks. Furthermore, we propose a joint training strategy and an effective
matching pipeline for our online MOT framework with the RS module. Our method
achieves competitive results on MOT17 and MOT20 datasets.


title:
Federated Learning for the Classification of Tumor Infiltrating
  Lymphocytes

abstract:
  We evaluate the performance of federated learning (FL) in developing deep
learning models for analysis of digitized tissue sections. A classification
application was considered as the example use case, on quantifiying the
distribution of tumor infiltrating lymphocytes within whole slide images
(WSIs). A deep learning classification model was trained using 50*50 square
micron patches extracted from the WSIs. We simulated a FL environment in which
a dataset, generated from WSIs of cancer from numerous anatomical sites
available by The Cancer Genome Atlas repository, is partitioned in 8 different
nodes. Our results show that the model trained with the federated training
approach achieves similar performance, both quantitatively and qualitatively,
to that of a model trained with all the training data pooled at a centralized
location. Our study shows that FL has tremendous potential for enabling
development of more robust and accurate models for histopathology image
analysis without having to collect large and diverse training data at a single
location.


title: Push-Subgradient Is of the Optimal Convergence Rate

abstract:
  The push-sum based subgradient is an important method for distributed convex
optimization over unbalanced directed graphs, which is known to converge at a
rate of $O(\ln t/\sqrt{t})$. This paper shows that the push-subgradient
algorithm actually converges at a rate of $O(1/\sqrt{t})$, which is the same as
that of the single-agent subgradient and thus optimal. The proposed tool for
analyzing push-sum based algorithms is of independent interest.


title:
Multi Antenna Radar System for American Sign Language (ASL) Recognition
  Using Deep Learning

abstract:
  This paper investigates RF-based system for automatic American Sign Language
(ASL) recognition. We consider radar for ASL by joint spatio-temporal
preprocessing of radar returns using time frequency (TF) analysis and
high-resolution receive beamforming. The additional degrees of freedom offered
by joint temporal and spatial processing using a multiple antenna sensor can
help to recognize ASL conversation between two or more individuals. This is
performed by applying beamforming to collect spatial images in an attempt to
resolve individuals communicating at the same time through hand and arm
movements. The spatio-temporal images are fused and classified by a
convolutional neural network (CNN) which is capable of discerning signs
performed by different individuals even when the beamformer is unable to
separate the respective signs completely. The focus group comprises individuals
with varying expertise with sign language, and real time measurements at 77 GHz
frequency are performed using Texas Instruments (TI) cascade radar.


title: In icy ocean worlds, size matters!

abstract:
  The ice shell and subsurface ocean on icy worlds are strongly coupled
together -- heat and salinity flux from the ice shell induced by the ice
thickness gradient drives circulation in the ocean, and in turn, the heat
transport by ocean circulation shapes the ice shell. Since measurements in the
near future are likely to remain constrained to above the ice shell,
understanding this ocean-ice interaction is crucial. Using an ocean box model
and a series of experiments simulating the 2D ocean circulation, we find that
large icy moons with strong gravity tend to have stronger ocean heat transport
under the same ice-shell topography. As a result, the equilibrium ice shell
geometry is expected to be flatter on moons with larger size, and vice versa.
This finding is broadly consistent with the observed ice shell geometry for
Enceladus and Europa.


title: DDNeRF: Depth Distribution Neural Radiance Fields

abstract:
  In recent years, the field of implicit neural representation has progressed
significantly. Models such as neural radiance fields (NeRF), which uses
relatively small neural networks, can represent high-quality scenes and achieve
state-of-the-art results for novel view synthesis. Training these types of
networks, however, is still computationally very expensive. We present depth
distribution neural radiance field (DDNeRF), a new method that significantly
increases sampling efficiency along rays during training while achieving
superior results for a given sampling budget. DDNeRF achieves this by learning
a more accurate representation of the density distribution along rays. More
specifically, we train a coarse model to predict the internal distribution of
the transparency of an input volume in addition to the volume's total density.
This finer distribution then guides the sampling procedure of the fine model.
This method allows us to use fewer samples during training while reducing
computational resources.


title:
A Bayesian framework for incorporating exposure uncertainty into health
  analyses with application to air pollution and stillbirth

abstract:
  Studies of the relationships between environmental exposures and adverse
health outcomes often rely on a two-stage statistical modeling approach, where
exposure is modeled/predicted in the first stage and used as input to a
separately fit health outcome analysis in the second stage. Uncertainty in
these predictions is frequently ignored, or accounted for in an overly
simplistic manner, when estimating the associations of interest. Working in the
Bayesian setting, we propose a flexible kernel density estimation (KDE)
approach for fully utilizing posterior output from the first stage
modeling/prediction to make accurate inference on the association between
exposure and health in the second stage, derive the full conditional
distributions needed for efficient model fitting, detail its connections with
existing approaches, and compare its performance through simulation. Our KDE
approach is shown to generally have improved performance across several
settings and model comparison metrics. Using competing approaches, we
investigate the association between lagged daily ambient fine particulate
matter levels and stillbirth counts in New Jersey (2011-2015), observing an
increase in risk with elevated exposure three days prior to delivery. The newly
developed methods are available in the R package KDExp.


title:
Physics-constrained Unsupervised Learning of Partial Differential
  Equations using Meshes

abstract:
  Enhancing neural networks with knowledge of physical equations has become an
efficient way of solving various physics problems, from fluid flow to
electromagnetism. Graph neural networks show promise in accurately representing
irregularly meshed objects and learning their dynamics, but have so far
required supervision through large datasets. In this work, we represent meshes
naturally as graphs, process these using Graph Networks, and formulate our
physics-based loss to provide an unsupervised learning framework for partial
differential equations (PDE). We quantitatively compare our results to a
classical numerical PDE solver, and show that our computationally efficient
approach can be used as an interactive PDE solver that is adjusting boundary
conditions in real-time and remains sufficiently close to the baseline
solution. Our inherently differentiable framework will enable the application
of PDE solvers in interactive settings, such as model-based control of
soft-body deformations, or in gradient-based optimization methods that require
a fully differentiable pipeline.


title: A new parameterized monogamy relation between entanglement and equality

abstract:
  We provide a generalized definition of the monogamy relation for entanglement
measures. A monogamy equality rather than the usual inequality is presented
based on the monogamy weight, from which we give monogamy relations satisfied
by the $\alpha$th $(\alpha>0)$ power of the entanglement measures. Taking
concurrence as an example, we further demonstrate the significance and
advantages of these relations. In addition, we show that monogamy relations can
be recovered by considering multiple copies of states for every non-additive
entanglement measure that violates the inequalities. We also demonstrate that
the such relations for tripartite states can be generalized to multipartite
systems.


title:
The popular myth of the drunk fireworks in the Valencian Fallas: "If you
  run, it chases you"

abstract:
  There is a popular myth transferred from generation to generation related to
the festivity of the Valencian Fallas. During these days, it is traditional to
throw pyrotechnic devices of all kinds. There is a legend about the so-called
drunk fireworks, small rockets that move randomly at high speed. According to
folk wisdom, if you try to run away from a drunk firework, it will chase you
until it hits you. It is performed a Monte Carlo simulation without considering
aerodynamic effects to see if there is indeed a greater chance of a person
being hit when moving. The results indicate that the popular myth is correct,
at least in terms of having a higher probability of impact. Once again, it is
demonstrated that popular myths can be a source of valuable knowledge beyond
formal science.


title:
Extreme value theory for a sequence of supremum of a class of Gaussian
  processes with trend

abstract:
  We investigate extreme value theory of a stationary random sequence defined
by the all-time supremum of aggregated self-similar Gaussian processes with
trend. This study is mainly motivated by a recent contribution by Meijer et al.
[Extreme-value theory for large fork-join queues, with applications to
high-tech supply chains, 2021, preprint] where some extreme value theory for
the Brownian case is discussed. We aim to extend some of their results on this
theory. We show that a sequence of suitably normalised $k$th order statistics
converges in distribution to a limiting random variable which can be a negative
log transformed Erlang distributed random variable, a Normal random variable or
a mixture of them, according to three conditions deduced through the model
parameters. Remarkably, this phenomenon resembles that of the stationary Normal
sequence. We also show that various moments of the normalised $k$th order
statistics converge to the corresponding moments of the limiting random
variable.


title: Controllable Augmentations for Video Representation Learning

abstract:
  This paper focuses on self-supervised video representation learning. Most
existing approaches follow the contrastive learning pipeline to construct
positive and negative pairs by sampling different clips. However, this
formulation tends to bias to static background and have difficulty establishing
global temporal structures. The major reason is that the positive pairs, i.e.,
different clips sampled from the same video, have limited temporal receptive
field, and usually share similar background but differ in motions. To address
these problems, we propose a framework to jointly utilize local clips and
global videos to learn from detailed region-level correspondence as well as
general long-term temporal relations. Based on a set of controllable
augmentations, we achieve accurate appearance and motion pattern alignment
through soft spatio-temporal region contrast. Our formulation is able to avoid
the low-level redundancy shortcut by mutual information minimization to improve
the generalization. We also introduce local-global temporal order dependency to
further bridge the gap between clip-level and video-level representations for
robust temporal modeling. Extensive experiments demonstrate that our framework
is superior on three video benchmarks in action recognition and video
retrieval, capturing more accurate temporal dynamics.


title: Model Predictive Optimized Path Integral Strategies

abstract:
  We generalize the derivation of model predictive path integral control (MPPI)
to allow for a single joint distribution across controls in the control
sequence. This reformation allows for the implementation of adaptive importance
sampling (AIS) algorithms into the original importance sampling step while
still maintaining the benefits of MPPI such as working with arbitrary system
dynamics and cost functions. The benefit of optimizing the proposal
distribution by integrating AIS at each control step is demonstrated in
simulated environments including controlling multiple cars around a track. The
new algorithm is more sample efficient than MPPI, achieving better performance
with fewer samples. This performance disparity grows as the dimension of the
action space increases. Results from simulations suggest the new algorithm can
be used as an anytime algorithm, increasing the value of control at each
iteration versus relying on a large set of samples.


title:
Transformer Language Models without Positional Encodings Still Learn
  Positional Information

abstract:
  Transformers typically require some form of positional encoding, such as
positional embeddings, to process natural language sequences. Surprisingly, we
find that transformer language models without any explicit positional encoding
are still competitive with standard models, and that this phenomenon is robust
across different datasets, model sizes, and sequence lengths. Probing
experiments reveal that such models acquire an implicit notion of absolute
positions throughout the network, effectively compensating for the missing
information. We conjecture that causal attention enables the model to infer the
number of predecessors that each token can attend to, thereby approximating its
absolute position.


title:
Can the abyss swallow gravitational waves or why we do not observe
  echoes?

abstract:
  Here we propose a simple explanation why echoes from wormholes mimicking
black holes may be so small that they cannot be observed. The essence of the
effect is in the redistribution of the initial energy of gravitational wave
among multiple universes, connected by a wormhole.


title:
Convex bodies of constant width in spaces of constant curvature and the
  extremal area of Reuleaux triangles

abstract:
  Extending Blaschke and Lebesgue's classical result in the Euclidean plane, it
has been recently proved in spherical and the hyperbolic cases, as well, that
Reuleaux triangles have the minimal area among convex domains of constant width
$D$. We prove an essentially optimal stability version of this statement in
each of the three types of surfaces of constant curvature. In addition, we
summarize the fundamental properties of convex bodies of constant width in
spaces of constant curvature, and provide a characterization in the hyperbolic
case in terms of horospheres.


title:
Hybrid Handcrafted and Learnable Audio Representation for Analysis of
  Speech Under Cognitive and Physical Load

abstract:
  As a neurophysiological response to threat or adverse conditions, stress can
affect cognition, emotion and behaviour with potentially detrimental effects on
health in the case of sustained exposure. Since the affective content of speech
is inherently modulated by an individual's physical and mental state, a
substantial body of research has been devoted to the study of paralinguistic
correlates of stress-inducing task load. Historically, voice stress analysis
(VSA) has been conducted using conventional digital signal processing (DSP)
techniques. Despite the development of modern methods based on deep neural
networks (DNNs), accurately detecting stress in speech remains difficult due to
the wide variety of stressors and considerable variability in the individual
stress perception. To that end, we introduce a set of five datasets for task
load detection in speech. The voice recordings were collected as either
cognitive or physical stress was induced in the cohort of volunteers, with a
cumulative number of more than a hundred speakers. We used the datasets to
design and evaluate a novel self-supervised audio representation that leverages
the effectiveness of handcrafted features (DSP-based) and the complexity of
data-driven DNN representations. Notably, the proposed approach outperformed
both extensive handcrafted feature sets and novel DNN-based audio
representation learning approaches.


title:
Compatibility of balanced and SKT metrics on two-step solvable Lie
  groups

abstract:
  It has been conjectured by Fino and Vezzoni that a compact complex manifold
admitting both a compatible SKT and a compatible balanced metric also admits a
compatible K\"ahler metric. Using the shear construction and classification
results for two-step solvable SKT Lie algebras from our previous work, we prove
this conjecture for compact two-step solvmanifolds endowed with an invariant
complex structure which is either (a) of pure type or (b) of dimension six. In
contrast, we provide two counterexamples for a natural generalisation of this
conjecture in the homogeneous invariant setting. As part of the work, we obtain
further classification results for invariant SKT, balanced and K\"ahler
structures on two-step solvable Lie groups. In particular, we give the full
classification of left-invariant SKT structures on two-step solvable Lie groups
in dimension six.


title:
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic
  descriptions, and Conceptual Relations

abstract:
  We present a meta-learning framework for learning new visual concepts
quickly, from just one or a few examples, guided by multiple naturally
occurring data streams: simultaneously looking at images, reading sentences
that describe the objects in the scene, and interpreting supplemental sentences
that relate the novel concept with other concepts. The learned concepts support
downstream applications, such as answering questions by reasoning about unseen
images. Our model, namely FALCON, represents individual visual concepts, such
as colors and shapes, as axis-aligned boxes in a high-dimensional space (the
"box embedding space"). Given an input image and its paired sentence, our model
first resolves the referential expression in the sentence and associates the
novel concept with particular objects in the scene. Next, our model interprets
supplemental sentences to relate the novel concept with other known concepts,
such as "X has property Y" or "X is a kind of Y". Finally, it infers an optimal
box embedding for the novel concept that jointly 1) maximizes the likelihood of
the observed instances in the image, and 2) satisfies the relationships between
the novel concepts and the known ones. We demonstrate the effectiveness of our
model on both synthetic and real-world datasets.


title: Task-driven Modular Co-design of Vehicle Control Systems

abstract:
  When designing autonomous systems, we need to consider multiple trade-offs at
various abstraction levels, and the choices of single (hardware and software)
components need to be studied jointly. In this work we consider the problem of
designing the control algorithm as well as the platform on which it is
executed. In particular, we focus on vehicle control systems, and formalize
state-of-the-art control schemes as monotone feasibility relations. We then
show how, leveraging a monotone theory of co-design, we can study the embedding
of control synthesis problems into the task-driven co-design problem of a
robotic platform. The properties of the proposed approach are illustrated by
considering urban driving scenarios. We show how, given a particular task, we
can efficiently compute Pareto optimal design solutions.


title:
Joint Sensing, Communication and Localization of a Silent Abnormality
  Using Molecular Diffusion

abstract:
  In this paper, we propose a molecular communication system to localize an
abnormality in a diffusion based medium. We consider a general setup to perform
joint sensing, communication and localization. This setup consists of three
types of devices, each for a different task: mobile sensors for navigation and
molecule releasing (for communication), fusion centers (FC)s for sampling,
amplifying and forwarding the signal, and a gateway for making decision or
exchanging the information with an external device. The sensors move randomly
in the environment to reach the abnormality. We consider both collaborative and
non-collaborative sensors that simultaneously release their molecules to the
FCs when the number of activated sensors or the moving time reach a certain
threshold, respectively. The FCs amplify the received signal and forward it to
the gateway for decision making using either an ideal or a noisy communication
channel. A practical application of the proposed model is drug delivery in a
tissue of human body, in order to guide the nanomachine bound drug to the exact
location. The decision rules and probabilities of error are obtained for two
considered sensors types in both ideal and noisy communication channels.


title: Data-driven Prediction of Relevant Scenarios for Robust Optimization

abstract:
  In this work we study robust one- and two-stage problems with discrete
uncertainty sets which are known to be hard to solve even if the underlying
deterministic problem is easy. Popular solution methods iteratively generate
scenario constraints and possibly second-stage variables. This way, by solving
a sequence of smaller problems, it is often possible to avoid the complexity of
considering all scenarios simultaneously. A key ingredient for the performance
of the iterative methods is a good selection of start scenarios. In this paper
we propose a data-driven heuristic to seed the iterative solution method with a
set of starting scenarios that provide a strong lower bound early in the
process, and result in considerably smaller overall solution times compared to
other benchmark methods. Our heuristic learns the relevance of a scenario by
extracting information from training data based on a combined similarity
measure between robust problem instances and single scenarios. Our experiments
show that predicting even a small number of good start scenarios by our method
can considerably reduce the computation time of the iterative methods.


title:
Adaptive Observer for a Class of Systems with Switched Unknown
  Parameters Using DREM

abstract:
  In this note, we develop an adaptive observer for a class of nonlinear
systems with switched unknown parameters to estimate the states and parameters
simultaneously. The main challenge lies in how to eliminate the disturbance
effect of zero-input responses caused by the switching on the parameter
estimation. These responses depend on the unknown states at switching instants
(SASI) and constitute an additive disturbance to the parameter estimation,
which obstructs parameter convergence to zero. Our solution is to treat the
zero-input responses as excitations instead of disturbances. This is realized
by first augmenting the system parameter with the SASI and then developing an
estimator for the augmented parameter using the \textit{dynamic regression
extension and mixing} (DREM) technique. Thanks to its property of element-wise
parameter adaptation, the system parameter estimation is decoupled from the
SASI. As a result, the estimation errors of system states and parameters
converge to zero asymptotically. Furthermore, the robustness of the proposed
adaptive observer is guaranteed in the presence of disturbances and noise. A
numerical example validates the effectiveness of the proposed approach.


title:
Boundary conditions for isolated asymptotically anti-de Sitter
  spacetimes

abstract:
  We revisit the propagation of classical scalar fields in a spacetime which is
asymptotically anti-de Sitter. The lack of global hyperbolicity of the
underlying background gives rise to an ambiguity in the dynamical evolution of
solutions of the wave equation, requiring the prescription of extra boundary
conditions at the conformal infinity to be fixed. We show that the only
boundary conditions tha are compatible with the hypothesis that the system is
isolated, as defined by the (improved) energy-momentum tensor, are of Dirichlet
and Neumann types.


title: A Toy Model for Damped Water Waves

abstract:
  We consider a toy model for a damped water waves system in a domain $\Omega_t
\subset \mathbb{T} \times \mathbb{R}$. The toy model is based on the
paradifferential water waves equation derived in the work of
Alazard-Burq-Zuily. The form of damping we utilize we utilize is a modified
sponge layer proposed for the three-dimensional water waves system by Clamond,
et. al. We show that, in the case of small Cauchy data, solutions to the toy
model exhibit a quadratic lifespan. This is done via proving energy estimates
with the energy being constructed from appropriately chosen vector fields.


title:
Generation of Speaker Representations Using Heterogeneous Training Batch
  Assembly

abstract:
  In traditional speaker diarization systems, a well-trained speaker model is a
key component to extract representations from consecutive and partially
overlapping segments in a long speech session. To be more consistent with the
back-end segmentation and clustering, we propose a new CNN-based speaker
modeling scheme, which takes into account the heterogeneity of the speakers in
each training segment and batch. We randomly and synthetically augment the
training data into a set of segments, each of which contains more than one
speaker and some overlapping parts. A soft label is imposed on each segment
based on its speaker occupation ratio, and the standard cross entropy loss is
implemented in model training. In this way, the speaker model should have the
ability to generate a geometrically meaningful embedding for each multi-speaker
segment. Experimental results show that our system is superior to the baseline
system using x-vectors in two speaker diarization tasks. In the CALLHOME task
trained on the NIST SRE and Switchboard datasets, our system achieves a
relative reduction of 12.93% in DER. In Track 2 of CHiME-6, our system provides
13.24%, 12.60%, and 5.65% relative reductions in DER, JER, and WER,
respectively.