title:
Barrier Bayesian Linear Regression: Online Learning of Control Barrier
  Conditions for Safety-Critical Control of Uncertain Systems

abstract:
  In this work, we consider the problem of designing a safety filter for a
nonlinear uncertain control system. Our goal is to augment an arbitrary
controller with a safety filter such that the overall closed-loop system is
guaranteed to stay within a given state constraint set, referred to as being
safe. For systems with known dynamics, control barrier functions (CBFs) provide
a scalar condition for determining if a system is safe. For uncertain systems,
robust or adaptive CBF certification approaches have been proposed. However,
these approaches can be conservative or require the system to have a particular
parametric structure. For more generic uncertain systems, machine learning
approaches have been used to approximate the CBF condition. These works
typically assume that the learning module is sufficiently trained prior to
deployment. Safety during learning is not guaranteed. We propose a barrier
Bayesian linear regression (BBLR) approach that guarantees safe online learning
of the CBF condition for the true, uncertain system. We assume that the error
between the nominal system and the true system is bounded and exploit the
structure of the CBF condition. We show that our approach can safely expand the
set of certifiable control inputs despite system and learning uncertainties.
The effectiveness of our approach is demonstrated in simulation using a
two-dimensional pendulum stabilization task.


title:
Stochastic Geometry-Based Low Latency Routing in Massive LEO Satellite
  Networks

abstract:
  In this paper, the routing in massive low earth orbit (LEO) satellite
networks is studied. When the satellite-to-satellite communication distance is
limited, we choose different relay satellites to minimize the latency in a
constellation at a constant altitude. Firstly, the global optimum solution is
obtained in the ideal scenario when there are available satellites at all the
ideal locations. Next, we propose a nearest neighbor search algorithm for
realistic (non-ideal) scenarios with a limited number of satellites. The
proposed algorithm can approach the global optimum solution under an ideal
scenario through a finite number of iterations and a tiny range of searches.
Compared with other routing strategies, the proposed algorithm shows
significant advantages in terms of latency. Furthermore, we provide two
approximation techniques that can give tight lower and upper bounds for the
latency of the proposed algorithm, respectively. Finally, the relationships
between latency and constellation height, satellites' number, and communication
distance are investigated.


title: On Maximum Weighted Nash Welfare for Binary Valuations

abstract:
  We consider the problem of fairly allocating indivisible goods to agents with
weights representing their entitlements. A natural rule in this setting is the
maximum weighted Nash welfare (MWNW) rule, which selects an allocation
maximizing the weighted product of the agents' utilities. We show that when
agents have binary valuations, a specific version of MWNW is resource- and
population-monotone, satisfies group-strategyproofness, and can be implemented
in polynomial time.


title:
A Learnable Variational Model for Joint Multimodal MRI Reconstruction
  and Synthesis

abstract:
  Generating multi-contrasts/modal MRI of the same anatomy enriches diagnostic
information but is limited in practice due to excessive data acquisition time.
In this paper, we propose a novel deep-learning model for joint reconstruction
and synthesis of multi-modal MRI using incomplete k-space data of several
source modalities as inputs. The output of our model includes reconstructed
images of the source modalities and high-quality image synthesized in the
target modality. Our proposed model is formulated as a variational problem that
leverages several learnable modality-specific feature extractors and a
multimodal synthesis module. We propose a learnable optimization algorithm to
solve this model, which induces a multi-phase network whose parameters can be
trained using multi-modal MRI data. Moreover, a bilevel-optimization framework
is employed for robust parameter training. We demonstrate the effectiveness of
our approach using extensive numerical experiments.


title:
The essential spectrum, norm, and spectral radius of abstract
  multiplication operators

abstract:
  Let $E$ be a complex Banach lattice and $T$ is an operator in the centrum
$Z(E)=\{T: |T|\le \lambda I \mbox{ for some } \lambda\}$ of $E$. Then the
essential norm $\|T\|_{e}$ of $T$ equals the essential spectral radius
$r_{e}(T)$ of $T$. We also prove $r_{e}(T)=\max\{\|T_{A^{d}}\|,
r_{e}(T_{A})\}$, where $T_{A}$ is the atomic part of $T$ and $T_{A^{d}}$ is the
non-atomic part of $T$. Moreover $r_{e}(T_{A})=\limsup_{\mathcal
F}\lambda_{a}$, where $\mathcal F$ is the Fr\'echet filter on the set $A$ of
all positive atoms in $E$ of norm one and $\lambda_{a}$ is given by
$T_{A}a=\lambda_{a}a$ for all $a\in A$.


title:
Silicon nanophotonic chips featuring Sagnac loop reflectors as a basis
  for advanced optical spectral filters

abstract:
  We present and investigate theoretically photonic integrated filters based on
2 Sagnac coupled loop reflectors (SLRs) that are arranged in a self-coupled
optical waveguide. We recently presented photonic integrated filters based on
coupled and cascaded SLRs. In this paper, we advance this field by
investigating a unique approach of employing coupled SLRs formed by
self-coupled waveguides. This allows us to achieve high performance filter
functions including Fano-like resonances and wavelength interleaving with a
simpler design and a higher fabrication tolerance by tailoring coherent mode
interference in the device. Our design takes into account the device
fabrication issues as well as the requirements for practical applications. As a
guide for practical device fabrication, an analysis of the impact of the
structural parameters and fabrication tolerance on each filter function is also
provided. The Fano-like resonances show a low insertion loss (IL) of 1.1 dB, a
high extinction ratio of 30.2 dB, and a high slope rate (SR) of 747.64 dB/nm.
The combination of low IL and high SR promises this device for Fano resonance
applications. Our device also can achieve wavelength de-interleaving function
with high fabrication tolerance which is advantageous for applications such as
optical interleavers that require a symmetric flat-top filter shape. Optical
de-interleavers and interleavers are key components for optical signal
multiplexing and demultiplexing for wavelength division multiplexing optical
communication systems. Versatile spectral responses with a simple design,
compact device footprint, and high fabrication tolerance make this approach
highly promising for flexible response shaping for many applications.


title:
Revisiting quantum relativistic effects from phase transition by
  catastrophe theory

abstract:
  In this paper we start from the Schr\"odinger equation to revisit some
classical quantum mechanics from the perspective of phase transition process.
Here the relativistic effect of particles moving at high speed can be regarded
as the phase transition process when the velocity variable increases.
Considering that the catastrophe models could describe qualitatively any phase
transition process, we adopt the simplest folding catastrophe type as the
potential function in the Schr\"odinger equation to obtain a revised
Schr\"odinger relativistic equation through the dimensionless analysis first,
and then further to derive out the steady-state Klein-Gordon equation and Dirac
relativistic equation gradually. These results reveal that the quantum
relativistic effect could be considered as the phase transition process, which
could be described by adopting the catastrophe models as the potential function
in the classical Schr\"odinger equation.


title: On the equilateral pentagonal central configurations

abstract:
  An equilateral pentagon is a polygon in the plane with five sides of equal
length. In this paper we classify the central configurations of the $5$-body
problem having the five bodies at the vertices of an equilateral pentagon with
an axis of symmetry. We prove that there are two unique classes of such
equilateral pentagons providing central configurations, one concave equilateral
pentagon and one convex equilateral pentagon, the regular one. A key point of
our proof is the use of rational parameterizations to transform the
corresponding equations, which involve square roots, into polynomial equations.


title: Federated Learning with Partial Model Personalization

abstract:
  We consider two federated learning algorithms for training partially
personalized models, where the shared and personal parameters are updated
either simultaneously or alternately on the devices. Both algorithms have been
proposed in the literature, but their convergence properties are not fully
understood, especially for the alternating variant. We provide convergence
analyses of both algorithms in the general nonconvex setting with partial
participation and delineate the regime where one dominates the other. Our
experiments on real-world image, text, and speech datasets demonstrate that (a)
partial personalization can obtain most of the benefits of full model
personalization with a small fraction of personal parameters, and, (b) the
alternating update algorithm often outperforms the simultaneous update
algorithm.


title:
Ligand Additivity and Divergent Trends in Two Types of Delocalization
  Errors from Approximate Density Functional Theory

abstract:
  Despite its widespread use, the predictive accuracy of density functional
theory (DFT) is hampered by delocalization errors, especially for correlated
systems such as transition-metal complexes. Two complementary tuning strategies
have been developed to reduce delocalization error: eliminating the global
curvature with respect to charge addition or removal, and computing a linear
response Hubbard U as a measure of local curvature at the metal center at fixed
charge and applying it to the transition-metal complex in a DFT+U framework. We
investigate the relationship between the two measures of delocalization error
as we manipulate the ligand field strength by varying the number of
strong-field ligands in a series of heteroleptic complexes or by geometrically
constraining the metal-ligand bond length in homoleptic octahedral complexes.
We show that across these sets of complexes with varying ligand fields, an
inverse relationship generally exists between global and local curvatures. We
find that effects of ligand substitution on both measures of delocalization are
typically additive, but the two quantities seldom coincide. The observation of
ligand additivity suggests opportunities for evaluating errors on homoleptic
complexes to infer corrections for lower-symmetry complexes.


title: Observation of Mesoscopic Clathrate Structures in Ethanol-Water Mixtures

abstract:
  Water-alcohol mixtures exhibit many abnormal physicochemical properties, the
origins of which remain controversial. Here we use transmission electron
microscopy (TEM), nanoparticle tracking analysis (NTA), and atomic force
microscopy (AFM) to study ethanol-water mixtures. TEM reveals mesoscopic
clathrate structures with water molecules forming a crystalline matrix hosting
a high density of tiny cells. The presence of these mesoscopic clathrate
structures is further supported by a refractive index of 1.27+-0.02 at 405 nm
measured via NTA and the hydrophilic nature of the mesoscopic structures
implied by AFM observations, explaining many long-standing puzzles related to
water-alcohol mixtures.


title: Exploring the Universality of Hadronic Jet Classification

abstract:
  The modeling of jet substructure significantly differs between Parton Shower
Monte Carlo (PSMC) programs. Despite this, we observe that machine learning
classifiers trained on different PSMCs learn nearly the same function. This
means that when these classifiers are applied to the same PSMC for testing,
they result in nearly the same performance. This classifier universality
indicates that a machine learning model trained on one simulation and tested on
another simulation (or data) will likely be optimal. Our observations are based
on detailed studies of shallow and deep neural networks applied to simulated
Lorentz boosted Higgs jet tagging at the LHC.


title: C^0 estimates for Hessian quotient equations on HKT manifolds

abstract:
  We show the $C^0$ estimate for solutions to Hessian quotient equations on
hyperK\"ahler with torsion manifolds without any additional assumption on its
hypercomplex structure by a clever use of the cone condition directly.


title:
Distinguishing between dark-matter interactions with gravitational-wave
  detectors

abstract:
  Ground-based gravitational-wave interferometers could directly probe the
existence of ultralight dark matter ($\mathcal{O}(10^{-14}-10^{-11})$ eV/$c^2$)
that couples to standard-model particles in the detectors. Recently, many
techniques have been developed to extract a variety of potential dark-matter
signals from noisy gravitational-wave data; however, little effort has gone
into ways to distinguish between types of dark matter that could directly
interact with the interferometers. In this work, we employ the Wiener filter to
follow-up candidate dark-matter interaction signals. The filter captures the
stochastic nature of these signals, and, in simulations, successfully
identifies which type of dark matter interacts with the interferometers. We
apply the Wiener filter to outliers that remained in the LIGO/Virgo/KAGRA
search for dark photons in data from the most recent observing (O3), and show
that they are consistent with noise disturbances. Our proof-of-concept analysis
demonstrates that the Wiener filter can be a powerful technique to confirm or
deny the presence of dark-matter interaction signals in gravitational-wave
data, and distinguish between scalar and vector dark-matter interactions.


title: Canonical Mean Filter for Almost Zero-Shot Multi-Task classification

abstract:
  The support set is a key to providing conditional prior for fast adaption of
the model in few-shot tasks. But the strict form of support set makes its
construction actually difficult in practical application. Motivated by ANIL, we
rethink the role of adaption in the feature extractor of CNAPs, which is a
state-of-the-art representative few-shot method. To investigate the role,
Almost Zero-Shot (AZS) task is designed by fixing the support set to replace
the common scheme, which provides corresponding support sets for the different
conditional prior of different tasks. The AZS experiment results infer that the
adaptation works little in the feature extractor. However, CNAPs cannot be
robust to randomly selected support sets and perform poorly on some datasets of
Meta-Dataset because of its scattered mean embeddings responded by the simple
mean operator. To enhance the robustness of CNAPs, Canonical Mean Filter (CMF)
module is proposed to make the mean embeddings intensive and stable in feature
space by mapping the support sets into a canonical form. CMFs make CNAPs robust
to any fixed support sets even if they are random matrices. This attribution
makes CNAPs be able to remove the mean encoder and the parameter adaptation
network at the test stage, while CNAP-CMF on AZS tasks keeps the performance
with one-shot tasks. It leads to a big parameter reduction. Precisely, 40.48\%
parameters are dropped at the test stage. Also, CNAP-CMF outperforms CNAPs in
one-shot tasks because it addresses inner-task unstable performance problems.
Classification performance, visualized and clustering results verify that CMFs
make CNAPs better and simpler.


title: Emergent phenomena from centre vortices

abstract:
  Quark confinement is perhaps the most important emergent property of the
theory of quantum chromodynamics. Herein we review some key aspects of centre
vortices in SU(3) lattice gauge theory. Starting from the original Monte Carlo
gauge fields, a vortex identification procedure yields vortex-removed and
vortex-only backgrounds. The comparison between the original `untouched' Monte
Carlo gauge fields and these so called vortex-modified ensembles has provided a
variety of results that support the notion that centre vortices are fundamental
to confinement in pure gauge theory. For the first time we perform direct
numerical tests of the response of centre vortices to the presence of dynamical
quarks in SU(3).


title:
Dark matter halos of luminous AGNs from galaxy-galaxy lensing with the
  HSC Subaru Strategic Program

abstract:
  We assess the dark matter halo masses of luminous AGNs over the redshift
range 0.2 to 1.2 using galaxy-galaxy lensing based on imaging data from the
Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). We measure the weak
lensing signal of a sample of 8882 AGNs constructed using HSC and WISE
photometry. The lensing detection around AGNs has a signal-to-noise ratio of
15. As expected, we find that the lensing mass profile is consistent with that
of massive galaxies ($M_{*}\sim 10.8~M_\odot$). Surprisingly, the lensing
signal remains unchanged when the AGN sample is split into low and high stellar
mass hosts. Specifically, we find that the excess surface density (ESD) of
AGNs, residing in galaxies with high stellar masses, significantly differs from
that of the control sample. We further fit a halo occupation distribution model
to the data to infer the posterior distribution of parameters including the
average halo mass. We find that the characteristic halo mass of the full AGN
population lies near the knee ($\rm log(M_h/h^{-1}M_{\odot})=12.0$) of the
stellar-to-halo mass relation (SHMR). Illustrative of the results given above,
the halo masses of AGNs residing in host galaxies with high stellar masses
(i.e., above the knee of the SHMR) falls below the calibrated SHMR while the
halo mass of the low stellar mass sample is more consistent with the
established SHMR. These results indicate that massive halos with higher
clustering bias tend to suppress AGN activity, probably due to the lack of
available gas.


title:
Search for 70 \mu eV Dark Photon Dark Matter with a
  Dielectrically-Loaded Multi-Wavelength Microwave Cavity

abstract:
  Microwave cavities have been deployed to search for bosonic dark matter
candidates with masses of a few $\mu$eV. However, the sensitivity of these
cavity detectors is limited by their volume, and the traditionally-employed
half-wavelength cavities suffer from a significant volume reduction at higher
masses. ADMX-Orpheus mitigates this issue by operating a tunable,
dielectrically-loaded cavity at a higher-order mode, which allows the detection
volume to remain large. The ADMX-Orpheus inaugural run excludes dark photon
dark matter with kinetic mixing angle $\chi > 10^{-13}$ between 65.5 $\mu$eV
(15.8 GHz) and 69.3 $\mu$eV (16.8GHz), marking the highest-frequency tunable
microwave cavity dark matter search to date.


title:
Testing the seesaw mechanisms via displaced right-handed neutrinos from
  a light scalar at the HL-LHC

abstract:
  We investigate the pair production of right-handed neutrinos from a light
$B-L$ scalar decays based at the $U(1)_{B-L}$ model. The $B-L$ scalar mixes to
the SM Higgs, and the physical scalar is required to be lighter than the
observed Higgs. The pair-produced right-handed neutrinos are predicted to be
long-lived by the type-I seesaw mechanism, and yield potential distinct
signatures such as displaced vertex and time-delayed leptons at the
CMS/ATLAS/LHCb, as well as signatures at the far detectors including the
CODEX-b, FACET, FASER, MoEDAL-MAPP and MATHUSLA. We analyse the sensitivity
reach at the HL-LHC for the RH neutrinos with masses from 2.5-30 GeV, showing
that the active-sterile mixing to muons can be probed $V_{\mu N} \sim 10^{-5}$
at the CMS/ATLAS/LHCb, and one magnitude lower at the MATHUSLA, reaching the
parameter space interesting for type-I seesaw mechanisms.


title:
Investigating the stability of mass transfer in neutron star-helium
  white dwarf binaries

abstract:
  Neutron star-helium white dwarf (NS+He WD) binaries are important
evolutionary products of close-orbit binary star systems. They are often
observed as millisecond pulsars and may continue evolving into ultra-compact
X-ray binaries (UCXBs) and continuous gravitational wave (GW) sources that will
be detected by space-borne GW observatories, such as LISA, TianQin and Taiji.
Nevertheless, the stability of NS+He WD binaries undergoing mass transfer is
not well studied and still under debate. In this paper, we model the evolution
of NS+He WD binaries with WD masses ranging from 0.17-0.45 $M_{\odot}$,
applying the detailed stellar evolution code mesa. Contrary to previous studies
based on hydrodynamics, we find that apparently all NS+He WD binaries undergo
stable mass transfer. We find for such UCXBs that the larger the WD mass, the
larger the maximum mass-transfer rate and the smaller the minimum orbital
period during their evolution. Finally, we demonstrate numerically and
analytically that there is a tight correlation between WD mass and GW frequency
for UCXBs, independent of NS mass.


title:
Sum of the Hurwitz-Lerch Zeta Function over Prime Numbers: Derivation
  and Evaluation

abstract:
  For the function $f(m,p,q,n)$, where $k,s,a$ general complex numbers and $q$
any positive integer, we establish the sum of values of the Hurwitz-Lerch zeta
function $\Phi(f(m,p,q,n),k,a)$ taken at prime numbers $n$. Special cases of
this sum are evaluated in terms of products of trigonometric functions and
Catalan's constant $K$.


title:
DiversiTree: Computing Diverse Sets of Near-Optimal Solutions to
  Mixed-Integer Optimization Problems

abstract:
  While most methods for solving mixed-integer optimization problems seek a
single optimal solution, finding a diverse set of near-optimal solutions can
often be more useful. State of the art methods for generating diverse
near-optimal solutions usually take a two-phase approach, first finding a set
of near-optimal solutions and then finding a diverse subset. In contrast, we
present a method of finding a set of diverse solutions by emphasizing diversity
within the search for near-optimal solutions. Specifically, within a
branch-and-bound framework, we investigate parameterized node selection rules
that explicitly consider diversity. Our results indicate that our approach
significantly increases diversity of the final solution set. When compared with
existing methods for finding diverse near-optimal sets, our method runs with
similar run-time as regular node selection methods and gives a diversity
improvement of up to 140%. In contrast, popular node selection rules such as
best-first search gives an improvement of no more than 40%. Further, we find
that our method is most effective when diversity is emphasized more in node
selection when deeper in the tree and when the solution set has grown large
enough.


title: Gravitationally Lensed Orphan Afterglows of Gamma-Ray Bursts

abstract:
  The cosmological nature of gamma-ray bursts (GRBs) implies that a small
portion of them could be gravitationally lensed by foreground objects during
their propagation. The gravitational lensing effect on the GRB prompt emission
and on-axis afterglows has been discussed, and some candidates have been found
in the literature. In this work, considering the high detection rate of GRB
orphan afterglows in future wide-field survey era, we investigate the
gravitationally lensed orphan afterglows in view of three lens models, i.e.,
the point-mass model, the singular isothermal sphere model, and the
Chang-Refsdal model. The structure of the GRB jet itself is also incorporated
in calculating the lensed afterglow light curves. It is found that lensed
optical/X-ray orphan afterglows in principle could be diagnosed through their
temporal characteristics, and the optical band is the best band to observe the
galaxy-lensed orphan afterglows. Moreover, the event rate for galaxy-lensed
orphan afterglows is estimated to be $\lesssim 0.7\ \text{yr}^{-1}$ for the
whole sky. The optimistic detection rates of the Wide Field Survey Telescope
(WFST) and Large Synoptic Survey Telescope (LSST) for galaxy-lensed orphan
afterglows in the optical band are $\lesssim 0.02\ \text{yr}^{-1}$ and
$\lesssim 0.08\ \text{yr}^{-1}$, respectively.


title:
Stability of Multi-dimensional Nonlinear Piezoelectric Beam with
  Viscoelastic Infinite Memory

abstract:
  The long time behavior of a kind of fully magnetic effected nonlinear
piezoelectric beam with viscoelastic infinite memory is considered. The
well-posedness of this nonlinear coupled PDEs system is showed by mean of the
semigroup theories and Banach fixed point theorem. Based on frequency domain
analysis, it is proved that the corresponding coupled linear system can be
indirectly stabilized exponentially by only one viscoelastic infinite memory
term, which is located on one equation of these strongly coupled PDEs. Then the
exponential decay of the solution to the nonlinear coupled PDEs' system is
established by the energy estimation method under certain condition.


title: Global stability of discretized Anosov flows

abstract:
  The goal of this article is to establish several general properties of a
somewhat large class of partially hyperbolic diffeomorphisms called
\emph{discretized Anosov flows}. A general definition for these systems is
presented and is proven to be equivalent with the definition introduced in
[BFFP19], as well as with the notion of flow type partially hyperbolic
diffeomorphisms introduced in [BFT20].
  The set of discretized Anosov flows is shown to be $C^1$ open and closed
inside the set of partially hyperbolic diffeomorphisms. Every discretized
Anosov flow is proven to be dynamically coherent and plaque expansive. Unique
integrability of the center bundle is shown to happen for whole connected
components, notably the ones containing the time 1 map of an Anosov flow. For
general connected components, a result on uniqueness of invariant foliation is
obtained.
  Similar results are seen to happen for partially hyperbolic systems admitting
a uniformly compact center foliation extending the studies initiated in [BB16].


title: Matrix multiplication via matrix groups

abstract:
  In 2003, Cohn and Umans proposed a group-theoretic approach to bounding the
exponent of matrix multiplication. Previous work within this approach ruled out
certain families of groups as a route to obtaining $\omega = 2$, while other
families of groups remain potentially viable. In this paper we turn our
attention to matrix groups, whose usefulness within this framework was
relatively unexplored.
  We first show that groups of Lie type cannot prove $\omega=2$ within the
group-theoretic approach. This is based on a representation-theoretic argument
that identifies the second-smallest dimension of an irreducible representation
of a group as a key parameter that determines its viability in this framework.
Our proof builds on Gowers' result concerning product-free sets in quasirandom
groups. We then give another barrier that rules out certain natural matrix
group constructions that make use of subgroups that are far from being
self-normalizing.
  Our barrier results leave open several natural paths to obtain $\omega = 2$
via matrix groups. To explore these routes we propose working in the continuous
setting of Lie groups, in which we develop an analogous theory. Obtaining the
analogue of $\omega=2$ in this potentially easier setting is a key challenge
that represents an intermediate goal short of actually proving $\omega = 2$. We
give two constructions in the continuous setting, each of which evades one of
our two barriers.


title: IA-GCN: Interactive Graph Convolutional Network for Recommendation

abstract:
  Recently, Graph Convolutional Network (GCN) has become a novel state-of-art
for Collaborative Filtering (CF) based Recommender Systems (RS). It is a common
practice to learn informative user and item representations by performing
embedding propagation on a user-item bipartite graph, and then provide the
users with personalized item suggestions based on the representations. Despite
effectiveness, existing algorithms neglect precious interactive features
between user-item pairs in the embedding process. When predicting a user's
preference for different items, they still aggregate the user tree in the same
way, without emphasizing target-related information in the user neighborhood.
Such a uniform aggregation scheme easily leads to suboptimal user and item
representations, limiting the model expressiveness to some extent.
  In this work, we address this problem by building bilateral interactive
guidance between each user-item pair and proposing a new model named IA-GCN
(short for InterActive GCN). Specifically, when learning the user
representation from its neighborhood, we assign higher attention weights to
those neighbors similar to the target item. Correspondingly, when learning the
item representation, we pay more attention to those neighbors resembling the
target user. This leads to interactive and interpretable features, effectively
distilling target-specific information through each graph convolutional
operation. Our model is built on top of LightGCN, a state-of-the-art GCN model
for CF, and can be combined with various GCN-based CF architectures in an
end-to-end fashion. Extensive experiments on three benchmark datasets
demonstrate the effectiveness and robustness of IA-GCN.


title: From PHY to QoE: A Parameterized Framework Design

abstract:
  The rapid development of 5G communication technology has given birth to
various real-time broadband communication services, such as augmented reality
(AR), virtual reality (VR) and cloud games. Compared with traditional services,
consumers tend to focus more on their subjective experience when utilizing
these services. In the meantime, the problem of power consumption is
particularly prominent in 5G and beyond. The traditional design of physical
layer (PHY) receiver is based on maximizing spectrum efficiency or minimizing
error, but this will no longer be the best after considering energy efficiency
and these new-coming services. Therefore, this paper uses quality of experience
(QoE) as the optimization criterion of the PHY algorithm. In order to establish
the relationship between PHY and QoE, this paper models the end-to-end
transmission from UE perspective and proposes a five-layer framework based on
hierarchical analysis method, which includes system-level model, bitstream
model, packet model, service quality model and experience quality model. Real
data in 5G network is used to train the parameters of the involved models for
each type of services, respectively. The results show that the PHY algorithms
can be simplified in perspective of QoE.


title: Does the Market of Citations Reward Reproducible Work?

abstract:
  The field of bibliometrics, studying citations and behavior, is critical to
the discussion of reproducibility. Citations are one of the primary incentive
and reward systems for academic work, and so we desire to know if this
incentive rewards reproducible work. Yet to the best of our knowledge, only one
work has attempted to look at this combined space, concluding that
non-reproducible work is more highly cited. We show that answering this
question is more challenging than first proposed, and subtle issues can inhibit
a robust conclusion. To make inferences with more robust behavior, we propose a
hierarchical Bayesian model that incorporates the citation rate over time,
rather than the total number of citations after a fixed amount of time. In
doing so we show that, under current evidence the answer is more likely that
certain fields of study such as Medicine and Machine Learning (ML) do correlate
reproducible works with more citations, but other fields appear to have no
relationship. Further, we find that making code available and thoroughly
referencing prior works appear to also positively correlate with increased
citations. Our code and data can be found at
https://github.com/EdwardRaff/ReproducibleCitations .


title:
PharmMT: A Neural Machine Translation Approach to Simplify Prescription
  Directions

abstract:
  The language used by physicians and health professionals in prescription
directions includes medical jargon and implicit directives and causes much
confusion among patients. Human intervention to simplify the language at the
pharmacies may introduce additional errors that can lead to potentially severe
health outcomes. We propose a novel machine translation-based approach,
PharmMT, to automatically and reliably simplify prescription directions into
patient-friendly language, thereby significantly reducing pharmacist workload.
We evaluate the proposed approach over a dataset consisting of over 530K
prescriptions obtained from a large mail-order pharmacy. The end-to-end system
achieves a BLEU score of 60.27 against the reference directions generated by
pharmacists, a 39.6% relative improvement over the rule-based normalization.
Pharmacists judged 94.3% of the simplified directions as usable as-is or with
minimal changes. This work demonstrates the feasibility of a machine
translation-based tool for simplifying prescription directions in real-life.


title:
Marvelous Agglutinative Language Effect on Cross Lingual Transfer
  Learning

abstract:
  As for multilingual language models, it is important to select languages for
training because of the curse of multilinguality. (Conneau et al., 2020). It is
known that using languages with similar language structures is effective for
cross lingual transfer learning (Pires et al., 2019). However, we demonstrate
that using agglutinative languages such as Korean is more effective in cross
lingual transfer learning. This is a great discovery that will change the
training strategy of cross lingual transfer learning.


title: Scaling Bockchain with Adaptivity

abstract:
  This paper presents Balloon, a scalable blockchain consensus protocol which
could dynamically adapt its performance to the overall computation power
change. Balloon is based on a parallel chain architecture combined with a
greedy heaviest sub-chain selection strategy. It adopts an inovative block
sampling approach to assess the change of block generation rate in the network.
By introducing view change mechanism, Balllon is able to dynamically adjust the
number of parallel sub-chains. Balloon redefines the concept of block subtree
weight with view change in consideration, so that a total order of blocks could
be obtained safely. To deal with rapidly increasing block generation rate in
the blockchain network, participants of previous Nakamoto-style protocols are
required to continuously increase their mining difficulty so as to maintain an
expected security gurantee. Balloon, however, could accomadate a fixed
difficulty setup and assign superfluous block processing capability to new
sub-chains, which makes it more open and also economical.


title:
A no-go theorem for scalar fields with couplings from Ginzburg-Landau
  models

abstract:
  Recently Hod proved a no-go theorem that static scalar fields cannot form
spherically symmetric boson stars in the asymptotically flat background. On the
other side, scalar fields can be coupled to the gradient according to
next-to-leading order Ginzburg-Landau models. In the present work, we extend
Hod's discussions by considering couplings between static scalar fields and the
field gradient. For a non-negative coupling parameter, we show that there is no
asymptotically flat spherically symmetric boson stars made of coupled static
scalar fields.


title:
Correspondence of topological classification between quantum graph extra
  dimension and topological matter

abstract:
  In this paper, we study a classification of boundary conditions with
symmetries for a five-dimensional Dirac fermion on a quantum graph. We find
that there is a nontrivial correspondence between the classification of
boundary conditions at the vertex on the quantum graph and that of the
symmetry-protected topological phases of gapped free-fermion systems, which are
classified into ten symmetry classes by the time-reversal symmetry,
particle-hole symmetry and chiral symmetry. A Hermitian matrix which specifies
the boundary conditions in our model corresponds to a zero-dimensional
Hamiltonian in the gapped free-fermion systems. Furthermore, symmetries in our
model give the condition that restricts the parameter space of the boundary
conditions. These conditions are identical to the ones in the gapped
free-fermion systems that the Hamiltonian with the symmetries should satisfy.
We also show that the topological number for each symmetry class in our model
implies the presence of 4d massless fields localized at the vertex of the
quantum graph, like gapless boundary states for the free-fermion systems from
the bulk-boundary correspondence.


title:
LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in
  Integrated Silicon-Photonic Neural Networks

abstract:
  Compared to electronic accelerators, integrated silicon-photonic neural
networks (SP-NNs) promise higher speed and energy efficiency for emerging
artificial-intelligence applications. However, a hitherto overlooked problem in
SP-NNs is that the underlying silicon photonic devices suffer from intrinsic
optical loss and crosstalk noise, the impact of which accumulates as the
network scales up. Leveraging precise device-level models, this paper presents
the first comprehensive and systematic optical loss and crosstalk modeling
framework for SP-NNs. For an SP-NN case study with two hidden layers and 1380
tunable parameters, we show a catastrophic 84% drop in inferencing accuracy due
to optical loss and crosstalk noise.


title:
Solvable Leibniz superalgebras whose the nilradical has the
  characteristic sequence $(n-1,1 \ | \ m)$ and nilindex $n+m$

abstract:
  In this paper, we give a classification of solvable Leibniz superalgebras
whose nilradical is a nilpotent Leibniz superalgebra with nilindex $n + m$ and
characteristic sequence $(n-1, 1 \ | \ m)$.


title:
Electrically tunable moir\'e magnetism in twisted double bilayer
  antiferromagnets

abstract:
  The introduction of moir\'e superlattices to electronic materials can
dramatically alter electronic properties, promising emergent correlated and
topological phenomena. Its first demonstration in van der Waals magnets
exhibited noncollinear states and domain structures with, however, limited
manipulation. Here, we fabricated twisted double antiferromagnetic bilayer
CrI3, and by magneto-optical Kerr effect microscopy demonstrate the coexistence
of antiferromagnetic and ferromagnetic orders with nonzero net magnetization,
which is the hallmark of moir\'e magnetism. Such magnetic state exhibits
nonmonotonic temperature dependence and extends over a wide range of twist
angles with transitions at ~0{\deg} and above 20{\deg}. We further demonstrate
voltage-assisted magnetic switching and the linear magnetoelectric effect. The
observed nontrivial magnetic states and unprecedented control by twist angle,
temperature and electrical gating are supported by the simulated phase diagram
of the moir\'e magnetism. Our results illustrate the rich behaviors of twisted
antiferromagnets and the control over them.


title:
Reusing the Task-specific Classifier as a Discriminator:
  Discriminator-free Adversarial Domain Adaptation

abstract:
  Adversarial learning has achieved remarkable performances for unsupervised
domain adaptation (UDA). Existing adversarial UDA methods typically adopt an
additional discriminator to play the min-max game with a feature extractor.
However, most of these methods failed to effectively leverage the predicted
discriminative information, and thus cause mode collapse for generator. In this
work, we address this problem from a different perspective and design a simple
yet effective adversarial paradigm in the form of a discriminator-free
adversarial learning network (DALN), wherein the category classifier is reused
as a discriminator, which achieves explicit domain alignment and category
distinguishment through a unified objective, enabling the DALN to leverage the
predicted discriminative information for sufficient feature alignment.
Basically, we introduce a Nuclear-norm Wasserstein discrepancy (NWD) that has
definite guidance meaning for performing discrimination. Such NWD can be
coupled with the classifier to serve as a discriminator satisfying the
K-Lipschitz constraint without the requirements of additional weight clipping
or gradient penalty strategy. Without bells and whistles, DALN compares
favorably against the existing state-of-the-art (SOTA) methods on a variety of
public datasets. Moreover, as a plug-and-play technique, NWD can be directly
used as a generic regularizer to benefit existing UDA algorithms. Code is
available at https://github.com/xiaoachen98/DALN.


title: Infusing Knowledge from Wikipedia to Enhance Stance Detection

abstract:
  Stance detection infers a text author's attitude towards a target. This is
challenging when the model lacks background knowledge about the target. Here,
we show how background knowledge from Wikipedia can help enhance the
performance on stance detection. We introduce Wikipedia Stance Detection BERT
(WS-BERT) that infuses the knowledge into stance encoding. Extensive results on
three benchmark datasets covering social media discussions and online debates
indicate that our model significantly outperforms the state-of-the-art methods
on target-specific stance detection, cross-target stance detection, and
zero/few-shot stance detection.


title:
Data-Driven Evaluation of Training Action Space for Reinforcement
  Learning

abstract:
  Training action space selection for reinforcement learning (RL) is
conflict-prone due to complex state-action relationships. To address this
challenge, this paper proposes a Shapley-inspired methodology for training
action space categorization and ranking. To reduce exponential-time shapley
computations, the methodology includes a Monte Carlo simulation to avoid
unnecessary explorations. The effectiveness of the methodology is illustrated
using a cloud infrastructure resource tuning case study. It reduces the search
space by 80\% and categorizes the training action sets into dispensable and
indispensable groups. Additionally, it ranks different training actions to
facilitate high-performance yet cost-efficient RL model design. The proposed
data-driven methodology is extensible to different domains, use cases, and
reinforcement learning algorithms.


title: Latent Trait Item Response Models for Continuous Responses

abstract:
  A general framework of latent trait item response models for continuous
responses is given. In contrast to classical test theory models, which
traditionally distinguish between true scores and error scores, the responses
are clearly linked to latent traits. It is shown that classical test theory
models can be derived as special cases but the model class is much wider. It
provides, in particular, appropriate modelling of responses that are restricted
in some way, for example, if responses are positive or are restricted to an
interval. Restrictions of this sort are easily incorporated in the modeling
framework. Restriction to an interval is typically ignored in common models
yielding inappropriate models, for example, when modeling Likert-type data. The
model also extends common response time models, which can be treated as special
cases. Properties of the model class are derived and the role of the total
score is investigated, which leads to a modified total score. Several
applications illustrate the use of the model including an example, in which
covariates that may modify the response are taken into account.


title:
From 2D Images to 3D Model:Weakly Supervised Multi-View Face
  Reconstruction with Deep Fusion

abstract:
  We consider the problem of Multi-view 3D Face Reconstruction (MVR) with
weakly supervised learning that leverages a limited number of 2D face images
(e.g. 3) to generate a high-quality 3D face model with very light annotation.
Despite their encouraging performance, present MVR methods simply concatenate
multi-view image features and pay less attention to critical areas (e.g. eye,
brow, nose and mouth). To this end, we propose a novel model called Deep Fusion
MVR (DF-MVR) and design a multi-view encoding to a single decoding framework
with skip connections, able to extract, integrate, and compensate deep features
with attention from multi-view images. In addition, we develop a multi-view
face parse network to learn, identify, and emphasize the critical common face
area. Finally, though our model is trained with a few 2D images, it can
reconstruct an accurate 3D model even if one single 2D image is input. We
conduct extensive experiments to evaluate various multi-view 3D face
reconstruction methods. Our proposed model attains superior performance,
leading to 11.4% RMSE improvement over the existing best weakly supervised
MVRs. Source codes are available in the supplementary materials.


title:
Communication-Efficient Cluster Federated Learning in Large-scale
  Peer-to-Peer Networks

abstract:
  A traditional federated learning (FL) allows clients to collaboratively train
a global model under the coordination of a central server, which sparks great
interests in exploiting the private data distributed on clients. However, once
the central server suffers from a single point of failure, it will lead to
system crash. In addition, FL usually involves a large number of clients, which
requires expensive communication costs. These challenges inspire a
communication-efficient design of decentralized FL. In this paper, we propose
an efficient and privacy-preserving global model training protocol in the
context of FL in large-scale peer-to-peer networks, CFL. The proposed CFL
protocol aggregates local contributions hierarchically by a cluster-based
aggregation mode, as well as a leverged authenticated encryption scheme to
ensure the security communication, whose key is distributed by a modified
secure communication key establishment protocol. Theoretical analyses show that
CFL guarantees the privacy of local model update parameters, as well as
integrity and authenticity under the widespread internal semi-honest and
external malicious threat models. In particular, the proposed key revocation
based on public voting can effectively defense against external adversaries
hijacking honest participants to ensure the confidentiality of the
communication keys. Moreover, the modified secure communication key
establishment protocol indeed achieves high network connectivity probability to
ensure transmission security of the system.


title: Monads on multiprojective spaces and associated vector bundles

abstract:
  In this paper we establish the existence of monads on Cartesian products of
projective spaces. We construct vector bundles associated to monads on
$\mathbb{P}^{a_1}\times\mathbb{P}^{a_1}\times\mathbb{P}^{a_2}\times\mathbb{P}^{a_2}\times\cdots\times\mathbb{P}^{a_n}\times\mathbb{P}^{a_n}$.
Once the monad on $X$ exists the next natural question is if the cohomology
vector bundle associated to these monads are simple or not. We study these
vector bundles associated to monads on $X$ and prove their stability and
simplicity.


title:
Decomposition-based Generation Process for Instance-Dependent Partial
  Label Learning

abstract:
  Partial label learning (PLL) is a typical weakly supervised learning problem,
where each training example is associated with a set of candidate labels among
which only one is true. Most existing PLL approaches assume that the incorrect
labels in each training example are randomly picked as the candidate labels and
model the generation process of the candidate labels in a simple way. However,
these approaches usually do not perform as well as expected due to the fact
that the generation process of the candidate labels is always
instance-dependent. Therefore, it deserves to be modeled in a refined way. In
this paper, we consider instance-dependent PLL and assume that the generation
process of the candidate labels could decompose into two sequential parts,
where the correct label emerges first in the mind of the annotator but then the
incorrect labels related to the feature are also selected with the correct
label as candidate labels due to uncertainty of labeling. Motivated by this
consideration, we propose a novel PLL method that performs Maximum A
Posterior(MAP) based on an explicitly modeled generation process of candidate
labels via decomposed probability distribution models. Experiments on benchmark
and real-world datasets validate the effectiveness of the proposed method.


title: A new condition on the Jones polynomial of a fibered positive link

abstract:
  We give a new upper bound on the maximum degree of the Jones polynomial of a
fibered positive link. In particular, we prove that the maximum degree of the
Jones polynomial of a fibered positive knot is at most four times the minimum
degree. Using this result, we can complete the classification of all knots of
crossing number $\leq 12$ as positive or not positive, by showing that the
seven remaining knots for which positivity was unknown are not positive. That
classification was also done independently at around the same time by
Stoimenow.


title:
Enhanced exemplar autoencoder with cycle consistency loss in any-to-one
  voice conversion

abstract:
  Recent research showed that an autoencoder trained with speech of a single
speaker, called exemplar autoencoder (eAE), can be used for any-to-one voice
conversion (VC). Compared to large-scale many-to-many models such as AutoVC,
the eAE model is easy and fast in training, and may recover more details of the
target speaker.
  To ensure VC quality, the latent code should represent and only represent
content information. However, this is not easy to attain for eAE as it is
unaware of any speaker variation in model training. To tackle the problem, we
propose a simple yet effective approach based on a cycle consistency loss.
Specifically, we train eAEs of multiple speakers with a shared encoder, and
meanwhile encourage the speech reconstructed from any speaker-specific decoder
to get a consistent latent code as the original speech when cycled back and
encoded again. Experiments conducted on the AISHELL-3 corpus showed that this
new approach improved the baseline eAE consistently. The source code and
examples are available at the project page: http://project.cslt.org/.


title:
AdvEst: Adversarial Perturbation Estimation to Classify and Detect
  Adversarial Attacks against Speaker Identification

abstract:
  Adversarial attacks pose a severe security threat to the state-of-the-art
speaker identification systems, thereby making it vital to propose
countermeasures against them. Building on our previous work that used
representation learning to classify and detect adversarial attacks, we propose
an improvement to it using AdvEst, a method to estimate adversarial
perturbation. First, we prove our claim that training the representation
learning network using adversarial perturbations as opposed to adversarial
examples (consisting of the combination of clean signal and adversarial
perturbation) is beneficial because it eliminates nuisance information. At
inference time, we use a time-domain denoiser to estimate the adversarial
perturbations from adversarial examples. Using our improved representation
learning approach to obtain attack embeddings (signatures), we evaluate their
performance for three applications: known attack classification, attack
verification, and unknown attack detection. We show that common attacks in the
literature (Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD),
Carlini-Wagner (CW) with different Lp threat models) can be classified with an
accuracy of ~96%. We also detect unknown attacks with an equal error rate (EER)
of ~9%, which is absolute improvement of ~12% from our previous work.


title: Prediction of COVID-19 using chest X-ray images

abstract:
  COVID-19, also known as Novel Coronavirus Disease, is a highly contagious
disease that first surfaced in China in late 2019. SARS-CoV-2 is a coronavirus
that belongs to the vast family of coronaviruses that causes this disease. The
sickness originally appeared in Wuhan, China in December 2019 and quickly
spread to over 213 nations, becoming a global pandemic. Fever, dry cough, and
tiredness are the most typical COVID-19 symptoms. Aches, pains, and difficulty
breathing are some of the other symptoms that patients may face. The majority
of these symptoms are indicators of respiratory infections and lung
abnormalities, which radiologists can identify. Chest x-rays of COVID-19
patients seem similar, with patchy and hazy lungs rather than clear and healthy
lungs. On x-rays, however, pneumonia and other chronic lung disorders can
resemble COVID-19. Trained radiologists must be able to distinguish between
COVID-19 and an illness that is less contagious. Our AI algorithm seeks to give
doctors a quantitative estimate of the risk of deterioration. So that patients
at high risk of deterioration can be triaged and treated efficiently. The
method could be particularly useful in pandemic hotspots when screening upon
admission is important for allocating limited resources like hospital beds.


title:
Resource Consumption for Supporting Federated Learning in Wireless
  Networks

abstract:
  Federated learning (FL) has recently become one of the hottest focuses in
wireless edge networks with the ever-increasing computing capability of user
equipment (UE). In FL, UEs train local machine learning models and transmit
them to an aggregator, where a global model is formed and then sent back to
UEs. In wireless networks, local training and model transmission can be
unsuccessful due to constrained computing resources, wireless channel
impairments, bandwidth limitations, etc., which degrades FL performance in
model accuracy and/or training time. Moreover, we need to quantify the benefits
and cost of deploying edge intelligence, as model training and transmission
consume certain amount of resources. Therefore, it is imperative to deeply
understand the relationship between FL performance and multiple-dimensional
resources. In this paper, we construct an analytical model to investigate the
relationship between the FL model accuracy and consumed resources in FL
empowered wireless edge networks. Based on the analytical model, we explicitly
quantify the model accuracy, available computing resources and communication
resources. Numerical results validate the effectiveness of our theoretical
modeling and analysis, and demonstrate the trade-off between the communication
and computing resources for achieving a certain model accuracy.


title:
Defense against Adversarial Attacks on Hybrid Speech Recognition using
  Joint Adversarial Fine-tuning with Denoiser

abstract:
  Adversarial attacks are a threat to automatic speech recognition (ASR)
systems, and it becomes imperative to propose defenses to protect them. In this
paper, we perform experiments to show that K2 conformer hybrid ASR is strongly
affected by white-box adversarial attacks. We propose three defenses--denoiser
pre-processor, adversarially fine-tuning ASR model, and adversarially
fine-tuning joint model of ASR and denoiser. Our evaluation shows denoiser
pre-processor (trained on offline adversarial examples) fails to defend against
adaptive white-box attacks. However, adversarially fine-tuning the denoiser
using a tandem model of denoiser and ASR offers more robustness. We evaluate
two variants of this defense--one updating parameters of both models and the
second keeping ASR frozen. The joint model offers a mean absolute decrease of
19.3\% ground truth (GT) WER with reference to baseline against fast gradient
sign method (FGSM) attacks with different $L_\infty$ norms. The joint model
with frozen ASR parameters gives the best defense against projected gradient
descent (PGD) with 7 iterations, yielding a mean absolute increase of 22.3\% GT
WER with reference to baseline; and against PGD with 500 iterations, yielding a
mean absolute decrease of 45.08\% GT WER and an increase of 68.05\% adversarial
target WER.


title: Reliable Visualization for Deep Speaker Recognition

abstract:
  In spite of the impressive success of convolutional neural networks (CNNs) in
speaker recognition, our understanding to CNNs' internal functions is still
limited. A major obstacle is that some popular visualization tools are
difficult to apply, for example those producing saliency maps. The reason is
that speaker information does not show clear spatial patterns in the
temporal-frequency space, which makes it hard to interpret the visualization
results, and hence hard to confirm the reliability of a visualization tool. In
this paper, we conduct an extensive analysis on three popular visualization
methods based on CAM: Grad-CAM, Score-CAM and Layer-CAM, to investigate their
reliability for speaker recognition tasks. Experiments conducted on a
state-of-the-art ResNet34SE model show that the Layer-CAM algorithm can produce
reliable visualization, and thus can be used as a promising tool to explain
CNN-based speaker models. The source code and examples are available in our
project page: http://project.cslt.org/.


title:
Lightweight starshade position sensing with convolutional neural
  networks and simulation-based inference

abstract:
  Starshades are a leading technology to enable the direct detection and
spectroscopic characterization of Earth-like exoplanets. To keep the starshade
and telescope aligned over large separations, reliable sensing of the peak of
the diffracted light of the occluded star is required. Current techniques rely
on image matching or model fitting, both of which put substantial computational
burdens on resource-limited spacecraft computers. We present a lightweight
image processing method based on a convolutional neural network paired with a
simulation-based inference technique to estimate the position of the spot of
Arago and its uncertainty. The method achieves an accuracy of a few centimeters
across the entire pupil plane, while only requiring 1.6 MB in stored data
structures and 5.3 MFLOPs (million floating point operations) per image at test
time. By deploying our method at the Princeton Starshade Testbed, we
demonstrate that the neural network can be trained on simulated images and used
on real images, and that it can successfully be integrated in the control
system for closed-loop formation flying.


title:
Durable and Recoverable Hydrophilicity of Polyethylene Terephthalate
  Fabric Prepared with Plasma Selective Etching

abstract:
  Durable delustered PET (PET-TiO2) fabrics super hydrophilic surface has been
obtained by plasma selecting etching. The aging effect of their hydrophilicity
after plasma treatment has been investigated with storage time. After Ar/O2
radio frequency (RF) plasma treatment for only 7 min, PET-TiO2 fabric showed
water contact angle of 0o. After 10 month storage time, it keeps its water
contact angle below 75.7o. Further more, with Xenon light irradiation for 10
min, it is firstly found that it has well-recovered water contact angle to
5{\deg}. While the contact angle of PET fabric for 7 min returns to 123.0{\deg}
and its hydrophilicity disappeared almost completely and showed no response to
Xenon light irradiation. The water absorption rate of 7 min plasma treated
PET-TiO2 fabric increased by 57.54%. By field emission scanning electron
microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS) and X-ray
diffraction analysis(XRD) measurement, waviness structure of humps and ridges
with irregular particles or pits were found on the plasma treated PET-TiO2
fabric surface and increased Ti atomic percentage was observed. It is verified
that TiO2 particles inside PET-TiO2 fiber have been exposed to its surface by
plasma selective etching of its organic component. It suppresses the aging
effect and is characterized with durable and recoverable hydrophilicity. This
one step, quick, green and cost-resonable manufacture method has a pratical
application for durable superhydrophilic surfaces.


title:
Hierarchical Softmax for End-to-End Low-resource Multilingual Speech
  Recognition

abstract:
  Low resource speech recognition has been long-suffering from insufficient
training data. While neighbour languages are often used as assistant training
data, it would be difficult for the model to induct similar units (character,
subword, etc.) across the languages. In this paper, we assume similar units in
neighbour language share similar term frequency and form a Huffman tree to
perform multi-lingual hierarchical Softmax decoding. During decoding, the
hierarchical structure can benefit the training of low-resource languages.
Experimental results show the effectiveness of our method.


title:
Definition and evolution of transverse momentum dependent distribution
  of twist-three

abstract:
  We present an in-depth analysis of transverse momentum dependent (TMD)
distributions of twist-three. In particular, we focus on evolution equations,
symmetry relations, parameterization, interpretation, small-b asymptotic
behaviour and the structure of singularities. The starting point of discussion
are the correlators with the definite TMD-twist. By considering suitable
combinations of these correlators, we introduce physical TMD distribution of
twist-three that can be used for practical applications. We also establish
relations with generic TMD distribution of twist-three, and demonstrate that
their evolution equations are autonomous in the large-$N_c$ limit.


title: How to Design Autonomous Service Level Agreements for 6G

abstract:
  With the growing demand for network connectivity and diversity of network
applications, one primary challenge that network service providers are facing
is managing the commitments for Service Level Agreements~(SLAs). Service
providers typically monitor SLAs for management tasks such as improving their
service quality, customer billing and future network planning. Network service
customers, on their side, monitor SLAs to optimize network usage and apply,
when required, penalties related to service failures. In future 6G networks,
critical network applications such as remote surgery and connected vehicles
will require these SLAs to be more dynamic, flexible, and automated to match
their diverse requirements on network services. Moreover, these SLAs should be
transparent to all stakeholders to address the trustworthiness on network
services and service providers required by critical applications. Currently,
there is no standardised method to immutably record and audit SLAs, leading to
challenges in aspects such as SLA enforcement and accountability -- traits
essential for future network applications. This work explores new requirements
for future service contracts, that is, on the evolution of SLAs. Based on those
new requirements, we propose an end to end layered SLA architecture leveraging
Distributed Ledger Technology (DLT) and smart contracts. Our architecture is
inheritable by an existing telco-application layered architectural frameworks
to support future SLAs. We also discuss some limitations of DLT and smart
contracts and provide several directions of future studies.


title:
eGEN: An Energy-saving Modeling Language and Code Generator for
  Location-sensing of Mobile Apps

abstract:
  The demand for reducing the energy consumption of location-based applications
has increased in recent years. The abnormal battery-draining behavior of GPS
makes it difficult for the developers to decide on battery optimization during
the development phase directly. It will reduce the burden on developers if
battery-saving strategies are considered early, and relevant battery-aware code
is generated from the design phase artifacts. Therefore, we aim to develop tool
support, eGEN, to specify and create native location-based mobile apps. eGEN
consists of Domain-specific Modeling Language (DSML) and a code generator for
location-sensing. It is developed using Xtext and Xtend as an Eclipse plug-in,
and currently, it supports native Android apps. eGEN is evaluated through
controlled experiments by instrumenting the generated code in five
location-based open-source Android applications. The experimental results show
4.35 minutes of average GPS reduction per hour and 188 mA of average reduction
in battery consumption while showing only 97 meters degrade in location
accuracy over 3 kilometers of a cycling path. Hence, we believe that code
generated by eGEN would help developers to balance between energy and accuracy
requirements of location-based applications. The source code, documentation,
tool demo video, and tool installation video are available at
https://github.com/Kowndinya2000/egen.


title:
Smooth coarse-graining and colored noise dynamics in stochastic
  inflation

abstract:
  We consider stochastic inflation coarse-grained using a general class of
exponential filters. Such a coarse-graining prescription gives rise to
inflaton-Langevin equations sourced by colored noise that is correlated in
$e$-fold time. The dynamics are studied first in slow-roll for simple
potentials using first-order perturbative, semi-analytical calculations which
are later compared to numerical simulations. Subsequent calculations are
performed using an exponentially correlated noise which appears as a leading
order correction to the full slow-roll noise correlation functions of the type
$\big\langle \xi(N)\xi(N') \big\rangle_{(n)}\sim\left( \cosh\left[ n(N-N')+1
\right] \right)^{-1}$. We find that the power spectrum of curvature
perturbations $\mathcal{P}_{\zeta}$ is suppressed at early $e$-folds, with the
suppression controlled by $n$. Furthermore, we use the leading order,
exponentially correlated noise and perform a first passage time analysis to
compute the statistics of the stochastic $e$-fold distribution $\mathcal{N}$
and derive an approximate expression for the mean number of $e$-folds
$\big\langle \mathcal{N} \big\rangle$. Comparing analytical results with
numerical simulations of the inflaton dynamics, we show that the leading order
noise correlation function can be used as a very good approximation of the
exact noise, the latter being more difficult to simulate.


title: Ghost diffraction: A spatial statistical approach

abstract:
  We report the reconstruction of a transparency image in Ghost diffraction
scheme using a statistical optics approach. This is implemented by using a
static diffuser rather than a pseudo thermal light source with a rotating
diffuser. The experimental implementation makes use of spatial ergodicity and
spatial stationarity for spatially distributed random fields. A strategy to
realize the Ghost diffraction scheme through spatial intensity correlation with
a phase retrieval algorithm permits reconstruction of the transparency.


title: Reaction-Path Statistical Mechanics of Enzymatic Kinetics

abstract:
  We introduce a reaction-path statistical mechanics formalism based on the
principle of large deviations to quantify the kinetics of single-molecule
enzymatic reaction processes under the Michaelis-Menten mechanism, which
exemplifies an out-of-equilibrium process in the living system. Our theoretical
approach begins with the principle of equal a priori probabilities and defines
the reaction path entropy to construct a new nonequilibrium ensemble as a
collection of possible chemical reaction paths. As a result, we evaluate a
variety of path-based partition functions and free energies using the formalism
of statistical mechanics. They allow us to calculate the timescales of a given
enzymatic reaction, even in the absence of an explicit boundary condition that
is necessary for the equilibrium ensemble. We also consider the large deviation
theory under a closed-boundary condition of the fixed observation time to
quantify the enzyme-substrate unbinding rates. The result demonstrates the
presence of a phase-separation-like, bimodal behavior in unbinding events at a
finite timescale, and the behavior vanishes as its rate function converges to a
single phase in the long-time limit.


title:
Improving approximate vacuum prepared by the adiabatic quantum
  computation

abstract:
  According to the quantum adiabatic theorem, we can in principle obtain a true
vacuum of a quantum system starting from a trivial vacuum of a simple
Hamiltonian. In actual adiabatic digital quantum simulation with finite time
length and non-infinitesimal time steps, we can only obtain an approximate
vacuum that is supposed to be a superposition of a true vacuum and excited
states. We propose a procedure to improve the approximate vacuum.


title:
Automatic Pronunciation Assessment using Self-Supervised Speech
  Representation Learning

abstract:
  Self-supervised learning (SSL) approaches such as wav2vec 2.0 and HuBERT
models have shown promising results in various downstream tasks in the speech
community. In particular, speech representations learned by SSL models have
been shown to be effective for encoding various speech-related characteristics.
In this context, we propose a novel automatic pronunciation assessment method
based on SSL models. First, the proposed method fine-tunes the pre-trained SSL
models with connectionist temporal classification to adapt the English
pronunciation of English-as-a-second-language (ESL) learners in a data
environment. Then, the layer-wise contextual representations are extracted from
all across the transformer layers of the SSL models. Finally, the automatic
pronunciation score is estimated using bidirectional long short-term memory
with the layer-wise contextual representations and the corresponding text. We
show that the proposed SSL model-based methods outperform the baselines, in
terms of the Pearson correlation coefficient, on datasets of Korean ESL learner
children and Speechocean762. Furthermore, we analyze how different
representations of transformer layers in the SSL model affect the performance
of the pronunciation assessment task.


title: Multi-scale temporal network for continuous sign language recognition

abstract:
  Continuous Sign Language Recognition (CSLR) is a challenging research task
due to the lack of accurate annotation on the temporal sequence of sign
language data. The recent popular usage is a hybrid model based on "CNN + RNN"
for CSLR. However, when extracting temporal features in these works, most of
the methods using a fixed temporal receptive field and cannot extract the
temporal features well for each sign language word. In order to obtain more
accurate temporal features, this paper proposes a multi-scale temporal network
(MSTNet). The network mainly consists of three parts. The Resnet and two fully
connected (FC) layers constitute the frame-wise feature extraction part. The
time-wise feature extraction part performs temporal feature learning by first
extracting temporal receptive field features of different scales using the
proposed multi-scale temporal block (MST-block) to improve the temporal
modeling capability, and then further encoding the temporal features of
different scales by the transformers module to obtain more accurate temporal
features. Finally, the proposed multi-level Connectionist Temporal
Classification (CTC) loss part is used for training to obtain recognition
results. The multi-level CTC loss enables better learning and updating of the
shallow network parameters in CNN, and the method has no parameter increase and
can be flexibly embedded in other models. Experimental results on two publicly
available datasets demonstrate that our method can effectively extract sign
language features in an end-to-end manner without any prior knowledge,
improving the accuracy of CSLR and reaching the state-of-the-art.


title:
Spatiotemporal Augmentation on Selective Frequencies for Video
  Representation Learning

abstract:
  Recent self-supervised video representation learning methods focus on
maximizing the similarity between multiple augmented views from the same video
and largely rely on the quality of generated views. In this paper, we propose
frequency augmentation (FreqAug), a spatio-temporal data augmentation method in
the frequency domain for video representation learning. FreqAug stochastically
removes undesirable information from the video by filtering out specific
frequency components so that learned representation captures essential features
of the video for various downstream tasks. Specifically, FreqAug pushes the
model to focus more on dynamic features rather than static features in the
video via dropping spatial or temporal low-frequency components. In other
words, learning invariance between remaining frequency components results in
high-frequency enhanced representation with less static bias. To verify the
generality of the proposed method, we experiment with FreqAug on multiple
self-supervised learning frameworks along with standard augmentations.
Transferring the improved representation to five video action recognition and
two temporal action localization downstream tasks shows consistent improvements
over baselines.


title:
Configuration interaction projected density functional theory: effects
  of four-quasiparticle configurations and time-odd interactions

abstract:
  The effects of four-quasiparticle configurations and time-odd interactions
are investigated in the framework of configuration interaction projected
density functional theory by taking the yrast states of 60Fe as examples. Based
on the universal PC-PK1 density functional, the energies of the yrast states
with spin up to 20\hbar and the available B(E2) transition probabilities are
well reproduced. The yrast states are predicted to be of four-quasiparticle
structure above spin I = 16\hbar. The inclusion of the time-odd interactions
increases the kinetic moments of inertia and delays the appearance of the first
band crossing, and, thus, improves the description of the data.


title:
Figures of merit for a stochastic gravitational-wave background
  measurement by LISA: implications of LISA Pathfinder noise correlations

abstract:
  An important goal of the Laser Interferometer Space Antenna (LISA) is to
observe a stochastic gravitational-wave background (SGWB). A study of possible
correlated noise in LISA is relevant to establish limits for this future
measurement. To test noise investigation methods under somewhat realistic
conditions, we use the data of LISA Pathfinder (LPF). We calculate the
coherence between the LPF differential acceleration of the two test-masses with
magnetic fields, temperature, and micronewton cold gas thruster activity in the
spacecraft. We apply our observed correlations to LISA, and estimate how the
presence of such correlated noise would affect its search for a SGWB. In the
context of a figure of merit, we estimate the effect of noise on the LISA SGWB
search.


title: Statistics of biased tracers in variance-suppressed simulations

abstract:
  Cosmological simulations play an increasingly important role in analysing the
observed large-scale structure of the Universe. Recently, they have been
particularly important in building hybrid models that combine a perturbative
bias expansion with displacement fields extracted from N-body simulations to
describe the clustering of biased tracers. Here, we show that simulations that
employ a technique referred to as "Fixing-and-pairing" (F&P) can dramatically
improve the statistical precision of such hybrid models. Specifically, by
numerical and analytic means, we show that F&P simulations provide unbiased
estimates for all statistics employed by hybrid models while reducing, by up to
two orders of magnitude, their uncertainty on large scales. This roughly
implies that an EUCLID-like survey could be analysed using simulations of 2Gpc
a side -- a 10% of the survey volume. Our work establishes the robustness of
F&P for current hybrid theoretical models for galaxy clustering, an important
step towards achieving an optimal exploitation of large-scale structure
measurements.


title: Where Does General Relativity Break Down?

abstract:
  It is widely accepted by physicists and philosophers of physics alike that
there are certain contexts in which general relativity will "break down". In
such cases, one expects to need some as-yet undiscovered successor theory. This
paper will discuss certain pathologies of general relativity that might be
taken to signal that the theory is breaking down, and consider how one might
expect a successor theory to do better. The upshot will be an unconventional
interpretation of the "Strong Cosmic Censorship Hypothesis".


title:
A unified treatment of structural definitions on syntax for
  capture-avoiding substitution, context application, named substitution,
  partial differentiation, and so on

abstract:
  We introduce a category-theoreticabstraction of a syntax with auxiliary
functions, called an admissiblemonad morphism. Relying on an abstract form of
structural recursion,we then design generic tools to construct admissible monad
morphismsfrom basic data. These tools automate ubiquitous standard patternslike
(1) defining auxiliary functions in successive, potentiallydependent layers,
and (2) proving properties of auxiliary functions byinduction on syntax. We
cover significant examples from theliterature, including the standard
lambda-calculus withcapture-avoiding substitution, a lambda-calculus with
bindingevaluation contexts, the lambda-mu-calculus with named substitution,
andthe differential lambda-calculus.


title: CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction

abstract:
  In this paper, we present CrudeOilNews, a corpus of English Crude Oil news
for event extraction. It is the first of its kind for Commodity News and serve
to contribute towards resource building for economic and financial text mining.
This paper describes the data collection process, the annotation methodology
and the event typology used in producing the corpus. Firstly, a seed set of 175
news articles were manually annotated, of which a subset of 25 news were used
as the adjudicated reference test set for inter-annotator and system
evaluation. Agreement was generally substantial and annotator performance was
adequate, indicating that the annotation scheme produces consistent event
annotations of high quality. Subsequently the dataset is expanded through (1)
data augmentation and (2) Human-in-the-loop active learning. The resulting
corpus has 425 news articles with approximately 11k events annotated. As part
of active learning process, the corpus was used to train basic event extraction
models for machine labeling, the resulting models also serve as a validation or
as a pilot study demonstrating the use of the corpus in machine learning
purposes. The annotated corpus is made available for academic research purpose
at https://github.com/meisin/CrudeOilNews-Corpus.


title:
Controllable Missingness from Uncontrollable Missingness: Joint Learning
  Measurement Policy and Imputation

abstract:
  Due to the cost or interference of measurement, we need to control
measurement system. Assuming that each variable can be measured sequentially,
there exists optimal policy choosing next measurement for the former
observations. Though optimal measurement policy is actually dependent on the
goal of measurement, we mainly focus on retrieving complete data, so called as
imputation. Also, we adapt the imputation method to missingness varying with
measurement policy. However, learning measurement policy and imputation
requires complete data which is impossible to be observed, unfortunately. To
tackle this problem, we propose a data generation method and joint learning
algorithm. The main idea is that 1) the data generation method is inherited by
imputation method, and 2) the adaptation of imputation encourages measurement
policy to learn more than individual learning. We implemented some variations
of proposed algorithm for two different datasets and various missing rates.
From the experimental results, we demonstrate that our algorithm is generally
applicable and outperforms baseline methods.


title: Spatial Transformer Network on Skeleton-based Gait Recognition

abstract:
  Skeleton-based gait recognition models usually suffer from the robustness
problem, as the Rank-1 accuracy varies from 90\% in normal walking cases to
70\% in walking with coats cases. In this work, we propose a state-of-the-art
robust skeleton-based gait recognition model called Gait-TR, which is based on
the combination of spatial transformer frameworks and temporal convolutional
networks. Gait-TR achieves substantial improvements over other skeleton-based
gait models with higher accuracy and better robustness on the well-known gait
dataset CASIA-B. Particularly in walking with coats cases, Gait-TR get a 90\%
Rank-1 gait recognition accuracy rate, which is higher than the best result of
silhouette-based models, which usually have higher accuracy than the
silhouette-based gait recognition models. Moreover, our experiment on CASIA-B
shows that the spatial transformer can extract gait features from the human
skeleton better than the widely used graph convolutional network.


title:
On-Policy Pixel-Level Grasping Across the Gap Between Simulation and
  Reality

abstract:
  Grasp detection in cluttered scenes is a very challenging task for robots.
Generating synthetic grasping data is a popular way to train and test grasp
methods, as is Dex-net and GraspNet; yet, these methods generate training
grasps on 3D synthetic object models, but evaluate at images or point clouds
with different distributions, which reduces performance on real scenes due to
sparse grasp labels and covariate shift. To solve existing problems, we propose
a novel on-policy grasp detection method, which can train and test on the same
distribution with dense pixel-level grasp labels generated on RGB-D images. A
Parallel-Depth Grasp Generation (PDG-Generation) method is proposed to generate
a parallel depth image through a new imaging model of projecting points in
parallel; then this method generates multiple candidate grasps for each pixel
and obtains robust grasps through flatness detection, force-closure metric and
collision detection. Then, a large comprehensive Pixel-Level Grasp Pose Dataset
(PLGP-Dataset) is constructed and released; distinguished with previous
datasets with off-policy data and sparse grasp samples, this dataset is the
first pixel-level grasp dataset, with the on-policy distribution where grasps
are generated based on depth images. Lastly, we build and test a series of
pixel-level grasp detection networks with a data augmentation process for
imbalance training, which learn grasp poses in a decoupled manner on the input
RGB-D images. Extensive experiments show that our on-policy grasp method can
largely overcome the gap between simulation and reality, and achieves the
state-of-the-art performance. Code and data are provided at
https://github.com/liuchunsense/PLGP-Dataset.


title:
Deterministic, Near-Linear $\varepsilon$-Approximation Algorithm for
  Geometric Bipartite Matching

abstract:
  Given point sets $A$ and $B$ in $\mathbb{R}^d$ where $A$ and $B$ have equal
size $n$ for some constant dimension $d$ and a parameter $\varepsilon>0$, we
present the first deterministic algorithm that computes, in
$n\cdot(\varepsilon^{-1} \log n)^{O(d)}$ time, a perfect matching between $A$
and $B$ whose cost is within a $(1+\varepsilon)$ factor of the optimal under
any $\smash{\ell_p}$-norm. Although a Monte-Carlo algorithm with a similar
running time is proposed by Raghvendra and Agarwal [J. ACM 2020], the
best-known deterministic $\varepsilon$-approximation algorithm takes
$\Omega(n^{3/2})$ time. Our algorithm constructs a (refinement of a) tree cover
of $\mathbb{R}^d$, and we develop several new tools to apply a tree-cover based
approach to compute an $\varepsilon$-approximate perfect matching.


title:
An STM perspective on hexaborides: Surface states of the Kondo insulator
  SmB$_6$

abstract:
  Compounds within the hexaboride class of materials exhibit a wide variety of
interesting physical phenomena, including polaron formation and quadrupolar
order. In particular, SmB$_6$ has recently drawn attention as it is considered
a prototypical topological Kondo insulator. Evidence in favor of this concept,
however, has proven experimentally difficult and controversial, partly because
of the required temperatures and energy resolution. Here, a powerful tool is
Scanning Tunneling Microscopy (STM) with its unique ability to give local,
microscopic information that directly relates to the one-particle Green's
function. Yet, STM on hexaborides is met with its own set of challenges. This
article attempts to review the progress in STM investigations on hexaborides,
with emphasis on SmB$_6$ and its intriguing properties.


title:
Decoupling Nuclear Spins via Interaction-Induced Freezing in Nitrogen
  Vacancy Centers in Diamond

abstract:
  Nitrogen Vacancy (NV) centers in diamonds provide an ideal room-temperature
platform for emulating a wide range of quantum phenomena. The ability to
initialize, control, and readout proximal nuclear spins using the NV center's
electron spin make it possible to emulate more complex features. A recent
proposal based on Rydberg atoms presented a novel effect in the
strong-interaction regime, named Rydberg-biased freezing, where the dynamics of
a Rydberg atom freezes when two strongly interacting Rydberg atoms are driven
by laser fields with very different Rabi frequencies. We simulate an analogous
interaction-induced freezing phenomenon in an NV center system where the state
dynamics of the NV center's intrinsic nuclear spin freezes when the electron
and nuclear spins are simultaneously driven with unequal Rabi frequencies. We
develop on this idea and show that the NV nuclear spin can also be effectively
shielded from strong drive or noise fields in the frozen regime. Further, we
present the evolution of quantum correlations in the electron-nuclear spin
system by measuring its quantum discord and observe a clear suppression of
quantum correlations in this regime. We conclude that the interaction-induced
freezing phenomenon can decouple nuclear spins from stray fields and minimize
their correlation with the NV electron spin. This can be instrumental in
extending the storage times of NV nuclear-spin-based quantum memories in hybrid
quantum systems.


title:
Picture Fuzzy Interactional Aggregation Operators via Strict Triangular
  Norms and Applications to Multi-Criteria Decision Making

abstract:
  The picture fuzzy set, characterized by three membership degrees, is a
helpful tool for multi-criteria decision making (MCDM). This paper investigates
the structure of the closed operational laws in the picture fuzzy numbers
(PFNs) and proposes efficient picture fuzzy MCDM methods. We first introduce an
admissible order for PFNs and prove that all PFNs form a complete lattice under
this order. Then, we give some specific examples to show the non-closeness of
some existing picture fuzzy aggregation operators. To ensure the closeness of
the operational laws in PFNs, we construct a new class of picture fuzzy
operators based on strict triangular norms, which consider the interaction
between the positive degrees (negative degrees) and the neutral degrees. Based
on these new operators, we obtain the picture fuzzy interactional weighted
average (PFIWA) operator and the picture fuzzy interactional weighted geometric
(PFIWG) operator. They are proved to be monotonous, idempotent, bounded,
shift-invariant, and homogeneous. We also establish a novel MCDM method under
the picture fuzzy environment applying PFIWA and PFIWG operators. Furthermore,
we present an illustrative example for a clear understanding of our method. We
also give the comparative analysis among the operators induced by six classes
of famous triangular norms.


title:
A Study of Different Ways to Use The Conformer Model For Spoken Language
  Understanding

abstract:
  SLU combines ASR and NLU capabilities to accomplish speech-to-intent
understanding. In this paper, we compare different ways to combine ASR and NLU,
in particular using a single Conformer model with different ways to use its
components, to better understand the strengths and weaknesses of each approach.
We find that it is not necessarily a choice between two-stage decoding and
end-to-end systems which determines the best system for research or
application. System optimization still entails carefully improving the
performance of each component. It is difficult to prove that one direction is
conclusively better than the other. In this paper, we also propose a novel
connectionist temporal summarization (CTS) method to reduce the length of
acoustic encoding sequences while improving the accuracy and processing speed
of end-to-end models. This method achieves the same intent accuracy as the best
two-stage SLU recognition with complicated and time-consuming decoding but does
so at lower computational cost. This stacked end-to-end SLU system yields an
intent accuracy of 93.97% for the SmartLights far-field set, 95.18% for the
close-field set, and 99.71% for FluentSpeech.


title:
CD$^2$-pFed: Cyclic Distillation-guided Channel Decoupling for Model
  Personalization in Federated Learning

abstract:
  Federated learning (FL) is a distributed learning paradigm that enables
multiple clients to collaboratively learn a shared global model. Despite the
recent progress, it remains challenging to deal with heterogeneous data
clients, as the discrepant data distributions usually prevent the global model
from delivering good generalization ability on each participating client. In
this paper, we propose CD^2-pFed, a novel Cyclic Distillation-guided Channel
Decoupling framework, to personalize the global model in FL, under various
settings of data heterogeneity. Different from previous works which establish
layer-wise personalization to overcome the non-IID data across different
clients, we make the first attempt at channel-wise assignment for model
personalization, referred to as channel decoupling. To further facilitate the
collaboration between private and shared weights, we propose a novel cyclic
distillation scheme to impose a consistent regularization between the local and
global model representations during the federation. Guided by the cyclical
distillation, our channel decoupling framework can deliver more accurate and
generalized results for different kinds of heterogeneity, such as feature skew,
label distribution skew, and concept shift. Comprehensive experiments on four
benchmarks, including natural image and medical image analysis tasks,
demonstrate the consistent effectiveness of our method on both local and
external validations.


title: Quasishuffle double bialgebras

abstract:
  Quasishuffle Hopf algebras, usually defined on a commutative monoid, can be
more generally defined on any associative algebra V. If V is a commutative and
cocommutative bialgebra, the associated quasishuffle bialgebra QSh(V) inherits
a second coproduct $\delta$ of contraction and extraction of words,
cointeracting with the deconcatenation coproduct $\Delta$, making QSh(V) a
double bialgebra. In order to generalize the universal property of the Hopf
algebra of quasisymmetric functions QSym (a particular case of quasishuffle
Hopf algebra) as exposed by Aguiar, Bergeron and Sottile, we introduce the
notion of double bialgebra over V. A bialgebra over V is a bialgebra in the
category of right V-comodules and an extra condition is required on the second
coproduct for double bialgebras over V. We prove that the quasishuffle
bialgebra QSh(V) is a double bialgebra over V , and that it satisfies a
universal property: for any bialgebra B over V and for any character $\lambda$
of B, under a connectedness condition, there exists a unique morphism $\phi$ of
bialgebras over V from B to QSh(V) such that $\epsilon_\delta \circ
\phi=\lambda$. When V is a double bialgebra over V , we obtain a unique
morphism of double bialgebras over V from B to QSh(V), and show that this
morphism $\phi_1$ allows to obtain any morphism of bialgebra over V from B to
QSh(V) thanks to an action of a monoid of characters. This formalism is applied
to a double bialgebra of V-decorated graphs.


title: About Opposition and Duality in Paraconsistent Type Theory

abstract:
  A paraconsistent type theory (an extension of a fragment of intuitionistic
type theory by adding opposite types) is here extended by adding co-function
types. It is shown that, in the extended paraconsistent type system, the
opposite type constructor can be viewed as an involution operation that
transforms each type into its dual type. Moreover, intuitive interpretations of
opposite and co-function types under different interpretations of types are
discussed.


title: Vision Transformers for Single Image Dehazing

abstract:
  Image dehazing is a representative low-level vision task that estimates
latent haze-free images from hazy images. In recent years, convolutional neural
network-based methods have dominated image dehazing. However, vision
Transformers, which has recently made a breakthrough in high-level vision
tasks, has not brought new dimensions to image dehazing. We start with the
popular Swin Transformer and find that several of its key designs are
unsuitable for image dehazing. To this end, we propose DehazeFormer, which
consists of various improvements, such as the modified normalization layer,
activation function, and spatial information aggregation scheme. We train
multiple variants of DehazeFormer on various datasets to demonstrate its
effectiveness. Specifically, on the most frequently used SOTS indoor set, our
small model outperforms FFA-Net with only 25% #Param and 5% computational cost.
To the best of our knowledge, our large model is the first method with the PSNR
over 40 dB on the SOTS indoor set, dramatically outperforming the previous
state-of-the-art methods. We also collect a large-scale realistic remote
sensing dehazing dataset for evaluating the method's capability to remove
highly non-homogeneous haze.


title: SeCaV: A Sequent Calculus Verifier in Isabelle/HOL

abstract:
  We describe SeCaV, a sequent calculus verifier for first-order logic in
Isabelle/HOL, and the SeCaV Unshortener, an online tool that expands succinct
derivations into the full SeCaV syntax. We leverage the power of Isabelle/HOL
as a proof checker for our SeCaV derivations. The interactive features of
Isabelle/HOL make our system transparent. For instance, the user can simply
click on a side condition to see its exact definition. Our formalized soundness
and completeness proofs pertain exactly to the calculus as exposed to the user
and not just to some model of our tool. Users can also write their derivations
in the SeCaV Unshortener, which provides a lighter syntax, and expand them for
later verification. We have used both tools in our teaching.


title: A Quick Overview on the Quantum Control Approach to the Lambda Calculus

abstract:
  In this short overview, we start with the basics of quantum computing,
explaining the difference between the quantum and the classical control
paradigms. We give an overview of the quantum control line of research within
the lambda calculus, ranging from untyped calculi up to categorical and
realisability models. This is a summary of the last 10+ years of research in
this area, starting from Arrighi and Dowek's seminal work until today.


title:
Trapping a Free-propagating Single-photon into an Atomic Ensemble as a
  Quantum Stationary Light Pulse

abstract:
  Efficient photon-photon interaction is one of the key elements for realizing
quantum information processing. The interaction, however, must often be
mediated through an atomic medium due to the bosonic nature of photons, and the
interaction time, which is critically linked to the efficiency, depends on the
properties of the atom-photon interaction. While the electromagnetically
induced transparency effect does offer the possibility of photonic quantum
memory, it does not enhance the interaction time as it fully maps the photonic
state to an atomic state. The stationary light pulse (SLP) effect, on the
contrary, traps the photonic state inside an atomic medium with zero group
velocity, opening up the possibility of the enhanced interaction time. In this
work, we report the first experimental demonstration of trapping a
free-propagating single-photon into a cold atomic ensemble via the quantum SLP
(QSLP) process. We conclusively show that the quantum properties of the
single-photon state are preserved well during the QSLP process. Our work paves
the way to new approaches for efficient photon-photon interactions, exotic
photonic states, and many-body simulations in photonic systems.


title: Uniformly Valid Inference Based on the Lasso in Linear Mixed Models

abstract:
  Linear mixed models (LMMs) are suitable for clustered data and are common in
e.g. biometrics, medicine, or small area estimation. It is of interest to
obtain valid inference after selecting a subset of available variables. We
construct confidence sets for the fixed effects in Gaussian LMMs that are
estimated via a Lasso-type penalization which allows quantifying the joint
uncertainty of both variable selection and estimation. To this end, we exploit
the properties of restricted maximum likelihood (REML) estimators to separate
the estimation of the regression coefficients and covariance parameters. We
derive an appropriate normalizing sequence to prove the uniform Cramer
consistency of the REML estimators. We then show that the resulting confidence
sets for the fixed effects are uniformly valid over the parameter space of both
the regression coefficients and the covariance parameters. Their superiority to
naive post-selection least-squares confidence sets is validated in simulations
and illustrated with a study of the acid neutralization capacity of U.S. lakes.


title: Transducer-based language embedding for spoken language identification

abstract:
  The acoustic and linguistic features are important cues for the spoken
language identification (LID) task. Recent advanced LID systems mainly use
acoustic features that lack the usage of explicit linguistic feature encoding.
In this paper, we propose a novel transducer-based language embedding approach
for LID tasks by integrating an RNN transducer model into a language embedding
framework. Benefiting from the advantages of the RNN transducer's linguistic
representation capability, the proposed method can exploit both
phonetically-aware acoustic features and explicit linguistic features for LID
tasks. Experiments were carried out on the large-scale multilingual LibriSpeech
and VoxLingua107 datasets. Experimental results showed the proposed method
significantly improves the performance on LID tasks with 12% to 59% and 16% to
24% relative improvement on in-domain and cross-domain datasets, respectively.