title:
Facets of Random Symmetric Edge Polytopes, Degree Sequences, and
  Clustering

abstract:
  Symmetric edge polytopes are lattice polytopes associated with finite simple
graphs that are of interest in both theory and applications. We investigate the
facet structure of symmetric edge polytopes for various models of random
graphs. For an Erd\H{o}s-Renyi random graph, we identify a threshold
probability at which with high probability the symmetric edge polytope shares
many facet-supporting hyperplanes with that of a complete graph. We also
investigate the relationship between the Watts-Strogatz clustering coefficient
and the number of facets for a graph with either a fixed number of edges or a
fixed degree sequence. We use well-known Markov Chain Monte Carlo sampling
methods to generate empirical evidence that for a fixed degree sequence, higher
Watts-Strogatz clustering in a connected graph corresponds to higher facet
numbers in the associated symmetric edge polytope.


title:
Approximating pressure-driven Stokes flow using the principle of minimal
  excess dissipation

abstract:
  Stokes' equations model microscale fluid flows including the flows of
nanoliter-sized fluid samples in lab-on-a-chip systems. Helmholtz's dissipation
theorem guarantees that the solution of Stokes' equations in a given domain
minimizes viscous dissipation among all incompressible vector fields that are
compatible with the velocities imposed at the domain boundaries. Helmholtz's
dissipation theorem directly guarantees the uniqueness of solutions of Stokes
flow, and provides a practical method for calculating approximate solutions for
flow around moving bodies. However, although generalization of the principle to
domains with mixtures of velocity and stress boundary conditions is relatively
straight-forward (Keller et al., 1967), it appears to be little known. Here we
show that the principle of minimal excess dissipation can be used to derive
accurate analytical approximations for the flows in microchannels with
different cross-section shapes including when the channel walls are engineered
to have different distributions of slip boundary conditions. In addition to
providing a simple, rapid, method for approximating the conductances of
micro-channels, analysis of excess dissipation allows for a comparison
principle that can be used, for example, to show that the conductance of a
channel is always increased by adding additional slip boundary conditions.


title: The Art of Prompting: Event Detection based on Type Specific Prompts

abstract:
  We compare various forms of prompts to represent event types and develop a
unified framework to incorporate the event type specific prompts for
supervised, few-shot, and zero-shot event detection. The experimental results
demonstrate that a well-defined and comprehensive event type prompt can
significantly improve the performance of event detection, especially when the
annotated data is scarce (few-shot event detection) or not available (zero-shot
event detection). By leveraging the semantics of event types, our unified
framework shows up to 24.3\% F-score gain over the previous state-of-the-art
baselines.


title:
Prospects for a local detection of dark matter with future missions to
  Uranus and Neptune

abstract:
  We investigate the possibility of detecting the gravitational influence of
dark matter (DM) on the trajectory of prospective Doppler ranging missions to
Uranus and Neptune. In addition, we estimate the constraints such a mission can
provide on modified and massive gravity theories via extra-precession
measurements using orbiters around the ice giants. We employ Monte Carlo-Markov
Chain methods to reconstruct fictitious spacecraft trajectories in a simplified
solar system model with varying amounts of DM. We characterise the noise on the
Doppler link by the Allan deviation $\sigma_{\rm A}$, scaled on the Cassini-era
value of $\sigma^{\rm{Cass}}_{\rm A}= 3 \times 10^{-15}$. Additionally, we
compare the precision of prospective extra-precession measurements of Uranus
and Neptune with the expected rates from simulations, in the context of
modifications to the inverse square law. We estimate that the prospective
mission will be sensitive to DM densities of the order of $\rho_{\rm{DM}} \sim
9 \times 10^{-20} \, (\sigma_{\rm A}/\sigma_{\rm A}^{\rm{Cass}}) $ kg/m$^3$,
while the $1\sigma$ bound on the expected galactic density of $\rho_{\rm{DM}}
\sim 5 \times 10^{-22}$ kg/m$^3$ decreases as $1.0 \times 10^{-20} \,
(\sigma_{\rm A}/\sigma^{\rm{Cass}}_{\rm A})^{0.8}$ kg/m$^3$. An improvement of
two to three orders of magnitude from the baseline Allan deviation would
guarantee a local detection of DM. Only a moderate reduction in ranging noise
is required to rule out Milgrom's interpolating function with solar system
based observations, and improve constraints the graviton mass beyond current
local- or gravitational wave-based measurements. Our analysis also highlights
the potential of future ranging missions to improve measurements of the
standard gravitational parameters in the solar system.

categories: gr-qc astro-ph.CO astro-ph.EP astro-ph.IM


title:
PLGAN: Generative Adversarial Networks for Power-Line Segmentation in
  Aerial Images

abstract:
  Accurate segmentation of power lines in various aerial images is very
important for UAV flight safety. The complex background and very thin
structures of power lines, however, make it an inherently difficult task in
computer vision. This paper presents PLGAN, a simple yet effective method based
on generative adversarial networks, to segment power lines from aerial images
with different backgrounds. Instead of directly using the adversarial networks
to generate the segmentation, we take their certain decoding features and embed
them into another semantic segmentation network by considering more context,
geometry, and appearance information of power lines. We further exploit the
appropriate form of the generated images for high-quality feature embedding and
define a new loss function in the Hough-transform parameter space to enhance
the segmentation of very thin power lines. Extensive experiments and
comprehensive analysis demonstrate that our proposed PLGAN outperforms the
prior state-of-the-art methods for semantic segmentation and line detection.


title:
Dirac cone spectroscopy of strongly correlated phases in twisted
  trilayer graphene

abstract:
  Mirror-symmetric magic-angle twisted trilayer graphene (MATTG) hosts flat
electronic bands close to zero energy, and has been recently shown to exhibit
abundant correlated quantum phases with flexible electrical tunability. However
studying these phases proved challenging as these are obscured by intertwined
Dirac bands. In this work, we demonstrate a novel spectroscopy technique, that
allows to quantify the energy gaps and Chern numbers of the correlated states
in MATTG by driving band crossings between Dirac cone Landau levels and the
energy gaps in the flat bands. We uncover hard correlated gaps with Chern
numbers of C = 0 at integer moire unit cell fillings of nu = 2 and 3 and reveal
novel charge density wave states originating from van Hove singularities at
fractional fillings of nu = 5/3 and 11/3. In addition, we demonstrate the
existence of displacement field driven first-order phase transitions at charge
neutrality and half fillings of the moire unit cell nu = 2, which is consistent
with a theoretical strong-coupling analysis, implying the breaking of the C2T
symmetry. Overall these properties establish the diverse and electrically
tunable phase diagram of MATTG and provide an avenue for investigating
electronic quantum phases and strong correlations in multiple-band moire
systems.


title: CIR equations with multivariate L\'evy noise

abstract:
  The paper is devoted to the study of the short rate equation of the form $$ d
R(t)=F(R(t)) dt+\sum_{i=1}^{d}G_i(R(t-)) dZ_i(t), \quad R(0)=x\geq 0,\quad t>0,
$$ with deterministic functions $F,G_1,...,G_d$ and a multivariate L\'evy
process $Z=(Z_1,...,Z_d)$. The equation is supposed to have a nonnegative
solution which generates an affine term structure model. Two classes of noise
are considered. In the first one the coordinates of Z are independent processes
with regularly varying Laplace exponents. In the second class Z is a spherical
processes, which means that its L\'evy measure has a similar structure as that
of a stable process, but with radial part of a general form. For both classes a
precise form of the short rate generator is characterized. Under mild
assumptions it is shown that any equation of the considered type has the same
solution as the equation driven by a L\'evy process with independent stable
coordinates.
  The paper generalizes the classical results on the Cox-Ingersoll-Ross (CIR)
model as well as on its extended version where $Z$ is a one-dimensional L\'evy
process.


title:
Robotic and Generative Adversarial Attacks in Offline Writer-independent
  Signature Verification

abstract:
  This study explores how robots and generative approaches can be used to mount
successful false-acceptance adversarial attacks on signature verification
systems. Initially, a convolutional neural network topology and data
augmentation strategy are explored and tuned, producing an 87.12% accurate
model for the verification of 2,640 human signatures. Two robots are then
tasked with forging 50 signatures, where 25 are used for the verification
attack, and the remaining 25 are used for tuning of the model to defend against
them. Adversarial attacks on the system show that there exists an information
security risk; the Line-us robotic arm can fool the system 24% of the time and
the iDraw 2.0 robot 32% of the time. A conditional GAN finds similar success,
with around 30% forged signatures misclassified as genuine. Following fine-tune
transfer learning of robotic and generative data, adversarial attacks are
reduced below the model threshold by both robots and the GAN. It is observed
that tuning the model reduces the risk of attack by robots to 8% and 12%, and
that conditional generative adversarial attacks can be reduced to 4% when 25
images are presented and 5% when 1000 images are presented.


title:
Benchmark computations of the phase field crystal and functionalized
  Cahn-Hilliard equations via fully implicit, Nesterov accelerated schemes

abstract:
  We introduce a fast solver for the phase field crystal (PFC) and
functionalized Cahn-Hilliard (FCH) equations with periodic boundary conditions
on a rectangular domain that features the preconditioned Nesterov accelerated
gradient descent (PAGD) method. We discretize these problems with a Fourier
collocation method in space, and employ various second-order schemes in time.
We observe a significant speedup with this solver when compared to the
preconditioned gradient descent (PGD) method. With the PAGD solver, fully
implicit, second-order-in-time schemes are not only feasible to solve the PFC
and FCH equations, but also do so more efficiently than some semi-implicit
schemes in some cases where accuracy issues are taken into account. Benchmark
computations of five different schemes for the PFC and FCH equations are
conducted and the results indicate that, for the FCH experiments, the fully
implicit schemes (midpoint rule and BDF2 equipped with the PAGD as a nonlinear
time marching solver) perform better than their IMEX versions in terms of
computational cost needed to achieve a certain precision. For the PFC, the
results are not as conclusive as in the FCH experiments, which, we believe, is
due to the fact that the nonlinearity in the PFC is milder nature compared to
the FCH equation. We also discuss some practical matters in applying the PAGD.
We introduce an averaged Newton preconditioner and a sweeping-friction strategy
as heuristic ways to choose good preconditioner parameters. The
sweeping-friction strategy exhibits almost as good a performance as the case of
the best manually tuned parameters.


title:
Waveform Optimization with SINR Criteria for FDA Radar in the Presence
  of Signal-Dependent Mainlobe Interference

abstract:
  In this paper, we focus on the design of the transmit waveforms of a
frequency diverse array (FDA) in order to improve the output
signal-to-interference-plus-noise ratio (SINR) in the presence of
signal-dependent mainlobe interference. Since the classical multi-carrier
matched filtering-based FDA receiver cannot effectively utilize the waveform
diversity of FDA, a novel FDA receiver framework based on multi-channel mixing
and low-pass filtering is developed to keep the separation of the transmit
waveform at the receiver side, while preserving the FDA range-controllable
degrees of freedom. Furthermore, a range-angle minimum variance distortionless
response beamforming technique is introduced to synthesize receiver filter
weights with the ability to suppress a possible signal-dependent mainlobe
interference. The resulting FDA transmit waveform design problem is initially
formulated as an optimization problem consisting of a non-convex objective
function and multiple non-convex constraints. To efficiently slove this, we
introduce two algorithms, one based on a signal relaxation technique, and the
other based on the majorization minimization technique. The preferable
performance of the proposed multi-channel low-pass filtering receiver and the
optimized transmit waveforms is illustrated using numerical simulations,
indicating that the resulting FDA system is not only able to effectively
suppress mainlobe interference, but also to yield estimates with a higher SINR
than the FDA system without waveform optimization.


title: Minimizing Control for Credit Assignment with Strong Feedback

abstract:
  The success of deep learning attracted interest in whether the brain learns
hierarchical representations using gradient-based learning. However, current
biologically plausible methods for gradient-based credit assignment in deep
neural networks need infinitesimally small feedback signals, which is
problematic in biologically realistic noisy environments and at odds with
experimental evidence in neuroscience showing that top-down feedback can
significantly influence neural activity. Building upon deep feedback control
(DFC), a recently proposed credit assignment method, we combine strong feedback
influences on neural activity with gradient-based learning and show that this
naturally leads to a novel view on neural network optimization. Instead of
gradually changing the network weights towards configurations with low output
loss, weight updates gradually minimize the amount of feedback required from a
controller that drives the network to the supervised output label. Moreover, we
show that the use of strong feedback in DFC allows learning forward and
feedback connections simultaneously, using a learning rule fully local in space
and time. We complement our theoretical results with experiments on standard
computer-vision benchmarks, showing competitive performance to backpropagation
as well as robustness to noise. Overall, our work presents a fundamentally
novel view of learning as control minimization, while sidestepping biologically
unrealistic assumptions.


title: Designing FDA Radars Robust to Contaminated Shared Spectra

abstract:
  This paper considers the problem of jointly designing the transmit waveforms
and weights for a frequency diverse array (FDA) in a spectrally congested
environment in which unintentional spectral interferences exist. Exploiting the
properties of the interference signal induced by the processing of the
multi-channel mixing and low-pass filtering FDA receiver, the interference
covariance matrix structure is derived. With this, the receive weights are
formed using the minimum variance distortionless response (MVDR) method for
interference cancellation. Owing to the fact that the resulting output
signal-to-interference-plus-noise ratio (SINR) is a function of the transmit
waveforms and weights, as well as due to the ever-greater competition for the
finite available spectrum, a joint design scheme for the FDA transmit weights
and the spectrally compatible waveforms is proposed to efficiently use the
available spectrum while maintaining a sufficient receive SINR. The performance
of the proposed technique is verified using numerical simulations in terms of
the achievable SINR, spectral compatiblity, as well as several aspects of the
synthesized waveforms.


title:
Coherent FDA Radar Systems: Joint Design of Transmitting and Receiving
  Array Weighters

abstract:
  Due to the frequency offset across its array elements, frequency diverse
array (FDA) will generate angle-range-dependent and time-variant transmit
beampattern. Since existing investigations usually focus on FDA transmitter and
only instantaneous beampattern is considered, which cannot fully exploit the
time-range characteristics of FDA radar for enhanced performance, in this paper
we formulate a multi-carrier mixing receiver for coherent pulsed-Doppler FDA
radar to effectively retain the range information of FDA radar returned signals
in subsequent receiver processing. Accordingly, the joint transmitter and
receiver is systematically modeled with time-range relationship consideration.
More importantly, we optimally design the joint transmitting and receiving
weighters by maximizing the radiated energy within the desired range-angle
sections for given total energy. All proposed methods are verified by
simulation results.


title:
On the Ohmic-dominant heating mode of capacitively-coupled plasma
  inverted by boundary electron emission

abstract:
  Electron emission from the boundary is ubiquitous in capacitively coupled
plasma (CCP) and precipitates nonnegligible influences on the discharge
properties. Here we present the PIC-MCC simulation of an Ohmic-dominant heating
mode of capacitively coupled plasma where the stochastic heating vanishes and
only Ohmic heating sustains the discharge, due to sheath inversion by boundary
electron emission. The inverted CCP features negative sheath potential without
Bohm presheath, hence excluding plasma heating due to sheath edge oscillation.
The particle and energy transport of the proposed heating mode is analyzed. The
influences of boundary electron emission flux, source voltage, and neutral
pressure on the transition between classic and Ohmic-dominant CCP heating modes
are shown with designated simulation scans. A modified inverse sheath-plasma
coupling due to excessive ionization is discovered. In the end, key indicators
of the proposed heating mode in plasma diagnostics are provided for future
experimental verifications.


title:
Early Myocardial Infarction Detection with One-Class Classification over
  Multi-view Echocardiography

abstract:
  Myocardial infarction (MI) is the leading cause of mortality and morbidity in
the world. Early therapeutics of MI can ensure the prevention of further
myocardial necrosis. Echocardiography is the fundamental imaging technique that
can reveal the earliest sign of MI. However, the scarcity of echocardiographic
datasets for the MI detection is the major issue for training data-driven
classification algorithms. In this study, we propose a framework for early
detection of MI over multi-view echocardiography that leverages one-class
classification (OCC) techniques. The OCC techniques are used to train a model
for detecting a specific target class using instances from that particular
category only. We investigated the usage of uni-modal and multi-modal one-class
classification techniques in the proposed framework using the HMC-QU dataset
that includes apical 4-chamber (A4C) and apical 2-chamber (A2C) views in a
total of 260 echocardiography recordings. Experimental results show that the
multi-modal approach achieves a sensitivity level of 85.23% and F1-Score of
80.21%.


title: Methodical Advice Collection and Reuse in Deep Reinforcement Learning

abstract:
  Reinforcement learning (RL) has shown great success in solving many
challenging tasks via use of deep neural networks. Although using deep learning
for RL brings immense representational power, it also causes a well-known
sample-inefficiency problem. This means that the algorithms are data-hungry and
require millions of training samples to converge to an adequate policy. One way
to combat this issue is to use action advising in a teacher-student framework,
where a knowledgeable teacher provides action advice to help the student. This
work considers how to better leverage uncertainties about when a student should
ask for advice and if the student can model the teacher to ask for less advice.
The student could decide to ask for advice when it is uncertain or when both it
and its model of the teacher are uncertain. In addition to this investigation,
this paper introduces a new method to compute uncertainty for a deep RL agent
using a secondary neural network. Our empirical results show that using dual
uncertainties to drive advice collection and reuse may improve learning
performance across several Atari games.


title: Improving Efficiency and Equality in School Choice

abstract:
  How should students be assigned to schools? Two mechanisms have been
suggested and implemented around the world: deferred acceptance (DA) and top
trading cycles (TTC). These two mechanisms are widely considered excellent
choices owing to their outstanding stability and incentive properties. We show
theoretically and empirically that both mechanisms perform poorly with regard
to two key desiderata such as efficiency and equality, even in large markets.
In contrast, the rank-minimizing mechanism is significantly more efficient and
egalitarian. It is also Pareto optimal for the students, unlike DA, and
generates less justified envy than TTC.


title: FDA Beampattern Characteristics With Considering Time-Range Relations

abstract:
  Existing investigations show that frequency diverse array (FDA) will produce
angle-range-dependent and time-variant transmit beampattern, but the relations
between time and range and their characteristics still are not fully
investigated. In order to fully exploit the time and range dependent
characteristics of FDA transmit beampattern and address the effects of FDA
frequency offsets, we systematically reformulate the FDA antenna and system
model with specific time-range relation consideration. Two FDA transmit
beampatterns, namely, instantaneous and integral ones, are derived.
Accordingly, the FDA time and range dependent characteristics together with the
comparisons to conventional phased-array and co-located multiple-input
multiple-output (MIMO) antennas are analyzed. Both theoretical analysis and
simulation results reveal that the FDA time-range dependent characteristics
actually can be regarded as auto-scanning capability, which may provide many
promising applications.


title: On Hilbert's sixth problem

abstract:
  Feynman path integrals are now a standard tool in quantum physics and their
use in differential geometry leads to new mathematical insights. A logical
treatment of quantum phenomena seems to require a sustained mathematical
analysis of path integrals. The subject is complicated by the fact that their
application in flat space-time is quite different from how path integrals are
used in, say, topological quantum field theory, where there is no natural
notion of time translation. An historical background is given in this paper and
a few mathematical approaches to Feynman path integrals in the context of
nonrelativistic quantum mechanics and scalar quantum fields with polynomial
self-interactions are outlined.


title: Causal Transformer for Estimating Counterfactual Outcomes

abstract:
  Estimating counterfactual outcomes over time from observational data is
relevant for many applications (e.g., personalized medicine). Yet,
state-of-the-art methods build upon simple long short-term memory (LSTM)
networks, thus rendering inferences for complex, long-range dependencies
challenging. In this paper, we develop a novel Causal Transformer for
estimating counterfactual outcomes over time. Our model is specifically
designed to capture complex, long-range dependencies among time-varying
confounders. For this, we combine three transformer subnetworks with separate
inputs for time-varying covariates, previous treatments, and previous outcomes
into a joint network with in-between cross-attentions. We further develop a
custom, end-to-end training procedure for our Causal Transformer. Specifically,
we propose a novel counterfactual domain confusion loss to address confounding
bias: it aims to learn adversarial balanced representations, so that they are
predictive of the next outcome but non-predictive of the current treatment
assignment. We evaluate our Causal Transformer based on synthetic and
real-world datasets, where it achieves superior performance over current
baselines. To the best of our knowledge, this is the first work proposing
transformer-based architecture for estimating counterfactual outcomes from
longitudinal data.


title:
Seasonal variations of chemical species and haze in Titan's upper
  atmosphere

abstract:
  Seasonal variation is significant in Titan's atmosphere due to the large
change of solar insolation resulting from Titan's 26.7{\deg} axial tilt
relative to the plane of Saturn's orbit. Here we present an investigation of
hydrocarbon and nitrile species in Titan's upper atmosphere at 400-1200 km,
which includes the mesosphere and the lower thermosphere, over more than one
fourth of Titan's year (2006-2014, LS=318{\deg}-60{\deg}), using eighteen
stellar occultation observations obtained by Cassini/UVIS. Vertical profiles of
eight chemical species (CH4, C2H2, C2H4, C2H6, C4H2, C6H6, HCN, HC3N) and haze
particles are retrieved from these observations using an instrument forward
model, which considers the technical issue of pointing motion. The Markov-chain
Monte Carlo (MCMC) algorithm is used to obtain the posterior probability
distributions of parameters in the retrieval, which inherently tests the extent
to which species profiles can be constrained. The results show that no change
of the species profiles is noticeable before the equinox, while the decrease of
atmospheric temperature and significant upwelling in the summer hemisphere are
found five terrestrial years afterwards. Altitude of the detached haze layer
decreases towards the vernal equinox then it disappears, and no reappearance is
identified within the time range of our data, which is consistent with
observations from Cassini/ISS. This study provides observational constraints on
the seasonal change of Titan's upper atmosphere, and suggests further
investigations of the atmospheric chemistry and dynamics therein.


title:
Vortices in a Ginzburg Landau Theory of Superconductors with Nematic
  Order

abstract:
  In this work we explore the interplay between superconductivity and
nematicity in the framework of a Ginzburg Landau theory with a nematic order
parameter coupled to the superconductor order parameter, often used in the
description of superconductivity of Fe based materials. In particular, we focus
on the study of the vortex-vortex interaction in order to determine the way
nematicity affects its attractive or repulsive character. To do so, we use a
dynamical method based on the solutions of the Time Dependent Ginzburg Landau
equations in a bulk superconductor. An important contribution of our work is
the implementation of a pseudo-spectral method to solve the dynamics, known to
be highly efficient and of very high order in comparison to the usual finite
differences/elements methods. The coupling between the superconductor and the
(real) nematic order parameters is represented by two terms in the free energy:
a biquadratic term and a coupling of the nematic order parameter to the
covariant derivatives of the superconductor order parameter. Our results show
that there is a competing effect: while the former independently of its
competitive or cooperative character generates an attractive vortex-vortex
interaction, the latter always generates a repulsive interaction.


title:
Convergence and Implicit Regularization Properties of Gradient Descent
  for Deep Residual Networks

abstract:
  We prove linear convergence of gradient descent to a global minimum for the
training of deep residual networks with constant layer width and smooth
activation function. We further show that the trained weights, as a function of
the layer index, admits a scaling limit which is H\"older continuous as the
depth of the network tends to infinity. The proofs are based on non-asymptotic
estimates of the loss function and of norms of the network weights along the
gradient descent path. We illustrate the relevance of our theoretical results
to practical settings using detailed numerical experiments on supervised
learning problems.


title: Imposing Consistency for Optical Flow Estimation

abstract:
  Imposing consistency through proxy tasks has been shown to enhance
data-driven learning and enable self-supervision in various tasks. This paper
introduces novel and effective consistency strategies for optical flow
estimation, a problem where labels from real-world data are very challenging to
derive. More specifically, we propose occlusion consistency and zero forcing in
the forms of self-supervised learning and transformation consistency in the
form of semi-supervised learning. We apply these consistency techniques in a
way that the network model learns to describe pixel-level motions better while
requiring no additional annotations. We demonstrate that our consistency
strategies applied to a strong baseline network model using the original
datasets and labels provide further improvements, attaining the
state-of-the-art results on the KITTI-2015 scene flow benchmark in the
non-stereo category. Our method achieves the best foreground accuracy (4.33% in
Fl-all) over both the stereo and non-stereo categories, even though using only
monocular image inputs.


title:
Schwinger pair production rate and time for some space-dependent
  electromagnetic fields via worldline instantons formalism

abstract:
  Schwinger pair production in some of space-dependent electromagnetic fields
is studied analytically by using worldline instantons formalism for scalar
quantum electrodynamics. With the increase of the modified Keldysh parameter
$\gamma_{keb}$, the pair production rate decreases, the region of instanton
paths expands regardless of the electromagnetic field form. We also find that
all of the paths of the instantons for various electromagnetic fields are
exactly in the same plane while with the different elliptical shapes. Moreover,
the magnitudes of the normalized instantons action with different external
electromagnetic fields are all bounded within the region from $\pi$ to $2\pi$
in spatial inhomogeneity fields. We further analytically identify and obtain
two kinds of the pair-production time associated to the worldline instantons
action.


title:
Zeeman effect in centrosymmetric antiferromagnets controlled by electric
  field

abstract:
  Centrosymmetric antiferromagnets, although abundant in nature, seem less
promising than ferromagnets or ferroelectrics for practical applications in
semiconductor spintronics. As a matter of fact, the lack of spontaneous
polarization and magnetization hinders the efficient utilization of electronic
spin in these materials. Here, we propose a paradigm to harness electronic spin
in centrosymmetric antiferromagnets via Zeeman spin splittings of electronic
energy levels -- termed as spin Zeeman effect -- which is controlled by an
electric field. By symmetry analysis, we identify twenty-one centrosymmetric
antiferromagnetic point groups that accommodate such a spin Zeeman effect. We
further predicted by first-principles that near-room temperature
antiferromagnetic SrFe$_2$S$_2$O is an excellent candidate showcasing a Zeeman
splitting as large as $\sim$75 meV, induced by an electric field of 0.15 V/\AA.
Moreover, the electronic spin magnetization associated to the split energy
levels is switchable when reversing the electric field. Our work thus sheds
light on the electric-field control of electronic spin in antiferromagnets,
which broadens the scope of application of centrosymmetric antiferromagnets.


title:
The Rise of Intelligent Reflecting Surfaces in Integrated Sensing and
  Communications Paradigms

abstract:
  The intelligent reflecting surface (IRS) alters the behavior of wireless
media and, consequently, has potential to improve the performance and
reliability of wireless systems such as communications and radar remote
sensing. Recently, integrated sensing and communications (ISAC) has been widely
studied as a means to efficiently utilize spectrum and thereby save cost and
power. This article investigates the role of IRS in the future ISAC paradigms.
While there is a rich heritage of recent research into IRS-assisted
communications, the IRS-assisted radars and ISAC remain relatively unexamined.
We discuss the putative advantages of IRS deployment, such as coverage
extension, interference suppression, and enhanced parameter estimation, for
both communications and radar. We introduce possible IRS-assisted ISAC
scenarios with common and dedicated surfaces. The article provides an overview
of related signal processing techniques and the design challenges, such as
wireless channel acquisition, waveform design, and security.


title:
Boundary points, minimal $L^2$ integrals and concavity property IV --
  fibrations over open Riemann surfaces

abstract:
  In this article, we consider the minimal $L^2$ integrals related to modules
at boundary points on fibrations over open Riemann surfaces, and present a
characterization for the concavity property of the minimal $L^2$ integrals
degenerating to linearity.


title: Learning Spatially Varying Pixel Exposures for Motion Deblurring

abstract:
  Computationally removing the motion blur introduced by camera shake or object
motion in a captured image remains a challenging task in computational
photography. Deblurring methods are often limited by the fixed global exposure
time of the image capture process. The post-processing algorithm either must
deblur a longer exposure that contains relatively little noise or denoise a
short exposure that intentionally removes the opportunity for blur at the cost
of increased noise. We present a novel approach of leveraging spatially varying
pixel exposures for motion deblurring using next-generation focal-plane
sensor--processors along with an end-to-end design of these exposures and a
machine learning--based motion-deblurring framework. We demonstrate in
simulation and a physical prototype that learned spatially varying pixel
exposures (L-SVPE) can successfully deblur scenes while recovering high
frequency detail. Our work illustrates the promising role that focal-plane
sensor--processors can play in the future of computational imaging.


title: Visual Pressure Estimation and Control for Soft Robotic Grippers

abstract:
  Soft robotic grippers facilitate contact-rich manipulation, including robust
grasping of varied objects. Yet the beneficial compliance of a soft gripper
also results in significant deformation that can make precision manipulation
challenging. We present visual pressure estimation & control (VPEC), a method
that uses a single RGB image of an unmodified soft gripper from an external
camera to directly infer pressure applied to the world by the gripper. We
present inference results for a pneumatic gripper and a tendon-actuated gripper
making contact with a flat surface. We also show that VPEC enables precision
manipulation via closed-loop control of inferred pressure. We present results
for a mobile manipulator (Stretch RE1 from Hello Robot) using visual servoing
to do the following: achieve target pressures when making contact; follow a
spatial pressure trajectory; and grasp small objects, including a microSD card,
a washer, a penny, and a pill. Overall, our results show that VPEC enables
grippers with high compliance to perform precision manipulation.


title: Ditauonium spectroscopy

abstract:
  We examine the properties of ditauonium, an exotic atom consisting of a pair
of opposite-sign $\tau$ leptons bound together by the quantum electrodynamics
(QED) interaction in a hydrogen-like state. The energy levels, decay modes and
associated partial widths, as well as total widths and lifetimes of the ortho-
and para-ditauonium states are calculated. Higher-order QED corrections --
including Lamb shifts, hyperfine splitting structure, and rare partial decay
widths -- are incorporated up to next-to-next-to-leading-order (NNLO) accuracy.
Beyond the dominant diphoton and difermion decays, the rates of higher-order
decay channels -- including Dalitz, radiative, triple-photon, double-Dalitz,
four-fermion, and neutrinos final states -- are determined.


title:
Model-agnostic Multi-Domain Learning with Domain-Specific Adapters for
  Action Recognition

abstract:
  In this paper, we propose a multi-domain learning model for action
recognition. The proposed method inserts domain-specific adapters between
layers of domain-independent layers of a backbone network. Unlike a multi-head
network that switches classification heads only, our model switches not only
the heads, but also the adapters for facilitating to learn feature
representations universal to multiple domains. Unlike prior works, the proposed
method is model-agnostic and doesn't assume model structures unlike prior
works. Experimental results on three popular action recognition datasets
(HMDB51, UCF101, and Kinetics-400) demonstrate that the proposed method is more
effective than a multi-head architecture and more efficient than separately
training models for each domain.


title: On the formation of globular clusters: comparison with observations

abstract:
  The paper deals with the conditions required to form at least two stellar
generations in globular clusters under the constraints generated by feedback
from massive stars as well as radiative cooling and the metallicity of the
primordial clouds. Our calculations are based on two main constraints to the
star formation efficiency of the first stellar generation (1G) $\epsilon_{1G}$.
First, $\epsilon_{1G}$ is restricted to warrant that stellar winds and
supernovae do not disrupt the leftover gas out of which a second generation
(2G) would form. Second, $\epsilon_{1G}$ is also limited such that the
metallicity enhancement caused by trapped supernovae is, in agreement with the
observations, not larger than $\sim$ 0.1 dex. Several central parameters define
the globular clusters end result: the mass and radius of the primordial clouds,
their metallicity and $\epsilon_{1G}$. The parameter space composed by models
which fulfilled all constraints, is here shown to coincide remarkably well with
the scattered observed anti-correlation between the fraction of first
generation stars ($f_{\textrm{1G}}$) and total cluster mass. Our models also
discern, in agreement with the data, between single and multiple population
clusters in a metallicity versus mass (or radius) plane. Hence, our results
suggest that the presence of multiple stellar populations is closely linked to
the ability of proto-globular clusters to retain a fraction of leftover gas.


title:
Automated speech tools for helping communities process restricted-access
  corpora for language revival efforts

abstract:
  Many archival recordings of speech from endangered languages remain
unannotated and inaccessible to community members and language learning
programs. One bottleneck is the time-intensive nature of annotation. An even
narrower bottleneck occurs for recordings with access constraints, such as
language that must be vetted or filtered by authorised community members before
annotation can begin. We propose a privacy-preserving workflow to widen both
bottlenecks for recordings where speech in the endangered language is
intermixed with a more widely-used language such as English for meta-linguistic
commentary and questions (e.g. What is the word for 'tree'?). We integrate
voice activity detection (VAD), spoken language identification (SLI), and
automatic speech recognition (ASR) to transcribe the metalinguistic content,
which an authorised person can quickly scan to triage recordings that can be
annotated by people with lower levels of access. We report work-in-progress
processing 136 hours archival audio containing a mix of English and Muruwari.
Our collaborative work with the Muruwari custodian of the archival materials
show that this workflow reduces metalanguage transcription time by 20% even
given only minimal amounts of annotated training data: 10 utterances per
language for SLI and for ASR at most 39 minutes, and possibly as little as 39
seconds.


title:
A bound for twists of $\rm GL_3\times GL_2$ $L$-functions with composite
  modulus

abstract:
  Let $\pi$ be a Hecke-Maass cusp form for $\rm SL_3(\mathbf{Z})$ and let $g$
be a holomorphic or Maass cusp form for $\rm SL_2(\mathbf{Z})$. Let $\chi$ be a
primitive Dirichlet character of modulus $M=M_1M_2$ with $M_i$ prime, $i=1,2$.
Suppose that $M^{1/2+2\eta}<M_1<M^{1-2\eta}$ with $0<\eta<1/8$. Then we have $$
L\left(\frac{1}{2},\pi\otimes g \otimes \chi\right)\ll_{\pi,g,\varepsilon}
M^{3/2-\eta+\varepsilon}. $$


title:
Spatial Geometry of the Large-Scale Universe: The Role of Quantum
  Gravity, Dark Energy and other Unknowns

abstract:
  It has been known for some time that the usual inference drawn from the
observed near-flatness of the large-scale Universe - namely the existence of a
cosmological "flatness problem", which is then taken as a partial, but key
motivation for assuming the existence of an ultra-short, inflationary expansion
of the very early Universe - is in itself deeply problematic. The present
contribution consolidates the earlier results regarding the absence of a
cosmological flatness problem of the sort that could potentially be resolved by
inflation, by clearing up some common misunderstandings and by presenting some
arguments in more detail.


title:
Incremental Prompting: Episodic Memory Prompt for Lifelong Event
  Detection

abstract:
  Lifelong event detection aims to incrementally update a model with new event
types and data while retaining the capability on previously learned old types.
One critical challenge is that the model would catastrophically forget old
types when continually trained on new data. In this paper, we introduce
Episodic Memory Prompts (EMP) to explicitly preserve the learned task-specific
knowledge. Our method adopts continuous prompt for each task and they are
optimized to instruct the model prediction and learn event-specific
representation. The EMPs learned in previous tasks are carried along with the
model in subsequent tasks, and can serve as a memory module that keeps the old
knowledge and transferring to new tasks. Experiment results demonstrate the
effectiveness of our method. Furthermore, we also conduct a comprehensive
analysis of the new and old event types in lifelong learning.


title:
auton-survival: an Open-Source Package for Regression, Counterfactual
  Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data

abstract:
  Applications of machine learning in healthcare often require working with
time-to-event prediction tasks including prognostication of an adverse event,
re-hospitalization or death. Such outcomes are typically subject to censoring
due to loss of follow up. Standard machine learning methods cannot be applied
in a straightforward manner to datasets with censored outcomes. In this paper,
we present auton-survival, an open-source repository of tools to streamline
working with censored time-to-event or survival data. auton-survival includes
tools for survival regression, adjustment in the presence of domain shift,
counterfactual estimation, phenotyping for risk stratification, evaluation, as
well as estimation of treatment effects. Through real world case studies
employing a large subset of the SEER oncology incidence data, we demonstrate
the ability of auton-survival to rapidly support data scientists in answering
complex health and epidemiological questions.


title: P\'{o}lya-type inequalities on spheres and hemispheres

abstract:
  Given an eigenvalue $\lambda$ of the Laplace-Beltrami operator on $n-$spheres
and $-$hemi\-spheres, we characterise those with the lowest and highest orders
which equal $\lambda$ and for which P\'{o}lya's conjecture holds and fails. We
further derive P\'{o}lya-type inequalities by adding a correction term
providing sharp lower and upper bounds for all eigenvalues. This allows us to
measure the deviation from the leading term in the Weyl asymptotics for
eigenvalues on spheres and hemispheres. As a direct consequence, we obtain
similar results for domains which tile hemispheres.


title:
A Fictitious-play Finite-difference Method for Linearly Solvable Mean
  Field Games

abstract:
  A new numerical method for mean field games (MFGs) is proposed. The target
MFGs are derived from optimal control problems for multidimensional systems
with advection terms, which are difficult to solve numerically with existing
methods. For such MFGs, linearization using the Cole-Hopf transformation and
iterative computation using fictitious play are introduced. This leads to an
implementation-friendly algorithm that iteratively solves explicit schemes. The
convergence properties of the proposed scheme are mathematically proved by
tracking the error of the variable through iterations. Numerical calculations
show that the proposed method works stably for both one- and two-dimensional
control problems.


title:
Some quantitative homogenization results of parabolic equations with an
  interface

abstract:
  This paper considers a family of second-order parabolic equations in
divergence form with rapidly oscillating and time-dependent periodic
coefficients and an interface between two periodic structures. Following a
framework initiated by Blanc, Le Bris and Lions and a generalized two-scale
expansion in divergence form of elliptic homogenization with an interface by
Josien, we can determine the effective (or homogenized) equation with the
coefficient matrix being piecewise constant and discontinuous across the
interface. Moreover, we obtain the $O(\varepsilon)$ convergence rates in
$L^{2(d+2)/{d}}_{x,t}$ with $\varepsilon$-smoothing method and the uniform
interior Lipschitz estimates via compactness argument.


title:
Invisible-to-Visible: Privacy-Aware Human Instance Segmentation using
  Airborne Ultrasound via Collaborative Learning Variational Autoencoder

abstract:
  In action understanding in indoor, we have to recognize human pose and action
considering privacy. Although camera images can be used for highly accurate
human action recognition, camera images do not preserve privacy. Therefore, we
propose a new task for human instance segmentation from invisible information,
especially airborne ultrasound, for action recognition. To perform instance
segmentation from invisible information, we first convert sound waves to
reflected sound directional images (sound images). Although the sound images
can roughly identify the location of a person, the detailed shape is ambiguous.
To address this problem, we propose a collaborative learning variational
autoencoder (CL-VAE) that simultaneously uses sound and RGB images during
training. In inference, it is possible to obtain instance segmentation results
only from sound images. As a result of performance verification, CL-VAE could
estimate human instance segmentations more accurately than conventional
variational autoencoder and some other models. Since this method can obtain
human segmentations individually, it could be applied to human action
recognition tasks with privacy protection.


title:
Pricing and Remunerating Electricity Storage Flexibility Using Virtual
  Links

abstract:
  Ambitious renewable portfolio standards motivate the incorporation of energy
storage resources (ESR) as sources of flexibility. While the United States
government aims to promote ESR participation in electricity markets, work on
market design for properly remunerating ESRs is still in early stages. In this
paper, we propose a new energy market clearing framework that incorporates ESR
systems. The new market design avoids mixed-integer formulations or
formulations with complementarity constraints. Our market design also captures
energy transfer across time using a virtual link representation; this reveals
different incentives for ESR operations and establishes remuneration
mechanisms. The proposed framework also reveals roles of different physical
parameters (e.g. charge capacity and efficiency) on market prices. We use our
framework and illustrative case studies to showcase the market properties and
to analyze the interplay between ESRs and independent system operators (ISOs).


title: Blackhole Mergers Through Evection Resonances

abstract:
  Mechanisms have been proposed to enhance the merger rate of stellar mass
black hole binaries, such as the Von Zeipel-Lidov-Kozai mechanism (vZLK).
However, high inclinations are required in order to greatly excite the
eccentricity and to reduce the merger time through vZLK. Here, we propose a
novel pathway through which compact binaries could merge due to eccentricity
increase in general, including in a near coplanar configuration. Specifically,
a compact binary migrating in an AGN disk could be captured in an evection
resonance, when the precession rate of the binary equals their orbital period
around the supermassive black hole. In our study we include precession to due
first-order post Newtonian precession as well as that due to disk around one or
both components of the binary. Eccentricity is excited when the binary sweeps
through the resonance which happens only when it migrates on a timescale 10-100
times the libration timescale of the resonance. Libration timescale decreases
as the mass of the disk increases. The eccentricity excitation of the binary
can reduce the merger timescale by a factor up to $\sim 10^{3-5}$.


title: Observing frustrated quantum magnetism in two-dimensional ion crystals

abstract:
  Two-dimensional (2D) quantum magnetism is a paradigm in strongly correlated
many-body physics. The understanding of 2D quantum magnetism can be expedited
by employing a controllable quantum simulator that faithfully maps 2D-spin
Hamiltonians. The 2D quantum simulators can exhibit exotic phenomena such as
frustrated quantum magnetism and topological order and can be used to show
quantum computational advantages. Many experimental platforms are being
developed, including Rydberg atoms and superconducting annealers. However, with
trapped-ion systems, which showed the most advanced controllability and quantum
coherence, quantum magnetism was explored in one-dimensional chains. Here, we
report simulations of frustrated quantum magnetism with 2D ion crystals. We
create a variety of spin-spin interactions for quantum magnets, including those
that exhibit frustration by driving different vibrational modes and
adiabatically prepare the corresponding ground states. The experimentally
measured ground states are consistent with the theoretical predictions and are
highly degenerate for geometrically frustrated spin models in two dimensions.
Quantum coherence of the ground states is probed by reversing the time
evolution of the B-field to the initial value and then measuring the extent to
which the remaining state coincides with the initial state. Our results open
the door for quantum simulations with 2D ion crystals.


title:
Quantum gradient descent algorithms for nonequilibrium steady states and
  linear algebraic systems

abstract:
  The gradient descent approach is the key ingredient in variational quantum
algorithms and machine learning tasks, which is an optimization algorithm for
finding a local minimum of an objective function. The quantum versions of
gradient descent have been investigated and implemented in calculating
molecular ground states and optimizing polynomial functions. Based on the
quantum gradient descent algorithm and Choi-Jamiolkowski isomorphism, we
present approaches to simulate efficiently the nonequilibrium steady states of
Markovian open quantum many-body systems. Two strategies are developed to
evaluate the expectation values of physical observables on the nonequilibrium
steady states. Moreover, we adapt the quantum gradient descent algorithm to
solve linear algebra problems including linear systems of equations and
matrix-vector multiplications, by converting these algebraic problems into the
simulations of closed quantum systems with well-defined Hamiltonians. Detailed
examples are given to test numerically the effectiveness of the proposed
algorithms for the dissipative quantum transverse Ising models and
matrix-vector multiplications.


title:
Enabling Capabilities for Infrastructure and Workforce in Electronics
  and ASICs

abstract:
  The impressive progress in data rate capabilities, pattern recognition, and
spatial resolution of current detectors in experimental particle physics has
been possible thanks to the availability of sophisticated analog processors
combined with complex digital signal processors implemented in custom made
front end ASICs. The demands in terms of performance and increasingly smaller
feature sizes are significant. In order to meet these instrumentation
challenges, we need to educate and retain a skilled work force, as well as
aggregate design tools and acquired knowledge. We propose a multifaceted
initiative to ensure that these needs are met.


title: Guided Co-Modulated GAN for 360{\deg} Field of View Extrapolation

abstract:
  We propose a method to extrapolate a 360{\deg} field of view from a single
image that allows for user-controlled synthesis of the out-painted content. To
do so, we propose improvements to an existing GAN-based in-painting
architecture for out-painting panoramic image representation. Our method
obtains state-of-the-art results and outperforms previous methods on standard
image quality metrics. To allow controlled synthesis of out-painting, we
introduce a novel guided co-modulation framework, which drives the image
generation process with a common pretrained discriminative model. Doing so
maintains the high visual quality of generated panoramas while enabling
user-controlled semantic content in the extrapolated field of view. We
demonstrate the state-of-the-art results of our method on field of view
extrapolation both qualitatively and quantitatively, providing thorough
analysis of our novel editing capabilities. Finally, we demonstrate that our
approach benefits the photorealistic virtual insertion of highly glossy objects
in photographs.


title:
Long time asymptotic behavior for the nonlocal mKdV equation in
  space-time solitonic regions-II

abstract:
  We study the long time asymptotic behavior for the Cauchy problem of an
integrable real nonlocal mKdV equation with nonzero initial data in the
solitonic regions \begin{align*} &q_t(x,t)-6\sigma
q(x,t)q(-x,-t)q_{x}(x,t)+q_{xxx}(x,t)=0, &q(x,0)=q_{0}(x),\ \ \lim_{x\to
\pm\infty} q_{0}(x)=q_{\pm}, \end{align*} where $|q_{\pm}|=1$ and $q_{+}=\delta
q_{-}$, $\sigma\delta=-1$. In our previous article, we have obtained long time
asymptotics for the nonlocal mKdV equation in the solitonic region $-6<\xi<6$
with $\xi=\frac{x}{t}$. In this paper, we calculate the asymptotic expansion of
the solution $q(x,t)$ for other solitonic regions $\xi<-6$ and $\xi>6$. Based
on the Riemann-Hilbert problem of the the Cauchy problem, further using the
$\bar{\partial}$ steepest descent method, we derive different long time
asymptotic expansions of the solution $q(x,t)$ in above two different
space-time solitonic regions. In the region $\xi<-6$, phase function
$\theta(z)$ has four stationary phase points on the $\mathbb{R}$.
Correspondingly, $q(x,t)$ can be characterized with an
$\mathcal{N}(\Lambda)$-soliton on discrete spectrum, the leading order term on
continuous spectrum and an residual error term, which are affected by a
function ${\rm Im}\nu(\zeta_i)$. In the region $\xi>6$, phase function
$\theta(z)$ has four stationary phase points on $i\mathbb{R}$, the
corresponding asymptotic approximations can be characterized with an
$\mathcal{N}(\Lambda)$-soliton with diverse residual error order
$\mathcal{O}(t^{-1})$.


title:
Characterizing the Efficiency vs. Accuracy Trade-off for Long-Context
  NLP Models

abstract:
  With many real-world applications of Natural Language Processing (NLP)
comprising of long texts, there has been a rise in NLP benchmarks that measure
the accuracy of models that can handle longer input sequences. However, these
benchmarks do not consider the trade-offs between accuracy, speed, and power
consumption as input sizes or model sizes are varied. In this work, we perform
a systematic study of this accuracy vs. efficiency trade-off on two widely used
long-sequence models - Longformer-Encoder-Decoder (LED) and Big Bird - during
fine-tuning and inference on four datasets from the SCROLLS benchmark. To study
how this trade-off differs across hyperparameter settings, we compare the
models across four sequence lengths (1024, 2048, 3072, 4096) and two model
sizes (base and large) under a fixed resource budget. We find that LED
consistently achieves better accuracy at lower energy costs than Big Bird. For
summarization, we find that increasing model size is more energy efficient than
increasing sequence length for higher accuracy. However, this comes at the cost
of a large drop in inference speed. For question answering, we find that
smaller models are both more efficient and more accurate due to the larger
training batch sizes possible under a fixed resource budget.


title:
Identifying and Measuring Token-Level Sentiment Bias in Pre-trained
  Language Models with Prompts

abstract:
  Due to the superior performance, large-scale pre-trained language models
(PLMs) have been widely adopted in many aspects of human society. However, we
still lack effective tools to understand the potential bias embedded in the
black-box models. Recent advances in prompt tuning show the possibility to
explore the internal mechanism of the PLMs. In this work, we propose two
token-level sentiment tests: Sentiment Association Test (SAT) and Sentiment
Shift Test (SST) which utilize the prompt as a probe to detect the latent bias
in the PLMs. Our experiments on the collection of sentiment datasets show that
both SAT and SST can identify sentiment bias in PLMs and SST is able to
quantify the bias. The results also suggest that fine-tuning can possibly
augment the existing bias in PLMs.


title:
Gradient estimates for the insulated conductivity problem in the
  presence of $m$-convex inclusions. II

abstract:
  In this paper, the insulated conductivity model with two touching or
close-to-touching inclusions is considered in $\mathbb{R}^{d}$ with $d\geq3$.
We establish the pointwise upper bounds on the gradient of the solution for the
generalized $m$-convex inclusions under these two cases with $m\geq2$, which
show that the singular behavior of the gradient in the thin gap between two
inclusions is described by the first non-zero eigenvalue of an elliptic
operator of divergence form on $\mathbb{S}^{d-2}$. Finally, the sharpness of
the estimates is also proved for two touching axisymmetric insulators,
especially including curvilinear cubes.


title: The training response law explains how deep neural networks learn

abstract:
  Deep neural network is the widely applied technology in this decade. In spite
of the fruitful applications, the mechanism behind that is still to be
elucidated. We study the learning process with a very simple supervised
learning encoding problem. As a result, we found a simple law, in the training
response, which describes neural tangent kernel. The response consists of a
power law like decay multiplied by a simple response kernel. We can construct a
simple mean-field dynamical model with the law, which explains how the network
learns. In the learning, the input space is split into sub-spaces along
competition between the kernels. With the iterated splits and the aging, the
network gets more complexity, but finally loses its plasticity.


title:
Unsupervised Probabilistic Models for Sequential Electronic Health
  Records

abstract:
  We develop an unsupervised probabilistic model for heterogeneous Electronic
Health Record (EHR) data. Utilizing a mixture model formulation, our approach
directly models sequences of arbitrary length, such as medications and
laboratory results. This allows for subgrouping and incorporation of the
dynamics underlying heterogeneous data types. The model consists of a layered
set of latent variables that encode underlying structure in the data. These
variables represent subject subgroups at the top layer, and unobserved states
for sequences in the second layer. We train this model on episodic data from
subjects receiving medical care in the Kaiser Permanente Northern California
integrated healthcare delivery system. The resulting properties of the trained
model generate novel insight from these complex and multifaceted data. In
addition, we show how the model can be used to analyze sequences that
contribute to assessment of mortality likelihood.


title:
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable
  Selection with Theoretical Guarantees

abstract:
  We develop a simple and unified framework for nonlinear variable selection
that incorporates model uncertainty and is compatible with a wide range of
machine learning models (e.g., tree ensembles, kernel methods and neural
network). In particular, for a learned nonlinear model $f(\mathbf{x})$, we
consider quantifying the importance of an input variable $\mathbf{x}^j$ using
the integrated gradient measure $\psi_j = \Vert \frac{\partial}{\partial
\mathbf{x}^j} f(\mathbf{x})\Vert^2_2$. We then (1) provide a principled
approach for quantifying variable selection uncertainty by deriving its
posterior distribution, and (2) show that the approach is generalizable even to
non-differentiable models such as tree ensembles. Rigorous Bayesian
nonparametric theorems are derived to guarantee the posterior consistency and
asymptotic uncertainty of the proposed approach. Extensive simulation confirms
that the proposed algorithm outperforms existing classic and recent variable
selection methods.


title:
Systematic analysis of the proton mass radius based on photoproduction
  of vector charmoniums

abstract:
  In this work, the cross-section of the reaction $\gamma p \rightarrow
V(J/\psi,\psi(2S)) p$ from the production threshold to medium energy is studied
and systematically analyzed within two gluon exchange model. The obtained
numerical results are in agreement with experimental data and other theoretical
predictions. Under the assumption of the scalar form factor of dipole form, the
value of proton mass radius is calculated as $0.55\pm 0.09$ fm and $ 0.77\pm
0.12$ fm from the fit to the predicted $J/\psi $ and $\psi(2S)$ differential
cross-section, respectively. Finally the average value of proton mass radius is
estimated to be $\sqrt{\left<R^2_m \right>}=0.67\pm 0.11$ fm. Moreover, one
find that extracting mass radius from the near-threshold differential
cross-section of heavy quarkoniums is always affected by large $|t|_{min}$.
These obtained results may provide important theoretical reference for
understanding of nucleon structure and future relevant experiments.


title:
On extracting the positions of multiple unknown cracks that occur on the
  junction line of two elastic plates

abstract:
  This paper is concerned with the reconstruction issue of an inverse crack
problem in a two-dimensional bounded domain which may have a possible
application to the nondestructive evaluation of materials. It is assumed that
the domain consists of two elastic plates welded together and has some unknown
cracks on the junction line and the governing equation of in-plane displacement
is Navier's equation. The problem is to extract information about the location
of cracks from the observation data which is a single set of a loading surface
traction and the resulted in-plane displacement field on the boundary of the
domain. It is shown that the enclosure method combined with the Kelvin
transform yields explicit extraction formulae of such information from the
observation data.


title:
Large-scale Dynamics of Winds Driven by Line Force from a Thin Accretion
  Disk

abstract:
  Winds play a significant role in active galactic nuclei feedback process.
Previous simulations studying winds only focus on a small dynamical range.
Therefore, it is unknown how far the winds can go and what the properties of
the winds will be if they can move to large radii. We perform simulations to
study the large scale dynamics of winds driven by line force. We find that the
properties of the winds depend on both black hole mass ($M_{BH}$) and accretion
disk luminosity. When the accretion disk luminosity is $0.6L_{edd}$ ($L_{edd}$
being Eddington luminosity), independent of $M_{BH}$, the winds have kinetic
energy flux exceeding $1\% L_{edd}$ and can escape from the black hole
potential. For the case with the accretion disk luminosity equaling
0.3$L_{edd}$, the strength of the winds decreases with the decrease of
$M_{BH}$. If $M_{BH}$ decreases from $10^9$ to $10^6$ solar mass ($M_\odot$),
the winds kinetic energy flux decreases from $\sim 0.01 L_{edd}$ to $ \sim
10^{-6} L_{edd}$. In case of $M_{BH}\geq 10^7 M_\odot$, winds can escape from
black hole potential. In the case of $M_{BH}=10^6 M_\odot$, the winds can not
escape. We find that for the ultra-fast winds observed in hard X-ray bands
(\citealt{Gofford et al. 2015}), the observed dependence of the mass flux and
the kinetic energy flux on accretion disk luminosity can be well produced by
line force driven winds model. We also find that the properties of the
ultra-fast winds observed in soft X-ray bands can be explained by the line
force driven winds model.


title: A Geometric Approach on Circular Photon Orbits and Black Hole Shadow

abstract:
  Circular photon orbit and black hole shadow are significantly important
issues in physics and astronomy, and a number of breakthroughs have been
witnessed in recent years. Conventionally, the stable and unstable circular
photon orbits are obtained using the effective potential of test particles
moving in black hole spacetime. In this work, a pure geometric approach is
developed to calculate these circular photon orbits and black hole shadow
radius. Furthermore, it can be proved that our geometric approach is completely
equivalent to the conventional approach based on effective potentials of test
particles.


title: Cartan Connection for h-Matsumoto change

abstract:
  In the present paper, we have studied the Matsumoto change
$\overline{L}(x,y)= \frac{L^{2}(x,y)}{L(x,y) - \beta(x,y)} $ with an
\textsl{h-}vector $b_{i}(x,y)$. We have derived some fundamental tensors for
this transformation. We have also obtained the necessary and sufficient
condition for which the Cartan connection coefficients for both the spaces
$F^{n}=(M^n,L)$ and $\overline{F}^{\,n}=(M^{n},\overline{L})$ are same.


title:
Where to Go for the Holidays: Towards Mixed-Type Dialogs for
  Clarification of User Goals

abstract:
  Most dialog systems posit that users have figured out clear and specific
goals before starting an interaction. For example, users have determined the
departure, the destination, and the travel time for booking a flight. However,
in many scenarios, limited by experience and knowledge, users may know what
they need, but still struggle to figure out clear and specific goals by
determining all the necessary slots.
  In this paper, we identify this challenge and make a step forward by
collecting a new human-to-human mixed-type dialog corpus. It contains 5k dialog
sessions and 168k utterances for 4 dialog types and 5 domains. Within each
session, an agent first provides user-goal-related knowledge to help figure out
clear and specific goals, and then help achieve them.
  Furthermore, we propose a mixed-type dialog model with a novel Prompt-based
continual learning mechanism. Specifically, the mechanism enables the model to
continually strengthen its ability on any specific type by utilizing existing
dialog corpora effectively.


title: Dense Learning based Semi-Supervised Object Detection

abstract:
  Semi-supervised object detection (SSOD) aims to facilitate the training and
deployment of object detectors with the help of a large amount of unlabeled
data. Though various self-training based and consistency-regularization based
SSOD methods have been proposed, most of them are anchor-based detectors,
ignoring the fact that in many real-world applications anchor-free detectors
are more demanded. In this paper, we intend to bridge this gap and propose a
DenSe Learning (DSL) based anchor-free SSOD algorithm. Specifically, we achieve
this goal by introducing several novel techniques, including an Adaptive
Filtering strategy for assigning multi-level and accurate dense pixel-wise
pseudo-labels, an Aggregated Teacher for producing stable and precise
pseudo-labels, and an uncertainty-consistency-regularization term among scales
and shuffled patches for improving the generalization capability of the
detector. Extensive experiments are conducted on MS-COCO and PASCAL-VOC, and
the results show that our proposed DSL method records new state-of-the-art SSOD
performance, surpassing existing methods by a large margin. Codes can be found
at \textcolor{blue}{https://github.com/chenbinghui1/DSL}.


title: Deformed in-medium similarity renormalization group

abstract:
  We have developed an {\it ab initio} deformed in-medium similarity
renormalization group (IMSRG) for open-shell nuclei. This is a single-reference
IMSRG in deformed Hartree-Fock (HF) basis. Deformed wave functions are more
efficient in describing deformed nuclei. The broken spherical symmetry needs to
be restored by angular momentum projection, which is computational expensive.
The angular momentum mainly capture the static correlations and can be
estimated by the projection of the HF state. In this work, we do deformed IMSRG
calculation and add the correlation energy from projected HF as a leading order
approximation. As the test ground, we have calculated the deformed $^{8,10}\rm
Be$ isotopes with the optimized chiral interaction NNLO$_{\rm opt}$. The
results are benchmarked with the no-core shell model and valence space IMSRG
calculations. Then we systematically investigated the ground-state energies and
charge radii of even-even isotopes from light beryllium to medium-mass
magnesium. The calculated energies are extrapolated to infinite basis space by
an exponential form, and compared with the extrapolated valence-space IMSRG
results and experimental data available.


title:
Improving Cross-Modal Understanding in Visual Dialog via Contrastive
  Learning

abstract:
  Visual Dialog is a challenging vision-language task since the visual dialog
agent needs to answer a series of questions after reasoning over both the image
content and dialog history. Though existing methods try to deal with the
cross-modal understanding in visual dialog, they are still not enough in
ranking candidate answers based on their understanding of visual and textual
contexts. In this paper, we analyze the cross-modal understanding in visual
dialog based on the vision-language pre-training model VD-BERT and propose a
novel approach to improve the cross-modal understanding for visual dialog,
named ICMU. ICMU enhances cross-modal understanding by distinguishing different
pulled inputs (i.e. pulled images, questions or answers) based on four-way
contrastive learning. In addition, ICMU exploits the single-turn visual
question answering to enhance the visual dialog model's cross-modal
understanding to handle a multi-turn visually-grounded conversation.
Experiments show that the proposed approach improves the visual dialog model's
cross-modal understanding and brings satisfactory gain to the VisDial dataset.


title:
General inertial smoothing proximal gradient algorithm for the
  relaxation of matrix rank minimization problem

abstract:
  We consider the exact continuous relaxation model of matrix rank minimization
problem proposed by Yu and Zhang (Comput.Optim.Appl. 1-20, 2022). Motivated by
the inertial techinique, we propose a general inertial smoothing proximal
gradient algorithm(GIMSPG) for this kind of problems. It is shown that the
singular values of any accumulation point have a common support set and the
nonzero singular values have a unified lower bound. Besides, the zero singular
values of the accumulation point can be achieved within finite iterations.
Moreover, we prove that any accumulation point of the sequence generated by the
GIMSPG algorithm is a lifted stationary point of the continuous relaxation
model under the flexible parameter constraint. Finally, we carry out numerical
experiments on random data and image data respectively to illustrate the
efficiency of the GIMSPG algorithm.


title:
On Variants of Root Normalised Order-aware Divergence and a Divergence
  based on Kendall's Tau

abstract:
  This paper reports on a follow-up study of the work reported in Sakai, which
explored suitable evaluation measures for ordinal quantification tasks. More
specifically, the present study defines and evaluates, in addition to the
quantification measures considered earlier, a few variants of an ordinal
quantification measure called Root Normalised Order-aware Divergence (RNOD), as
well as a measure which we call Divergence based on Kendall's $\tau$ (DNKT).
The RNOD variants represent alternative design choices based on the idea of
Sakai's Distance-Weighted sum of squares (DW), while DNKT is designed to ensure
that the system's estimated distribution over classes is faithful to the target
priorities over classes. As this Priority Preserving Property (PPP) of DNKT may
be useful in some applications, we also consider combining some of the existing
quantification measures with DNKT. Our experiments with eight ordinal
quantification data sets suggest that the variants of RNOD do not offer any
benefit over the original RNOD at least in terms of system ranking consistency,
i.e., robustness of the system ranking to the choice of test data. Of all
ordinal quantification measures considered in this study (including Normalised
Match Distance, a.k.a. Earth Mover's Distance), RNOD is the most robust measure
overall. Hence the design choice of RNOD is a good one from this viewpoint.
Also, DNKT is the worst performer in terms of system ranking consistency.
Hence, if DNKT seems appropriate for a task, sample size design should take its
statistical instability into account.


title:
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External
  Data and Fine-Tuning Make a Difference

abstract:
  Few-shot learning (FSL) is an important and topical problem in computer
vision that has motivated extensive research into numerous methods spanning
from sophisticated meta-learning methods to simple transfer learning baselines.
We seek to push the limits of a simple-but-effective pipeline for more
realistic and practical settings of few-shot image classification. To this end,
we explore few-shot learning from the perspective of neural network
architecture, as well as a three stage pipeline of network updates under
different data supplies, where unsupervised external data is considered for
pre-training, base categories are used to simulate few-shot tasks for
meta-training, and the scarcely labelled data of an novel task is taken for
fine-tuning. We investigate questions such as: (1) How pre-training on external
data benefits FSL? (2) How state-of-the-art transformer architectures can be
exploited? and (3) How fine-tuning mitigates domain shift? Ultimately, we show
that a simple transformer-based pipeline yields surprisingly good performance
on standard benchmarks such as Mini-ImageNet, CIFAR-FS, CDFSL and Meta-Dataset.
Our code and demo are available at https://hushell.github.io/pmf.


title: Congestion Reduction via Personalized Incentives

abstract:
  With rapid population growth and urban development, traffic congestion has
become an inescapable issue, especially in large cities. Many congestion
reduction strategies have been proposed in the past, ranging from roadway
extension to transportation demand management. In particular, congestion
pricing schemes have been used as negative reinforcements for traffic control.
In this project, we study an alternative approach of offering positive
incentives to drivers to take different routes. More specifically, we propose
an algorithm to reduce traffic congestion and improve routing efficiency via
offering personalized incentives to drivers. We exploit the wide-accessibility
of smart devices to communicate with drivers and develop an incentive offering
mechanism using individuals' preferences and aggregate traffic information. The
incentives are offered after solving a large-scale optimization problem in
order to minimize the total travel time (or minimize any cost function of the
network such as total Carbon emission). Since this massive size optimization
problem needs to be solved continually in the network, we developed a
distributed computational approach. The proposed distributed algorithm is
guaranteed to converge under a mild set of assumptions that are verified with
real data. We evaluated the performance of our algorithm using traffic data
from the Los Angeles area. Our experiments show congestion reduction of up to
11% in arterial roads and highways.


title: Dynamical mean-field theory study of a ferromagnetic CrI3 monolayer

abstract:
  We have employed one of the well-known many-body techniques, density
functional theory plus dynamical mean-field theory (DFT + DMFT), to investigate
the electronic structure of ferromagnetic monolayer CrI3 as a function of
temperature and hole-doping concentration. The computed magnetic susceptibility
follows the Curie's law, indicating that the ferromagnetism of monolayer CrI3
originates from localized magnetic moments of Cr atoms rather than Stoner-type
itinerant ones. The DFT + DMFT calculations show a different coherent
temperature for each spin component, demonstrating apparent strong
spin-dependent electronic correlation effects in monolayer CrI3. Furthermore,
we have explored the doping-dependent electronic structure of monolayer CrI3
and found that its electronic and magnetic properties are easily tunable by the
hole-doping.


title:
Ensemble diverse hypotheses and knowledge distillation for unsupervised
  cross-subject adaptation

abstract:
  Recognizing human locomotion intent and activities is important for
controlling the wearable robots while walking in complex environments. However,
human-robot interface signals are usually user-dependent, which causes that the
classifier trained on source subjects performs poorly on new subjects. To
address this issue, this paper designs the ensemble diverse hypotheses and
knowledge distillation (EDHKD) method to realize unsupervised cross-subject
adaptation. EDH mitigates the divergence between labeled data of source
subjects and unlabeled data of target subjects to accurately classify the
locomotion modes of target subjects without labeling data. Compared to previous
domain adaptation methods based on the single learner, which may only learn a
subset of features from input signals, EDH can learn diverse features by
incorporating multiple diverse feature generators and thus increases the
accuracy and decreases the variance of classifying target data, but it
sacrifices the efficiency. To solve this problem, EDHKD (student) distills the
knowledge from the EDH (teacher) to a single network to remain efficient and
accurate. The performance of the EDHKD is theoretically proved and
experimentally validated on a 2D moon dataset and two public human locomotion
datasets. Experimental results show that the EDHKD outperforms all other
methods. The EDHKD can classify target data with 96.9%, 94.4%, and 97.4%
average accuracy on the above three datasets with a short computing time (1
ms). Compared to a benchmark (BM) method, the EDHKD increases 1.3% and 7.1%
average accuracy for classifying the locomotion modes of target subjects. The
EDHKD also stabilizes the learning curves. Therefore, the EDHKD is significant
for increasing the generalization ability and efficiency of the human intent
prediction and human activity recognition system, which will improve
human-robot interactions.


title:
Saga: A Platform for Continuous Construction and Serving of Knowledge At
  Scale

abstract:
  We introduce Saga, a next-generation knowledge construction and serving
platform for powering knowledge-based applications at industrial scale. Saga
follows a hybrid batch-incremental design to continuously integrate billions of
facts about real-world entities and construct a central knowledge graph that
supports multiple production use cases with diverse requirements around data
freshness, accuracy, and availability. In this paper, we discuss the unique
challenges associated with knowledge graph construction at industrial scale,
and review the main components of Saga and how they address these challenges.
Finally, we share lessons-learned from a wide array of production use cases
powered by Saga.


title: Bayesian information engine that optimally exploits noisy measurements

abstract:
  We have experimentally realized an information engine consisting of an
optically trapped, heavy bead in water. The device raises the trap center after
a favorable "up" thermal fluctuation, thereby increasing the bead's average
gravitational potential energy. In the presence of measurement noise, poor
feedback decisions degrade its performance; below a critical signal-to-noise
ratio, the engine shows a phase transition and cannot store any gravitational
energy. However, using Bayesian estimates of the bead's position to make
feedback decisions can extract gravitational energy at all measurement noise
strengths and has maximum performance benefit at the critical signal-to-noise
ratio.


title: MetaSets: Meta-Learning on Point Sets for Generalizable Representations

abstract:
  Deep learning techniques for point clouds have achieved strong performance on
a range of 3D vision tasks. However, it is costly to annotate large-scale point
sets, making it critical to learn generalizable representations that can
transfer well across different point sets. In this paper, we study a new
problem of 3D Domain Generalization (3DDG) with the goal to generalize the
model to other unseen domains of point clouds without any access to them in the
training process. It is a challenging problem due to the substantial geometry
shift from simulated to real data, such that most existing 3D models
underperform due to overfitting the complete geometries in the source domain.
We propose to tackle this problem via MetaSets, which meta-learns point cloud
representations from a group of classification tasks on carefully-designed
transformed point sets containing specific geometry priors. The learned
representations are more generalizable to various unseen domains of different
geometries. We design two benchmarks for Sim-to-Real transfer of 3D point
clouds. Experimental results show that MetaSets outperforms existing 3D deep
learning methods by large margins.


title:
Structural Analysis of Branch-and-Cut and the Learnability of Gomory
  Mixed Integer Cuts

abstract:
  The incorporation of cutting planes within the branch-and-bound algorithm,
known as branch-and-cut, forms the backbone of modern integer programming
solvers. These solvers are the foremost method for solving discrete
optimization problems and thus have a vast array of applications in machine
learning, operations research, and many other fields. Choosing cutting planes
effectively is a major research topic in the theory and practice of integer
programming. We conduct a novel structural analysis of branch-and-cut that pins
down how every step of the algorithm is affected by changes in the parameters
defining the cutting planes added to the input integer program. Our main
application of this analysis is to derive sample complexity guarantees for
using machine learning to determine which cutting planes to apply during
branch-and-cut. These guarantees apply to infinite families of cutting planes,
such as the family of Gomory mixed integer cuts, which are responsible for the
main breakthrough speedups of integer programming solvers. We exploit geometric
and combinatorial structure of branch-and-cut in our analysis, which provides a
key missing piece for the recent generalization theory of branch-and-cut.


title:
Rapid 3D Multiparametric Mapping of Brain Metastases with Deep
  Learning-Based Phase-Sensitive MR Fingerprinting

abstract:
  In MR fingerprinting (MRF) reconstruction, measured data is pattern-matched
to simulated signals to extract quantitative tissue parameters. A critical
drawback to this approach is the exponentially increasing compute time for
mapping of multiple parameters. Previously, a deep learning (DL) reconstruction
method called DRONE was shown to overcome this constraint by mapping the
magnitude time-series signal to the underlying tissue parameters. However,
relaxometry from magnitude images is susceptible to errors arising from
ambiguities in the zero crossing of the signal or the non-zero noise mean. The
aim of this study is to develop rapid acquisition and quantification methods to
enable accurate multiparametric tissue mapping from complex data. An optimized
EPI based MRF sequence is developed along with a novel phasesensitive DL
quantification allowing the use of real-valued neural networks to reconstruct
complex measured data and providing an additional quantitative map of the
phase. Phantom experiments demonstrate the accuracy of the proposed approach. A
comparison to previous DRONE methods in a healthy subject shows improved
fidelity to known T1 and T2 values for the phase-sensitive approach. By
processing the estimated phase map with conventional quantitative
susceptibility mapping algorithms, we demonstrate the feasibility of
simultaneous quantification of proton density, T1, T2, transmitter B1+ field
and the quantitative susceptibility maps. In vivo experiments in a healthy
volunteer and a subject with metastatic brain cancer are used to illustrate
potential applications of this technology for treatment response assessment and
tumor characterization.


title: Feature Compression for Rate Constrained Object Detection on the Edge

abstract:
  Recent advances in computer vision has led to a growth of interest in
deploying visual analytics model on mobile devices. However, most mobile
devices have limited computing power, which prohibits them from running large
scale visual analytics neural networks. An emerging approach to solve this
problem is to offload the computation of these neural networks to computing
resources at an edge server. Efficient computation offloading requires
optimizing the trade-off between multiple objectives including compressed data
rate, analytics performance, and computation speed. In this work, we consider a
"split computation" system to offload a part of the computation of the YOLO
object detection model. We propose a learnable feature compression approach to
compress the intermediate YOLO features with light-weight computation. We train
the feature compression and decompression module together with the YOLO model
to optimize the object detection accuracy under a rate constraint. Compared to
baseline methods that apply either standard image compression or learned image
compression at the mobile and perform image decompression and YOLO at the edge,
the proposed system achieves higher detection accuracy at the low to medium
rate range. Furthermore, the proposed system requires substantially lower
computation time on the mobile device with CPU only.


title:
Machine Learning Approaches to Automated Mechanism Design for Public
  Project Problem

abstract:
  Mechanism design is a central research branch in microeconomics. An effective
mechanism can significantly improve performance and efficiency of social
decisions under desired objectives, such as to maximize social welfare or to
maximize revenue for agents. However, mechanism design is challenging for many
common models including the public project problem model which we study in this
thesis. A typical public project problem is a group of agents crowdfunding a
public project (e.g., building a bridge). The mechanism will decide the payment
and allocation for each agent (e.g., how much the agent pays, and whether the
agent can use it) according to their valuations. The mechanism can be applied
to various economic scenarios, including those related to cyber security. There
are different constraints and optimized objectives for different public project
scenarios (sub-problems), making it unrealistic to design a universal mechanism
that fits all scenarios, and designing mechanisms for different settings
manually is a taxing job. Therefore, we explore automated mechanism design
(AMD) of public project problems under different constraints.
  In this thesis, we focus on the public project problem, which includes many
sub-problems (excludable/non-excludable, divisible/indivisible,
binary/non-binary). We study the classical public project model and extend this
model to other related areas such as the zero-day exploit markets. For
different sub-problems of the public project problem, we adopt different novel
machine learning techniques to design optimal or near-optimal mechanisms via
automated mechanism design. We evaluate our mechanisms by theoretical analysis
or experimentally comparing our mechanisms against existing mechanisms. The
experiments and theoretical results show that our mechanisms are better than
state-of-the-art automated or manual mechanisms.


title:
XDBERT: Distilling Visual Information to BERT from Cross-Modal Systems
  to Improve Language Understanding

abstract:
  Transformer-based models are widely used in natural language understanding
(NLU) tasks, and multimodal transformers have been effective in visual-language
tasks. This study explores distilling visual information from pretrained
multimodal transformers to pretrained language encoders. Our framework is
inspired by cross-modal encoders' success in visual-language tasks while we
alter the learning objective to cater to the language-heavy characteristics of
NLU. After training with a small number of extra adapting steps and finetuned,
the proposed XDBERT (cross-modal distilled BERT) outperforms pretrained-BERT in
general language understanding evaluation (GLUE), situations with adversarial
generations (SWAG) benchmarks, and readability benchmarks. We analyze the
performance of XDBERT on GLUE to show that the improvement is likely visually
grounded.


title:
Stochastic Search for a Parametric Cost Function Approximation: Energy
  storage with rolling forecasts

abstract:
  Rolling forecasts have been almost overlooked in the renewable energy storage
literature. In this paper, we provide a new approach for handling uncertainty
not just in the accuracy of a forecast, but in the evolution of forecasts over
time. Our approach shifts the focus from modeling the uncertainty in a
lookahead model to accurate simulations in a stochastic base model. We develop
a robust policy for making energy storage decisions by creating a
parametrically modified lookahead model, where the parameters are tuned in the
stochastic base model. Since computing unbiased stochastic gradients with
respect to the parameters require restrictive assumptions, we propose a
simulation-based stochastic approximation algorithm based on numerical
derivatives to optimize these parameters. While numerical derivatives,
calculated based on the noisy function evaluations, provide biased gradient
estimates, an online variance reduction technique built in the framework of our
proposed algorithm, will enable us to control the accumulated bias errors and
establish the finite-time rate of convergence of the algorithm. Our numerical
experiments show the performance of this algorithm in finding policies
outperforming the deterministic benchmark policy.


title:
Thermal contributions to the structural phase transitions of crystalline
  aluminum under ultrahigh pressures

abstract:
  The \emph{ab initio} computations of thermodynamic properties of condensed
matters under extreme conditions is a long-time pursuit. In this work, the
pressure induced structural phase transitions of crystalline aluminum up to
$600$ GPa at room temperature is investigated by the criterion of free energy
derived directly from the partition function formulated in ensemble theory with
the interatomic interactions characterized by density functional theory
computations. The transition pressures of the
FCC$\rightarrow$HCP$\rightarrow$BCC phase transition are determined at $194$
and $361$ GPa, the axial ratio of the HCP structure is found to be equal to
$1.62$ and the discontinuities in the equations of states are confirmed to be
associated with $-0.674\%$ and $-0.897\%$ volume changes, which all validate
the accuracy of measurements by one of the recent experiments but disagree with
the results by others. Compared with the results obtained by the widely used
criterion of enthalpy at $0$K, this work shows that the thermal contributions
play a nontrivial role for the structural stability of aluminum even under a
room-temperature circumstance.


title:
A review of path following control strategies for autonomous robotic
  vehicles: theory, simulations, and experiments

abstract:
  This article presents an in-depth review of the topic of path following for
autonomous robotic vehicles, with a specific focus on vehicle motion in two
dimensional space (2D). From a control system standpoint, path following can be
formulated as the problem of stabilizing a path following error system that
describes the dynamics of position and possibly orientation errors of a vehicle
with respect to a path, with the errors defined in an appropriate reference
frame. In spite of the large variety of path following methods described in the
literature we show that, in principle, most of them can be categorized in two
groups: stabilization of the path following error system expressed either in
the vehicle's body frame or in a frame attached to a "reference point" moving
along the path, such as a Frenet-Serret (F-S) frame or a Parallel Transport
(P-T) frame. With this observation, we provide a unified formulation that is
simple but general enough to cover many methods available in the literature. We
then discuss the advantages and disadvantages of each method, comparing them
from the design and implementation standpoint. We further show experimental
results of the path following methods obtained from field trials testing with
under-actuated and fully-actuated autonomous marine vehicles. In addition, we
introduce open-source Matlab and Gazebo/ROS simulation toolboxes that are
helpful in testing path following methods prior to their integration in the
combined guidance, navigation, and control systems of autonomous vehicles.


title:
Upper and lower $L^2$-decay bounds for a class of derivative nonlinear
  Schr\"odinger equations

abstract:
  We consider the initial value problem for cubic derivative nonlinear
Schr\"odinger equations possessing weakly dissipative structure in one space
dimension. We show that the small data solution decays like $O((\log
t)^{-1/4})$ in $L^2$ as $t\to +\infty$. Furthermore, we find that this
$L^2$-decay rate is optimal by giving a lower estimate of the same order.


title:
Graph Pooling for Graph Neural Networks: Progress, Challenges, and
  Opportunities

abstract:
  Graph neural networks have emerged as a leading architecture for many
graph-level tasks such as graph classification and graph generation with a
notable improvement. Among these tasks, graph pooling is an essential component
of graph neural network architectures for obtaining a holistic graph-level
representation of the entire graph. Although a great variety of methods have
been proposed in this promising and fast-developing research field, to the best
of our knowledge, little effort has been made to systematically summarize these
methods. To set the stage for the development of future works, in this paper,
we attempt to fill this gap by providing a broad review of recent methods on
graph pooling. Specifically, 1) we first propose a taxonomy of existing graph
pooling methods and provide a mathematical summary for each category; 2) next,
we provide an overview of the libraries related to graph pooling, including the
commonly used datasets, model architectures for downstream tasks, and
open-source implementations; 3) then, we further outline in brief the
applications that incorporate the idea of graph pooling in a number of domains;
4) and finally, we discuss some critical challenges faced by the current
studies and share our insights on potential directions for improving graph
pooling in the future.


title: Quantum cloning of steering

abstract:
  The property of steering in a global state allows an observer to affect the
postmeasured states of a subsystem by performing different local measurements
on the other part. We show that, in general, this property cannot be perfectly
cloned by any joint operation between a steered subsystem and a third system.
The perfect cloning is viable if and only if the initial state is of
zero-discord. We also investigate the process of cloning the steered qubit of a
Bell state by using a universal cloning machine. The quantum steering, as a
type of quantum correlation existing in the states without a local-hidden-state
model, can be found in both of the copy subsystems. This breaks the recent
conclusion of no-cloning of quantum steering in [npj Quantum Inf. 2, 16020
(2016)] based on a mutual information criterion for quantum steering.


title:
Quantum phases of spin-orbital-angular-momentum coupled bosonic gases in
  optical lattices

abstract:
  Spin-orbit coupling plays an important role in understanding exotic quantum
phases. In this work, we present a scheme to combine
spin-orbital-angular-momentum (SOAM) coupling and strong correlations in
ultracold atomic gases. Essential ingredients of this setting is the interplay
of SOAM coupling and Raman-induced spin-flip hopping, engineered by lasers that
couples different hyperfine spin states. In the presence of SOAM coupling only,
we find rich quantum phases in the Mott-insulating regime, which support
different types of spin textures such as spin vortex and nonrotating Mott core.
Based on an effective exchange model, we find that these competing spin
textures are a result of the interplay of Dzyaloshinskii-Moriya and Heisenberg
exchange interactions. In the presence of both SOAM coupling and Raman-induced
spin-flip hopping, more many-body phases appear, including
canted-antiferromagnetic and stripe phases. Our prediction suggests that SOAM
coupling could induce rich exotic many-body phases in the strongly interacting
regime.