title: On the Representation Collapse of Sparse Mixture of Experts

abstract:
  Sparse mixture of experts provides larger model capacity while requiring a
constant computational overhead. It employs the routing mechanism to distribute
input tokens to the best-matched experts according to their hidden
representations. However, learning such a routing mechanism encourages token
clustering around expert centroids, implying a trend toward representation
collapse. In this work, we propose to estimate the routing scores between
tokens and experts on a low-dimensional hypersphere. We conduct extensive
experiments on cross-lingual language model pre-training and fine-tuning on
downstream tasks. Experimental results across seven multilingual benchmarks
show that our method achieves consistent gains. We also present a comprehensive
analysis on the representation and routing behaviors of our models. Our method
alleviates the representation collapse issue and achieves more consistent
routing than the baseline mixture-of-experts methods.


title: Quantum Theory of Nonlinear Thermal Response

abstract:
  The Linear behavior of thermal transport has been widely explored, both
theoretically and ex?perimentally. On the other hand, the nonlinear thermal
response has not been fully discussed. In light of the thermal vector potential
theory [Phys. Rev. Lett. 114, 196601 (2015)], we develop a general formulation
to calculate the linear and nonlinear dynamic thermal responses. In the DC
limit, we recover the well-known Mott relation and the Wiedemann-Franz (WF) law
at the linear order response, which link the thermoelectric conductivity
{\eta}, thermal conductivity \k{appa} and electric conductivity {\sigma}
together. To be specific, the linear Mott relation describes the linear {\eta}
is proportional to the first derivative of {\sigma} with respect to Fermi
energy (for brevity we call the first derivative, the others are similar); and
the linear WF law shows the linear \k{appa} is proportional to the zero
derivative (i.e. the {\sigma} itself). We found there are higher-order Mott
relation and WF law which follow an order-dependent relation. At the second
order, the Mott relation indicates that the second order {\sigma} is
proportional to the zero derivative of the second order {\eta}; but the second
WF law shows that the second {\sigma} is proportional to the first derivative
of \k{appa}. At the third order, the derivative order in?creases once. Although
we only did explicit calculate up to the third order response, we can deduce
that the n-th order electric conductivity is proportional to the n-2-th
derivative of the n-th order thermoelectric conductivity for the nonlinear Mott
relation; and the n-th order electric conductivity is proportional to the
n-1-th derivative of the n-th order thermal conductivity for the nonlinear WF
law.


title: CLASSY III: The Properties of Starburst-Driven Warm Ionized Outflows

abstract:
  We report the results of analyses of galactic outflows in a sample of 45
low-redshift starburst galaxies in the COS Legacy Archive Spectroscopic SurveY
(CLASSY), augmented by five additional similar starbursts with COS data. The
outflows are traced by blueshifted absorption-lines of metals spanning a wide
range of ionization potential. The high quality and broad spectral coverage of
CLASSY data enable us to disentangle the absorption due to the static ISM from
that due to outflows. We further use different line multiplets and doublets to
determine the covering fraction, column density, and ionization state as a
function of velocity for each outflow. We measure the outflow's mean velocity
and velocity width, and find that both correlate in a highly significant way
with the star-formation rate, galaxy mass, and circular velocity over ranges of
four orders-of-magnitude for the first two properties. We also estimate outflow
rates of metals, mass, momentum, and kinetic energy. We find that, at most,
only about 20% of silicon created and ejected by supernovae in the starburst is
carried in the warm phase we observe. The outflows' mass-loading factor
increases steeply and inversely with both circular and outflow velocity
(log-log slope $\sim$ -1.6), and reaches $\sim 10$ for dwarf galaxies. We find
that the outflows typically carry about 10 to 100% of the momentum injected by
massive stars and about 1 to 20% of the kinetic energy. We show that these
results place interesting constraints on, and new insights into, models and
simulations of galactic winds.


title: Cosmological memory effect in scalar-tensor theories

abstract:
  The cosmological memory effect is a permanent change in the relative
separation of test particles located in a FLRW spacetime due to the passage of
gravitational waves. In the case of a spatially flat FLRW spacetime filled with
a perfect fluid in general relativity, it is known that only tensor
perturbations contribute to the memory effect while scalar and vector
perturbations do not. In this paper, we show that in the context of
scalar-tensor theories, the scalar perturbations associated to the scalar
graviton contribute to the memory effect as well. We find that, depending on
the mass and coupling, the influence of cosmic expansion on the memory effect
due to the scalar perturbations can be either stronger or weaker than the one
induced by the tensor perturbations. As a byproduct, in an appendix, we develop
a general framework which can be used to study coupled wave equations in any
curved spacetime region which admits a foliation by time slices.


title:
Robustness Testing of Data and Knowledge Driven Anomaly Detection in
  Cyber-Physical Systems

abstract:
  The growing complexity of Cyber-Physical Systems (CPS) and challenges in
ensuring safety and security have led to the increasing use of deep learning
methods for accurate and scalable anomaly detection. However, machine learning
(ML) models often suffer from low performance in predicting unexpected data and
are vulnerable to accidental or malicious perturbations. Although robustness
testing of deep learning models has been extensively explored in applications
such as image classification and speech recognition, less attention has been
paid to ML-driven safety monitoring in CPS. This paper presents the preliminary
results on evaluating the robustness of ML-based anomaly detection methods in
safety-critical CPS against two types of accidental and malicious input
perturbations, generated using a Gaussian-based noise model and the Fast
Gradient Sign Method (FGSM). We test the hypothesis of whether integrating the
domain knowledge (e.g., on unsafe system behavior) with the ML models can
improve the robustness of anomaly detection without sacrificing accuracy and
transparency. Experimental results with two case studies of Artificial Pancreas
Systems (APS) for diabetes management show that ML-based safety monitors
trained with domain knowledge can reduce on average up to 54.2% of robustness
error and keep the average F1 scores high while improving transparency.


title: A note on the generalized Geroch conjecture for complete spin manifolds

abstract:
  Let $W$ be a closed area enlargeable manifold in the sense of Gromov-Lawson
and $M$ a noncompact spin manifold, we show that the connected sum $M\# W$
admits no complete metric of positive scalar curvature. When $W=T^n$, this
provides a positive answer to the generalized Geroch conjecture in the spin
setting.


title:
Optimality Conditions and Numerical Algorithms for A Class of Linearly
  Constrained Minimax Optimization Problems

abstract:
  It is well known that there have been many numerical algorithms for solving
nonsmooth minimax problems, numerical algorithms for nonsmooth minimax problems
with joint linear constraints are very rare. This paper aims to discuss
optimality conditions and develop practical numerical algorithms for minimax
problems with joint linear constraints. First of all, we use the properties of
proximal mapping and KKT system to establish optimality conditions. Secondly,
we propose a framework of alternating coordinate algorithm for the minimax
problem and analyze its convergence properties. Thirdly, we develop a proximal
gradient multi-step ascent decent method (PGmsAD) as a numerical algorithm and
demonstrate that the method can find an $\epsilon$-stationary point for this
kind of nonsmooth nonconvex-nonconcave problem in ${\cal
O}(\epsilon^{-2}\log\epsilon^{-1})$ iterations. Finally, we apply PGmsAD to
generalized absolute value equations, generalized linear projection equations
and linear regression problems and report the efficiency of PGmsAD on
large-scale optimization.


title:
Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud
  Completion

abstract:
  Point clouds scanned by real-world sensors are always incomplete, irregular,
and noisy, making the point cloud completion task become increasingly more
important. Though many point cloud completion methods have been proposed, most
of them require a large number of paired complete-incomplete point clouds for
training, which is labor exhausted. In contrast, this paper proposes a novel
Reconstruction-Aware Prior Distillation semi-supervised point cloud completion
method named RaPD, which takes advantage of a two-stage training scheme to
reduce the dependence on a large-scale paired dataset. In training stage 1, the
so-called deep semantic prior is learned from both unpaired complete and
unpaired incomplete point clouds using a reconstruction-aware pretraining
process. While in training stage 2, we introduce a semi-supervised prior
distillation process, where an encoder-decoder-based completion network is
trained by distilling the prior into the network utilizing only a small number
of paired training samples. A self-supervised completion module is further
introduced, excavating the value of a large number of unpaired incomplete point
clouds, leading to an increase in the network's performance. Extensive
experiments on several widely used datasets demonstrate that RaPD, the first
semi-supervised point cloud completion method, achieves superior performance to
previous methods on both homologous and heterologous scenarios.


title:
Ordinal-ResLogit: Interpretable Deep Residual Neural Networks for
  Ordered Choices

abstract:
  This study presents an Ordinal version of Residual Logit (Ordinal-ResLogit)
model to investigate the ordinal responses. We integrate the standard ResLogit
model into COnsistent RAnk Logits (CORAL) framework, classified as a binary
classification algorithm, to develop a fully interpretable deep learning-based
ordinal regression model. As the formulation of the Ordinal-ResLogit model
enjoys the Residual Neural Networks concept, our proposed model addresses the
main constraint of machine learning algorithms, known as black-box. Moreover,
the Ordinal-ResLogit model, as a binary classification framework for ordinal
data, guarantees consistency among binary classifiers. We showed that the
resulting formulation is able to capture underlying unobserved heterogeneity
from the data as well as being an interpretable deep learning-based model.
Formulations for market share, substitution patterns, and elasticities are
derived. We compare the performance of the Ordinal-ResLogit model with an
Ordered Logit Model using a stated preference (SP) dataset on pedestrian wait
time and a revealed preference (RP) dataset on travel distance. Our results
show that Ordinal-ResLogit outperforms the traditional ordinal regression model
for both datasets. Furthermore, the results obtained from the Ordinal-ResLogit
RP model show that travel attributes such as driving and transit cost have
significant effects on choosing the location of non-mandatory trips. In terms
of the Ordinal-ResLogit SP model, our results highlight that the road-related
variables and traffic condition are contributing factors in the prediction of
pedestrian waiting time such that the mixed traffic condition significantly
increases the probability of choosing longer waiting times.


title: Functional Covering of Point Processes

abstract:
  We introduce a new distortion measure for point processes called
functional-covering distortion. It is inspired by intensity theory and is
related to both the covering of point processes and logarithmic loss
distortion. We obtain the distortion-rate function with feedforward under this
distortion measure for a large class of point processes. For Poisson processes,
the rate-distortion function is obtained under a general condition called
constrained functional-covering distortion, of which both covering and
functional-covering are special cases. Also for Poisson processes, we
characterize the rate-distortion region for a two-encoder CEO problem and show
that feedforward does not enlarge this region.


title: Numerical solution to the time-independent Gross-Pitaevskii equation

abstract:
  We solve the time-independent Gross-Pitaevskii equation modeling the
Bose-Einstein condensate trapped in an anistropic harmonic potential using a
pseudospectral method. Numerically obtained values for an energy and a chemical
potential for the condensate with positive and negative scattering length have
been compared with those from the literature. The results show that they are in
good agreement when an atomic interaction is not too strong.


title:
Phase-Shift Design and Channel Modeling for Focused Beams in
  IRS-Assisted FSO Systems

abstract:
  Interest in free-space optics (FSO) is rapidly growing as a potential
solution for the backhaul of next-generation mobile or low-orbit satellite
communications. Various techniques have been suggested for employing an
intelligent reflecting surface (IRS) in FSO systems, such as anomalous
reflection, power amplification, and beam splitting. It is possible to deliver
more power to the receiver (Rx) by collimating or focusing the reflected beam
at the Rx lens. In this study, we propose a phase-shift design of an IRS for
beam focusing. In addition, we propose a new pointing error model and an outage
performance analysis applicable when the beam width is comparable to or less
than the aperture size of the Rx. The analytical results are validated by Monte
Carlo simulations. This study provides essential preliminary results for future
researches that assume a focused beam in FSO systems.


title:
Unleashing the Power of Compiler Intermediate Representation to Enhance
  Neural Program Embeddings

abstract:
  Neural program embeddings have demonstrated considerable promise in a range
of program analysis tasks, including clone identification, program repair, code
completion, and program synthesis. However, most existing methods generate
neural program embeddings directly from the program source codes, by learning
from features such as tokens, abstract syntax trees, and control flow graphs.
This paper takes a fresh look at how to improve program embeddings by
leveraging compiler intermediate representation (IR). We first demonstrate
simple yet highly effective methods for enhancing embedding quality by training
embedding models alongside source code and LLVM IR generated by default
optimization levels (e.g., -O2). We then introduce IRGen, a framework based on
genetic algorithms (GA), to identify (near-)optimal sequences of optimization
flags that can significantly improve embedding quality.


title: Rigidity transitions in zero-temperature polygons

abstract:
  We study geometrical clues of a rigidity transition due to the emergence of a
system-spanning state of self stress in under-constrained systems of individual
polygons and spring networks constructed from such polygons. When a polygon
with harmonic bond edges and an area spring constraint is subject to an
expansive strain, we observe that convexity of the polygon is a necessary
condition for such a self stress. We prove that the cyclic configuration of the
polygon is a sufficient condition for the self stress. This correspondence of
geometry and rigidity is akin to the straightening of a one dimensional chain
of springs to rigidify it. We predict the onset of the rigidity transition
using a purely geometrical method. We also estimate the transition strain for a
given initial configuration by approximating irregular polygons as regular
polygons. These findings help determine the rigidity of an area-preserving
polygon just by looking at it. Since two-dimensional spring networks can be
considered as a network of polygons, we look for similar geometric features in
under-constrained spring networks under isotropic expansive strain. In
particular, we observe that all polygons attain convexity at the rigidity
transition such that the fraction of convex, but not cyclic, polygons predicts
the onset of the rigidity transition. Interestingly, acyclic polygons in the
network correlate with larger tensions, thus, forming effective force chains.


title: Functional Calibration under Non-Probability Survey Sampling

abstract:
  Non-probability sampling is prevailing in survey sampling, but ignoring its
selection bias leads to erroneous inferences. We offer a unified nonparametric
calibration method to estimate the sampling weights for a non-probability
sample by calibrating functions of auxiliary variables in a reproducing kernel
Hilbert space. The consistency and the limiting distribution of the proposed
estimator are established, and the corresponding variance estimator is also
investigated. Compared with existing works, the proposed method is more robust
since no parametric assumption is made for the selection mechanism of the
non-probability sample. Numerical results demonstrate that the proposed method
outperforms its competitors, especially when the model is misspecified. The
proposed method is applied to analyze the average total cholesterol of Korean
citizens based on a non-probability sample from the National Health Insurance
Sharing Service and a reference probability sample from the Korea National
Health and Nutrition Examination Survey.


title: Refinement on spectral Tur\'{a}n's theorem

abstract:
  A well-known result in extremal spectral graph theory, known as Nosal's
theorem, states that if $G$ is a triangle-free graph on $n$ vertices, then
$\lambda (G) \le \lambda (K_{\lfloor \frac{n}{2}\rfloor, \lceil \frac{n}{2}
\rceil })$, equality holds if and only if $G=K_{\lfloor \frac{n}{2}\rfloor,
\lceil \frac{n}{2} \rceil }$. Nikiforov [Linear Algebra Appl. 427 (2007)]
extended Nosal's theorem to $K_{r+1}$-free graphs for every integer $r\ge 2$.
This is known as the spectral Tur\'{a}n theorem. Recently, Lin, Ning and Wu
[Combin. Probab. Comput. 30 (2021)] proved a refinement on Nosal's theorem for
non-bipartite triangle-free graphs. In this paper, we provide alternative
proofs for the result of Nikiforov and the result of Lin, Ning and Wu. Our
proof can allow us to extend the later result to non-$r$-partite $K_{r+1}$-free
graphs. Our result refines the theorem of Nikiforov and it also can be viewed
as a spectral version of a theorem of Brouwer.


title:
Experimental Simulations of Shock Textures in BCC Iron: Implications for
  Iron Meteorites

abstract:
  Neumann band in iron meteorites, which is deformation twins in kamacite
(Fe-Ni alloy), has been known to be a characteristic texture indicating ancient
collisions on parent bodies of meteorites. We conducted a series of shock
recovery experiments on bcc iron with the projectile velocity at 1.5 km/sec at
various initial temperatures, room temperature, 670 K, and 1100 K, and
conducted an annealing experiment on the shocked iron. We also conducted
numerical simulations with the iSALE-2D code to investigate peak pressure and
temperature distributions in the nontransparent targets. The effects of
pressure and temperature on the formation and disappearance of the twins
(Neumann band) were explored based on laboratory and numerical experiments. The
twin was formed in the run products of the experiments conducted at room
temperature and 670 K, whereas it was not observed in the run product formed by
the impact at 1100 K. The present experiments combined with the numerical
simulations revealed that the twin was formed by impacts with various shock
pressures from 1.5-2 GPa to around 13 GPa. The twin in iron almost disappeared
by annealing at 1070 K. The iron meteorites with Neumann bands were shocked at
this pressure range and temperatures at least up to 670 K, and were not heated
to the temperatures above 1070 K after the Neumann band formation.


title:
On the pressure gain of stable flow systems with variable cross-section
  area

abstract:
  For the clarification of total pressure gain performance of rotating
detonation propulsion systems, the extended Hugoniot curve is proposed and
discussed for the stable flow systems with variable cross-section area (SFSVA).
The dimensionless pressure integral ({\theta}) along the system walls is found
to have critical impact on the pressure gain of the SFSVA with given inlet Mach
number (M0) and heat release (q). The key to obtain positive pressure gain of a
SFSVA is to achieve the matching {\theta}, M0 and q.


title:
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and
  Results

abstract:
  In this paper, we summarize the 1st NTIRE challenge on stereo image
super-resolution (restoration of rich details in a pair of low-resolution
stereo images) with a focus on new solutions and results. This challenge has 1
track aiming at the stereo image super-resolution problem under a standard
bicubic degradation. In total, 238 participants were successfully registered,
and 21 teams competed in the final testing phase. Among those participants, 20
teams successfully submitted results with PSNR (RGB) scores better than the
baseline. This challenge establishes a new benchmark for stereo image SR.


title:
Search for a lighter neutral custodial fiveplet scalar in the
  Georgi-Machacek model

abstract:
  Many researches from both the theoretical and experimental sides have been
performed to search for a new Higgs Boson lighter than the 125 $GeV$ Higgs
boson which was discovered at the LHC in 2012. In this paper we explore the
possibility of constraining a lighter neutral custodial fiveplet scalar
$H_{5}^{0}$ in the Georgi-Machacek (GM) model by the latest results of the
search for a lighter Higgs boson decaying into two photons from LHC data. The
custodial-singlet mass eigenstate $h$ or $H$ is considered to be the LHC
observed 125 $GeV$ Higgs boson. A new set of constrained parameters that is
favoured by low-mass $H_{5}^{0}$, is proposed to generate events efficiently.
The production of $H_{5}^{0}$ from the scan based on the constrained parameters
is compared to the latest results of the search for a lighter Higgs boson
decaying into two photons by CMS Collaboration, after the theoretical
constraints from GM model and the constraints from all existing relevant
experimental measurements including the recent results of the Higgs boson
searches from the LHC. The numerical analyses of the surviving GM parameter
space are performed. The tendencies and correlations of the GM input parameters
from the phenomenological studies are summarized. In addition the discovery
potential of other interesting decay channels of this low-mass neutral
custodial fiveplet scalar are discussed.


title:
Role of long-range coupling on the properties of single polarons in
  models with dual electron-phonon couplings

abstract:
  We use the Variational Exact Diagonalization to investigate the single
polaron properties for four different dual models, combining a short-range
off-diagonal (Peierls) plus a longer-range diagonal (Holstein or
breathing-mode) coupling. This allows us to investigate the sensitivity of
various polaron properties both to the range of the diagonal coupling and to
the specific diagonal coupling chosen. We find strong sensitivity to the range
for all duals models as the adiabatic limit is approached, however considerable
sensitivity is observed for some quantities even for large phonon frequencies.
Also, the strong dependence of the results on the specific form of the diagonal
coupling is observed everywhere in the parameter space. Taken together, these
results suggest that careful consideration must be given to the specific
coupling and its proper range when quantitative comparisons with experiments
are sought.


title: Stellar Atmospheric Parameters of M-type Stars from LAMOST DR8

abstract:
  The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low
Resolution Spectroscopic Survey (LRS) provides massive spectroscopic data of
M-type stars, and the derived stellar parameters could bring vital help to
various studies. We adopt the ULySS package to perform $\chi^2$ minimization
with model spectra generated from the MILES interpolator, and determine the
stellar atmospheric parameters for the M-type stars from LAMOST LRS Data
Release (DR) 8. Comparison with the stellar parameters from APOGEE Stellar
Parameter and Chemical Abundance Pipeline (ASPCAP) suggests that most of our
results have good consistency. For M dwarfs, we achieve dispersions better than
74 K, 0.19 dex and 0.16 dex for $T_{\rm eff}$, $\log{g}$ and [Fe/H], while for
M giants, the internal uncertainties are 58 K, 0.32 dex and 0.26 dex,
respectively. Compared to ASPCAP we also find a systematic underestimation of
$\Delta {T_{\rm eff}} =$ $-$176 K for M dwarfs, and a systematic overestimation
of $\Delta {\log{g}} =$ 0.30 dex for M giants. However, such differences are
less significant when we make comparison with common stars from other
literature, which indicates that systematic biases exist in the difference of
ASPCAP and other measurements. A catalog of 763,136 spectra corresponding to
616,314 M-type stars with derived stellar parameters is presented. We determine
the stellar parameters for stars with $T_{\rm eff}$ higher than 2,900 K, with
$\log{g}$ from -0.24 dex to 5.9 dex. The typical precisions are 45 K, 0.25 dex
and 0.22 dex, for $T_{\rm eff}$, $\log{g}$ and [Fe/H], respectively, which are
estimated from the duplicate observations of the same stars.


title:
Scotogenic Dirac neutrino model embedded with leptoquarks: one pathway
  to addressing all

abstract:
  If the leptoquarks proposed to account for the intriguing anomalies observed
in the $B$-meson decays, $R_{D^{(\ast)}}$ and $R_{K^{(\ast)}}$, as well as in
the anomalous magnetic moment of the muon, $(g-2)_\mu$, can be embedded into
the scotogenic Dirac neutrino model, all these flavor anomalies, together with
the origin of neutrino masses and the nature of dark matter, would be
potentially addressed in a unified picture. Among the minimal seesaw, one-loop,
and two-loop realizations of the dimension-4 effective operator
$\mathcal{L}_{4}$ for the Dirac neutrino masses, we show that plenty of
diagrams associated with the two-loop realizations of $\mathcal{L}_{4}$ can
support the coexistence of leptoquarks and dark matter candidates. After a
simple match of these leptoquarks to those that can accommodate all the flavor
anomalies, we establish the scotogenic Dirac neutrino models embedded with
leptoquarks, which could address all the problems mentioned above.


title: A Best Cost-Sharing Rule for Selfish Bin Packing

abstract:
  In selfish bin packing, each item is regarded as a player, who aims to
minimize the cost-share by choosing a bin it can fit in. To have a least number
of bins used, cost-sharing rules play an important role. The currently best
known cost sharing rule has a lower bound on $PoA$ larger than 1.45, while a
general lower bound 4/3 on $PoA$ applies to any cost-sharing rule under which
no items have incentive unilaterally moving to an empty bin. In this paper, we
propose a novel and simple rule with a $PoA$ matching the lower bound, thus
completely resolving this game. The new rule always admits a Nash equilibrium
and its $PoS$ is one. Furthermore, the well-known bin packing algorithm $BFD$
(Best-Fit Decreasing) is shown to achieve a strong equilibrium, implying that a
stable packing with an asymptotic approximation ratio of $11/9$ can be produced
in polynomial time.


title: Laser pulse shape designer for direct-drive inertial confinement fusion

abstract:
  A pulse shape designer for direct drive inertial confinement fusion has been
developed, it aims at high compression of the fusion fuel while keeping
hydrodynamics instability within tolerable level. Fast linear analysis on
implosion instability enables the designer to fully scan the vast pulse
configuration space at a practical computational cost, machine learning helps
to summarize pulse performance into an implicit scaling metric that promotes
the pulse shape evolution. The designer improves its credibility by
incorporating various datasets including extra high-precision simulations or
experiments. When tested on the double-cone ignition scheme [J. Zhang et al,
Phil. Trans. R. Soc. A. 378.2184 (2020)], optimized pulses reach the assembly
requirements, show significant imprint mitigation and adiabatic shaping
capability, and have the potential to achieve better implosion performance in
real experiments. This designer serves as an efficient alternative to
traditional empirical pulse shape tuning procedure, reduces workload and time
consumption. The designer can be used to quickly explore the unknown parameter
space for new direct-drive schemes, assists design iteration and reduces
experiment risk.


title:
Interventional Multi-Instance Learning with Deconfounded Instance-Level
  Prediction

abstract:
  When applying multi-instance learning (MIL) to make predictions for bags of
instances, the prediction accuracy of an instance often depends on not only the
instance itself but also its context in the corresponding bag. From the
viewpoint of causal inference, such bag contextual prior works as a confounder
and may result in model robustness and interpretability issues. Focusing on
this problem, we propose a novel interventional multi-instance learning (IMIL)
framework to achieve deconfounded instance-level prediction. Unlike traditional
likelihood-based strategies, we design an Expectation-Maximization (EM)
algorithm based on causal intervention, providing a robust instance selection
in the training phase and suppressing the bias caused by the bag contextual
prior. Experiments on pathological image analysis demonstrate that our IMIL
method substantially reduces false positives and outperforms state-of-the-art
MIL methods.


title:
Energetics of three interacting mass-imbalanced bodies in a
  three-dimensional spherical harmonic trap

abstract:
  We consider a system of three particles, either three identical bosons or two
identical fermions plus an impurity, within an isotropic three-dimensional trap
interacting via a contact interaction. Using two approaches, one using an
infinite sum of basis states for the wavefunction and the other a closed form
wavefunction, we calculate the allowable energy eigenstates of the system as a
function of the interaction strength, including the strongly and weakly
interacting limits. For the fermionic case this is done while maintaining
generality regarding particle masses. We find that the two methods of
calculating the spectrum are in excellent agreement and we find that both
methods are unable to specify the energy of Efimov states and in fact the
energies of Efimov states diverge in the same manner. We show that the numeric
limitations of the summation approach correspond to the effective range
boundary condition of the closed form approach to a high degree of accuracy.


title: Noncolliding Macdonald walks with an absorbing wall

abstract:
  The branching rule is one of the most fundamental properties of the Macdonald
symmetric polynomials. It expresses a Macdonald polynomial as a nonnegative
linear combination of Macdonald polynomials with smaller number of variables.
Taking a limit of the branching rule under the principal specialization when
the number of variables goes to infinity, we obtain a Markov chain of $m$
noncolliding particles with negative drift and an absorbing wall at zero. The
chain depends on the Macdonald parameters $(q,t)$ and may be viewed as a
discrete deformation of the Dyson Brownian motion. The trajectory of the Markov
chain is equivalent to a certain Gibbs ensemble of plane partitions with an
arbitrary cascade front wall.
  In the Jack limit $q=t^{\beta/2}\to1$ the absorbing wall disappears, and the
Macdonald noncolliding walks turn into the $\beta$-noncolliding random walks
studied by Huang (arXiv:1708.07115). Taking $q=0$ (Hall-Littlewood
degeneration) and further sending $t\to 1$, we obtain a continuous time
particle system on $\mathbb{Z}_{\ge0}$ with inhomogeneous jump rates and
absorbing wall at zero.


title: Radio signal of PSR B0950+08 is detected over the whole phase

abstract:
  Pulsars, known as the "lighthouses" in the universe, are thought to emit
periodic pulses with duty-cycle ~10%. In this report, the 160,min-data of a
nearby pulsar, PSR B0950+08, observed with the Five-hundred-meter Aperture
Spherical radio Telescope (FAST) is analysed. Thanks to the extremely high
sensitivity of FAST, it is found that the radiation of PSR B0950+08 could be
detected over the entire pulse period. To investigate the radiative
characteristics of the pulsar's bridge emission, a function, \Theta(n), is
defined to reveal the weak radiation there. It is suggested that the narrow
peaks of both the main and the inter pulses could be radiated at low altitude,
while other weak emission (e.g., the "bridges") from high magnetosphere far
away from the surface though its radiative mechanism is still a matter of
debate. The measured mean pulse behaviors are consistent with previous results
in the phase of strong emission, and both the frequency-independent separation
between the interpulse and main pulse and the narrow pulse width may support a
double-pole model. Nonetheless, in order to finalize the magnetospheric
geometry, further polarization observation with FAST is surely required, which
would only be believable in the phase of weak emission if the baseline is
determined with certainty in the future.


title: Optimizing doping parameters of target to enhance direct-drive implosion

abstract:
  Direct-drive is an important approach to achieving the ignition of inertial
confinement fusion. To enhance implosion performance while keeping the risk of
hydrodynamic instability at a low level, we have designed a procedure to
optimize the parameters of the target doped with mid- or high-$Z$ atoms. In the
procedure, a one-dimensional implosion can be automatically simulated, while
its implosion performance and high-dimensional instability are integrally
evaluated at the same time. To find the optimal doping parameters, the
procedure is performed in the framework of global optimization algorithm, where
we have used the particle swarm optimization in the current work. In the
optimization, the opacity of mixture materials is quickly obtained by using an
interpolation method, showing only a slight difference from the data of TOPS,
which is an online doping program of Los Alamos National Laboratory. To test
the procedure, optimization has been carried out for the CH ablator in the
double cone ignition scheme [Phil. Trans. R. Soc. A. 378.2184 (2020)] by doping
with Si and Cl. Both one- and two-dimensional simulations show that doping with
either Si or Cl can efficiently mitigate the instability during the
acceleration phase and does not result in significant degradation of the peak
areal density. The results from one- and two-dimensional simulations
qualitatively match with each other, demonstrating the validity of our
optimization procedure.


title:
Who Is Missing? Characterizing the Participation of Different
  Demographic Groups in a Korean Nationwide Daily Conversation Corpus

abstract:
  A conversation corpus is essential to build interactive AI applications.
However, the demographic information of the participants in such corpora is
largely underexplored mainly due to the lack of individual data in many
corpora. In this work, we analyze a Korean nationwide daily conversation corpus
constructed by the National Institute of Korean Language (NIKL) to characterize
the participation of different demographic (age and sex) groups in the corpus.


title: Does Interference Exist When Training a Once-For-All Network?

abstract:
  The Once-For-All (OFA) method offers an excellent pathway to deploy a trained
neural network model into multiple target platforms by utilising the
supernet-subnet architecture. Once trained, a subnet can be derived from the
supernet (both architecture and trained weights) and deployed directly to the
target platform with little to no retraining or fine-tuning. To train the
subnet population, OFA uses a novel training method called Progressive
Shrinking (PS) which is designed to limit the negative impact of interference
during training. It is believed that higher interference during training
results in lower subnet population accuracies. In this work we take a second
look at this interference effect. Surprisingly, we find that interference
mitigation strategies do not have a large impact on the overall subnet
population performance. Instead, we find the subnet architecture selection bias
during training to be a more important aspect. To show this, we propose a
simple-yet-effective method called Random Subnet Sampling (RSS), which does not
have mitigation on the interference effect. Despite no mitigation, RSS is able
to produce a better performing subnet population than PS in four
small-to-medium-sized datasets; suggesting that the interference effect does
not play a pivotal role in these datasets. Due to its simplicity, RSS provides
a $1.9\times$ reduction in training times compared to PS. A $6.1\times$
reduction can also be achieved with a reasonable drop in performance when the
number of RSS training epochs are reduced. Code available at
https://github.com/Jordan-HS/RSS-Interference-CVPRW2022.


title:
Sensitivity of the deeply bound pionic atoms to the pion-nucleon sigma
  term $\sigma_{\pi N}$

abstract:
  We discuss the sensitivity of the observables of the deeply bound pionic
atoms to the pion-nucleon sigma term $\sigma_{\pi N}$ to investigate the
possibility of the precise determination of the value of $\sigma_{\pi N}$ by
the accurate data of the deeply bound pionic atoms expected to be obtained at
RIBF/RIKEN. We evaluate that the 1 MeV variation of the $\sigma_{\pi N}$ value
$\Delta \sigma_{\pi N} = 1$ MeV causes the shift of the binding energy $|\Delta
B_\pi (1s)| = 5 \sim 7.5$ keV of the 1$s$ pionic atoms in Sn isotopes for the
cases considered in this article. The width of the $1s$ state in the light Sn
isotopes has good sensitivity to the $\sigma_{\pi N}$ value, too. We also study
the sensitivity of the formation spectra of the deeply bound pionic atoms to
the value of the $\sigma_{\pi N}$ term. The combined analyses of the
observables of the deeply bound pionic atoms are found to be helpful to
determine the $\sigma_{\pi N}$ term precisely. One of the interesting
combination of the observables is the energy gap of the 1$s$ and $2p$ states
($B_{\pi}(1s) - B_\pi(2p)$) which experimental error is significantly smaller
than that of the absolute value of the binding energy itself of each state. The
expected experimental error of the energy gap is $10 \sim 15$ keV in Sn region
which corresponds to the uncertainty of the $\sigma_{\pi N}$ value around 3 MeV
in our evaluation.


title:
Primary accelerometer calibration with two-axis automatic positioning
  stage

abstract:
  In this study, we developed an automated, multipoint primary accelerometer
calibration system using a two-axis positioning stage and a heterodyne laser
interferometer. The proposed system offers low-cost, convenient, and automated
multipoint accelerometer calibration, enabling less calibration lead time. The
positioning stage also offers better positioning repeatability of 1 um, which
is impossible through manual alignments. We measured the surface deformation of
a laser reflection adaptor for a single-ended accelerometer by measuring more
than 450 measurement positions. Visualizing the deformation of laser reflection
surfaces facilitates understanding the effects of deformation or nonrectilinear
motion, which are among the most significant uncertainty components in
high-frequency accelerometer calibrations.


title:
Efficient Progressive High Dynamic Range Image Restoration via Attention
  and Alignment Network

abstract:
  HDR is an important part of computational photography technology. In this
paper, we propose a lightweight neural network called Efficient
Attention-and-alignment-guided Progressive Network (EAPNet) for the challenge
NTIRE 2022 HDR Track 1 and Track 2. We introduce a multi-dimensional
lightweight encoding module to extract features. Besides, we propose
Progressive Dilated U-shape Block (PDUB) that can be a progressive
plug-and-play module for dynamically tuning MAccs and PSNR. Finally, we use
fast and low-power feature-align module to deal with misalignment problem in
place of the time-consuming Deformable Convolutional Network (DCN). The
experiments show that our method achieves about 20 times compression on MAccs
with better mu-PSNR and PSNR compared to the state-of-the-art method. We got
the second place of both two tracks during the testing phase. Figure1. shows
the visualized result of NTIRE 2022 HDR challenge.


title: von Neumann type trace inequality for dual quaternion matrices

abstract:
  As a powerful tool to represent rigid body motion in 3D spaces, dual
quaternions have been successfully applied to robotics, 3D motion modelling and
control, and computer graphics. Due to the important applications in
multi-agent formation control, this paper addresses the concept of spectral
norm of dual quaternion matrices. We introduce a von Neumann type trace
inequality and a Hoffman-Wielandt type inequality for general dual quaternion
matrices, where the latter characterizes a simultaneous perturbation bound on
all singular values of a dual quaternion matrix. In particular, we also present
two variants of the above two inequalities expressed by eigenvalues of dual
quaternion Hermitian matrices. Our results are helpful for the further study of
dual quaternion matrix theory, algorithmic design, and applications.


title: Measurement of cosmogenic $^9$Li and $^8$He production rates at RENO

abstract:
  We report the measured production rates of unstable isotopes $^9$Li and
$^8$He produced by cosmic muon spallation on $^{12}$C using two identical
detectors of the RENO experiment. Their beta-decays accompanied by a neutron
make a significant contribution to backgrounds of reactor antineutrino events
in precise determination of the smallest neutrino mixing angle. The mean muon
energy of its near (far) detector with an overburden of 120 (450) m.w.e. is
estimated as 33.1 +- 2.3 (73.6 +- 4.4) GeV. Based on roughly 3100 days of data,
the cosmogenic production rate of $^9$Li ($^8$He) isotope is measured to be
44.2 +- 3.1 (10.6 +- 7.4) per day at near detector and 10.0 +- 1.1 (2.1 +- 1.5)
per day at far detector. This corresponds to yields of $^9$Li ($^8$He), 4.80 +-
0.36 (1.15 +- 0.81) and 9.9 +- 1.1 (2.1 +- 1.5) at near and far detectors,
respectively, in a unit of 10$^{-8}$ $\mu^{-1}$ g${^-1}$ cm${^2}$. Combining
the measured $^9$Li yields with other available underground measurements, an
excellent power-law relationship of the yield with respect to the mean muon
energy is found to have an exponent of $\alpha$ = 0.76 +- 0.05.


title:
Characterization of GaN-based HEMTs Down to 4.2 K for Cryogenic
  Applications

abstract:
  The cryogenic performance of GaN-based HEMTs (high-electron-mobility
transistors) is systematically investigated by the direct current (DC) and
low-frequency noise (LFN) characteristics within the temperature (T) range from
300 K to 4.2 K. The important electrical merits of the device, including drain
saturation current (IDsat), on-resistance (RON), transductance, subthreshold
swing (SS), gate leakage current, and Schottky barrier height, are
comprehensively characterized and their temperature-dependent behavior was
statistically analyzed. In addition, the LFN of the device shows an evident
behavior of 1/f noise from 10 Hz to 10 kHz in the measured temperature range
and can be significantly reduced at cryogenic temperature. These results are of
great importance to motivate further studies into the GaN-based cryo-devices
and systems.


title: Cumulants of the chiral order parameter at lower RHIC energies

abstract:
  We study cumulants of the chiral order parameter as function of beam energy
as a possible signal for the presence of a critical end point and first-order
phase transition in the QCD phase diagram. We model the expansion of a
heavy-ion collision by a fluid dynamic expansion coupled to the explicit
propagation of the chiral order parameter sigma via a Langevin equation. We
evolve the medium until a parametrized freeze-out condition is met where we
calculate event-by-event fluctuations and cumulants of sigma which are expected
to follow the trend of net-proton number cumulants. We emphasize the role of a
nonequilibrium first-order phase transition: The presence of an unstable phase
causes the well-known bending of the trajectories in the space of temperature
and baryochemical potential. For these cases at lower beam energies, the system
crosses the freeze-out line more than once, allowing us to calculate a range of
cumulants for each initial condition which are overall enhanced for the second
hit of the freeze-out line. We thus find not only the critical end point but
also the phase transition of the underlying model clearly reflected in the
cumulants. The impact of volume fluctuations is demonstrated to play a
measurable role for fluid dynamical evolutions that last significantly long.


title:
Reinforcement Learning with Intrinsic Affinity for Personalized Asset
  Management

abstract:
  The common purpose of applying reinforcement learning (RL) to asset
management is the maximization of profit. The extrinsic reward function used to
learn an optimal strategy typically does not take into account any other
preferences or constraints. We have developed a regularization method that
ensures that strategies have global intrinsic affinities, i.e., different
personalities may have preferences for certain assets which may change over
time. We capitalize on these intrinsic policy affinities to make our RL model
inherently interpretable. We demonstrate how RL agents can be trained to
orchestrate such individual policies for particular personality profiles and
still achieve high returns.


title: An intersection problem of graph-directed attractors and an application

abstract:
  Let $(K_1,\ldots,K_n)$ be a Cantor type graph-directed attractors in
$\mathbb{R}^d$. By creating an auxiliary graph, we provide an effective
criterion of whether $K_i\cap K_j$ is empty for every pair of $1\leq i,j\leq
n$. Moreover, the emptiness can be checked by examining only a finite number of
the attractor's geometric approximations. The method is also applicable for
more general graph-directed systems. As an application, we are able to
determine the connectedness of all $d$-dimensional generalized Sierpi\'nski
sponges of which the corresponding IFSs are allowed to have rotational and
reflectional components.


title:
LingYi: Medical Conversational Question Answering System based on
  Multi-modal Knowledge Graphs

abstract:
  The medical conversational system can relieve the burden of doctors and
improve the efficiency of healthcare, especially during the pandemic. This
paper presents a medical conversational question answering (CQA) system based
on the multi-modal knowledge graph, namely "LingYi", which is designed as a
pipeline framework to maintain high flexibility. Our system utilizes automated
medical procedures including medical triage, consultation, image-text drug
recommendation and record. To conduct knowledge-grounded dialogues with
patients, we first construct a Chinese Medical Multi-Modal Knowledge Graph
(CM3KG) and collect a large-scale Chinese Medical CQA (CMCQA) dataset. Compared
with the other existing medical question-answering systems, our system adopts
several state-of-the-art technologies including medical entity disambiguation
and medical dialogue generation, which is more friendly to provide medical
services to patients. In addition, we have open-sourced our codes which contain
back-end models and front-end web pages at https://github.com/WENGSYX/LingYi.
The datasets including CM3KG at https://github.com/WENGSYX/CM3KG and CMCQA at
https://github.com/WENGSYX/CMCQA are also released to further promote future
research.


title: Vision System of Curling Robots: Thrower and Skip

abstract:
  We built a vision system of curling robot which can be expected to play with
human curling player. Basically, we built two types of vision systems for
thrower and skip robots, respectively. First, the thrower robot drives towards
a given point of curling sheet to release a stone. Our vision system in the
thrower robot initialize 3DoF pose on two dimensional curling sheet and updates
the pose to decide for the decision of stone release. Second, the skip robot
stands at the opposite side of the thrower robot and monitors the state of the
game to make a strategic decision. Our vision system in the skip robot
recognize every stones on the curling sheet precisely. Since the viewpoint is
quite perspective, many stones are occluded by each others so it is challenging
to estimate the accurate position of stone. Thus, we recognize the ellipses of
stone handles outline to find the exact midpoint of the stones using
perspective Hough transform. Furthermore, we perform tracking of a thrown stone
to produce a trajectory for ice condition analysis. Finally, we implemented our
vision systems on two mobile robots and successfully perform a single turn and
even careful gameplay. Specifically, our vision system includes three cameras
with different viewpoint for their respective purposes.


title: K-LITE: Learning Transferable Visual Models with External Knowledge

abstract:
  Recent state-of-the-art computer vision systems are trained from natural
language supervision, ranging from simple object category names to descriptive
captions. This free form of supervision ensures high generality and usability
of the learned visual models, based on extensive heuristics on data collection
to cover as many visual concepts as possible. Alternatively, learning with
external knowledge about images is a promising way which leverages a much more
structured source of supervision. In this paper, we propose K-LITE
(Knowledge-augmented Language-Image Training and Evaluation), a simple strategy
to leverage external knowledge to build transferable visual systems: In
training, it enriches entities in natural language with WordNet and Wiktionary
knowledge, leading to an efficient and scalable approach to learning image
representations that can understand both visual concepts and their knowledge;
In evaluation, the natural language is also augmented with external knowledge
and then used to reference learned visual concepts (or describe new ones) to
enable zero-shot and few-shot transfer of the pre-trained models. We study the
performance of K-LITE on two important computer vision problems, image
classification and object detection, benchmarking on 20 and 13 different
existing datasets, respectively. The proposed knowledge-augmented models show
significant improvement in transfer learning performance over existing methods.


title:
The first- and second-order magneto-optical effects and intrinsically
  anomalous transport in 2D van der Waals layered magnets CrXY (X = S, Se, Te;
  Y = Cl, Br, I)

abstract:
  Recently, the two-dimensional magnetic semiconductor CrSBr has attracted
considerable attention due to its excellent air-stable property and high
magnetic critical temperature. Here, we systematically investigate the
electronic structure, magnetocrystalline anisotropy energy, first-order
magneto-optical effects (Kerr and Faraday effects) and second-order
magneto-optical effects (Schafer-Hubert and Voigt effects) as well as
intrinsically anomalous transport properties (anomalous Hall, anomalous Nernst,
and anomalous thermal Hall effects) of two-dimensional van der Waals layered
magnets CrXY (X = S, Se, Te; Y = Cl, Br, I) by using the first-principles
calculations. Our results show that monolayer and bilayer CrXY (X = S, Se) are
narrow band gap semiconductors, whereas monolayer and bilayer CrTeY are
multi-band metals. The magnetic ground states of bilayer CrXY and the easy
magnetization axis of monolayer and bilayer CrXY are confirmed by the
magnetocrystalline anisotropy energy calculations. Utilizing magnetic group
theory analysis, the first-order magneto-optical effects as well as anomalous
Hall, anomalous Nernst, and anomalous thermal Hall effects are identified to
exist in ferromagnetic state with out-of-plane magnetization. The second-order
magneto-optical effects are not restricted by the above symmetry requirements,
and therefore can arise in ferromagnetic and antiferromagnetic states with
in-plane magnetization. The calculated results are compared with the available
theoretical and experimental data of other two-dimensional magnets and some
conventional ferromagnets. The present work reveals that monolayer and bilayer
CrXY with superior magneto-optical responses and anomalous transport properties
provide an excellent material platform for the promising applications of
magneto-optical devices, spintronics, and spin caloritronics.


title:
Improving Self-Supervised Speech Representations by Disentangling
  Speakers

abstract:
  Self-supervised learning in speech involves training a speech representation
network on a large-scale unannotated speech corpus, and then applying the
learned representations to downstream tasks. Since the majority of the
downstream tasks of SSL learning in speech largely focus on the content
information in speech, the most desirable speech representations should be able
to disentangle unwanted variations, such as speaker variations, from the
content. However, disentangling speakers is very challenging, because removing
the speaker information could easily result in a loss of content as well, and
the damage of the latter usually far outweighs the benefit of the former. In
this paper, we propose a new SSL method that can achieve speaker
disentanglement without severe loss of content. Our approach is adapted from
the HuBERT framework, and incorporates disentangling mechanisms to regularize
both the teacher labels and the learned representations. We evaluate the
benefit of speaker disentanglement on a set of content-related downstream
tasks, and observe a consistent and notable performance advantage of our
speaker-disentangled representations.


title:
Disentangling Spatial-Temporal Functional Brain Networks via
  Twin-Transformers

abstract:
  How to identify and characterize functional brain networks (BN) is
fundamental to gain system-level insights into the mechanisms of brain
organizational architecture. Current functional magnetic resonance (fMRI)
analysis highly relies on prior knowledge of specific patterns in either
spatial (e.g., resting-state network) or temporal (e.g., task stimulus) domain.
In addition, most approaches aim to find group-wise common functional networks,
individual-specific functional networks have been rarely studied. In this work,
we propose a novel Twin-Transformers framework to simultaneously infer common
and individual functional networks in both spatial and temporal space, in a
self-supervised manner. The first transformer takes space-divided information
as input and generates spatial features, while the second transformer takes
time-related information as input and outputs temporal features. The spatial
and temporal features are further separated into common and individual ones via
interactions (weights sharing) and constraints between the two transformers. We
applied our TwinTransformers to Human Connectome Project (HCP) motor task-fMRI
dataset and identified multiple common brain networks, including both
task-related and resting-state networks (e.g., default mode network).
Interestingly, we also successfully recovered a set of individual-specific
networks that are not related to task stimulus and only exist at the individual
level.


title: A Quadratic Harmonic Approximation

abstract:
  We obtain the quadratic term in Euler's asymptotic expansion of the nth
harmonic number by a simple modification of Young's elementary determination of
the linear term.


title: Cross-stitched Multi-modal Encoders

abstract:
  In this paper, we propose a novel architecture for multi-modal speech and
text input. We combine pretrained speech and text encoders using multi-headed
cross-modal attention and jointly fine-tune on the target problem. The
resultant architecture can be used for continuous token-level classification or
utterance-level prediction acting on simultaneous text and speech. The
resultant encoder efficiently captures both acoustic-prosodic and lexical
information. We compare the benefits of multi-headed attention-based fusion for
multi-modal utterance-level classification against a simple concatenation of
pre-pooled, modality-specific representations. Our model architecture is
compact, resource efficient, and can be trained on a single consumer GPU card.


title:
Tight Last-Iterate Convergence of the Extragradient Method for
  Constrained Monotone Variational Inequalities

abstract:
  The monotone variational inequality is a central problem in mathematical
programming that unifies and generalizes many important settings such as smooth
convex optimization, two-player zero-sum games, convex-concave saddle point
problems, etc. The extragradient method by Korpelevich [1976] is one of the
most popular methods for solving monotone variational inequalities. Despite its
long history and intensive attention from the optimization and machine learning
community, the following major problem remains open. What is the last-iterate
convergence rate of the extragradient method for monotone and Lipschitz
variational inequalities with constraints? We resolve this open problem by
showing a tight $O\left(\frac{1}{\sqrt{T}}\right)$ last-iterate convergence
rate for arbitrary convex feasible sets, which matches the lower bound by
Golowich et al. [2020]. Our rate is measured in terms of the standard gap
function. The technical core of our result is the monotonicity of a new
performance measure -- the tangent residual, which can be viewed as an
adaptation of the norm of the operator that takes the local constraints into
account. To establish the monotonicity, we develop a new approach that combines
the power of the sum-of-squares programming with the low dimensionality of the
update rule of the extragradient method. We believe our approach has many
additional applications in the analysis of iterative methods.


title:
Estimating probabilistic dynamic origin-destination demands using
  multi-day traffic data on computational graphs

abstract:
  System-level decision making in transportation needs to understand day-to-day
variation of network flows, which calls for accurate modeling and estimation of
probabilistic dynamic travel demand on networks. Most existing studies estimate
deterministic dynamic origin-destination (OD) demand, while the day-to-day
variation of demand and flow is overlooked. Estimating probabilistic
distributions of dynamic OD demand is challenging due to the complexity of the
spatio-temporal networks and the computational intensity of the
high-dimensional problems. With the availability of massive traffic data and
the emergence of advanced computational methods, this paper develops a
data-driven framework that solves the probabilistic dynamic origin-destination
demand estimation (PDODE) problem using multi-day data. Different statistical
distances (e.g., lp-norm, Wasserstein distance, KL divergence, Bhattacharyya
distance) are used and compared to measure the gap between the estimated and
the observed traffic conditions, and it is found that 2-Wasserstein distance
achieves a balanced accuracy in estimating both mean and standard deviation.
The proposed framework is cast into the computational graph and a
reparametrization trick is developed to estimate the mean and standard
deviation of the probabilistic dynamic OD demand simultaneously. We demonstrate
the effectiveness and efficiency of the proposed PDODE framework on both small
and real-world networks. In particular, it is demonstrated that the proposed
PDODE framework can mitigate the overfitting issues by considering the demand
variation. Overall, the developed PDODE framework provides a practical tool for
public agencies to understand the sources of demand stochasticity, evaluate
day-to-day variation of network flow, and make reliable decisions for
intelligent transportation systems.


title:
Dark Spot Detection from SAR Images Based on Superpixel Deeper Graph
  Convolutional Network

abstract:
  Synthetic Aperture Radar (SAR) is the main instrument utilized for the
detection of oil slicks on the ocean surface. In SAR images, some areas
affected by ocean phenomena, such as rain cells, upwellings, and internal
waves, or discharge from oil spills appear as dark spots on images. Dark spot
detection is the first step in the detection of oil spills, which then become
oil slick candidates. The accuracy of dark spot segmentation ultimately affects
the accuracy of oil slick identification. Although some advanced deep learning
methods that use pixels as processing units perform well in remote sensing
image semantic segmentation, detecting some dark spots with weak boundaries
from noisy SAR images remains a huge challenge. We propose a dark spot
detection method based on superpixels deeper graph convolutional networks
(SGDCN) in this paper, which takes the superpixels as the processing units and
extracts features for each superpixel. The features calculated from superpixel
regions are more robust than those from fixed pixel neighborhoods. To reduce
the difficulty of learning tasks, we discard irrelevant features and obtain an
optimal subset of features. After superpixel segmentation, the images are
transformed into graphs with superpixels as nodes, which are fed into the
deeper graph convolutional neural network for node classification. This graph
neural network uses a differentiable aggregation function to aggregate the
features of nodes and neighbors to form more advanced features. It is the first
time using it for dark spot detection. To validate our method, we mark all dark
spots on six SAR images covering the Baltic Sea and construct a dark spots
detection dataset, which has been made publicly available
(https://drive.google.com/drive/folders/12UavrntkDSPrItISQ8iGefXn2gIZHxJ6?usp=sharing).
The experimental results demonstrate that our proposed SGDCN is robust and
effective.


title: Optimal reconciliation with immutable forecasts

abstract:
  The practical importance of coherent forecasts in hierarchical forecasting
has inspired many studies on forecast reconciliation. Under this approach,
so-called base forecasts are produced for every series in the hierarchy and are
subsequently adjusted to be coherent in a second reconciliation step.
Reconciliation methods have been shown to improve forecast accuracy, but will,
in general, adjust the base forecast of every series. However, in an
operational context, it is sometimes necessary or beneficial to keep forecasts
of some variables unchanged after forecast reconciliation. In this paper, we
formulate reconciliation methodology that keeps forecasts of a pre-specified
subset of variables unchanged or "immutable". In contrast to existing
approaches, these immutable forecasts need not all come from the same level of
a hierarchy, and our method can also be applied to grouped hierarchies. We
prove that our approach preserves unbiasedness in base forecasts. Our method
can also account for correlations between base forecasting errors and ensure
non-negativity of forecasts. We also perform empirical experiments, including
an application to sales of a large scale online retailer, to assess the impacts
of our proposed methodology.


abstract:
  Recent advances in deep learning and computer vision offer an excellent
opportunity to investigate high-level visual analysis tasks such as human
localization and human pose estimation. Although the performance of human
localization and human pose estimation has significantly improved in recent
reports, they are not perfect and erroneous localization and pose estimation
can be expected among video frames. Studies on the integration of these
techniques into a generic pipeline that is robust to noise introduced from
those errors are still lacking. This paper fills the missing study. We explored
and developed two working pipelines that suited the visual-based positioning
and pose estimation tasks. Analyses of the proposed pipelines were conducted on
a badminton game. We showed that the concept of tracking by detection could
work well, and errors in position and pose could be effectively handled by a
linear interpolation technique using information from nearby frames. The
results showed that the Visual-based Positioning and Pose Estimation could
deliver position and pose estimations with good spatial and temporal
resolutions.


title:
Physics-Informed Quantum Communication Networks: A Vision Towards the
  Quantum Internet

abstract:
  Quantum communications is a promising technology that will play a fundamental
role in the design of future networks. In fact, significant efforts are being
dedicated by both the quantum physics and the classical communications
communities on developing new architectures, solutions, and practical
implementations of quantum communication networks (QCNs). Although these
efforts led to various advances in today's technologies, there still exists a
non-trivial gap between the research efforts of the two communities on
designing and optimizing the QCN performance. For instance, most prior works by
the classical communications community ignore important quantum physics-based
constraints when designing QCNs. For example, many works on entanglement
distribution do not account for the decoherence of qubits inside quantum
memories and, thus, their designs become impractical since they assume an
infinite quantum states' lifetime. In this paper, we introduce a novel
framework, dubbed physics-informed QCNs, for designing and analyzing the
performance of QCNs, by relying on the quantum physics principles that underly
the different QCN components. The need of the proposed approach is then
assessed and its fundamental role in designing practical QCNs is analyzed
across various open research areas. Moreover, we identify novel
physics-informed performance metrics and controls that enable QCNs to leverage
the state-of-the-art advancements in quantum technologies to enhance their
performance. Finally, we analyze multiple pressing challenges and open research
directions in QCNs that must be treated using a physics-informed approach to
lead practically viable results. Ultimately, this work attempts to bridge the
gap between the classical communications and the quantum physics communities in
the area of QCNs to foster the development of future communication networks (6G
and beyond, and the quantum Internet).


title: Solving The Long-Tailed Problem via Intra- and Inter-Category Balance

abstract:
  Benchmark datasets for visual recognition assume that data is uniformly
distributed, while real-world datasets obey long-tailed distribution. Current
approaches handle the long-tailed problem to transform the long-tailed dataset
to uniform distribution by re-sampling or re-weighting strategies. These
approaches emphasize the tail classes but ignore the hard examples in head
classes, which result in performance degradation. In this paper, we propose a
novel gradient harmonized mechanism with category-wise adaptive precision to
decouple the difficulty and sample size imbalance in the long-tailed problem,
which are correspondingly solved via intra- and inter-category balance
strategies. Specifically, intra-category balance focuses on the hard examples
in each category to optimize the decision boundary, while inter-category
balance aims to correct the shift of decision boundary by taking each category
as a unit. Extensive experiments demonstrate that the proposed method
consistently outperforms other approaches on all the datasets.


title:
JanusAQP: Efficient Partition Tree Maintenance for Dynamic Approximate
  Query Processing

abstract:
  Approximate query processing over dynamic databases, i.e., under
insertions/deletions, has applications ranging from high-frequency trading to
internet-of-things analytics. We present JanusAQP, a new dynamic AQP system,
which supports SUM, COUNT, AVG, MIN, and MAX queries under insertions and
deletions to the dataset. JanusAQP extends static partition tree synopses,
which are hierarchical aggregations of datasets, into the dynamic setting. This
paper contributes new methods for: (1) efficient initialization of the data
synopsis in the presence of incoming data, (2) maintenance of the data synopsis
under insertions/deletions, and (3) re-optimization of the partitioning to
reduce the approximation error. JanusAQP reduces the error of a
state-of-the-art baseline by more than 60% using only 10% storage cost.
JanusAQP can process more than 100K updates per second in a single node setting
and keep the query latency at a millisecond level.


title: Scalable Motif Counting for Large-scale Temporal Graphs

abstract:
  One fundamental problem in temporal graph analysis is to count the
occurrences of small connected subgraph patterns (i.e., motifs), which benefits
a broad range of real-world applications, such as anomaly detection, structure
prediction, and network representation learning. However, existing works
focused on exacting temporal motif are not scalable to large-scale temporal
graph data, due to their heavy computational costs or inherent inadequacy of
parallelism. In this work, we propose a scalable parallel framework for exactly
counting temporal motifs in large-scale temporal graphs. We first categorize
the temporal motifs based on their distinct properties, and then design
customized algorithms that offer efficient strategies to exactly count the
motif instances of each category. Moreover, our compact data structures, namely
triple and quadruple counters, enable our algorithms to directly identify the
temporal motif instances of each category, according to edge information and
the relationship between edges, therefore significantly improving the counting
efficiency. Based on the proposed counting algorithms, we design a hierarchical
parallel framework that features both inter- and intra-node parallel
strategies, and fully leverages the multi-threading capacity of modern CPU to
concurrently count all temporal motifs. Extensive experiments on sixteen
real-world temporal graph datasets demonstrate the superiority and capability
of our proposed framework for temporal motif counting, achieving up to 538*
speedup compared to the state-of-the-art methods. The source code of our method
is available at: https://github.com/steven-ccq/FAST-temporal-motif.


title:
Constraints on primordial curvature perturbations from the scalar
  induced gravitational waves

abstract:
  The scalar induced gravitational waves are produced from primordial curvature
perturbations in the second order of perturbations. We constrain the fractional
energy density of scalar induced gravitational waves from gravitational waves
observations. If there is no detection of the scalar induced gravitational
waves, the fractional energy density of scalar induced gravitational waves is
constrained by some upper limits. Depends on these upper limits, we can obtain
the constraints on the power spectrum of the primordial curvature
perturbations. For a power-law scalar power spectrum, the constraints from FAST
project have a significant impact on the amplitude and spectral index, namely
$\ln(10^{10}A_s)=3.024^{+0.015}_{-0.012}$ and $n_s=0.9468^{+0.0010}_{-0.0007}$
at $68\%$ confidence level. We also consider the effects of LIGO, Virgo, LISA
and IPTA detectors, while the constraints from CMB+BAO are totally within their
upper limits of scalar induced gravitational waves.


title: Genera and crossing numbers of $2$-bridge knots

abstract:
  In this paper, we determine the average genus of all the $2$-bridge knots
with a given crossing number. As a consequence, we obtain the oblique asymptote
of this value as the crossing number grows.


title: A $p$-adic arithmetic inner product formula

abstract:
  Fix a prime number $p$ and let $E/F$ be a CM extension of number fields in
which $p$ splits relatively. Let $\pi$ be an automorphic representation of a
quasi-split unitary group of even rank with respect to $E/F$ such that $\pi$ is
ordinary above $p$ with respect to the Siegel parabolic subgroup. We construct
the cyclotomic $p$-adic $L$-function of $\pi$, and show, under certain
conditions, that if its order of vanishing at the trivial character is $1$,
then the rank of the Selmer group of the Galois representation of $E$
associated with $\pi$ is at least $1$. Furthermore, under a certain modularity
hypothesis, we use special cycles on unitary Shimura varieties to construct
some explicit elements in the Selmer group called Selmer theta lifts; and we
prove a precise formula relating their $p$-adic heights to the derivative of
the $p$-adic $L$-function. In parallel to Perrin-Riou's $p$-adic analogue of
the Gross--Zagier formula, our formula is the $p$-adic analogue of the
arithmetic inner product formula recently established by Chao~Li and the second
author.


title:
Adiabatic temperature change in ErAl2/metal PIT wires: A practical
  method for estimating the magnetocaloric response of magnetocaloric
  composites

abstract:
  We report the adiabatic temperature change in ErAl2 magnetocaloric wires
fabricated by a powder-in-tube (PIT) process. The adiabatic temperature change
of the PIT wires is found to be determined by not only the volume fraction of
ErAl2 core but also the magnitude relationship between the specific heat of the
ErAl2 core and the metal sheath. We propose a quantitative analysis method for
calculating the temperature and core volume fraction dependence of adiabatic
temperature change in the PIT wire, whose formula is applicable to also various
magnetocaloric composites, useful to estimate the magnetocaloric response prior
to fabrication.


title:
Existence of weakly quasisymmetric magnetic fields in asymmetric
  toroidal domains with non-tangential quasisymmetry

abstract:
  A quasisymmetry is a special symmetry that enhances the ability of a magnetic
field to trap charged particles. Quasisymmetric magnetic fields may allow the
realization of next generation fusion reactors (stellarators) with superior
performance when compared with classical (tokamak) designs. Nevertheless, the
existence of such magnetic configurations lacks mathematical proof due to the
complexity of the governing equations. Here, we prove the existence of weakly
quasisymmetric magnetic fields by constructing explicit examples. This result
is achieved by a tailored parametrization of both magnetic field and hosting
toroidal domain, which are optimized to fulfill quasisymmetry. The obtained
solutions hold in a toroidal volume, are smooth, possess nested flux surfaces,
are not invariant under continuous Euclidean isometries, have a non-vanishing
current, exhibit a direction of quasisymmetry that is not tangential to the
toroidal boundary, and fit within the framework of anisotropic
magnetohydrodynamics.


title: Strongly Quasiconvex subgroups in graphs of groups

abstract:
  Given a graph of groups $\mathcal{G} = (\Gamma, \{G_v\}, \{G_e\})$ with
certain conditions on vertex groups and $G$ acts acylindrically on its
Bass-Serre tree $T$. Let $H$ be a finitely generated subgroup of $G$. We prove
the following statements equivalence: $H$ has finite height, $(G, T, H)$ is a
$A/QI$--triple, $H$ is strongly quasiconvex and virtually free in $G$. We also
give a condition to determine whether strong quasiconvexity in a group is
preserved under amalgams.


title:
Mechanical analysis of a dynamical phase transition for particles in a
  channel

abstract:
  We analyse biased ensembles of trajectories for a two-dimensional system of
particles, evolving by Langevin dynamics in a channel geometry. This bias
controls the degree of particle clustering. On biasing to large clustering, we
observe a dynamical phase transition where the particles break symmetry and
accumulate at one of the walls. We analyse the mechanical properties of this
symmetry-broken state using the Irving-Kirkwood stress tensor. The biased
ensemble is characterised by body forces which originate in random thermal
noises, but have finite averages in the presence of the bias. We discuss the
connection of these forces to Doob's transform and optimal control theory.


title: DAME: Domain Adaptation for Matching Entities

abstract:
  Entity matching (EM) identifies data records that refer to the same
real-world entity. Despite the effort in the past years to improve the
performance in EM, the existing methods still require a huge amount of labeled
data in each domain during the training phase. These methods treat each domain
individually, and capture the specific signals for each dataset in EM, and this
leads to overfitting on just one dataset. The knowledge that is learned from
one dataset is not utilized to better understand the EM task in order to make
predictions on the unseen datasets with fewer labeled samples. In this paper,
we propose a new domain adaptation-based method that transfers the task
knowledge from multiple source domains to a target domain. Our method presents
a new setting for EM where the objective is to capture the task-specific
knowledge from pretraining our model using multiple source domains, then
testing our model on a target domain. We study the zero-shot learning case on
the target domain, and demonstrate that our method learns the EM task and
transfers knowledge to the target domain. We extensively study fine-tuning our
model on the target dataset from multiple domains, and demonstrate that our
model generalizes better than state-of-the-art methods in EM.


title:
Compositional Semantics and Inference System for Temporal Order based on
  Japanese CCG

abstract:
  Natural Language Inference (NLI) is the task of determining whether a premise
entails a hypothesis. NLI with temporal order is a challenging task because
tense and aspect are complex linguistic phenomena involving interactions with
temporal adverbs and temporal connectives. To tackle this, temporal and
aspectual inference has been analyzed in various ways in the field of formal
semantics. However, a Japanese NLI system for temporal order based on the
analysis of formal semantics has not been sufficiently developed. We present a
logic-based NLI system that considers temporal order in Japanese based on
compositional semantics via Combinatory Categorial Grammar (CCG) syntactic
analysis. Our system performs inference involving temporal order by using
axioms for temporal relations and automated theorem provers. We evaluate our
system by experimenting with Japanese NLI datasets that involve temporal order.
We show that our system outperforms previous logic-based systems as well as
current deep learning-based models.


title:
RI/MOM and RI/SMOM renormalization of quark bilinear operators using the
  overlap fermion

abstract:
  We present the vector, scalar and tensor renormalization constants (RCs)
using the overlap fermion on dynamical fermion gauge configurations with either
the Domain wall fermion action or HISQ actions, using RI/MOM and RI/SMOM as the
intermediate scheme at the lattice spacing $a$ from 0.04 fm to 0.12 fm. The
results show that RI/MOM and RI/SMOM can provide consistent renormalization
constants under the $\bar{\textrm{MS}}$ scheme, after proper $a^2p^2$
extrapolations. But at $p\sim 2$ GeV, both the RI/MOM and RI/SMOM suffer from
nonperturbative effects which cannot be removed by the perturbative matching.
The comparison between the results with different sea actions also suggests
that the renormalization constant is discernibly sensitive to the lattice
spacing but not to the bare gauge coupling in the gauge action.


title: Pointlike sets with respect to ER

abstract:
  We show that pointlike sets are decidable for the pseudovariety of finite
semigroups whose idempotent-generated subsemigroup is R-trivial. Notably, our
proof is constructive: we provide an explicit relational morphism which
computes the ER-pointlike subsets of a given finite semigroup.


title: Synthetic Target Domain Supervision for Open Retrieval QA

abstract:
  Neural passage retrieval is a new and promising approach in open retrieval
question answering. In this work, we stress-test the Dense Passage Retriever
(DPR) -- a state-of-the-art (SOTA) open domain neural retrieval model -- on
closed and specialized target domains such as COVID-19, and find that it lags
behind standard BM25 in this important real-world setting. To make DPR more
robust under domain shift, we explore its fine-tuning with synthetic training
examples, which we generate from unlabeled target domain text using a
text-to-text generator. In our experiments, this noisy but fully automated
target domain supervision gives DPR a sizable advantage over BM25 in
out-of-domain settings, making it a more viable model in practice. Finally, an
ensemble of BM25 and our improved DPR model yields the best results, further
pushing the SOTA for open retrieval QA on multiple out-of-domain test sets.


title: Properties of Orbits and Normal Numbers in the Binary Dynamical System

abstract:
  In 1930 G. H. Hardy and J. E. Littlewood derived a result concerning the rate
of divergence of certain series of cosecants. In more recent terminology, their
result can be interpreted as a result about the behaviour of orbits in
dynamical systems arising from rotations on the unit circle. In general terms,
this behaviour is related to the question of `how often' a point under
successive rotations gets `close' to $1$. Now, the expansion of numbers in
$[0,1)$ to the base $2$ can be associated with a different system -- the binary
dynamical system. This article considers orbit behaviour in the binary system
that corresponds to the behaviour that was, in effect, observed by Hardy and
Littlewood in systems involving rotations. Now, except for a countable set, the
sequence of binary digits of a number in $[0,1)$ may be arranged as an infinite
sequence of consecutive, finite blocks, each block consisting of all zeros or
all ones. The relationships between the lengths of these blocks determine
Hardy-Littlewood types of behaviour in the binary system. This behaviour is
considered and results relating to normal and simply normal numbers are
obtained. There also are suggestions for further investigation.


title: Impacts of Public Information on Flexible Information Acquisition

abstract:
  Interacting agents receive public information at no cost and flexibly acquire
private information at a cost proportional to entropy reduction. When a
policymaker provides more public information, agents acquire less private
information, thus lowering information costs. Does more public information
raise or reduce uncertainty faced by agents? Is it beneficial or detrimental to
welfare? To address these questions, we examine the impacts of public
information on flexible information acquisition in a linear-quadratic-Gaussian
game with arbitrary quadratic material welfare. More public information raises
uncertainty if and only if the game exhibits strategic complementarity, which
can be harmful to welfare. However, when agents acquire a large amount of
information, more provision of public information increases welfare through a
substantial reduction in the cost of information. We give a necessary and
sufficient condition for welfare to increase with public information and
identify optimal public information disclosure, which is either full or partial
disclosure depending upon the welfare function and the slope of the best
response.


title:
Wetting hysteresis induces effective unidirectional water transport
  through a fluctuating nanochannel

abstract:
  We propose a water pump that actively transports water molecules through
nanochannels. Spatially asymmetric thermal fluctuations imposed on the channel
radius cause unidirectional water flow without osmotic pressure, which can be
attributed to hysteresis in the cyclic transition between the wetting/drying
states. We show that the water transport depends on fluctuations, such as
white, Brownian, and pink noises. Because of the high-frequency components in
white noise, fast switching of open and close states inhibits channel wetting.
Conversely, pink and Brownian noises generate high-pass filtered net flow.
Brownian fluctuation leads to a faster water transport rate, whereas pink noise
has a higher capability to overcome osmotic pressure in the opposite direction.
A trade-off relationship exists between the resonant frequency of the
fluctuation and the flow amplification. The proposed pump can be considered as
an analogy for the reversed Carnot cycle, which is the upper limit on the
energy conversion efficiency.


title: Inertia of two-qutrit entanglement witnesses

abstract:
  Entanglement witnesses (EWs) are a fundamental tool for the detection of
entanglement. We investigate the inertias of bipartite EWs constructed by the
partial transpose of NPT states. Furthermore, we find out most of the inertias
of the patial transpose of the two-qutrit bipartite NPT states. As an
application, we extend our results to high dimensional states.


title:
Homogenization with quasistatic Tresca's friction law: qualitative and
  quantitative results

abstract:
  The problems of frictional contacts are the key to the investigation of
mechanical performances of composite materials under varying service
environments. The paper considers a linear elasticity system with strongly
heterogeneous coefficients and quasistatic Tresca's friction law, and we study
the homogenization theories under the frameworks of H-convergence and small
$\epsilon$-periodicity. The qualitative result is based on H-convergence, which
shows the original oscillating solutions will converge weakly to the
homogenized solution, while our quantitative result provides an estimate of
asymptotic errors in the $H^1$ norm for the periodic homogenization. We also
design several numerical experiments to validate the convergence rates in the
quantitative analysis.


title:
Integral, mean and covariance of the simplex-truncated multivariate
  normal distribution

abstract:
  Compositional data, which is data consisting of fractions or probabilities,
is common in many fields including ecology, economics, physical science and
political science. If these data would otherwise be normally distributed, their
spread can be conveniently represented by a multivariate normal distribution
truncated to the non-negative space under a unit simplex. Here this
distribution is called the simplex-truncated multivariate normal distribution.
For calculations on truncated distributions, it is often useful to obtain rapid
estimates of their integral, mean and covariance; these quantities
characterising the truncated distribution will generally possess different
values to the corresponding non-truncated distribution. In this paper, three
different approaches that can estimate the integral, mean and covariance of any
simplex-truncated multivariate normal distribution are described and compared.
These three approaches are (1) naive rejection sampling, (2) a method described
by Gessner et al. that unifies subset simulation and the Holmes-Diaconis-Ross
algorithm with an analytical version of elliptical slice sampling, and (3) a
semi-analytical method that expresses the integral, mean and covariance in
terms of integrals of hyperrectangularly-truncated multivariate normal
distributions, the latter of which are readily computed in modern mathematical
and statistical packages. Strong agreement is demonstrated between all three
approaches, but the most computationally efficient approach depends strongly
both on implementation details and the dimension of the simplex-truncated
multivariate normal distribution. For computations in low-dimensional
distributions, the semi-analytical method is fast and thus should be
considered. As the dimension increases, the Gessner et al. method becomes the
only practically efficient approach of the methods tested here.


title: Superconducting bimodal ionic photo-memristor

abstract:
  Memristive circuit elements constitute a cornerstone for novel electronic
applications, such as neuromorphic computing, called to revolutionize
information technologies. By definition, memristors are sensitive to the
history of electrical stimuli, to which they respond by varying their
electrical resistance across a continuum of nonvolatile states. Recently, much
effort has been devoted to developing devices that present an analogous
response to optical excitation. Here we realize a new class of device, a
tunnelling photo-memristor, whose behaviour is bimodal: both electrical and
optical stimuli can trigger the switching across resistance states in a way
determined by the dual optical-electrical history. This unique behaviour is
obtained in a device of ultimate simplicity: an interface between a
high-temperature superconductor and a transparent semiconductor. The
microscopic mechanism at play is a reversible nanoscale redox reaction between
both materials, whose oxygen content determines the electron tunnelling rate
across their interface. Oxygen exchange is controlled here via illumination by
exploiting a competition between electrochemistry, photovoltaic effects and
photo-assisted ion migration. In addition to their fundamental interest, the
unveiled electro-optic memory effects have considerable technological
potential. Especially in combination with high-temperature superconductivity
which, beyond facilitating the high connectivity required in neuromorphic
circuits, brings photo-memristive effects to the realm of superconducting
electronics.

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


title: Dispersive approach to HVP for muon g-2

abstract:
  The dispersive approaches to the hadronic vacuum polarisation contributions
to the muon anomalous magnetic moment $a_\mu$ are described. The main issues
are discussed followed by perspectives in the next years.


title: Spectroscopy of light baryons with strangeness -1, -2, and -3

abstract:
  The present article contains the descriptive study of light strange baryons
$\Lambda$, $\Sigma$, $\Xi$, and $\Omega$. The Regge phenomenology with linear
Regge trajectories has been employed and the relations between Regge slopes,
intercepts, and baryon masses have been extracted. With the aid of these
relations, ground state masses are obtained for $\Xi$ and $\Omega$ baryons.
Regge parameters have also been estimated to calculate the excited state masses
for $\Lambda$, $\Sigma$, $\Xi$, and $\Omega$ baryons in both the $(J,M^{2})$
and $(n,M^{2})$ planes. The obtained results are compared with available
experimental data and various theoretical approaches. We predict the
spin-parity of recently observed light baryons and our predictions may be
useful in future experimental searches of these baryons and their $J^{P}$
assignments.


title: BeiDou-G2: Past and Present

abstract:
  In January 2022, the defunct satellite BeiDou-G2 was pulled out of
geostationary orbit by Shijian-21 to a graveyard orbit. For safe docking and
operation, it was necessary to determine the rotation state in advance. In this
paper, we show the evolution of the rotation of the BeiDou-G2 satellite based
on the photometry observation data for the past 10 years. The rotational speed
of BeiDou-G2 was found to be annual oscillation, mainly due to the solar
radiation. Based on the evolution of BeiDou-G2's rotation speed and its orbit,
we confirmed that in the last 10 years, the satellite had six abnormal events.
These abnormal events were mainly due to the increase in the rotation speed
caused by suspected fuel leakages. Additionally, the abnormal events included
one collision in 2012, which was inferred to be the trigger of the fuel
leakages in the following years. No rotational speed abnormalities occurred
again after 2017, probably due to the complete release of the residual fuel.
The parameters and the propagating models after one incidence of solar panel
damage in 2014 and one fragment in 2016, with the standard errors for
propagating over 1 year of the rotational axis less than 3{\deg} and rotational
speed being 0.11{\deg}/s, were believed to be able to satisfy the accuracy
requirements of the rotation state well at the moment of docking.


title:
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine
  Translation

abstract:
  Domain Adaptation (DA) of Neural Machine Translation (NMT) model often relies
on a pre-trained general NMT model which is adapted to the new domain on a
sample of in-domain parallel data. Without parallel data, there is no way to
estimate the potential benefit of DA, nor the amount of parallel samples it
would require. It is however a desirable functionality that could help MT
practitioners to make an informed decision before investing resources in
dataset creation. We propose a Domain adaptation Learning Curve prediction
(DaLC) model that predicts prospective DA performance based on in-domain
monolingual samples in the source language. Our model relies on the NMT encoder
representations combined with various instance and corpus-level features. We
demonstrate that instance-level is better able to distinguish between different
domains compared to corpus-level frameworks proposed in previous studies.
Finally, we perform in-depth analyses of the results highlighting the
limitations of our approach, and provide directions for future research.


title:
Dynamical transitions in scalarization and descalarization through black
  hole accretion

abstract:
  We present the first fully nonlinear study on the accretion of scalar fields
onto a seed black hole in anti-de Sitter spacetime in Einstein-Maxwell-scalar
theory. Intrinsic critical phenomena in the dynamical transition between the
bald and scalarized black holes are disclosed. In scalarizations, the
transition is discontinuous and a metastable black hole acts as an attractor at
the threshold. We construct a new physical mechanism to dynamically descalarize
an isolated scalarized black hole. The first results on critical phenomena in
descalarizations are revealed. The dynamical descalarizations can be either
discontinuous or continuous at the threshold, distinguished by whether or not
an attractor appears.


title: Ion Diffusion Velocity Measurements in a Multi-Ion-Species Plasma Shock

abstract:
  Collisional plasma shocks generated from supersonic flows are an important
feature in many astrophysical and laboratory high-energy-density plasmas.
Compared to single-ion-species plasma shocks, plasma shock fronts with multiple
ion species contain additional structure, including interspecies ion separation
driven by gradients in species concentration, temperature, pressure, and
electric potential. We present time-resolved density and temperature
measurements of two ion species in collisional plasma shocks produced by
head-on merging of supersonic plasma jets, allowing determination of the
diffusion velocity of the ion species. Our results demonstrate that the lighter
ion species diffuse faster than the heavier species within a shock front,
consistent with predictions from the inter-ion-species transport theory.
Results from the experiment approach quantitative agreement with theoretical
prediction and 1D ion-Fokker-Planck kinetic simulation.


title:
Character bounds for regular semisimple elements and asymptotic results
  on Thompson's conjecture

abstract:
  For every integer $k$ there exists a bound $B=B(k)$ such that if the
characteristic polynomial of $g\in \operatorname{SL}_n(q)$ is the product of
$\le k$ pairwise distinct monic irreducible polynomials over $\mathbb{F}_q$,
then every element $x$ of $\operatorname{SL}_n(q)$ of support at least $B$ is
the product of two conjugates of $g$. We prove this and analogous results for
the other classical groups over finite fields; in the orthogonal and symplectic
cases, the result is slightly weaker. With finitely many exceptions $(p,q)$, in
the special case that $n=p$ is prime, if $g$ has order $\frac{q^p-1}{q-1}$,
then every non-scalar element $x \in \operatorname{SL}_p(q)$ is the product of
two conjugates of $g$. The proofs use the Frobenius formula together with upper
bounds for values of unipotent and quadratic unipotent characters in finite
classical groups.


title:
User-Centric Conversational Recommendation with Multi-Aspect User
  Modeling

abstract:
  Conversational recommender systems (CRS) aim to provide highquality
recommendations in conversations. However, most conventional CRS models mainly
focus on the dialogue understanding of the current session, ignoring other rich
multi-aspect information of the central subjects (i.e., users) in
recommendation. In this work, we highlight that the user's historical dialogue
sessions and look-alike users are essential sources of user preferences besides
the current dialogue session in CRS. To systematically model the multi-aspect
information, we propose a User-Centric Conversational Recommendation (UCCR)
model, which returns to the essence of user preference learning in CRS tasks.
Specifically, we propose a historical session learner to capture users'
multi-view preferences from knowledge, semantic, and consuming views as
supplements to the current preference signals. A multi-view preference mapper
is conducted to learn the intrinsic correlations among different views in
current and historical sessions via self-supervised objectives. We also design
a temporal look-alike user selector to understand users via their similar
users. The learned multi-aspect multi-view user preferences are then used for
the recommendation and dialogue generation. In experiments, we conduct
comprehensive evaluations on both Chinese and English CRS datasets. The
significant improvements over competitive models in both recommendation and
dialogue generation verify the superiority of UCCR.


title: Bottom complexes

abstract:
  The bottom complex of a finite polyhedal pointed rational cone is the lattice
polytopal complex of the compact faces of the convex hull of nonzero lattice
points in the cone. The algebra, associated to the bottom complex of a cone,
defines a flat deformation of the affine toric variety, associated to the
polyhedral cone, set-theoretically. We describe three explicit infinite
families of abstract polytopal complexes, defining such flat deformations
scheme-theoretically.