title:
Onception: Active Learning with Expert Advice for Real World Machine
  Translation

abstract:
  Active learning can play an important role in low-resource settings (i.e.,
where annotated data is scarce), by selecting which instances may be more
worthy to annotate. Most active learning approaches for Machine Translation
assume the existence of a pool of sentences in a source language, and rely on
human annotators to provide translations or post-edits, which can still be
costly. In this article, we assume a real world human-in-the-loop scenario in
which: (i) the source sentences may not be readily available, but instead
arrive in a stream; (ii) the automatic translations receive feedback in the
form of a rating, instead of a correct/edited translation, since the
human-in-the-loop might be a user looking for a translation, but not be able to
provide one. To tackle the challenge of deciding whether each incoming pair
source-translations is worthy to query for human feedback, we resort to a
number of stream-based active learning query strategies. Moreover, since we not
know in advance which query strategy will be the most adequate for a certain
language pair and set of Machine Translation models, we propose to dynamically
combine multiple strategies using prediction with expert advice. Our
experiments show that using active learning allows to converge to the best
Machine Translation systems with fewer human interactions. Furthermore,
combining multiple strategies using prediction with expert advice often
outperforms several individual active learning strategies with even fewer
interactions.


title: Multiphase curved boundary condition in lattice Boltzmann method

abstract:
  The boundary treatment is fundamental for modeling fluid flows; especially,
the curved boundary condition, which accurately describes the boundary
geometry, can effectively improve the accuracy of simulations with complex
boundaries. However, although curved boundary conditions have been successfully
applied in single-phase lattice Boltzmann simulations, they cause dramatical
mass leakage or increase when it is directly used for multiphase flows. We find
that the principal reason is the absence of nonideal effect, followed by the
calculation error. In this paper, incorporating the nonideal effect into the
linear interpolation scheme and compensating the interpolating error, we
propose a multiphase curved boundary condition to treat wetting boundaries with
complex geometries. A series of static and dynamic multiphase simulations with
large density ratio verify that the present scheme is accurate and ensures mass
conservation.


title: Reduced-quaternion inframonogenic functions on the ball

abstract:
  A function $f$ from a domain in $\mathbb{R}^3$ to the quaternions is said to
be inframonogenic if $\overline{\partial}\, f\overline{\partial} =0$, where
$\overline{\partial} = \partial/\partial x_0+ (\partial/\partial
x_1)e_1+(\partial/\partial x_2) e_2$. All inframonogenic functions are
biharmonic. In the context of functions $f=f_0+f_1e_1+f_2e_2$ taking values in
the reduced quaternions, we show that the homogeneous polynomials of degree $n$
form a subspace of dimension $6n+3$. We use them to construct an explicit,
computable orthogonal basis for the Hilbert space of square-integrable
inframonogenic functions defined in the ball in $\mathbb{R}^3$.


title: ReVar: Strengthening Policy Evaluation via Reduced Variance Sampling

abstract:
  This paper studies the problem of data collection for policy evaluation in
Markov decision processes (MDPs). In policy evaluation, we are given a target
policy and asked to estimate the expected cumulative reward it will obtain in
an environment formalized as an MDP. We develop theory for optimal data
collection within the class of tree-structured MDPs by first deriving an oracle
data collection strategy that uses knowledge of the variance of the reward
distributions. We then introduce the Reduced Variance Sampling (ReVar)
algorithm that approximates the oracle strategy when the reward variances are
unknown a priori and bound its sub-optimality compared to the oracle strategy.
Finally, we empirically validate that ReVar leads to policy evaluation with
mean squared error comparable to the oracle strategy and significantly lower
than simply running the target policy.


title:
Revealing the Excitation Causality between Climate and Political
  Violence via a Neural Forward-Intensity Poisson Process

abstract:
  The causal mechanism between climate and political violence is fraught with
complex mechanisms. Current quantitative causal models rely on one or more
assumptions: (1) the climate drivers persistently generate conflict, (2) the
causal mechanisms have a linear relationship with the conflict generation
parameter, and/or (3) there is sufficient data to inform the prior
distribution. Yet, we know conflict drivers often excite a social
transformation process which leads to violence (e.g., drought forces
agricultural producers to join urban militia), but further climate effects do
not necessarily contribute to further violence. Therefore, not only is this
bifurcation relationship highly non-linear, there is also often a lack of data
to support prior assumptions for high resolution modeling. Here, we aim to
overcome the aforementioned causal modeling challenges by proposing a neural
forward-intensity Poisson process (NFIPP) model. The NFIPP is designed to
capture the potential non-linear causal mechanism in climate induced political
violence, whilst being robust to sparse and timing-uncertain data. Our results
span 20 recent years and reveal an excitation-based causal link between extreme
climate events and political violence across diverse countries. Our
climate-induced conflict model results are cross-validated against qualitative
climate vulnerability indices. Furthermore, we label historical events that
either improve or reduce our predictability gain, demonstrating the importance
of domain expertise in informing interpretation.


title: Riemann problem of Euler equations with singular sources

abstract:
  This paper is concerned with the Riemann problem of one-dimensional Euler
equations with a singular source.
  The exact solution of this Riemann problem contains a stationary
discontinuity induced by the singular source, which is different from all the
simple waves in the Riemann solution of classical Euler equations.
  We propose an eigenvalue-based monotonicity criterion to select the physical
curve of this stationary discontinuity.
  By including this stationary discontinuity as an elementary wave, the
structure of Riemann solution becomes diverse, e.g. the number of waves is not
fixed and interactions between two waves become possible.
  Under the double CRP framework, we prove all possible structures of the
Riemann solution.


title: Fluorine Abundances in the Galactic Nuclear Star Cluster

abstract:
  Abundances of fluorine ($^{19}$F), as well as isotopic ratios of
$^{16}$O/$^{17}$O, are derived in a sample of luminous young
($\sim$10$^{7}$--10$^{8}$ yrs) red giants in the Galactic center (with
galactocentric distances ranging from 0.6--30 pc), using high-resolution
infrared spectra and vibration-rotation lines of H$^{19}$F near
$\lambda$2.3$\mu$m. Five of the six red giants are members of the Nuclear star
cluster that orbits the central supermassive black hole. Previous
investigations of the chemical evolution of $^{19}$F in Galactic thin and thick
disk stars have revealed that the nucleosynthetic origins of $^{19}$F may be
rather complex, resulting from two, or more, astrophysical sites; fluorine
abundances behave as a primary element with respect to Fe abundances for thick
disk stars and as a secondary element in thin disk stars. The Galactic center
red giants analyzed fall within the thin disk relation of F with Fe, having
near-solar, to slightly larger, abundances of Fe ($<$[Fe/H]$>$=+0.08$\pm$0.04),
with a slight enhancement of the F/Fe abundance ratio
($<$[F/Fe]$>$=+0.28$\pm$0.17). In terms of their F and Fe abundances, the
Galactic center stars follow the thin disk population, which requires an
efficient source of $^{19}$F that could be the winds from core-He burning Wolf
Rayet stars, or thermally-pulsing AGB stars, or a combination of both. The
observed increase of [F/Fe] with increasing [Fe/H] found in thin disk and
Galactic center stars is not predicted by any published chemical evolution
models that are discussed, thus a quantitative understanding of yields from the
various possible sources of $^{19}$F remains unknown.


title:
Surrogate "Level-Based" Lagrangian Relaxation for Mixed-Integer Linear
  Programming

abstract:
  Mixed-Integer Linear Programming problems, playing a prominent role in
Operations Research, are prone to the curse of dimensionality; specifically,
the combinatorial complexity of the associated MILP problems increases
exponentially with the increase of the problem size. To efficiently solve the
"separable" MILP combinatorial problems, decomposition, and coordination
Surrogate "Level-Based" Lagrangian Relaxation method is developed. The new
method efficiently exploits the underlying geometric-convergence potential -
the best convergence theoretically possible - inherent to Polyak's step-sizing
formula in the dual space without the need to know the optimal dual value
required for convergence. Unlike all the previous methods to obtain "level"
estimates of the optimal dual value by adjusting hyperparameters, the key novel
idea to obtain "level" value is through a hyper-parameter-free optimization: a
novel "multiplier-convergence-feasibility" Linear Programming constraint
satisfaction problem needs to be solved, from which, in conjunction with the
Polyak's formula, the sought-for "level" estimate is inferred. Testing results
for medium- and large-scale instances of standard Generalized Assignment
Problems from OR-library demonstrate that 1. "level" estimates generally
decrease and the dual values increase faster than those obtained by using other
existing methods, 2. the new method is computationally efficient in terms of
the quality of solutions (for several large-scale cases, optimal solutions are
obtained) and in terms of the CPU time, 3. the new method is robust with
respect to the choice of initial parameters such as step-sizes and multipliers.
Moreover, the procedure to estimate the "level" value is independent of
linearity and separability of the problem, and thus the new method also paves
the way for efficient resolution of general Mixed-Integer Programming (MIP)
problems.


title:
Structure and Distribution Metric for Quantifying the Quality of
  Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural
  Operators for Regression

abstract:
  We propose two bounded comparison metrics that may be implemented to
arbitrary dimensions in regression tasks. One quantifies the structure of
uncertainty and the other quantifies the distribution of uncertainty. The
structure metric assesses the similarity in shape and location of uncertainty
with the true error, while the distribution metric quantifies the supported
magnitudes between the two. We apply these metrics to Gaussian Processes (GPs),
Ensemble Deep Neural Nets (DNNs), and Ensemble Deep Neural Operators (DNOs) on
high-dimensional and nonlinear test cases. We find that comparing a model's
uncertainty estimates with the model's squared error provides a compelling
ground truth assessment. We also observe that both DNNs and DNOs, especially
when compared to GPs, provide encouraging metric values in high dimensions with
either sparse or plentiful data.


title: Update Compression for Deep Neural Networks on the Edge

abstract:
  An increasing number of artificial intelligence (AI) applications involve the
execution of deep neural networks (DNNs) on edge devices. Many practical
reasons motivate the need to update the DNN model on the edge device
post-deployment, such as refining the model, concept drift, or outright change
in the learning task. In this paper, we consider the scenario where retraining
can be done on the server side based on a copy of the DNN model, with only the
necessary data transmitted to the edge to update the deployed model. However,
due to bandwidth constraints, we want to minimise the transmission required to
achieve the update. We develop a simple approach based on matrix factorisation
to compress the model update -- this differs from compressing the model itself.
The key idea is to preserve existing knowledge in the current model and
optimise only small additional parameters for the update which can be used to
reconstitute the model on the edge. We compared our method to similar
techniques used in federated learning; our method usually requires less than
half of the update size of existing methods to achieve the same accuracy.


title:
Recovery Time Metric Demonstrated on Real-world Electric Grid for
  Hurricane Impacted Outages

abstract:
  This work proposes a methodology for estimating recovery times for
transmission lines and substations, and is demonstrated on a real-world
1269-bus power system model of Puerto Rico under 20 hurricane scenarios, or
stochastic realizations of asset failure under the meteorological conditions of
Hurricane Maria. The method defines base recovery times for system components
and identifies factors that impact these base values by means of multipliers.
While the method is tested on transmission lines and substation failures due to
hurricanes, it is based on a generic process that could be applied to any
system component or event as a general recovery time estimation framework. The
results show that given the two failure modes under study (transmission towers
and substations), transmission towers appear to have a greater impact on
recovery time estimates despite substations being given longer base outage
times. Additionally, average recovery times for the simulated hurricanes across
20 scenarios is ~28,000 work crew days.


title: Observational window effects on multi-object Reverberation Mapping

abstract:
  Contemporary reverberation mapping campaigns are employing wide-area
photometric data and high-multiplex spectroscopy to efficiently monitor
hundreds of active galactic nuclei (AGN). However, the interaction of the
window function(s) imposed by the observation cadence with the reverberation
lag and AGN variability time scales (intrinsic to each source over a range of
luminosities) impact our ability to recover these fundamental physical
properties. Time dilation effects due to the sample source redshift
distribution introduces added complexity. We present comprehensive analysis of
the implications of observational cadence, seasonal gaps and campaign baseline
duration (i.e., the survey window function) for reverberation lag recovery. We
find the presence of a significant seasonal gap dominates the efficacy of any
given campaign strategy for lag recovery across the parameter space,
particularly for those sources with lags above $\sim$100 days in the
observed-frame. Using the OzDES survey as a baseline, we consider the
implications of this analysis for the 4MOST/TiDES campaign providing concurrent
follow-up of the LSST deep-drilling fields, as well as upcoming programs. We
conclude the success of such surveys will be critically limited by the seasonal
visibility of some potential field choices, but show significant improvement
from extending the baseline. Optimising the sample selection to fit the window
function will improve survey efficacy.


title: Efficient Search of Live-Coding Screencasts from Online Videos

abstract:
  Programming videos on the Internet are valuable resources for learning
programming skills. To find relevant videos, developers typically search online
video platforms (e.g., YouTube) with keywords on topics they wish to learn.
Developers often look for live-coding screencasts, in which the videos' authors
perform live coding. Yet, not all programming videos are live-coding
screencasts. In this work, we develop a tool named PSFinder to identify
live-coding screencasts. PSFinder leverages a classifier to identify whether a
video frame contains an IDE window. It uses a sampling strategy to pick a
number of frames from an input video, runs the classifer on these frames, and
then determines whether the video is a live-coding screencast based on frames
classified as containing IDE window. In our preliminary experiment, PSFinder
can effectively identify live-coding screencasts as it achieves an F1-score of
0.97.


title: Automorphy lifting with adequate image

abstract:
  Let $F$ be a CM number field. We generalize existing automorphy lifting
theorems for regular residually irreducible $p$-adic Galois representations
over $F$ by relaxing the big image assumption on the residual representation.


title: Character stacks are PORC count

abstract:
  We compute the number of points over finite fields of the character stack
associated to a compact surface group and a reductive group with connected
centre. We find that the answer is a Polynomial On Residue Classes (PORC). The
key ingredients in the proof are Lusztig's Jordan decomposition of complex
characters and Deriziotis's results on genus numbers of finite reductive
groups. As a consequence of our main theorem, we obtain an expression for the
$E$-polynomial of the character stack.


title:
High-density disc reflection spectroscopy of low-mass active galactic
  nuclei

abstract:
  The standard alpha-disc model predicts an anti-correlation between the
density of the inner accretion disc and the black hole mass times square of the
accretion rate, as seen in higher mass ($M_{\rm BH}>10^{6} M_{\odot}$) active
galactic nuclei (AGNs). In this work, we test the predictions of the alpha-disc
model and study the properties of the inner accretion flow for the low-mass end
($M_{\rm BH}\approx 10^{5-6}M_{\odot}$) of AGNs. We utilize a new high-density
disc reflection model where the density parameter varies from $n_{\rm
e}=10^{15}$ to $10^{20}$ cm$^{-3}$ and apply it to the broadband X-ray (0.3-10
keV) spectra of the low-mass AGN sample. The sources span a wide range of
Eddington fractions and are consistent with being sub-Eddington or
near-Eddington. The X-ray spectra reveal a soft X-ray excess below $\sim 1.5$
keV which is well modeled by high-density reflection from an ionized accretion
disc of density $n_{\rm e}\sim 10^{18}$ cm$^{-3}$ on average. The results
suggest a radiation pressure-dominated disc with an average of 70% fraction of
the disc power transferred to the corona, consistent with that observed in
higher mass AGNs. We show that the disc density higher than $10^{15}$ cm$^{-3}$
can result from the radiation pressure compression when the disc surface does
not hold a strong magnetic pressure gradient. We find tentative evidence for a
drop in black hole spin at low-mass regimes.


title:
Resonant superfluidity in the Rabi-coupled spin-dependent Fermi-Hubbard
  model

abstract:
  We investigate the ground-state phase diagram of the one-dimensional
attractive Fermi-Hubbard model with spin-dependent hoppings and an on-site Rabi
coupling by using the density-matrix renormalization group method. In
particular, we show that even in the limit of one component being immobile the
pair superfluidity can be resonantly enhanced in the vicinity of the quantum
phase transition to a polarized phase. We derive an effective spin-1/2 XXZ
model in order to understand the ground-state properties in the strong
attraction limit.


title: Multi-Agent Active Search using Detection and Location Uncertainty

abstract:
  Active search refers to the task of autonomous robots (agents) detecting
objects of interest (targets) in a search space using decision making
algorithms that adapt to the history of their observations. It has important
applications in search and rescue missions, wildlife patrolling and environment
monitoring. Active search algorithms must contend with two types of
uncertainty: detection uncertainty and location uncertainty. Prior work has
typically focused on one of these while ignoring or engineering away the other.
The more common approach in robotics is to focus on location uncertainty and
remove detection uncertainty by thresholding the detection probability to zero
or one. On the other hand, it is common in the sparse signal processing
literature to assume the target location is accurate and focus on the
uncertainty of its detection. In this work, we propose an inference method to
jointly handle both target detection and location uncertainty. We then build a
decision making algorithm on this inference method that uses Thompson sampling
to enable efficient active search in both the single agent and multi-agent
settings. We perform experiments in simulation over varying number of agents
and targets to show that our inference and decision making algorithms
outperform competing baselines that only account for either target detection or
location uncertainty.


title:
Weighted Sum Age of Information Minimization in Wireless Networks with
  Aerial IRS

abstract:
  In this letter, we analyze a terrestrial wireless communication network
assisted by an aerial intelligent reflecting surface (IRS). We consider a
packet scheduling problem at the ground base station (BS) aimed at improving
the information freshness by selecting packets based on their AoI. To further
improve the communication quality, the trajectory of the unmanned aerial
vehicle (UAV) which carries the IRS is optimized with joint active and passive
beamforming design. To solve the formulated non-convex problem, we propose an
iterative alternating optimization problem based on a successive convex
approximation (SCA) algorithm. The simulation results shows significant
performance improvement in terms of weighted sum AoI, and the SCA solution
converges quickly with low computational complexity.


title: Covalent Adsorption of functional groups on [N]-carbophenes

abstract:
  Starting from the planar molecule 1,3,5-trihydroxybenzene, Du et al. reported
synthesizing one of a couple of possible 2D materials: graphenylene or
3-carbophene. 3-carbophene is a member of a novel class of two-dimensional
covalent organic framework, [N]-carbophenes (carbophenes). Using a high
throughput method, we computed the formation energies and conduction properties
of 3-, 4-, 5-, and 6-carbophenes with hydroxyl (OH), carbonyl (CO), nitro
(NO$_2$), amine (NH$_2$), carboxyl (COOH) functional groups replacing hydrogen
terminating agents. Five hundred and nine structures with randomly picked
motifs, with functionalizations from a single functional group per cell to
fully functionalized were studied. Our results demonstrate a negatively sloped
linear relationship between the degree of functionalization and formation
energy when the type of functional group and type of carbophene are held
constant. The decrease in formation energy with functionalization makes Du's
synthesis of functionalized 3-carbophene more creditable. The type of
carbophene, type of functional group, and the degree of functionalization all
play a role in the band structure of the materials. For example, CO functional
groups may lead to a mid-gap state pinned to the Fermi level, whereas the other
functional groups studied keep the semiconducting nature of pristine
carbophene. Thus, because carbonyl functional groups are often present in
defected carbon systems, care should be taken to limit the amount of oxygen in
carbophene devices where the band gap is important. Thus, this work strengthens
the hypothesis of Junkermeier et al.'s hypothesis that Du et al. synthesized
3-carbophene and not graphenylene.


title: On the Stability of Krasnosel'ski\v{\i}-Mann Iterations

abstract:
  Firstly, we invoke the weak convergence (resp. strong convergence) of
translated basic methods involving nonexpansive operators to establish the weak
convergence (resp. strong convergence) of the associated method with both
perturbation and approximation. Then we employ the technique obtaining the
result above to extend convergence results from the classic
Krasnosel'ski\v{\i}-Mann iterations to their relaxation variants for finding a
common fixed point of associated nonexpansive operators. At last, we show
applications on generalized proximal point algorithms for solving monotone
inclusion problems.


title:
Merger and post-merger of binary neutron stars with a quark-hadron
  crossover equation of state

abstract:
  Fully general-relativistic binary-neutron-star (BNS) merger simulations with
quark-hadron crossover (QHC) equations of state (EOSs) are studied for the
first time. In contrast to EOSs with purely hadronic matter or with a
first-order quark-hadron transition, in the transition region QHC EOSs show a
peak in sound speed, and thus a stiffening. We study the effects of such
stiffening in the merger and post-merger gravitational (GW) signals. Through
simulations in the binary-mass range $2.5 < M/M_{\odot} < 2.75$, characteristic
differences due to different EOSs appear in the frequency of the main peak of
the post-merger GW spectrum ($f_2$), extracted through Bayesian inference. In
particular, we found that (i) for lower-mass binaries, since the maximum baryon
number density ($n_{\rm max}$) after the merger stays below $3-4$ times the
nuclear-matter density ($n_0$), the characteristic stiffening of the QHC models
in that density range results in a lower $f_2$ than that computed for the
underlying hadronic EOS and thus also than that for EOSs with a first-order
phase transition, and (ii) for higher-mass binaries, where $n_{\rm max}$ may
exceed $4-5 n_0$ depending on the EOS model, whether $f_2$ in QHC models is
higher or lower than that in the underlying hadronic model depends on the
height of the sound-speed peak. Comparing the values of $f_2$ for different
EOSs and BNS masses gives important clues on how to discriminate different
types of quark dynamics in the high-density end of EOSs and is relevant to
future kHz GW observations with third-generation GW detectors.


title:
Do twin spectral peaks of olivine particles in the thermal infrared
  diagnose their sizes and porosities?

abstract:
  A well-established constraint on the size of non-porous olivine grains or the
porosity of aggregates consisting of small olivine grains from prominent narrow
peaks in thermal infrared spectra characteristic of crystalline silicates is
reexamined. To thoroughly investigate thermal infrared peaks, we make
theoretical argument for the absorption and scattering of light by non-porous,
non-spherical olivine particles, which is followed by numerical verification.
Our study provides perfectly rational explanations of the physics behind the
small-particle effect of emission peaks in the framework of classical
electrodynamics and convincing evidence of small-particle's emission peaks in
the literature. While resonant absorption excited by surface roughness on the
order of submicrometer scales can be identified even for non-porous olivine
particles with a radius of $10~{\rm \mu m}$, it makes only a negligible
contribution to thermal infrared spectra of the particles. In contrast, the
porosity of non-spherical particles has a significant impact on the strength
and wavelength of the peaks, while the resonant absorption excited by an
ensemble of small grains takes place at a wavelength different than one expects
for surface roughness. We finally reaffirm that twin peaks of olivine in
thermal infrared spectra of dust particles in astronomical environments are the
intrinsic diagnostic characters of submicrometer-sized small grains and their
aggregate particles in fluffy and porous configurations.


title:
Design of Detectors at the Electron Ion Collider with Artificial
  Intelligence

abstract:
  Artificial Intelligence (AI) for design is a relatively new but active area
of research across many disciplines. Surprisingly when it comes to designing
detectors with AI this is an area at its infancy. The Electron Ion Collider is
the ultimate machine to study the strong force. The EIC is a large-scale
experiment with an integrated detector that extends for about $\pm$35 meters to
include the central, far-forward, and far-backward regions. The design of the
central detector is made by multiple sub-detectors, each in principle
characterized by a multidimensional design space and multiple design criteria
also called objectives. Simulations with Geant4 are typically compute
intensive, and the optimization of the detector design may include
non-differentiable terms as well as noisy objectives. In this context, AI can
offer state of the art solutions to solve complex combinatorial problems in an
efficient way. In particular, one of the proto-collaborations, ECCE, has
explored during the detector proposal the possibility of using multi-objective
optimization to design the tracking system of the EIC detector. This document
provides an overview of these techniques and recent progress made during the
EIC detector proposal. Future high energy nuclear physics experiments can
leverage AI-based strategies to design more efficient detectors by optimizing
their performance driven by physics criteria and minimizing costs for their
realization.


title:
Computing Continuous Dynamic Time Warping of Time Series in Polynomial
  Time

abstract:
  Dynamic Time Warping is arguably the most popular similarity measure for time
series, where we define a time series to be a one-dimensional polygonal curve.
The drawback of Dynamic Time Warping is that it is sensitive to the sampling
rate of the time series. The Fr\'echet distance is an alternative that has
gained popularity, however, its drawback is that it is sensitive to outliers.
  Continuous Dynamic Time Warping (CDTW) is a recently proposed alternative
that does not exhibit the aforementioned drawbacks. CDTW combines the
continuous nature of the Fr\'echet distance with the summation of Dynamic Time
Warping, resulting in a similarity measure that is robust to sampling rate and
to outliers. In a recent experimental work of Brankovic et al., it was
demonstrated that clustering under CDTW avoids the unwanted artifacts that
appear when clustering under Dynamic Time Warping and under the Fr\'echet
distance. Despite its advantages, the major shortcoming of CDTW is that there
is no exact algorithm for computing CDTW, in polynomial time or otherwise.
  In this work, we present the first exact algorithm for computing CDTW of
one-dimensional curves. Our algorithm runs in time $O(n^5)$ for a pair of
one-dimensional curves, each with complexity at most $n$. In our algorithm, we
propagate continuous functions in the dynamic program for CDTW, where the main
difficulty lies in bounding the complexity of the functions. We believe that
our result is an important first step towards CDTW becoming a practical
similarity measure between curves.


title:
Generalized SAV-exponential integrator schemes for Allen-Cahn type
  gradient flows

abstract:
  The energy dissipation law and the maximum bound principle (MBP) are two
important physical features of the well-known Allen-Cahn equation. While some
commonly-used first-order time stepping schemes have turned out to preserve
unconditionally both energy dissipation law and MBP for the equation,
restrictions on the time step size are still needed for existing second-order
or even higher-order schemes in order to have such simultaneous preservation.
In this paper, we develop and analyze novel first- and second-order linear
numerical schemes for a class of Allen-Cahn type gradient flows. Our schemes
combine the generalized scalar auxiliary variable (SAV) approach and the
exponential time integrator with a stabilization term, while the standard
central difference stencil is used for discretization of the spatial
differential operator. We not only prove their unconditional preservation of
the energy dissipation law and the MBP in the discrete setting, but also derive
their optimal temporal error estimates under fixed spatial mesh. Numerical
experiments are also carried out to demonstrate the properties and performance
of the proposed schemes.


title: Seshadri constants of parabolic vector bundles

abstract:
  Let $X$ be a complex projective variety, and let $E_{\ast}$ be a parabolic
vector bundle on $X$. We introduce the notion of \textit{parabolic Seshadri
constants} of $E_{\ast}$. It is shown that these constants are analogous to the
classical Seshadri constants of vector bundles, in particular, they have
parallel definitions and properties. We prove a Seshadri criterion for
parabolic ampleness of $E_{\ast}$ in terms of parabolic Seshadri constants. We
also compute parabolic Seshadri constants for symmetric powers and tensor
products of parabolic vector bundles.


title:
Interface superconductivity in FeSe thin films on SrTiO$_3$grown by the
  PLD technique

abstract:
  In this study, we fabricate 5-30-nm-thick films of FeSe/STO using pulsed
laser deposition (PLD). The grown films exhibit superconductivity with an onset
$T_{\mathrm{c}}$ that is much higher than that of bulk FeSe under ambient
pressure. The observed $T_{\mathrm{c}}$ values are exceptionally high in terms
of the strain vs. $T_{\mathrm{c}}$ relationship of the same material
established so far. Furthermore, $T_{\mathrm{c}}$ increases as the film
thickness decreases, except for films thinner than 10 nm. This thickness
dependence of $T_{\mathrm{c}}$ is in good agreement with the results reported
for films grown by molecular beam epitaxy (MBE) that exhibit interface
superconductivity. These results indicate the realization of interface
superconductivity in PLD-grown FeSe/STO. Furthermore, our PLD technique
requires no post-annealing to realize interface superconductivity, which is
different from MBE techniques. Because the PLD technique has the advantage that
various interfaces can be fabricated easily by simply altering the target
materials, our results open novel routes to study interface superconductivity
toward higher $T_{\mathrm{c}}$ by systematic control of the interface.


title:
High Dimensional Statistical Analysis and its Application to ALMA Map of
  NGC 253

abstract:
  In astronomy, if we denote the dimension of data as $d$ and the number of
samples as $n$, we often meet a case with $n \ll d$. Traditionally, such a
situation is regarded as ill-posed, and there was no choice but to throw away
most of the information in data dimension to let $d < n$. The data with $n \ll
d$ is referred to as high-dimensional low sample size (HDLSS). {}To deal with
HDLSS problems, a method called high-dimensional statistics has been developed
rapidly in the last decade. In this work, we first introduce the
high-dimensional statistical analysis to the astronomical community. We apply
two representative methods in the high-dimensional statistical analysis
methods, the noise-reduction principal component analysis (NRPCA) and
regularized principal component analysis (RPCA), to a spectroscopic map of a
nearby archetype starburst galaxy NGC 253 taken by the Atacama Large
Millimeter/Submillimeter Array (ALMA). The ALMA map is a typical HDLSS dataset.
First we analyzed the original data including the Doppler shift due to the
systemic rotation. The high-dimensional PCA could describe the spatial
structure of the rotation precisely. We then applied to the Doppler-shift
corrected data to analyze more subtle spectral features. The NRPCA and RPCA
could quantify the very complicated characteristics of the ALMA spectra.
Particularly, we could extract the information of the global outflow from the
center of NGC 253. This method can also be applied not only to spectroscopic
survey data, but also any type of data with small sample size and large
dimension.


title:
Metric Entropy Duality and the Sample Complexity of Outcome
  Indistinguishability

abstract:
  We give the first sample complexity characterizations for outcome
indistinguishability, a theoretical framework of machine learning recently
introduced by Dwork, Kim, Reingold, Rothblum, and Yona (STOC 2021). In outcome
indistinguishability, the goal of the learner is to output a predictor that
cannot be distinguished from the target predictor by a class $D$ of
distinguishers examining the outcomes generated according to the predictors'
predictions.
  In the distribution-specific and realizable setting where the learner is
given the data distribution together with a predictor class $P$ containing the
target predictor, we show that the sample complexity of outcome
indistinguishability is characterized by the metric entropy of $P$ w.r.t. the
dual Minkowski norm defined by $D$, and equivalently by the metric entropy of
$D$ w.r.t. the dual Minkowski norm defined by $P$. This equivalence makes an
intriguing connection to the long-standing metric entropy duality conjecture in
convex geometry. Our sample complexity characterization implies a variant of
metric entropy duality, which we show is nearly tight. In the distribution-free
setting, we focus on the case considered by Dwork et al. where $P$ contains all
possible predictors, hence the sample complexity only depends on $D$. In this
setting, we show that the sample complexity of outcome indistinguishability is
characterized by the fat-shattering dimension of $D$.
  We also show a strong sample complexity separation between realizable and
agnostic outcome indistinguishability in both the distribution-free and the
distribution-specific settings. This is in contrast to distribution-free (resp.
distribution-specific) PAC learning where the sample complexity in both the
realizable and the agnostic settings can be characterized by the VC dimension
(resp. metric entropy).


title: Fast Road Segmentation via Uncertainty-aware Symmetric Network

abstract:
  The high performance of RGB-D based road segmentation methods contrasts with
their rare application in commercial autonomous driving, which is owing to two
reasons: 1) the prior methods cannot achieve high inference speed and high
accuracy in both ways; 2) the different properties of RGB and depth data are
not well-exploited, limiting the reliability of predicted road. In this paper,
based on the evidence theory, an uncertainty-aware symmetric network (USNet) is
proposed to achieve a trade-off between speed and accuracy by fully fusing RGB
and depth data. Firstly, cross-modal feature fusion operations, which are
indispensable in the prior RGB-D based methods, are abandoned. We instead
separately adopt two light-weight subnetworks to learn road representations
from RGB and depth inputs. The light-weight structure guarantees the real-time
inference of our method. Moreover, a multiscale evidence collection (MEC)
module is designed to collect evidence in multiple scales for each modality,
which provides sufficient evidence for pixel class determination. Finally, in
uncertainty-aware fusion (UAF) module, the uncertainty of each modality is
perceived to guide the fusion of the two subnetworks. Experimental results
demonstrate that our method achieves a state-of-the-art accuracy with real-time
inference speed of 43+ FPS. The source code is available at
https://github.com/morancyc/USNet.


title: Monocular Depth Distribution Alignment with Low Computation

abstract:
  The performance of monocular depth estimation generally depends on the amount
of parameters and computational cost. It leads to a large accuracy contrast
between light-weight networks and heavy-weight networks, which limits their
application in the real world. In this paper, we model the majority of accuracy
contrast between them as the difference of depth distribution, which we call
"Distribution drift". To this end, a distribution alignment network (DANet) is
proposed. We firstly design a pyramid scene transformer (PST) module to capture
inter-region interaction in multiple scales. By perceiving the difference of
depth features between every two regions, DANet tends to predict a reasonable
scene structure, which fits the shape of distribution to ground truth. Then, we
propose a local-global optimization (LGO) scheme to realize the supervision of
global range of scene depth. Thanks to the alignment of depth distribution
shape and scene depth range, DANet sharply alleviates the distribution drift,
and achieves a comparable performance with prior heavy-weight methods, but uses
only 1% floating-point operations per second (FLOPs) of them. The experiments
on two datasets, namely the widely used NYUDv2 dataset and the more challenging
iBims-1 dataset, demonstrate the effectiveness of our method. The source code
is available at https://github.com/YiLiM1/DANet.


title:
Predicted novel type of photoinduced topological phase transition
  accompanied by collision and collapse of Dirac-cone pair in organic salt
  $\alpha$-(BEDT-TTF)$_2$I$_3$

abstract:
  Photoinduced topological phase transitions in the Dirac-electron systems have
attracted intensive research interest since its theoretical prediction in
graphene, where the application of circularly polarized light opens a gap at
the Dirac points and renders the system a topologically nontrivial Chern
insulator phase through breaking the time-reversal symmetry. However, most of
the previously studied phenomena in two-dimensional Dirac systems are basically
based on the same physicical mechanism, i.e., the gap opening in the Dirac
electron bands with circularly polarized light, and it is lacking in variety of
the physics. In this paper, we theoretically predict a novel type of
photoinduced topological phase transition accompanied by collision and collapse
of gapped Dirac points in the organic salt $\alpha$-(BEDT-TTF)$_2$I$_3$. By
constructing the Floquet theory for this compound, we demonstrate that the
irradiation of elliptically polarized light causes collision of the Dirac
points through the photoinduced band deformation and their collapse, which
eventually results in the topological phase transition from a topological
semimetal with gapped Dirac cones to a normal insulator when the elliptical
axis is oriented at a specfic angle with respect to the crystallographic axes.
We argue that this novel photoinduced phase transition can be experimentally
detected by the measurement of Hall conductivity. The present work enriches the
fundamental physics of photoinduced topological phase transitions and thus
contribute to development of this rapidly growing research field.


title:
MetaCon: Unified Predictive Segments System with Trillion Concept
  Meta-Learning

abstract:
  Accurate understanding of users in terms of predicative segments play an
essential role in the day to day operation of modern internet enterprises.
Nevertheless, there are significant challenges that limit the quality of data,
especially on long tail predictive tasks. In this work, we present MetaCon, our
unified predicative segments system with scalable, trillion concepts meta
learning that addresses these challenges. It builds on top of a flat concept
representation that summarizes entities' heterogeneous digital footprint,
jointly considers the entire spectrum of predicative tasks as a single learning
task, and leverages principled meta learning approach with efficient first
order meta-optimization procedure under a provable performance guarantee in
order to solve the learning task. Experiments on both proprietary production
datasets and public structured learning tasks demonstrate that MetaCon can lead
to substantial improvements over state of the art recommendation and ranking
approaches.


title: PUTN: A Plane-fitting based Uneven Terrain Navigation Framework

abstract:
  Autonomous navigation of ground robots has been widely used in indoor
structured 2D environments, but there are still many challenges in outdoor 3D
unstructured environments, especially in rough, uneven terrains. This paper
proposed a plane-fitting based uneven terrain navigation framework (PUTN) to
solve this problem. The implementation of PUTN is divided into three steps.
First, based on Rapidly-exploring Random Trees (RRT), an improved sample-based
algorithm called Plane Fitting RRT* (PF-RRT*) is proposed to obtain a sparse
trajectory. Each sampling point corresponds to a custom traversability index
and a fitted plane on the point cloud. These planes are connected in series to
form a traversable strip. Second, Gaussian Process Regression is used to
generate traversability of the dense trajectory interpolated from the sparse
trajectory, and the sampling tree is used as the training set. Finally, local
planning is performed using nonlinear model predictive control (NMPC). By
adding the traversability index and uncertainty to the cost function, and
adding obstacles generated by the real-time point cloud to the constraint
function, a safe motion planning algorithm with smooth speed and strong
robustness is available. Experiments in real scenarios are conducted to verify
the effectiveness of the method.


title:
Dynamical phase transitions in the photodriven charge-ordered
  Dirac-electron system

abstract:
  Photoinduced phase transitions and charge dynamics in the interacting
Dirac-electron system with a charge-ordered ground state are theoretically
studied by taking an organic salt $\alpha$-(BEDT-TTF)$_2$I$_3$. By analysing
the extended Hubbard model for this compound using a combined method of
numerical simulations based on the time-dependent Schr\"odinger equation and
the Floquet theory, we observe successive dynamical phase transitions from the
charge-ordered insulator to a gapless Dirac semimetal and, eventually, to a
Chern insulator phase under irradiation with circularly polarized light. These
phase transitions occur as a consequence of two major effects of circularly
polarized light, i.e., closing of the charge gap through melting the charge
order and opening of the topological gap by breaking the time reversal symmetry
at the Dirac points. We demonstrate that these photoinduced phenomena are
governed by charge dynamics of driven correlated Dirac electrons.


title:
Smeared Mass Source Wormholes in Modified $f(R)$ Gravity with the
  Lorentzian Density Distribution Function

abstract:
  Wormholes are speculative structures linking disparate space-time points.
Their geometry can be obtained by solving Einstein equations with tolerating
the violation of null energy conditions. Recently, many researchers have
studied different wormholes according to different criteria, and they achieved
remarkable results. In this paper, we investigate a series of exact solutions
of the static wormhole with smeared mass source geometry in modified $f(R)$
gravity theories. In fact, we consider the Lorentzian density distribution
which is coming from a particle-like source. To be more specific, the modified
gravity models we consider here are some power laws. We compute resulting
solutions according to the wormhole field equations. We also specify parameters
such as the radial pressure and transverse pressure as well as various energy
conditions such as null energy conditions, weak energy conditions and strong
energy conditions. Finally, by plotting some figures, in addition to
identifying the wormhole throat, we describe the results of either the
violation or the satisfaction of the energy conditions completely.


title: Explicit String bundles

abstract:
  While higher bundles are of clear relevance to higher gauge theory, examples
other than abelian bundle gerbes are hard to come across. One would in
particular like to see 2-bundles where the structure 2-group is the String
2-group associated to a compact simple simply-connected Lie group. This talk
will outline a method to construct many examples over homogeneous spaces. We
shall also consider one example in detail, giving explicit formulas for the
crossed-module-valued Cech cocycle arising from a local trivialisation.


title: Quantum states from mixtures of equilibrium distributions

abstract:
  We construct and explore a family of states for quantum systems in contact
with two or more heath reservoirs. The reservoirs are described by equilibrium
distributions. The interaction of each reservoir with the bulk of the system is
encoded in a probability, which characterises the particle exchange among them
and depends in general on the particle momentum. The convex combination of the
reservoir distributions, weighted with the aforementioned probabilities,
defines a new distribution. We establish the existence of an
emission-absorption regime in which the new distribution generates a
non-equilibrium quantum state. We develop a systematic field theory framework
for constructing this state and illustrate its physical properties on a simple
model. In this context we derive the particle current full counting statistics,
the heat current and the Lorenz number. The entropy production and the relative
quantum fluctuations are also determined.

categories: cond-mat.stat-mech hep-th math-ph math.MP


title:
Investigation of the Relationship between Geomagnetic Activity and Solar
  Wind Parameters Based on A Novel Neural Network (Potential Learning)

abstract:
  Predicting geomagnetic conditions based on in-situ solar wind observations
allows us to evade disasters caused by large electromagnetic disturbances
originating from the Sun to save lives and protect economic activity. In this
study, we aimed to examine the relationship between the Kp index, representing
global magnetospheric activity level, and solar wind conditions using an
interpretable neural network known as potential learning (PL). Data analyses
based on neural networks are difficult to interpret; however, PL learns by
focusing on the "potentiality of input neurons" and can identify which inputs
are significantly utilized by the network. Using the full advantage of PL, we
extracted the influential solar wind parameters that disturb the magnetosphere
under southward Interplanetary magnetic field (IMF) conditions. The input
parameters of PL were the three components of the IMF (Bx, By, -Bz(Bs)), solar
wind flow speed (Vx), and proton number density (Np) in geocentric solar
ecliptic (GSE) coordinates obtained from the OMNI solar wind database between
1998 and 2019. Furthermore, we classified these input parameters into two
groups (targets), depending on the Kp level: Kp = 6- to 9 (positive target) and
Kp = 0 to 1+ (negative target). Negative target samples were randomly selected
to ensure that numbers of positive and negative targets were equal. The PL
results revealed that solar wind flow speed is an influential parameter for
increasing Kp under southward IMF conditions, which was in good agreement with
previous reports on the statistical relationship between the Kp index and solar
wind velocity, and the Kp formulation based on the IMF and solar wind plasma
parameters. Based on this new neural network, we aim to construct a more
correct and parameter-dependent space weather forecasting model.


title:
Spectral Efficiency of Unicast and Multigroup Multicast Transmission in
  Cell-free Distributed Massive MIMO Systems

abstract:
  In this paper, we consider a joint unicast and multi-group multicast
cell-free distributed massive multiple-input multiple-output (MIMO) system,
while accounting for co-pilot assignment strategy based channel estimation,
pilot contamination and different precoding schemes. Under the co-pilot
assignment strategy, we derive the minimum-mean-square error (MMSE) channel
state information (CSI) estimation for unicast and multicast users. Given the
acquired CSI, the closed-form expressions for downlink achievable rates with
maximum ratio transmission (MRT), zero-forcing (ZF) and MMSE beamforming are
derived. Based on these expressions, we propose an efficient power allocation
scheme by solving a multi-objective optimization problem (MOOP) between
maximizing the minimum spectral efficiency (SE) of multicast users and
maximizing the average SE of unicast users with non-dominated sorting genetic
algorithm II (NSGA-II). Moreover, the MOOP is converted into a deep learning
(DL) problem and solved by an unsupervised learning method to further promote
computational efficiency. Numerical results verify the accuracy of the derived
closed-form expressions and the effectiveness of the joint unicast and
multigroup multicast transmission scheme in cell-free distributed massive MIMO
systems. The SE analysis under various system parameters and the trade-off
regions between these two conflicting optimization objectives offers numerous
flexibilities for system optimization.


title: How to Stay Socially Distant: A Geometric Approach

abstract:
  We introduce the notion of social distance width (SDW) in geometric domains,
to model and quantify the ability for two or more agents to maintain social
distancing while moving within their respective, possibly shared, domains. We
first study the social distance width of two polygonal curves in one and two
dimensions, providing conditional lower bounds and matching algorithms.
  We then consider social distance width among polygons, which measures the
minimum distance that two agents can maintain while restricted to travel in or
on the boundary of the same polygon. We investigate several variants of the
problem in this setting, for some of which we provide linear time algorithms.
  We draw connections between our proposed social distancing measure and
existing related work in computational geometry, hoping that our new measure
may spawn investigations into further interesting problems that arise.


title: The Exponential Map for Hopf Algebras

abstract:
  We give an analogue of the classical exponential map on Lie groups for Hopf
$*$-algebras with differential calculus. The major difference with the
classical case is the interpretation of the value of the exponential map,
classically an element of the Lie group. We give interpretations as states on
the Hopf algebra, elements of a Hilbert $C^{*} $-bimodule of $\frac{1}{2}$
densities and elements of the dual Hopf algebra. We give examples for complex
valued functions on the groups $S_{3}$ and $\mathbb{Z}$, Woronowicz's matrix
quantum group $\mathbb{C}_{q}[SU_2] $ and the Sweedler-Taft algebra.


title:
Laser-cavity locking at the $10^{-7}$ instability scale utilizing beam
  elipticity

abstract:
  Ultrastable lasers form the back bone of precision measurements in science
and technology. Such lasers attain their stability through frequency locking to
reference cavities. State-of-the-art locking performances to date had been
achieved using frequency-modulation based methods, complemented with active
drift cancellation systems. We demonstrate an all passive, modulation-free
laser-cavity locking technique (squash locking) that utilizes changes in beam
ellipticity for error signal generation, and a coherent polarization
post-selection for noise resilience. By comparing two identically built
proof-of-principle systems, we show a frequency locking instability of $5
\times 10^{-7}$ relative to the cavity linewidth at 10 s averaging. The results
surpass the demonstrated performances of methods engineered over the last five
decades, opening a new path for further advancing the precision and simplicity
of laser frequency stabilization.


title:
Multi-Objective Multi-Agent Planning for Discovering and Tracking
  Unknown and Varying Number of Mobile Objects

abstract:
  We consider the online planning problem for a team of agents to discover and
track an unknown and time-varying number of moving objects from measurements
with uncertain measurement-object origins. Since the onboard sensors have
limited field of views (FoV), the usual planning strategy based solely on
either tracking detected objects or discovering hidden objects is not adequate.
We propose a new multi-objective multi-agent partially observable Markov
decision process (MM-POMDP) based on information-theoretic criteria and a
state-of-the-art online multi-object tracker. The resulting multi-agent
planning problem is exponentially complex due to the unknown data association
between objects and multi-sensor measurements, and hence, computing an optimal
control action is intractable. We prove that the proposed multi-objective value
function is a monotone submodular set function, and develop a greedy algorithm
that can achieve an 0.5OPT compared to an optimal algorithm. We demonstrate the
proposed solution via a series of numerical examples.


title:
Cross validation for model selection: a primer with examples from
  ecology

abstract:
  The growing use of model-selection principles in ecology for statistical
inference is underpinned by information criteria (IC) and cross-validation (CV)
techniques. Although IC techniques, such as Akaike's Information Criterion,
have been historically more popular in ecology, CV is a versatile and
increasingly used alternative. CV uses data splitting to estimate model scores
based on (out-of-sample) predictive performance, which can be used even when it
is not possible to derive a likelihood (e.g., machine learning) or count
parameters precisely (e.g., mixed-effects models and penalised regression).
Here we provide a primer to understanding and applying CV in ecology. We review
commonly applied variants of CV, including approximate methods, and make
recommendations for their use based on the statistical context. We explain some
important -- but often overlooked -- technical aspects of CV, such as bias
correction, estimation uncertainty, score selection, and parsimonious selection
rules. We also address misconceptions (and truths) about impediments to the use
of CV, including computational cost and ease of implementation, and clarify the
relationship between CV and information-theoretic approaches to model
selection. The paper includes two ecological case studies which illustrate the
application of the techniques. We conclude that CV-based model selection should
be widely applied to ecological analyses, because of its robust estimation
properties and the broad range of situations for which it is applicable. In
particular, we recommend using leave-one-out (LOO) or approximate LOO CV to
minimise bias, or otherwise K-fold CV using bias correction if K<10. To
mitigate overfitting, we recommend calibrated selection via the modified
one-standard-error rule which accounts for the predominant cause of
overfitting: score-estimation uncertainty.


title: On large partial ovoids of symplectic and Hermitian polar spaces

abstract:
  In this paper we provide constructive lower bounds on the sizes of the
largest partial ovoids of the symplectic polar spaces ${\cal W}(3, q)$, $q$ odd
square, $q \not\equiv 0 \pmod{3}$, ${\cal W}(5, q)$ and of the Hermitian polar
spaces ${\cal H}(4, q^2)$, $q$ even or $q$ odd square, $q \not\equiv 0
\pmod{3}$, ${\cal H}(6, q^2)$, ${\cal H}(8, q^2)$.


title: ChiTransformer:Towards Reliable Stereo from Cues

abstract:
  Current stereo matching techniques are challenged by restricted searching
space, occluded regions and sheer size. While single image depth estimation is
spared from these challenges and can achieve satisfactory results with the
extracted monocular cues, the lack of stereoscopic relationship renders the
monocular prediction less reliable on its own, especially in highly dynamic or
cluttered environments. To address these issues in both scenarios, we present
an optic-chiasm-inspired self-supervised binocular depth estimation method,
wherein a vision transformer (ViT) with gated positional cross-attention (GPCA)
layers is designed to enable feature-sensitive pattern retrieval between views
while retaining the extensive context information aggregated through
self-attentions. Monocular cues from a single view are thereafter conditionally
rectified by a blending layer with the retrieved pattern pairs. This crossover
design is biologically analogous to the optic-chasma structure in the human
visual system and hence the name, ChiTransformer. Our experiments show that
this architecture yields substantial improvements over state-of-the-art
self-supervised stereo approaches by 11%, and can be used on both rectilinear
and non-rectilinear (e.g., fisheye) images.


title:
Two-colorings of the normed spaces without long monochromatic unit
  arithmetic progressions

abstract:
  Given a natural $n$, we construct a two-coloring of an $n$-dimensional space
with the maximum metric satisfying the following. For any finite set of reals
$S$ with diameter greater than $5^{n}$ such that the distance between any two
consecutive points of $S$ does not exceed one, no isometric copy of $S$ is
monochromatic. As a corollary, we prove that any normed space can be
two-colored such that all sufficiently long unit arithmetic progressions
contain points of both colors.


title:
A thermal convection limit of spiral state in wide-gap spherical Couette
  flow

abstract:
  The symmetries of flow structures are often prescribed by their mechanical
instability and geometry. Here, as an example, we present the homotopy of the
rotating 3-fold spiral state that is robust in a spherical Couette flow towards
the hybrid system with a thermal stratification effect. It has not yet been
confirmed that the rotating wave state smoothly connects to the thermal
stratification system. Through continuation, the most dangerous mode at a
purely spherical Couette flow of $m=4$ modes of spherical harmonics is replaced
by $l=4$ and $m=3$ in a purely thermal convective system. For the state
obtained at the limit under only the thermal effect, the residual quantities of
both the torque to the outer sphere and meridional circulation are discussed in
detail.


title:
Neighborhood persistency of the linear optimization relaxation of
  integer linear optimization

abstract:
  For an integer linear optimization (ILO) problem, persistency of its linear
optimization (LO) relaxation is a property that for every optimal solution of
the relaxation that assigns integer values to some variables, there exists an
optimal solution of the ILO problem in which these variables retain the same
values. Although persistency has been used to develop heuristic, approximation,
and fixed-parameter algorithms for special cases of ILO, its applicability
remains unknown in the literature. In this paper we reveal a maximal subclass
of ILO such that its LO relaxation has persistency. Specifically, we show that
the LO relaxation of ILO on unit-two-variable-per-inequality (UTVPI) systems
has persistency and is (in a certain sense) maximal among such ILO. Our
persistency result generalizes the results of Nemhauser and Trotter, Hochbaum
et al., and Fiorini et al. Even more, we propose a stronger property called
\emph{neighborhood persistency} and show that the LO relaxation of ILO on UTVPI
systems in general has this property. Using this stronger result, we obtain a
fixed-parameter algorithm (where the parameter is the solution size) and
another proof of two-approximability for ILO on UTVPI systems where objective
functions and variables are non-negative.


title:
Jointly modeling rating responses and times with fuzzy numbers: An
  application to psychometric data

abstract:
  In several research areas, ratings data and response times have been
successfully used to unfold the stage-wise process through which human raters
provide their responses to questionnaires and social surveys. A limitation of
the standard approach to analyze this type of data is that it requires the use
of independent statistical models. Although this provides an effective way to
simplify the data analysis, it could potentially involve difficulties with
regards to statistical inference and interpretation. In this sense, a joint
analysis could be more effective. In this research article, we describe a way
to jointly analyze ratings and response times by means of fuzzy numbers. A
probabilistic tree model framework has been adopted to fuzzify ratings data and
four-parameter triangular fuzzy numbers have been used in order to integrate
crisp responses and times. Finally, a real case study on psychometric data is
discussed in order to illustrate the proposed methodology. Overall, we provide
initial findings to the problem of using fuzzy numbers as abstract models for
representing ratings data with additional information (i.e., response times).
The results indicate that using fuzzy numbers lead to theoretically sound and
more parsimonious data analysis methods, which limit some statistical issues
that may occur with standard data analysis procedures (e.g., the problem of
multiple hypothesis testing).


title: Learning Temporal Consistency for Source-Free Video Domain Adaptation

abstract:
  Video-based Unsupervised Domain Adaptation (VUDA) methods improve the
robustness of video models, enabling them to be applied to action recognition
tasks across different environments. However, these methods require constant
access to source data during the adaptation process. Yet in many real-world
applications, subjects and scenes in the source video domain should be
irrelevant to those in the target video domain. With the increasing emphasis on
data privacy, such methods that require source data access would raise serious
privacy issues. Therefore, to cope with such concern, a more practical domain
adaptation scenario is formulated as the Source-Free Video-based Domain
Adaptation (SFVDA). Though there are a few methods for Source-Free Domain
Adaptation (SFDA) on image data, these methods yield degenerating performance
in SFVDA due to the multi-modality nature of videos, with the existence of
additional temporal features. In this paper, we propose a novel Attentive
Temporal Consistent Network (ATCoN) to address SFVDA by learning temporal
consistency, guaranteed by two novel consistency objectives, namely feature
consistency and source prediction consistency, performed across local temporal
features. ATCoN further constructs effective overall temporal features by
attending to local temporal features based on prediction confidence. Empirical
results demonstrate the state-of-the-art performance of ATCoN across various
cross-domain action recognition benchmarks.


title:
Active Self-Semi-Supervised Learning for Few Labeled Samples Fast
  Training

abstract:
  Faster training and fewer annotations are two key issues for applying deep
models to various practical domains. Now, semi-supervised learning has achieved
great success in training with few annotations. However, low-quality labeled
samples produced by random sampling make it difficult to continue to reduce the
number of annotations. In this paper we propose an active self-semi-supervised
training framework that bootstraps semi-supervised models with good prior
pseudo-labels, where the priors are obtained by label propagation over
self-supervised features. Because the accuracy of the prior is not only
affected by the quality of features, but also by the selection of the labeled
samples. We develop active learning and label propagation strategies to obtain
better prior pseudo-labels. Consequently, our framework can greatly improve the
performance of models with few annotations and greatly reduce the training
time. Experiments on three semi-supervised learning benchmarks demonstrate
effectiveness. Our method achieves similar accuracy to standard semi-supervised
approaches in about 1/3 of the training time, and even outperform them when
fewer annotations are available (84.10\% in CIFAR-10 with 10 labels).


title:
Two- and three-body hadronic decays of charmed mesons involving a tensor
  meson

abstract:
  We study the quasi-two-body $D\to TP$ decays and the three-body $D$ decays
proceeding through intermediate tensor resonances, where $T$ and $P$ denote
tensor and pseudoscalar mesons, respectively. We employ $D\to T$ transition
form factors based upon light-cone sum rules and the covariant light-front
quark model to evaluate the decay rates, with the former giving a better
agreement with current data. Though the tree amplitudes with the emitted meson
being a tensor meson vanish under factorization approximation, contributions
proportional to the tensor decay constant $f_T$ can be produced from vertex and
hard spectator-scattering corrections. We also investigate the finite-width
effects of the tensor mesons and find that, contrary to three-body $B$ decays,
the tensor-mediated $D$ decays are more seriously affected and the narrow width
approximation has to be corrected. More experimental data are required in order
to extract information topological amplitudes associated with quasi-two-body
$D\to TP$ decays. Among the data, the $D^+\to f_2\pi^+$ and $D^+\to \bar
K_2^{*0}\pi^+$ branching fractions are not self consistent and further
clarification is called for.


title:
Quantum equation of motion and two-loop cutoff renormalization for
  $\phi^3$ model

abstract:
  We present two-loop renormalization of $\phi^3$-model effective action by
using the background field method and cutoff momentum regularization. In this
paper, we also study a derivation of the quantum equation of motion and its
application to the renormalization.


title: MLNav: Learning to Safely Navigate on Martian Terrains

abstract:
  We present MLNav, a learning-enhanced path planning framework for
safety-critical and resource-limited systems operating in complex environments,
such as rovers navigating on Mars. MLNav makes judicious use of machine
learning to enhance the efficiency of path planning while fully respecting
safety constraints. In particular, the dominant computational cost in such
safety-critical settings is running a model-based safety checker on the
proposed paths. Our learned search heuristic can simultaneously predict the
feasibility for all path options in a single run, and the model-based safety
checker is only invoked on the top-scoring paths. We validate in high-fidelity
simulations using both real Martian terrain data collected by the Perseverance
rover, as well as a suite of challenging synthetic terrains. Our experiments
show that: (i) compared to the baseline ENav path planner on board the
Perserverance rover, MLNav can provide a significant improvement in multiple
key metrics, such as a 10x reduction in collision checks when navigating real
Martian terrains, despite being trained with synthetic terrains; and (ii) MLNav
can successfully navigate highly challenging terrains where the baseline ENav
fails to find a feasible path before timing out.


title: Region-Aware Face Swapping

abstract:
  This paper presents a novel Region-Aware Face Swapping (RAFSwap) network to
achieve identity-consistent harmonious high-resolution face generation in a
local-global manner: \textbf{1)} Local Facial Region-Aware (FRA) branch
augments local identity-relevant features by introducing the Transformer to
effectively model misaligned cross-scale semantic interaction. \textbf{2)}
Global Source Feature-Adaptive (SFA) branch further complements global
identity-relevant cues for generating identity-consistent swapped faces.
Besides, we propose a \textit{Face Mask Predictor} (FMP) module incorporated
with StyleGAN2 to predict identity-relevant soft facial masks in an
unsupervised manner that is more practical for generating harmonious
high-resolution faces. Abundant experiments qualitatively and quantitatively
demonstrate the superiority of our method for generating more
identity-consistent high-resolution swapped faces over SOTA methods, \eg,
obtaining 96.70 ID retrieval that outperforms SOTA MegaFS by 5.87$\uparrow$.


title:
Numerical reconstruction of radiative sources from partial boundary
  measurements

abstract:
  We consider an inverse source problem in the stationary radiative transport
through an absorbing and scattering medium in two dimensions. Using the
angularly resolved radiation measured on an arc of the boundary, we propose a
numerical algorithm to recover the source in the convex hull of this arc. The
method involves an unstable step of inverting a bounded operator whose range is
not closed. We show that the continuity constant of the discretized inverse
grows at most linearly with the discretization step, thus stabilizing the
problem.


title:
All You Need is LUV: Unsupervised Collection of Labeled Images using
  Invisible UV Fluorescent Indicators

abstract:
  Large-scale semantic image annotation is a significant challenge for
learning-based perception systems in robotics. Current approaches often rely on
human labelers, which can be expensive, or simulation data, which can visually
or physically differ from real data. This paper proposes Labels from
UltraViolet (LUV), a novel framework that enables rapid, labeled data
collection in real manipulation environments without human labeling. LUV uses
transparent, ultraviolet-fluorescent paint with programmable ultraviolet LEDs
to collect paired images of a scene in standard lighting and UV lighting to
autonomously extract segmentation masks and keypoints via color segmentation.
We apply LUV to a suite of diverse robot perception tasks to evaluate its
labeling quality, flexibility, and data collection rate. Results suggest that
LUV is 180-2500 times faster than a human labeler across the tasks. We show
that LUV provides labels consistent with human annotations on unpainted test
images. The networks trained on these labels are used to smooth and fold
crumpled towels with 83% success rate and achieve 1.7mm position error with
respect to human labels on a surgical needle pose estimation task. The low cost
of LUV makes it ideal as a lightweight replacement for human labeling systems,
with the one-time setup costs at $300 equivalent to the cost of collecting
around 200 semantic segmentation labels on Amazon Mechanical Turk. Code,
datasets, visualizations, and supplementary material can be found at
https://sites.google.com/berkeley.edu/luv


title:
Efficient quadrature-squeezing from biexcitonic parametric gain in
  atomically thin semiconductors

abstract:
  Modification of electromagnetic quantum fluctuations in the form of
quadrature-squeezing is a central quantum resource, which can be generated from
nonlinear optical processes. Such a process is facilitated by coherent
two-photon excitation of the strongly bound biexciton in atomically thin
semiconductors. We show theoretically that interfacing an atomically thin
semiconductor with an optical cavity allows to harness this two-photon
resonance and use the biexcitonic parametric gain to generate squeezed light
with input power an order of magnitude below current state-of-the-art devices
with conventional third-order nonlinear materials that rely on far off-resonant
nonlinearities. Furthermore, the squeezing bandwidth is found to be in the
range of several meV. These results identify atomically thin semiconductors as
a promising candidate for on-chip squeezed-light sources.


title:
PHTrans: Parallelly Aggregating Global and Local Representations for
  Medical Image Segmentation

abstract:
  The success of Transformer in computer vision has attracted increasing
attention in the medical imaging community. Especially for medical image
segmentation, many excellent hybrid architectures based on convolutional neural
networks (CNNs) and Transformer have been presented and achieve impressive
performance. However, most of these methods, which embed modular Transformer
into CNNs, struggle to reach their full potential. In this paper, we propose a
novel hybrid architecture for medical image segmentation called PHTrans, which
parallelly hybridizes Transformer and CNN in main building blocks to produce
hierarchical representations from global and local features and adaptively
aggregate them, aiming to fully exploit their strengths to obtain better
segmentation performance. Specifically, PHTrans follows the U-shaped
encoder-decoder design and introduces the parallel hybird module in deep
stages, where convolution blocks and the modified 3D Swin Transformer learn
local features and global dependencies separately, then a sequence-to-volume
operation unifies the dimensions of the outputs to achieve feature aggregation.
Extensive experimental results on both Multi-Atlas Labeling Beyond the Cranial
Vault and Automated Cardiac Diagnosis Challeng datasets corroborate its
effectiveness, consistently outperforming state-of-the-art methods.


title:
Smoothness of integrated density of states and level statistics of the
  Anderson model when single site distribution is convolution with the Cauchy
  distribution

abstract:
  In this work we consider the Anderson model on $\ell^2(\mathbb{Z}^d)$ when
the single site distribution (SSD) is given by $\mu_1 * \mu_2$, where $\mu_1$
is the Cauchy distribution and $\mu_2$ is any probability measure. For this
model we prove that the integrated density of states (IDS) is infinitely
differentiable irrespective of the disorder strength. Also, we investigate the
local eigenvalue statistics of this model in $d\ge 2$, without any assumption
on the localization property.


title:
CP-ViT: Cascade Vision Transformer Pruning via Progressive Sparsity
  Prediction

abstract:
  Vision transformer (ViT) has achieved competitive accuracy on a variety of
computer vision applications, but its computational cost impedes the deployment
on resource-limited mobile devices.
  We explore the sparsity in ViT and observe that informative patches and heads
are sufficient for accurate image recognition.
  In this paper, we propose a cascade pruning framework named CP-ViT by
predicting sparsity in ViT models progressively and dynamically to reduce
computational redundancy while minimizing the accuracy loss. Specifically, we
define the cumulative score to reserve the informative patches and heads across
the ViT model for better accuracy. We also propose the dynamic pruning ratio
adjustment technique based on layer-aware attention range. CP-ViT has great
general applicability for practical deployment, which can be applied to a wide
range of ViT models and can achieve superior accuracy with or without
fine-tuning.
  Extensive experiments on ImageNet, CIFAR-10, and CIFAR-100 with various
pre-trained models have demonstrated the effectiveness and efficiency of
CP-ViT. By progressively pruning 50\% patches, our CP-ViT method reduces over
40\% FLOPs while maintaining accuracy loss within 1\%.


title:
A Neuro-vector-symbolic Architecture for Solving Raven's Progressive
  Matrices

abstract:
  Neither deep neural networks nor symbolic AI alone have approached the kind
of intelligence expressed in humans. This is mainly because neural networks are
not able to decompose distinct objects from their joint representation (the
so-called binding problem), while symbolic AI suffers from exhaustive rule
searches, among other problems. These two problems are still pronounced in
neuro-symbolic AI which aims to combine the best of the two paradigms. Here, we
show that the two problems can be addressed with our proposed
neuro-vector-symbolic architecture (NVSA) by exploiting its powerful operators
on fixed-width holographic vectorized representations that serve as a common
language between neural networks and symbolic logical reasoning. The efficacy
of NVSA is demonstrated by solving the Raven's progressive matrices. NVSA
achieves a new record of 97.7% average accuracy in RAVEN, and 98.8% in I-RAVEN
datasets, with two orders of magnitude faster execution than the symbolic
logical reasoning on CPUs.


title:
A family of special case of sequential warped product manifolds with
  semi-Riemannian Einstein metrics

abstract:
  We derive the general formulas for a special configuration of the sequential
warped product semi-Riemannian manifold to be Einstein, where the base-manifold
is the product of two manifolds both equipped with a conformal metrics.
Subsequently we study the case in which these two manifolds are conformal to a
$n'$-dimensional and $\widetilde n$-dimensional pseudo-Euclidean space,
respectively. For the latter case, we prove the existence of a family of
solutions that are invariant under the action of a $(n'-1)$-dimensional group
of transformations to the case of positive constant Ricci curvature
($\lambda>0$).


title: Reinforced Meta Active Learning

abstract:
  In stream-based active learning, the learning procedure typically has access
to a stream of unlabeled data instances and must decide for each instance
whether to label it and use it for training or to discard it. There are
numerous active learning strategies which try to minimize the number of labeled
samples required for training in this setting by identifying and retaining the
most informative data samples. Most of these schemes are rule-based and rely on
the notion of uncertainty, which captures how small the distance of a data
sample is from the classifier's decision boundary. Recently, there have been
some attempts to learn optimal selection strategies directly from the data, but
many of them are still lacking generality for several reasons: 1) They focus on
specific classification setups, 2) They rely on rule-based metrics, 3) They
require offline pre-training of the active learner on related tasks. In this
work we address the above limitations and present an online stream-based meta
active learning method which learns on the fly an informativeness measure
directly from the data, and is applicable to a general class of classification
problems without any need for pretraining of the active learner on related
tasks. The method is based on reinforcement learning and combines episodic
policy search and a contextual bandits approach which are used to train the
active learner in conjunction with training of the model. We demonstrate on
several real datasets that this method learns to select training samples more
efficiently than existing state-of-the-art methods.


title: Heat Fluctuations in Chemically Active Systems

abstract:
  Chemically active systems such as living cells are maintained out of thermal
equilibrium due to chemical events which generate heat and lead to active
fluctuations. A key question is to understand on which time and length scales
active fluctuations dominate thermal fluctuations. Here, we formulate a
stochastic field theory with Poisson white noise to describe the heat
fluctuations which are generated by stochastic chemical events and lead to
active temperature fluctuations. We find that on large length and time scales,
active fluctuations always dominate thermal fluctuations. However, at
intermediate length and time scales, multiple crossovers exist which highlight
the different characteristics of active and thermal fluctuations. Our work
provides a framework to characterize fluctuations in active systems and reveals
that local equilibrium holds at certain length and time scales.


title: Geometric Aspects of Data-Processing of Markov Chains

abstract:
  We consider data-processing of Markov chains through the lens of information
geometry. We first develop a theory of congruent Markov morphisms in the
context of Markov kernels that we show to correspond to the congruent
embeddings with respect to the lumping operation. Furthermore, we inspect
information projections onto geodesically convex sets of Markov kernels, and
show that under some conditions, m-projecting onto doubly convex submanifolds
can be regarded as a data-processing operation. Finally, we show that the
family of lumpable kernels can be meaningfully endowed with the structure of a
foliated manifold.


title: Observation of edge solitons in topological trimer arrays

abstract:
  We report the experimental observation of nonlinear light localization and
edge soliton formation at the edges of fs-laser written trimer waveguide
arrays, where transition from non-topological to topological phases is
controlled by the spacing between neighboring trimers. We found that, in the
former regime, edge solitons occur only above a considerable power threshold,
whereas in the latter one they bifurcate from linear states. Edge solitons are
observed in a broad power range where their propagation constant falls into one
of the topological gaps of the system, while partial delocalization is observed
when considerable nonlinearity drives the propagation constant into an allowed
band, causing coupling with bulk modes. Our results provide direct experimental
evidence of the coexistence and selective excitation in the same or in
different topological gaps of two types of topological edge solitons with
different internal structures, which can rarely be observed even in
nontopological systems. This also constitutes the first experimental evidence
of formation of topological solitons in a nonlinear system with more than one
topological gap.


title: Self-testing randomness from a nuclear spin system

abstract:
  Randomness is a very important resource for cryptography, algorithms, and
scientific simulations. Since all classical processes are considered to be
intrinsically deterministic, we must build quantum random number generators
which utilize quantum processes to generate true randomness. Quantum random
number generators have been realized in different quantum systems, including
quantum optical systems, and trapped ions. Here we present a proof-of-concept
random number generator based on a nuclear spin system for the first time. The
state preparation and measurements are performed with high-fidelity operations
in our system. The entropy of randomness in the experimental data is quantified
by two dimension witness certification protocols, which require no detailed
models to describe the experimental devices but only some general assumptions,
such as the limited dimensionality and the independence of the experimental
devices.


title:
Deciding cuspidality of manipulators through computer algebra and
  algorithms in real algebraic geometry

abstract:
  Cuspidal robots are robots with at least two inverse kinematic solutions that
can be connected by a singularity-free path. Deciding the cuspidality of a
generic 3R robots has been studied in the past, but extending the study to
six-degree-of-freedom robots can be a challenging problem. Many robots can be
modeled as a polynomial map together with a real algebraic set so that the
notion of cuspidality can be extended to these data.In this paper we design an
algorithm that, on input a polynomial map in $n$ indeterminates, and $s$
polynomials in the same indeterminates describing a real algebraic set of
dimension $d$, decides the cuspidality of the restriction of the map to the
real algebraic set under consideration. Moreover, if $D$ and $\tau$ are
respectively the maximum degree and a bound on the bit size of the coefficients
of the input polynomials, this algorithm runs in time log-linear in $\tau$ and
polynomial in $((s+d)nD)^{O(n^2)}$.It relies on many high-level algorithms in
computer algebra which use advanced methods on real algebraic sets and critical
loci of polynomial maps. As far as we know, this is the first algorithm that
tackles the cuspidality problem from a general point of view.


title:
Multi-Objective reward generalization: Improving performance of Deep
  Reinforcement Learning for selected applications in stock and cryptocurrency
  trading

abstract:
  We investigate the potential of Multi-Objective, Deep Reinforcement Learning
for stock and cryptocurrency trading. More specifically, we build on the
generalized setting \`a la Fontaine and Friedman arXiv:1809.06364 (where the
reward weighting mechanism is not specified a priori, but embedded in the
learning process) by complementing it with computational speed-ups, and adding
the cumulative reward's discount factor to the learning process. Firstly, we
verify that the resulting Multi-Objective algorithm generalizes well, and we
provide preliminary statistical evidence showing that its prediction is more
stable than the corresponding Single-Objective strategy's. Secondly, we show
that the Multi-Objective algorithm has a clear edge over the corresponding
Single-Objective strategy when the reward mechanism is sparse (i.e., when
non-null feedback is infrequent over time). Finally, we discuss the
generalization properties of the discount factor. The entirety of our code is
provided in open source format.


title:
Equilibrium-Independent Stability Analysis for Distribution Systems with
  Lossy Transmission Lines

abstract:
  Power distribution systems are becoming much more active with increased
penetration of distributed energy resources. Because of the intermittent nature
of these resources, the stability of distribution systems under large
disturbances and time-varying conditions is becoming a key issue in practical
operations. Because the transmission lines in distribution systems are lossy,
standard approaches in power system stability analysis do not readily apply and
the understanding of transient stability remains open even for simplified
models.
  This paper proposes a novel equilibrium-independent transient stability
analysis of distribution systems with lossy lines. We certify network-level
stability by breaking the network into subsystems, and by looking at the
equilibrium-independent passivity of each subsystem, the network stability is
certified through a diagonal stability property of the interconnection matrix.
This allows the analysis scale to large networked systems with time-varying
equilibria. The proposed method gracefully extrapolates between lossless and
lossy systems, and provides a simple yet effective approach to optimize control
efforts with guaranteed stability regions. Case studies verify that the
proposed method is much less conservative than existing approaches and also
scales to large systems.


title:
Quenched {\Lambda} spin-orbit splitting by relativistic Fock diagram in
  single-{\Lambda} hypernuclei

abstract:
  We extend the relativistic Hartree-Fock (RHF) theory to study the structure
of single-$\Lambda$ Hypernuclei. The density dependence is taken in both
meson-nucleon and meson-hyperon coupling strengths, and the induced
$\Lambda$-nucleon ($\Lambda N$) effective interactions are determined by
fitting $\Lambda$ separation energies to the experimental data for several
single-$\Lambda$ hypernuclei. The equilibrium of nuclear dynamics described by
the RHF model in normal atomic nuclei, namely, the balance between nuclear
attractive and repulsive interactions, is then found to be drastically changed
in single-$\Lambda$ Hypernuclei, revealing a different role of Fock terms via
$\Lambda$ hyperon from the nucleon exchange. Since only one hyperon exists in a
single-$\Lambda$ hypernucleus, the overwhelmed $\Lambda N$ and $\Lambda\Lambda$
attractions via the Hartree than the $\Lambda\Lambda$ repulsion from the Fock
terms require an alternation of meson-hyperon coupling strengths in RHF to
rebalance the effective nuclear force with the strangeness degree of freedom,
leading to an improved description of $\Lambda$ Dirac mass and correspondingly
a systematically reduced $\sigma$-$\Lambda$ coupling strength
$g_{\sigma\Lambda}$ in current models as compared to those relativistic
mean-field (RMF) approaches without Fock terms. As a result, the effective
$\Lambda$ spin-orbit coupling potential in the ground state of hypernuclei are
suppressed, and these RHF models predict correspondingly a quenching effects in
$\Lambda$ spin-orbit splitting in comparison with the RMF cases. Furthermore,
the $\Lambda$ spin-orbit splitting could decrease efficiently by evolving the
hyperon-relevant couplings $g_{\sigma\Lambda}$ and $g_{\omega\Lambda}$
simultaneously, where to reconcile with the empirical value the RHF models
address a larger parameter space of meson-hyperon couplings.


title:
Concave likelihood-based regression with finite-support response
  variables

abstract:
  We propose likelihood-based methods for regression when the response variable
has finite support. Our work is motivated by the fact that, in practice,
observed data are discrete and bounded. The proposed methods assume a model
which includes models previously considered for interval-censored variables
with log-concave distributions as special cases. The resulting log-likelihood
is concave, which we use to establish asymptotic normality of its maximizer as
the number of observations $n$ tends to infinity with the number of parameters
$d$ fixed, and rates of convergence of $L_1$-regularized estimators when the
true parameter vector is sparse and $d$ and $n$ both tend to infinity with
$\log(d) / n \to 0$. We consider an inexact proximal Newton algorithm for
computing estimates and give theoretical guarantees for its convergence. The
range of possible applications is wide, including but not limited to survival
analysis in discrete time, the modeling of outcomes on scored surveys and
questionnaires, and, more generally, interval-censored regression. The
applicability and usefulness of the proposed methods are illustrated in
simulations and two data examples.


title:
Language Adaptive Cross-lingual Speech Representation Learning with
  Sparse Sharing Sub-networks

abstract:
  Unsupervised cross-lingual speech representation learning (XLSR) has recently
shown promising results in speech recognition by leveraging vast amounts of
unlabeled data across multiple languages. However, standard XLSR model suffers
from language interference problem due to the lack of language specific
modeling ability. In this work, we investigate language adaptive training on
XLSR models. More importantly, we propose a novel language adaptive
pre-training approach based on sparse sharing sub-networks. It makes room for
language specific modeling by pruning out unimportant parameters for each
language, without requiring any manually designed language specific component.
After pruning, each language only maintains a sparse sub-network, while the
sub-networks are partially shared with each other. Experimental results on a
downstream multilingual speech recognition task show that our proposed method
significantly outperforms baseline XLSR models on both high resource and low
resource languages. Besides, our proposed method consistently outperforms other
adaptation methods and requires fewer parameters.


title:
Resonant control of elastic collisions between $^{23}$Na$^{40}$K
  molecules and $^{40}$K atoms

abstract:
  We have demonstrated the resonant control of the elastic scattering cross
sections in the vicinity of Feshbach resonances between $^{23}$Na$^{40}$K
molecules and $^{40}$K atoms by studying the thermalization between them. The
elastic scattering cross sections vary by more than two orders of magnitude
close to the resonance, and can be well described by an asymmetric Fano
profile. The parameters that characterize the magnetically tunable s-wave
scattering length are determined from the elastic scattering cross sections.
The observation of resonantly controlled elastic scattering cross sections
opens up the possibility to study strongly interacting atom-molecule mixtures
and improve our understanding of the complex atom-molecule Feshbach resonances.


title:
Multiply charged naphthalene and its C10H8 isomers: bonding,
  spectroscopy and implications in AGN environments

abstract:
  Naphthalene (C10H8) is the simplest polycyclic aromatic hydrocarbon (PAH) and
an important component in a series of astrochemical reactions involving
hydrocarbons. Its molecular charge state affects the stability of its isomeric
structures, which is specially relevant in ionised astrophysical environments.
We thus perform an extensive computational search for low-energy molecular
structures of neutral, singly, and multiply charged naphthalene and its isomers
with charge states +q = 0-4 and investigate their geometric properties and
bonding situations. We find that isomerisation reactions should be frequent for
higher charged states and that open chains dominate their low-energy
structures. We compute both the scaled-harmonic and anharmonic infrared spectra
of selected low-energy species and provide the calculated scaling factors for
the naphthalene neutral, cation, and dication global minima. All simulated
spectra reproduce satisfactorily the experimental data and, thus, are adequate
for aiding observations. Moreover, the potential presence of these species in
the emission spectra of the circumnuclear regions of active galactic nuclei
(AGNs), with high energetic X-ray photon fluxes, is explored using the
experimental value of the naphthalene photodissociation cross-section,
\sigma_{ph-d}, to determine its half-life, t_{1/2}, at a photon energy of 2.5
keV in a set of relevant sources. Finally, we show that the computed IR bands
of the triply and quadruply charged species are able to reproduce some features
of the selected AGN sources.


title:
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and
  Modality-level Attention Fusion

abstract:
  Multi-modal magnetic resonance (MR) imaging provides great potential for
diagnosing and analyzing brain gliomas. In clinical scenarios, common MR
sequences such as T1, T2 and FLAIR can be obtained simultaneously in a single
scanning process. However, acquiring contrast enhanced modalities such as T1ce
requires additional time, cost, and injection of contrast agent. As such, it is
clinically meaningful to develop a method to synthesize unavailable modalities
which can also be used as additional inputs to downstream tasks (e.g., brain
tumor segmentation) for performance enhancing. In this work, we propose an
end-to-end framework named Modality-Level Attention Fusion Network (MAF-Net),
wherein we innovatively conduct patchwise contrastive learning for extracting
multi-modal latent features and dynamically assigning attention weights to fuse
different modalities. Through extensive experiments on BraTS2020, our proposed
MAF-Net is found to yield superior T1ce synthesis performance (SSIM of 0.8879
and PSNR of 22.78) and accurate brain tumor segmentation (mean Dice scores of
67.9%, 41.8% and 88.0% on segmenting the tumor core, enhancing tumor and whole
tumor).