title:
Unraveling the temperature dynamics and hot electron generation in
  tunable gap-plasmon metasurface absorbers

abstract:
  Localized plasmons formed in ultrathin metallic nanogaps can lead to robust
absorption of incident light. Plasmonic metasurfaces based on this effect can
efficiently generate energetic charge carriers, also known as hot electrons,
owing to their ability to squeeze and enhance electromagnetic fields in
confined subwavelength spaces. However, it is very challenging to accurately
identify and quantify the dynamics of hot carriers, mainly due to their
ultrafast time decay. Their non-equilibrium temperature response is one of the
key factors missing to understand the short time decay and overall transient
tunable absorption performance of gap-plasmon metasurfaces. Here, we
systematically study the temperature dynamics of hot electrons and their
transition into thermal carriers at various timescales from femto to
nanoseconds by using the two-temperature model. Additionally, the hot electron
temperature and generation rate threshold values are investigated by using a
hydrodynamic nonlocal model approach that is more accurate when ultrathin gaps
are considered. The derived temperature dependent material properties are used
to study the ultrafast transient nonlinear modification in the absorption
spectrum before plasmon-induced lattice heating is established leading to
efficient tunable nanophotonic absorber designs. We also examine the damage
threshold of these plasmonic absorbers under various pulsed laser
illuminations, an important quantity to derive the ultimate input intensity
limits that can be used in various emerging nonlinear optics and other tunable
nanophotonic applications. The presented results elucidate the role of hot
electrons in the response of gap-plasmon metasurface absorbers which can be
used to design more efficient photocatalysis, photovoltaics, and photodetection
devices.


title: Periodic stellar variability from almost a million NGTS light curves

abstract:
  We analyse 829,481 stars from the Next Generation Transit Survey (NGTS) to
extract variability periods. We utilise a generalisation of the autocorrelation
function (the G-ACF), which applies to irregularly sampled time series data. We
extract variability periods for 16,880 stars from late-A through to mid-M
spectral types and periods between 0.1 and 130 days with no assumed variability
model. We find variable signals associated with a number of astrophysical
phenomena, including stellar rotation, pulsations and multiple-star systems.
The extracted variability periods are compared with stellar parameters taken
from Gaia DR2, which allows us to identify distinct regions of variability in
the Hertzsprung-Russell Diagram. We explore a sample of rotational
main-sequence objects in period-colour space, in which we observe a dearth of
rotation periods between 15 and 25 days. This 'bi-modality' was previously only
seen in space-based data. We demonstrate that stars in sub-samples above and
below the period gap appear to arise from a stellar population not
significantly contaminated by excess multiple systems. We also observe a small
population of long-period variable M-dwarfs, which highlight a departure from
the predictions made by rotational evolution models fitted to solar-type
main-sequence objects. The NGTS data spans a period and spectral type range
that links previous rotation studies such as those using data from Kepler, K2
and MEarth.


title:
Speaker-wire vortices in stratified anabatic Prandtl slope flows and
  their secondary instabilities

abstract:
  Stationary longitudinal vortical rolls emerge in katabatic and anabatic
Prandtl slope flows due to the dominance of the normal component of the
buoyancy force over flow shear. Here, we further identify self pairing of these
longitudinal rolls as a unique flow structure. The topology of the
counter-rotating vortex pair bears a striking resemblance to speaker-wires and
their interaction with each other is a precursor to further destabilization and
breakdown of the flow field into smaller structures. On its own, a speaker-wire
vortex retains its unique topology without any vortex reconnection or breakup.
For a fixed slope angle $\alpha=3^{\circ}$ and at a constant Prandtl number, we
analyse the saturated state of speaker-wire vortices and perform a bi-global
linear stability analysis based on their stationary state. We establish the
existence of both fundamental and subharmonic secondary instabilities depending
on the circulation and transverse wavelength of the base state of speaker-wire
vortices. The dominance of subharmonic modes relative to the fundamental mode
helps explain the relative stability of a single vortex pair compared to the
vortex dynamics in presence of two or an even number of pairs.These instability
modes are essential for the bending and merging of multiple speaker-wire
vortices, which break up and lead to more dynamically unstable states,
eventually paving the way for transition towards turbulence. This process is
demonstrated via direct numerical simulations with which we are able to track
the nonlinear temporal evolution of these instabilities.


title: SurferBot: A wave-propelled aquatic vibrobot

abstract:
  Nature has evolved a vast array of strategies for propulsion at the air-fluid
interface. Inspired by a survival mechanism initiated by the honeybee (Apis
mellifera) trapped on the surface of water, we here present the SurferBot: a
centimeter-scale vibrating robotic device that self-propels on a fluid surface
using analogous hydrodynamic mechanisms as the stricken honeybee. This low-cost
and easily assembled device is capable of rectilinear motion thanks to forces
arising from a wave-generated, unbalanced momentum flux, achieving speeds on
the order of centimeters per second. Owing to the dimensions of the SurferBot
and amplitude of the capillary wave field, we find that the magnitude of the
propulsive force is similar to that of the honeybee. In addition to a detailed
description of the fluid mechanics underpinning the SurferBot propulsion, other
modes of SurferBot locomotion are discussed. More broadly, we propose that the
SurferBot can be used to explore fundamental aspects of active and driven
particles at fluid interfaces, as well as in robotics and fluid mechanics
pedagogy.


title: Calibrated Model Criticism Using Split Predictive Checks

abstract:
  Checking how well a fitted model explains the data is one of the most
fundamental parts of a Bayesian data analysis. However, existing model checking
methods suffer from trade-offs between being well-calibrated, automated, and
computationally efficient. To overcome these limitations, we propose split
predictive checks (SPCs), which combine the ease-of-use and speed of posterior
predictive checks with the good calibration properties of predictive checks
that rely on model-specific derivations or inference schemes. We develop an
asymptotic theory for two types of SPCs: single SPCs and the divided SPC. Our
results demonstrate that they offer complementary strengths: single SPCs
provide superior power in the small-data regime or when the misspecification is
significant and divided SPCs provide superior power as the dataset size
increases or when the form of misspecification is more subtle. We validate the
finite-sample utility of SPCs through extensive simulation experiments in
exponential family and hierarchical models, and provide four real-data examples
where SPCs offer novel insights and additional flexibility beyond what is
available when using posterior predictive checks.


title: The root extraction problem in braid group-based cryptography

abstract:
  The root extraction problem in braid groups is the following: given a braid
$\beta \in \mathcal{B}_n$ and a number $k\in \mathbb{N}$, find $\alpha\in
\mathcal{B}_n$ such that $\alpha^k=\beta$. In the last decades, many
cryptosystems such as authentication schemes and digital signatures based on
the root extraction problem have been proposed. In this paper, we first
describe these cryptosystems built around braid groups. Then we prove that, in
general, these authentication schemes and digital signature are not secure by
presenting for each of them a possible attack.


title: Towards a Hybrid RF/Optical Lunar Communication System (LunarComm)

abstract:
  The prospect of mankind returning to the Moon has garnered a great amount of
attention in recent years. Dozens of lunar missions are planned for the coming
decade which will require the development of a sustainable communication
infrastructure with high data rates and minimal latency. Space communication
systems thus far have relied on Radio Frequency (RF) links alone, but recent
developments in laser communications have demonstrated that Free Space Optical
(FSO) links can achieve much higher data rates. Upon considering the respective
benefits and drawbacks of RF and FSO links, we make a case for the integration
of these two technologies into a hybrid RF/FSO lunar communications
architecture which leverages small satellites in a Low Earth Orbit (LEO)
constellation. We include a case study for this technology designed in
Analytical Graphics Systems Tool Kit (STK) software. Results are presented in
terms of chain access duration, propagation delay, transmission loss,
Signal-to-Noise Ratio (SNR), and Bit Error Rate (BER). This architecture shows
potential to revolutionize extraterrestrial communications and pave the way for
highly ambitious future missions in space.


title: CEE inner TOF prototype design and preliminary test results

abstract:
  The Cooling Storage Ring (CSR) External-target Experiment(CEE) is the first
multi-purpose nuclear physics experimental device to operate in the GeV energy
range at the Heavy-Ion Research Facility(HIRFL-CSR) in Lanzhou, China. The
primary goals of the CEE are to study the bulk properties of dense matter and
to understand the quantum chromo-dynamic (QCD) phase diagram by measuring the
charged particles produced in heavy-ion collisions at the target region with a
large acceptance. An inner time of flight (iTOF) system has been proposed to
measure the multiplicity, angular distribution, and time information of the
charged particles. Herein, we introduce the performance requirements of iTOF
according to calculations and GEANT4 simulations. The proposed system is
characterized by high granularity and time performance, hence, the conceptual
design of the iTOF wall adopts high granularity Multi-gap Resistive Plate
Chambers (MRPC) with a time resolution of around 30 ps. To evaluate the MRPC
design, the cosmic ray test was performed. A timing resolution better than 28
ps and an efficiency better than 98% has been achieved for MIPs, as interpreted
by the cosmic ray GEANT4 simulation of time jitter components.


title:
Deep Equilibrium Assisted Block Sparse Coding of Inter-dependent
  Signals: Application to Hyperspectral Imaging

abstract:
  In this study, the problem of computing a sparse representation for datasets
of inter-dependent signals, given a fixed dictionary, is considered. A dataset
of inter-dependent signals is defined as a matrix whose columns demonstrate
strong dependencies. A computational efficient sparse coding optimization
problem is derived by employing regularization terms that are adapted to the
properties of the signals of interest. Exploiting the merits of the learnable
regularization techniques, a neural network is employed to act as structure
prior and reveal the underlying signal interdependencies. To solve the
optimization problem Deep unrolling and Deep equilibrium based algorithms are
developed, forming highly interpretable and concise deep-learning-based
architectures, that process the input dataset in a block-by-block fashion.
Extensive simulation results, in the context of hyperspectral image denoising,
are provided, that demonstrate that the proposed algorithms outperform
significantly other sparse coding approaches and exhibit superior performance
against recent state-of-the-art deep-learning-based denoising models. In a
wider perspective, our work provides a unique bridge between a classic
approach, that is the sparse representation theory, and modern representation
tools that are based on deep learning modeling.


title:
Large-Format, Transmission-Line-Coupled Kinetic Inductance Detector
  Arrays for HEP at Millimeter Wavelengths

abstract:
  The kinetic inductance detector (KID) is a versatile and scalable detector
technology with a wide range of applications. These superconducting detectors
offer significant advantages: simple and robust fabrication, intrinsic
multiplexing that will allow thousands of detectors to be read out with a
single microwave line, and simple and low cost room temperature electronics.
These strengths make KIDs especially attractive for HEP science via mm-wave
cosmological studies. Examples of these potential cosmological observations
include studying cosmic acceleration (Dark Energy) through measurements of the
kinetic Sunyaev-Zeldovich effect, precision cosmology through ultra-deep
measurements of small-scale CMB anisotropy, and mm-wave spectroscopy to map out
the distribution of cosmological structure at the largest scales and highest
redshifts. The principal technical challenge for these kinds of projects is the
successful deployment of large-scale high-density focal planes -- a need that
can be addressed by KID technology. In this paper, we present an overview of
microstrip-coupled KIDs for use in mm-wave observations and outline the
research and development needed to advance this class of technology and enable
these upcoming large-scale experiments.


title: Large-mass, low-threshold sapphire detector for rare event searches

abstract:
  Low mass nuclear recoil dark matter and
coherent-elastic-neutrino-nucleus-scattering (CENNS) searches confront similar
challenges in choosing ultra-low threshold and large-mass detectors. We report
experimental results from the first-of-its-kind 100 g single-crystal sapphire
detector design with a diameter of 76 mm and thickness of 4 mm. The detector is
designed to be sensitive for low-energy rare interactions with an intention to
investigate the low mass region of dark matter phase-space and search for CENNS
at the reactor site. Sapphire is a crystal of aluminum oxide (Al2O3) and has
been found to be a good candidate for light mass spin-dependent dark matter
search experiments due to its lower atomic mass compared to other detector
materials such as germanium and silicon. Using the data collected from the test
facility at Texas A&M University, we were able to resolve low energy lines from
calibration sources and estimated that our newly developed sapphire detector
has a baseline recoil energy resolution of 18 eV. These detectors are operated
at 0 V with the phonon-assisted detection providing a quenching-free
low-threshold operation.


title:
Evidence of cluster dipole states in germanium detectors operating at
  temperatures below 10 K

abstract:
  By studying charge trapping in germanium (Ge) detectors operating at
temperatures below 10 K, we demonstrate for the first time that the formation
of cluster dipole states from residual impurities is responsible for charge
trapping. Two planar detectors with different impurity levels and types are
used in this study. When drifting the localized charge carriers created by
$\alpha$ particles from the top surface across a detector under a lower bias
voltage, significant charge trapping is observed when compared to operating at
a higher bias voltage. The amount of charge trapping shows a strong dependence
on the type of charge carriers. Electrons are trapped more than holes in a
p-type detector while holes are trapped more than electrons in a n-type
detector. When both electrons and holes are drifted simultaneously using the
widespread charge carriers created by $\gamma$ rays inside the detector, the
amount of charge trapping shows no dependence on the polarity of bias voltage.


title:
Hydrated Organic Matter: Functional Relationships with the Mass Physical
  Static and Dynamic Properties of Marine Mud

abstract:
  The mass physical and dynamic properties of marine mud deposits are a
function of the interaction among a maximum of four sediment Phases as follows:
(1) clay mineral solids that often include silt and sand size particles of
different mineralogy, (2) semisolid hydrated organic matter, (3) free pore
water, and (4) free gas when present. Historically, the total water was removed
by oven drying which includes the seawater of hydration of the organic matter
(OM); thus, the total water content was considered free pore water in several
research disciplines. The semisolid hydrated OM resists transport through the
mud pore space and the water that is bound to the OM is not free pore water.
Calculations of marine mud mass-physical properties without consideration and
correction for the presence of hydrated organic matter can introduce
significant error in the amount of free water content of the pore space in the
mud and the quantitative differences in both porosity and void ratio.
Simulations reveal absolute relative differences due to the presence of
hydrated OM that can reach meaningful values even when the %TOC values are
below ~3%. Relevance is determined by comparing the differences due to the
hydrated OM Phase with the differences in the fluid saturation Phase at 100%
and at 90%. Understanding the presence and characteristics of hydrated OM is
expected to engage important future research in several sciences and in
geotechnical engineering of terrestrial and marine mud deposits.


title: The Planck boundary within the hyperspace of the circle of pseudo-arcs

abstract:
  In this paper we point out an interesting geometric structure of nonnegative
metric curvature emerging from the hyperspaces of decomposable, non-locally
connected homogeneous continua, where "smooth" and "non-smooth" partitions live
together, similarly to the macroscopic and the quantum realms of the Universe.


title:
Edgeworth expansions for integer valued additive functionals of
  uniformly elliptic Markov chains

abstract:
  We obtain asymptotic expansions for probabilities $\mathbb{P}(S_N=k)$ of
partial sums of uniformly bounded integer-valued functionals $S_N=\sum_{n=1}^N
f_n(X_n)$ of uniformly elliptic inhomogeneous Markov chains. The expansions
involve products of polynomials and trigonometric polynomials, and they hold
without additional assumptions. As an application of the explicit formulas of
the trigonometric polynomials, we show that for every $r\geq1\,$, $S_N$ obeys
the standard Edgeworth expansions of order $r$ in a conditionally stable way if
and only if for every $m$, and every $\ell$ the conditional distribution of
$S_N$ given $X_{j_1},...,X_{j_\ell}$ mod $m$ is $o_\ell(\sigma_N^{1-r})$ close
to uniform, uniformly in the choice of $j_1,...,j_\ell$, where
$\sigma_N=\sqrt{\text{Var}(S_N)}.$


title:
A Multigrid Preconditioner for Spatially Adaptive High-order Meshless
  Method on Fluid-solid Interaction Problems

abstract:
  We present a monolithic geometric multigrid preconditioner for solving
fluid-solid interaction problems in Stokes limit. The problems are discretized
by a spatially adaptive high-order meshless method, the generalized moving
least squares (GMLS) with adaptive $h$-refinement. In Stokes limit, solid
kinematics can be dominated by the singularities governing the lubrication
effects. Resolving those singularities with adaptive $h$-refinement can lead to
an ill-conditioned linear system of equations. For constructing the
interpolation and restriction operators - the key ingredients of the multigrid
preconditioner, we utilize the geometric information of hierarchical sets of
GMLS nodes generated in adaptive $h$-refinement. We build decoupled smoothers
through physics-based splitting and then combine them via a multiplicative
overlapping Schwarz approach. Through numerical examples with the inclusion of
different numbers and shapes of solid bodies, we demonstrate the performance
and assess the scalability of the designed preconditioner. As the total degrees
of freedom and the number of solid bodies $N_s$ increase, the proposed
monolithic geometric multigrid preconditioner can ensure convergence and good
scalability when using the Krylov iterative method for solving the linear
systems of equations generated from the spatially adaptive GMLS discretization.
More specifically, for a fixed number of solid bodies, as the discretization
resolution is incrementally refined, the number of iterations of the linear
solver can be maintained at the same level, indicating nearly linear
scalability of our preconditioner with respect to the total degrees of freedom.
When $N_s$ increases, the number of iterations is nearly proportional to
$\sqrt{N_s}$, implying the sublinear optimality with respect to the number of
solid bodies.


title:
Characteristics of grassy ELMs and its impact on the divertor heat flux
  width

abstract:
  BOUT++ turbulence simulations are conducted for a 60s steady-state long pulse
high \{beta}p EAST grassy ELM discharge. BOUT++ linear simulations show that
the unstable mode spectrum covers a range of toroidal mode numbers from low-n
(n=10~15) peeling-ballooning modes (P-B) to high-n (n=40~80) drift-Alfv\'en
instabilities. Nonlinear simulations show that the ELM crash is trigged by
low-n peeling modes and fluctuation is generated at the peak pressure gradient
position and radially spread outward into the Scrape-Off-Layer (SOL), even
though the drift-Alfv\'en instabilities dominate the linear growth phase.
However, drift-Alfv\'en turbulence delays the onset of the grassy ELM and
enhances the energy loss with the fluctuation extending to pedestal top region.
Simulations further show that if the peeling drive is removed, the fluctuation
amplitude drops by an order of magnitude and the ELM crashes disappear. The
divertor heat flux width is ~2 times larger than the estimates based on the HD
model and the ITPA multi-tokamak scaling (or empirical Eich scaling) due to the
strong radial turbulence transport.


title: On the topology of leaves of singular Riemannian foliations

abstract:
  In this paper, we establish a number of results about the topology of the
leaves of a closed singular Riemannian foliation $(M,\fol)$. If $M$ is simply
connected, we prove that the leaves are finitely covered by nilpotent spaces,
and characterize the fundamental group of the generic leaves. If $M$ has
virtually nilpotent fundamental group, we prove that the leaves have virtually
nilpotent fundamental group as well.


title:
Economic state classification and portfolio optimisation with
  application to stagflationary environments

abstract:
  Motivated by the current fears of a potentially stagflationary global
economic environment, this paper uses new and recently introduced mathematical
techniques to study multivariate time series pertaining to country inflation
(CPI), economic growth (GDP) and equity index behaviours. We begin by assessing
the temporal evolution among various economic phenomena, and complement this
analysis with "economic driver analysis", where we decouple country economic
trajectories and determine what is most important in their association. Next,
we study the temporal self-similarity of global inflation, growth and equity
index returns to identify the most anomalous historic periods, and windows in
the past that are most similar to current market dynamics. We then introduce a
new algorithm to construct economic state classifications and compute an
economic state integral, where countries are determined to belong in one of
four candidate states based on their inflation and growth behaviours. Finally,
we implement a decade-by-decade portfolio optimisation to determine which
equity indices and portfolio assets have been most beneficial in maximising
portfolio risk-adjusted returns in various market conditions. This could be of
great interest to those looking for asset allocation guidance in the current
period of high economic uncertainty.


title: Timing performance of the Timepix4 front-end

abstract:
  A characterisation of the Timepix4 pixel front-end with a strong focus on
timing performance is presented. Externally generated test pulses were used to
probe the per-pixel time-to-digital converter (TDC) and measure the time-bin
sizes by precisely controlling the test-pulse arrival time in steps of 10 ps.
The results indicate that the TDC can achieve a time resolution of 60 ps,
provided that a calibration is performed to compensate for frequency variation
in the voltage controlled oscillators of the pixel TDCs. The internal clock
distribution system of Timepix4 was used to control the arrival time of
internally generated analog test pulses in steps of about 20 ps. The analog
test pulse mechanism injects a controlled amount of charge directly into the
analog front-end (AFE) of the pixel, and was used to measure the time
resolution as a function of signal charge, independently of the TDC. It was
shown that for the default configuration, the AFE time resolution in the
hole-collecting mode is limited to 105 ps. However, this can be improved up to
about 60 ps by increasing the preamplifier bias-current at the cost of
increased power dissipation. For the electron-collecting mode, an AFE time
resolution of 47 ps was measured for a bare Timepix4 device at a signal charge
of 21 ke. It was observed that additional input capacitance from a bonded
sensor reduces this figure to 62 ps.


title:
Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling
  and Design

abstract:
  Being able to predict the performance of circuits without running expensive
simulations is a desired capability that can catalyze automated design. In this
paper, we present a supervised pretraining approach to learn circuit
representations that can be adapted to new circuit topologies or unseen
prediction tasks. We hypothesize that if we train a neural network (NN) that
can predict the output DC voltages of a wide range of circuit instances it will
be forced to learn generalizable knowledge about the role of each circuit
element and how they interact with each other. The dataset for this supervised
learning objective can be easily collected at scale since the required DC
simulation to get ground truth labels is relatively cheap. This representation
would then be helpful for few-shot generalization to unseen circuit metrics
that require more time consuming simulations for obtaining the ground-truth
labels. To cope with the variable topological structure of different circuits
we describe each circuit as a graph and use graph neural networks (GNNs) to
learn node embeddings. We show that pretraining GNNs on prediction of output
node voltages can encourage learning representations that can be adapted to new
unseen topologies or prediction of new circuit level properties with up to 10x
more sample efficiency compared to a randomly initialized model. We further
show that we can improve sample efficiency of prior SoTA model-based
optimization methods by 2x (almost as good as using an oracle model) via
fintuning pretrained GNNs as the feature extractor of the learned models.


title:
Ab initio prediction of the $^4{\rm He}(d,\gamma)\,^6\rm Li$ big bang
  radiative capture

abstract:
  The rate at which helium ($^4$He) and deuterium ($d$) fuse together to
produce lithium-6 ($^6$Li) and a $\gamma$ ray, $^4$He$(d,\gamma)^6$Li, is a
critical puzzle piece in resolving the roughly three orders of magnitude
discrepancy between big bang predictions and astronomical observations for the
primordial abundance of $^6$Li. The accurate determination of this radiative
capture rate requires the quantitative and predictive description of the fusion
probability across the big bang energy window ($30$ keV $\lesssim E\lesssim
400$ keV), where measurements are hindered by low counting rates. We present
first-principles (or, ab initio) predictions of the $^4$He$(d,\gamma)^6$Li
astrophysical S-factor using validated nucleon-nucleon and three-nucleon
interactions derived within the framework of chiral effective field theory. By
employing the ab initio no-core shell model with continuum to describe $^4{\rm
He}$-$d$ scattering dynamics and bound $^6\rm Li$ product on an equal footing,
we accurately and consistently determine the contributions of the main
electromagnetic transitions driving the radiative capture process. Our results
reveal an enhancement of the capture probability below 100 keV owing to
previously neglected magnetic dipole (M1) transitions and reduce by an average
factor of 7 the uncertainty of the thermonuclear capture rate between $0.002$
and $2$ GK.


title:
Thermal noise-limited laser stabilization to an 8 mL volume
  Fabry-P\'erot reference cavity with microfabricated mirrors

abstract:
  Lasers stabilized to vacuum-gap Fabry-P\'erot optical reference cavities
display extraordinarily low noise and high stability, with linewidths much less
than 1 Hz. These lasers can expand into new applications and ubiquitous use
with the development of compact, portable cavities that are manufacturable at
scale. Here we demonstrate an 8 mL volume Fabry-P\'erot cavity constructed with
mirrors that are fabricated lithographically with finesse near 1 million. A
laser locked to the cavity exhibits phase noise limited by the cavity thermal
noise for offset frequencies ranging from 1 Hz to $\approx$ 1 kHz, with a
fractional frequency stability of 7$\times$10$^{-15}$ at 1 second. Furthermore,
the use of microfabricated mirrors allows us to expand the design space of
centimeter-scale cavities, and we explore the noise implications of pushing
towards cavity volumes of 2 mL or less.


title: Current Implicit Policies May Not Eradicate COVID-19

abstract:
  Successful predictive modeling of epidemics requires an understanding of the
implicit feedback control strategies which are implemented by populations to
modulate the spread of contagion. While this task of capturing endogenous
behavior can be achieved through intricate modeling assumptions, we find that a
population's reaction to case counts can be described through a second order
affine dynamical system with linear control which fits well to the data across
different regions and times throughout the COVID-19 pandemic. The model fits
the data well both in and out of sample across the 50 states of the United
States, with comparable $R^2$ scores to state of the art ensemble predictions.
In contrast to recent models of epidemics, rather than assuming that
individuals directly control the contact rate which governs the spread of
disease, we assume that individuals control the rate at which they vary their
number of interactions, i.e. they control the derivative of the contact rate.
We propose an implicit feedback law for this control input and verify that it
correlates with policies taken throughout the pandemic. A key takeaway of the
dynamical model is that the "stable" point of case counts is non-zero, i.e.
COVID-19 will not be eradicated under the current collection of policies and
strategies, and additional policies are needed to fully eradicate it quickly.
Hence, we suggest alternative implicit policies which focus on making
interventions (such as vaccinations and mobility restrictions) a function of
cumulative case counts, for which our results suggest a better possibility of
eradicating COVID-19.


title:
Shallow Fusion of Weighted Finite-State Transducer and Language Model
  for Text Normalization

abstract:
  Text normalization (TN) systems in production are largely rule-based using
weighted finite-state transducers (WFST). However, WFST-based systems struggle
with ambiguous input when the normalized form is context-dependent. On the
other hand, neural text normalization systems can take context into account but
they suffer from unrecoverable errors and require labeled normalization
datasets, which are hard to collect. We propose a new hybrid approach that
combines the benefits of rule-based and neural systems. First, a
non-deterministic WFST outputs all normalization candidates, and then a neural
language model picks the best one -- similar to shallow fusion for automatic
speech recognition. While the WFST prevents unrecoverable errors, the language
model resolves contextual ambiguity. The approach is easy to extend and we show
it is effective. It achieves comparable or better results than existing
state-of-the-art TN models.


title:
Helium-like X-ray line complexes show that the hottest plasma on the O
  supergiant zeta Puppis is in its wind

abstract:
  We present an analysis of Chandra grating spectra of key helium-like line
complexes to put constraints on the location with respect to the photosphere of
the hottest ($T \gtrsim{6 \times 10^6}$ K) plasma in the wind of the O
supergiant zeta Pup and to explore changes in the 18 years between two sets of
observations of this star. We fit two models -- one empirical and one
wind-shock-based -- to the S XV, Si XIII, and Mg XI line complexes and show
that an origin in the wind flow, above $r \approx 1.5$ R$_{\ast}$, is strongly
favored over an origin less than 0.3 R$_{\ast}$ above the photosphere ($r
\lesssim 1.3$ R$_{\ast}$), especially in the more recent, very long-exposure
data set. There is a modest increase in the line and continuum fluxes, line
widths, wind absorption signatures, and of the hot plasma's distance from the
photosphere in the 18 years since the first Chandra grating observation of zeta
Pup. Both modes of modeling include the effects of dielectronic recombination
satellite emission line blending on the helium-like complexes -- the first time
this has been accounted for in the analysis of He-like line ratios in O stars.


title: On the existence of left and right eigenvalues

abstract:
  In this note, we consider arbitrary finite-dimensional real algebras
containing a copy of complex numbers. It is proved that matrices with entries
from an arbitrary finite-dimensional real algebra containing a square root of
negative one in its left (resp. right) associate set have left (resp. right)
eigenvalues. A quick consequence of our main result is the existence of left
and right eigenvalues for matrices with entries from finite-dimensional
alternatives algebras containing a copy of complex numbers, e.g., octonions,
and more generally matrices with entries from the real Cayley-Dickson algebras.


title:
Three-dimensional Gaussian fluctuations of non-commutative random
  surface growth with a reflecting wall

abstract:
  We consider the multi-time correlation and covariance structure of a random
surface growth with a wall introduced in arXiv:0904.2607. It is shown that the
correlation functions associated with the model along space-like paths have
determinantal structure, which yields the convergence of height fluctuations to
that of a Gaussian free field. We also construct a continuous-time
non-commutative random walk on $U(\mathfrak{so}_{N+1})$, which matches the
random surface growth when restricting to the Gelfand-Tsetlin subalgebra of
$U(\mathfrak{so}_{N+1})$. As an application, we prove the convergence of
moments to an explicit Gaussian free field and get the covariance functions of
the associated random point process along both the space-like paths and
time-like paths. In particular, it does not match the three-dimensional
Gaussian field from spectra of overlapping stochastic Wishart matrices in
arXiv:2112.13728 even along the space-like paths.


title:
Growth factors of random butterfly matrices and the stability of
  avoiding pivoting

abstract:
  Random butterfly matrices were introduced by Parker in 1995 to remove the
need for pivoting when using Gaussian elimination. The growing applications of
butterfly matrices have often eclipsed the mathematical understanding of how or
why butterfly matrices are able to accomplish these given tasks. To help begin
to close this gap using theoretical and numerical approaches, we explore the
impact on the growth factor of preconditioning a linear system by butterfly
matrices. These results are compared to other common methods found in
randomized numerical linear algebra. In these experiments, we show
preconditioning using butterfly matrices has a more significant dampening
impact on large growth factors than other common preconditioners and a smaller
increase to minimal growth factor systems. Moreover, we are able to determine
the full distribution of the growth factors for a subclass of random butterfly
matrices. Previous results by Trefethen and Schreiber relating to the
distribution of random growth factors were limited to empirical estimates of
the first moment for Ginibre matrices.


title: Efficient hot carrier dynamics in near-infrared photocatalytic metals

abstract:
  Photoexcited metals can produce highly-energetic hot carriers whose
controlled generation and extraction is a promising avenue for technological
applications. While hot carrier dynamics in Au-group metals have been widely
investigated, a microscopic description of the dynamics of photoexcited
carriers in the mid-infrared and near-infrared Pt-group metals range is still
scarce. Since these materials are widely used in catalysis and, more recently,
in plasmonic catalysis, their microscopic carrier dynamics characterization is
crucial. We employ \emph{ab initio} many-body perturbation theory to
investigate the hot carrier generation, relaxation times, and mean free path in
bulk Pd and Pt. We show that the direct optical transitions of photoexcited
carriers in this metals are mainly generated in the near-infrared range. We
also find that the electron-phonon mass enhancement parameter for Pt is 16 $\%$
higher than Pd, a result that help explains several experimental results
showing diverse trends. Moreover, we predict that Pd (Pt) hot electrons possess
total relaxation times of up to 35 fs (24 fs), taking place at approximately
0.5 eV (1.0 eV) above the Fermi energy. Finally, an efficient hot electron
generation and extraction can be achieved in nanofilms of Pd (110) and Pd (100)
when subject to excitation energies ranging from 0.4 to 1.6 eV.


title:
Dose rate effects in radiation-induced changes to phenyl-based polymeric
  scintillators

abstract:
  Results on the effects of ionizing radiation on the signal produced by
plastic scintillating rods manufactured by Eljen Technology company are
presented for various matrix materials, dopant concentrations, fluors (EJ-200
and EJ-260), anti-oxidant concentrations, scintillator thickness, doses, and
dose rates. The light output before and after irradiation is measured using an
alpha source and a photomultiplier tube, and the light transmission by a
spectrophotometer. Assuming an exponential decrease in the light output with
dose, the change in light output is quantified using the exponential dose
constant $D$. The $D$ values are similar for primary and secondary doping
concentrations of 1 and 2 times, and for antioxidant concentrations of 0, 1,
and 2 times, the default manufacturer's concentration. The $D$ value depends
approximately linearly on the logarithm of the dose rate for dose rates between
2.2 Gy/hr and 70 Gy/hr for all materials. For EJ-200 polyvinyltoluene-based
(PVT) scintillator, the dose constant is approximately linear in the logarithm
of the dose rate up to 3400 Gy/hr, while for polystyrene-based (PS)
scintillator or for both materials with EJ-260 fluors, it remains constant or
decreases (depending on doping concentration) above about 100 Gy/hr. The
results from rods of varying thickness and from the different fluors suggest
damage to the initial light output is a larger effect than color center
formation for scintillator thickness $\leq1$ cm. For the blue scintillator
(EJ-200), the transmission measurements indicate damage to the fluors. We also
find that while PVT is more resistant to radiation damage than PS at dose rates
higher than about 100 Gy/hr for EJ-200 fluors, they show similar damage at
lower dose rates and for EJ-260 fluors.


title:
Hybrid of monolithic and staggered solution techniques for the
  computational analysis of fracture, assessed on fibrous network mechanics

abstract:
  The computational analysis of fiber network fracture is an emerging field
with application to paper, rubber-like materials, hydrogels, soft biological
tissue, and composites. Fiber networks are often described as probabilistic
structures of interacting one-dimensional elements, such as truss-bars and
beams. Failure may then be modeled as strong discontinuities in the
displacement field that are directly embedded within the structural finite
elements. As for other strain-softening materials, the tangent stiffness matrix
can be non-positive definite, which diminishes the robustness of the solution
of the coupled (monolithic) two-field problem. Its uncoupling, and thus the use
of a staggered solution method where the field variables are solved
alternatingly, avoids such difficulties and results in a stable, but
sub-optimally converging solution method. In the present work, we evaluate the
staggered against the monolithic solution approach and assess their
computational performance in the analysis of fiber network failure. We then
propose a hybrid solution technique that optimizes the performance and
robustness of the computational analysis. It represents a matrix regularization
technique that retains a positive definite element stiffness matrix while
approaching the tangent stiffness matrix of the monolithic problem. The
approach is general and may also accelerate the computational analysis of other
failure problems.


title:
Multi-Agent Asynchronous Cooperation with Hierarchical Reinforcement
  Learning

abstract:
  Hierarchical multi-agent reinforcement learning (MARL) has shown a
significant learning efficiency by searching policy over higher-level,
temporally extended actions (options). However, standard policy gradient-based
MARL methods have a difficulty generalizing to option-based scenarios due to
the asynchronous executions of multi-agent options. In this work, we propose a
mathematical framework to enable policy gradient optimization over asynchronous
multi-agent options by adjusting option-based policy distribution as well as
trajectory probability. We study our method under a set of multi-agent
cooperative setups with varying inter-dependency levels, and evaluate the
effectiveness of our method on typical option-based multi-agent cooperation
tasks.


title:
Disentangled3D: Learning a 3D Generative Model with Disentangled
  Geometry and Appearance from Monocular Images

abstract:
  Learning 3D generative models from a dataset of monocular images enables
self-supervised 3D reasoning and controllable synthesis. State-of-the-art 3D
generative models are GANs which use neural 3D volumetric representations for
synthesis. Images are synthesized by rendering the volumes from a given camera.
These models can disentangle the 3D scene from the camera viewpoint in any
generated image. However, most models do not disentangle other factors of image
formation, such as geometry and appearance. In this paper, we design a 3D GAN
which can learn a disentangled model of objects, just from monocular
observations. Our model can disentangle the geometry and appearance variations
in the scene, i.e., we can independently sample from the geometry and
appearance spaces of the generative model. This is achieved using a novel
non-rigid deformable scene formulation. A 3D volume which represents an object
instance is computed as a non-rigidly deformed canonical 3D volume. Our method
learns the canonical volume, as well as its deformations, jointly during
training. This formulation also helps us improve the disentanglement between
the 3D scene and the camera viewpoints using a novel pose regularization loss
defined on the 3D deformation field. In addition, we further model the inverse
deformations, enabling the computation of dense correspondences between images
generated by our model. Finally, we design an approach to embed real images
into the latent space of our disentangled generative model, enabling editing of
real images.


title:
The DESC Stellarator Code Suite Part II: Perturbation and continuation
  methods

abstract:
  A new perturbation and continuation method is presented for computing and
analyzing stellarator equilibria. The method is formally derived from a series
expansion about the equilibrium condition $F \equiv J \times B - \nabla p = 0$,
and an efficient algorithm for computing solutions to 2nd and 3rd order
perturbations is developed. The method has been implemented in the DESC
stellarator equilibrium code, using automatic differentiation to compute the
required derivatives. Examples are shown demonstrating its use for computing
complicated equilibria, perturbing a tokamak into a stellarator, and performing
parameter scans in pressure, rotational transform and boundary shape in a
fraction of the time required for a full solution.


title:
Precision-aware Deterministic and Probabilistic Error Bounds for
  Floating Point Summation

abstract:
  We analyze the forward error in the floating point summation of real numbers,
for computations in low precision or extreme-scale problem dimensions that push
the limits of the precision. We present a systematic recurrence for a
martingale on a computational tree, which leads to explicit and interpretable
bounds without asymptotic big-O terms. Two probability parameters strengthen
the precision-awareness of our bounds: one parameter controls the first order
terms in the summation error, while the second one is designed for controlling
higher order terms in low precision or extreme-scale problem dimensions. Our
systematic approach yields new deterministic and probabilistic error bounds for
three classes of mono-precision algorithms: general summation, shifted general
summation, and compensated (sequential) summation. Extension of our systematic
error analysis to mixed-precision summation algorithms that allow any number of
precisions yields the first probabilistic bounds for the mixed-precision FABsum
algorithm. Numerical experiments illustrate that the probabilistic bounds are
accurate, and that among the three classes of mono-precision algorithms,
compensated summation is generally the most accurate. As for mixed precision
algorithms, our recommendation is to minimize the magnitude of intermediate
partial sums relative to the precision in which they are computed.


title:
Sample Recycling for Nested Simulation with Application in Portfolio
  Risk Measurement

abstract:
  Nested simulation is a natural approach to tackle nested estimation problems
in operations research and financial engineering. The outer-level simulation
generates outer scenarios and the inner-level simulations are run in each outer
scenario to estimate the corresponding conditional expectation. The resulting
sample of conditional expectations is then used to estimate different risk
measures of interest. Despite its flexibility, nested simulation is notorious
for its heavy computational burden. We introduce a novel simulation procedure
that reuses inner simulation outputs to improve efficiency and accuracy in
solving nested estimation problems. We analyze the convergence rates of the
bias, variance, and MSE of the resulting estimator. In addition, central limit
theorems and variance estimators are presented, which lead to asymptotically
valid confidence intervals for the nested risk measure of interest. We conduct
numerical studies on two financial risk measurement problems. Our numerical
studies show consistent results with the asymptotic analysis and show that the
proposed approach outperforms the standard nested simulation and a state-of-art
regression approach for nested estimation problems.


title:
Extracting Godl [sic] from the Salt Mines: Ethereum Miners Extracting
  Value

abstract:
  Cryptocurrency miners have great latitude in deciding which transactions they
accept, including their own, and the order in which they accept them. Ethereum
miners in particular use this flexibility to collect MEV-Miner Extractable
Value-by structuring transactions to extract additional revenue. Ethereum also
contains numerous bots that attempt to obtain MEV based on
public-but-not-yet-confirmed transactions. Private relays shelter operations
from these selfsame bots by directly submitting transactions to mining pools.
  In this work, we develop an algorithm to detect MEV exploitation present in
previously mined blocks. We use our implementation of the detector to analyze
MEV usage and profit redistribution, finding that miners make the lion's share
of the profits, rather than independent users of the private relays. More
specifically, (i) 73% of private transactions hide trading activity or
re-distribute miner rewards, and 87.6% of MEV collection is accomplished with
privately submitted transactions, (ii) our algorithm finds more than $6M worth
of MEV profit in a period of 12 days, two thirds of which go directly to
miners, and (iii) MEV represents 9.2% of miners' profit from transaction fees.
  Furthermore, in those 12 days, we also identify four blocks that contain
enough MEV profits to make time-bandit forking attacks economically viable for
large miners, undermining the security and stability of Ethereum as a whole.


title:
Scalable fabrication of ultrahigh-finesse micromirrors with variable
  geometry

abstract:
  Due to their unrivaled performance and versatility, high-finesse
Fabry-P\'erot resonators have enabled scientific and technological
breakthroughs in fields ranging from cavity QED to optical clocks and precision
metrology. Scalable production of compact Fabry-P\'erot resonators could enable
miniaturaization and integration of these technologies for new applications.
However, it has proven difficult to identify a microfabrication approach that
can yield world-class resonator performance and produce the range of required
resonator geometries. In this paper, we demonstrate a wafer-scale fabrication
technique that produces arrays of ultrahigh-finesse ($\geq 10^6$) mirrors with
a user-defined radius of curvature spanning four orders of magnitude
($10^{-4}-10^0$ m). We employ photoresist reflow and reactive ion etching to
shape and transfer mirror templates into a substrate while maintaining
sub-Angstrom roughness. This substrate is coated and used to create arrays of
compact Fabry-P\'erot resonators with finesse values as high as 1.3 million and
measured excess loss $< 1$ ppm. Optical ringdown measurements of 43 devices
across 5 substrates reveal that the fabricated cavity mirrors with both small
and large radii of curvature produce an average coating-limited finesse of 1.05
million. This versatile new approach opens the door to scalable fabrication of
high-finesse miniaturized Fabry-P\'erot cavities needed for next-generation
quantum optics and frequency metrology technologies.


title:
Self-Contrastive Learning based Semi-Supervised Radio Modulation
  Classification

abstract:
  This paper presents a semi-supervised learning framework that is new in being
designed for automatic modulation classification (AMC). By carefully utilizing
unlabeled signal data with a self-supervised contrastive-learning pre-training
step, our framework achieves higher performance given smaller amounts of
labeled data, thereby largely reducing the labeling burden of deep learning. We
evaluate the performance of our semi-supervised framework on a public dataset.
The evaluation results demonstrate that our semi-supervised approach
significantly outperforms supervised frameworks thereby substantially enhancing
our ability to train deep neural networks for automatic modulation
classification in a manner that leverages unlabeled data.


title: High-order harmonic generation from C$_{20}$ isomers

abstract:
  High-order harmonic responses from three $C_{20}$ isomers: fullerene, ring,
and bowl, are calculated within the modified Lewenstein model for molecular
systems. Spectra for all three structures exhibit intense modulations of the
harmonic spectrum along the plateau and some of them can be interpreted as a
consequence of multi-center interference effects. Each structure shows
characteristic modulation patterns in peak harmonic intensities, which are
directly related to zeroes in the recombination matrix element as a function of
the three components of momentum. Different C$_{20}$ isomers lead to different
harmonic polarizations depending on the geometric configuration of carbon atoms
and molecular orientation.


title:
Theory-agnostic framework for inspiral tests of general relativity with
  higher-harmonic gravitational waves

abstract:
  Recent gravitational wave observations show evidence for the presence of
higher harmonics, thus possibly indicating that these waves were generated in
the inspiral of compact objects with asymmetric mass ratios. Signals with
higher harmonics contain a trove of information that can lead to a better
estimation of system parameters and possibly to more stringent tests of general
relativity. Gravitational wave model that include higher harmonics, however,
have only been developed within general relativity, while models to test
theory-agnostic deviations from general relativity have been purely based on
the signal's dominant mode. We here extend the parameterized post-Einsteinian
framework to include the $\ell=2, 3$ and $4$ higher harmonics to first
post-Newtonian order, therefore providing a ready-to-use Fourier-domain
waveform model for tests of general relativity with higher harmonics. We find
that the deformations to the higher harmonics of the Fourier phase can be
easily mapped to the deformation of the dominant harmonic, while the
deformations to the higher-harmonics of the Fourier amplitude in general cannot
in a theory-agnostic way. Nonetheless, we develop a simple ansatz for the
deformations of the waveform amplitude (through a re-scaling deformation of the
time-domain amplitude) that both minimizes the number of independent amplitude
deformations parameters and captures the predictions of all known modified
theories to date.


title: Graph Neural Networks in IoT: A Survey

abstract:
  The Internet of Things (IoT) boom has revolutionized almost every corner of
people's daily lives: healthcare, home, transportation, manufacturing, supply
chain, and so on. With the recent development of sensor and communication
technologies, IoT devices including smart wearables, cameras, smartwatches, and
autonomous vehicles can accurately measure and perceive their surrounding
environment. Continuous sensing generates massive amounts of data and presents
challenges for machine learning. Deep learning models (e.g., convolution neural
networks and recurrent neural networks) have been extensively employed in
solving IoT tasks by learning patterns from multi-modal sensory data. Graph
Neural Networks (GNNs), an emerging and fast-growing family of neural network
models, can capture complex interactions within sensor topology and have been
demonstrated to achieve state-of-the-art results in numerous IoT learning
tasks. In this survey, we present a comprehensive review of recent advances in
the application of GNNs to the IoT field, including a deep dive analysis of GNN
design in various IoT sensing environments, an overarching list of public data
and source code from the collected publications, and future research
directions. To keep track of newly published works, we collect representative
papers and their open-source implementations and create a Github repository at
https://github.com/GuiminDong/GNN4IoT.


title: A Simple Yet Effective Pretraining Strategy for Graph Few-shot Learning

abstract:
  Recently, increasing attention has been devoted to the graph few-shot
learning problem, where the target novel classes only contain a few labeled
nodes. Among many existing endeavors, episodic meta-learning has become the
most prevailing paradigm, and its episodic emulation of the test environment is
believed to equip the graph neural network models with adaptability to novel
node classes. However, in the image domain, recent results have shown that
feature reuse is more likely to be the key of meta-learning to few-shot
extrapolation. Based on such observation, in this work, we propose a simple
transductive fine-tuning based framework as a new paradigm for graph few-shot
learning. In the proposed paradigm, a graph encoder backbone is pretrained with
base classes, and a simple linear classifier is fine-tuned by the few labeled
samples and is tasked to classify the unlabeled ones. For pretraining, we
propose a supervised contrastive learning framework with data augmentation
strategies specific for few-shot node classification to improve the
extrapolation of a GNN encoder. Finally, extensive experiments conducted on
three benchmark datasets demonstrate the superior advantage of our framework
over the state-of-the-art methods.


title:
Improving Mispronunciation Detection with Wav2vec2-based Momentum
  Pseudo-Labeling for Accentedness and Intelligibility Assessment

abstract:
  Current leading mispronunciation detection and diagnosis (MDD) systems
achieve promising performance via end-to-end phoneme recognition. One challenge
of such end-to-end solutions is the scarcity of human-annotated phonemes on
natural L2 speech. In this work, we leverage unlabeled L2 speech via a
pseudo-labeling (PL) procedure and extend the fine-tuning approach based on
pre-trained self-supervised learning (SSL) models. Specifically, we use Wav2vec
2.0 as our SSL model, and fine-tune it using original labeled L2 speech samples
plus the created pseudo-labeled L2 speech samples. Our pseudo labels are
dynamic and are produced by an ensemble of the online model on-the-fly, which
ensures that our model is robust to pseudo label noise. We show that
fine-tuning with pseudo labels gains a 5.35% phoneme error rate reduction and
2.48% MDD F1 score improvement over a labeled-samples-only fine-tuning
baseline. The proposed PL method is also shown to outperform conventional
offline PL methods. Compared to the state-of-the-art MDD systems, our MDD
solution achieves a more accurate and consistent phonetic error diagnosis. In
addition, we conduct an open test on a separate UTD-4Accents dataset, where our
system recognition outputs show a strong correlation with human perception,
based on accentedness and intelligibility.


title:
Resolvent estimates for one-dimensional Schr\"odinger operators with
  complex potentials

abstract:
  We study one-dimensional Schr\"odinger operators $\operatorname{H} =
-\partial_x^2 + V$ with unbounded complex potentials $V$ and derive asymptotic
estimates for the norm of the resolvent, $\Psi(\lambda) := \| (\operatorname{H}
- \lambda)^{-1} \|$, as $|\lambda| \to +\infty$, separately considering
$\lambda \in \operatorname{Ran} V$ and $\lambda \in \mathbb{R}_+$. In each
case, our analysis yields an exact leading order term and an explicit remainder
for $\Psi(\lambda)$ and we show these estimates to be optimal. We also discuss
several extensions of the main results, their interrelation with some aspects
of semigroup theory and illustrate them with examples.


title: Design, fabrication, and tuning of a THz-driven electron gun

abstract:
  We present the design, fabrication, and low power testing of a THz-driven
field emission electron gun. The two cell standing-wave gun is designed to be
powered by a 110 GHz gyrotron and produce 360 keV electrons with 500 kW of
input power. Several gun structures were fabricated using a high precision
diamond turned mandrel and copper electroforming. The field emission source is
a copper tip with a 50 $\mu$m radius inserted halfway into first cell. The
frequencies of the cavity resonances were mechanically tuned using azimuthal
compression. This work presents electromagnetic and particle simulations of the
design and cold test measurements of the fabricated structures.


title: Detrended Fluctuation Analysis for Continuous Real Variable Functions

abstract:
  Based on the well-known Detrended Fluctuation Analysis (DFA) for time series,
in this work we describe a DFA for continuous real variable functions. Under
certain conditions, DFA accurately predicts the long-term auto-correlation of
the time series, depending on the value of certain scaling parameter. We show
that for continuous functions, the proposed continuous DFA also exhibits
fractal properties and approximates a power law with scaling exponent one.


title: Design of a Biomimetic Tactile Sensor for Material Classification

abstract:
  Tactile sensing typically involves active exploration of unknown surfaces and
objects, making it especially effective at processing the characteristics of
materials and textures. A key property extracted by human tactile perception is
surface roughness, which relies on measuring vibratory signals using the
multi-layered fingertip structure. Existing robotic systems lack tactile
sensors that are able to provide high dynamic sensing ranges, perceive material
properties, and maintain a low hardware cost. In this work, we introduce the
reference design and fabrication procedure of a miniature and low-cost tactile
sensor consisting of a biomimetic cutaneous structure, including the artificial
fingerprint, dermis, epidermis, and an embedded magnet-sensor structure which
serves as a mechanoreceptor for converting mechanical information to digital
signals. The presented sensor is capable of detecting high-resolution magnetic
field data through the Hall effect and creating high-dimensional time-frequency
domain features for material texture classification. Additionally, we
investigate the effects of different superficial sensor fingerprint patterns
for classifying materials through both simulation and physical experimentation.
After extracting time series and frequency domain features, we assess a
k-nearest neighbors classifier for distinguishing between different materials.
The results from our experiments show that our biomimetic tactile sensors with
fingerprint ridges can classify materials with more than 8% higher accuracy and
lower variability than ridge-less sensors. These results, along with the low
cost and customizability of our sensor, demonstrate high potential for lowering
the barrier to entry for a wide array of robotic applications, including
model-less tactile sensing for texture classification, material inspection, and
object recognition.


title: Combinatorics of Triangular Partitions

abstract:
  The aim of this paper is to develop the combinatorics of constructions
associated to what we call \emph{triangular partitions}. As introduced in
arXiv:2102.07931, these are the partitions whose cells are those lying below
the line joining points $(r,0)$ and $(0,s)$, for any given positive reals $r$
and $s$. Classical notions such as Dyck paths and parking functions are
naturally generalized by considering the set of partitions included in a given
triangular partition. One of our striking results is that the restriction of
the Young lattice to triangular partition has a planar Hasse diagram, with many
nice properties. It follows that we may generalize the "first-return"
recurrence, for the enumeration of classical Dyck paths, to the enumeration of
all partitions contained in a fixed triangular one.


title: Self-Supervised Leaf Segmentation under Complex Lighting Conditions

abstract:
  As an essential prerequisite task in image-based plant phenotyping, leaf
segmentation has garnered increasing attention in recent years. While
self-supervised learning is emerging as an effective alternative to various
computer vision tasks, its adaptation for image-based plant phenotyping remains
rather unexplored. In this work, we present a self-supervised leaf segmentation
framework consisting of a self-supervised semantic segmentation model, a
color-based leaf segmentation algorithm, and a self-supervised color correction
model. The self-supervised semantic segmentation model groups the semantically
similar pixels by iteratively referring to the self-contained information,
allowing the pixels of the same semantic object to be jointly considered by the
color-based leaf segmentation algorithm for identifying the leaf regions.
Additionally, we propose to use a self-supervised color correction model for
images taken under complex illumination conditions. Experimental results on
datasets of different plant species demonstrate the potential of the proposed
self-supervised framework in achieving effective and generalizable leaf
segmentation.


title: On the Excitations of a Balian-Werthamer Superconductor

abstract:
  My contribution to this collection of articles in honor of David Lee and John
Reppy on their 90th birthdays is a reflection on the remarkable phenomenology
of the excitation spectra of superfluid $^3$He, in particular the B-phase which
was identified by NMR and acoustic spectroscopy as Balian-Werthamer state shown
in 1963 to be the ground state of a spin-triplet, p-wave superconductor within
weak-coupling BCS theory. The superfluid phases of $^3$He provide paradigms for
electronic superconductors with broken space-time symmetries and non-trivial
ground-state topology. Indeed broken spin- and orbital rotation symmetries lead
to a rich spectrum of collective modes of the order parameter that can be
detected using NMR, acoustic and microwave spectroscopies. The topology of the
BW state implies its low-temperature, low-energy transport properties are
dominated by gapless Majorana modes confined on boundaries or interfaces. Given
the central role the BW state played I discuss the acoustic and electromagnetic
signatures of the BW state, the latter being relevant if an electronic analog
of superfluid $^3$He-B is realized.


title: Robust, Automated, and Accurate Black-box Variational Inference

abstract:
  Black-box variational inference (BBVI) now sees widespread use in machine
learning and statistics as a fast yet flexible alternative to Markov chain
Monte Carlo methods for approximate Bayesian inference. However, stochastic
optimization methods for BBVI remain unreliable and require substantial
expertise and hand-tuning to apply effectively. In this paper, we propose
Robust, Automated, and Accurate BBVI (RAABBVI), a framework for reliable BBVI
optimization. RAABBVI is based on rigorously justified automation techniques,
includes just a small number of intuitive tuning parameters, and detects
inaccurate estimates of the optimal variational approximation. RAABBVI
adaptively decreases the learning rate by detecting convergence of the
fixed--learning-rate iterates, then estimates the symmetrized Kullback--Leiber
(KL) divergence between the current variational approximation and the optimal
one. It also employs a novel optimization termination criterion that enables
the user to balance desired accuracy against computational cost by comparing
(i) the predicted relative decrease in the symmetrized KL divergence if a
smaller learning were used and (ii) the predicted computation required to
converge with the smaller learning rate. We validate the robustness and
accuracy of RAABBVI through carefully designed simulation studies and on a
diverse set of real-world model and data examples.


title: Towards Learning Neural Representations from Shadows

abstract:
  We present a method that learns neural scene representations from only
shadows present in the scene. While traditional shape-from-shadow (SfS)
algorithms reconstruct geometry from shadows, they assume a fixed scanning
setup and fail to generalize to complex scenes. Neural rendering algorithms, on
the other hand, rely on photometric consistency between RGB images but largely
ignore physical cues such as shadows, which have been shown to provide valuable
information about the scene. We observe that shadows are a powerful cue that
can constrain neural scene representations to learn SfS, and even outperform
NeRF to reconstruct otherwise hidden geometry. We propose a graphics-inspired
differentiable approach to render accurate shadows with volumetric rendering,
predicting a shadow map that can be compared to the ground truth shadow. Even
with just binary shadow maps, we show that neural rendering can localize the
object and estimate coarse geometry. Our approach reveals that sparse cues in
images can be used to estimate geometry using differentiable volumetric
rendering. Moreover, our framework is highly generalizable and can work
alongside existing 3D reconstruction techniques that otherwise only use
photometric consistency. Our code is made available in our supplementary
materials.


title:
Improving Capstone Research Projects: Using Computational Thinking to
  Provide Choice and Structured Active Learning

abstract:
  This work presents a structured systematic process for undergraduate capstone
research projects embodying computational thinking (CT) practices. Students
learn to conduct research with a decision support system utilizing CT. The
system is demonstrated through a case study of a capstone research project
course. The course is a 3rd year single semester capstone in an aviation
program. CT was integrated over a decade, through 21 semesters of coordinating
and delivering the course. The CT practices evolved and were utilized for more
aspects over time. The CT system facilitated a significant reduction in staff
workload by eliminating the need for direct one-on-one supervision and enabling
the streamlining of marking. This resulted in fairer marking by eliminating
supervisor bias. Student feedback shows a high degree of satisfaction, with
comments highlighting choice and learning.


title:
Leveraging Evolutionary Algorithms for Feasible Hexapod Locomotion
  Across Uneven Terrain

abstract:
  Optimizing gait stability for legged robots is a difficult problem. Even on
level surfaces, effectively traversing across different textures (e.g., carpet)
rests on dynamically tuning parameters in multidimensional space. Inspired by
biology, evolutionary algorithms (EA) remain an attractive solution for
feasibly implementing robotic locomotion with both energetic economy and rapid
parameter convergence. Here, we leveraged this class of algorithms to evolve a
stable hexapod gait controller capable of traversing uneven terrain and
obstacles. Gait parameters were evolved in a rigid body dynamics simulation on
an 8 x 3 meter obstacle course comprised of random step field, linear obstacles
and inclined surfaces. Using a fitness function that jointly optimized
locomotion velocity and stability, we found that multiple successful gait
parameter evolutions yielded specialized functionality for each leg. Specific
gait parameters were identified as critical to developing a rough terrain gait.


title:
Electronic structure of $\alpha-$RuCl$_3$ by fixed-node and fixed-phase
  diffusion Monte Carlo methods

abstract:
  Layered material $\alpha-$RuCl$_3$ has caught wide attention due to its
possible realization of Kitaev's spin liquid and its electronic structure that
involves the interplay of electron-electron correlations and spin-orbit
effects. Several DFT$+U$ studies have suggested that both electron-electron
correlations and spin-orbit effects are crucial for accurate description of the
band gap. This work studies the importance of these two effects using
fixed-node and fixed-phase diffusion Monte Carlo calculations both in
spin-averaged and explicit spin-orbit formalisms. In the latter, the
Slater-Jastrow trial function is constructed from two-component spin-orbitals
using our recent quantum Monte Carlo (QMC) developments and thoroughly tested
effective core potentials. Our results show that the gap in the ideal crystal
is already accurately described by spin-averaged case with the dominant role
being played by the magnetic ground state with significant exchange and
electron correlation effects. We find qualitative agreement between hybrid DFT,
DFT+$U$, and QMC. In addition, QMC results agree very well with available
experiments, and we identify the values of exact Fock exchange mixing that
provide comparable gaps. Explicit spin-orbit QMC calculations reveal that the
effect of spin-orbit coupling on the gap is minor, of the order of 0.2 eV which
corresponds to the strength of the spin-orbit of the Ru atom.

categories: cond-mat.str-el cond-mat.mtrl-sci physics.chem-ph physics.comp-ph


title: Empirical Standards for Repository Mining

abstract:
  The purpose of scholarly peer review is to evaluate the quality of scientific
manuscripts. However, study after study demonstrates that peer review neither
effectively nor reliably assesses research quality. Empirical standards attempt
to address this problem by modelling a scientific community's expectations for
each kind of empirical study conducted in that community. This should enhance
not only the quality of research but also the reliability and predictability of
peer review, as scientists adopt the standards in both their researcher and
reviewer roles. However, these improvements depend on the quality and adoption
of the standards. This tutorial will therefore present the empirical standard
for mining software repositories, both to communicate its contents and to get
feedback from the attendees. The tutorial will be organized into three parts:
(1) brief overview of the empirical standards project; (2) detailed
presentation of the repository mining standard; (3) discussion and suggestions
for improvement.


title: Transcendental splitting fields of division algebras

abstract:
  We examine when division algebras can share common splitting fields of
bounded transcendence degree. In particular, we show that one can find fields
for which one has infinitely many Brauer classes of the same index and period
at least $3$, all nonisomorphic and having the same set of splitting fields of
transcendence degree at most $1$. On the other hand, we show that one fixes any
division algebra over a field, then any division algebras sharing the same
splitting fields of transcendence degree at most $3$ must generate the same
cyclic subgroup of the Brauer group. In particular, there are only a finite
number of such division algebras. We also show that a similar finiteness
statement holds for splitting fields of transcendence degree at most $2$.


title:
4-bit Conformer with Native Quantization Aware Training for Speech
  Recognition

abstract:
  Reducing the latency and model size has always been a significant research
problem for live Automatic Speech Recognition (ASR) application scenarios.
Along this direction, model quantization has become an increasingly popular
approach to compress neural networks and reduce computation cost. Most of the
existing practical ASR systems apply post-training 8-bit quantization. To
achieve a higher compression rate without introducing additional performance
regression, in this study, we propose to develop 4-bit ASR models with native
quantization aware training, which leverages native integer operations to
effectively optimize both training and inference. We conducted two experiments
on state-of-the-art Conformer-based ASR models to evaluate our proposed
quantization technique. First, we explored the impact of different precisions
for both weight and activation quantization on the LibriSpeech dataset, and
obtained a lossless 4-bit Conformer model with 7.7x size reduction compared to
the float32 model. Following this, we for the first time investigated and
revealed the viability of 4-bit quantization on a practical ASR system that is
trained with large-scale datasets, and produced a lossless Conformer ASR model
with mixed 4-bit and 8-bit weights that has 5x size reduction compared to the
float32 model.


title:
Five and three quantum dot systems as apparatuses for measuring
  energy-levels

abstract:
  A quantum dot (QD) system provides various quantum physics of nanostructures.
So far, many types of semiconductor QD structures have been fabricated and
investigated experimentally and analyzed theoretically. Presently, QD systems
have attracted considerable attention as units for the qubit system of quantum
computers. Therefore, it is vital to integrate QD systems as measurement
devices in addition to qubits. Here, we theoretically investigate the side-QD
system as a measurement apparatus for energy-levels of the target QDs. We
formulate the transport properties of both three and five QDs based on the
Green functions method. The effects of the energy-difference of two side-QDs on
the measurement current are calculated. The trade-off between the strength of
the measurement and the back-action induced by the measurement is discussed. It
is found that the medium coupling strength is appropriate for reading out the
difference of the two energy-levels.


title:
Snowmass2021 Cosmic Frontier White Paper: Probing dark matter with
  small-scale astrophysical observations

abstract:
  The current understanding of dark matter comes largely from measurements of
the total matter content in the universe, from the distribution of gravitating
matter on very large scales, and from rotation curves and velocity dispersions
on sub-galactic scales. However, small-scale structure may well hold the key to
unlocking the particle physics of the still-mysterious 85% of matter in the
universe. Novel small-scale astrophysical probes of new particles and dark
matter will become possible with a large data volume of high-precision
measurements enabled by next-generation gravitational-wave detectors and
advanced astrometry instruments. Cosmic microwave background and large-scale
structure surveys will provide complementary constraints on dark matter models
with unique small-scale signatures. We lay out the studies of small-scale
structures and compact objects as dark matter probes, and summarize the
requirements to achieve the goals.


title:
Investigating the Properties of Neural Network Representations in
  Reinforcement Learning

abstract:
  In this paper we investigate the properties of representations learned by
deep reinforcement learning systems. Much of the earlier work in representation
learning for reinforcement learning focused on designing fixed-basis
architectures to achieve properties thought to be desirable, such as
orthogonality and sparsity. In contrast, the idea behind deep reinforcement
learning methods is that the agent designer should not encode representational
properties, but rather that the data stream should determine the properties of
the representation -- good representations emerge under appropriate training
schemes. In this paper we bring these two perspectives together, empirically
investigating the properties of representations that support transfer in
reinforcement learning. This analysis allows us to provide novel hypotheses
regarding impact of auxiliary tasks in end-to-end training of non-linear
reinforcement learning methods. We introduce and measure six representational
properties over more than 25 thousand agent-task settings. We consider DQN
agents with convolutional networks in a pixel-based navigation environment. We
develop a method to better understand \emph{why} some representations work
better for transfer, through a systematic approach varying task similarity and
measuring and correlating representation properties with transfer performance.


title: Ultrafast laser stress figuring for accurate deformation of thin mirrors

abstract:
  Fabricating freeform mirrors relies on accurate optical figuring processes
capable of arbitrarily modifying low-spatial frequency height without creating
higher-spatial frequency errors. We present a scalable process to accurately
figure thin mirrors using stress generated by a focused ultrafast laser. We
applied ultrafast laser stress figuring (ULSF) to four thin fused silica
mirrors to correct them to 10-20 nm RMS over 28 Zernike terms, in 2-3
iterations, without significantly affecting higher-frequency errors. We
measured the mirrors over a month and found that dielectric-coated mirrors were
stable but stability of aluminum-coated mirrors was inconclusive. The accuracy
and throughput for ULSF is on par with existing deterministic figuring
processes, yet ULSF doesn't significantly affect mid-spatial frequency errors,
can be applied after mirror coating, and can scale to higher throughput using
mature laser processing technologies. ULSF offers new potential to rapidly and
accurately shape freeform mirrors.


title:
Pressure-induced superconductivity in flat-band Kagome compounds
  Pd$_3$P$_2$(S$_{1-x}$Se$_x$)$_8$

abstract:
  We performed high-pressure transport studies on the flat-band Kagome
compounds, Pd$_3$P$_2$(S$_{1-x}$Se$_x$)$_8$ ($x$ = 0, 0.25), with a diamond
anvil cell. For both compounds, the resistivity exhibits an insulating behavior
with pressure up to 17 GPa. With pressure above 20 GPa, a metallic behavior is
observed at high temperatures in Pd$_3$P$_2$S$_8$, and superconductivity
emerges at low temperatures. The onset temperature of superconducting
transition $T_{\rm C}$ rises monotonically from 2 K to 5 K and does not
saturate with pressure up to 43 GPa. For the Se-doped compound
Pd$_3$P$_2$(S$_{0.75}$Se$_{0.25}$)$_8$, the $T_{\rm C}$ is about 1.5 K higher
than that of the undoped one over the whole pressure range, and reaches 6.5 K
at 43 GPa. The upper critical field with field applied along the $c$ axis at
typical pressures is about 50$\%$ of the Pauli limit, suggests a 3D
superconductivity. The Hall coefficient is low and exhibits a peaked behavior
at about 40 K, which suggests a multi-band electronic structure.


title: High-resolution Face Swapping via Latent Semantics Disentanglement

abstract:
  We present a novel high-resolution face swapping method using the inherent
prior knowledge of a pre-trained GAN model. Although previous research can
leverage generative priors to produce high-resolution results, their quality
can suffer from the entangled semantics of the latent space. We explicitly
disentangle the latent semantics by utilizing the progressive nature of the
generator, deriving structure attributes from the shallow layers and appearance
attributes from the deeper ones. Identity and pose information within the
structure attributes are further separated by introducing a landmark-driven
structure transfer latent direction. The disentangled latent code produces rich
generative features that incorporate feature blending to produce a plausible
swapping result. We further extend our method to video face swapping by
enforcing two spatio-temporal constraints on the latent space and the image
space. Extensive experiments demonstrate that the proposed method outperforms
state-of-the-art image/video face swapping methods in terms of hallucination
quality and consistency. Code can be found at:
https://github.com/cnnlstm/FSLSD_HiRes.


title:
Entity-driven Fact-aware Abstractive Summarization of Biomedical
  Literature

abstract:
  As part of the large number of scientific articles being published every
year, the publication rate of biomedical literature has been increasing.
Consequently, there has been considerable effort to harness and summarize the
massive amount of biomedical research articles. While transformer-based
encoder-decoder models in a vanilla source document-to-summary setting have
been extensively studied for abstractive summarization in different domains,
their major limitations continue to be entity hallucination (a phenomenon where
generated summaries constitute entities not related to or present in source
article(s)) and factual inconsistency. This problem is exacerbated in a
biomedical setting where named entities and their semantics (which can be
captured through a knowledge base) constitute the essence of an article. The
use of named entities and facts mined from background knowledge bases
pertaining to the named entities to guide abstractive summarization has not
been studied in biomedical article summarization literature. In this paper, we
propose an entity-driven fact-aware framework for training end-to-end
transformer-based encoder-decoder models for abstractive summarization of
biomedical articles. We call the proposed approach, whose building block is a
transformer-based model, EFAS, Entity-driven Fact-aware Abstractive
Summarization. We conduct experiments using five state-of-the-art
transformer-based models (two of which are specifically designed for long
document summarization) and demonstrate that injecting knowledge into the
training/inference phase of these models enables the models to achieve
significantly better performance than the standard source document-to-summary
setting in terms of entity-level factual accuracy, N-gram novelty, and semantic
equivalence while performing comparably on ROUGE metrics. The proposed approach
is evaluated on ICD-11-Summ-1000, and PubMed-50k.


title:
On the abundance of $k$-fold semi-monotone minimal sets in bimodal
  circle maps

abstract:
  Inspired by a twist maps theorem of Mather we study recurrent invariant sets
that are ordered like rigid rotation under the action of the lift of a bimodal
circle map $g$ to the $k$-fold cover. For each irrational in the interior of
the rotation set the collection of the $k$-fold ordered semi-Denjoy minimal
sets with that rotation number contains a $(k-1)$-dimensional ball in the weak
topology on their unique invariant measures. We also describe completely their
periodic orbit analogs for rational rotation numbers. The main tool is a
generalization of a construction of Hedlund and Morse which generates the
symbolic analogs of these $k$-fold well-ordered invariant sets.


title: Using Active Speaker Faces for Diarization in TV shows

abstract:
  Speaker diarization is one of the critical components of computational media
intelligence as it enables a character-level analysis of story portrayals and
media content understanding. Automated audio-based speaker diarization of
entertainment media poses challenges due to the diverse acoustic conditions
present in media content, be it background music, overlapping speakers, or
sound effects. At the same time, speaking faces in the visual modality provide
complementary information and not prone to the errors seen in the audio
modality. In this paper, we address the problem of speaker diarization in TV
shows using the active speaker faces. We perform face clustering on the active
speaker faces and show superior speaker diarization performance compared to the
state-of-the-art audio-based diarization methods. We additionally report a
systematic analysis of the impact of active speaker face detection quality on
the diarization performance. We also observe that a moderately well-performing
active speaker system could outperform the audio-based diarization systems.


title: Homogenization for Time-periodic KPP Reactions

abstract:
  We prove homogenization for reaction-advection-diffusion equations with KPP
reactions, in the time-periodic spatially stationary ergodic setting, and find
an explicit formula for the homogenized dynamic. We also extend this result to
models with non-local diffusion and KPP reactions.


title: Blacklight: A General-Relativistic Ray-Tracing and Analysis Tool

abstract:
  We describe the Blacklight code, intended for post-processing
general-relativistic magnetohydrodynamic simulation data. Beyond polarized ray
tracing of synchrotron radiation, it can produce a number of outputs that aid
in analyzing data sets, such as maps of auxiliary quantities and false-color
renderings. Additional features include support for adaptive mesh refinement
input, slow-light calculations, and adaptive ray tracing. The code is written
with ease of use, readability, and transparency as primary objectives, while it
still achieves high performance. Blacklight is publicly available and released
into the public domain.


title: Khovanov-Lauda-Rouquier subalgebras and redotted Webster algebras

abstract:
  We define Khovanov-Lauda-Rouquier subalgebras which are generalizations of
redotted versions of Webster's tensor product algebras of type $A_1$. Quotient
algebras of these subalgebras are isomorphic to Webster's tensor product
algebras in general type.


title: PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation

abstract:
  In this paper, we propose a new deep learning-based method for estimating
room layout given a pair of 360 panoramas. Our system, called Position-aware
Stereo Merging Network or PSMNet, is an end-to-end joint layout-pose estimator.
PSMNet consists of a Stereo Pano Pose (SP2) transformer and a novel
Cross-Perspective Projection (CP2) layer. The stereo-view SP2 transformer is
used to implicitly infer correspondences between views, and can handle noisy
poses. The pose-aware CP2 layer is designed to render features from the
adjacent view to the anchor (reference) view, in order to perform view fusion
and estimate the visible layout. Our experiments and analysis validate our
method, which significantly outperforms the state-of-the-art layout estimators,
especially for large and complex room spaces.


title: Federated Domain Adaptation for ASR with Full Self-Supervision

abstract:
  Cross-device federated learning (FL) protects user privacy by collaboratively
training a model on user devices, therefore eliminating the need for
collecting, storing, and manually labeling user data. While important topics
such as the FL training algorithm, non-IID-ness, and Differential Privacy have
been well studied in the literature, this paper focuses on two challenges of
practical importance for improving on-device ASR: the lack of ground-truth
transcriptions and the scarcity of compute resource and network bandwidth on
edge devices. First, we propose a FL system for on-device ASR domain adaptation
with full self-supervision, which uses self-labeling together with data
augmentation and filtering techniques. The system can improve a strong
Emformer-Transducer based ASR model pretrained on out-of-domain data, using
in-domain audio without any ground-truth transcriptions. Second, to reduce the
training cost, we propose a self-restricted RNN Transducer (SR-RNN-T) loss, a
variant of alignment-restricted RNN-T that uses Viterbi alignments from
self-supervision. To further reduce the compute and network cost, we
systematically explore adapting only a subset of weights in the
Emformer-Transducer. Our best training recipe achieves a $12.9\%$ relative WER
reduction over the strong out-of-domain baseline, which equals $70\%$ of the
reduction achievable with full human supervision and centralized training.


title:
X-ray Selected Narrow-Line Active Galactic Nuclei in the COSMOS Field:
  Nature of Optically Dull Active Galactic Nuclei

abstract:
  X-ray emission detection in a galaxy is one of the efficient tools for
selecting Active Galactic Nuclei (AGNs). However, many X-ray-selected AGNs are
not easily selected as AGNs by their optical emission. These galaxies,
so-called optically dull (OD) AGNs, are fascinating since their X-ray emission
is bright even though the AGN signature in the optical regime is absent. In a
deep multiwavelength survey over 2 deg$^2$ of the Cosmic Evolution Survey
(COSMOS) field, we have looked for the OD AGNs using photometric,
spectroscopic, and X-ray data. We identified 310 non-broad line sources with
optical spectra as AGN using X-ray selection up to redshift $z\sim 1.5$. We
inspected the spectra to check for any AGN signature in their optical emission
lines: [Ne V] forbidden emission line, Mass Excitation diagram (MEx), color
excitation diagram (TBT), and excess in [O II ] emission line. Finally, we
found 48 AGNs show AGN signatures in the optical spectrum classified as
narrow-line AGN and 180 AGNs that did not show any AGN signature as OD AGN
sample. The simple explanation of OD AGN's nature is due to a bright host
galaxy that dilutes the AGN light or dust materials obscuring the AGN light.
The bright host galaxy dilution explains nearly $70\%$ of our OD AGN sample. At
the same time, the dust material obscuration is unlikely for the main reason.
By estimating the Eddington ratio, we also found that 95/180 of our OD AGNs
have a lower accretion rate of $(\lambda_\text{Edd})\lesssim 10^{-2}$ than the
typical AGN value. We expected the lower accretion rate sources that suffer
from neither host galaxy dilution nor obscuration to have Radiatively
Inefficient Flow (RIAF) in their accretion disk. Finally, nine sources have
been identified to be most likely host the RIAF disk.


title: Light Clients for Lazy Blockchains

abstract:
  Decoupling consensus from transaction verification and execution is an
important technique to increase the throughput of blockchains, a technique
known as a lazy blockchain. Lazy blockchains can end up containing invalid
transactions such as double spends, but these can easily be filtered out by
full nodes that can check if there have been previous conflicting transactions.
However, creating light (SPV) clients that do not see the whole transaction
history on top of these chains becomes a challenge: A record of a transaction
on the chain does not necessarily entail transaction confirmation. In this
paper, we devise a protocol that enables the creation of efficient light
clients for lazy blockchains. The number of interaction rounds and the
communication complexity of our protocol are only logarithmic in the blockchain
execution time. Our construction is based on a bisection game that traverses
the Merkle tree containing the ledger of all -- valid or invalid --
transactions. We prove that our proof system is succinct, complete and sound,
and we illustrate how it can be applied to both the UTXO as well as the account
based models. Lastly, we empirically demonstrate the feasibility of our scheme
by providing experimental results.


title: Deeply Interleaved Two-Stream Encoder for Referring Video Segmentation

abstract:
  Referring video segmentation aims to segment the corresponding video object
described by the language expression. To address this task, we first design a
two-stream encoder to extract CNN-based visual features and transformer-based
linguistic features hierarchically, and a vision-language mutual guidance
(VLMG) module is inserted into the encoder multiple times to promote the
hierarchical and progressive fusion of multi-modal features. Compared with the
existing multi-modal fusion methods, this two-stream encoder takes into account
the multi-granularity linguistic context, and realizes the deep interleaving
between modalities with the help of VLGM. In order to promote the temporal
alignment between frames, we further propose a language-guided multi-scale
dynamic filtering (LMDF) module to strengthen the temporal coherence, which
uses the language-guided spatial-temporal features to generate a set of
position-specific dynamic filters to more flexibly and effectively update the
feature of current frame. Extensive experiments on four datasets verify the
effectiveness of the proposed model.


title:
A meta-probabilistic-programming language for bisimulation of
  probabilistic and non-well-founded type systems

abstract:
  We introduce a formal meta-language for probabilistic programming, capable of
expressing both programs and the type systems in which they are embedded. We
are motivated here by the desire to allow an AGI to learn not only relevant
knowledge (programs/proofs), but also appropriate ways of reasoning
(logics/type systems). We draw on the frameworks of cubical type theory and
dependent typed metagraphs to formalize our approach. In doing so, we show that
specific constructions within the meta-language can be related via bisimulation
(implying path equivalence) to the type systems they correspond. This allows
our approach to provide a convenient means of deriving synthetic denotational
semantics for various type systems. Particularly, we derive bisimulations for
pure type systems (PTS), and probabilistic dependent type systems (PDTS). We
discuss further the relationship of PTS to non-well-founded set theory, and
demonstrate the feasibility of our approach with an implementation of a
bisimulation proof for a small-scale type system in Guarded Cubical Agda.


title:
Stability of three-dimensional stochastic Navier-Stokes equation with
  Markov switching

abstract:
  A right continuous Markov chain is introduced in the noise terms of the
three-dimensional stochastic Navier-Stokes equation, and we call such
stochastic system as stochastic Navier-Stokes equation with Markov switching.
In the present article, we study the $p$-th moment exponential stability and
the almost surely exponential stability of the solution to the equation.


title:
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs
  via PAC-Bayes Objectives

abstract:
  Deep Probabilistic Programming (DPP) allows powerful models based on
recursive computation to be learned using efficient deep-learning optimization
techniques. Additionally, DPP offers a unified perspective, where inference and
learning algorithms are treated on a par with models as stochastic programs.
Here, we offer a framework for representing and learning flexible PAC-Bayes
bounds as stochastic programs using DPP-based methods. In particular, we show
that DPP techniques may be leveraged to derive generalization bounds that draw
on the compositionality of DPP representations. In turn, the bounds we
introduce offer principled training objectives for higher-order probabilistic
programs. We offer a definition of a higher-order generalization bound, which
naturally encompasses single- and multi-task generalization perspectives
(including transfer- and meta-learning) and a novel class of bound based on a
learned measure of model complexity. Further, we show how modified forms of all
higher-order bounds can be efficiently optimized as objectives for DPP
training, using variational techniques. We test our framework using single- and
multi-task generalization settings on synthetic and biological data, showing
improved performance and generalization prediction using flexible DPP model
representations and learned complexity measures.