title:
On the blowup mechanism of smooth solutions to 1D quasilinear strictly
  hyperbolic systems with large initial data

abstract:
  For the first order 1D $n\times n$ quasilinear strictly hyperbolic system
$\partial_tu+F(u)\partial_xu=0$ with $u(x, 0)=\varepsilon u_0(x)$, where
$\varepsilon>0$ is small, $u_0(x)\not\equiv 0$ and $u_0(x)\in C_0^2(\mathbb
R)$, when at least one eigenvalue of $F(u)$ is genuinely nonlinear, it is
well-known that on the finite blowup time $T_{\varepsilon}$, the derivatives
$\partial_{t,x}u$ blow up while the solution $u$ keeps to be small. For the 1D
scalar equation or $2\times 2$ strictly hyperbolic system (corresponding to
$n=1, 2$), if the smooth solution $u$ blows up in finite time, then the blowup
mechanism can be well understood (i.e., only the blowup of $\partial_{t,x}u$
happens). In the present paper, for the $n\times n$ ($n\geq 3$) strictly
hyperbolic system with a class of large initial data, we are concerned with the
blowup mechanism of smooth solution $u$ on the finite blowup time and the
detailed singularity behaviours of $\partial_{t,x}u$ near the blowup point. Our
results are based on the efficient decomposition of $u$ along the different
characteristic directions, the suitable introduction of the modulated
coordinates and the global weighted energy estimates.


title: Modality-Balanced Embedding for Video Retrieval

abstract:
  Video search has become the main routine for users to discover videos
relevant to a text query on large short-video sharing platforms. During
training a query-video bi-encoder model using online search logs, we identify a
modality bias phenomenon that the video encoder almost entirely relies on text
matching, neglecting other modalities of the videos such as vision, audio. This
modality imbalanceresults from a) modality gap: the relevance between a query
and a video text is much easier to learn as the query is also a piece of text,
with the same modality as the video text; b) data bias: most training samples
can be solved solely by text matching. Here we share our practices to improve
the first retrieval stage including our solution for the modality imbalance
issue. We propose MBVR (short for Modality Balanced Video Retrieval) with two
key components: manually generated modality-shuffled (MS) samples and a dynamic
margin (DM) based on visual relevance. They can encourage the video encoder to
pay balanced attentions to each modality. Through extensive experiments on a
real world dataset, we show empirically that our method is both effective and
efficient in solving modality bias problem. We have also deployed our MBVR in a
large video platform and observed statistically significant boost over a highly
optimized baseline in an A/B test and manual GSB evaluations.


title: Massive Parallelization of Massive Sample-size Survival Analysis

abstract:
  Large-scale observational health databases are increasingly popular for
conducting comparative effectiveness and safety studies of medical products.
However, increasing number of patients poses computational challenges when
fitting survival regression models in such studies. In this paper, we use
graphics processing units (GPUs) to parallelize the computational bottlenecks
of massive sample-size survival analyses. Specifically, we develop and apply
time- and memory-efficient single-pass parallel scan algorithms for Cox
proportional hazards models and forward-backward parallel scan algorithms for
Fine-Gray models for analysis with and without a competing risk using a cyclic
coordinate descent optimization approach We demonstrate that GPUs accelerate
the computation of fitting these complex models in large databases by
orders-of-magnitude as compared to traditional multi-core CPU parallelism. Our
implementation enables efficient large-scale observational studies involving
millions of patients and thousands of patient characteristics.


title: On The Diameter of Pancake Graphs

abstract:
  The Pancake graph($P_n$) represents the group of all permutations on n
elements, namely $S_n$, with respect to the generating set containing all
prefix reversals. The diameter of a graph is the maximum of all distances on
the graph, where the distance between two vertices is the shortest path between
them. In the case of the $P_n$, it is the maximum of the shortest generating
sequence of each permutation in $S_n$. Here we propose a method to realise
better upper bounds to the diameter of $P_n$ that has its focus on Graph
Theoretical concepts rather than Algebra.


title:
Completion Delay of Random Linear Network Coding in Full-Duplex Relay
  Networks

abstract:
  As the next-generation wireless networks thrive, full-duplex and relaying
techniques are combined to improve the network performance. Random linear
network coding (RLNC) is another popular technique to enhance the efficiency
and reliability in wireless communications. In this paper, in order to explore
the potential of RLNC in full-duplex relay networks, we investigate two
fundamental perfect RLNC schemes and theoretically analyze their completion
delay performance. The first scheme is a straightforward application of
conventional perfect RLNC studied in wireless broadcast, so it involves no
additional process at the relay. Its performance serves as an upper bound among
all perfect RLNC schemes. The other scheme allows sufficiently large buffer and
unconstrained linear coding at the relay. It attains the optimal performance
and serves as a lower bound among all RLNC schemes. For both schemes,
closed-form formulae to characterize the expected completion delay at a single
receiver as well as for the whole system are derived. Numerical results are
also demonstrated to justify the theoretical characterizations, and compare the
two new schemes with the existing one.


title:
On invariant properties of natural differential operators associated to
  geometric structures on $\mathbb{R}^n$

abstract:
  We provide a general framework to study invariant properties of various
gradient-like and Laplace-like differential operators naturally associated to
geometric structures on $\mathbb{R}^n$, which encompass Euclidean, Minkowski,
pseudo-Euclidean and symplectic structures.


title:
Securing Signal-free Intersections against Strategic Jamming Attacks: A
  Macroscopic Approach

abstract:
  We consider the security-by-design of a signal-free intersection for
connected and autonomous vehicles in the face of strategic jamming attacks. We
use a fluid model to characterize macroscopic traffic flow through the
intersection, where the saturation rate is derived from a vehicle coordination
algorithm. We model jamming attacks as sudden increase in communication latency
induced on vehicle-to-infrastructure connectivity; such latency triggers the
safety mode for vehicle coordination and thus reduces the intersection
saturation rate. A strategic attacker selects the attacking rate, while a
system operator selects key design parameters, either the saturation rate or
the recovery rate. Both players' actions induce technological costs and jointly
determine the mean travel delay. By analyzing the equilibrium of the security
game, we study the preferable level of investment in the intersection's nominal
discharging capability or recovery capability, for balance between
hardware/infrastructure cost and security-by-design.


title: Local wild mapping class groups and cabled braids

abstract:
  We will define and study some generalisations of pure $\mathfrak{g}$-braid
groups that occur in the theory of connections on curves, for any complex
reductive Lie algebra $\mathfrak{g}$. They make up local pieces of the wild
mapping class groups, which are fundamental groups of (universal) deformations
of wild Riemann surfaces, underlying the braiding of Stokes data and
generalising the usual mapping class groups. We will establish a general
product decomposition for the local wild mapping class groups, and in many
cases define a fission tree controlling this decomposition. Further in type A
we will show one obtains cabled versions of braid groups, related to braid
operads.


title:
Sardino: Ultra-Fast Dynamic Ensemble for Secure Visual Sensing at Mobile
  Edge

abstract:
  Adversarial example attack endangers the mobile edge systems such as vehicles
and drones that adopt deep neural networks for visual sensing. This paper
presents {\em Sardino}, an active and dynamic defense approach that renews the
inference ensemble at run time to develop security against the adaptive
adversary who tries to exfiltrate the ensemble and construct the corresponding
effective adversarial examples. By applying consistency check and data fusion
on the ensemble's predictions, Sardino can detect and thwart adversarial
inputs. Compared with the training-based ensemble renewal, we use HyperNet to
achieve {\em one million times} acceleration and per-frame ensemble renewal
that presents the highest level of difficulty to the prerequisite exfiltration
attacks. Moreover, the robustness of the renewed ensembles against adversarial
examples is enhanced with adversarial learning for the HyperNet. We design a
run-time planner that maximizes the ensemble size in favor of security while
maintaining the processing frame rate. Beyond adversarial examples, Sardino can
also address the issue of out-of-distribution inputs effectively. This paper
presents extensive evaluation of Sardino's performance in counteracting
adversarial examples and applies it to build a real-time car-borne traffic sign
recognition system. Live on-road tests show the built system's effectiveness in
maintaining frame rate and detecting out-of-distribution inputs due to the
false positives of a preceding YOLO-based traffic sign detector.


title: What quenches ${g_A}$ in nuclei ?

abstract:
  The long-standing puzzle of how $g_A$ is "quenched" in nuclei from its
free-space value of 1.276 to $\sim 1$ is explained in terms of strong nuclear
correlations dictated by hidden scale invariance, "invisible" in matter-free
space, but emergent in dense matter. Superallowed Gamow-Teller transitions in
doubly magic nuclei are suggested as a means to test this idea. The possible
role of the trace anomaly of QCD in inducing a most likely small {\it
fundamental} renormalization is discussed. The scale symmetry is argued to be
responsible in the approach for the pseudo-conformal sound speed
$v_{pcs}^2/c^2\approx 1/3$ in compact stars. The possible implication of this
observation on the claim of a "first-priniciples solution" to the quenched
$g_A$ problem and also on the search for $0\nu\beta\beta$ decay is discussed.


title: Combinatorial Yamabe flow on hyperbolic bordered surfaces

abstract:
  This paper studies the combinatorial Yamabe flow on hyperbolic bordered
surfaces. We show that the flow exists for all time and converges exponentially
fast to conformal factor which produces a hyperbolic surface whose lengths of
boundary components are equal to prescribed positive numbers. This provides an
algorithm to such problems.


title: Semi-Supervised Super-Resolution

abstract:
  Super-Resolution is the technique to improve the quality of a low-resolution
photo by boosting its plausible resolution. The computer vision community has
extensively explored the area of Super-Resolution. However, previous
Super-Resolution methods require vast amounts of data for training which
becomes problematic in domains where very few low-resolution, high-resolution
pairs might be available. One such area is statistical downscaling, where
super-resolution is increasingly being used to obtain high-resolution climate
information from low-resolution data. Acquiring high-resolution climate data is
extremely expensive and challenging. To reduce the cost of generating
high-resolution climate information, Super-Resolution algorithms should be able
to train with a limited number of low-resolution, high-resolution pairs. This
paper tries to solve the aforementioned problem by introducing a
semi-supervised way to perform super-resolution that can generate sharp,
high-resolution images with as few as 500 paired examples. The proposed
semi-supervised technique can be used as a plug-and-play module with any
supervised GAN-based Super-Resolution method to enhance its performance. We
quantitatively and qualitatively analyze the performance of the proposed model
and compare it with completely supervised methods as well as other unsupervised
techniques. Comprehensive evaluations show the superiority of our method over
other methods on different metrics. We also offer the applicability of our
approach in statistical downscaling to obtain high-resolution climate images.


title:
I Cannot See Students Focusing on My Presentation; Are They Following
  Me? Continuous Monitoring of Student Engagement through "Stungage"

abstract:
  Monitoring students' engagement and understanding their learning pace in a
virtual classroom becomes challenging in the absence of direct eye contact
between the students and the instructor. Continuous monitoring of eye gaze and
gaze gestures may produce inaccurate outcomes when the students are allowed to
do productive multitasking, such as taking notes or browsing relevant content.
This paper proposes Stungage - a software wrapper over existing online meeting
platforms to monitor students' engagement in real-time by utilizing the facial
video feeds from the students and the instructor coupled with a local on-device
analysis of the presentation content. The crux of Stungage is to identify a few
opportunistic moments when the students should visually focus on the
presentation content if they can follow the lecture. We investigate these
instances and analyze the students' visual, contextual, and cognitive presence
to assess their engagement during the virtual classroom while not directly
sharing the video captures of the participants and their screens over the web.
Our system achieves an overall F2-score of 0.88 for detecting student
engagement. Besides, we obtain 92 responses from the usability study with an
average SU score of 74.18.


title: Phishing Fraud Detection on Ethereum using Graph Neural Network

abstract:
  Blockchain has widespread applications in the financial field but has also
attracted increasing cybercrimes. Recently, phishing fraud has emerged as a
major threat to blockchain security, calling for the development of effective
regulatory strategies. Nowadays network science has been widely used in
modeling Ethereum transaction data, further introducing the network
representation learning technology to analyze the transaction patterns. In this
paper, we consider phishing detection as a graph classification task and
propose an end-to-end Phishing Detection Graph Neural Network framework
(PDGNN). Specifically, we first construct a lightweight Ethereum transaction
network and extract transaction subgraphs of collected phishing accounts. Then
we propose an end-to-end detection model based on Chebyshev-GCN to precisely
distinguish between normal and phishing accounts. Extensive experiments on five
Ethereum datasets demonstrate that our PDGNN significantly outperforms general
phishing detection methods and scales well in large transaction networks.


title:
Tri-photon at muon collider: a new process to probe the anomalous
  quartic gauge couplings

abstract:
  The muon collider has recently received a great deal of attention because of
its ability to achieve both high energy and high luminosity.It plays as a gauge
boson collider because the vector boson scattering (VBS) becomes the dominant
production topology for Standard Model processes starting from a few TeV of
collision energy.In this paper, we propose that the process of $\mu^+\mu^-$
annihilation into tri-photon is also very sensitive to the search of anomalous
quartic gauge couplings (aQGCs). We investigate the projected constraints on
the transverse operators contributing to aQGCs through $\mu^+\mu^-\to
Z^\ast/\gamma^\ast\to \gamma\gamma\gamma$ at muon colliders. For the muon
collider with $\sqrt{s}=3$ TeV and $\mathcal{L}=1\;{\rm ab}^{-1}$, the expected
constraints are about two orders of magnitude stronger than those at the 13 TeV
LHC.


title:
Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit
  Neural Representation

abstract:
  Point clouds upsampling is a challenging issue to generate dense and uniform
point clouds from the given sparse input. Most existing methods either take the
end-to-end supervised learning based manner, where large amounts of pairs of
sparse input and dense ground-truth are exploited as supervision information;
or treat up-scaling of different scale factors as independent tasks, and have
to build multiple networks to handle upsampling with varying factors. In this
paper, we propose a novel approach that achieves self-supervised and
magnification-flexible point clouds upsampling simultaneously. We formulate
point clouds upsampling as the task of seeking nearest projection points on the
implicit surface for seed points. To this end, we define two implicit neural
functions to estimate projection direction and distance respectively, which can
be trained by two pretext learning tasks. Experimental results demonstrate that
our self-supervised learning based scheme achieves competitive or even better
performance than supervised learning based state-of-the-art methods. The source
code is publicly available at https://github.com/xnowbzhao/sapcu.


title: Groups, drift and harmonic measures

abstract:
  In this short note we will describe an old problem and a new approach which
casts light upon it. The old problem is to understand the nature of harmonic
measures for cocompact Fuchsian groups. The new approach is to compute
numerically the value of the drift and, in particular, get new results on the
dimension of the measure in some new examples.


title:
UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm
  Detection using generative-based and mutation-based data augmentation

abstract:
  Sarcasm is a term that refers to the use of words to mock, irritate, or amuse
someone. It is commonly used on social media. The metaphorical and creative
nature of sarcasm presents a significant difficulty for sentiment analysis
systems based on affective computing. The methodology and results of our team,
UTNLP, in the SemEval-2022 shared task 6 on sarcasm detection are presented in
this paper. We put different models, and data augmentation approaches to the
test and report on which one works best. The tests begin with traditional
machine learning models and progress to transformer-based and attention-based
models. We employed data augmentation based on data mutation and data
generation. Using RoBERTa and mutation-based data augmentation, our best
approach achieved an F1-sarcastic of 0.38 in the competition's evaluation
phase. After the competition, we fixed our model's flaws and achieved an
F1-sarcastic of 0.414.


title:
Microbulk Micromegas in non-flammable mixtures of argon and neon at high
  pressure

abstract:
  We report on a systematic characterization of microbulk Micromegas readouts
in high-pressure Ar+1%iC4H10 and Ne+2%iC4H10 mixtures. Experimental data on
gain, electron transmission and energy resolution are presented for a wide
range of drift and amplification voltages and pressures from 1 bar to 10 bar
for argon and from 5 bar to 10 bar in neon, in steps of 1 bar. Maximum gains
higher than 1.7e3 (1.7e4) in argon (neon) are measured for all pressures,
remarkably without the significant decrease with pressure typically observed in
other amplification structures. A competitive energy resolution at 22.1 keV,
but with a slight degradation with pressure, is observed:from 10.8% at 1 bar to
15.6% FWHM at 10 bar in argon and from 8.3% at 5 bar to 15.0% FWHM at1 10 bar
in neon. The experimental setup, procedure and the results will be presented
and discussed in detail. The work is motivated by the TREX-DM experiment, that
is operating in the Laboratorio Subterr\'aneo de Canfranc with the mentioned
mixtures, although the results may be of interest for other applications of
time projection chambers at high pressures.


title:
Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path
  and Beyond

abstract:
  The vast majority of existing algorithms for unsupervised domain adaptation
(UDA) focus on adapting from a labeled source domain to an unlabeled target
domain directly in a one-off way. Gradual domain adaptation (GDA), on the other
hand, assumes a path of $(T-1)$ unlabeled intermediate domains bridging the
source and target, and aims to provide better generalization in the target
domain by leveraging the intermediate ones. Under certain assumptions, Kumar et
al. (2020) proposed a simple algorithm, Gradual Self-Training, along with a
generalization bound in the order of $e^{O(T)}
\left(\varepsilon_0+O\left(\sqrt{log(T)/n}\right)\right)$ for the target domain
error, where $\varepsilon_0$ is the source domain error and $n$ is the data
size of each domain. Due to the exponential factor, this upper bound becomes
vacuous when $T$ is only moderately large. In this work, we analyze gradual
self-training under more general and relaxed assumptions, and prove a
significantly improved generalization bound as
$\widetilde{O}\left(\varepsilon_0 + T\Delta + T/\sqrt{n} + 1/\sqrt{nT}\right)$,
where $\Delta$ is the average distributional distance between consecutive
domains. Compared with the existing bound with an exponential dependency on $T$
as a multiplicative factor, our bound only depends on $T$ linearly and
additively. Perhaps more interestingly, our result implies the existence of an
optimal choice of $T$ that minimizes the generalization error, and it also
naturally suggests an optimal way to construct the path of intermediate domains
so as to minimize the accumulative path length $T\Delta$ between the source and
target. To corroborate the implications of our theory, we examine gradual
self-training on multiple semi-synthetic and real datasets, which confirms our
findings. We believe our insights provide a path forward toward the design of
future GDA algorithms.


title:
On an inhomogeneous slip-inflow boundary value problems for a steady
  viscous compressible channel flows

abstract:
  We prove the existence and uniqueness of strong solutions to the steady
isentropic compressible Navier-Stokes equations with inflow boundary conditions
for density and mixed boundary conditions for the velocity around a shear flow.
In particular, the Dirichlet boundary condition on inflow and outflow part of
the boundary while the full Navier boundary conditions on the wall $\Gamma_{0}$
for the velocity filed are considered. For our result, there are no
restrictions on the amplitude of friction coefficients $\alpha$, and the
appropriately large hypothesis for the viscous coefficients $\mu$ is enough.
One of the substantial ingredients of our proof is an elegant transformation
induced by the flow field. With the help of this transformation, we can
overcome the difficulties caused by the hyperbolicity of continuity equation,
establish the a priori estimates for a linearized system and apply the fixed
point arguments.


title: Type II Seesaw Leptogenesis

abstract:
  The Type II Seesaw Mechanism provides a minimal framework to explain the
neutrino masses involving the introduction of a single triplet Higgs to the
Standard Model. However, this simple extension was believed to be unable to
successfully explain the observed baryon asymmetry of the universe through
Leptogenesis. In our previous work (Phys. Rev. Lett. 128, 141801), we
demonstrated that the triplet Higgs of the Type II Seesaw Mechanism alone can
simultaneously generate the observed baryon asymmetry of the universe and the
neutrino masses while playing a role in setting up Inflation. This is
achievable with a triplet Higgs mass as low as 1 TeV, and predicts that the
neutral component obtains a small vacuum expectation value $v_\Delta < 10 $
keV. We find that our model has very rich phenomenology and can be tested by
various terrestrial experiments as well as by astronomical observations.
Particularly, we show that the successful parameter region may be probed at a
future 100 TeV collider, upcoming lepton flavor violation experiments such as
Mu3e, and neutrinoless double beta decay experiments. Additionally, the
tensor-to-scalar ratio from the inflationary scenario will be probed by the
LiteBIRD telescope, and observable isocurvature perturbations may be produced
for some parameter choices. In this article, we present all the technical
details of our calculations and further discussion of its phenomenological
implications.


title: The Bowen$\unicode{x2013}$Series coding and zeros of zeta functions

abstract:
  We give a discussion of the classical Bowen$\unicode{x2013}$Series coding
and, in particular, its application to the study of zeta functions and their
zeros. In the case of compact surfaces of constant negative curvature $-1$ the
analytic extension of the Selberg zeta function to the entire complex plane is
classical, and can be achieved using the Selberg trace formula. However, an
alternative dynamical approach is to use the Bowen$\unicode{x2013}$Series
coding on the boundary at infinity to obtain a piecewise analytic expanding map
from which the extension of the zeta function can be obtained using properties
of the associated transfer operator. This latter method has the advantage that
it also applies in the case of infinite area surfaces provided they do not have
cusps. For such examples the location of the zeros is somewhat more mysterious.
However, in particularly simple examples there is a striking structure to the
zeros when we take appropriate limits. We will try to give some insight into
this phenomenon.
  The survey is based on lectures given by the first author during the Workshop
on Statistical Properties of Nonequilibrium Dynamical Systems which took place
in July 2016 at South University of Science and Technology of China in
ShenZhen.


title:
Stochastic subgradient for composite convex optimization with functional
  constraints

abstract:
  In this paper we consider optimization problems with stochastic composite
objective function subject to (possibly) infinite intersection of constraints.
The objective function is expressed in terms of expectation operator over a sum
of two terms satisfying a stochastic bounded gradient condition, with or
without strong convexity type properties. In contrast to the classical
approach, where the constraints are usually represented as intersection of
simple sets, in this paper we consider that each constraint set is given as the
level set of a convex but not necessarily differentiable function. Based on the
flexibility offered by our general optimization model we consider a stochastic
subgradient method with random feasibility updates. At each iteration, our
algorithm takes a stochastic proximal (sub)gradient step aimed at minimizing
the objective function and then a subsequent subgradient step minimizing the
feasibility violation of the observed random constraint. We analyze the
convergence behavior of the proposed algorithm for diminishing stepsizes and
for the case when the objective function is convex or has a quadratic
functional growth, unifying the nonsmooth and smooth cases. We prove sublinear
convergence rates for this stochastic subgradient algorithm, which are known to
be optimal for subgradient methods on this class of problems. When the
objective function has a linear least-square form and the constraints are
polyhedral, it is shown that the algorithm converges linearly. Numerical
evidence supports the effectiveness of our method in real problems.


title:
A Greedy and Optimistic Approach to Clustering with a Specified
  Uncertainty of Covariates

abstract:
  In this study, we examine a clustering problem in which the covariates of
each individual element in a dataset are associated with an uncertainty
specific to that element. More specifically, we consider a clustering approach
in which a pre-processing applying a non-linear transformation to the
covariates is used to capture the hidden data structure. To this end, we
approximate the sets representing the propagated uncertainty for the
pre-processed features empirically. To exploit the empirical uncertainty sets,
we propose a greedy and optimistic clustering (GOC) algorithm that finds better
feature candidates over such sets, yielding more condensed clusters. As an
important application, we apply the GOC algorithm to synthetic datasets of the
orbital properties of stars generated through our numerical simulation
mimicking the formation process of the Milky Way. The GOC algorithm
demonstrates an improved performance in finding sibling stars originating from
the same dwarf galaxy. These realistic datasets have also been made publicly
available.


title: TigerLily: Finding drug interactions in silico with the Graph

abstract:
  Tigerlily is a TigerGraph based system designed to solve the drug interaction
prediction task. In this machine learning task, we want to predict whether two
drugs have an adverse interaction. Our framework allows us to solve this highly
relevant real-world problem using graph mining techniques in these steps:
  (a) Using PyTigergraph we create a heterogeneous biological graph of drugs
and proteins.
  (b) We calculate the personalized PageRank scores of drug nodes in the
TigerGraph Cloud.
  (c) We embed the nodes using sparse non-negative matrix factorization of the
personalized PageRank matrix.
  (d) Using the node embeddings we train a gradient boosting based drug
interaction predictor.


title: Hunting $B_c^\ast$ via Conservation Laws

abstract:
  To distinguish $B_c^{\ast +}$ and $B_c^+$ in the experiments, we propose two
methods based on the conservation laws. I. From the angular momentum
conservation, a nonzero helicity of $J/\psi$ of $B_c^{(\ast)+ } \to J/\psi
\pi^+$ would be an evidence of $B_c^{\ast +}$. II. Since $B_c^+ \to B^+ \phi$
is kinematically forbidden, $ B_c^{\ast +} \to B^+ \phi$ provides a clean
channel to probe $B_c^{\ast +}$. Particularly, our results show that $B_c^{\ast
+}$ is promising to be observed at LHC via $B_c^{(\ast)+} \to J/\psi \pi^+$. On
the other hand, we find that ${\cal B} ( B_c^{\ast+} \to B^+ \phi) = ( 7.0 \pm
3.0 ) \times 10 ^{-9} $, which is also feasible to be measured at the
forthcoming experiments at HL-LHC and FCC-hh.


title:
Time dependent numerical model for the very high energy emissions of
  distant gamma-ray busrt GRB 201216C

abstract:
  Recently, the MAGIC Collaboration reported a $\sim 5\sigma$ statistical
significance of the very-high-energy (VHE) emission from a distant GRB, GRB
201216C. Such distant GRB may be effectively absorbed by the extragalactic
background light (EBL). The origin of the VHE emission from such distant
objects is still unknown. Here, we propose a numerical model for studying the
afterglow emission of this distant GRB. The model solves the continuity
equation governing the temporal evolution of electron distribution, and the
broad-band observed data can be explained by the synchrotron plus synchrotron
self-Compton (SSC) radiation of the forward shock. The predicted observed 0.1
TeV flux can reach $\sim 10^{-9} -10^{-10}\rm erg ~ cm^{-2} ~ s^{-1}$ at $t
\sim 10^3 -10^4 \rm s$, even with strong EBL absorption, such strong Sub-TeV
emissions still can be observed by MAGIC telescope. Using this numerical model,
the shock parameters in the modeling are similar with two other Sub-TeV GRBs
(i.e., GRB 190114C and GRB 180720B), implying that the Sub-TeV GRBs have some
commonalities: they have energetic burst energy, low circum-burst medium
density and low magnetic equipartition factor. We regard GRB 201216C as a
typical GRB, and estimate the maximum redshift of GRB that can be detected by
MAGIC telescope, i.e., $z \sim 1.6$. We also find that the VHE photon energy of
such distant GRB can only reach $\sim 0.1 ~\rm TeV$. Improving the low energy
sensitivity of the VHE telescope is very important to detect the Sub-TeV
emissions of these distant GRBs.


title:
OMG: Observe Multiple Granularities for Natural Language-Based Vehicle
  Retrieval

abstract:
  Retrieving tracked-vehicles by natural language descriptions plays a critical
role in smart city construction. It aims to find the best match for the given
texts from a set of tracked vehicles in surveillance videos. Existing works
generally solve it by a dual-stream framework, which consists of a text
encoder, a visual encoder and a cross-modal loss function. Although some
progress has been made, they failed to fully exploit the information at various
levels of granularity. To tackle this issue, we propose a novel framework for
the natural language-based vehicle retrieval task, OMG, which Observes Multiple
Granularities with respect to visual representation, textual representation and
objective functions. For the visual representation, target features, context
features and motion features are encoded separately. For the textual
representation, one global embedding, three local embeddings and a color-type
prompt embedding are extracted to represent various granularities of semantic
features. Finally, the overall framework is optimized by a cross-modal
multi-granularity contrastive loss function. Experiments demonstrate the
effectiveness of our method. Our OMG significantly outperforms all previous
methods and ranks the 9th on the 6th AI City Challenge Track2. The codes are
available at https://github.com/dyhBUPT/OMG.


title:
Long wave interaction with a partially immersed body. Part II: Numerical
  results

abstract:
  In this manuscript we perform an extensive numerical study of the long wave
interaction problem with a fixed partially immersed body into a fluid layer.
The incident wave is assumed to be an isolated solitary wave. The body in this
study is assumed to be fixed with a rectangular section which is not touching
the bottom of the channel. The mathematical modelling of this problem is based
on Part I (Khakimzyanov and Dutykh 2020) of this series and considered models
include the Nonlinear Shallow Water Equations (NSWE), fully nonlinear weakly
dispersive Serre-Green-Naghdi Equations (SGN equations) (completed with
appropriate compatibility conditions on solid/fluid boundaries) and the free
surface irrotational full Euler equations (FEE). We study the influence of the
floating body elongation, immersion depth and incident wave amplitude on the
wave field before and after the obstacle. The comparison of all three models
predictions and the data of small-scale laboratory experiments is performed.
Moreover, in the framework of the FEE model we investigate the anomalous wave
run-up behind the floating body in the close presence of a vertical wall. We
demonstrate the cases where the vertical wall creates extreme wave amplitudes
behind the body, but also we show the cases where the wall attenuates wave
amplitudes comparing to the wave field without a wall.


title:
How to Attain Communication-Efficient DNN Training? Convert, Compress,
  Correct

abstract:
  In this paper, we introduce $\mathsf{CO}_3$, an algorithm for
communication-efficiency federated Deep Neural Network (DNN)
training.$\mathsf{CO}_3$ takes its name from three processing applied steps
which reduce the communication load when transmitting the local gradients from
the remote users to the Parameter Server.Namely:(i) gradient quantization
through floating-point conversion, (ii) lossless compression of the quantized
gradient, and (iii) quantization error correction.We carefully design each of
the steps above so as to minimize the loss in the distributed DNN training when
the communication overhead is fixed.In particular, in the design of steps (i)
and (ii), we adopt the assumption that DNN gradients are distributed according
to a generalized normal distribution.This assumption is validated numerically
in the paper. For step (iii), we utilize an error feedback with memory decay
mechanism to correct the quantization error introduced in step (i). We argue
that this coefficient, similarly to the learning rate, can be optimally tuned
to improve convergence. The performance of $\mathsf{CO}_3$ is validated through
numerical simulations and is shown having better accuracy and improved
stability at a reduced communication payload.


title: Hole spin qubits in thin curved quantum wells

abstract:
  Hole spin qubits are frontrunner platforms for scalable quantum computers
because of their large spin-orbit interaction which enables ultrafast
all-electric qubit control at low power. The fastest spin qubits to date are
defined in long quantum dots with two tight confinement directions, when the
driving field is aligned to the smooth direction. However, in these systems the
lifetime of the qubit is strongly limited by charge noise, a major issue in
hole qubits. We propose here a different, scalable qubit design, compatible
with planar CMOS technology, where the hole is confined in a curved germanium
quantum well surrounded by silicon. This design takes full advantage of the
strong spin-orbit interaction of holes, and at the same time suppresses charge
noise in a wide range of configurations, enabling highly coherent, ultrafast
qubit gates. While here we focus on a Si/Ge/Si curved quantum well, our design
is also applicable to different semiconductors. Strikingly, these devices allow
for ultrafast operations even in short quantum dots by a transversal electric
field. This additional driving mechanism relaxes the demanding design
constraints, and opens up a new way to reliably interface spin qubits in a
single quantum dot to microwave photons. By considering state-of-the-art
high-impedance resonators and realistic qubit designs, we estimate interaction
strengths of a few hundreds of MHz, largely exceeding the decay rate of spins
and photons. Reaching such a strong coupling regime in hole spin qubits will be
a significant step towards high-fidelity entangling operations between distant
qubits, as well as fast single-shot readout, and will pave the way towards the
implementation of a large-scale semiconducting quantum processor.


title:
Index Modulation Pattern Design for Non-Orthogonal Multicarrier Signal
  Waveforms

abstract:
  Spectral efficiency improvement is a key focus in most wireless communication
systems and achieved by various means such as using large antenna arrays and/or
advanced modulation schemes and signal formats. This work proposes to further
improve spectral efficiency through combining non-orthogonal spectrally
efficient frequency division multiplexing (SEFDM) systems with index modulation
(IM), which can efficiently make use of the indices of activated subcarriers as
communication information. Recent research has verified that IM may be used
with SEFDM to alleviate inter-carrier interference (ICI) and improve error
performance. This work proposes new SEFDM signal formats based on novel
activation pattern designs, which limit the locations of activated subcarriers
and enable a variable number of activated subcarriers in each SEFDM subblock.
SEFDM-IM system designs are developed by jointly considering activation
patterns, modulation schemes and signal waveform formats, with a set of
solutions evaluated under different spectral efficiency scenarios. Detailed
modelling of coded systems and simulation studies reveal that the proposed
designs not only lead to better bit error rate (BER) but also lower
peak-to-average power ratio (PAPR) and reduced computational complexity
relative to other reported index-modulated systems.


title: Hamiltonian Particle-in-Cell methods for Vlasov-Poisson equations

abstract:
  In this paper, Particle-in-Cell algorithms for the Vlasov-Poisson system are
presented based on its Poisson bracket structure. The Poisson equation is
solved by finite element methods, in which the appropriate finite element
spaces are taken to guarantee that the semi-discretized system possesses a well
defined discrete Poisson bracket structure. Then, splitting methods are applied
to the semi-discretized system by decomposing the Hamiltonian function. The
resulting discretizations are proved to be Poisson bracket preserving.
Moreover, the conservative quantities of the system are also well preserved. In
numerical experiments, we use the presented numerical methods to simulate
various physical phenomena. Due to the huge computational effort of the
practical computations, we employ the strategy of parallel computing. The
numerical results verify the efficiency of the new derived numerical
discretizations.


title: Correlations of multiplicative functions with automorphic L-functions

abstract:
  Let $\lambda_{\phi}(n)$ be the Fourier coefficients of a Hecke holomorphic or
Hecke--Maass cusp form on ${\rm SL}_2(\mathbb Z)$, and $f$ be any
multiplicative function that satisfies two mild hypotheses. We establish a
non-trivial upper bound for the correlation $\sum_{n \leq
X}f(n)\lambda_{\phi}(n+h)$ uniformly in $0<|h|\ll X$. As applications, we
consider some special cases, including $\lambda_{\pi}(n),
\,\mu(n)\lambda_{\pi}(n)$ and any divisor-bounded multiplicative function. Here
$\lambda_{\pi}(n)$ denotes the $n$-th Dirichlet coefficient of $\text{GL}_m$
automorphic $L$-function $L(s,\pi)$ for an automorphic irreducible cuspidal
representation $\pi$, and $\mu(n)$ denotes the M\"obius function. In
particular, some savings are achieved for shifted convolution problems on ${\rm
GL}_m\times {\rm GL}_2\, (m\geq 4)$ and Hypothesis C for the first time.


title:
A gate-programmable van der Waals metal-ferroelectric-semiconductor
  memory

abstract:
  Ferroelecticity, one of the keys to realize nonvolatile memories owing to the
remanent electric polarization, has been an emerging phenomenon in the
two-dimensional (2D) limit. Yet the demonstrations of van der Waals (vdW)
memories using 2D ferroelectric materials as an ingredient are very limited.
Especially, gate-tunable ferroelectric vdW memristive device, which holds
promises in future neuromorphic applications, remains challenging. Here, we
show a prototype gate-programmable memory by vertically assembling graphite,
CuInP2S6, and MoS2 layers into a metal-ferroelectric-semiconductor
architecture. The resulted devices exhibit two-terminal switchable
electro-resistance with on-off ratios exceeding 105 and long-term retention,
akin to a conventional memristor but strongly coupled to the ferroelectric
characteristics of the CuInP2S6 layer. By controlling the top gate, Fermi level
of MoS2 can be set inside (outside) of its band gap to quench (enable) the
memristive behaviour, yielding a three-terminal gate programmable nonvolatile
vdW memory. Our findings pave the way for the engineering of
ferroelectric-mediated memories in future implementations of nanoelectronics.


title:
Static and spherically symmetric general relativity solutions in Minimal
  Theory of Bigravity

abstract:
  We investigate static and spherically symmetric solutions in the Minimal
Theory of Bigravity (MTBG). First, we show that a pair of Schwarzschild-de
Sitter spacetimes with different cosmological constants and black hole masses
written in the spatially-flat Gullstrand-Painlev\'e (GP) coordinates is a
solution in the self-accelerating branch of MTBG, while it cannot be a solution
in the normal branch. We then illustrate how Schwarzschild-de Sitter solutions
can become compatible with the normal branch when using different coordinates.
We also confirm that the self-accelerating branch of MTBG admits static and
spherically symmetric general relativity solutions with matter written in the
spatially-flat coordinates, including neutron stars with arbitrary matter
equations of state. Finally, we show that in the self-accelerating branch
nontrivial solutions are given by the Schwarzschild-de Sitter metrics written
in nonstandard coordinates.


title:
Zeros of the Selberg zeta function for symmetric infinite area
  hyperbolic surfaces

abstract:
  In the present paper we give a simple mathematical foundation for describing
the zeros of the Selberg zeta functions $Z_X$ for certain very symmetric
infinite area surfaces $X$. For definiteness, we consider the case of three
funneled surfaces. We show that the zeta function is a complex almost periodic
function which can be approximated by complex trigonometric polynomials on
large domains (in Theorem 4.2). As our main application, we provide an
explanation of the striking empirical results of Borthwick (arXiv:1305.4850)
(in Theorem 1.5) in terms of convergence of the affinely scaled zero sets to
standard curves $\mathcal C$.


title:
Entanglement properties of superconducting qubits coupled to a
  semi-infinite transmission line

abstract:
  Quantum entanglement, a key resource in quantum information processing, is
reduced by interaction between the quantum system concerned and its unavoidable
noisy environment. Therefore it is of particular importance to study the
dynamical properties of entanglement in open quantum systems. In this work, we
mainly focus on two qubits coupled to an adjustable environment, namely a
semi-infinite transmission line. The two qubits' relaxations, through
individual channels or collective channel or both, can be adjusted by the
qubits' transition frequencies. We examine entanglement dynamics in this model
system with initial Werner state, and show that the phenomena of entanglement
sudden death and revival can be observed. Due to the hardness of preparing the
Werner state experimentally, we introduce a new type of entangled state called
pseudo-Werner state, which preserves as much entangling property as the Werner
state, and more importantly, is experiment friendly. Furthermore, we provide
detailed procedures for generating pseudo-Werner state and studying
entanglement dynamics with it, which can be straightforwardly implemented in a
superconducting waveguide quantum electrodynamics system.


title: Introduction to Charged Lepton Flavour Violation

abstract:
  Neutrino masses are evidence of lepton flavour violation, but no violation in
the interactions among the charged leptons has been observed yet. Many models
of Physics Beyond the Standard Model (BSM) predict Charged Lepton Flavour
Violation (CLFV) in a wide spectrum of processes with rates in reach of
upcoming experiments. The experimental searches that provide the current best
limits on the CLFV searches are reviewed, with a particular emphasis on the
muon-based experiments that give the most stringent constraints on BSM
parameter space. The next generation of muon-based experiments (MEG-II, Mu2e,
COMET, Mu3e) aim to reach improvements by many orders of magnitude w.r.t. the
current best limits, thanks to several technological advancements. We review
popular heavy BSM theories, and we present state-of-the-art calculations of the
predicted CLFV branching ratios, focusing on the more sensitive $\mu\to e$
sector.


title: Additive divisor problem for multiplicative functions

abstract:
  Let $\tau$ denote the divisor function, and $f$ be any multiplicative
function that satisfies some mild hypotheses. We establish the asymptotic
formula or non-trivial upper bound for the shifted convolution sum $\sum_{n
\leq X}f(n)\tau(n-1)$. We also derive several applications to multiplicative
functions in the automorphic context, including the functions
$\lambda_{\pi}(n), \,\mu(n)\lambda_{\pi}(n)$ and $\lambda_{\phi}(n)^l$. Here
$\lambda_{\pi}(n)$ denotes the $n$-th Dirichlet coefficient of $\text{GL}_m$
automorphic $L$-function $L(s,\pi)$ for an automorphic irreducible cuspidal
representation $\pi$, $\lambda_{\phi}(n)$ denotes the $n$-th Fourier
coefficient of a holomorphic or Maass cusp form $\phi$ on ${\rm SL}_2(\mathbb
Z)$, and $\mu(n)$ denotes the M\"obius function.
  We present two different arguments. The first one mainly relies on the
uniform estimates for the binary additive divisor problem, while the second is
based on the recent estimates of Bettin--Chandee for trilinear forms in
Kloosterman fractions. In addition, the Bourgain-K\'atai-Sarnak-Ziegler
criterion and Linnik's dispersion method are both employed in these two
arguments.


title:
Interaction of a Relativistic Magnetized Collisionless Shock with a
  Dense Clump

abstract:
  The interactions between a relativistic magnetized collisionless shock and
dense clumps have been expected to play a crucial role on the magnetic field
amplification and cosmic-ray acceleration. We investigate this process by
two-dimensional Particle-In-Cell (PIC) simulations for the first time, where
the clump size is much larger than the gyroradius of downstream particles. We
also perform relativistic magnetohydrodynamic (MHD) simulations for the same
condition to see the kinetic effects. We find that particles escape from the
shocked clump along magnetic field lines in the PIC simulations, so that the
vorticity is lower than that in the MHD simulations. Moreover, in both the PIC
and MHD simulations, the shocked clump quickly decelerates because of the
Lorentz contraction. Owing to the escape and the deceleration, the shocked
clump cannot amplify the downstream magnetic field in relativistic
collisionless shocks. This large-scale PIC simulation opens a new window to
understand large-scale behaviors in collisionless plasma systems.


title: Lee-Yang theory of the two-dimensional quantum Ising model

abstract:
  Determining the phase diagram of interacting quantum many-body systems is an
important task for a wide range of problems such as the understanding and
design of quantum materials. For classical equilibrium systems, the Lee-Yang
formalism provides a rigorous foundation of phase transitions, and these ideas
have also been extended to the quantum realm. Here, we develop a Lee-Yang
theory of quantum phase transitions that can include thermal fluctuations
caused by a finite temperature, and it thereby provides a link between the
classical Lee-Yang formalism and recent theories of phase transitions at zero
temperature. Our methodology exploits analytic properties of the moment
generating function of the order parameter in systems of finite size, and it
can be implemented in combination with tensor-network calculations.
Specifically, the onset of a symmetry-broken phase is signaled by the zeros of
the moment generating function approaching the origin in the complex plane of a
counting field that couples to the order parameter. Moreover, the zeros can be
obtained by measuring or calculating the high cumulants of the order parameter.
We determine the phase diagram of the two-dimensional quantum Ising model and
thereby demonstrate the potential of our method to predict the critical
behavior of two-dimensional quantum systems at finite temperatures.


title: Convergence in relative error for the Porous Medium equation in a tube

abstract:
  Given a bounded domain $D \subset \mathbb{R}^N$ and $m > 1$, we study the
long-time behaviour of solutions to the Porous Medium equation (PME) posed in a
tube \[ \partial_tu = \Delta u^m \quad \text{ in } D \times \mathbb{R}, \quad t
> 0, \] with homogeneous Dirichlet boundary conditions on the boundary
$\partial D \times \mathbb{R}$ and suitable initial datum at $t=0$. In two
previous works, V\'azquez and Gilding & Goncerzewicz proved that a wide class
of solutions exhibit a traveling wave behaviour, when computed at a logarithmic
time-scale and suitably renormalized. In this paper, we show that, for large
times, solutions converge in relative error to the Friendly Giant, i.e., the
unique nonnegative solution to the PME posed in the section $D$ of the tube
(with homogeneous Dirichlet boundary conditions) having a special self-similar
form. In addition, sharp rates of convergence and uniform bounds for the
location of the free boundary of solutions are given.


title: High-resolution ALMA observations of transition disk candidates in Lupus

abstract:
  Transition disks with small inner dust cavities are interesting targets for
the study of disk clearing mechanisms. Such disks have been identified through
a deficit in the infrared part of their SED, but spatially resolved millimeter
imaging is required to confirm the presence of an inner dust cavity. We use
high-resolution ALMA observations of 30 mas resolution in Band 6 continuum and
$^{12}$CO 2--1 emission of 10 transition disk candidates in the Lupus star
forming region, in order to confirm the presence of inner dust cavities and
infer the responsible mechanism. The continuum data are analyzed using
visibility modeling and the SEDs are compared with radiative transfer models.
Out of the six transition disk candidates selected from their SED, only one
disk revealed an inner dust cavity of 4 au in radius. Three of the other disks
are highly inclined, which limits the detectability of an inner dust cavity but
it is also demonstrated to be the possible cause for the infrared deficit in
their SED. The two remaining SED-selected disks are very compact, with dust
radii of only $\sim$3 au. From the four candidates selected from low-resolution
images, three new transition disks with large inner cavities $>$20 au are
identified, bringing the total number of transition disks with large cavities
in Lupus to 13. SED-selected transition disks with small cavities are biased
towards highly inclined and compact disks, which casts doubt on the use of
their occurrence rates in estimating dispersal timescales of photoevaporation.
Using newly derived disk dust masses and radii, we re-evaluate the
size-luminosity and $M_{\rm dust}-M_{\rm star}$ relations. These relations can
be understood if the bright disks are dominated by disks with substructure
whereas faint disks are dominated by drift-dominated disks. (Abridged)


title:
Empirical Evaluation and Theoretical Analysis for Representation
  Learning: A Survey

abstract:
  Representation learning enables us to automatically extract generic feature
representations from a dataset to solve another machine learning task.
Recently, extracted feature representations by a representation learning
algorithm and a simple predictor have exhibited state-of-the-art performance on
several machine learning tasks. Despite its remarkable progress, there exist
various ways to evaluate representation learning algorithms depending on the
application because of the flexibility of representation learning. To
understand the current representation learning, we review evaluation methods of
representation learning algorithms and theoretical analyses. On the basis of
our evaluation survey, we also discuss the future direction of representation
learning. Note that this survey is the extended version of Nozawa and Sato
(2022).


title:
The Devil is in the Frequency: Geminated Gestalt Autoencoder for
  Self-Supervised Visual Pre-Training

abstract:
  The self-supervised Masked Image Modeling (MIM) schema, following
"mask-and-reconstruct" pipeline of recovering contents from masked image, has
recently captured the increasing interest in the multimedia community, owing to
the excellent ability of learning visual representation from unlabeled data.
Aiming at learning representations with high semantics abstracted, a group of
works attempts to reconstruct non-semantic pixels with large-ratio masking
strategy, which may suffer from "over-smoothing" problem, while others directly
infuse semantics into targets in off-line way requiring extra data. Different
from them, we shift the perspective to the Fourier domain which naturally has
global perspective and present a new Masked Image Modeling (MIM), termed
Geminated Gestalt Autoencoder (Ge$^2$-AE) for visual pre-training.
Specifically, we equip our model with geminated decoders in charge of
reconstructing image contents from both pixel and frequency space, where each
other serves as not only the complementation but also the reciprocal
constraints. Through this way, more robust representations can be learned in
the pre-trained encoders, of which the effectiveness is confirmed by the
juxtaposing experimental results on downstream recognition tasks. We also
conduct several quantitative and qualitative experiments to investigate the
learning behavior of our method. To our best knowledge, this is the first MIM
work to solve the visual pre-training through the lens of frequency domain.


title: Human and automated approaches for finite trigonometric sums

abstract:
  We show that identities involving trigonometric sums recently proved by
Harshitha, Vasuki and Yathirajsharma, using Ramanujan's theory of theta
functions, were either already in the literature or can be proved easily by
adapting results that can be found in the literature. Also we prove two
conjectures given in that paper. After mentioning many other works dealing with
identities for various trigonometric sums, we end this paper by describing an
automated approach for proving such trigonometric identities.


title:
Preference Enhanced Social Influence Modeling for Network-Aware Cascade
  Prediction

abstract:
  Network-aware cascade size prediction aims to predict the final reposted
number of user-generated information via modeling the propagation process in
social networks. Estimating the user's reposting probability by social
influence, namely state activation plays an important role in the information
diffusion process. Therefore, Graph Neural Networks (GNN), which can simulate
the information interaction between nodes, has been proved as an effective
scheme to handle this prediction task. However, existing studies including
GNN-based models usually neglect a vital factor of user's preference which
influences the state activation deeply. To that end, we propose a novel
framework to promote cascade size prediction by enhancing the user preference
modeling according to three stages, i.e., preference topics generation,
preference shift modeling, and social influence activation. Our end-to-end
method makes the user activating process of information diffusion more adaptive
and accurate. Extensive experiments on two large-scale real-world datasets have
clearly demonstrated the effectiveness of our proposed model compared to
state-of-the-art baselines.


title: A generalization of the Lodha-Moore group

abstract:
  We generalized the Lodha-Moore group into $n$-adic and showed analogues in
the Lodha-Moore group of properties between the Thompson group $F$ and the
generalized Thompson group $F(n)$.


title:
Long-time behaviour and stability for quasilinear doubly degenerate
  parabolic equations of higher order

abstract:
  We study the long-time behaviour of solutions to quasilinear doubly
degenerate parabolic problems of fourth order. The equations model for instance
the dynamic behaviour of a non-Newtonian thin-film flow on a flat impermeable
bottom and with zero contact angle. We consider a shear-rate dependent fluid
the rheology of which is described by a constitutive power-law or Ellis-law for
the fluid viscosity. In all three cases, positive constants (i.e. positive flat
films) are the only positive steady-state solutions. Moreover, we can give a
detailed picture of the long-time behaviour of solutions with respect to the
$H^1(\Omega)$-norm. In the case of shear-thickening power-law fluids, one
observes that solutions which are initially close to a steady state, converge
to equilibrium in finite time. In the shear-thinning power-law case, we find
that steady states are polynomially stable in the sense that, as time tends to
infinity, solutions which are initially close to a steady state, converge to
equilibrium at rate $1/t^{1/\beta}$ for some $\beta > 0$. Finally, in the case
of an Ellis-fluid, steady states are exponentially stable in $H^1(\Omega)$.


title: Symmetric wormholes in Einstein-vector-Gauss-Bonnet theory

abstract:
  We construct wormholes in Einstein-vector-Gauss-Bonnet theory where a real
massless vector field is coupled to the higher curvature Gauss-Bonnet
invariant. We consider three coupling functions which depend on the square of
the vector field. The respective domains of existence of wormholes possess as
their boundaries i) black holes, ii) solutions with a singular throat, iii)
solutions with a degenerate throat and iv) solutions with cusp singularities.
Depending on the coupling function wormhole solutions can feature a single
throat or an equator surrounded by a double throat. The wormhole solutions need
a thin shell of matter at the throat, in order to be symmetrically continued
into the second asymptotically flat region. These wormhole spacetimes allow for
bound and unbound particle motion as well as light rings.


title: The Bass-Quillen phenomenon for reductive group torsors

abstract:
  We complete the proof of the Nisnevich conjecture in equal characteristic:
for a smooth variety $X$ over a field, a smooth divisor $D \subset X$, and a
reductive $X$-group $G$ that is totally isotropic along $D$, we show that each
generically trivial $G$-torsor over $X\setminus D$ trivializes Zariski
(semi)locally on $X$. We then use it to give a new proof for the generalization
of the Bass-Quillen conjecture to torsors under reductive groups in equal
characteristic: for a regular ring $R$ that contains a field and a totally
isotropic reductive $R$-group scheme $G$, we show that each generically trivial
$G$-torsor over the affine space $\mathbf{A}^d_R$ descends to $R$,
equivalently, that $H^1_{\mathrm{Nis}}(R, G) \xrightarrow{\sim}
H^1_{\mathrm{Nis}}(\mathbf{A}^d_R, G)$. We base our arguments on an extension
of the Gabber-Quillen presentation lemma, on the relative version of the
Grothendieck-Serre conjecture, and on an extension result for $G$-torsors over
a smooth relative curve.


title: A Clifford inequality for semistable curves

abstract:
  Let $X$ be a semistable curve and $L$ a line bundle whose multidegree is
uniform, i.e., in the range between those of the structure sheaf and the
dualizing sheaf of $X$. We establish an upper bound for $h^0(X,L)$, which
generalizes the classic Clifford inequality for smooth curves. The bound
depends on the total degree of $L$ and connectivity properties of the dual
graph of $X$. It is sharp, in the sense that on any semistable curve there
exist line bundles with uniform multidegree that achieve the bound.


title: Table Enrichment System for Machine Learning

abstract:
  Data scientists are constantly facing the problem of how to improve
prediction accuracy with insufficient tabular data. We propose a table
enrichment system that enriches a query table by adding external attributes
(columns) from data lakes and improves the accuracy of machine learning
predictive models. Our system has four stages, join row search, task-related
table selection, row and column alignment, and feature selection and
evaluation, to efficiently create an enriched table for a given query table and
a specified machine learning task. We demonstrate our system with a web UI to
show the use cases of table enrichment.


title: Two-Body Strong Decay of the 2P and 3P Charmonium states

abstract:
  Two-body open charm strong decays of the $2P$ and $3P$ charmonium states are
studied by the Bethe-Salpeter(BS) method combined with the $^3P_0$ model. The
wave functions and mass spectra of the $2P$ and $3P$ charmonium states are
obtained by solving the BS equation with the relativistic correction. The
strong decay widths and relative ratios of the $2P$ and $3P$ charmonium states
are calculated. Comparing our results with the experimental data, we obtain
some interesting results. Considering the $X^*(3860)$ as the $\chi_{c0}(2P)$,
the total strong decay width is smaller than the experimental data. But the
strong decay width depends on the parameter $\gamma$ in the $^3P_0$ model, and
the mass and width of the $X^*(3860)$ have large errors, we cannot rule out the
possibility that the $X^*(3860)$ is the $\chi_{c0}(2P)$. The $X(4160)$ is a
good candidate for the $\chi_{c0}(3P)$, not only the strong decay width of the
$\chi_{c0}(3P)$ is same as the experimental data, but the relative ratios
$\frac{\Gamma(\chi_{c0}(3P)\to D\bar D)}{\Gamma(\chi_{c0}(3P)\to D^*\bar
D^*)}\approx0.0013<0.09$, and $\frac{\Gamma({\chi_{c0}(3P)\to D\bar
D^*})}{\Gamma({\chi_{c0}(3P)\to D^*\bar D^*})}=0<0.22$ are consistent with the
experimental results of the $X(4160)$. Taking the $X(4274)$ as the
$\chi_{c1}(3P)$, the strong decay width is consistent with the experimental
data, so the $X(4274)$ is a good candidate for the $\chi_{c1}(3P)$. Assigning
the $X(4350)$ as the $\chi_{c2}(3P)$, the corresponding strong decay width is
slightly larger than the experimental data. To identify if the $X(4350)$ is
$\chi_{c2}(3P)$, many more investigations are needed. All of the strong decay
widths and relative ratios of the $2P$ and $3P$ charmonium states can provide
the useful information to discover and confirm these particles in the future.


title:
Modx: Binary Level Partial Imported Third-Party Library Detection
  through Program Modularization and Semantic Matching

abstract:
  With the rapid growth of software, using third-party libraries (TPLs) has
become increasingly popular. The prosperity of the library usage has provided
the software engineers with handful of methods to facilitate and boost the
program development. Unfortunately, it also poses great challenges as it
becomes much more difficult to manage the large volume of libraries. Researches
and studies have been proposed to detect and understand the TPLs in the
software. However, most existing approaches rely on syntactic features, which
are not robust when these features are changed or deliberately hidden by the
adversarial parties. Moreover, these approaches typically model each of the
imported libraries as a whole, therefore, cannot be applied to scenarios where
the host software only partially uses the library code segments.
  To detect both fully and partially imported TPLs at the semantic level, we
propose ModX, a framework that leverages novel program modularization
techniques to decompose the program into finegrained functionality-based
modules. By extracting both syntactic and semantic features, it measures the
distance between modules to detect similar library module reuse in the program.
Experimental results show that ModX outperforms other modularization tools by
distinguishing more coherent program modules with 353% higher module quality
scores and beats other TPL detection tools with on average 17% better in
precision and 8% better in recall.


title:
Coherent Resonant Coupling between Atoms and a Mechanical Oscillator
  Mediated by Cavity-Vacuum Fluctuations

abstract:
  We show that an atom can be coupled to a mechanical oscillator via quantum
vacuum fluctuations of a cavity field enabling energy transfer processes
between them. In a hybrid quantum system consisting of a cavity resonator with
a movable mirror and an atom, these processes are dominated by two
pair-creation mechanisms: the counter-rotating (atom-cavity system) and
dynamical Casimir interaction terms (optomechanical system). Because of these
two pair-creation mechanisms, the resonant atom-mirror coupling is the result
of high-order virtual processes with different transition paths well described
in our theoretical framework. We perform a unitary transformation to the
atom-mirror system Hamiltonian, exhibiting two kinds of multiple-order
transitions of the pair creation. By tuning the frequency of the atom, we show
that photon frequency conversion can be realized within a cavity of multiple
modes. Furthermore, when involving two atoms coupled with the same mechanical
mode, a single vibrating excitation of the mechanical oscillator can be
simultaneously absorbed by the two atoms. Considering recent advances in strong
and ultrastrong coupling for cavity optomechanics and other systems, we believe
our proposals can be implemented using available technology.


title: Sampling theorems in the OLCT and the OLCHT domains by polar coordinates

abstract:
  The sampling theorem for the offset linear canonical transform (OLCT) of
bandlimited functions in polar coordinates is an important mathematical tool in
many fields of signal processing and medical imaging. This paper investigates
two sampling theorems for interpolating \Omega bandlimited and highest
frequency bandlimited functions f(r,{\theta}) in the OLCT and the offset linear
canonical Hankel transform (OLCHT) domains by polar coordinates. Based on the
classical Stark's interpolation formulas, we derive the sampling theorems for
\Omega bandlimited functions f(r,{\theta}) in the OLCT and the OLCHT domains,
respectively. The first interpolation formula is concise and applicable. Due to
the consistency of the OLCHT order, the second interpolation formula is
superior to the first interpolation formula in computational complexity.


title: Linear Battery Models for Power Systems Analysis

abstract:
  Mathematical models are just models. The desire to describe battery energy
storage system (BESS) operation using computationally tractable model
formulations has motivated a long-standing discussion in both the scientific
and industrial communities. Linear BESS models are the most widely used so far.
However, finding suitable linear BESS models has been controversial.
  This paper focuses on the description of linear BESS models. Four linear BESS
formulations are presented, among the most popularly used. A new formulation is
also proposed. The 5 BESS models are tested in 100 random BESS and 1.450 random
samples of daily profiles of renewable generation. Two classical problems of
power systems, namely, the set-point tracking problem and the transmission
expansion planning problem, are selected for numerical analysis. Five thousand
simulations are used to draw a better interpretation of each linear formulation
presented and showcase specific challenges of BESS models. Practical
recommendations are provided based on the findings.


title:
GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural
  Networks for Passage Retrieval

abstract:
  Recently, retrieval models based on dense representations are dominant in
passage retrieval tasks, due to their outstanding ability in terms of capturing
semantics of input text compared to the traditional sparse vector space models.
A common practice of dense retrieval models is to exploit a dual-encoder
architecture to represent a query and a passage independently. Though
efficient, such a structure loses interaction between the query-passage pair,
resulting in inferior accuracy. To enhance the performance of dense retrieval
models without loss of efficiency, we propose a GNN-encoder model in which
query (passage) information is fused into passage (query) representations via
graph neural networks that are constructed by queries and their top retrieved
passages. By this means, we maintain a dual-encoder structure, and retain some
interaction information between query-passage pairs in their representations,
which enables us to achieve both efficiency and efficacy in passage retrieval.
Evaluation results indicate that our method significantly outperforms the
existing models on MSMARCO, Natural Questions and TriviaQA datasets, and
achieves the new state-of-the-art on these datasets.


title:
Fast optimization of common basis for matrix set through Common Singular
  Value Decomposition

abstract:
  SVD (singular value decomposition) is one of the basic tools of machine
learning, allowing to optimize basis for a given matrix. However, sometimes we
have a set of matrices $\{A_k\}_k$ instead, and would like to optimize a single
common basis for them: find orthogonal matrices $U$, $V$, such that $\{U^T A_k
V\}$ set of matrices is somehow simpler. For example DCT-II is orthonormal
basis of functions commonly used in image/video compression - as discussed
here, this kind of basis can be quickly automatically optimized for a given
dataset. While also discussed gradient descent optimization might be
computationally costly, there is proposed CSVD (common SVD): fast general
approach based on SVD. Specifically, we choose $U$ as built of eigenvectors of
$\sum_i (w_k)^q (A_k A_k^T)^p$ and $V$ of $\sum_k (w_k)^q (A_k^T A_k)^p$, where
$w_k$ are their weights, $p,q>0$ are some chosen powers e.g. 1/2, optionally
with normalization e.g. $A \to A - rc^T$ where $r_i=\sum_j A_{ij}, c_j =\sum_i
A_{ij}$.


title: Solvability of Superlinear Fractional Parabolic Equations

abstract:
  We study necessary conditions and sufficient conditions for the existence of
local-in-time solutions of the Cauchy problem for superlinear fractional
parabolic equations. Our conditions are sharp and clarify the relationship
between the solvability of the Cauchy problem and the strength of the
singularities of the initial measure.


title: RIS-Assisted Cooperative NOMA with SWIPT

abstract:
  This paper studies the application of reconfigurable intelligent surface
(RIS) to cooperative non-orthogonal multiple access (C-NOMA) networks with
simultaneous wireless information and power transfer (SWIPT). We aim for
maximizing the rate of the strong user with guaranteed weak user's quality of
service (QoS) by jointly optimizing power splitting factors, beamforming
coefficients, and RIS reflection coefficients in two transmission phases. The
formulated problem is difficult to solve due to its complex and non-convex
constraints. To tackle this challenging problem, we first use alternating
optimization (AO) framework to transform it into three subproblems, and then
use the penalty-based arithmetic-geometric mean approximation (PBAGM) algorithm
and the successive convex approximation (SCA)-based method to solve them.
Numerical results verify the superiority of the proposed algorithm over the
baseline schemes.


title: Noether charge formalism for Weyl transverse gravity

abstract:
  Weyl transverse gravity is a gravitational theory that is invariant under
transverse diffeomorphisms and Weyl transformations. It is characterised by
having the same classical solutions as general relativity while solving some of
its issues with the cosmological constant. In this work, we first find the
Noether currents and charges corresponding to local symmetries of Weyl
transverse gravity as well as a prescription for the symplectic form. We then
employ these results to derive the first law of black hole mechanics in Weyl
transverse gravity (both in vacuum and in the presence of a perfect fluid),
identifying the total energy, the total angular momentum, and the Wald entropy
of black holes. We further obtain the first law and Smarr formula for
Schwarzschild-anti-de Sitter and pure de Sitter spacetimes, discussing the
contributions of the varying cosmological constant, which naturally appear in
Weyl transverse gravity. Lastly, we derive the first law of causal diamonds in
vacuum.


title:
Uniform in time solutions for a chemotaxis with potential consumption
  model

abstract:
  In this work we investigate the following chemo-attraction with consumption
model in bounded domains of \, $\mathbb{R}^N$ ($N=1,2,3$):
  $$ \partial_t u - \Delta u = - \nabla \cdot (u \nabla v), \quad
  \partial_t v - \Delta v = - u^s v
  $$ where $s\ge 1$, endowed with isolated boundary conditions and initial
conditions for $(u,v)$. The main novelty in the model is the nonlinear
potential consumption term $u^sv$.
  Through the convergence of solutions of an adequate truncated model, two main
results are established; existence of uniform in time weak solutions in $3D$
domains, and uniqueness and regularity in $2D$ (or $1D$) domains. Both results
are proved imposing minimal regularity assumptions on the boundary of the
domain.


title: Joint Multi-view Unsupervised Feature Selection and Graph Learning

abstract:
  Despite the recent progress, the existing multi-view unsupervised feature
selection methods mostly suffer from two limitations. First, they generally
utilize either cluster structure or similarity structure to guide the feature
selection, neglecting the possibility of a joint formulation with mutual
benefits. Second, they often learn the similarity structure by either global
structure learning or local structure learning, lacking the capability of graph
learning with both global and local structural awareness. In light of this,
this paper presents a joint multi-view unsupervised feature selection and graph
learning (JMVFG) approach. Particularly, we formulate the multi-view feature
selection with orthogonal decomposition, where each target matrix is decomposed
into a view-specific basis matrix and a view-consistent cluster indicator.
Cross-space locality preservation is incorporated to bridge the cluster
structure learning in the projected space and the similarity learning (i.e.,
graph learning) in the original space. Further, a unified objective function is
presented to enable the simultaneous learning of the cluster structure, the
global and local similarity structures, and the multi-view consistency and
inconsistency, upon which an alternating optimization algorithm is developed
with theoretically proved convergence. Extensive experiments demonstrate the
superiority of our approach for both multi-view feature selection and graph
learning tasks.


title:
Electromagnetic multipole structure of a spin-one particle: Abel
  tomography case

abstract:
  We investigate the three-dimensional~(3D) and two-dimensional~(2D) charge
distributions of a spin-one particle in terms of the multipole expansion. On
account of the geometrical difference between 2D and 3D spaces, projecting the
3D electric distribution to the 2D one in the Breit frame brings about the
influence of the quadrupole distribution upon the monopole one. Thus, the 2D
charge distribution becomes spin-dependent. This effect should be sorted out
from the relativistic effects arising from the Lorentz boost. We first provide
the connections between the 2D and 3D distributions in the Breit frame in terms
of the angle-dependent Abel transformation. We then provide the differential
equations that enable us to map 2D distributions in the Breit frame to those in
the infinite momentum frame.


title:
Label-free metabolic imaging of non-alcoholic-fatty-liver-disease
  (NAFLD) liver by volumetric dynamic optical coherence tomography

abstract:
  Label-free metabolic imaging of non-alcoholic fatty liver disease (NAFLD)
mouse liver is demonstrated ex vivo by dynamic optical coherence tomography
(OCT). The NAFLD mouse is a methionine choline-deficient (MCD)-diet model, and
two mice fed MCD diet for 1 and 2 weeks are involved in addition to a
normal-diet mouse. The dynamic OCT is based on repeating raster scan and
logarithmic intensity variance (LIV) analysis which enables volumetric
metabolic imaging with a standard-speed (50,000 A-lines/s) OCT system.
Metabolic domains associated with lipid droplet accumulation and inflammation
are clearly visualized three-dimensionally. Particularly, the normal-diet liver
exhibits highly metabolic vessel-like structures of peri-vascular hepatic
zones. The 1-week MCD-diet liver shows ring-shaped highly metabolic structures
formed with lipid droplets. The 2-week MCD-diet liver exhibits fragmented
vessel-like structures associated with inflammation. These results imply that
volumetric LIV imaging is useful for visualizing and assessing NAFLD
abnormalities.


title: Quantum Fluctuations Approach to the Nonequilibrium $GW$ approximation

abstract:
  The quantum dynamics of fermionic or bosonic many-body systems following
external excitation can be successfully studied using nonequilibrium Green
functions (NEGF) or reduced density matrix methods. Approximations are
introduced via a proper choice of the selfenergy or decoupling of the
BBGKY-hierarchy. These approximations are based on Feynman's diagram approaches
or on cluster expansions into single-particle and correlation operators. Here
we develop a different approach where, instead of equations of motion for the
many-particle NEGF, equations for the correlation functions of fluctuations are
analyzed. We present a derivation of the first two equations of the alternative
hierarchy of fluctuations and discuss possible decoupling approximations. In
particular, we derive the polarization approximation (PA) which is shown to be
equivalent to the nonequilibrium $GW$ approximation with exchange effects of
NEGF theory within the generalized Kadanoff-Baym ansatz for weak coupling. The
main advantage of the quantum fluctuations approach is that the standard
ensemble average can be replaced by a semiclassical average over different
initial realizations, as was demonstrated before by Lacroix and co-workers.
Here we introduce the stochastic $GW$ (SGW) approximation and the stochastic
polarization approximation (SPA) which are demonstrated to be equivalent to the
$GW$ approximation without and with exchange, respectively, in the weak
coupling limit. In addition to the standard stochastic approach to sample
initial configurations we also present an exact approach. Our numerical tests
confirm that our approach has the same favorable linear scaling with the
computation time as the recently developed G1--G2 scheme. At the same time the
SPA and SGW approaches scale more favorably with the system size than the
G1--G2 scheme, allowing to extend nonequilibrium $GW$ calculations to bigger
systems.


title: Minimum entropy of graphs with given size

abstract:
  The first degree-based graph entropy of a graph is the Shannon entropy of its
degree sequence. Its correct interpretation as a measure of uniformity of the
degree sequence requires the determination of its extremal values given natural
constraints. In this paper, we prove that the graphs with given size that
minimize the first degree-based graph entropy are precisely the colex graphs.


title:
The Variational Problem and Background Field in Renormalization Group
  Method for Non-Linear Sigma Models

abstract:
  We study the variational problem as described by Balaban in his
renormalization group method for Yang-Mills theories in $d= 3, 4$ and adapt it
to a class of Non-Linear Sigma Models in $d=2$. The result of the variational
problem is a minimal configuration which serves as a classical background field
in the renormalization group analysis.


title:
High-Density Polyethylene Degradation into Low Molecular Weight Gases at
  1823 K: An Atomistic Simulation

abstract:
  Using molecular dynamics simulation, we present a comprehensive study of the
volatile thermal degradation of high-density polyethylene (HDPE) across a
temperature range of 300 K to 1823 K. We find that degradation at temperatures
higher than $\sim$ 1373 K generates significant quantities of reducing gases
such as CH$_{\text{n}}$ and hydrogen molecules which are beneficial to the
steelmaking industry. Our results provide a new understanding of HDPE's phase
transformation from solid to gas that occurs during superheating at
steelmaking's electric arc furnace environment offering a new method for
eliminating end-of-life HDPE from landfill.


title:
Deterministic Low-Diameter Decompositions for Weighted Graphs and
  Distributed and Parallel Applications

abstract:
  This paper presents new deterministic and distributed low-diameter
decomposition algorithms for weighted graphs. In particular, we show that if
one can efficiently compute approximate distances in a parallel or a
distributed setting, one can also efficiently compute low-diameter
decompositions. This consequently implies solutions to many fundamental
distance based problems using a polylogarithmic number of approximate distance
computations.
  Our low-diameter decomposition generalizes and extends the line of work
starting from [Rozho\v{n}, Ghaffari STOC 2020] to weighted graphs in a very
model-independent manner. Moreover, our clustering results have additional
useful properties, including strong-diameter guarantees, separation properties,
restricting cluster centers to specified terminals, and more. Applications
include:
  -- The first near-linear work and polylogarithmic depth randomized and
deterministic parallel algorithm for low-stretch spanning trees (LSST) with
polylogarithmic stretch. Previously, the best parallel LSST algorithm required
$m \cdot n^{o(1)}$ work and $n^{o(1)}$ depth and was inherently randomized. No
deterministic LSST algorithm with truly sub-quadratic work and sub-linear depth
was known.
  -- The first near-linear work and polylogarithmic depth deterministic
algorithm for computing an $\ell_1$-embedding into polylogarithmic dimensional
space with polylogarithmic distortion. The best prior deterministic algorithms
for $\ell_1$-embeddings either require large polynomial work or are inherently
sequential.
  Even when we apply our techniques to the classical problem of computing a
ball-carving with strong-diameter $O(\log^2 n)$ in an unweighted graph, our new
clustering algorithm still leads to an improvement in round complexity from
$O(\log^{10} n)$ rounds [Chang, Ghaffari PODC 21] to $O(\log^{4} n)$.


title: LBL System for Underwater Acoustic Positioning: Concept and Equations

abstract:
  GNSS (Global Navigation Satellite System) positioning is not available
underwater due to the very short range of electromagnetic waves in the sea
water medium. In this article a LBL (Long Base Line) acoustic repeater system
of the GNSS positioning is presented. The system is hyperbolic, i.e., based on
time differences and it does not need very accurate atomic clocks to
synchronize repeaters.


title: Ab initio study of resonance states of exotic 10Li nucleus

abstract:
  The spectrum, partial and total decay widths of the states of the exotic 10Li
nucleus are studied in an ab initio approach. The spectrum is calculated using
No-Core Shell Model and corresponding extrapolation procedure. The calculations
use the well-proven Daejeon16 potential. The NCSM-based approach, which
includes a method for constructing the basis of functions of cluster channels
and a procedure for matching the cluster form factors obtained within this
method with the asymptotic wave functions, is applied to compute the widths of
the levels. The results obtained quite adequately reproduce the pattern given
by the most advanced experiments. Detailed predictions of the spectral
characteristics of 10Li nucleus states are given.


title: On well-splitting posets

abstract:
  We introduce a class of proper posets which is preserved under countable
support iterations, includes $\omega^\omega$-bounding, Cohen, Miller, and
Mathias posets associated to filters with the Hurewicz covering properties, and
has the property that the ground model reals remain splitting and unbounded in
corresponding extensions. Our results may be considered as a possible path
towards solving variations of the famous Roitman problem.


title:
Unusual band splitting and superconducting gap evolution with sulfur
  substitution in FeSe

abstract:
  High-resolution angle-resolved photoemission measurements were taken on
FeSe$_{1-x}$S$_x$ (x=0, 0.04, and 0.08) superconductors. With an ultrahigh
energy resolution of 0.4 meV, unusual two hole bands near the Brillouin-zone
center, which was possibly a result of additional symmetry breaking, were
identified in all the sulfur-substituted samples. In addition, in both of the
hole bands highly anisotropic superconducting gaps with resolution limited
nodes were evidenced. We find that the larger superconducting gap on the outer
hole band is reduced linearly to the nematic transition temperature while the
gap on the inner hole is nearly S-substitution independent. Our observations
strongly suggest that the superconducting gap increases with enhanced
nematicity although the superconducting transition temperature is not only
governed by the pairing strength, demonstrating strong constraints on theories
in the FeSe family.


title:
Inverse problems for Dirac operators with constant delay: uniqueness,
  characterization, uniform stability

abstract:
  We initiate studying inverse spectral problems for Dirac-type
functional-differential operators with constant delay. For simplicity, we
restrict ourselves to the case when the delay parameter is not less than one
half of the interval. For the considered case, however, we give answers to the
full range of questions usually raised in the inverse spectral theory.
Specifically, reconstruction of two complex $L_2$-potentials is studied from
either complete spectra or subspectra of two boundary value problems with one
common boundary condition. We give conditions on the subspectra that are
necessary and sufficient for the unique determination of the potentials.
Moreover, necessary and sufficient conditions for the solvability of both
inverse problems are obtained. For the inverse problem involving the complete
spectra, we establish also uniform stability in each ball. For this purpose, we
use recent results on uniform stability of sine-type functions with
asymptotically separated zeros.


title: Geometrical bounds on irreversibility in squeezed thermal bath

abstract:
  Irreversible entropy production (IEP) plays an important role in quantum
thermodynamic processes. Here we investigate the geometrical bounds of IEP in
nonequilibrium thermodynamics by exemplifying a system coupled to a squeezed
thermal bath subject to dissipation and dephasing, respectively. We find that
the geometrical bounds of the IEP always shift in contrary way under
dissipation and dephasing, where the lower and upper bounds turning to be
tighter occurs in the situation of dephasing and dissipation, respectively.
However, either under dissipation or under dephasing, we may reduce both the
critical time of the IEP itself and the critical time of the bounds for
reaching an equilibrium by harvesting the benefits of squeezing effects, in
which the values of the IEP, quantifying the degree of thermodynamic
irreversibility, also becomes smaller. Therefore, due to the nonequilibrium
nature of the squeezed thermal bath, the system-bath interaction energy brings
prominent impact on the IEP, leading to tightness of its bounds. Our results
are not contradictory with the second law of thermodynamics by involving
squeezing of the bath as an available resource, which can improve the
performance of quantum thermodynamic devices.


title: Visio-Linguistic Brain Encoding

abstract:
  Enabling effective brain-computer interfaces requires understanding how the
human brain encodes stimuli across modalities such as visual, language (or
text), etc. Brain encoding aims at constructing fMRI brain activity given a
stimulus. There exists a plethora of neural encoding models which study brain
encoding for single mode stimuli: visual (pretrained CNNs) or text (pretrained
language models). Few recent papers have also obtained separate visual and text
representation models and performed late-fusion using simple heuristics.
However, previous work has failed to explore: (a) the effectiveness of image
Transformer models for encoding visual stimuli, and (b) co-attentive
multi-modal modeling for visual and text reasoning. In this paper, we
systematically explore the efficacy of image Transformers (ViT, DEiT, and BEiT)
and multi-modal Transformers (VisualBERT, LXMERT, and CLIP) for brain encoding.
Extensive experiments on two popular datasets, BOLD5000 and Pereira, provide
the following insights. (1) To the best of our knowledge, we are the first to
investigate the effectiveness of image and multi-modal Transformers for brain
encoding. (2) We find that VisualBERT, a multi-modal Transformer, significantly
outperforms previously proposed single-mode CNNs, image Transformers as well as
other previously proposed multi-modal models, thereby establishing new
state-of-the-art. The supremacy of visio-linguistic models raises the question
of whether the responses elicited in the visual regions are affected implicitly
by linguistic processing even when passively viewing images. Future fMRI tasks
can verify this computational insight in an appropriate experimental setting.


title:
Taylor expansions of Jacobi forms and linear relations among theta
  series

abstract:
  We study Taylor expansions of Jacobi forms of lattice index. As the main
result, we give an embedding from certain space of such forms, whether
scalar-valued or vector-valued, integral-weight or half-integral-weight, of any
level, with any character, into a product of finitely many spaces of modular
forms. As an application, we investigate linear relations among Jacobi theta
series of lattice index. Many linear relations among the second powers of such
theta series associated with the $D_4$ lattice and $A_3$ lattice are obtained,
along with relations among the third powers of series associated with the $A_2$
lattice. We present the complete SageMath code for the $D_4$ lattice.


title:
Factual Error Correction for Abstractive Summaries Using Entity
  Retrieval

abstract:
  Despite the recent advancements in abstractive summarization systems
leveraged from large-scale datasets and pre-trained language models, the
factual correctness of the summary is still insufficient. One line of trials to
mitigate this problem is to include a post-editing process that can detect and
correct factual errors in the summary. In building such a post-editing system,
it is strongly required that 1) the process has a high success rate and
interpretability and 2) has a fast running time. Previous approaches focus on
regeneration of the summary using the autoregressive models, which lack
interpretability and require high computing resources. In this paper, we
propose an efficient factual error correction system RFEC based on entities
retrieval post-editing process. RFEC first retrieves the evidence sentences
from the original document by comparing the sentences with the target summary.
This approach greatly reduces the length of text for a system to analyze. Next,
RFEC detects the entity-level errors in the summaries by considering the
evidence sentences and substitutes the wrong entities with the accurate
entities from the evidence sentences. Experimental results show that our
proposed error correction system shows more competitive performance than
baseline methods in correcting the factual errors with a much faster speed.


title:
Controlled light scattering of a single nanoparticle by wavefront
  shaping

abstract:
  Controlling light scattering by nanoparticles is fundamentally important for
the understanding and the control of light inside photonic nanostructures, as
well as for nanoparticle scattering itself, including Mie scattering. Here, we
theoretically and numerically investigate the possibility to manipulate
nanoparticle scattering through wavefront shaping that was initially developed
to control scattering of light through opaque random media that consist of
large numbers of scattering nanoparticles. We find that even a single
nanoparticle supports multiple strongly scattering eigenchannels, which opens
the opportunity to manipulate scattering with wavefront shaping previously
developed for multiple scattered light through opaque random media. We find
that these scattering eigenchannels are related to different resonant leaky
modes of the scatterer. Moreover, we investigate the spectral correlation of
these highly scattering eigenchannels, and demonstrate the coexistence of short
range and long range correlations. Our work proposes a new tool to control
light-matter interactions with resonant modes via wavefront shaping and
constitutes a step towards exploring novel spectral correlations in the
scattering of light by nano scatterers, including Mie spheres.


title:
Configuration-Aware Safe Control for Mobile Robotic Arm with Control
  Barrier Functions

abstract:
  Collision avoidance is a widely investigated topic in robotic applications.
When applying collision avoidance techniques to a mobile robot, how to deal
with the spatial structure of the robot still remains a challenge. In this
paper, we design a configuration-aware safe control law by solving a Quadratic
Programming (QP) with designed Control Barrier Functions (CBFs) constraints,
which can safely navigate a mobile robotic arm to a desired region while
avoiding collision with environmental obstacles. The advantage of our approach
is that it correctly and in an elegant way incorporates the spatial structure
of the mobile robotic arm. This is achieved by merging geometric restrictions
among mobile robotic arm links into CBFs constraints. Simulations on a rigid
rod and the modeled mobile robotic arm are performed to verify the feasibility
and time-efficiency of proposed method. Numerical results about the time
consuming for different degrees of freedom illustrate that our method scales
well with dimension.


title:
Type II Dirac Seesaw with Observable $\Delta N_{\rm eff}$ in the light
  of W-mass Anomaly

abstract:
  We propose a type II seesaw model for light Dirac neutrinos to provide an
explanation for the recently reported anomaly in W boson mass by the CDF
collaboration with $7\sigma$ statistical significance. In the minimal model,
the required enhancement in W boson mass is obtained at tree level due to the
vacuum expectation value of a real scalar triplet, which also plays a role in
generating light Dirac neutrino mass. Depending upon the couplings and masses
of newly introduced particles, we can have thermally or non-thermally generated
relativistic degrees of freedom $\Delta N_{\rm eff}$ in the form of right
handed neutrinos which can be observed at future cosmology experiments.
Extending the model to a radiative Dirac seesaw scenario can also accommodate
dark matter and lepton anomalous magnetic moment.


title: Precursors of fatty alcohols in the ISM: Discovery of n-propanol

abstract:
  Theories on the origins of life propose that early cell membranes were
synthesized from amphiphilic molecules simpler than phospholipids such as fatty
alcohols. The discovery in the interstellar medium (ISM) of ethanolamine, the
simplest phospholipid head group, raises the question whether simple
amphiphilic molecules are also synthesized in space. We investigate whether
precursors of fatty alcohols are present in the ISM. For this, we have carried
out a spectral survey at 7, 3, 2 and 1 mm toward the Giant Molecular Cloud
G+0.693-0.027 located in the Galactic Center using the IRAM 30m and Yebes 40m
telescopes. Here, we report the detection in the ISM of the primary alcohol
n-propanol (in both conformers Ga-n-C3H7OH and Aa-n-C3H7OH), a precursor of
fatty alcohols. The derived column densities of n-propanol are (5.5+-0.4)x10^13
cm^-2 for the Ga conformer and (3.4+-0.3)x10^13 cm^-2 for the Aa conformer,
which imply molecular abundances of (4.1+-0.3)x10^-10 for Ga-n-C3H7OH and of
(2.5+-0.2)x10^-10 for Aa-n-C3H7OH. We also searched for the AGa conformer of
n-butanol (AGa-n-C4H9OH) without success yielding an upper limit to its
abundance of <4.1x10^-11. The inferred CH3OH:C2H5OH:C3H7OH:C4H9OH abundance
ratios go as 1:0.04:0.006:<0.0004 toward G+0.693-0.027, i.e. they decrease
roughly by one order of magnitude for increasing complexity. We also report the
detection of both syn and anti conformers of vinyl alcohol, with column
densities of (1.11+-0.08)x10^14 cm^-2 and (1.3+-0.4)x10^13 cm^-2, and
abundances of (8.2+-0.6)x10^-10 and (9.6+-3.0)x10^-11, respectively. The
detection of n-propanol, together with the recent discovery of ethanolamine in
the ISM, opens the possibility that precursors of lipids according to theories
of the origin of life, could have been brought to Earth from outer space.