title: Weighted Connected Matchings

abstract:
  A matching $M$ is a $\mathscr{P}$-matching if the subgraph induced by the
endpoints of the edges of $M$ satisfies property $\mathscr{P}$. As examples,
for appropriate choices of $\mathscr{P}$, the problems Induced Matching,
Uniquely Restricted Matching, Connected Matching and Disconnected Matching
arise. For many of these problems, finding a maximum $\mathscr{P}$-matching is
a knowingly NP-Hard problem, with few exceptions, such as connected matchings,
which has the same time complexity as usual Maximum Matching problem. The
weighted variant of Maximum Matching has been studied for decades, with many
applications, including the well-known Assignment problem. Motivated by this
fact, in addition to some recent researches in weighted versions of acyclic and
induced matchings, we study the Maximum Weight Connected Matching. In this
problem, we want to find a matching $M$ such that the endpoint vertices of its
edges induce a connected subgraph and the sum of the edge weights of $M$ is
maximum. Unlike the unweighted Connected Matching problem, which is in P for
general graphs, we show that Maximum Weight Connected Matching is NP-Hard even
for bounded diameter bipartite graphs, starlike graphs, planar bipartite, and
bounded degree planar graphs, while solvable in linear time for trees and
subcubic graphs. When we restrict edge weights to be non negative only, we show
that the problem turns to be polynomially solvable for chordal graphs, while it
remains NP-Hard for most of the cases when weights can be negative. Our final
contributions are on parameterized complexity. On the positive side, we present
a single exponential time algorithm when parameterized by treewidth. In terms
of kernelization, we show that, even when restricted to binary weights,
Weighted Connected Matching does not admit a polynomial kernel when
parameterized by vertex cover under standard complexity-theoretical hypotheses.


title:
A new generation of effective core potentials from correlated and
  spin-orbit calculations: selected heavy elements

abstract:
  We introduce new correlation consistent effective core potentials (ccECPs)
for the elements I, Te, Bi, Ag, Au, Pd, Ir, Mo, and W with $4d$, $5d$, $6s$ and
$6p$ valence spaces. These ccECPs are given as a sum of spin-orbit averaged
relativistic effective potential (AREP) and effective spin-orbit (SO) terms.
The construction involves several steps with increasing refinements from more
simple to fully correlated methods. The optimizations are carried out with
objective functions that include weighted many-body atomic spectra,
norm-conservation criteria, and spin-orbit splittings. Transferability tests
involve molecular binding curves of corresponding hydride and oxide dimers. The
constructed ccECPs are systematically better and in a few cases on par with
previous effective core potential (ECP) tables on all tested criteria and
provide a significant increase in accuracy for valence-only calculations with
these elements. Our study confirms the importance of the AREP part in
determining the overall quality of the ECP even in the presence of sizable
spin-orbit effects. The subsequent quantum Monte Carlo (QMC) calculations point
out the importance of accurate trial wave functions which in some cases (mid
series transition elements) require treatment well beyond single-reference.


title:
Estimation of Clinical Workload and Patient Activity using Deep Learning
  and Optical Flow

abstract:
  Contactless monitoring using thermal imaging has become increasingly proposed
to monitor patient deterioration in hospital, most recently to detect fevers
and infections during the COVID-19 pandemic. In this letter, we propose a novel
method to estimate patient motion and observe clinical workload using a similar
technical setup but combined with open source object detection algorithms
(YOLOv4) and optical flow. Patient motion estimation was used to approximate
patient agitation and sedation, while worker motion was used as a surrogate for
caregiver workload. Performance was illustrated by comparing over 32000 frames
from videos of patients recorded in an Intensive Care Unit, to clinical
agitation scores recorded by clinical workers.


title: Depicting a generalized shift move in crown diagrams

abstract:
  This paper gives a diagrammatic way to perform a generalized shift move on a
crown diagram of a smooth 4-manifold. Applications include a simplified proof
that if two crown diagrams are related by a generalized shift move, then they
are slide-equivalent; a method for converting a genus g > 1 Lefschetz fibration
into a crown diagram; and the fact that the vanishing cycles of such a crown
diagram are slide-equivalent to a standard inclusion of the Lefschetz vanishing
cycles into a genus g + 1 surface.


title: The strongly irradiated planets in Praesepe

abstract:
  We present an analysis of XMM-Newton observations of four stars in the young
(670 Myr) open cluster Praesepe. The planets hosted by these stars all lie
close in radius-period space to the radius-period valley and/or the Neptunian
desert, two features that photoevaporation by X-ray and extreme ultraviolet
(EUV) photons could be driving. Although the stars are no longer in the
saturated regime, strong X-ray and extreme ultraviolet irradiation is still
ongoing. Based on EUV time evolution slopes we derived in a previous paper, in
all four cases, two-thirds of their EUV irradiation is still to come. We
compare the XMM-Newton light curves to those simultaneously measured with K2 at
optical wavelengths, allowing us to search for correlated variability between
the X-ray and optical light curves. We find that the X-ray flux decreases and
flattens off while the optical flux rises throughout for K2-100, something that
could result from active regions disappearing from view as the star spins.
Finally, we also investigate possible futures for the four planets in our
sample with simulations of their atmosphere evolution still to come, finding
that complete photoevaporative stripping of the envelope of three of the four
planets is possible, depending on the current planet masses.


title: Enhancement of optical forces at bound state in the continuum

abstract:
  Light-actuated motors, vehicles, and even space sails have drawn tremendous
attention for basic science and applications in space, biomedical, and sensing
domains. Optical bound states in the continuum (BIC) are topological
singularities of the scattering matrix, known for their unique light trapping
capability and enhanced light-matter interaction. We show that BIC modes enable
the generation of enhanced and tunable optical forces and torques. A sharp and
controllable lineshape is observed in forces and torques spectra when
approaching high-Q resonance BIC modes. Wavelength and polarization tunability
are presented as an effective method to control the forces on BIC enclosed
structures. Finally Finite-size simulations are performed to evaluate the
practical application for a BIC assisted metavehicle.


title:
"This is Fake! Shared it by Mistake": Assessing the Intent of Fake News
  Spreaders

abstract:
  Individuals can be misled by fake news and spread it unintentionally without
knowing it is false. This phenomenon has been frequently observed but has not
been investigated. Our aim in this work is to assess the intent of fake news
spreaders. To distinguish between intentional versus unintentional spreading,
we study the psychological explanations of unintentional spreading. With this
foundation, we then propose an influence graph, using which we assess the
intent of fake news spreaders. Our extensive experiments show that the assessed
intent can help significantly differentiate between intentional and
unintentional fake news spreaders. Furthermore, the estimated intent can
significantly improve the current techniques that detect fake news. To our best
knowledge, this is the first work to model individuals' intent in fake news
spreading.


title:
Discovering Concepts in Learned Representations using Statistical
  Inference and Interactive Visualization

abstract:
  Concept discovery is one of the open problems in the interpretability
literature that is important for bridging the gap between non-deep learning
experts and model end-users. Among current formulations, concepts defines them
by as a direction in a learned representation space. This definition makes it
possible to evaluate whether a particular concept significantly influences
classification decisions for classes of interest. However, finding relevant
concepts is tedious, as representation spaces are high-dimensional and hard to
navigate. Current approaches include hand-crafting concept datasets and then
converting them to latent space directions; alternatively, the process can be
automated by clustering the latent space. In this study, we offer another two
approaches to guide user discovery of meaningful concepts, one based on
multiple hypothesis testing, and another on interactive visualization. We
explore the potential value and limitations of these approaches through
simulation experiments and an demo visual interface to real data. Overall, we
find that these techniques offer a promising strategy for discovering relevant
concepts in settings where users do not have predefined descriptions of them,
but without completely automating the process.


title:
Wireless Transmission of Images With The Assistance of Multi-level
  Semantic Information

abstract:
  Semantic-oriented communication has been considered as a promising to boost
the bandwidth efficiency by only transmitting the semantics of the data. In
this paper, we propose a multi-level semantic aware communication system for
wireless image transmission, named MLSC-image, which is based on the deep
learning techniques and trained in an end to end manner. In particular, the
proposed model includes a multilevel semantic feature extractor, that extracts
both the highlevel semantic information, such as the text semantics and the
segmentation semantics, and the low-level semantic information, such as local
spatial details of the images. We employ a pretrained image caption to capture
the text semantics and a pretrained image segmentation model to obtain the
segmentation semantics. These high-level and low-level semantic features are
then combined and encoded by a joint semantic and channel encoder into symbols
to transmit over the physical channel. The numerical results validate the
effectiveness and efficiency of the proposed semantic communication system,
especially under the limited bandwidth condition, which indicates the
advantages of the high-level semantics in the compression of images.


title:
DeepSSN: a deep convolutional neural network to assess spatial scene
  similarity

abstract:
  Spatial-query-by-sketch is an intuitive tool to explore human spatial
knowledge about geographic environments and to support communication with scene
database queries. However, traditional sketch-based spatial search methods
perform insufficiently due to their inability to find hidden multi-scale map
features from mental sketches. In this research, we propose a deep
convolutional neural network, namely Deep Spatial Scene Network (DeepSSN), to
better assess the spatial scene similarity. In DeepSSN, a triplet loss function
is designed as a comprehensive distance metric to support the similarity
assessment. A positive and negative example mining strategy using qualitative
constraint networks in spatial reasoning is designed to ensure a consistently
increasing distinction of triplets during the training process. Moreover, we
develop a prototype spatial scene search system using the proposed DeepSSN, in
which the users input spatial query via sketch maps and the system can
automatically augment the sketch training data. The proposed model is validated
using multi-source conflated map data including 131,300 labeled scene samples
after data augmentation. The empirical results demonstrate that the DeepSSN
outperforms baseline methods including k-nearest-neighbors, multilayer
perceptron, AlexNet, DenseNet, and ResNet using mean reciprocal rank and
precision metrics. This research advances geographic information retrieval
studies by introducing a novel deep learning method tailored to spatial scene
queries.


title: An introduction to symmetric edge graphs

abstract:
  Given a graph $X,$ we define the symmetric edge graph of $X,$ denoted as
$\gamma(X)$ and study various properties of $\gamma(X)$ with respect to $X.$ If
$X^{\prime \prime}$ denotes the Kronecker double cover of $X,$ we characterize
all graphs which satisfy $\gamma(X)=L(X^{\prime \prime}),$ where $L(Y)$ is the
line graph of $Y.$


title:
A Novel Encoder-Decoder Network with Guided Transmission Map for Single
  Image Dehazing

abstract:
  A novel Encoder-Decoder Network with Guided Transmission Map (EDN-GTM) for
single image dehazing scheme is proposed in this paper. The proposed EDN-GTM
takes conventional RGB hazy image in conjunction with its transmission map
estimated by adopting dark channel prior as the inputs of the network. The
proposed EDN-GTM utilizes U-Net for image segmentation as the core network and
utilizes various modifications including spatial pyramid pooling module and
Swish activation to achieve state-of-the-art dehazing performance. Experiments
on benchmark datasets show that the proposed EDN-GTM outperforms most of
traditional and deep learning-based image dehazing schemes in terms of PSNR and
SSIM metrics. The proposed EDN-GTM furthermore proves its applicability to
object detection problems. Specifically, when applied to an image preprocessing
tool for driving object detection, the proposed EDN-GTM can efficiently remove
haze and significantly improve detection accuracy by 4.73% in terms of mAP
measure. The code is available at: https://github.com/tranleanh/edn-gtm.


title:
Consideration of the Intricacies Inherent in Molecular Beam Epitaxy of
  the NaCl/GaAs System

abstract:
  The high cost of substrates for III-V growth can be cost limiting for
technologies that require large semiconductor areas. Thus, being able to
separate device layers and reuse the original substrate is highly desirable,
but existing techniques to lift off a film from a substrate have substantial
drawbacks. This work discusses some of the complexities with the growth of a
water-soluble, alkali-halide salt thin film between a III-V substrate and
overlayer. Much of the difficulty stems from the growth of GaAs on an actively
decomposing NaCl surface at elevated temperatures. Interestingly, the presence
of an in-situ electron beam incident on the NaCl surface, prior to and during
GaAs deposition, affects the crystallinity and morphology of the III-V
overlayer. Here we investigate a wide range of growth temperatures and the
timing of the impinging flux of both elemental sources and high energy
electrons at different points during the growth. We show that an assortment of
morphologies (discrete islands, porous material, and fully dense layers with
sharp interfaces) and crystallinity (amorphous, crystalline, and highly
textured) occur depending on the specific growth conditions, driven largely by
changes in GaAs nucleation which is greatly affected by the presence of the
reflection high energy electron diffraction beam.


title: Observability of Evaporating Lava Worlds

abstract:
  Lava worlds belong to a class of short orbital period planets reaching
dayside temperatures high enough to melt their silicate crust. Theory predicts
that the resulting lava oceans outgas their volatile components, attaining
equilibrium with the overlying vapour. This creates a tenuous, silicate-rich
atmosphere that may be confined to the permanent dayside of the planet. The
launch of JWST will provide the much needed sensitivity and spectral coverage
to characterise these worlds. In this paper, we assess the observability of
characterisable spectral features by self-consistently modelling silicate
atmospheres for all the currently confirmed targets having sufficient
substellar temperatures (> 1500 K). To achieve this we use outgassed
equilibrium chemistry and radiative transfer methods to compute
temperature-pressure profiles, atmospheric chemical compositions and emission
spectra. We explore varying melt compositions, free of highly-volatile
elements, accounting for possible atmospheric evolution. Our models include a
large number of neutral and ionic species, as well as all up-to-date opacities.
The results indicate that SiO and SiO2 infrared features are the best, unique
identifiers of silicate atmospheres, detectable using MIRI instrument of JWST.
Detection of these two species in emission would allow for strong constraints
on atmospheric thermal structure and possibly the composition of the melt. We
also propose that certain species, e.g., TiO, may be directly tied to different
classes of melts, possibly revealing surface and interior dynamics. Currently,
there are nearly a dozen confirmed lava planets ideal for characterisation of
silicate atmospheres using JWST, with two of these already accepted for the
initial General Observers program.


title: Weak pion-production and the second resonance region

abstract:
  In this work we report the calculation of the total cross section for
pion-production by the dispersion of neutrinos on nucleons. We use a consistent
formalism for the intermediate resonance states of spin $3/2$ , taking care on
the conditions imposed by the invariance on contact transformations and using
the Sachs parametrization for the form factors. In addition, we incorporate
states in the second resonance region up to 1.6 GeV and implement different
approaches for all the dressed resonance propagators. In addition to the
$\Delta$(1232), we include the N$^*$(1440),N$^*$(1535)and N$^*$(1520) resonance
contributions, and it is shown that this inclusion improve the description of
the total cross section regards a model where only the $\Delta$(1230) resonance
is considered. Also results for antineutrinos are properly described.


title: Impact of 3D Structure on Magnetic Reconnection

abstract:
  Results from 2.5D and 3D studies of the onset and development of the tearing
instability are presented, using high fidelity resistive MHD simulations. A
limited parameter study of the strength of the reconnecting field (or shear
angle) was performed. An initially simple 1D equilibrium was used, consisting
of a modified force-free current sheet, with periodic boundary conditions in
all directions. In all cases, the linear and non-linear evolution led to a
primary current sheet between two large flux ropes. The global reconnection
rate during this later stage was analyzed in all simulations. It was found that
in 2.5D the primary current sheet fragmented due to plasmoids, and as expected,
the global reconnection rate, calculated using multiple methods, increases with
the strength of the reconnecting field due to a stronger Alfv\'{e}n speed. In
3D, the presence of interacting oblique modes of the tearing instability
complicates the simple 2.5D picture, entangling the magnetic field of the
inflow and introducing a negative effect on the reconnection rate. The two
competing effects of stronger Alfv\'{e}n speed and entangling, which both
increase with the strength of the reconnecting field, resulted in a decrease in
the reconnection rate with increasing reconnecting field. For all simulations,
the 3D rates were less than in 2.5D, but suggest that as one goes to weak
reconnecting field (or strong guide field), the system becomes more 2.5D like
and the 2.5D and 3D rates converge. These results have relevance to situations
like nano-flare heating and flare current sheets in the corona.


title:
Providing Real-time Assistance for Repairing Runtime Exceptions using
  Stack Overflow Posts

abstract:
  Runtime Exceptions (REs) are an important class of bugs that occur frequently
during code development. Traditional Automatic Program Repair (APR) tools are
of limited use in this "in-development" use case, since they require a
test-suite to be available as a patching oracle. Thus, developers typically
tend to manually resolve their in-development REs, often by referring to
technical forums, such as Stack Overflow (SO). To automate this manual process
we extend our previous work, MAESTRO, to provide real-time assistance to
developers for repairing Java REs by recommending a relevant patch-suggesting
SO post and synthesizing a repair patch from this post to fix the RE in the
developer's code. MAESTRO exploits a library of Runtime Exception Patterns
(REPs) semi-automatically mined from SO posts, through a relatively
inexpensive, one-time, incremental process. An REP is an abstracted sequence of
statements that triggers a given RE. REPs are used to index SO posts, retrieve
a post most relevant to the RE instance exhibited by a developer's code and
then mediate the process of extracting a concrete repair from the SO post,
abstracting out post-specific details, and concretizing the repair to the
developer's buggy code. We evaluate MAESTRO on a published RE benchmark
comprised of 78 instances. MAESTRO is able to generate a correct repair patch
at the top position in 27% of the cases, within the top-3 in 40% of the cases
and overall return a useful artifact in 81% of the cases. Further, the use of
REPs proves instrumental to all aspects of MAESTRO's performance, from ranking
and searching of SO posts to synthesizing patches from a given post. In
particular, 45% of correct patches generated by MAESTRO could not be produced
by a baseline technique not using REPs, even when provided with MAESTRO's
SO-post ranking. MAESTRO is also fast, needing around 1 second, on average, to
generate its output.


title: MAGIS-100 Environmental Characterization and Noise Analysis

abstract:
  We investigate and analyze site specific systematics for the MAGIS-100 atomic
interferometry experiment at Fermi National Accelerator Laboratory. As atom
interferometers move out of the laboratory environment passive and active
mitigation for noise sources must be implemented. To inform the research and
development of the experiment design, we measure ambient temperature, humidity,
and vibrations of the installation site. We find that temperature fluctuations
will necessitate enclosures for critical subsystems and a temperature
controlled laser room for the laser system. We also measure and analyze the
vibration spectrum above and below ground for the installation site. The
seismic vibration effect of gravity gradient noise is also modeled using input
from a low-noise seismometer at multiple locations and a mitigation scheme is
studied using a stochastic simulation and characterized by a suppression
factor.


title:
Prospects to Explore High-redshift Black Hole Formation with Multi-band
  Gravitational Waves Observatories

abstract:
  The assembly of massive black holes in the early universe remains a poorly
constrained open question in astrophysics. The merger and accretion of light
seeds (remnants of Population III stars with mass below $\sim 1000M_{\odot}$)
or heavy seeds (in the mass range $10^4-10^6 M_{\odot}$) could both explain the
formation of massive black holes, but the abundance of seeds and their merging
mechanism are highly uncertain. In the next decades, the gravitational-wave
observatories coming online are expected to observe very high-redshift mergers,
shedding light on the seeding of the first black holes. In this Letter we
explore the potential and limitations for LISA, Cosmic Explorer and Einstein
Telescope to constrain the mixture ratio of light and heavy seeds as well as
the probability that central black holes in merging galaxies merge as well.
Since the third generation ground-based gravitational-wave detectors will only
observe light seed mergers, we demonstrate two scenarios in which the inference
of the seed mixture ratio and merging probability can be limited. The synergy
of multi-band gravitational-wave observations and electromagnetic observations
will likely be necessary in order to fully characterize the process of
high-redshift black hole formation.


title:
METAL: The Metal Evolution, Transport, and Abundance in the Large
  Magellanic Cloud Hubble program. III. Interstellar Depletions, Dust-to-Metal,
  and Dust-to-Gas Ratios Versus Metallicity

abstract:
  The metallicity and gas density dependence of interstellar depletions, the
dust-to-gas (D/G), and dust-to-metal (D/M) ratios have important implications
for how accurately we can trace the chemical enrichment of the universe; either
by using FIR dust emission as a tracer of the ISM; or by using spectroscopy of
damped Lyman-alpha systems (DLAs) to measure chemical abundances over a wide
range of redshifts. We collect and compare large samples of depletion
measurements in the Milky Way (MW), LMC (Z=0.5 Zsun), and SMC (Z=0.2 Zsun). The
relation between the depletions of different elements do not strongly vary
between the three galaxies, implying that abundance ratios should trace
depletions accurately down to 20% solar metallicity. From the depletions, we
derive D/G and D/M. The D/G increases with density, consistent with the more
efficient accretion of gas-phase metals onto dust grains in the denser ISM. For
log N(H) > 21 cm^-2, the depletion of metallicity tracers (S, Zn) exceeds -0.5
dex, even at 20% solar metallicity. The gas fraction of metals increases from
the MW to the LMC (factor 3) and SMC (factor 6), compensating the reduction in
total heavy element abundances and resulting in those three galaxies having the
same neutral gas-phase metallicities. The D/G derived from depletions are a
factor of 2 (LMC) and 5 (SMC) higher than the D/G derived from FIR, 21 cm, and
CO emission, likely due to the combined uncertainties on the dust FIR opacity
and on the depletion of carbon and oxygen.


title:
Sampling Strategy for Fine-Tuning Segmentation Models to Crisis Area
  under Scarcity of Data

abstract:
  The use of remote sensing in humanitarian crisis response missions is
well-established and has proven relevant repeatedly. One of the problems is
obtaining gold annotations as it is costly and time consuming which makes it
almost impossible to fine-tune models to new regions affected by the crisis.
Where time is critical, resources are limited and environment is constantly
changing, models has to evolve and provide flexible ways to adapt to a new
situation. The question that we want to answer is if prioritization of samples
provide better results in fine-tuning vs other classical sampling methods under
annotated data scarcity? We propose a method to guide data collection during
fine-tuning, based on estimated model and sample properties, like predicted IOU
score. We propose two formulas for calculating sample priority. Our approach
blends techniques from interpretability, representation learning and active
learning. We have applied our method to a deep learning model for semantic
segmentation, U-Net, in a remote sensing application of building detection -
one of the core use cases of remote sensing in humanitarian applications.
Preliminary results shows utility in prioritization of samples for tuning
semantic segmentation models under scarcity of data condition.


title: Ellipsars: Ring-like explosions from flattened stars

abstract:
  The stellar cataclysms producing astronomical transients have long been
modeled as either a point-like explosion or jet-like engine ignited at the
center of a spherically symmetric star. However, many stars are observed, or
are expected on theoretical grounds, not to be precisely spherically symmetric,
but rather to have a slightly flattened geometry similar to that of an oblate
spheroid. Here we present axisymmetric two-dimensional hydrodynamical
simulations of the dynamics of point-like explosions initiated at the center of
an aspherical massive star with a range of oblateness. We refer to these
exploding aspherical stars as "ellipsars" in reference to the elliptical shape
of the iso-density contours of their progenitors in the two-dimensional
axisymmetric case. We find that ellipsars are capable of accelerating expanding
rings of relativistic ejecta which may lead to the production of astronomical
transients including low-luminosity GRBs, relativistic supernovae, and Fast
Blue Optical Transients (FBOTs.)


title: Boosting Graph Neural Networks by Injecting Pooling in Message Passing

abstract:
  There has been tremendous success in the field of graph neural networks
(GNNs) as a result of the development of the message-passing (MP) layer, which
updates the representation of a node by combining it with its neighbors to
address variable-size and unordered graphs. Despite the fruitful progress of MP
GNNs, their performance can suffer from over-smoothing, when node
representations become too similar and even indistinguishable from one another.
Furthermore, it has been reported that intrinsic graph structures are smoothed
out as the GNN layer increases. Inspired by the edge-preserving bilateral
filters used in image processing, we propose a new, adaptable, and powerful MP
framework to prevent over-smoothing. Our bilateral-MP estimates a pairwise
modular gradient by utilizing the class information of nodes, and further
preserves the global graph structure by using the gradient when the aggregating
function is applied. Our proposed scheme can be generalized to all ordinary MP
GNNs. Experiments on five medium-size benchmark datasets using four
state-of-the-art MP GNNs indicate that the bilateral-MP improves performance by
alleviating over-smoothing. By inspecting quantitative measurements, we
additionally validate the effectiveness of the proposed mechanism in preventing
the over-smoothing issue.


title:
Spectral Propagation Graph Network for Few-shot Time Series
  Classification

abstract:
  Few-shot Time Series Classification (few-shot TSC) is a challenging problem
in time series analysis. It is more difficult to classify when time series of
the same class are not completely consistent in spectral domain or time series
of different classes are partly consistent in spectral domain. To address this
problem, we propose a novel method named Spectral Propagation Graph Network
(SPGN) to explicitly model and propagate the spectrum-wise relations between
different time series with graph network. To the best of our knowledge, SPGN is
the first to utilize spectral comparisons in different intervals and involve
spectral propagation across all time series with graph networks for few-shot
TSC. SPGN first uses bandpass filter to expand time series in spectral domain
for calculating spectrum-wise relations between time series. Equipped with
graph networks, SPGN then integrates spectral relations with label information
to make spectral propagation. The further study conveys the bi-directional
effect between spectral relations acquisition and spectral propagation. We
conduct extensive experiments on few-shot TSC benchmarks. SPGN outperforms
state-of-the-art results by a large margin in $4\% \sim 13\%$. Moreover, SPGN
surpasses them by around $12\%$ and $9\%$ under cross-domain and cross-way
settings respectively.


title:
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Time-Series
  Representation Learning

abstract:
  Unsupervised/self-supervised time series representation learning is a
challenging problem because of its complex dynamics and sparse annotations.
Existing works mainly adopt the framework of contrastive learning with the
time-based augmentation techniques to sample positives and negatives for
contrastive training. Nevertheless, they mostly use segment-level augmentation
derived from time slicing, which may bring about sampling bias and incorrect
optimization with false negatives due to the loss of global context. Besides,
they all pay no attention to incorporate the spectral information in feature
representation. In this paper, we propose a unified framework, namely Bilinear
Temporal-Spectral Fusion (BTSF). Specifically, we firstly utilize the
instance-level augmentation with a simple dropout on the entire time series for
maximally capturing long-term dependencies. We devise a novel iterative
bilinear temporal-spectral fusion to explicitly encode the affinities of
abundant time-frequency pairs, and iteratively refines representations in a
fusion-and-squeeze manner with Spectrum-to-Time (S2T) and Time-to-Spectrum
(T2S) Aggregation modules. We firstly conducts downstream evaluations on three
major tasks for time series including classification, forecasting and anomaly
detection. Experimental results shows that our BTSF consistently significantly
outperforms the state-of-the-art methods.


title:
Orthogonal Matrices for MBAT Vector Symbolic Architectures, and a "Soft"
  VSA Representation for JSON

abstract:
  Vector Symbolic Architectures (VSAs) give a way to represent a complex object
as a single fixed-length vector, so that similar objects have similar vector
representations. These vector representations then become easy to use for
machine learning or nearest-neighbor search. We review a previously proposed
VSA method, MBAT (Matrix Binding of Additive Terms), which uses multiplication
by random matrices for binding related terms. However, multiplying by such
matrices introduces instabilities which can harm performance. Making the random
matrices be orthogonal matrices provably fixes this problem. With respect to
larger scale applications, we see how to apply MBAT vector representations for
any data expressed in JSON. JSON is used in numerous programming languages to
express complex data, but its native format appears highly unsuited for machine
learning. Expressing JSON as a fixed-length vector makes it readily usable for
machine learning and nearest-neighbor search. Creating such JSON vectors also
shows that a VSA needs to employ binding operations that are non-commutative.
  VSAs are now ready to try with full-scale practical applications, including
healthcare, pharmaceuticals, and genomics.
  Keywords: MBAT (Matrix Binding of Additive Terms), VSA (Vector Symbolic
Architecture), HDC (Hyperdimensional Computing), Distributed Representations,
Binding, Orthogonal Matrices, Recurrent Connections, Machine Learning, Search,
JSON, VSA Applications


title: GrASP: Gradient-Based Affordance Selection for Planning

abstract:
  Planning with a learned model is arguably a key component of intelligence.
There are several challenges in realizing such a component in large-scale
reinforcement learning (RL) problems. One such challenge is dealing effectively
with continuous action spaces when using tree-search planning (e.g., it is not
feasible to consider every action even at just the root node of the tree). In
this paper we present a method for selecting affordances useful for planning --
for learning which small number of actions/options from a continuous space of
actions/options to consider in the tree-expansion process during planning. We
consider affordances that are goal-and-state-conditional mappings to
actions/options as well as unconditional affordances that simply select
actions/options available in all states. Our selection method is gradient
based: we compute gradients through the planning procedure to update the
parameters of the function that represents affordances. Our empirical work
shows that it is feasible to learn to select both primitive-action and option
affordances, and that simultaneously learning to select affordances and
planning with a learned value-equivalent model can outperform model-free RL.


title: A Neural Network Model of Continual Learning with Cognitive Control

abstract:
  Neural networks struggle in continual learning settings from catastrophic
forgetting: when trials are blocked, new learning can overwrite the learning
from previous blocks. Humans learn effectively in these settings, in some cases
even showing an advantage of blocking, suggesting the brain contains mechanisms
to overcome this problem. Here, we build on previous work and show that neural
networks equipped with a mechanism for cognitive control do not exhibit
catastrophic forgetting when trials are blocked. We further show an advantage
of blocking over interleaving when there is a bias for active maintenance in
the control signal, implying a tradeoff between maintenance and the strength of
control. Analyses of map-like representations learned by the networks provided
additional insights into these mechanisms. Our work highlights the potential of
cognitive control to aid continual learning in neural networks, and offers an
explanation for the advantage of blocking that has been observed in humans.


title: SHAS: Approaching optimal Segmentation for End-to-End Speech Translation

abstract:
  Speech translation models are unable to directly process long audios, like
TED talks, which have to be split into shorter segments. Speech translation
datasets provide manual segmentations of the audios, which are not available in
real-world scenarios, and existing segmentation methods usually significantly
reduce translation quality at inference time. To bridge the gap between the
manual segmentation of training and the automatic one at inference, we propose
Supervised Hybrid Audio Segmentation (SHAS), a method that can effectively
learn the optimal segmentation from any manually segmented speech corpus.
First, we train a classifier to identify the included frames in a segmentation,
using speech representations from a pre-trained wav2vec 2.0. The optimal
splitting points are then found by a probabilistic Divide-and-Conquer algorithm
that progressively splits at the frame of lowest probability until all segments
are below a pre-specified length. Experiments on MuST-C and mTEDx show that the
translation of the segments produced by our method approaches the quality of
the manual segmentation on 5 languages pairs. Namely, SHAS retains 95-98% of
the manual segmentation's BLEU score, compared to the 87-93% of the best
existing methods. Our method is additionally generalizable to different domains
and achieves high zero-shot performance in unseen languages.


title: Experimental evidence of nonlinear focusing in standing water waves

abstract:
  Nonlinear wave focusing originating from the universal modulation instability
(MI) is responsible for the formation of strong wave localizations on the water
surface and in nonlinear wave guides, such as optical Kerr media and plasma.
Such extreme wave dynamics can be described by breather solutions of the
nonlinear Schr\"odinger equation (NLSE) like by way of example the famed
doubly-localized Peregrine breathers (PB), which typify particular cases of MI.
On the other hand, it has been suggested that the MI relevance weakens when the
wave field becomes broadband or directional. Here, we provide experimental
evidence of nonlinear and distinct PB-type focusing in standing water waves
describing the scenario of two counter-propagating wave trains. The collected
collinear wave measurements are in excellent agreement with the hydrodynamic
coupled NLSE (CNLSE) and suggest that MI can undisturbedly prevail during the
interplay of several wave systems and emphasize the potential role of exact
NLSE solutions in extreme wave formation beyond the formal narrowband and
uni-directional limits. Our work may inspire further experimental
investigations in various nonlinear wave guides governed by CNLSE frameworks as
well as theoretical progress to predict strong wave coherence in directional
fields.


title:
Coherent photoproduction of the neutral pion and eta meson on the
  deuteron at incident energies below 1.15 GeV

abstract:
  Coherent photoproduction of the neutral pion and eta meson on the deuteron,
$\gamma{d}${$\to$}$\pi^0\eta{d}$, has been experimentally studied at incident
photon energies ranging from the reaction threshold to 1.15 GeV. The total
cross section demonstrates a rapid rise below 1 GeV. The data are
underestimated by the existing theoretical calculations based on quasi-free
$\pi^0 \eta$ photoproduction on the nucleon followed by deuteron coalescence.
At the same time, the data are rather well reproduced by the calculations
taking into account the final-state interaction. We have also measured for the
first time the differential cross sections: the $\pi^0 \eta$ invariant-mass
distribution $d\sigma/dM_{\pi \eta}$, the $\pi^0 d$ invariant-mass distribution
$d\sigma/dM_{\pi d}$, the $\eta d$ invariant-mass distribution
$d\sigma/dM_{\eta d}$, and the distribution over the deuteron emission angle
$d\sigma/d\Omega_d$ in the overall center-of-mass frame. The measured cross
section $d\sigma/d\Omega_d$ does not exhibit strongly backward-peaking behavior
predicted by the calculations. At all incident energies, an increase in
$d\sigma/dM_{\eta d}$ near the $\eta d$ threshold is observed, which indicates
a bound or virtual $\eta d$ state resulting from a strong attraction between
$\eta$ and a deuteron. The possibilities of using coherent $\pi^0\eta$
photoproduction on a nucleus to study the $\eta$-nuclear interaction are also
discussed.


title: Exact Solutions of a Deep Linear Network

abstract:
  This work finds the exact solutions to a deep linear network with weight
decay and stochastic neurons, a fundamental model for understanding the
landscape of neural networks. Our result implies that weight decay strongly
interacts with the model architecture and can create bad minima in a network
with more than $1$ hidden layer, qualitatively different for a network with
only $1$ hidden layer. As an application, we also analyze stochastic nets and
show that their prediction variance vanishes to zero as the stochasticity, the
width, or the depth tends to infinity.


title: Quasi-triangle inequality for absolute correlation distance

abstract:
  We show that absolute correlation distance satisfies a K-relaxed triangle
inequality, with the best K = 2.


title:
Extracting High-Resolution, Multi-Mode Surface Wave Dispersion Data from
  Distributed Acoustic Sensing Measurements using the Multichannel Analysis of
  Surface Waves

abstract:
  We rigorously assess the potential for extracting high-resolution, multi-mode
surface wave dispersion data from distributed acoustic sensing (DAS)
measurements using active-source multichannel analysis of surface waves (MASW).
We have utilized both highly-controlled, broadband vibroseis shaker trucks and
more-variable, narrow-band sledgehammer sources to excite the near surface, and
compare the DAS-derived dispersion data obtained from both source types
directly with concurrently acquired traditional geophone-derived dispersion
data. We find that the differences between the two sensing approaches are
minimal and well within the dispersion uncertainty bounds associated with each
individual measurement type when the following conditions are met for DAS: (a)
a tight-buffered or strain-sensing fiber-optic cable is used, (b) the cable is
buried in a shallow trench to enhance coupling, and (c) short gauge lengths and
small channel separations are used. We also show that frequency-dependent
normalization of the dispersion image following MASW processing removes the
effects of scaling, integration, and differentiation on the measured waveforms,
thereby allowing nearly identical dispersion data to be extracted from geophone
waveforms and DAS waveforms without requiring them to first be converted into
equivalent units. We demonstrate that the short wavelength DAS dispersion
measurements are limited by both the gauge length and the more commonly
considered channel separation. We further show that it is possible to extract
essentially equivalent surface wave dispersion data from seismic measurements
made using a traditional geophone array or two different DAS cables. Finally,
we show that shear wave velocity profiles recovered from the DAS data using an
uncertainty-consistent, multi-mode inversion agree favorably with cone
penetration tests performed at the site.


title:
Rapidly alternating flux states of GX 339-4 during its 2021 outburst
  captured by Insight-HXMT

abstract:
  The low mass X-ray binary GX 339-4 went into a new outburst in 2021. At the
end of the hard to soft transition of this outburst, Insight-HXMT found that
the source rapidly alternated between low flux and high flux states on a
timescale of hours. Two high flux states lasted only for a period comparable to
the orbital period of the observatory. Time-resolved spectral analysis shows
that the sudden changes of flux are confined in the hard X-ray band (>4 keV).
The variable non-thermal emission, including the power-law continuum from the
corona and the reflected emission from the inner accretion disk, is responsible
for the observed variability. The strength of disk thermal emission and the
inner radius of the accretion disk are consistent between the two flux states.
Assuming the lamppost geometry, our best-fit disk reflection models suggest a
very low corona height (within 3 $R_{\rm g}$) and there is no evidence of
significant variation in the corona geometry either. The observed rapidly
alternating flux states suggest that the intrinsic power of the corona must
change during the state transition. We discuss possible mechanisms for the
observed sudden changes in the coronal power of GX 339-4.


title:
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving
  Scenarios

abstract:
  Visual detection is a key task in autonomous driving, and it serves as one
foundation for self-driving planning and control. Deep neural networks have
achieved promising results in various computer vision tasks, but they are known
to be vulnerable to adversarial attacks. A comprehensive understanding of deep
visual detectors' vulnerability is required before people can improve their
robustness. However, only a few adversarial attack/defense works have focused
on object detection, and most of them employed only classification and/or
localization losses, ignoring the objectness aspect. In this paper, we identify
a serious objectness-related adversarial vulnerability in YOLO detectors and
present an effective attack strategy aiming the objectness aspect of visual
detection in autonomous vehicles. Furthermore, to address such vulnerability,
we propose a new objectness-aware adversarial training approach for visual
detection. Experiments show that the proposed attack targeting the objectness
aspect is 45.17% and 43.50% more effective than those generated from
classification and/or localization losses on the KITTI and COCO_traffic
datasets, respectively. Also, the proposed adversarial defense approach can
improve the detectors' robustness against objectness-oriented attacks by up to
21% and 12% mAP on KITTI and COCO_traffic, respectively.


title:
Heterogeneous Mixed Populations of Coordinating, Anticoordinating, and
  Imitating Individuals

abstract:
  Decision-making individuals are typically either an imitator, who mimics the
action of the most successful individual(s), a conformist (or coordinating
individual), who chooses an action if enough others have done so, or a
nonconformist (or anticoordinating individual), who chooses an action if few
others have done so. Researchers have studied the asymptotic behavior of
populations comprising one or two of these types of decision-makers, but not
altogether, which we do for the first time. We consider a population of
heterogeneous individuals, each either cooperates or defects, and earns payoffs
according to their possibly unique payoff matrix and the total number of
cooperators in the population. Over a discrete sequence of time, the
individuals revise their choices asynchronously based on the best-response or
imitation update rule. Those who update based on the best-response are a
conformist (resp. nonconformist) if their payoff matrix is that of a
coordination (resp. anticoordination) game. We take the distribution of
cooperators over the three types of individuals with the same payoff matrix as
the state of the system. First, we provide our simulation results, showing that
a population may admit zero, one or more equilibria at the same time, and
several non-singleton minimal positively invariant sets. Second, we find the
necessary and sufficient condition for equilibrium existence. Third, we perform
stability analysis and find that only those equilibria where the imitators
either all cooperate or all defect are likely to be stable. Fourth, we proceed
to the challenging problem of characterizing the minimal positively invariant
sets and find conditions for the existence of such sets. Finally, we study the
stochastic stability of the states under the perturbed dynamics, where the
agents are allowed to make mistakes in their decisions with a certain small
probability.


title:
Discovery, Timing, and Multiwavelength Observations of the Black Widow
  Millisecond Pulsar PSR J1555-2908

abstract:
  We report the discovery of PSR J1555-2908, a 1.79 ms radio and gamma-ray
pulsar in a 5.6 hr binary system with a minimum companion mass of 0.052
$M_\odot$. This fast and energetic ($\dot E = 3 \times 10^{35}$ erg/s)
millisecond pulsar was first detected as a gamma-ray point source in Fermi LAT
sky survey observations. Guided by a steep spectrum radio point source in the
Fermi error region, we performed a search at 820 MHz with the Green Bank
Telescope that first discovered the pulsations. The initial radio pulse timing
observations provided enough information to seed a search for gamma-ray
pulsations in the LAT data, from which we derive a timing solution valid for
the full Fermi mission. In addition to the radio and gamma-ray pulsation
discovery and timing, we searched for X-ray pulsations using NICER but no
significant pulsations were detected. We also obtained time-series r-band
photometry that indicates strong heating of the companion star by the pulsar
wind. Material blown off the heated companion eclipses the 820 MHz radio pulse
during inferior conjunction of the companion for ~10% of the orbit, which is
twice the angle subtended by its Roche lobe in an edge-on system.


title:
Characterization of photoinduced normal state through charge density
  wave in superconducting YBa$_2$Cu$_3$O$_{6.67}$

abstract:
  The normal state of high-Tc cuprates has been considered one of the essential
topics in high-temperature superconductivity research. However, compared to the
high magnetic fields study of it, understanding a photoinduced normal state
remains elusive. Here, we explore a photoinduced normal state of
YBa$_2$Cu$_3$O$_{6.67}$ (YBCO) through a charge density wave (CDW) with
time-resolved resonant soft x-ray scattering, as well as a high-magnetic field
x-ray scattering. In the non-equilibrium state in which people predict a
quenched superconducting state based on the previous optical spectroscopies, we
experimentally observed a similar analogy to the competition between
superconductivity and CDW shown in the equilibrium state. We further observe
that the broken pairing states in the superconducting CuO$_2$ plane via the
optical pump lead to nucleation of three-dimensional CDW precursor correlation,
revealing that the photoinduced CDW is similar to phenomena shown under
magnetic fields. Ultimately, these findings provide a critical clue that the
characteristics of the photoinduced normal state show a solid resemblance to
those under magnetic fields in equilibrium conditions.


title:
Multiclass histogram-based thresholding using kernel density estimation
  and scale-space representations

abstract:
  We present a new method for multiclass thresholding of a histogram which is
based on the nonparametric Kernel Density (KD) estimation, where the unknown
parameters of the KD estimate are defined using the Expectation-Maximization
(EM) iterations. The method compares the number of extracted minima of the KD
estimate with the number of the requested clusters minus one. If these numbers
match, the algorithm returns positions of the minima as the threshold values,
otherwise, the method gradually decreases/increases the kernel bandwidth until
the numbers match. We verify the method using synthetic histograms with known
threshold values and using the histogram of real X-ray computed tomography
images. After thresholding of the real histogram, we estimated the porosity of
the sample and compare it with the direct experimental measurements. The
comparison shows the meaningfulness of the thresholding.


title: No-Regret Learning in Dynamic Stackelberg Games

abstract:
  In a Stackelberg game, a leader commits to a randomized strategy, and a
follower chooses their best strategy in response. We consider an extension of a
standard Stackelberg game, called a discrete-time dynamic Stackelberg game,
that has an underlying state space that affects the leader's rewards and
available strategies and evolves in a Markovian manner depending on both the
leader and follower's selected strategies. Although standard Stackelberg games
have been utilized to improve scheduling in security domains, their deployment
is often limited by requiring complete information of the follower's utility
function. In contrast, we consider scenarios where the follower's utility
function is unknown to the leader; however, it can be linearly parameterized.
Our objective then is to provide an algorithm that prescribes a randomized
strategy to the leader at each step of the game based on observations of how
the follower responded in previous steps. We design a no-regret learning
algorithm that, with high probability, achieves a regret bound (when compared
to the best policy in hindsight) which is sublinear in the number of time
steps; the degree of sublinearity depends on the number of features
representing the follower's utility function. The regret of the proposed
learning algorithm is independent of the size of the state space and polynomial
in the rest of the parameters of the game. We show that the proposed learning
algorithm outperforms existing model-free reinforcement learning approaches.


title:
Proceedings of the Robust Artificial Intelligence System Assurance
  (RAISA) Workshop 2022

abstract:
  The Robust Artificial Intelligence System Assurance (RAISA) workshop will
focus on research, development and application of robust artificial
intelligence (AI) and machine learning (ML) systems. Rather than studying
robustness with respect to particular ML algorithms, our approach will be to
explore robustness assurance at the system architecture level, during both
development and deployment, and within the human-machine teaming context. While
the research community is converging on robust solutions for individual AI
models in specific scenarios, the problem of evaluating and assuring the
robustness of an AI system across its entire life cycle is much more complex.
Moreover, the operational context in which AI systems are deployed necessitates
consideration of robustness and its relation to principles of fairness,
privacy, and explainability.


title: Families of Prym varieties of abelian coverings and Shimura varieties

abstract:
  Under the condition that the Prym map is injective in characteristic $p$, we
prove that the special subvarieties in the moduli space of abelian varieties of
dimension $l$ and polarization type $D$, $A_{l,D}$, arising from families of
abelian covers of $\P^1$ are of a very restrictive nature. In other words, if
the family is one-dimensional or if it contains an eigenspace of certain type
for the group action on the cohomology of fibers, then the Shimura varieties
arising from such families can only be constructed by the group action of the
family.


title:
HW/SW Co-design for Reliable In-memory Brain-inspired Hyperdimensional
  Computing

abstract:
  Brain-inspired hyperdimensional computing (HDC) is continuously gaining
remarkable attention. It is a promising alternative to traditional
machine-learning approaches due to its ability to learn from little data,
lightweight implementation, and resiliency against errors. However, HDC is
overwhelmingly data-centric similar to traditional machine-learning algorithms.
In-memory computing is rapidly emerging to overcome the von Neumann bottleneck
by eliminating data movements between compute and storage units. In this work,
we investigate and model the impact of imprecise in-memory computing hardware
on the inference accuracy of HDC. Our modeling is based on 14nm FinFET
technology fully calibrated with Intel measurement data. We accurately model,
for the first time, the voltage-dependent error probability in SRAM-based and
FeFET-based in-memory computing. Thanks to HDC's resiliency against errors, the
complexity of the underlying hardware can be reduced, providing large energy
savings of up to 6x. Experimental results for SRAM reveal that
variability-induced errors have a probability of up to 39 percent. Despite such
a high error probability, the inference accuracy is only marginally impacted.
This opens doors to explore new tradeoffs. We also demonstrate that the
resiliency against errors is application-dependent. In addition, we investigate
the robustness of HDC against errors when the underlying in-memory hardware is
realized using emerging non-volatile FeFET devices instead of mature CMOS-based
SRAMs. We demonstrate that inference accuracy does remain high despite the
larger error probability, while large area and power savings can be obtained.
All in all, HW/SW co-design is the key for efficient yet reliable in-memory
hyperdimensional computing for both conventional CMOS technology and upcoming
emerging technologies.


title: The Existence of Pseudoharmonic Maps For Small Horizontal Energy

abstract:
  In this paper, we consider the pseudoharmonic heat flow with small initial
horizontal energy and give the existence of pseudoharmonic maps from closed
pseudo-Hermitian manifolds to closed Riemannian manifolds.


title: Optimal $L^2$ Extensions of Openness Type

abstract:
  We study the following optimal $L^2$ extension problem of openness type:
given a complex manifold $M$, a closed subvariety $S\subset M$ and a
holomorphic vector bundle $E\rightarrow M$, for any $L^2$ holomorphic section
$f$ defined on some open neighborhood $U$ of $S$, find an $L^2$ holomorphic
section $F$ on $M$ such that $F|_S = f|_S$, and the $L^2$ norm of $F$ on $M$ is
optimally controlled by the $L^2$ norm of $f$ on $U$.
  Using the $L^2$ techniques of Guan-Zhou (Ann. of Math., 2015) and Zhou-Zhu
(J. Differential Geom., 2018), together with a log-concavity for certain
minimal $L^2$ integrals, we solve the above optimal $L^2$ extension problem of
openness type on weakly pseudoconvex K\"ahler manifolds, by establishing an
optimal $L^2$ extension theorem of openness type. Moreover, we prove a product
property for certain minimal $L^2$ extensions, which generalizes the product
property of Bergman kernels. Using a tensor power trick, we give another proof
to a weak version of the above $L^2$ extension theorem. As an application, we
prove that, under some additional conditions, the uniform estimate in
Guan-Zhou's optimal $L^2$ extension theorem can be improved.
  We discuss the relation between $L^2$ extension problems of openness type and
that of Ohsawa-Takegoshi type. We present a different method in some step to
approach the equality part of Suita's conjecture, which bases on a necessary
condition for the aforementioned log-concavity degenerating to linearity. The
same method is also applicable to generalized Suita conjectures with jets and
weights.


title:
Remarks on a conjecture of Huneke and Wiegand and the vanishing of
  (co)homology

abstract:
  In this paper we study a long-standing conjecture of Huneke and Wiegand which
is concerned with the torsion submodule of certain tensor products of modules
over one-dimensional local domains. We utilize Hochster's theta invariant and
show that the conjecture is true for two periodic modules. We also make use of
a result of Orlov and formulate a new condition which, if true over
hypersurface rings, forces the conjecture of Huneke and Wiegand to be true over
complete intersection rings of arbitrary codimension. Along the way we
investigate the interaction between the vanishing of Tate (co)homology and
torsion in tensor products of modules, and obtain new results that are of
independent interest.


title:
Low Ambiguity Zone: Theoretical Bounds and Doppler-Resilient Sequence
  Design in Integrated Sensing and Communication Systems

abstract:
  In radar sensing and communications, designing Doppler resilient sequences
(DRSs) with low ambiguity function for delay over the entire signal duration
and Doppler shift over the entire signal bandwidth is an extremely difficult
task. However, in practice, the Doppler frequency range is normally much
smaller than the bandwidth of the transmitted signal, and it is relatively easy
to attain quasi-synchronization for delays far less than the entire signal
duration. Motivated by this observation, we propose a new concept called low
ambiguity zone (LAZ) which is a small area of the corresponding ambiguity
function of interest defined by the certain Doppler frequency and delay. Such
an LAZ will reduce to a zero ambiguity zone (ZAZ) if the maximum ambiguity
values of interest are zero. In this paper, we derive a set of theoretical
bounds on periodic LAZ/ZAZ of unimodular DRSs with and without spectral
constraints, which include the existing bounds on periodic global ambiguity
function as special cases. These bounds may be used as theoretical design
guidelines to measure the optimality of sequences against Doppler effect. We
then introduce four optimal constructions of DRSs with respect to the derived
ambiguity lower bounds based on some algebraic tools such as characters over
finite field and cyclic difference sets.


title:
Small examples of discriminantal arrangements associated to non-very
  generic arrangements

abstract:
  Discriminantal arrangements introduced by Manin and Schectmanin in $1989$ are
induced arrangements in the space of all translations of an original
arrangement $\mathcal{A}$. The combinatorics of the discriminantal arrangement
depends on the original arrangement $\mathcal{A}$ therefore in 1997 Bayer and
Brandt defined two classes of original arrangements. One consists of the
arrangements such that the intersection lattice of the induced discriminantal
arrangement has maximum cardinality. Those arrangements are called \textit{very
generic}. The others are called \textit{non-very generic}. In this paper, we
will provide both a characterization and a classification of few non-very
generic arrangements in low dimensional space in terms of permutation groups.


title:
SDSS J1042-0018 a broad line AGN but mis-classified as a HII galaxy in
  the BPT diagram by flux ratios of narrow emission lines

abstract:
  In the manuscript, we discuss properties of the SDSS J1042-0018 which is a
broad line AGN but mis-classified as a HII galaxy in the BPT diagram (SDSS
J1042-0018 called as a mis-classified broad line AGN). The emission lines
around H$\alpha$ and around H$\beta$ are well described by different model
functions, considering broad Balmer lines to be described by Gaussian or
Lorentz functions. Different model functions lead to different determined
narrow emission line fluxes, but the different narrow emission line flux ratios
lead the SDSS J1042-0018 as a HII galaxy in the BPT diagram. In order to
explain the unique properties of the mis-classified broad line AGN SDSS
J1042-0018, two methods are proposed, the starforming contributions and the
compressed NLRs with high electron densities near to critical densities of
forbidden emission lines. Fortunately, the strong starforming contributions can
be preferred in the SDSS J1042-0018. The mis-classified broad line AGN SDSS
J1042-0018, well explained by starforming contributions, could provide further
clues on the applications of BPT diagrams to the normal broad line AGN.


title: The Transfer Performance of Economic Models

abstract:
  Whether a model's performance on a given domain can be extrapolated to other
settings depends on whether it has learned generalizable structure. We
formulate this as the problem of theory transfer, and provide a tractable way
to measure a theory's transferability. We derive confidence intervals for
transferability that ensure coverage in finite samples, and apply our approach
to evaluate the transferability of predictions of certainty equivalents across
different subject pools. We find that models motivated by economic theory
perform more reliably than black-box machine learning methods at this transfer
prediction task.


title:
Local breaking of the spin degeneracy in the vortex states of Ising
  superconductors: Induced antiphase ferromagnetic order

abstract:
  Ising spin-orbital coupling is usually easy to identify in the Ising
superconductors via an in-plane critical field enhancement, but we show that
the Ising spin-orbital coupling also manifests in the vortex physics for
perpendicular magnetic fields. By self-consistently solving the Bogoliubov-de
Gennes equations of a model Hamiltonian built on the honeycomb lattice with the
Ising spin-orbital coupling pertinent to the transition metal dichalcogenides,
we numerically investigate the local breaking of the spin and sublattice
degeneracies in the presence of a perpendicular magnetic field. It is revealed
that the ferromagnetic orders are induced inside the vortex core region by the
Ising spin-orbital coupling. The induced magnetic orders are antiphase in terms
of their opposite polarizations inside the two nearest-neighbor vortices with
one of the two polarizations coming dominantly from one sublattice sites,
implying the local breaking of the spin and sublattice degeneracies. The
finite-energy peaks of the local-density-of-states for spin-up and spin-down
in-gap states are split and shifted oppositely by the Ising spin-orbital
coupling, and the relative shifts of them on sublattices $A$ and $B$ are also
of opposite algebraic sign. The calculated results and the proposed scenario
may not only serve as experimental signatures for identifying the Ising
spin-orbital coupling in the Ising superconductors, but also be prospective in
manipulation of electron spins in motion through the orbital effect in the
superconducting vortex states.


title: Augmenting Neural Networks with Priors on Function Values

abstract:
  The need for function estimation in label-limited settings is common in the
natural sciences. At the same time, prior knowledge of function values is often
available in these domains. For example, data-free biophysics-based models can
be informative on protein properties, while quantum-based computations can be
informative on small molecule properties. How can we coherently leverage such
prior knowledge to help improve a neural network model that is quite accurate
in some regions of input space -- typically near the training data -- but
wildly wrong in other regions? Bayesian neural networks (BNN) enable the user
to specify prior information only on the neural network weights, not directly
on the function values. Moreover, there is in general no clear mapping between
these. Herein, we tackle this problem by developing an approach to augment BNNs
with prior information on the function values themselves. Our probabilistic
approach yields predictions that rely more heavily on the prior information
when the epistemic uncertainty is large, and more heavily on the neural network
when the epistemic uncertainty is small.


title: A Clustering Approach to Integrative Analysis of Multiomic Cancer Data

abstract:
  Rapid technological advances have allowed for molecular profiling across
multiple omics domains from a single sample for clinical decision making in
many diseases, especially cancer. As tumor development and progression are
dynamic biological processes involving composite genomic aberrations, key
challenges are to effectively assimilate information from these domains to
identify genomic signatures and biological entities that are druggable, develop
accurate risk prediction profiles for future patients, and identify novel
patient subgroups for tailored therapy and monitoring.
  We propose integrative probabilistic frameworks for high-dimensional
multiple-domain cancer data that coherently incorporate dependence within and
between domains to accurately detect tumor subtypes, thus providing a catalogue
of genomic aberrations associated with cancer taxonomy. We propose an
innovative, flexible and scalable Bayesian nonparametric framework for
simultaneous clustering of both tumor samples and genomic probes. We describe
an efficient variable selection procedure to identify relevant genomic
aberrations that can potentially reveal underlying drivers of a disease.
Although the work is motivated by several investigations related to lung
cancer, the proposed methods are broadly applicable in a variety of contexts
involving high-dimensional data. The success of the methodology is demonstrated
using artificial data and lung cancer omics profiles publicly available from
The Cancer Genome Atlas.


title:
The Abduction of Sherlock Holmes: A Dataset for Visual Abductive
  Reasoning

abstract:
  Humans have remarkable capacity to reason abductively and hypothesize about
what lies beyond the literal content of an image. By identifying concrete
visual clues scattered throughout a scene, we almost can't help but draw
probable inferences beyond the literal scene based on our everyday experience
and knowledge about the world. For example, if we see a "20 mph" sign alongside
a road, we might assume the street sits in a residential area (rather than on a
highway), even if no houses are pictured. Can machines perform similar visual
reasoning?
  We present Sherlock, an annotated corpus of 103K images for testing machine
capacity for abductive reasoning beyond literal image contents. We adopt a
free-viewing paradigm: participants first observe and identify salient clues
within images (e.g., objects, actions) and then provide a plausible inference
about the scene, given the clue. In total, we collect 363K (clue, inference)
pairs, which form a first-of-its-kind abductive visual reasoning dataset. Using
our corpus, we test three complementary axes of abductive reasoning. We
evaluate the capacity of models to: i) retrieve relevant inferences from a
large candidate corpus; ii) localize evidence for inferences via bounding
boxes, and iii) compare plausible inferences to match human judgments on a
newly-collected diagnostic corpus of 19K Likert-scale judgments. While we find
that fine-tuning CLIP-RN50x64 with a multitask objective outperforms strong
baselines, significant headroom exists between model performance and human
agreement. We provide analysis that points towards future work.


title:
The leap to ordinal: detailed functional prognosis after traumatic brain
  injury with a flexible modelling approach

abstract:
  When a patient is admitted to the intensive care unit (ICU) after a traumatic
brain injury (TBI), an early prognosis is essential for baseline risk
adjustment and shared decision making. TBI outcomes are commonly categorised by
the Glasgow Outcome Scale-Extended (GOSE) into 8, ordered levels of functional
recovery at 6 months after injury. Existing ICU prognostic models predict
binary outcomes at a certain threshold of GOSE (e.g., prediction of survival
[GOSE>1] or functional independence [GOSE>4]). We aimed to develop ordinal
prediction models that concurrently predict probabilities of each GOSE score.
From a prospective cohort (n=1,550, 65 centres) in the ICU stratum of the
Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI)
patient dataset, we extracted all clinical information within 24 hours of ICU
admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of
2 design elements on ordinal model performance: (1) the baseline predictor set,
ranging from a concise set of 10 validated predictors to a token-embedded
representation of all possible predictors, and (2) the modelling strategy, from
ordinal logistic regression to multinomial deep learning. With repeated k-fold
cross-validation, we found that expanding the baseline predictor set
significantly improved ordinal prediction performance while increasing
analytical complexity did not. Half of these gains could be achieved with the
addition of 8 high-impact predictors (2 demographic variables, 4 protein
biomarkers, and 2 severity assessments) to the concise set. At best, ordinal
models achieved 0.76 (95% CI: 0.74-0.77) ordinal discrimination ability
(ordinal c-index) and 57% (95% CI: 54%-60%) explanation of ordinal variation in
6-month GOSE (Somers' D). Our results motivate the search for informative
predictors for higher GOSE and the development of ordinal dynamic prediction
models.


title:
Should Storage-Centric Tariffs be Extended to Commercial Flexible
  Demand?

abstract:
  Further electrification of the economy is expected to sharpen ramp rates and
increase peak loads. Flexibility from the demand side, which new technologies
might facilitate, can help these operational challenges. Electric utilities
have begun implementing new tariffs and other mechanisms to encourage the
deployment of energy storage. This paper examines whether making these new
tariffs technology agnostic and extending them to flexible demand would
significantly improve the procurement of operational flexibility. In
particular, we consider how a commercial consumer might adjust its flexible
demand when subject to Pacific Gas and Electric Company's storage-centric
electric tariff. We show that extending this tariff to consumers with flexible
demand would reduce the utility's net demand ramp rates during peak hours. If
consumers have a high level of demand flexibility, this tariff also reduces the
net demand during peak hours and decreases total electric bills when compared
to the base tariff.


title:
Magnetic excitations in double perovskite iridates
  La$_{2}$$\mathit{M}$IrO$_{6}$ ($\mathit{M}$ = Co, Ni, and Zn) mediated by
  3$\mathit{d}$-5$\mathit{d}$ hybridization

abstract:
  By performing resonant inelastic x-ray scattering (RIXS) measurements at the
Ir $\mathit{L_{\mathrm{3}}}$ edge, we have investigated the low-energy
elementary excitations in a series of double perovskite iridate single
crystals, La$_{2}$$\mathit{M}$IrO$_{6}$ ($\mathit{M}$ = Co, Ni, and Zn). Almost
dispersionless magnetic excitations at $\sim$ 42(6) meV and $\sim$ 35(5) meV
have been observed in crystals containing magnetic 3$\mathit{d}$ ions,
La$_{2}$CoIrO$_{6}$ and La$_{2}$NiIrO$_{6}$, respectively. In contrast, this
low-energy magnetic excitation is absent in La$_{2}$ZnIrO$_{6}$ in which the
3$\mathit{d}$ ions are non-magnetic, suggesting the importance of
3$\mathit{d}$-5$\mathit{d}$ hybridization in the magnetic properties of these
double perovskite iridates. The magnetic excitation is suppressed completely
above the magnetic ordering temperature, suggesting the inadequacy of using a
simple spin Hamiltonian to describe magnetism of these materials.


title: $\Lambda_b\to p$ transition form factors in perturbative QCD

abstract:
  We reanalyze the $\Lambda_b\to p$ transition form factors in the perturbative
QCD (PQCD) approach by including higher-twist light-cone distribution
amplitudes (LCDAs) of a $\Lambda_b$ baryon and a proton. The previous PQCD
evaluation performed decades ago with only the leading-twist $\Lambda_b$ baryon
and proton LCDAs gave the form factors, which are two orders of magnitude
smaller than indicated by experimental data. We find that the twist-4
$\Lambda_b$ baryon LCDAs and the twist-4 and -5 proton LCDAs contribute
dominantly, and the enhanced form factors become consistent with those from
lattice QCD and other nonperturbative methods. The estimated branching ratios
of the semileptonic decays $\Lambda_b\to p\ell\bar{\nu}_\ell$ and the hadronic
decay $\Lambda_b\to p\pi$ are also close to the data. It implies that the $b$
quark mass is not really heavy enough, and higher-power contributions play a
crucial role, similar to the observation made in analyses of $B$ meson
transition form factors. With the formalism established in this work, we are
ready to study various exclusive heavy baryon decays systematically in the PQCD
approach.


title:
Understanding Hyperdimensional Computing for Parallel Single-Pass
  Learning

abstract:
  Hyperdimensional computing (HDC) is an emerging learning paradigm that
computes with high dimensional binary vectors. It is attractive because of its
energy efficiency and low latency, especially on emerging hardware -- but HDC
suffers from low model accuracy, with little theoretical understanding of what
limits its performance. We propose a new theoretical analysis of the limits of
HDC via a consideration of what similarity matrices can be "expressed" by
binary vectors, and we show how the limits of HDC can be approached using
random Fourier features (RFF). We extend our analysis to the more general class
of vector symbolic architectures (VSA), which compute with high-dimensional
vectors (hypervectors) that are not necessarily binary. We propose a new class
of VSAs, finite group VSAs, which surpass the limits of HDC. Using
representation theory, we characterize which similarity matrices can be
"expressed" by finite group VSA hypervectors, and we show how these VSAs can be
constructed. Experimental results show that our RFF method and group VSA can
both outperform the state-of-the-art HDC model by up to 7.6\% while maintaining
hardware efficiency.


title:
Mechanical feedback controls the emergence of dynamical memory in
  growing tissue monolayers

abstract:
  The growth of a tissue, which depends on cell-cell interactions and
biologically relevant process such as cell division and apoptosis, is regulated
by a mechanical feedback mechanism. We account for these effects in a minimal
two-dimensional model in order to investigate the consequences of mechanical
feedback, which is controlled by a critical pressure, $p_c$. A cell can only
grow and divide if the pressure it experiences, due to interaction with its
neighbors, is less than $p_c$. Because temperature is an irrelevant variable in
the model, the cell dynamics is driven by self-generated active forces (SGAFs)
that are created by cell division. It is shown that even in the absence of
intercellular interactions, cells undergo diffusive behavior. The SGAF driven
diffusion is indistinguishable from the well-known dynamics of a free Brownian
particle at a fixed finite temperature. When intercellular interactions are
taken into account, we find persistent temporal correlations in the force-force
autocorrelation function ($FAF$) that extends over timescale of several cell
division times. The time-dependence of the $FAF$ reveals memory effects, which
increases as pc increases. The observed non-Markovian effects emerge due to the
interplay of cell division and mechanical feedback, and is inherently a
non-equilibrium phenomenon.

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


title:
Spatial active noise control based on individual kernel interpolation of
  primary and secondary sound fields

abstract:
  A spatial active noise control (ANC) method based on the individual kernel
interpolation of primary and secondary sound fields is proposed. Spatial ANC is
aimed at cancelling unwanted primary noise within a continuous region by using
multiple secondary sources and microphones. A method based on the kernel
interpolation of a sound field makes it possible to attenuate noise over the
target region with flexible array geometry. Furthermore, by using the kernel
function with directional weighting, prior information on primary noise source
directions can be taken into consideration. However, whereas the sound field to
be interpolated is a superposition of primary and secondary sound fields, the
directional weight for the primary noise source was applied to the total sound
field in previous work; therefore, the performance improvement was limited. We
propose a method of individually interpolating the primary and secondary sound
fields and formulate a normalized least-mean-square algorithm based on this
interpolation method. Experimental results indicate that the proposed method
outperforms the method based on total kernel interpolation.


title: Did bio-homochirality arise from spin-polarized electron?

abstract:
  The origin of bio-homochirality is a subject of much debate. The emergence of
chirality and life on earth is a break of symmetry to be compared with the
breaks of symmetry in the evolution of the universe. Based on a perspective of
asymmetry transfer, the chirality at molecular level might stem from the
electron spin at subatomic level. Accordingly, in this paper a spin-induced
chiral selectivity (SICS) mechanism and its outreach are introduced and
discussed. The stress force or spin torque derived from quantum electrodynamics
(QED) might be the driving force for the transfer of asymmetry and the
formation of molecular chirality. Some recent experimental results seem to
support the SICS conjecture. If that spin-polarized electrons (SPEs) caused
life to become chirally selective was right, a magnetic half-metal material
such as greigite (Fe3S4), a mineral present in a primordial site where life
could have emerged, might act as a spin filter to produce SPEs, which then
induced the asymmetric synthesis of chiral molecules via the SICS mechanism.
All these tentative thoughts may help understand how homochirality and life
could have arisen on the early Earth.


title:
Existence of global-in-time solutions to a system of fully nonlinear
  parabolic equations

abstract:
  We consider the Cauchy problem for a system of fully nonlinear parabolic
equations. In this paper, we shall show the existence of global-in-time
solutions to the problem. Our condition to ensure the global existence is
specific to the fully nonlinear parabolic system.


title: Higher Genus Correlation Functions in CFTs with $T\bar{T}$ Deformation

abstract:
  Since the definition of $T\bar{T}$ deformation in the curved Riemann surface
is obstructive in the literature, we propose a way to generalize the
deformation in the higher genus Riemann surface by sewing prescription. We
construct the correlation functions of conformal field theories (CFTs) on genus
two Riemann surfaces with the $T\bar{T}$ deformation in terms of the
perturbative CFT approach. Thanks to sewing construction to form higher genus
Riemann surfaces from lower genus ones and conformal symmetry, we
systematically obtain the first order $T\bar{T}$ correction to the higher genus
correlation functions in the $T\bar{T}$ deformed CFTs, e.g. partition function
and one/higher-point correlation functions. In the weak-coupling limit, the
higher genus correlation functions can be decomposed by lower genus correlation
functions. Our results offer $T\bar{T}$ deformed field theories data to allow
us to extract quantum chaos signals and multiple-interval R\'{e}nyi entropies
in deformed field theories.


title:
Creating an institutional ecosystem for cash transfer programming:
  Lessons from post-disaster governance in Indonesia

abstract:
  Humanitarian and disaster management actors have increasingly adopted cash
transfer to reduce the sufferings and vulnerability of the survivors. Case
transfers have also been used as a critical instrument in the current COVID-19
pandemic. Unfortunately, academic work on humanitarian and disaster-cash
transfer related issues remains limited. This article explores how NGOs and
governments implement humanitarian cash transfer in a post-disaster setting
using an exploratory research strategy. It asks What are institutional
constraints and opportunities faced by humanitarian emergency responders in
ensuring an effective humanitarian cash transfer and how humanitarian actors
address such institutional conditions. We introduced a new conceptual
framework, namely humanitarian and disaster management ecosystem for cash
transfer. This framework allows non-governmental actors to restore complex
relations between the state, disaster survivors or citizen, local market
economy and civil society. Mixed methods and multistage research strategy were
used to collect and analyze primary and secondary data. The findings suggest
that implementing cash transfers in the context of post tsunamigenic
earthquakes and liquefaction hazards, NGOs must co-create an ecosystem of
response that not only aimed at restoring peoples access to cash and basic
needs but first they must restore relations between the states and their
citizen while linking the at-risk communities with the private sectors to
jump-starting local livelihoods and market economy.


title:
Weakly-Supervised Semantic Segmentation with Visual Words Learning and
  Hybrid Pooling

abstract:
  Weakly-Supervised Semantic Segmentation (WSSS) methods with image-level
labels generally train a classification network to generate the Class
Activation Maps (CAMs) as the initial coarse segmentation labels. However,
current WSSS methods still perform far from satisfactorily because their
adopted CAMs 1) typically focus on partial discriminative object regions and 2)
usually contain useless background regions. These two problems are attributed
to the sole image-level supervision and aggregation of global information when
training the classification networks. In this work, we propose the visual words
learning module and hybrid pooling approach, and incorporate them in the
classification network to mitigate the above problems. In the visual words
learning module, we counter the first problem by enforcing the classification
network to learn fine-grained visual word labels so that more object extents
could be discovered. Specifically, the visual words are learned with a
codebook, which could be updated via two proposed strategies, i.e.
learning-based strategy and memory-bank strategy. The second drawback of CAMs
is alleviated with the proposed hybrid pooling, which incorporates the global
average and local discriminative information to simultaneously ensure object
completeness and reduce background regions. We evaluated our methods on PASCAL
VOC 2012 and MS COCO 2014 datasets. Without any extra saliency prior, our
method achieved 70.6% and 70.7% mIoU on the $val$ and $test$ set of PASCAL VOC
dataset, respectively, and 36.2% mIoU on the $val$ set of MS COCO dataset,
which significantly surpassed the performance of state-of-the-art WSSS methods.


title: Electromagnetically induced walking

abstract:
  In atomic devices, optical control and detection of neutral atoms involves
Doppler broadening due to the momentum uncertainty in atom wave packet. Here,
we show how to remove the Doppler broadening near topological edge-state energy
in the synthetic dimensions of Rydberg atoms and propose coherent walking of
single atoms. Attractive and repulsive oscillating forces for the atom walk
result from the back action process and depend on internal states of the atoms.


title:
Royalflush Speaker Diarization System for ICASSP 2022 Multi-channel
  Multi-party Meeting Transcription Challenge

abstract:
  This paper describes the Royalflush speaker diarization system submitted to
the Multi-channel Multi-party Meeting Transcription Challenge(M2MeT). Our
system comprises speech enhancement, overlapped speech detection, speaker
embedding extraction, speaker clustering, speech separation and system fusion.
In this system, we made three contributions. First, we propose an architecture
of combining the multi-channel and U-Net-based models, aiming at utilizing the
benefits of these two individual architectures, for far-field overlapped speech
detection. Second, in order to use overlapped speech detection model to help
speaker diarization, a speech separation based overlapped speech handling
approach, in which the speaker verification technique is further applied, is
proposed. Third, we explore three speaker embedding methods, and obtained the
state-of-the-art performance on the CNCeleb-E test set. With these proposals,
our best individual system significantly reduces DER from 15.25% to 6.40%, and
the fusion of four systems finally achieves a DER of 6.30% on the far-field
Alimeeting evaluation set.


title:
Flatfield Calibrations with Astrophysical Sources for the Nancy Grace
  Roman Space Telescope's Coronagraph Instrument

abstract:
  The Nancy Grace Roman Space Telescope Coronagraph Instrument is a
high-contrast imager, polarimeter, and spectrometer that will enable the study
of exoplanets and circumstellar disks at visible wavelengths
($\sim$550--850~nm) at contrasts 2--3 orders of magnitude better than can
currently be achieved by ground or space-based direct imaging facilities. To
capitalize on this sensitivity, precise flux calibration will be required. The
Roman Coronagraph, like other space-based missions, will use on-orbit
flatfields to measure and correct for phenomena that impact the measured total
effective throughput. However, the Coronagraph does not have internal lamp
sources, therefore we have developed a method to perform flatfield calibrations
using observations of extended sources, such as Uranus and Neptune, using a
combination of rastering the Coronagraph's Fast Steering Mirror, tiling the
planet across the field of view, and matched-filter image processing. Here we
outline the process and present the results of simulations using images of
Uranus and Neptune from the Hubble Space Telescopes Wide Field Camera 3, in
filters approximate to the Coronagraph's Band 1 and Band 4. The simulations are
performed over the Coronagraph's direct imaging and polarimetric modes. We
model throughput effects in 3 different spatial frequency regimes including 1)
high spatial frequency detector pixel-to-pixel quantum efficiency variations,
2) medium spatial frequency "measles" caused by particle deposition on the
detector or other focal-plane optics post-launch, and 3) low spatial frequency
detector fringing caused by self-interference due to internal reflections in
the detector substrate as well as low spatial frequency vignetting at the edges
of the Coronagraph's field of view. We show that Uranus and Neptune can be used
as astrophysical flat sources with high precision ($\sim$0.5% relative error)


title: Scale Estimation with Dual Quadrics for Monocular Object SLAM

abstract:
  The scale ambiguity problem is inherently unsolvable to monocular SLAM
without the metric baseline between moving cameras. In this paper, we present a
novel scale estimation approach based on an object-level SLAM system. To obtain
the absolute scale of the reconstructed map, we derive a nonlinear optimization
method to make the scaled dimensions of objects conforming to the distribution
of their sizes in the physical world, without relying on any prior information
of gravity direction. We adopt the dual quadric to represent objects for its
ability to fit objects compactly and accurately. In the proposed monocular
object-level SLAM system, dual quadrics are fastly initialized based on
constraints of 2-D detections and fitted oriented bounding box and are further
optimized to provide reliable dimensions for scale estimation.


title:
Complete vectorlike fourth family and new U(1)$^\prime$ for muon
  anomalies

abstract:
  I introduce a model to explain the recent anomalies in the muon anomalous
magnetic moment and the rare semi-leptonic $B$ meson decays. The Standard Model
is extended by a $U(1)^\prime$ gauge symmetry and a complete fourth family
fermions which are vector-like under the gauge symmetry. We found parameter
points which can explain the anomalies consistently with the other observables.
We then propose an interesting possibility to have signals with four muons or
more from vector-like lepton pair production.


title: Syntomic complexes and $p$-adic \'etale Tate twists

abstract:
  The primary goal of this paper is to identify syntomic complexes with the
$p$-adic \'etale Tate twists of Geisser--Schneider--Sato on regular
$p$-torsionfree schemes. Our methods apply naturally to a broader class of
schemes that we call "$F$-smooth". The $F$-smoothness of regular schemes leads
to new results on the absolute prismatic cohomology of regular schemes.


title: Some formulas for fully degenerate Bernoulli numbers and polynomials

abstract:
  The aim of this paper is to study the fully degenerate Bernoulli polynomials
and numbers, which are a degenerate version of Bernoulli polynomials and
numbers and arise naturally from the Volkenborn integral of the degenerate
exponential functions on Zp. We find some explicit expressions for the fully
degenerate Bernoulli polynomials and numbers in terms of the degenerate
Stirling numbers of the second kind, the degenerate r-Stirling numbers of the
second kind and of the degenerate Stirling polynomials.
  We also consider the degenerate poly-Bernoulli polynomials and derive
explicit representations for them in terms of the same degenerate Stirling
numbers and polynomials.


title: L0Learn: A Scalable Package for Sparse Learning using L0 Regularization

abstract:
  We introduce L0Learn: an open-source package for sparse regression and
classification using L0 regularization. L0Learn implements scalable,
approximate algorithms, based on coordinate descent and local combinatorial
optimization. The package is built using C++ and has a user-friendly R
interface. Our experiments indicate that L0Learn can scale to problems with
millions of features, achieving competitive run times with state-of-the-art
sparse learning packages. L0Learn is available on both CRAN and GitHub.


title: Measuring disentangled generative spatio-temporal representation

abstract:
  Disentangled representation learning offers useful properties such as
dimension reduction and interpretability, which are essential to modern deep
learning approaches. Although deep learning techniques have been widely applied
to spatio-temporal data mining, there has been little attention to further
disentangle the latent features and understanding their contribution to the
model performance, particularly their mutual information and correlation across
features. In this study, we adopt two state-of-the-art disentangled
representation learning methods and apply them to three large-scale public
spatio-temporal datasets. To evaluate their performance, we propose an internal
evaluation metric focusing on the degree of correlations among latent variables
of the learned representations and the prediction performance of the downstream
tasks. Empirical results show that our modified method can learn disentangled
representations that achieve the same level of performance as existing
state-of-the-art ST deep learning methods in a spatio-temporal sequence
forecasting problem. Additionally, we find that our methods can be used to
discover real-world spatial-temporal semantics to describe the variables in the
learned representation.


title: Survey on Graph Neural Network Acceleration: An Algorithmic Perspective

abstract:
  Graph neural networks (GNNs) have been a hot spot of recent research and are
widely utilized in diverse applications. However, with the use of huger data
and deeper models, an urgent demand is unsurprisingly made to accelerate GNNs
for more efficient execution. In this paper, we provide a comprehensive survey
on acceleration methods for GNNs from an algorithmic perspective. We first
present a new taxonomy to classify existing acceleration methods into five
categories. Based on the classification, we systematically discuss these
methods and highlight their correlations. Next, we provide comparisons from
aspects of the efficiency and characteristics of these methods. Finally, we
suggest some promising prospects for future research.


title:
Decreasing Annotation Burden of Pairwise Comparisons with
  Human-in-the-Loop Sorting: Application in Medical Image Artifact Rating

abstract:
  Ranking by pairwise comparisons has shown improved reliability over ordinal
classification. However, as the annotations of pairwise comparisons scale
quadratically, this becomes less practical when the dataset is large. We
propose a method for reducing the number of pairwise comparisons required to
rank by a quantitative metric, demonstrating the effectiveness of the approach
in ranking medical images by image quality in this proof of concept study.
Using the medical image annotation software that we developed, we actively
subsample pairwise comparisons using a sorting algorithm with a human rater in
the loop. We find that this method substantially reduces the number of
comparisons required for a full ordinal ranking without compromising
inter-rater reliability when compared to pairwise comparisons without sorting.


title: AdaPrompt: Adaptive Model Training for Prompt-based NLP

abstract:
  Prompt-based learning, with its capability to tackle zero-shot and few-shot
NLP tasks, has gained much attention in community. The main idea is to bridge
the gap between NLP downstream tasks and language modeling (LM), by mapping
these tasks into natural language prompts, which are then filled by pre-trained
language models (PLMs). However, for prompt learning, there are still two
salient gaps between NLP tasks and pretraining. First, prompt information is
not necessarily sufficiently present during LM pretraining. Second,
task-specific data are not necessarily well represented during pretraining. We
address these two issues by proposing AdaPrompt, adaptively retrieving external
data for continual pretraining of PLMs by making use of both task and prompt
characteristics. In addition, we make use of knowledge in Natural Language
Inference models for deriving adaptive verbalizers. Experimental results on
five NLP benchmarks show that AdaPrompt can improve over standard PLMs in
few-shot settings. In addition, in zero-shot settings, our method outperforms
standard prompt-based methods by up to 26.35\% relative error reduction.


title:
A nitrogen-rich atmosphere on ancient Mars consistent with isotopic
  evolution models

abstract:
  The ratio of nitrogen isotopes in the Martian atmosphere is a key constraint
on the planet's atmospheric evolution. However, enrichment of the heavy isotope
expected due to atmospheric loss from sputtering and photochemical processes is
greater than measurements. A massive, multi-bar early CO2-dominated atmosphere
and recent volcanic outgassing have been proposed to explain this discrepancy,
and many previous models have assumed atmospheric nitrogen rapidly reached a
steady state where loss to space balanced volcanic outgassing. Here we show
using time-dependent models that the abundance and isotopic composition of
nitrogen in the Martian atmosphere can be explained by a family of evolutionary
scenarios in which the initial partial pressure of nitrogen is sufficiently
high that a steady state is not reached and nitrogen levels gradually decline
to present-day values over 4 billion years. Our solutions do not require a
multi-bar early CO2 atmosphere and are consistent with volcanic outgassing
indicated by both geologic mapping and the atmospheric 36Ar/38Ar ratio. Monte
Carlo simulations that include these scenarios estimate that the partial
pressure of N2 was 60 - 740 mbar (90% confidence, with a median value of 310
mbar) at 3.8 billion years ago when the valley networks formed. We suggest that
such a high nitrogen partial pressure could have contributed substantially to
warming on early Mars.