title: Bursting Drops

abstract:
  For decades, researchers worldwide have investigated phenomena related to
natural, artificial oil leakages such as oil drop formation within water
bodies, their rise, and oil slick evolution after they breach the water-air
interface. Despite this, the event leading to slick formation -the bursting of
oil drops at the liquid-air interface has remained unnoticed thus far. In this
work, we investigate this and report a counterintuitive jetting reversal that
releases a daughter oil droplet inside the bulk as opposed to the upwards
shooting jets observed in bursting air bubbles. We show that the daughter
droplet size thus produced can be correlated to the bulk liquid properties and
that its formation can be suppressed by increasing the bulk viscosity, by an
overlaying layer of oil or by the addition of microparticles. We further
demonstrate the significance of our results by synthesizing colloidal pickered
droplets and show applications of bursting compound drops in double emulsions
and studies on raindrop impact on a slick. These results could be immensely
transformative for diverse areas, including climatology, oceanic, atmospheric
sciences, colloidal synthesis and drug delivery.


title: $W$-Boson Mass, Electroweak Precision Tests and SMEFT

abstract:
  Recently the CDF collaboration at the Tevatron reported a significant
discrepancy between the direct measurement of the $W$-boson mass and its
Standard Model (SM) prediction based on electroweak precision tests (EWPTs). In
this paper we explore the potential origin of this discrepancy from physics
beyond the SM. Explicitly, we work on a set of six-dimensional operators in the
SM effective field theory which are relevant to the EWPTs. By fitting to the
data, we demonstrate that an upward shift in $m_W$ is driven by the operator
$\mathcal{O}_{T}=\frac{1}{2}(H^{\dagger}\overset{\text{$\leftrightarrow$}}{D}_{\mu}H)^2$
with a coefficient $c_T ({\rm TeV}/\Lambda)^2 \gtrsim 0.01$. This suggests that
the new physics scale favored by the CDF data could be within the reach of
current and near-future LHC searches. We also comment on other further indirect
tests of the physical origin for this anomalous discrepancy such as the Higgs
couplings.


title:
Design and Experimental Verification of a Novel
  Error-Backpropagation-Based Background Calibration for Time Interleaved ADC
  in Digital Communication Receivers

abstract:
  A novel background calibration technique for Time-Interleaved
Analog-to-Digital Converters (TI-ADCs) is presented in this paper. This
technique is applicable to equalized digital communication receivers. As shown
by Tsai et al. [1] and Luna et al. [2], in a digital receiver it is possible to
treat the TI-ADC errors as part of the communication channel and take advantage
of the adaptive equalizer to compensate them. Therefore calibration becomes an
integral part of the channel equalization. No special purpose analog or digital
calibration blocks or algorithms are required. However, there is a large class
of receivers where the equalization technique cannot be directly applied
because other signal processing blocks are located between the TI-ADC and the
equalizer. The technique presented here generalizes earlier works to this class
of receivers. The error backpropagation algorithm, traditionally used in
machine learning, is applied to the error computed at the receiver slicer and
used to adapt an auxiliary equalizer adjacent to the TI-ADC, called the
Compensation Equalizer (CE). Simulations using a dual polarization optical
coherent receiver model demonstrate accurate and robust mismatch compensation
across different application scenarios. Several Quadrature Amplitude Modulation
(QAM) schemes are tested in simulations and experimentally. Measurements on an
emulation platform which includes an 8 bit, 4 GS/s TI-ADC prototype chip
fabricated in 130nm CMOS technology, show an almost ideal mitigation of the
impact of the mismatches on the receiver performance when 64-QAM and 256-QAM
schemes are tested. An absolute improvement in the TI-ADC performance of
$\sim$15 dB in both SNDR and SFDR is measured.


title: Spurious poles in a finite volume

abstract:
  Using effective-range expansion for the two-body amplitudes may generate
spurious sub-threshold poles outside of the convergence range of the expansion.
In the infinite volume, the emergence of such poles leads to the
inconsistencies in the three-body equations, e.g., to the breakdown of
unitarity. We investigate the effect of the spurious poles on the three-body
quantization condition in a finite volume and show that it leads to a peculiar
dependence of the energy levels on the box size $L$. Furthermore, within a
simple model, it is demonstrated that the procedure for the removal of these
poles, which was recently proposed in Ref.[1] in the infinite volume, can be
adapted to the finite-volume calculations. The structure of the exact energy
levels is reproduced with an accuracy that systematically improves order by
order in the EFT expansion.


title:
Unbiased Multilevel Monte Carlo methods for intractable distributions:
  MLMC meets MCMC

abstract:
  Constructing unbiased estimators from Markov chain Monte Carlo (MCMC) outputs
has recently increased much attention in statistics and machine learning
communities. However, the existing unbiased MCMC framework only works when the
quantity of interest is an expectation, which rules out many practical
applications. In this paper, we propose a general method to construct unbiased
estimators for function of expectations. We further generalize this method to
estimate nested expectations. Our idea is based on the combination and
generalization of the unbiased MCMC and Multilevel Monte Carlo (MLMC) methods.
In contrast to traditional sequential methods, our estimator can be easily
implemented on parallel processors independently. We prove our estimator has a
finite variance, a finite computational complexity, and achieves
$\varepsilon$-accuracy within $O(1/\varepsilon^2)$ computational cost under
mild conditions. We also illustrate our estimator on both synthetic and real
data examples.


title:
Consistency of Monte Carlo Estimators for Risk-Neutral PDE-Constrained
  Optimization

abstract:
  We apply the sample average approximation (SAA) method to risk-neutral
optimization problems governed by nonlinear partial differential equations
(PDEs) with random inputs. We analyze the consistency of the SAA optimal values
and SAA solutions. Our analysis exploits problem structure in PDE-constrained
optimization problems, allowing us to construct deterministic, compact subsets
of the feasible set that contain the solutions to the risk-neutral problem and
eventually those to the SAA problems. The construction is used to study the
consistency using results established in the literature on stochastic
programming. The assumptions of our framework are verified on three nonlinear
optimization problems under uncertainty.


title:
Convergence of randomized urn models with irreducible and reducible
  replacement policy

abstract:
  Generalized Friedman urn is one of the simplest and most useful models
considered in probability theory. Since Athreya and Ney (1972) showed the
almost sure convergence of urn proportions in a randomized urn model with
irreducible replacement matrix under the $L\log L$ moment assumption, this
assumption has been regarded as the weakest moment assumption, but the
necessary has never been shown. In this paper, we study the strong and weak
convergence of generalized Friedman urns. It is proved that, when the random
replacement matrix is irreducible in probability, the sufficient and necessary
moment assumption for the almost sure convergence of the urn proportions is
that the expectation of the replacement matrix is finite, which is less
stringent than the $L\log L$ moment assumption, and when the replacement is
reducible, the $L\log L$ moment assumption is the weakest sufficient condition.
The rate of convergence and the strong and weak convergence of non-homogenous
generalized Friedman urns are also derived.


title:
Listen only to me! How well can target speech extraction handle false
  alarms?

abstract:
  Target speech extraction (TSE) extracts the speech of a target speaker in a
mixture given auxiliary clues characterizing the speaker, such as an enrollment
utterance. TSE addresses thus the challenging problem of simultaneously
performing separation and speaker identification. There has been much progress
in extraction performance following the recent development of neural networks
for speech enhancement and separation. Most studies have focused on processing
mixtures where the target speaker is actively speaking. However, the target
speaker is sometimes silent in practice, i.e., inactive speaker (IS). A typical
TSE system will tend to output a signal in IS cases, causing false alarms. This
is a severe problem for the practical deployment of TSE systems. This paper
aims at understanding better how well TSE systems can handle IS cases. We
consider two approaches to deal with IS, (1) training a system to directly
output zero signals or (2) detecting IS with an extra speaker verification
module. We perform an extensive experimental comparison of these schemes in
terms of extraction performance and IS detection using the LibriMix dataset and
reveal their pros and cons.


title:
OutfitTransformer: Learning Outfit Representations for Fashion
  Recommendation

abstract:
  Learning an effective outfit-level representation is critical for predicting
the compatibility of items in an outfit, and retrieving complementary items for
a partial outfit. We present a framework, OutfitTransformer, that uses the
proposed task-specific tokens and leverages the self-attention mechanism to
learn effective outfit-level representations encoding the compatibility
relationships between all items in the entire outfit for addressing both
compatibility prediction and complementary item retrieval tasks. For
compatibility prediction, we design an outfit token to capture a global outfit
representation and train the framework using a classification loss. For
complementary item retrieval, we design a target item token that additionally
takes the target item specification (in the form of a category or text
description) into consideration. We train our framework using a proposed
set-wise outfit ranking loss to generate a target item embedding given an
outfit, and a target item specification as inputs. The generated target item
embedding is then used to retrieve compatible items that match the rest of the
outfit. Additionally, we adopt a pre-training approach and a curriculum
learning strategy to improve retrieval performance. Since our framework learns
at an outfit-level, it allows us to learn a single embedding capturing
higher-order relations among multiple items in the outfit more effectively than
pairwise methods. Experiments demonstrate that our approach outperforms
state-of-the-art methods on compatibility prediction, fill-in-the-blank, and
complementary item retrieval tasks. We further validate the quality of our
retrieval results with a user study.


title:
Explanation Graph Generation via Pre-trained Language Models: An
  Empirical Study with Contrastive Learning

abstract:
  Pre-trained sequence-to-sequence language models have led to widespread
success in many natural language generation tasks. However, there has been
relatively less work on analyzing their ability to generate structured outputs
such as graphs. Unlike natural language, graphs have distinct structural and
semantic properties in the context of a downstream NLP task, e.g., generating a
graph that is connected and acyclic can be attributed to its structural
constraints, while the semantics of a graph can refer to how meaningfully an
edge represents the relation between two node concepts. In this work, we study
pre-trained language models that generate explanation graphs in an end-to-end
manner and analyze their ability to learn the structural constraints and
semantics of such graphs. We first show that with limited supervision,
pre-trained language models often generate graphs that either violate these
constraints or are semantically incoherent. Since curating large amount of
human-annotated graphs is expensive and tedious, we propose simple yet
effective ways of graph perturbations via node and edge edit operations that
lead to structurally and semantically positive and negative graphs. Next, we
leverage these graphs in different contrastive learning models with Max-Margin
and InfoNCE losses. Our methods lead to significant improvements in both
structural and semantic accuracy of explanation graphs and also generalize to
other similar graph generation tasks. Lastly, we show that human errors are the
best negatives for contrastive learning and also that automatically generating
more such human-like negative graphs can lead to further improvements. Our code
and models are publicly available at https://github.com/swarnaHub/ExplagraphGen


title:
The polarization quaternion and its applications: a joint representation
  of the Q/U Stokes parameters and E/B mode polarizations

abstract:
  In this work, we point out that the Q/U Stokes parameters and E/B mode
polarizations are the four components of a unique quaternion, which describes
at the same time the directions and the parity states of spherical linear
polarizations. We then point out that, with this polarization quaternion, the
mathematical form of all Q/U and E/B transforms are greatly simplified, to an
extent that requires only one quaternion multiplication for each transform. A
preliminary application of the polarization quaternion is shown as an example
to detect peculiar pixel domain patterns within the E- and B-families, which
are the former and latter halves of the polarization quaternion.


title:
The physical connection between central stellar surface density and
  stellar spin in SAMI and MaNGA nearby galaxies

abstract:
  The stellar surface density within the inner 1 kpc ($\Sigma_{1}$) has become
a popular tool for understanding the growth of galaxies and its connection with
the quenching of star formation. The emerging picture suggests that building a
central dense core is a necessary condition for quenching. However, it is not
clear whether changes in $\Sigma_{1}$ trace changes in stellar kinematics and
the growth of dispersion-dominated bulges. In this paper, we combine imaging
from the Sloan Digital Sky Survey with stellar kinematics from the Sydney-AAO
Multi-object Integral-field unit (SAMI) and Mapping Nearby Galaxies at Apache
Point Observatory (MaNGA) surveys to quantify the correlation between
$\Sigma_{1}$ and the proxy for stellar spin parameter within one effective
radius ($\lambda_{re}$) for 1599 nearby galaxies. We show that, on the
star-forming main sequence and at fixed stellar mass, changes in $\Sigma_{1}$
are mirrored by changes in $\lambda_{re}$. While forming stars, main sequence
galaxies remain rotationally-dominated systems, with their $\Sigma_{1}$
increasing but their stellar spin staying either constant or slightly
increasing. The picture changes below the main sequence, where $\Sigma_{1}$ and
$\lambda_{re}$ are no longer correlated. Passive systems show a narrower range
of $\Sigma_{1}$, but a wider range of $\lambda_{re}$ compared to star-forming
galaxies. Our results indicate that, from a structural point of view, passive
galaxies are a more heterogeneous population than star-forming systems, and may
have followed a variety of evolutionary paths. This also suggests that, if
dispersion-dominated bulges still grow significantly at $z\sim$0, this
generally takes place during, or after, the quenching phase.


title:
Distributed Hardware Accelerated Secure Joint Computation on the COPA
  Framework

abstract:
  Performance of distributed data center applications can be improved through
use of FPGA-based SmartNICs, which provide additional functionality and enable
higher bandwidth communication. Until lately, however, the lack of a simple
approach for customizing SmartNICs to application requirements has limited the
potential benefits. Intel's Configurable Network Protocol Accelerator (COPA)
provides a customizable FPGA framework that integrates both hardware and
software development to improve computation and communication performance. In
this first case study, we demonstrate the capabilities of the COPA framework
with an application from cryptography -- secure Multi-Party Computation (MPC)
-- that utilizes hardware accelerators connected directly to host memory and
the COPA network. We find that using the COPA framework gives significant
improvements to both computation and communication as compared to traditional
implementations of MPC that use CPUs and NICs. A single MPC accelerator running
on COPA enables more than 17Gbps of communication bandwidth while using only 1%
of Stratix 10 resources. We show that utilizing the COPA framework enables
multiple MPC accelerators running in parallel to fully saturate a 100Gbps link
enabling higher performance compared to traditional NICs.


title: Effective Mutation Rate Adaptation through Group Elite Selection

abstract:
  Evolutionary algorithms are sensitive to the mutation rate (MR); no single
value of this parameter works well across domains. Self-adaptive MR approaches
have been proposed but they tend to be brittle: Sometimes they decay the MR to
zero, thus halting evolution. To make self-adaptive MR robust, this paper
introduces the Group Elite Selection of Mutation Rates (GESMR) algorithm. GESMR
co-evolves a population of solutions and a population of MRs, such that each MR
is assigned to a group of solutions. The resulting best mutational change in
the group, instead of average mutational change, is used for MR selection
during evolution, thus avoiding the vanishing MR problem. With the same number
of function evaluations and with almost no overhead, GESMR converges faster and
to better solutions than previous approaches on a wide range of continuous test
optimization problems. GESMR also scales well to high-dimensional
neuroevolution for supervised image-classification tasks and for reinforcement
learning control tasks. Remarkably, GESMR produces MRs that are optimal in the
long-term, as demonstrated through a comprehensive look-ahead grid search.
Thus, GESMR and its theoretical and empirical analysis demonstrate how
self-adaptation can be harnessed to improve performance in several applications
of evolutionary computation.


title: Forecast and backcast of the solar cycles

abstract:
  Solar cycle is modeled as a forced and damped harmonic oscillator and the
amplitudes, frequencies, phases and decay factors of such a harmonic oscillator
are estimated by non-linear fitting the equation of sinusoidal and transient
parts to the sunspot and irradiance (proxy for the sunspot) data for the years
1700-2008. We find that:(i) amplitude and frequency (or period of $\sim$11 yr)
of the sinusoidal part remain constant for all the solar cycles; (ii) the
amplitude of the transient part is phase locked with the phase of the
sinusoidal part; (iii) for all the cycles, the period and decay factor (that is
much less than 1) of the transient part remain approximately constant. The
constancy of the amplitudes and the frequencies of the sinusoidal part and a
very small decay factor from the transient part suggests that the solar
activity cycle mainly consists of a persistent oscillatory part that might be
compatible with long-period ($\sim$22 yr) Alfven oscillations. For all the
cycles, with the estimated physical parameters (amplitudes, phases and periods)
and, by an autoregressive model, we forecast (especially for coming solar cycle
25) and backcast (to check whether Maunder minimum type solar activity exists
or not) the solar cycles. We find that amplitude of coming solar cycle 25 is
almost same as the amplitude of the previous solar cycle 24. We also find that
sun might not have experienced a deep Maunder minimum (MM) type of activity
during 1645-1700 AD corroborating some of the paleoclimatic inferences and, MM
type of activity will not be imminent in near future, until at least 200 years.


title:
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction
  for High-dimensional Uncertainty Quantification

abstract:
  Multi-fidelity modelling arises in many situations in computational science
and engineering world. It enables accurate inference even when only a small set
of accurate data is available. Those data often come from a high-fidelity
model, which is computationally expensive. By combining the realizations of the
high-fidelity model with one or more low-fidelity models, the multi-fidelity
method can make accurate predictions of quantities of interest. This paper
proposes a new dimension reduction framework based on rotated multi-fidelity
Gaussian process regression and a Bayesian active learning scheme when the
available precise observations are insufficient. By drawing samples from the
trained rotated multi-fidelity model, the so-called supervised dimension
reduction problems can be solved following the idea of the sliced average
variance estimation (SAVE) method combined with a Gaussian process regression
dimension reduction technique. This general framework we develop can
effectively solve high-dimensional problems while the data are insufficient for
applying traditional dimension reduction methods. Moreover, a more accurate
surrogate Gaussian process model of the original problem can be obtained based
on our trained model. The effectiveness of the proposed rotated multi-fidelity
Gaussian process(RMFGP) model is demonstrated in four numerical examples. The
results show that our method has better performance in all cases and
uncertainty propagation analysis is performed for last two cases involving
stochastic partial differential equations.


title: Nielsen Realization for sphere twists on 3-manifolds

abstract:
  For a 3-manifold M, the twist group Twist(M) is the subgroup of the mapping
class group Mod(M) generated by twists about embedded 2-spheres. We study the
Nielsen realization problem for subgroups of Twist(M). We prove that a
nontrivial subgroup G<Twist(M) is realized by diffeomorphisms if and only if G
is cyclic and M is a connected sum of lens spaces. We also apply our methods to
the Burnside problem for 3-manifolds and show that Diff(M) does not contain an
infinite torsion group when M is reducible and not a connected sum of lens
spaces.


title: Markov categories, causal theories, and the do-calculus

abstract:
  We give a category-theoretic treatment of causal models that formalizes the
syntax for causal reasoning over a directed acyclic graph (DAG) by associating
a free Markov category with the DAG in a canonical way. This framework enables
us to define and study important concepts in causal reasoning from an abstract
and "purely causal" point of view, such as causal independence/separation,
causal conditionals, and decomposition of intervention effects. Our results
regarding these concepts abstract away from the details of the commonly adopted
causal models such as (recursive) structural equation models or causal Bayesian
networks. They are therefore more widely applicable and in a way conceptually
clearer. Our results are also intimately related to Judea Pearl's celebrated
do-calculus, and yield a syntactic version of a core part of the calculus that
is inherited in all causal models. In particular, it induces a simpler and
specialized version of Pearl's do-calculus in the context of causal Bayesian
networks, which we show is as strong as the full version.


title:
Very High-energy Afterglow Emission of GRB 190829A: Evidence for Its
  Hadronic Origin?

abstract:
  The detection of multi-TeV gamma-rays from the afterglow phase of GRB 190829A
by High Energy Stereoscopic System (H.E.S.S.) telescope is an addition to the
already existing list of two more GRBs observed in the very high energy (VHE)
gamma-rays in recent years. Jets of blazars and GRBs have many similarities and
the photohadronic model is very successful in explaining the VHE gamma-ray
spectra from the high energy blazars. Recently, the photohadronic model has
been successfully applied to study the sub-TeV gamma-rays from the afterglow
phases of GRB 180720B and GRB 190114C. We employed this model again to explain
the VHE spectra observed for the two consecutive nights from GRB 190829A. We
show that the spectra of GRB 190829A can be due to the interactions of high
energy protons with the synchrotron self-Compton photons in the forward shock
region of the GRB jet, similar to the low emission state of the VHE flaring
events of high energy blazars. We speculate that, if in future, it is possible
to observe the VHE gamma-ray spectra from nearby GRBs in their afterglow
phases, then some of them could only be explained by employing two different
spectral indices. If confirmed, such VHE spectra could be interpreted as a
result of the interactions of the high energy protons with the photons, both
from the synchrotron background and the synchrotron self-Compton background in
the forward shock region.


title: ACuTE: Automatic Curriculum Transfer from Simple to Complex Environments

abstract:
  Despite recent advances in Reinforcement Learning (RL), many problems,
especially real-world tasks, remain prohibitively expensive to learn. To
address this issue, several lines of research have explored how tasks, or data
samples themselves, can be sequenced into a curriculum to learn a problem that
may otherwise be too difficult to learn from scratch. However, generating and
optimizing a curriculum in a realistic scenario still requires extensive
interactions with the environment. To address this challenge, we formulate the
curriculum transfer problem, in which the schema of a curriculum optimized in a
simpler, easy-to-solve environment (e.g., a grid world) is transferred to a
complex, realistic scenario (e.g., a physics-based robotics simulation or the
real world). We present "ACuTE", Automatic Curriculum Transfer from Simple to
Complex Environments, a novel framework to solve this problem, and evaluate our
proposed method by comparing it to other baseline approaches (e.g., domain
adaptation) designed to speed up learning. We observe that our approach
produces improved jumpstart and time-to-threshold performance even when adding
task elements that further increase the difficulty of the realistic scenario.
Finally, we demonstrate that our approach is independent of the learning
algorithm used for curriculum generation, and is Sim2Real transferable to a
real world scenario using a physical robot.


title: Levi-Civita Ricci-flat metrics on non-K\"{a}hler Calabi-Yau manifolds

abstract:
  In this paper, we provide new examples of Levi-Civita Ricci-flat Hermitian
metrics on certain compact non-K\"{a}hler Calabi-Yau manifolds, including every
compact Hermitian Weyl-Einstein manifold, every compact locally conformal
hyperK\"{a}hler manifold, certain suspensions of Brieskorn manifolds, and every
generalized Hopf manifold provided by suspensions of exotic spheres. These
examples generalize previous constructions on Hopf manifolds. Additionally, we
also construct new examples of compact Hermitian manifolds with nonnegative
first Chern class that admit constant strictly negative Riemannian scalar
curvature. Further, we remark some applications of our main results in the
study of the Chern-Ricci flow on compact Hermitian Weyl-Einstein manifolds. In
particular, we describe the Gromov-Hausdorff limit for certain explicit
finite-time collapsing solutions which generalize previous constructions on
Hopf manifolds.


title: Impact of Dynamical Fermions on Centre Vortex Structure

abstract:
  This presentation examines the centre vortex structure of Monte-Carlo
generated gauge-field configurations using modern visualisation techniques.
This time, the manner in which light dynamical fermion degrees of freedom
impact the centre-vortex structure is explored. Focusing on the thin vortices
identified by plaquettes having a non-trivial centre phase, the vortex
structure is illustrated through 3D renderings of oriented plaquettes. The
impact of light dynamical fermions is not subtle, changing both the density of
vortices and the complexity of the vortex structures observed. The role of
vortex branching points in full QCD is highlighted in the survey of results
presented.


title: Equilibrium Finding in Normal-Form Games Via Greedy Regret Minimization

abstract:
  We extend the classic regret minimization framework for approximating
equilibria in normal-form games by greedily weighing iterates based on regrets
observed at runtime. Theoretically, our method retains all previous convergence
rate guarantees. Empirically, experiments on large randomly generated games and
normal-form subgames of the AI benchmark Diplomacy show that greedy weights
outperforms previous methods whenever sampling is used, sometimes by several
orders of magnitude.


title: Hierarchical Optimal Power Flow with Improved Gradient Evaluation

abstract:
  Existing algorithms to solve alternating-current optimal power flow (AC-OPF)
often exploit linear approximations to simplify system models and accelerate
computations. In this paper, we improve a recent hierarchical OPF algorithm,
which rested on primal-dual gradients evaluated in a linearized distribution
power flow model. Specifically, we identify a risk of voltage violation arising
from the model linearization, and propose a more accurate gradient evaluation
method to eliminate that risk. We further develop a hierarchical primal-dual
algorithm to solve OPF based on the proposed gradient evaluation method.
Numerical results on IEEE networks show that our algorithm can enhance voltage
safety with satisfactory computational efficiency.


title:
Improved Approximations for Euclidean $k$-means and $k$-median, via
  Nested Quasi-Independent Sets

abstract:
  Motivated by data analysis and machine learning applications, we consider the
popular high-dimensional Euclidean $k$-median and $k$-means problems. We
propose a new primal-dual algorithm, inspired by the classic algorithm of Jain
and Vazirani and the recent algorithm of Ahmadian, Norouzi-Fard, Svensson, and
Ward. Our algorithm achieves an approximation ratio of $2.406$ and $5.912$ for
Euclidean $k$-median and $k$-means, respectively, improving upon the 2.633
approximation ratio of Ahmadian et al. and the 6.1291 approximation ratio of
Grandoni, Ostrovsky, Rabani, Schulman, and Venkat.
  Our techniques involve a much stronger exploitation of the Euclidean metric
than previous work on Euclidean clustering. In addition, we introduce a new
method of removing excess centers using a variant of independent sets over
graphs that we dub a "nested quasi-independent set". In turn, this technique
may be of interest for other optimization problems in Euclidean and $\ell_p$
metric spaces.


title:
Operator estimates for non-periodically perforated domains with
  Dirichlet and nonlinear Robin conditions: vanishing limit

abstract:
  We consider a general second order linear equation in a finely perforated
domain. The shapes of cavities and their distribution in the domain are
arbitrary and non-periodic; they are supposed to satisfy minimal natural
geometric conditions. On the boundaries of the cavities we impose either the
Dirichlet or a nonlinear Robin condition; the choice of the type of the
boundary condition for each cavity is arbitrary. Then we suppose that for some
cavities the nonlinear Robin condition is sign-definite in certain sense.
Provided such cavities and ones with the Dirichlet condition are distributed
rather densely in the domain and the characteristic sizes of the cavities and
the minimal distances between the cavities satisfy certain simple condition, we
show that a solution to our problem vanishes as the perforation becomes finer.
Our main result are order sharp estimates for the $L_2$- and $W_2^1$-norms of
the solution uniform in the $L_2$-norm of the right hand side in the equation.


title:
A parallel iterative procedure for weak Galerkin methods for second
  order elliptic problems

abstract:
  A parallelizable iterative procedure based on domain decomposition is
presented and analyzed for weak Galerkin finite element methods for second
order elliptic equations. The convergence analysis is established for the
decomposition of the domain into individual elements associated to the weak
Galerkin methods or into larger subdomains. A series of numerical tests are
illustrated to verify the theory developed in this paper.


title:
Cello: Efficient Computer Systems Optimization with Predictive Early
  Termination and Censored Regression

abstract:
  Sample-efficient machine learning (SEML) has been widely applied to find
optimal latency and power tradeoffs for configurable computer systems. Instead
of randomly sampling from the configuration space, SEML reduces the search cost
by dramatically reducing the number of configurations that must be sampled to
optimize system goals (e.g., low latency or energy). Nevertheless, SEML only
reduces one component of cost -- the total number of samples collected -- but
does not decrease the cost of collecting each sample. Critically, not all
samples are equal; some take much longer to collect because they correspond to
slow system configurations. This paper present Cello, a computer systems
optimization framework that reduces sample collection costs -- especially those
that come from the slowest configurations. The key insight is to predict ahead
of time whether samples will have poor system behavior (e.g., long latency or
high energy) and terminate these samples early before their measured system
behavior surpasses the termination threshold, which we call it predictive early
termination. To predict the future system behavior accurately before it
manifests as high runtime or energy, Cello uses censored regression to produces
accurate predictions for running samples. We evaluate Cello by optimizing
latency and energy for Apache Spark workloads. We give Cello a fixed amount of
time to search a combined space of hardware and software configuration
parameters. Our evaluation shows that compared to the state-of-the-art SEML
approach in computer systems optimization, Cello improves latency by 1.19X for
minimizing latency under a power constraint, and improves energy by 1.18X for
minimizing energy under a latency constraint.


title: The Complexity of Temporal Vertex Cover in Small-Degree Graphs

abstract:
  Temporal graphs naturally model graphs whose underlying topology changes over
time. Recently, the problems TEMPORAL VERTEX COVER (or TVC) and SLIDING-WINDOW
TEMPORAL VERTEX COVER(or $\Delta$-TVC for time-windows of a fixed-length
$\Delta$) have been established as natural extensions of the classic problem
VERTEX COVER on static graphs with connections to areas such as surveillance in
sensor networks. In this paper we initiate a systematic study of the complexity
of TVC and $\Delta$-TVC on sparse graphs. Our main result shows that for every
$\Delta\geq 2$, $\Delta$-TVC is NP-hard even when the underlying topology is
described by a path or a cycle. This resolves an open problem from literature
and shows a surprising contrast between $\Delta$-TVC and TVC for which we
provide a polynomial-time algorithm in the same setting. To circumvent this
hardness, we present a number of exact and approximation algorithms for
temporal graphs whose underlying topologies are given by a path, that have
bounded vertex degree in every time step, or that admit a small-sized temporal
vertex cover.


title: Word Embeddings Are Capable of Capturing Rhythmic Similarity of Words

abstract:
  Word embedding systems such as Word2Vec and GloVe are well-known in deep
learning approaches to NLP. This is largely due to their ability to capture
semantic relationships between words. In this work we investigated their
usefulness in capturing rhythmic similarity of words instead. The results show
that vectors these embeddings assign to rhyming words are more similar to each
other, compared to the other words. It is also revealed that GloVe performs
relatively better than Word2Vec in this regard. We also proposed a first of its
kind metric for quantifying rhythmic similarity of a pair of words.


title:
Unifying gravitational waves with $W$ boson, FIMP dark matter, and
  Majorana Seesaw mechanism

abstract:
  We study a minimal extension of the Standard Model by introducing three
right-handed neutrinos and a new scotogenic scalar doublet, whose mass
splittings between neutral and charged components are responsible for the newly
measured $W$ boson mass in the CDF group. Not only does the interaction of a
right-handed neutrino and a scotogenic scalar produce the non-vanishing
Majorana neutrino masses, but it also explains the Universe's missing matter in
the form of FIMP dark matter. The correlation among the three concerns is well
demonstrated by mass splittings. We also study the influence of the mass
splitting on the first order electroweak phase transition, and find that a new
mass splitting shall enhance the transition strength further. This model thus
predicts the appearance of gravitational waves during the phase transition
where we can unveil its properties in the forthcoming detectors such as
U-DECIGO.


title:
First- and second-order gradient couplings to NV centers engineered by
  the geometric symmetry

abstract:
  The magnetic fields with the first- and second-order gradient are engineered
in several mechanically controlled hybrid systems. The current-carrying
nanowires with different geometries can induce a tunable magnetic field
gradient because of their geometric symmetries, and therefore develop various
couplings to nitrogen-vacancy (NV) centers. For instance, a straight nanowire
can guarantee the Jaynes-Cummings (JC) spin-phonon interaction and may indicate
a potential route towards the application on quantum measurement. Especially,
two parallel straight nanowires can develop the coherent down-conversion
spin-phonon interaction through a second-order gradient of the magnetic field,
and it can induce a bundle emission of the antibunched phonon pairs via an
entirely different magnetic mechanism. Maybe, this investigation is further
believed to support NV's future applications in the area of quantum
manipulation, quantum sensing, and precision measurement, etc.


title:
Consistency Learning via Decoding Path Augmentation for Transformers in
  Human Object Interaction Detection

abstract:
  Human-Object Interaction detection is a holistic visual recognition task that
entails object detection as well as interaction classification. Previous works
of HOI detection has been addressed by the various compositions of subset
predictions, e.g., Image -> HO -> I, Image -> HI -> O. Recently, transformer
based architecture for HOI has emerged, which directly predicts the HOI
triplets in an end-to-end fashion (Image -> HOI). Motivated by various
inference paths for HOI detection, we propose cross-path consistency learning
(CPC), which is a novel end-to-end learning strategy to improve HOI detection
for transformers by leveraging augmented decoding paths. CPC learning enforces
all the possible predictions from permuted inference sequences to be
consistent. This simple scheme makes the model learn consistent
representations, thereby improving generalization without increasing model
capacity. Our experiments demonstrate the effectiveness of our method, and we
achieved significant improvement on V-COCO and HICO-DET compared to the
baseline models. Our code is available at https://github.com/mlvlab/CPChoi.


title:
Dependable Intrusion Detection System for IoT: A Deep Transfer
  Learning-based Approach

abstract:
  Security concerns for IoT applications have been alarming because of their
widespread use in different enterprise systems. The potential threats to these
applications are constantly emerging and changing, and therefore, sophisticated
and dependable defense solutions are necessary against such threats. With the
rapid development of IoT networks and evolving threat types, the traditional
machine learning-based IDS must update to cope with the security requirements
of the current sustainable IoT environment. In recent years, deep learning, and
deep transfer learning have progressed and experienced great success in
different fields and have emerged as a potential solution for dependable
network intrusion detection. However, new and emerging challenges have arisen
related to the accuracy, efficiency, scalability, and dependability of the
traditional IDS in a heterogeneous IoT setup. This manuscript proposes a deep
transfer learning-based dependable IDS model that outperforms several existing
approaches. The unique contributions include effective attribute selection,
which is best suited to identify normal and attack scenarios for a small amount
of labeled data, designing a dependable deep transfer learning-based ResNet
model, and evaluating considering real-world data. To this end, a comprehensive
experimental performance evaluation has been conducted. Extensive analysis and
performance evaluation show that the proposed model is robust, more efficient,
and has demonstrated better performance, ensuring dependability.


title: Robust Molecular Computation by Active Mechanics

abstract:
  The living cell expends energetic and material resources to reliably process
information from its environment. To do so, it utilises unreliable molecular
circuitry that is subject to thermal and other fluctuations. Here, we argue
that active, physical processes can provide error correcting mechanisms for
information processing. We analyse a model in which fluctuating receptor
activation induces contractile stresses that recruit further receptors,
dynamically controlling resource usage and accuracy. We show that this active
scheme can outperform passive, static clusters (as formed, for instance, by
protein crosslinking). We consider simple binary environments, informative
decision trees, and chemical computations; in each case, active stresses serve
to contextually build signalling platforms that dynamically suppress error and
allows for robust cellular computation.


title:
An interpolation of the generalized duality formula for the Schur
  multiple zeta values to complex functions

abstract:
  One of the important research subjects in the study of multiple zeta
functions is to clarify the linear relations and functional equations among
them. The Schur multiple zeta functions are a generalization of the multiple
zeta functions of Euler-Zagier type. In this paper, we give an interpolation of
the sums in the generalized duality formula, called Ohno relation, for Schur
multiple zeta values. Moreover, we prove that the Ohno relation for Schur
multiple zeta values is valid for complex numbers.


title:
Nonparametric Bayes Differential Analysis of Multigroup DNA Methylation
  Data

abstract:
  DNA methylation datasets in cancer studies are comprised of sample
measurements on a large number of genomic locations called
cytosine-phosphate-guanine (CpG) sites with complex correlation structures. A
fundamental goal of these investigations is the development of statistical
techniques that identify disease genomic signatures across multiple patient
groups determined by different experimental or biological conditions. We
propose BayesDiff, a nonparametric Bayesian approach for differential analysis
relying on a novel class of first order mixture models called the Sticky
Poisson-Dirichlet process or two-restaurant two-cuisine franchise (2R2CF). The
BayesDiff methodology flexibly utilizes information from all CpG sites,
adaptively accommodating any serial dependence in the data corresponding to the
widely varying inter-probe distances, to perform simultaneous inferences about
the differential genomic signature of the patient groups. In simulation
studies, we demonstrate the effectiveness of the BayesDiff procedure relative
to existing statistical techniques for differential DNA methylation. The
methodology is applied to analyze a gastrointestinal (GI) cancer DNA
methylation dataset that displays both serial correlations and interaction
patterns. The results support and complement known aspects of DNA methylation
and gene association in upper GI cancers.


title:
Autonomous Mobile Clinics: Empowering Affordable Anywhere Anytime
  Healthcare Access

abstract:
  We are facing a global healthcare crisis today as the healthcare cost is ever
climbing, but with the aging population, government fiscal revenue is ever
dropping. To create a more efficient and effective healthcare system, three
technical challenges immediately present themselves: healthcare access,
healthcare equity, and healthcare efficiency. An autonomous mobile clinic
solves the healthcare access problem by bringing healthcare services to the
patient by the order of the patient's fingertips. Nevertheless, to enable a
universal autonomous mobile clinic network, a three-stage technical roadmap
needs to be achieved: In stage one, we focus on solving the inequity challenge
in the existing healthcare system by combining autonomous mobility and
telemedicine. In stage two, we develop an AI doctor for primary care, which we
foster from infancy to adulthood with clean healthcare data. With the AI
doctor, we can solve the inefficiency problem. In stage three, after we have
proven that the autonomous mobile clinic network can truly solve the target
clinical use cases, we shall open up the platform for all medical verticals,
thus enabling universal healthcare through this whole new system.


title:
Towards Homogeneous Modality Learning and Multi-Granularity Information
  Exploration for Visible-Infrared Person Re-Identification

abstract:
  Visible-infrared person re-identification (VI-ReID) is a challenging and
essential task, which aims to retrieve a set of person images over visible and
infrared camera views. In order to mitigate the impact of large modality
discrepancy existing in heterogeneous images, previous methods attempt to apply
generative adversarial network (GAN) to generate the modality-consisitent data.
However, due to severe color variations between the visible domain and infrared
domain, the generated fake cross-modality samples often fail to possess good
qualities to fill the modality gap between synthesized scenarios and target
real ones, which leads to sub-optimal feature representations. In this work, we
address cross-modality matching problem with Aligned Grayscale Modality (AGM),
an unified dark-line spectrum that reformulates visible-infrared dual-mode
learning as a gray-gray single-mode learning problem. Specifically, we generate
the grasycale modality from the homogeneous visible images. Then, we train a
style tranfer model to transfer infrared images into homogeneous grayscale
images. In this way, the modality discrepancy is significantly reduced in the
image space. In order to reduce the remaining appearance discrepancy, we
further introduce a multi-granularity feature extraction network to conduct
feature-level alignment. Rather than relying on the global information, we
propose to exploit local (head-shoulder) features to assist person Re-ID, which
complements each other to form a stronger feature descriptor. Comprehensive
experiments implemented on the mainstream evaluation datasets include SYSU-MM01
and RegDB indicate that our method can significantly boost cross-modality
retrieval performance against the state of the art methods.


title:
An Adaptive Alternating-direction-method-based Nonnegative Latent Factor
  Model

abstract:
  An alternating-direction-method-based nonnegative latent factor model can
perform efficient representation learning to a high-dimensional and incomplete
(HDI) matrix. However, it introduces multiple hyper-parameters into the
learning process, which should be chosen with care to enable its superior
performance. Its hyper-parameter adaptation is desired for further enhancing
its scalability. Targeting at this issue, this paper proposes an Adaptive
Alternating-direction-method-based Nonnegative Latent Factor (A2NLF) model,
whose hyper-parameter adaptation is implemented following the principle of
particle swarm optimization. Empirical studies on nonnegative HDI matrices
generated by industrial applications indicate that A2NLF outperforms several
state-of-the-art models in terms of computational and storage efficiency, as
well as maintains highly competitive estimation accuracy for an HDI matrix's
missing data.


title:
HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with
  Data Augmentation for Multilingual News Similarity

abstract:
  This paper describes our system designed for SemEval-2022 Task 8:
Multilingual News Article Similarity. We proposed a linguistics-inspired model
trained with a few task-specific strategies. The main techniques of our system
are: 1) data augmentation, 2) multi-label loss, 3) adapted R-Drop, 4) samples
reconstruction with the head-tail combination. We also present a brief analysis
of some negative methods like two-tower architecture. Our system ranked 1st on
the leaderboard while achieving a Pearson's Correlation Coefficient of 0.818 on
the official evaluation set.


title:
Statistical Perspective on Functional and Causal Neural Connectomics:
  The Time-Aware PC Algorithm

abstract:
  The representation of the flow of information between neurons in the brain
based on their activity is termed the causal functional connectome. Such
representation incorporates the dynamic nature of neuronal activity and causal
interactions between them. In contrast to connectome, the causal functional
connectome is not directly observed and needs to be inferred from neural time
series. A popular statistical framework for inferring causal connectivity from
observations is the directed probabilistic graphical modeling. Its common
formulation is not suitable for neural time series since was developed for
variables with independent and identically distributed static samples. In this
work, we propose to model and estimate the causal functional connectivity from
neural time series using a novel approach that adapts directed probabilistic
graphical modeling to the time series scenario. In particular, we develop the
Time-Aware PC (TPC) algorithm for estimating the causal functional
connectivity, which adapts the PC algorithm a state-of-the-art method for
statistical causal inference. We show that the model outcome of TPC has the
properties of reflecting causality of neural interactions such as being
non-parametric, exhibits the directed Markov property in a time-series setting,
and is predictive of the consequence of counterfactual interventions on the
time series. We demonstrate the utility of the methodology to obtain the causal
functional connectome for several datasets including simulations, benchmark
datasets, and recent multi-array electro-physiological recordings from the
mouse visual cortex.


title:
Storage and manipulation of single x-ray photons via nuclear hyperfine
  splitting

abstract:
  We introduce a technique to store and manipulate single x-ray photons which
relies on dynamically controlled absorption via nuclear hyperfine magnetic
splitting. This scheme is inherently suitable for storage, on-demand generation
and dynamical manipulation of single x-ray photons, for instance, the
manipulation of the temporal shape, temporal splitting, the interference
between x-ray photons and the control of the polarization. Our approach opens
up new paths in x-ray quantum information.


title: The random dynamical pitchfork bifurcation with additive L\'evy noises

abstract:
  This paper concerns the effects of additive non-Gaussian L\'evy noises on the
pitchfork bifurcation. We consider two types of noises, $\alpha$-stable process
and the truncated process. Under both $\alpha$-stable process and the truncated
process, the classical pitchfork bifurcation model exists a unique invariant
measure. The Lyapunov exponent associated with the invariant measure is always
negative for the system under the truncated case. While the stochastic
pitchfork bifurcation still occurs. In both cases, the attractivity uniformity,
the finite-time Lyapunov exponent, and the dichotomy spectrum behave varies
with the bifurcation parameter changing.
  Compared with Brownian motion, there is two key difficulties for the L\'evy
processes. The stationary density can not be solved explicitly, thus we have to
estimate it properly. This is overcome by the strong maximum principle. The
bilateral suprema for L\'evy processes need to be analyzed. This is acquired by
the strong Markov property. Based on them, we establish the main results
indicating stochastic pitchfork bifurcation.


title: Repair Crew Routing for Power Distribution Network Restoration

abstract:
  As extreme weather events become more frequent and disruptive, service
restoration is increasingly important for many infrastructures, e.g., power
grids and communication networks. In many studies on service restoration, the
logistics issue of traveling over the road network, however, is often
overlooked due to the complexity of considering multiple networks
simultaneously, resulting in prolonged disruption time. In this work, we
address such a problem arising in power systems, where technical crew and
utility trucks travel to a number of sites to repair damaged equipment, with
the goal of minimizing the total service disruption time within the service
region. We call this problem the Power Restoration Traveling Repairman Problem
(PRTRP). What makes it significantly more challenging than a typical routing
problem is that the service disruption time in a location depends on the
interaction of the routing sequence with both networks, i.e., the road network
and the power grid. To solve the problem, we develop an exact method based on
bi-directional dynamic programming. We then improve the method by reducing the
search space with solution upper and lower bounds, and threshold rules derived
from the precedence relations in the power grid. We also propose efficient
heuristic variants of the method. We present computational results and compare
our method with benchmark heuristics.


title:
A spectroscopic study of blue supergiant stars in Local Group spiral
  galaxies: Andromeda and Triangulum

abstract:
  Low-resolution LAMOST and Keck spectra of blue supergiant stars distributed
over the disks of the Local Group spiral galaxies M 31 and M 33 are analyzed to
determine stellar effective temperatures, gravities, metallicities, and
reddening. Logarithmic metallicities at the center of the galaxies (in solar
units) of $0.30\pm0.09$ and $0.11\pm0.04$ and metallicity gradients of
$-0.37\pm0.13$ dex/$R_{25}$ and $-0.36\pm0.16$ dex/$R_{25}$ are measured for M
31 and M 33, respectively. For M 33 the 2-dimensional distribution of
metallicity indicates a deviation from azimutal symmetry with an off-centre
peak. The flux-weighted gravity-luminosity relationship of blue supergiant
stars is used to determine a distance modulus of 24.51$\pm$0.13 mag for M 31
and 24.93$\pm$0.07 mag for M 33. For M 31 the flux-weighted gravity--luminosity
relationship (FGLR) distance agrees well with other methods. For M 33 the
FGLR-based distance is larger than the distances from Cepheids studies but it
is in good agreement with work on eclipsing binaries, planetary nebulae ,
long-period variables, and the tip of the red giant branch.


title: Electron transport in the single-layer semiconductor

abstract:
  Two-dimensional (2D) materials are a new class of materials with interesting
physical properties and applications ranging from nanoelectronics to sensing
and photonics. In addition to graphene, the most studied 2D material,
monolayers of other layered materials such as semiconducting dichalcogenides
MoS2 or WSe2 are gaining in importance as promising channel materials for
field-effect transistors (FETs) and phototransistors. However, it is unclear
that how the specific process of electron transport is affected by temperature.
So, nowadays the electron dynamics of single-layer semiconductor cannot be
understood fundamentally. Here, we develop an analytical theory distinguishing
from traditional energy band theory, backed up by Monte-Carlo simulations, that
predicts the process of electron transport and the effect of temperature on the
electron transport in the single-layer semiconductor. In this paper, A new
model is built to deal with electron transporting in the sing-layer
semiconductor. The resistance is decided by the barrier rather than the
electron scattering in the single-layer semiconductor, which is macroscopic
quantum effect. Electron transport in FETs with different dielectric
configurations are investigated at different temperatures and a new control
factor that is decided by top-gate voltage or bottom-gate voltage is introduced
to describe the effect of gate voltage on the electron transport in 2D
semiconductor. The results of simulation show the drain current is mainly
determined by some elements, such as temperature, top-gate voltage, bottom-gate
voltage and source-drain voltage.


title:
A numerical algorithm for inverse problem from partial boundary
  measurement arising from mean field game problem

abstract:
  In this work, we consider a novel inverse problem in mean-field games (MFG).
We aim to recover the MFG model parameters that govern the underlying
interactions among the population based on a limited set of noisy partial
observations of the population dynamics under the limited aperture. Due to its
severe ill-posedness, obtaining a good quality reconstruction is very
difficult. Nonetheless, it is vital to recover the model parameters stably and
efficiently in order to uncover the underlying causes for population dynamics
for practical needs.
  Our work focuses on the simultaneous recovery of running cost and interaction
energy in the MFG equations from a \emph{finite number of boundary
measurements} of population profile and boundary movement. To achieve this
goal, we formalize the inverse problem as a constrained optimization problem of
a least squares residual functional under suitable norms. We then develop a
fast and robust operator splitting algorithm to solve the optimization using
techniques including harmonic extensions, three-operator splitting scheme, and
primal-dual hybrid gradient method. Numerical experiments illustrate the
effectiveness and robustness of the algorithm.


title: A unified theory of second sound in two dimensional materials

abstract:
  We develop a unified theory for the second sound in two dimensional
materials. Previously studied drifting and driftless second sound are two
limiting cases of the theory, corresponding to the drift and diffusive part of
the energy flux, respectively. We find that due to the presence of quadratic
flexural phonons the drifting second sound does not exist in the thermodynamic
limit, while the driftless mode is less affected. This is understood as a
result of infinite effective inertia of flexual phonons, due to their constant
density states and divergent Bose-Einstein distribution in the long wave length
limit. Consequently, the group velocity of the drifting mode is smaller than
that of the driftless mode. However, upon tensile strain, the velocity of
drifting mode becomes larger. Both of them increase with tensile strain due to
the linearization of the flexural phonon dispersion. Our results clarify
several puzzles encountered previously and pave the way for exploring wave-like
heat transport beyond hydrodynamic regime.


title: Neural Lagrangian Schr\"odinger Bridge

abstract:
  Population dynamics is the study of temporal and spatial variation in the
size of populations of organisms and is a major part of population ecology. One
of the main difficulties in analyzing population dynamics is that we can only
obtain observation data with coarse time intervals from fixed-point
observations due to experimental costs or other constraints. Recently, modeling
population dynamics by using continuous normalizing flows (CNFs) and dynamic
optimal transport has been proposed to infer the expected trajectory of samples
from a fixed-point observed population. While the sample behavior in CNF is
deterministic, the actual sample in biological systems moves in an essentially
random yet directional manner. Moreover, when a sample moves from point A to
point B in dynamical systems, its trajectory is such that the corresponding
action has the smallest possible value, known as the principle of least action.
To satisfy these requirements of the sample trajectories, we formulate the
Lagrangian Schr\"odinger bridge (LSB) problem and propose to solve it
approximately using neural SDE with regularization. We also develop a model
architecture that enables faster computation. Our experiments show that our
solution to the LSB problem can approximate the dynamics at the population
level and that using the prior knowledge introduced by the Lagrangian enables
us to estimate the trajectories of individual samples with stochastic behavior.


title:
A Uniqueness Result for the Calderon Problem for $U(N)$-connections
  coupled to spinors

abstract:
  In this paper we define a Dirichlet-to-Neumann map for a twisted Dirac
Laplacian acting on bundle-valued spinors over a spin manifold. We show that
this map is a pseudodifferential operator of order 1 whose symbol determines
the Taylor series of the metric and connection at the boundary. We go on to
show that if two real-analytic connections couple to a spinor via the
Yang--Mills--Dirac equations with appropriate boundary conditions, and have
equal Dirichlet-to-Neumann maps, then the two connections are locally gauge
equivalent. In the abelian case, the connections are globally gauge equivalent.


title: Fusion of Self-supervised Learned Models for MOS Prediction

abstract:
  We participated in the mean opinion score (MOS) prediction challenge, 2022.
This challenge aims to predict MOS scores of synthetic speech on two tracks,
the main track and a more challenging sub-track: out-of-domain (OOD). To
improve the accuracy of the predicted scores, we have explored several model
fusion-related strategies and proposed a fused framework in which seven
pretrained self-supervised learned (SSL) models have been engaged. These
pretrained SSL models are derived from three ASR frameworks, including Wav2Vec,
Hubert, and WavLM. For the OOD track, we followed the 7 SSL models selected on
the main track and adopted a semi-supervised learning method to exploit the
unlabeled data. According to the official analysis results, our system has
achieved 1st rank in 6 out of 16 metrics and is one of the top 3 systems for 13
out of 16 metrics. Specifically, we have achieved the highest LCC, SRCC, and
KTAU scores at the system level on main track, as well as the best performance
on the LCC, SRCC, and KTAU evaluation metrics at the utterance level on OOD
track. Compared with the basic SSL models, the prediction accuracy of the fused
system has been largely improved, especially on OOD sub-track.


title: Defect Identification, Categorization, and Repair: Better Together

abstract:
  Just-In-Time defect prediction (JIT-DP) models can identify defect-inducing
commits at check-in time. Even though previous studies have achieved a great
progress, these studies still have the following limitations: 1) useful
information (e.g., semantic information and structure information) are not
fully used; 2) existing work can only predict a commit as buggy one or clean
one without more information about what type of defect it is; 3) a commit may
involve changes in many files, which cause difficulty in locating the defect;
4) prior studies treat defect identification and defect repair as separate
tasks, none aims to handle both tasks simultaneously. In this paper, to handle
aforementioned limitations, we propose a comprehensive defect prediction and
repair framework named CompDefect, which can identify whether a changed
function (a more fine-grained level) is defect-prone, categorize the type of
defect, and repair such a defect automatically if it falls into several
scenarios, e.g., defects with single statement fixes, or those that match a
small set of defect templates. Generally, the first two tasks in CompDefect are
treated as a multiclass classification task, while the last one is treated as a
sequence generation task. The whole input of CompDefect consists of three parts
(exampled with positive functions): the clean version of a function (i.e., the
version before defect introduced), the buggy version of a function and the
fixed version of a function. In multiclass classification task, CompDefect
categorizes the type of defect via multiclass classification with the
information in both the clean version and the buggy version. In code sequence
generation task, CompDefect repairs the defect once identified or keeps it
unchanged.


title:
Why Shape Coding? Asymptotic Analysis of the Entropy Rate for Digital
  Images

abstract:
  This paper focuses on the ultimate limit theory of image compression. It
proves that for an image source, there exists a coding method with shapes that
can achieve the entropy rate under a certain condition where the shape-pixel
ratio in the encoder/decoder is $O({1 \over {\log t}})$. Based on the new
finding, an image coding framework with shapes is proposed and proved to be
asymptotically optimal for stationary and ergodic processes.


title: Stability and Generalization of Differentially Private Minimax Problems

abstract:
  In the field of machine learning, many problems can be formulated as the
minimax problem, including reinforcement learning, generative adversarial
networks, to just name a few. So the minimax problem has attracted a huge
amount of attentions from researchers in recent decades. However, there is
relatively little work on studying the privacy of the general minimax paradigm.
In this paper, we focus on the privacy of the general minimax setting,
combining differential privacy together with minimax optimization paradigm.
Besides, via algorithmic stability theory, we theoretically analyze the high
probability generalization performance of the differentially private minimax
algorithm under the strongly-convex-strongly-concave condition. To the best of
our knowledge, this is the first time to analyze the generalization performance
of general minimax paradigm, taking differential privacy into account.


title:
A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and
  Challenges

abstract:
  Legal judgment prediction (LJP) applies Natural Language Processing (NLP)
techniques to predict judgment results based on fact descriptions
automatically. Recently, large-scale public datasets and advances in NLP
research have led to increasing interest in LJP. Despite a clear gap between
machine and human performance, impressive results have been achieved in various
benchmark datasets. In this paper, to address the current lack of comprehensive
survey of existing LJP tasks, datasets, models and evaluations, (1) we analyze
31 LJP datasets in 6 languages, present their construction process and define a
classification method of LJP with 3 different attributes; (2) we summarize 14
evaluation metrics under four categories for different outputs of LJP tasks;
(3) we review 12 legal-domain pretrained models in 3 languages and highlight 3
major research directions for LJP; (4) we show the state-of-art results for 8
representative datasets from different court cases and discuss the open
challenges. This paper can provide up-to-date and comprehensive reviews to help
readers understand the status of LJP. We hope to facilitate both NLP
researchers and legal professionals for further joint efforts in this problem.


title:
State capital involvement, managerial sentiment and firm innovation
  performance Evidence from China

abstract:
  In recent years, more and more state-owned enterprises (SOEs) have been
embedded in the restructuring and governance of private enterprises through
equity participation, providing a more advantageous environment for private
enterprises in financing and innovation. However, there is a lack of knowledge
about the underlying mechanisms of SOE intervention on corporate innovation
performance. Hence, in this study, we investigated the association of state
capital intervention with innovation performance, meanwhile further
investigated the potential mediating and moderating role of managerial
sentiment and financing constraints, respectively, using all listed non-ST
firms from 2010 to 2020 as the sample. The results revealed two main findings:
1) state capital intervention would increase innovation performance through
managerial sentiment; 2) financing constraints would moderate the effect of
state capital intervention on firms' innovation performance.


title:
SUMD: Super U-shaped Matrix Decomposition Convolutional neural network
  for Image denoising

abstract:
  In this paper, we propose a novel and efficient CNN-based framework that
leverages local and global context information for image denoising. Due to the
limitations of convolution itself, the CNN-based method is generally unable to
construct an effective and structured global feature representation, usually
called the long-distance dependencies in the Transformer-based method. To
tackle this problem, we introduce the matrix decomposition module(MD) in the
network to establish the global context feature, comparable to the Transformer
based method performance. Inspired by the design of multi-stage progressive
restoration of U-shaped architecture, we further integrate the MD module into
the multi-branches to acquire the relative global feature representation of the
patch range at the current stage. Then, the stage input gradually rises to the
overall scope and continuously improves the final feature. Experimental results
on various image denoising datasets: SIDD, DND, and synthetic Gaussian noise
datasets show that our model(SUMD) can produce comparable visual quality and
accuracy results with Transformer-based methods.


title: Tweet Emotion Dynamics: Emotion Word Usage in Tweets from US and Canada

abstract:
  Over the last decade, Twitter has emerged as one of the most influential
forums for social, political, and health discourse. In this paper, we introduce
a massive dataset of more than 45 million geo-located tweets posted between
2015 and 2021 from US and Canada (TUSC), especially curated for natural
language analysis. We also introduce Tweet Emotion Dynamics (TED) -- metrics to
capture patterns of emotions associated with tweets over time. We use TED and
TUSC to explore the use of emotion-associated words across US and Canada;
across 2019 (pre-pandemic), 2020 (the year the pandemic hit), and 2021 (the
second year of the pandemic); and across individual tweeters. We show that
Canadian tweets tend to have higher valence, lower arousal, and higher
dominance than the US tweets. Further, we show that the COVID-19 pandemic had a
marked impact on the emotional signature of tweets posted in 2020, when
compared to the adjoining years. Finally, we determine metrics of TED for
170,000 tweeters to benchmark characteristics of TED metrics at an aggregate
level. TUSC and the metrics for TED will enable a wide variety of research on
studying how we use language to express ourselves, persuade, communicate, and
influence, with particularly promising applications in public health, affective
science, social science, and psychology.


title: Superconductivity in TlBi$_2$ with a large Kadowaki-Woods ratio

abstract:
  In this article, the superconducting and normal state properties of TlBi$_2$
with the AlB$_2$-type structure were studied by the resistivity, magnetization
and specific heat measurements. It was found that bulk superconductivity with
$T_{C}$ = 6.2 K emerges in TlBi$_2$, which is a phonon-mediated $s$-wave
superconductor with a strong electron-phonon coupling ($\lambda$$_{ep}$ = 1.38)
and a large superconducting gap ($\Delta_{0}$/$k_{B}T_{C}$ = 2.25). We found
that the $\rho$($T$) exhibits an unusual $T$-linear dependence above 50 K, and
can be well described by the Fermi-liquid theory below 20 K. Interestingly, its
Kadowaki-Woods ratio $A/\gamma^{2}$ [9.2$\times$10$^{-5}$ $\mu\Omega$ cm(mol
K$^{2}$/mJ)$^{2}$] is unexpectedly one order of magnitude larger than that
obtained in many heavy Fermi compounds, although the electron correlation is
not so strong.


title: Discrete Vector-Valued Nonuniform Gabor Frames

abstract:
  Gabor frames have interested many mathematicians and physicists due to their
potential applications in time-frequency analysis, in particular, signal
processing. A Gabor system is a collection of vectors which is obtained by
applying modulation and shift operators to non-zero functions in signal spaces.
In many applications, for example, signal processing related to Gabor systems,
the corresponding shifts may not be uniform. That is, the set associated with
shifts may not be a group under usual addition. We analyze discrete
vector-valued nonuniform Gabor frames (DVNUG frames, in short) in discrete
vector-valued nonuniform signal spaces, where the indexing set associated with
shifts may not be a subgroup of real numbers under usual addition, but a
spectrum which is based on the theory of spectral pairs. First, we give
necessary and sufficient conditions for the existence of DVNUG Bessel sequences
in discrete vector-valued nonuniform signal spaces in terms of Fourier
transformations of the modulated window sequences. We provide a
characterization of DVNUG frames in discrete vector-valued nonuniform signal
spaces. It is shown that DVNUG frames are stable under small perturbation of
window sequences associated with given discrete vector-valued nonuniform Gabor
systems. We observed that the arithmetic mean sequences associated with window
sequences of a given DVNUG frame collectively constitutes a discrete nonuniform
Gabor frame. Finally, we discuss an interplay between window sequences of DVNUG
systems and their corresponding coordinates.


title:
A novel stereo matching pipeline with robustness and unfixed disparity
  search range

abstract:
  Stereo matching is an essential basis for various applications, but most
stereo matching methods have poor generalization performance and require a
fixed disparity search range. Moreover, current stereo matching methods focus
on the scenes that only have positive disparities, but ignore the scenes that
contain both positive and negative disparities, such as 3D movies. In this
paper, we present a new stereo matching pipeline that first computes semi-dense
disparity maps based on binocular disparity, and then completes the rest
depending on monocular cues. The new stereo matching pipeline have the
following advantages: It 1) has better generalization performance than most of
the current stereo matching methods; 2) relaxes the limitation of a fixed
disparity search range; 3) can handle the scenes that involve both positive and
negative disparities, which has more potential applications, such as view
synthesis in 3D multimedia and VR/AR. Experimental results demonstrate the
effectiveness of our new stereo matching pipeline.


title: Resolved imaging of exoplanets with the solar gravitational lens

abstract:
  We discuss the feasibility of direct multipixel imaging of exoplanets with
the solar gravitational lens (SGL) in the context of a realistic deep space
mission. For this, we consider an optical telescope, placed in the image plane
that forms in the strong interference region of the SGL. We consider an
Earth-like exoplanet located in our immediate stellar neighborhood and model
its characteristics using our own Earth. We estimate photon fluxes from such a
compact, extended, resolved exoplanet. This light appears in the form of an
Einstein ring around the Sun, seen through the solar corona. The solar corona
background contributes a significant amount of stochastic noise and represents
the main noise source for observations utilizing the SGL. We estimate the
magnitude of this noise. We compute the resulting signal-no-noise ratios and
related integration times that are needed to perform imaging measurements under
realistic conditions. We conclude that an imaging mission is challenging but
feasible, using technologies that are either already available or in active
development. Under realistic conditions, megapixel imaging of Earth-like
exoplanets in our galactic neighborhood requires only weeks or months of
integration time, not years as previously thought.

categories: astro-ph.EP astro-ph.GA astro-ph.IM astro-ph.SR gr-qc


title:
Structured Graph Variational Autoencoders for Indoor Furniture layout
  Generation

abstract:
  We present a structured graph variational autoencoder for generating the
layout of indoor 3D scenes. Given the room type (e.g., living room or library)
and the room layout (e.g., room elements such as floor and walls), our
architecture generates a collection of objects (e.g., furniture items such as
sofa, table and chairs) that is consistent with the room type and layout. This
is a challenging problem because the generated scene should satisfy multiple
constrains, e.g., each object must lie inside the room and two objects cannot
occupy the same volume. To address these challenges, we propose a deep
generative model that encodes these relationships as soft constraints on an
attributed graph (e.g., the nodes capture attributes of room and furniture
elements, such as class, pose and size, and the edges capture geometric
relationships such as relative orientation). The architecture consists of a
graph encoder that maps the input graph to a structured latent space, and a
graph decoder that generates a furniture graph, given a latent code and the
room graph. The latent space is modeled with auto-regressive priors, which
facilitates the generation of highly structured scenes. We also propose an
efficient training procedure that combines matching and constrained learning.
Experiments on the 3D-FRONT dataset show that our method produces scenes that
are diverse and are adapted to the room layout.


title: On complex roots of the independence polynomial

abstract:
  It is known from the work of Shearer (1985) (and also Scott and Sokal (2005))
that the independence polynomial $Z_G(\lambda)$ of a graph $G$ of maximum
degree at most $d+1$ does not vanish provided that $\vert{\lambda}\vert \leq
\frac{d^d}{(d+1)^{d+1}}$. Significant extensions of this result have recently
been given in the case $\Re \lambda \geq 0$ by Peters and Regts (2019) and
Bencs and Csikv\'ari (arxiv:1807.08963). In this paper, our motivation is to
further extend these results and find zero free regions when $\Re \lambda \leq
0$.
  We begin by giving new geometric criteria for establishing zero-free regions
as well as for carrying out semi-rigorous numerical explorations. We then
provide two examples of the (rigorous) use of these criteria, by establishing
two new zero-free regions in the left-half plane. We also improve upon the
results of Bencs and P\'eter Csikv\'ari (arxiv:1807.08963) for the right
half-plane using our framework. By a direct application of the interpolation
method of Barvinok, combined with extensions due to Patel and Regts, these
results also imply deterministic polynomial time approximation algorithms for
the independence polynomial of bounded degree graphs in the new zero-free
regions.


title: Evaluation of Automatic Text Summarization using Synthetic Facts

abstract:
  Despite some recent advances, automatic text summarization remains
unreliable, elusive, and of limited practical use in applications. Two main
problems with current summarization methods are well known: evaluation and
factual consistency. To address these issues, we propose a new automatic
reference-less text summarization evaluation system that can measure the
quality of any text summarization model with a set of generated facts based on
factual consistency, comprehensiveness, and compression rate. As far as we
know, our evaluation system is the first system that measures the overarching
quality of the text summarization models based on factuality, information
coverage, and compression rate.


title:
Second-harmonic generation in germanium-on-insulator from visible to
  telecom wavelengths

abstract:
  The second-order $\chi^{2}$ process underpins many important nonlinear
optical applications in the field of classical and quantum optics. Generally,
the $\chi^{2}$ process manifests itself only in a non-centrosymmetric
dielectric medium via an anharmonic electron oscillation when driven by an
intense optical field. Due to inversion symmetry, group-IV semiconductors like
silicon (Si) and germanium (Ge) are traditionally not considered as ideal
candidates for second-order nonlinear optics applications. Here, we report the
experimental observation of the second-harmonic generation (SHG) in a
Ge-on-insulator (GOI) sample under femtosecond optical pumping. Specially, we
report the first-time measurement of the SHG signal from a GOI sample in the
telecom S-band by pumping at $\sim$$3000$ nm.


title: Three-dimensional de Sitter holography and bulk correlators at late time

abstract:
  We propose an explicitly calculable example of holography on 3-dimensional de
Sitter space by providing a prescription to analytic continue a higher-spin
holography on 3-dimensional anti-de Sitter space. Applying the de Sitter
holography, we explicitly compute bulk correlation functions on 3-dimensional
de Sitter space at late time in a higher-spin gravity. These expressions are
consistent with recent analysis based on bulk Feynman diagrams. Our explicit
computations reveal how holographic computations could provide fruitful
information.


title:
Cohomologies and deformations of Relative Rota-Baxter operators on
  Lie-Yamaguti algebras

abstract:
  In this paper, we establish the cohomology of relative Rota-Baxter operators
on Lie-Yamaguti algebras via the Yamaguti cohomology. Then we use this type of
cohomology to characterize deformations of relative Rota-Baxter operators on
Lie-Yamaguti algebras. We show that if two linear or formal deformations of a
relative Rota-Baxter operator are equivalent, then their infinitesimals are in
the same cohomology class in the first cohomology group. Moreover, an order $n$
deformation of a relative Rota-Baxter operator can be extended to an order
$n+1$ deformation if and only if the obstruction class in the second cohomology
group is trivial.


title: Adapting BigScience Multilingual Model to Unseen Languages

abstract:
  We benchmark different strategies of adding new languages (German and Korean)
into the BigScience's pretrained multilingual language model with 1.3 billion
parameters that currently supports 13 languages. We investigate the factors
that affect the language adaptability of the model and the trade-offs between
computational costs and expected performance.


title: Augmentation-Free Graph Contrastive Learning

abstract:
  Graph contrastive learning (GCL) is the most representative and prevalent
self-supervised learning approach for graph-structured data. Despite its
remarkable success, existing GCL methods highly rely on an augmentation scheme
to learn the representations invariant across different augmentation views. In
this work, we revisit such a convention in GCL through examining the effect of
augmentation techniques on graph data via the lens of spectral theory. We found
that graph augmentations preserve the low-frequency components and perturb the
middle- and high-frequency components of the graph, which contributes to the
success of GCL algorithms on homophilic graphs but hinders its application on
heterophilic graphs, due to the high-frequency preference of heterophilic data.
Motivated by this, we propose a novel, theoretically-principled, and
augmentation-free GCL method, named AF-GCL, that (1) leverages the features
aggregated by Graph Neural Network to construct the self-supervision signal
instead of augmentations and therefore (2) is less sensitive to the graph
homophily degree. Theoretically, We present the performance guarantee for
AF-GCL as well as an analysis for understanding the efficacy of AF-GCL.
Extensive experiments on 14 benchmark datasets with varying degrees of
heterophily show that AF-GCL presents competitive or better performance on
homophilic graphs and outperforms all existing state-of-the-art GCL methods on
heterophilic graphs with significantly less computational overhead.


title: Learning to Induce Causal Structure

abstract:
  The fundamental challenge in causal induction is to infer the underlying
graph structure given observational and/or interventional data. Most existing
causal induction algorithms operate by generating candidate graphs and then
evaluating them using either score-based methods (including continuous
optimization) or independence tests. In this work, instead of proposing scoring
function or independence tests, we treat the inference process as a black box
and design a neural network architecture that learns the mapping from both
observational and interventional data to graph structures via supervised
training on synthetic graphs. We show that the proposed model generalizes not
only to new synthetic graphs but also to naturalistic graphs.


title:
Lyapunov-Guided Embedding for Hyperparameter Selection in Recurrent
  Neural Networks

abstract:
  Recurrent Neural Networks (RNN) are ubiquitous computing systems for
sequences and multivariate time series data. While several robust architectures
of RNN are known, it is unclear how to relate RNN initialization, architecture,
and other hyperparameters with accuracy for a given task. In this work, we
propose to treat RNN as dynamical systems and to correlate hyperparameters with
accuracy through Lyapunov spectral analysis, a methodology specifically
designed for nonlinear dynamical systems. To address the fact that RNN features
go beyond the existing Lyapunov spectral analysis, we propose to infer relevant
features from the Lyapunov spectrum with an Autoencoder and an embedding of its
latent representation (AeLLE). Our studies of various RNN architectures show
that AeLLE successfully correlates RNN Lyapunov spectrum with accuracy.
Furthermore, the latent representation learned by AeLLE is generalizable to
novel inputs from the same task and is formed early in the process of RNN
training. The latter property allows for the prediction of the accuracy to
which RNN would converge when training is complete. We conclude that
representation of RNN through Lyapunov spectrum along with AeLLE, and assists
with hyperparameter selection of RNN, provides a novel method for organization
and interpretation of variants of RNN architectures.


title:
Gravitational instability with a dark matter background: Exploring the
  different scenarios

abstract:
  We study the Jeans-type gravitational instability for a self-gravitating
medium composed of two species, baryonic (bright) and dark matter particles,
using a hybrid quantum-classical fluid approach. Baryonic matter is treated
classically, which is appropriate for most astrophysical environments, e.g.,
Bok globules, while dark matter is treated through a quantum hydrodynamic
approach allowing for possible nonlinearities. These nonlinearities may arise
in bosonic dark matter due to attractive or repulsive short-range
self-interaction (attractive interaction being more relevant for axions) or
from the Pauli exclusion principle for fermionic dark matter, e.g., massive
neutrinos. This allows us to explore, in a very broad context, the impact of a
dark matter background on the Jeans process for different scenarios discussed
in the literature. In the simplest case, it is shown that the effect of a dark
matter background on the Jeans mass depends only on the ratio of densities and
velocity dispersions of baryonic and dark matter particles. Taking advantage of
that, we confront the established stability criterion with Bok globule
stability observations and show that the model adequately accounts for the data
with dark matter parameters close to those predicted independently from
numerical simulations.


title: Semantic Information Market For The Metaverse: An Auction Based Approach

abstract:
  In this paper, we address the networking and communications problems of
creating a digital copy in the Metaverse digital twin. Specifically, a virtual
service provider (VSP) which is responsible for creating and rendering the
Metaverse, is required to use the data collected by IoT devices to create the
virtual copy of the physical world. However, due to the huge volume of the
collected data by IoT devices (e.g., images and videos) and the limited
bandwidth, the VSP might become unable to retrieve all the required data from
the physical world. Furthermore, the Metaverse needs fast replication (e.g.,
rendering) of the digital copy adding more restrictions on the data
transmission delay. To solve the aforementioned challenges, we propose to equip
the IoT devices with semantic information extraction algorithms to minimize the
size of the transmitted data over the wireless channels. Since many IoT devices
will be interested to sell their semantic information to the VSP, we propose a
truthful reverse auction mechanism that helps the VSP select only IoT devices
that can improve the quality of its virtual copy of objects through the
semantic information. We conduct extensive simulations on a dataset that
contains synchronized camera and radar images, and show that our novel design
enables a fast replication of the digital copy with high accuracy.


title:
How to Find Your Friendly Neighborhood: Graph Attention Design with
  Self-Supervision

abstract:
  Attention mechanism in graph neural networks is designed to assign larger
weights to important neighbor nodes for better representation. However, what
graph attention learns is not understood well, particularly when graphs are
noisy. In this paper, we propose a self-supervised graph attention network
(SuperGAT), an improved graph attention model for noisy graphs. Specifically,
we exploit two attention forms compatible with a self-supervised task to
predict edges, whose presence and absence contain the inherent information
about the importance of the relationships between nodes. By encoding edges,
SuperGAT learns more expressive attention in distinguishing mislinked
neighbors. We find two graph characteristics influence the effectiveness of
attention forms and self-supervision: homophily and average degree. Thus, our
recipe provides guidance on which attention design to use when those two graph
characteristics are known. Our experiment on 17 real-world datasets
demonstrates that our recipe generalizes across 15 datasets of them, and our
models designed by recipe show improved performance over baselines.


title:
Exploring the Fate of Stellar Core Collapse with Supernova Relic
  Neutrinos

abstract:
  Core collapse of massive stars leads to the different fate for various
physical factors, which gives different spectra of the emitted neutrinos. We
focus on the supernova relic neutrinos (SRNs) as a probe to investigate the
stellar collapse fate. We present the SRN fluxes and event rate spectra at a
detector for three resultant states after stellar core collapse, the typical
mass neutron star, the higher mass neutron star, or the failed supernova
forming a black hole, based on different nuclear equations of state. Then
possible SRN fluxes are formed as mixtures of the three components. We also
show the expected sensitivities at the next-generation water-based Cherenkov
detectors, SK-Gd and Hyper-Kamiokande, as constraining the mixture fractions.
This study provides a practical example of extracting astrophysical constraints
through the SRN measurement.


title: Absorption spectra of electrified hydrogen molecules

abstract:
  Molecular hydrogen normally has only weak, quadrupole transitions between its
rovibrational states, but in a static electric field it acquires a dipole
moment and a set of allowed transitions. Here we use published ab initio
calculations of the static electrical response tensors of the H2 molecule to
construct the perturbed rovibrational eigensystem and its ground state
absorptions. We restrict attention to two simple field configurations that are
relevant to condensed hydrogen molecules in the interstellar medium: a uniform
electric field, and the field of a point-like charge. The energy eigenstates
are mixtures of vibrational and angular momentum eigenstates so there are many
transitions that satisfy the dipole selection rules. We find that mixing is
strongest amongst the states with high vibrational excitation, leading to
hundreds of absorption lines across the optical and near infrared. These
spectra are very different to that of the field-free molecule, so if they
appeared in astronomical data they would be difficult to assign. Furthermore in
a condensed environment the excited states likely have short lifetimes to
internal conversion, giving the absorption lines a diffuse appearance. We
therefore suggest electrified H2 as a possible carrier of the Diffuse
Interstellar Bands (DIBs). We further argue that in principle it may be
possible to account for all of the DIBs with this one carrier. However, despite
electrification the transitions are not very strong and a large column of
condensed H2 would be required, making it difficult to reconcile this
possibility with our current understanding of the ISM.


title:
Properties and applications of the Ap\'ery set of good semigroups in
  $\mathbb{N}^d$

abstract:
  In this article we discuss some applications of the construction of the
Ap\'ery set of a good semigroup in $\mathbb{N}^d$ given in the previous paper
[Partition of the complement of good semigroup ideals and Ap\'ery sets,
Communications in Algebra, 49, No. 10, 4136-4158 (2021))]. In particular we
study: the duality of a symmetric and almost symmetric good semigroup, the
Ap\'ery set of non-local good semigroups and the Ap\'ery set of value
semigroups of plane curves.


title:
Accurate Portraits of Scientific Resources and Knowledge Service
  Components

abstract:
  With the advent of the cloud computing era, the cost of creating, capturing
and managing information has gradually decreased. The amount of data in the
Internet is also showing explosive growth, and more and more scientific and
technological resources are uploaded to the network. Different from news and
social media data ubiquitous in the Internet, the main body of scientific and
technological resources is composed of academic-style resources or entities
such as papers, patents, authors, and research institutions. There is a rich
relationship network between resources, from which a large amount of
cutting-edge scientific and technological information can be mined. There are a
large number of management and classification standards for existing scientific
and technological resources, but these standards are difficult to completely
cover all entities and associations of scientific and technological resources,
and cannot accurately extract important information contained in scientific and
technological resources. How to construct a complete and accurate
representation of scientific and technological resources from structured and
unstructured reports and texts in the network, and how to tap the potential
value of scientific and technological resources is an urgent problem. The
solution is to construct accurate portraits of scientific and technological
resources in combination with knowledge graph related technologies.


title:
Gross-Neveu and deconfined quantum criticalities in an extended Hubbard
  model on the square lattice

abstract:
  We investigate the ground state phase diagram of an extended Hubbard model
with $\pi$-flux hopping term at half-filling on a square lattice, with unbiased
large-scale auxiliary-field quantum Monte Carlo simulations. As a function of
interaction strength, there emerge two interaction-driven quantum critical
points, with the first between the Dirac semimetal and an insulating phase of
weak valence bond solid (VBS) order, and the second separating the VBS order
and an antiferromagnetic insulating phase. These intriguing quantum critical
points are respectively bestowed with Gross-Neveu and deconfined quantum
criticalities. We also investigate the dynamical properties of the spin
excitation and find the spin gap open near the first transition and close at
the second. The relevance of our findings in realizing deconfined quantum
criticality in fermion systems and the implication to lattice models with
further extended interactions such as those in quantum Moir\'e systems, are
discussed.


title: Zitterbewegung of Klein-Gordon particles and Landau levels

abstract:
  In this paper, we consider the Zitterbewegung (ZB) effect for Klein-Gordon
particles induced by a cosmic string, near which curvature effects play a
dominant role. An analytical expression for the current is obtained and
analyzed.


title: Almost prime ideals of S-unital and local rings

abstract:
  Let $P$ be a (right) ideal of a ring $R$. $P$ is an almost prime (right)
ideal of $R$ if for each pair $(A,B)$ of (right) ideals of $R$, $AB\subseteq P$
and $AB\not\subseteq P^2$ implies $A\subseteq P$ or $B \subseteq P$. In the
current study, we give various results on almost prime (right) ideals of
noncommutative rings in the classes of S-unital, local or decomposable rings.


title:
Research on Cross-media Science and Technology Information Data
  Retrieval

abstract:
  Since the era of big data, the Internet has been flooded with all kinds of
information. Browsing information through the Internet has become an integral
part of people's daily life. Unlike the news data and social data in the
Internet, the cross-media technology information data has different
characteristics. This data has become an important basis for researchers and
scholars to track the current hot spots and explore the future direction of
technology development. As the volume of science and technology information
data becomes richer, the traditional science and technology information
retrieval system, which only supports unimodal data retrieval and uses outdated
data keyword matching model, can no longer meet the daily retrieval needs of
science and technology scholars. Therefore, in view of the above research
background, it is of profound practical significance to study the cross-media
science and technology information data retrieval system based on deep semantic
features, which is in line with the development trend of domestic and
international technologies.