title:
Probing the initial longitudinal density profile and electromagnetic
  field in ultrarelativistic heavy-ion collisions with heavy quarks

abstract:
  Heavy quarks are valuable probes of the electromagnetic field and the initial
condition of the quark-gluon plasma (QGP) matter produced in high-energy
nuclear collisions. Within an improved Langevin model that is coupled to a
(3+1)-dimensional viscous hydrodynamic model, we explore the origin of the
directed flow coefficient ($v_1$) of heavy mesons and their decay leptons, and
its splitting ($\Delta v_{1}$) between opposite charges. We find that while the
rapidity dependence of the heavy flavor $v_1$ is mainly driven by the titled
energy density profile of the QGP with respect to the longitudinal direction at
the RHIC energy, it is dominated by the electromagnetic field at the LHC
energy. The $\Delta v_{1}$ serves as a novel probe of the spacetime evolution
profile of the electromagnetic field. Our results of $D$ mesons and their decay
electrons are consistent with the available data at RHIC and LHC, and our
predictions on the heavy flavor decay muons can be further tested by future
measurements.


title: Filter-enhanced MLP is All You Need for Sequential Recommendation

abstract:
  Recently, deep neural networks such as RNN, CNN and Transformer have been
applied in the task of sequential recommendation, which aims to capture the
dynamic preference characteristics from logged user behavior data for accurate
recommendation. However, in online platforms, logged user behavior data is
inevitable to contain noise, and deep recommendation models are easy to overfit
on these logged data. To tackle this problem, we borrow the idea of filtering
algorithms from signal processing that attenuates the noise in the frequency
domain. In our empirical experiments, we find that filtering algorithms can
substantially improve representative sequential recommendation models, and
integrating simple filtering algorithms (eg Band-Stop Filter) with an all-MLP
architecture can even outperform competitive Transformer-based models.
Motivated by it, we propose \textbf{FMLP-Rec}, an all-MLP model with learnable
filters for sequential recommendation task. The all-MLP architecture endows our
model with lower time complexity, and the learnable filters can adaptively
attenuate the noise information in the frequency domain. Extensive experiments
conducted on eight real-world datasets demonstrate the superiority of our
proposed method over competitive RNN, CNN, GNN and Transformer-based methods.
Our code and data are publicly available at the link:
\textcolor{blue}{\url{https://github.com/RUCAIBox/FMLP-Rec}}.


title:
Research Needs for Realization of Zero-Carbon Power Grids with Selected
  Case Studies

abstract:
  The attainment of carbon neutrality requires a research agenda that addresses
the technical and economic challenges that will be encountered as we progress
toward 100% renewable electricity generation. Increasing proportions of
variable renewable energy (VRE) sources (such as wind turbines and photovoltaic
systems) render the supply-and-demand balance of VRE-dominated power grids
difficult. The operational characteristics and effects of VRE inverters also
require attention. Here, we examine the implications of the paradigm shift to
carbon neutrality and summarize the associated research challenges in terms of
system planning, operation, and sta-bility, and the need for energy storage
integration, demand-side participation, distributed con-trol and estimation,
and energy sector coupling. We also highlight the existing literature gaps, and
our recent studies that can fill in the gaps, thereby facilitating the
improvement of grid op-eration and estimation. The numerical results of
comparative case studies are also provided on the operational stability and
economics of power grids with a high level of VRE sources, assist-ing
stakeholders in establishing specific roadmaps and making relevant decisions.


title: CINO: A Chinese Minority Pre-trained Language Model

abstract:
  Multilingual pre-trained language models have shown impressive performance on
cross-lingual tasks. It greatly facilitates the applications of natural
language processing on low-resource languages. However, there are still some
languages that the existing multilingual models do not perform well on. In this
paper, we propose CINO (Chinese Minority Pre-trained Language Model), a
multilingual pre-trained language model for Chinese minority languages. It
covers Standard Chinese, Cantonese, and six other Chinese minority languages.
To evaluate the cross-lingual ability of the multilingual models on the
minority languages, we collect documents from Wikipedia and build a text
classification dataset WCM (Wiki-Chinese-Minority). We test CINO on WCM and two
other text classification tasks. Experiments show that CINO outperforms the
baselines notably. The CINO model and the WCM dataset are available at
http://cino.hfl-rc.com.


title: Black Hole Shadow with Soft Hair

abstract:
  Light bending by the strong gravity around the black hole will form the
so-called black hole shadow, the shape of which can shed light on the structure
of the near-horizon geometry to possibly reveal novel physics of strong gravity
and black hole. In this work, we adopt both analytical and ray-tracing methods
to study the black hole shadow in the presence of the infrared structure of
gravity theory, which manifests the asymptotic symmetries of spacetime as the
supertranslation soft hairs of the black hole. Though the black hole metrics
with and without the soft hair are related by large gauge transformations, the
near horizon geometries relevant for the shape of the shadow are quite
different. Moreover, the Hamiltonian for the geodesic seems intrinsically
different, i.e., the loss of separability due to the breaking of spherical
symmetry by soft hair. By applying ray-tracing computations, we find that the
soft hair, although not affecting the shape of the shadow, may change the
average size and position of the shadow. Images resulting from soft hair black
holes with surrounding accretion flows are also discussed.


title: ConvNeXt-backbone HoVerNet for nuclei segmentation and classification

abstract:
  This manuscript gives a brief description of the algorithm used to
participate in CoNIC Challenge 2022. After the baseline was made available, we
follow the method in it and replace the ResNet baseline with ConvNeXt one.
Moreover, we propose to first convert RGB space to Haematoxylin-Eosin-DAB(HED)
space, then use Haematoxylin composition of origin image to smooth semantic one
hot label. Afterwards, nuclei distribution of train and valid set are explored
to select the best fold split for training model for final test phase
submission. Results on validation set shows that even with channel of each
stage smaller in number, HoVerNet with ConvNeXt-tiny backbone still improves
the mPQ+ by 0.04 and multi r2 by 0.0144


title:
On a Fractional Nirenberg problem involving the square root of the
  Laplacian on $\mathbb{S}^{3}$

abstract:
  In this paper, we are devoted to establishing the compactness and existence
results of the solutions to the fractional Nirenberg problem for $n=3,$
$\sigma=1/2,$ when the prescribing $\sigma$-curvature function satisfies the
$(n-2\sigma)$-flatness condition near its critical points. The compactness
results are new and optimal. In addition, we obtain a degree-counting formula
of all solutions. From our results, we can know where blow up occur. Moreover,
for any finite distinct points, the sequence of solutions that blow up
precisely at these points can be constructed. We extend the results of Li in
\cite[CPAM, 1996]{LYY} from the local problem to nonlocal cases.


title: Name Your Style: An Arbitrary Artist-aware Image Style Transfer

abstract:
  Image style transfer has attracted widespread attention in the past few
years. Despite its remarkable results, it requires additional style images
available as references, making it less flexible and inconvenient. Using text
is the most natural way to describe the style. More importantly, text can
describe implicit abstract styles, like styles of specific artists or art
movements. In this paper, we propose a text-driven image style transfer (TxST)
that leverages advanced image-text encoders to control arbitrary style
transfer. We introduce a contrastive training strategy to effectively extract
style descriptions from the image-text model (i.e., CLIP), which aligns
stylization with the text description. To this end, we also propose a novel and
efficient attention module that explores cross-attentions to fuse style and
content features. Finally, we achieve an arbitrary artist-aware image style
transfer to learn and transfer specific artistic characters such as Picasso,
oil painting, or a rough sketch. Extensive experiments demonstrate that our
approach outperforms the state-of-the-art methods on both image and textual
styles. Moreover, it can mimic the styles of one or many artists to achieve
attractive results, thus highlighting a promising direction in image style
transfer.


title:
The preliminary statistical analysis of LAMOST DR8 low resolution AFGK
  stars

abstract:
  We download the LAMOST DR8 low resolution catalog 6,478,063 AFGK tpye stars
and plot the figures of effective temperature, gravitational acceleration, and
metal abundance. Some small and medium mass stars are evolved from pre-main
sequence or main sequence stage to planetary nebula stage or white dwarf stage
by the stellar evolution code \texttt{MESA}. We analyze the observed
statistical data and model calculation results, and then obtain some basic
conclusions preliminarily. Most red giant and asymptotic giant stars with
log$g$ less than 0.85 have poor metal abundance. Most hot A type main-sequence
stars are metal rich stars with log$g$ from 3.5 to 4.5. The conclusions are
reasonable within a certain error range. The theory of a gap area in the H-R
diagram for stellar evolutions of medium mass stars is reflected in the
statistical figures. The central core hydrogen burning stage and the central
core helium burning stage correspond to the peak structures in the
gravitational acceleration statistical figures respectively. The metal
abundances among A, F, G, and K type stars have a wide distribution. We can not
simply replace the metal abundances of these stars with the metal abundance of
the Sun when doing a fine research work.


title:
Towards Machine Learning for Placement and Routing in Chip Design: a
  Methodological Overview

abstract:
  Placement and routing are two indispensable and challenging (NP-hard) tasks
in modern chip design flows. Compared with traditional solvers using heuristics
or expert-well-designed algorithms, machine learning has shown promising
prospects by its data-driven nature, which can be of less reliance on knowledge
and priors, and potentially more scalable by its advanced computational
paradigms (e.g. deep networks with GPU acceleration). This survey starts with
the introduction of basics of placement and routing, with a brief description
on classic learning-free solvers. Then we present detailed review on recent
advance in machine learning for placement and routing. Finally we discuss the
challenges and opportunities for future research.


title:
A Holistic Review on Advanced Bi-directional EV Charging Control
  Algorithms

abstract:
  The rapid growth of electric vehicles (EVs) has promised a next-generation
transportation system with reduced carbon emission. The fast development of EVs
and charging facilities is driving the evolution of Internet of Vehicles (IoV)
to Internet of Electric Vehicles (IoEV). IoEV benefits from both smart grid and
Internet of Things (IoT) technologies which provide advanced bi-directional
charging services and real-time data processing capability, respectively. The
major design challenges of the IoEV charging control lie in the randomness of
charging events and the mobility of EVs. In this article, we present a holistic
review on advanced bi-directional EV charging control algorithms. For
Grid-to-Vehicle (G2V), we introduce the charging control problem in two
scenarios: 1) Operation of a single charging station and 2) Operation of
multiple charging stations in coupled transportation and power networks. For
Vehicle-to-Grid (V2G), we discuss how EVs can perform energy trading in the
electricity market and provide ancillary services to the power grid. Besides, a
case study is provided to illustrate the economic benefit of the joint
optimization of routing and charging scheduling of multiple EVs in the IoEV.
Last but not the least, we will highlight some open problems and future
research directions of charging scheduling problems for IoEVs.


title:
Learning Parameters for a Generalized Vidale-Wolfe Response Model with
  Flexible Ad Elasticity and Word-of-Mouth

abstract:
  In this research, we investigate a generalized form of Vidale-Wolfe (GVW)
model. One key element of our modeling work is that the GVW model contains two
useful indexes representing advertiser's elasticity and the word-of-mouth (WoM)
effect, respectively. Moreover, we discuss some desirable properties of the GVW
model, and present a deep neural network (DNN)-based estimation method to learn
its parameters. Furthermore, based on three realworld datasets, we conduct
computational experiments to validate the GVW model and identified properties.
In addition, we also discuss potential advantages of the GVW model over
econometric models. The research outcome shows that both the ad elasticity
index and the WoM index have significant influences on advertising responses,
and the GVW model has potential advantages over econometric models of
advertising, in terms of several interesting phenomena drawn from practical
advertising situations. The GVW model and its deep learning-based estimation
method provide a basis to support big data-driven advertising analytics and
decision makings; in the meanwhile, identified properties and experimental
findings of this research illuminate critical managerial insights for
advertisers in various advertising forms.


title:
Colliding Beam Polarization effect on Higgs production at Lepton
  Collider

abstract:
  Higgs related production processes e.g., annihilation, $W$ and $Z$ boson
fusion are observed for different configurations of colliding electron and
positron beam polarizations mainly in International Linear Collider (ILC),
operating at $\sqrt{s}=200-1000$ GeV. This study indicates that the enhancement
or reduction of production cross section depending on center-of-mass (CM)
energy and the type of combination used which may help in the selection of
different production processes. This study also shows that number of measurable
observable increases by polarizing beams, which are required for determination
of new physical parameters of physics models beyond the standard model (SM).
Observable are left-right asymmetry, effective polarization and effective
luminosity.


title: On D-modules of categories II

abstract:
  In our paper "On D-module of categories I", we provide two different methods
of constructing D-module structures on the complex computing periodic cyclic
homology associated to a family of stable infinity categories. One is based on
a canonical extension of factorization homology. Another method uses the pair
of Hochschild cohomology and Hochschild homology, Kodaira-Spencer map for a
family of stable infinity categories, Koszul dualities,and the relation between
dg Lie algebras and pointed formal stacks. In this paper, we prove that two
resulting structures coindice.


title:
On the Application of Quasi-Degradation to Network NOMA in Downlink CoMP
  Systems

abstract:
  The application of network non-orthogonal multiple access (N-NOMA) technique
to coordinated multi-point (CoMP) systems has attracted significant attention
due to its superior capability to improve connectivity and maintain reliable
transmission for CoMP users simultaneously. Based on the concept of
quasi-degraded channel for N-NOMA, this paper studies the precoding design for
downlink N-NOMA scenarios with two base stations (BSs) equipped with multiple
antennas. In specific, under quasi-degraded channels, simple linear precoding
based N-NOMA can achieve the same minimal total transmission power as
theoretically optimal but complicated dirty paper coding (DPC) scheme, when the
users' target rates and minimal transmission power of each BS are given. In
this paper, the channel quasi-degradation (QD) condition is first rigorously
derived for the scenario with single CoMP user and two NOMA users. The
closed-form optimal precoders for N-NOMA under quasi-degraded channels are also
provided. Then, based on QD condition, a novel hybrid N-NOMA (H-N-NOMA) scheme
is proposed, which is a mixture of N-NOMA and conventional zero-forcing
beamforming (ZFBF) scheme. Further, for the scenarios with more users, a
low-complexity QD based user pairing (QDUP) algorithm is proposed. Numerical
results are presented to reveal the impact factors of QD channels, and also
demonstrate the superior performance of the proposed H-N-NOMA/QDUP scheme. It
is shown that the proposed H-N-NOMA/QDUP scheme can effectively exploit the
benefit of multi user diversity.


title: Compact hidden charm pentaquark states and QCD isomers

abstract:
  We make an exhaustive investigation on the pentaquark states $qqqc\bar{c}$
($q=u, d$ and $s$) and discuss the effect of color structures in a multiquark
color flux-tube model. We exhibit a novel picture of the structure and
properties of the states $P_c$ and $P_{cs}$ observed by the LHCb Collaboration.
We can describe the states as the compact pentaquark states in the model. The
spin-parity of the group of $P_c(4312)^+$ and $P_c(4337)^+$ is $\frac{1}{2}^-$
while that of the group of $P_c(4380)^+$, $P_c(4440)^+$ and $P_c(4457)^+$ is
$\frac{3}{2}^-$. Their structures are pentagon, diquark, pentagon, diquark, and
octet, respectively. The members in each group can be analogically called QCD
isomers because of their the same spin-parity and quark content but different
color structures. The singlet $P_{cs}(4459)^0$ has pentagon structure and
spin-parity of $\frac{1}{2}^-$. In addition, we also predict the $P_{cs}$,
$P_{c ss}$ and $P_{csss}$ families in the model. The five-body confinement
potential based on the color flux-tube picture, which is a collective degree of
freedom and induces QCD isomer phenomenon, plays an important role in the
formation of the compact states.


title: Binding of anisotropic curvature-inducing proteins onto membrane tubes

abstract:
  Bin/Amphiphysin/Rvs superfamily proteins and other curvature-inducing
proteins have anisotropic shapes and anisotropically bend biomembrane. Here, we
report how the anisotropic proteins bind the membrane tube and are
orientationally ordered using mean-field theory including an
orientation-dependent excluded volume. The proteins exhibit a second-order or
first-order nematic transition with increasing protein density depending on the
radius of the membrane tube. The tube curvatures for the maximum protein
binding and orientational order are different and varied by the protein density
and rigidity. As the external force along the tube axis increases, a
first-order transition from a large tube radius with low protein density to a
small radius with high density occurs once, and subsequently, the protein
orientation tilts to the tube-axis direction. When an isotropic bending energy
is used for the proteins with an elliptic shape, the force-dependence curves
become symmetric and the first-order transition occurs twice. This theory
quantitatively reproduces the results of meshless membrane simulation for short
proteins, whereas deviations are seen for long proteins owing to the formation
of protein clusters.


title: Optimizing Information Freshness in RIS-assisted NOMA-based IoT Networks

abstract:
  This paper investigates the benefits of integrating reconfigurable
intelligent surface (RIS) on minimizing the average sum age of information
(AoI) in uplink non-orthogonal multiple access-based Internet-of-Things (IoT)
networks. In this setup, an optimization problem is formulated to optimize the
RIS configuration, the transmit power per IoT device and the clustering policy
of IoT devices. The formulated problem is a mixed-integer non-convex one, and
in order to solve it we obtain first the RIS configuration by adopting a
semi-definite relaxation (SDR) approach. Afterwards, the joint power allocation
and user-clustering problem is solved using the concept of bi-level
optimization and is decomposed into an outer user clustering problem and an
inner power allocation problem. Optimal closed-form expressions are derived for
the inner problem and the Hungarian method is employed to solve the outer one.
Numerical results demonstrate the performance superiority of our approach.


title: Primitively universal quaternary quadratic forms

abstract:
  A (positive definite and integral) quadratic form $f$ is said to be
$\textit{universal}$ if it represents all positive integers, and is said to be
$\textit{primitively universal}$ if it represents all positive integers
primitively. We also say $f$ is $\textit{primitively almost universal}$ if it
represents almost all positive integers primitively. Conway and Schneeberger
proved (see [1]) that there are exactly $204$ equivalence classes of universal
quaternary quadratic forms. Recently, Earnest and Gunawardana proved in [4]
that among $204$ equivalence classes of universal quaternary quadratic forms,
there are exactly $152$ equivalence classes of primitively almost universal
quaternary quadratic forms. In this article, we prove that there are exactly
$107$ equivalence classes of primitively universal quaternary quadratic forms.
We also determine the set of all positive integers that are not primitively
represented by each of the remaining $152-107=45$ equivalence classes of
primitively almost universal quaternary quadratic forms.


title: Introduction to phase transmission fibre-optic sensing of seismic waves

abstract:
  This manuscript is concerned with phase changes of signals transmitted
through deforming optical fibres. As a first result, it establishes an exact
relation between observable phase changes and the deformation tensor along the
fibre. This relation is non-linear, and it includes effects related to both
local changes in fibre length and deformation-induced changes of the local
speed of light or refractive index. In seismic applications, where the norm of
the earthquake-induced deformation tensor is orders of magnitude smaller than
1, a useful first-order relation can be derived. It simply connects phase
changes to an integral over in-line strain along the fibre times the local
refractive index. Under the assumption that spatial variations of the
refractive index are fast compared to the seismic wavelength, this permits a
direct synthesis of phase change measurements from distributed strain
measurements, for instance, from Distributed Acoustic Sensing (DAS). A more
detailed analysis of the first-order relation reveals that a perfectly straight
fibre would produce zero phase change measurements, unless deformation was
sufficiently widespread to affect the starting and end points of the fibre. If
this condition is not met, non-zero measurements can only result from curvature
of the fibre or from a heterogeneous distribution of the refractive index.
Segments of the fibre that are strongly curved generally make larger
contributions to the observed phase change.


title:
On an anisotropic double phase problem with singular and sign changing
  nonlinearity

abstract:
  This article consists of study of anisotropic double phase problems with
singular term and sign changing subcritical as well as critical nonlinearity.
Seeking the help of well known Nehari manifold technique, we establish
existence of at least two opposite sign energy solutions in the subcritical
case and one negative energy solution in the critical case. The results in the
critical case is even new in the classical $p$-Laplacian case.


title: KL Divergence Estimation with Multi-group Attribution

abstract:
  Estimating the Kullback-Leibler (KL) divergence between two distributions
given samples from them is well-studied in machine learning and information
theory. Motivated by considerations of multi-group fairness, we seek KL
divergence estimates that accurately reflect the contributions of
sub-populations to the overall divergence. We model the sub-populations coming
from a rich (possibly infinite) family $\mathcal{C}$ of overlapping subsets of
the domain. We propose the notion of multi-group attribution for $\mathcal{C}$,
which requires that the estimated divergence conditioned on every
sub-population in $\mathcal{C}$ satisfies some natural accuracy and fairness
desiderata, such as ensuring that sub-populations where the model predicts
significant divergence do diverge significantly in the two distributions. Our
main technical contribution is to show that multi-group attribution can be
derived from the recently introduced notion of multi-calibration for importance
weights [HKRR18, GRSW21]. We provide experimental evidence to support our
theoretical results, and show that multi-group attribution provides better KL
divergence estimates when conditioned on sub-populations than other popular
algorithms.


title: Point Set Self-Embedding

abstract:
  This work presents an innovative method for point set self-embedding, that
encodes the structural information of a dense point set into its sparser
version in a visual but imperceptible form. The self-embedded point set can
function as the ordinary downsampled one and be visualized efficiently on
mobile devices. Particularly, we can leverage the self-embedded information to
fully restore the original point set for detailed analysis on remote servers.
This task is challenging since both the self-embedded point set and the
restored point set should resemble the original one. To achieve a learnable
self-embedding scheme, we design a novel framework with two jointly-trained
networks: one to encode the input point set into its self-embedded sparse point
set and the other to leverage the embedded information for inverting the
original point set back. Further, we develop a pair of up-shuffle and
down-shuffle units in the two networks, and formulate loss terms to encourage
the shape similarity and point distribution in the results. Extensive
qualitative and quantitative results demonstrate the effectiveness of our
method on both synthetic and real-scanned datasets.


title: Local central limit theorem for gradient field models

abstract:
  We consider the gradient field model in $\left[ -N,N\right] ^{2}\cap
\mathbb{Z}^{2}$ with a uniformly convex interaction potential. Naddaf-Spencer
\cite{NS} and Miller \cite{Mi} proved that the macroscopic averages of linear
statistics of the field converge to a continuum Gaussian free field. In this
paper we prove the distribution of $\phi(0)/\sqrt{\log N}$ converges uniformly
to a Gaussian density, with a Berry-Esseen type bound. This implies the
distribution of $\phi(0)$ is sufficiently `Gaussian like' between $[-\sqrt
{\log N}, \sqrt {\log N}]$.


title:
Sufficient dimension reduction via distance covariance for functional
  and longitudinal data

abstract:
  We propose a method of sufficient dimension reduction for functional data
using distance covariance. We consider the case where the response variable is
a scalar but the predictor is a random function. Our method has several
advantages. It requires very mild conditions on the predictor, unlike the
existing methods require the restrictive linear conditional mean assumption and
constant covariance assumption. It also does not involve the inverse of the
covariance operator which is not bounded. The link function between the
response and the predictor can be arbitrary and our method maintains the model
free advantage without estimating the link function. Moreover, our method is
naturally applicable to sparse longitudinal data. We use functional principal
component analysis with truncation as the regularization mechanism in the
development. The justification for validity of the proposed method is provided
and under some regularization conditions, statistical consistency of our
estimator is established. Simulation studies and real data analysis are also
provided to demonstrate the performance of our method.


title: Femtosecond laser-shockwave induced densification in fused silica

abstract:
  Tightly focused femtosecond laser-beam in the non-ablative regime can induce
a shock-wave enough to reach locally pressures in the giga-Pascal range or
more. In a single beam configuration, the location of the highest-pressure zone
is nested within the laser-focus zone, making it difficult to differentiate the
effect of the shock-wave pressure from photo-induced and plasma relaxation
effect. To circumvent this difficulty, we consider two spatially separated
focused beams that individually act as quasi-simultaneous pressure-wave
emitters. The zone where both shock-waves interfere constructively forms a
region of extreme pressure range, physically separated from the regions under
direct laser exposure. Here, we present evidences of pressured-induced
densification in fused silica in between the foci of the two beams, which can
be exclusively attributed to the superposition of the pressure waves emitted by
each focused laser-beam. Specifically, we show how the beams gap and pulses
time-delay affect the structural properties of fused silica using Raman
characterization, beam deflection technique, and selective etching techniques.
The method is generic and can be implemented in a variety of transparent
substrates for high-pressure physics studies and, unlike classical methods,
such as the use of diamond anvils, offers a means to create arbitrary-shaped
laser-induced high-pressure impacted zones by scanning the two beams across the
specimen volume


title:
'Tis but Thy Name: Semantic Question Answering Evaluation with 11M Names
  for 1M Entities

abstract:
  Classic lexical-matching-based QA metrics are slowly being phased out because
they punish succinct or informative outputs just because those answers were not
provided as ground truth. Recently proposed neural metrics can evaluate
semantic similarity but were trained on small textual similarity datasets
grafted from foreign domains. We introduce the Wiki Entity Similarity (WES)
dataset, an 11M example, domain targeted, semantic entity similarity dataset
that is generated from link texts in Wikipedia. WES is tailored to QA
evaluation: the examples are entities and phrases and grouped into semantic
clusters to simulate multiple ground-truth labels. Human annotators
consistently agree with WES labels, and a basic cross encoder metric is better
than four classic metrics at predicting human judgments of correctness.


title:
Suppression of ferromagnetism and influence of disorder in
  silicon-substituted CeRh6Ge4

abstract:
  We report a study of isoelectronic chemical substitution in the recently
discovered quantum critical ferromagnet CeRh$_6$Ge$_4$. Upon silicon-doping,
the ferromagnetic ordering temperature of CeRh$_6$(Ge$_{1-x}$Si$_x$)$_4$ is
continuously suppressed, and no transition is observed beyond
$x_c$$\approx$0.125. Non-Fermi liquid behavior with $C/T \propto$log($T^*/T$)
is observed close to $x_c$, indicating the existence of strong quantum
fluctuations, while the $T$-linear behavior observed upon pressurizing the
parent compound is absent in the resistivity, which appears to be a consequence
of the disorder induced by silicon doping. Our findings provide evidence for
the role played by disorder on the unusual ferromagnetic quantum criticality in
CeRh$_6$Ge$_4$, and provides further evidence for understanding the origin of
this behavior.


title:
A nanodiamonds-engineered optical-fiber plasmonic interface for
  sensitivity-enhanced biosensing

abstract:
  Benefitting from the excellent characteristics such as low cytotoxicity,
functionalization versatility, and tunable fluorescence, nanodiamonds (NDs)
have shown enormous application potentials in the biomedical field. Herein, we
proposed, for the first time to our best knowledge, to integrate NDs on a
plasmonic interface constructed on a side-polished fiber using drop-casting
method. The added NDs engineers the plasmonic interface towards improving the
sensing field, thus enhancing the sensitivity, which, moreover, is
significantly dependent on the number of drop-casting cycles (DCs) and the used
concentration of NDs dispersion solution. Experimental results suggest that
properly increasing the NDs dispersion concentration is beneficial to obtain a
higher sensitivity while using a fewer number of DCs, but the excessive
concentration extremely deteriorates the resonance dip. Experimentally, using
the optimal 0.2 mg/mL concentration and 3 DCs, we achieve the highest RI
sensitivity of 3582 nm/RIU, which shows an enhancement of 73.8% compared to the
case without NDs modification. The sensitivity enhancement in biosensing is
also proved by employing bovine serum albumin as a demo. The behind mechanism
is explored via characterizations and simulations. This work opens up a new
application form for NDs, i.e. integrating NDs with a plasmonic interface
towards high-performance biosensing.


title: Fractional integration with singularity on Light-cone

abstract:
  We study a family of convolution operators whose kernels have a singularity
on the light-cone in $\mathbb{R}^{n+1}$. As a result, we prove a desired ${\bf
L}^p\to {\bf L}^q$ norm inequality.


title:
Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs
  to be Forgotten

abstract:
  As the use of machine learning (ML) models is becoming increasingly popular
in many real-world applications, there are practical challenges that need to be
addressed for model maintenance. One such challenge is to 'undo' the effect of
a specific subset of dataset used for training a model. This specific subset
may contain malicious or adversarial data injected by an attacker, which
affects the model performance. Another reason may be the need for a service
provider to remove data pertaining to a specific user to respect the user's
privacy. In both cases, the problem is to 'unlearn' a specific subset of the
training data from a trained model without incurring the costly procedure of
retraining the whole model from scratch. Towards this goal, this paper presents
a Markov chain Monte Carlo-based machine unlearning (MCU) algorithm. MCU helps
to effectively and efficiently unlearn a trained model from subsets of training
dataset. Furthermore, we show that with MCU, we are able to explain the effect
of a subset of a training dataset on the model prediction. Thus, MCU is useful
for examining subsets of data to identify the adversarial data to be removed.
Similarly, MCU can be used to erase the lineage of a user's personal data from
trained ML models, thus upholding a user's "right to be forgotten". We
empirically evaluate the performance of our proposed MCU algorithm on
real-world phishing and diabetes datasets. Results show that MCU can achieve a
desirable performance by efficiently removing the effect of a subset of
training dataset and outperform an existing algorithm that utilizes the
remaining dataset.


title:
Hilbert Boundary Value Problems for Hyper Monogenic Functions on The
  Hyperplane

abstract:
  This paper systematically studies Hilbert boundary value problems for hyper
monogenic functions on the hyperplane for the solutions being of any integer
orders at the infinity, where the negative order cases are new even when
restricted to the complex plane context. The explicit solution formulas are
given and the solvability conditions are specified. The results are proved
through using the Clifford symmetric extension method to reduce Hilbert
boundary value problems to Riemann boundary value problems.


title: Rethinking and Refining the Distinct Metric

abstract:
  Distinct-$n$ score\cite{Li2016} is a widely used automatic metric for
evaluating diversity in language generation tasks. However, we observed that
the original approach for calculating distinct scores has evident biases that
tend to assign higher penalties to longer sequences. We refine the calculation
of distinct scores by scaling the number of distinct tokens based on their
expectations. We provide both empirical and theoretical evidence to show that
our method effectively removes the biases existing in the original distinct
score. Our experiments show that our proposed metric,
\textit{Expectation-Adjusted Distinct (EAD)}, correlates better with human
judgment in evaluating response diversity. To foster future research, we
provide an example implementation at
\url{https://github.com/lsy641/Expectation-Adjusted-Distinct}.


title:
Using Multi-scale SwinTransformer-HTC with Data augmentation in CoNIC
  Challenge

abstract:
  Colorectal cancer is one of the most common cancers worldwide, so early
pathological examination is very important. However, it is time-consuming and
labor-intensive to identify the number and type of cells on H&E images in
clinical. Therefore, automatic segmentation and classification task and
counting the cellular composition of H&E images from pathological sections is
proposed by CoNIC Challenge 2022. We proposed a multi-scale Swin transformer
with HTC for this challenge, and also applied the known normalization methods
to generate more augmentation data. Finally, our strategy showed that the
multi-scale played a crucial role to identify different scale features and the
augmentation arose the recognition of model.


title: Unsupervised Representation Learning for Point Clouds: A Survey

abstract:
  Point cloud data have been widely explored due to its superior accuracy and
robustness under various adverse situations. Meanwhile, deep neural networks
(DNNs) have achieved very impressive success in various applications such as
surveillance and autonomous driving. The convergence of point cloud and DNNs
has led to many deep point cloud models, largely trained under the supervision
of large-scale and densely-labelled point cloud data. Unsupervised point cloud
representation learning, which aims to learn general and useful point cloud
representations from unlabelled point cloud data, has recently attracted
increasing attention due to the constraint in large-scale point cloud
labelling. This paper provides a comprehensive review of unsupervised point
cloud representation learning using DNNs. It first describes the motivation,
general pipelines as well as terminologies of the recent studies. Relevant
background including widely adopted point cloud datasets and DNN architectures
is then briefly presented. This is followed by an extensive discussion of
existing unsupervised point cloud representation learning methods according to
their technical approaches. We also quantitatively benchmark and discuss the
reviewed methods over multiple widely adopted point cloud datasets. Finally, we
share our humble opinion about several challenges and problems that could be
pursued in the future research in unsupervised point cloud representation
learning. A project associated with this survey has been built at
https://github.com/xiaoaoran/3d_url_survey.


title:
LCP-dropout: Compression-based Multiple Subword Segmentation for Neural
  Machine Translation

abstract:
  In this study, we propose a simple and effective preprocessing method for
subword segmentation based on a data compression algorithm. Compression-based
subword segmentation has recently attracted significant attention as a
preprocessing method for training data in Neural Machine Translation. Among
them, BPE/BPE-dropout is one of the fastest and most effective method compared
to conventional approaches. However, compression-based approach has a drawback
in that generating multiple segmentations is difficult due to the determinism.
To overcome this difficulty, we focus on a probabilistic string algorithm,
called locally-consistent parsing (LCP), that has been applied to achieve
optimum compression. Employing the probabilistic mechanism of LCP, we propose
LCP-dropout for multiple subword segmentation that improves BPE/BPE-dropout,
and show that it outperforms various baselines in learning from especially
small training data.


title: Minimal Absent Words on Run-Length Encoded Strings

abstract:
  A string $w$ is called a minimal absent word (MAW) for another string $T$ if
$w$ does not occur (as a substring) in $T$ and any proper substring of $w$
occurs in $T$. State-of-the-art data structures for reporting the set
$\mathsf{MAW}(T)$ of MAWs from a given string $T$ of length $n$ require $O(n)$
space, can be built in $O(n)$ time, and can report all MAWs in
$O(|\mathsf{MAW}(T)|)$ time upon a query. This paper initiates the problem of
computing MAWs from a compressed representation of a string. In particular, we
focus on the most basic compressed representation of a string, run-length
encoding (RLE), which represents each maximal run of the same characters $a$ by
$a^p$ where $p$ is the length of the run. Let $m$ be the RLE-size of string
$T$. After categorizing the MAWs into five disjoint sets $\mathcal{M}_1$,
$\mathcal{M}_2$, $\mathcal{M}_3$, $\mathcal{M}_4$, $\mathcal{M}_5$ using RLE,
we present matching upper and lower bounds for the number of MAWs in
$\mathcal{M}_i$ for $i = 1,2,4,5$ in terms of RLE-size $m$, except for
$\mathcal{M}_3$ whose size is unbounded by $m$. We then present a compact
$O(m)$-space data structure that can report all MAWs in optimal
$O(|\mathsf{MAW}(T)|)$ time.


title: A Construction for Clifford Algebras

abstract:
  This paper begins with a basic construction that produces Clifford algebras
inductively, starting with a base algebra A that is associative and has an
involution. This basic construction is an analog of the Cayley-Dickson
Construction that produces the complex numbers, quaternions and octonions
starting from the real numbers. Our basic construction always produces
associative algebras and can be iterated an indefinite number of times. We
generalize the basic construction to a group theoretic construction where a
group G acts on the algebra A, and show how this group theoretic construction
is related to matrix algebras. The paper then concentrates on applications of
this algebra to the Dirac Equation and the Majorana-Dirac Equation.


title:
Displacement-noise-free neutron interferometer for gravitational wave
  detection using a single Mach-Zehnder configuration

abstract:
  The improvement of sensitivity to gravitational waves (GWs) at lower
frequencies is still challenging on account of displacement noise. One of the
solutions is the neutron displacement-noise-free interferometer (DFI). We focus
on a simplification of the detector configuration by taking advantage of the
ability to adjust the neutron speed depending on the configuration. The new
configuration consists of two beamsplitters and two mirrors, which constitute a
single Mach-Zehnder interferometer (MZI). It is simpler than the configuration
with two MZIs in previous research. All displacement noise of mirrors and
beamsplitters can be canceled in the frequency domain. This cancellation can be
explained intuitively using a phasor diagram.


title:
Second-harmonic optical circular dichroism of plasmonic chiral
  helicoid-III nanoparticles

abstract:
  While plasmonic particles can provide optical resonances in a wide spectral
range from the lower visible up to the near-infrared, often symmetry effects
are utilized to obtain particular optical responses. By breaking certain
spatial symmetries, chiral structures arise and provide robust chiroptical
responses to these plasmonic resonances. Here, we observe strong chiroptical
responses in the linear and nonlinear optical regime for chiral L-handed
helicoid III nanoparticles and quantify them by means of an asymmetric factor,
the so-called g-factor. We calculate the linear-optical g-factors for two
distinct chiroptical resonances to -0.12 and -0.43 and the nonlinear optical
g-factors to -1.45 and -1.63. The results demonstrate that the chirality of the
helicoid-III nanoparticles is strongly enhanced in the nonlinear regime.


title: Feasibility study of an accelerator neutrino experiment in China

abstract:
  Future accelerator neutrino experiments will provide a powerful tool to
measure standard oscillation parameters and search for new physics. In this
context, we discuss the prospects of building an accelerator neutrino
experiment in China. The feasibility of such facilities is investigated by
evaluating their prospects to the standard mixing parameters. As an example, we
consider an SPPC-based neutrino beamline and CJPL-based neutrino detector with
1736 km baseline length. We find this setup able to significantly improve the
precision on $\delta_{CP}$, $\theta_{23}$ and $\Delta m_{31}^2$.


title: Emergent geometry and duality in the carbon nucleus

abstract:
  The carbon atom provides the backbone for the complex organic chemistry
composing the building blocks of life. The physics of the carbon nucleus in its
predominant isotope, $^{12}$C, is similarly full of multifaceted complexity.
Some nuclear states of $^{12}$C can be preferentially treated as a collection
of independent particles held by the mean field of the nucleus, while other
states behave more as a collection of three alpha-particle clusters. But these
two pictures are not mutually exclusive, and some states can be described in
either fashion$^{1,2}$. In this work, we provide the first model-independent
tomographic scan of the three-dimensional geometry of the nuclear states of
$^{12}$C using the {\it ab initio} framework of nuclear lattice effective field
theory. We find that the well-known but enigmatic Hoyle state is composed of a
"bent-arm" or obtuse triangular arrangement of alpha clusters. We identify all
of the low-lying nuclear states of $^{12}$C as having an intrinsic shape
composed of three alpha clusters forming either an equilateral triangle or an
obtuse triangle. From these basic structural formations, the various nuclear
states correspond to different rotational and vibrational excitations as well
as either distortions or large-amplitude displacements of the alpha clusters.
The states with the equilateral triangle formation also have a dual description
in terms of particle-hole excitations in the mean-field picture. We compare our
theoretical calculations with experimental data for binding energies,
quadrupole moments, electromagnetic transitions, charge densities, and form
factors. The overall agreement is good, and further studies using
higher-fidelity interactions are planned.


title: Rectified Max-Value Entropy Search for Bayesian Optimization

abstract:
  Although the existing max-value entropy search (MES) is based on the widely
celebrated notion of mutual information, its empirical performance can suffer
due to two misconceptions whose implications on the exploration-exploitation
trade-off are investigated in this paper. These issues are essential in the
development of future acquisition functions and the improvement of the existing
ones as they encourage an accurate measure of the mutual information such as
the rectified MES (RMES) acquisition function we develop in this work. Unlike
the evaluation of MES, we derive a closed-form probability density for the
observation conditioned on the max-value and employ stochastic gradient ascent
with reparameterization to efficiently optimize RMES. As a result of a more
principled acquisition function, RMES shows a consistent improvement over MES
in several synthetic function benchmarks and real-world optimization problems.


title:
Red Light, Green Light Game of Multi-Robot Systems with Safety Barrier
  Certificates

abstract:
  In this paper, we propose the safety barrier certificates for uncertain
multi-robot systems playing red light, green light game. According to the rule
of the game, the robots are allowed to move forward after a doll shouts `green
light' and must stop when it shouts `red light'. Following this rule, a
two-mode nominal controller is designed where one mode is for moving forward
and the other one is for slowing down and being motionless. Then, multiple
exponential control barrier functions(ECBFs) are developed to handle safety
constraints for limited playground, collision avoidance, and saturation of the
velocity. While designing the nominal controller and ECBFs, an estimated
braking time and robust inequality constraints are derived to deal with the
system uncertainty. Consequently, a controller guaranteeing safety barrier
certificates of each robot has been formulated by a quadratic programming with
the nominal controller and the robust inequality constraints. Finally, red
light, green light game is simulated to validate the proposed safety-critical
control system.


title:
Asymptotic analysis on positive solutions of the Lane-Emden system with
  nearly critical exponents

abstract:
  We concern a family $\{(u_{\varepsilon},v_{\varepsilon})\}_{\varepsilon > 0}$
of solutions of the Lane-Emden system on a smooth bounded convex domain
$\Omega$ in $\mathbb{R}^N$ \[\begin{cases} -\Delta u_{\varepsilon} =
v_{\varepsilon}^p &\text{in } \Omega,\\ -\Delta v_{\varepsilon} =
u_{\varepsilon}^{q_{\varepsilon}} &\text{in } \Omega,\\ u_{\varepsilon},\,
v_{\varepsilon} > 0 &\text{in } \Omega,\\ u_{\varepsilon} = v_{\varepsilon} =0
&\text{on } \partial\Omega \end{cases}\] for $N \ge 4$,
$\max\{1,\frac{3}{N-2}\} < p < q_{\varepsilon}$ and small \[\varepsilon :=
\frac{N}{p+1} + \frac{N}{q_{\varepsilon}+1} - (N-2) > 0.\] This system appears
as the extremal equation of the Sobolev embedding $W^{2,(p+1)/p}(\Omega)
\hookrightarrow L^{q_{\varepsilon}+1}(\Omega)$, and is also closely related to
the Calder\'on-Zygmund estimate. Under the a natural energy condition
\[\sup_{\varepsilon > 0} \left(\|u_{\varepsilon}\|_{W^{2,{p+1 \over
p}}(\Omega)} + \|v_{\varepsilon}\|_{W^{2,{q_{\varepsilon}+1 \over
q_{\varepsilon}}}(\Omega)}\right) < \infty,\] we prove that the multiple
bubbling phenomena may arise for the family
$\{(u_{\varepsilon},v_{\varepsilon})\}_{\varepsilon > 0}$, and establish a
detailed qualitative and quantitative description. If $p < \frac{N}{N-2}$, the
nonlinear structure of the system makes the interaction between bubbles so
strong, so the determination process of the blow-up rates and locations is
completely different from that of the classical Lane-Emden equation. If $p \ge
\frac{N}{N-2}$, the blow-up scenario is relatively close to (but not the same
as) that of the classical Lane-Emden equation, and only one-bubble solutions
can exist. Even in the latter case, the standard approach does not work well,
which forces us to devise a new method. Using our analysis, we also deduce a
general existence theorem valid on any smooth bounded domains.


title:
Summary: Chicago Quantum Exchange (CQE) Pulse-level Quantum Control
  Workshop

abstract:
  Quantum information processing holds great promise for pushing beyond the
current frontiers in computing. Specifically, quantum computation promises to
accelerate the solving of certain problems, and there are many opportunities
for innovation based on applications in chemistry, engineering, and finance. To
harness the full potential of quantum computing, however, we must not only
place emphasis on manufacturing better qubits, advancing our algorithms, and
developing quantum software. To scale devices to the fault tolerant regime, we
must refine device-level quantum control.
  On May 17-18, 2021, the Chicago Quantum Exchange (CQE) partnered with IBM
Quantum and Super.tech to host the Pulse-level Quantum Control Workshop. At the
workshop, representatives from academia, national labs, and industry addressed
the importance of fine-tuning quantum processing at the physical layer. The
purpose of this report is to summarize the topics of this meeting for the
quantum community at large.


title:
Measurements of Absolute Branching Fractions of $D^0\to K_L^0\phi$,
  $K_L^0\eta$, $K_L^0\omega$, and $K_L^0\eta^{\prime}$

abstract:
  We report the first measurements of the absolute branching fractions of
$D^0\to K_L^0\phi$, $D^0\to K_L^0\eta$, $D^0\to K_L^0\omega$, and $D^0\to
K_L^0\eta^{\prime}$, obtained by analyzing $2.93\,\rm fb^{-1}$ of $e^+e^-$
collision data taken at a center-of-mass energy of 3.773 GeV with the BESIII
detector. Taking the world averages of the branching fractions of $D^0\to
K_S^0\phi$, $D^0\to K_S^0\eta$, $D^0\to K_S^0\omega$, and $D^0\to
K_S^0\eta^{\prime}$, the $K_S^0$-$K_L^0$ asymmetry $\mathcal{R}(D^0)$ in these
decay modes are obtained. The CP asymmetries in these decays are also
determined. No significant $CP$ violation is observed.


title:
A versatile metasurface enabling superwettability for self-cleaning and
  dynamic color response

abstract:
  Metasurfaces provide applications for a variety of flat elements and devices
due to the ability to modulate light with subwavelength structures. The working
principle meanwhile gives rise to the crucial problem and challenge to protect
the metasurface from dust or clean the unavoidable contaminants during daily
usage. Here, taking advantage of the intelligent bioinspired surfaces which
exhibit self-cleaning properties, we show a versatile dielectric metasurface
benefiting from the obtained superhydrophilic or quasi-superhydrophobic states.
The design is realized by embedding the metasurface inside a large area of
wettability supporting structures, which is highly efficient in fabrication,
and achieves both optical and wettability functionality at the same time. The
superhydrophilic state enables an enhanced optical response with water, while
the quasi-superhydrophobic state imparts the fragile antennas an ability to
self-clean dust contamination. Furthermore, the metasurface can be easily
switched and repeated between these two wettability or functional states by
appropriate treatments in a repeatable way, without degrading the optical
performance. The proposed design strategy will bring new opportunities to smart
metasurfaces with improved optical performance, versatility and physical
stability.


title:
Bandit Learning with General Function Classes: Heteroscedastic Noise and
  Variance-dependent Regret Bounds

abstract:
  We consider learning a stochastic bandit model, where the reward function
belongs to a general class of uniformly bounded functions, and the additive
noise can be heteroscedastic. Our model captures contextual linear bandits and
generalized linear bandits as special cases. While previous works (Kirschner
and Krause, 2018; Zhou et al., 2021) based on weighted ridge regression can
deal with linear bandits with heteroscedastic noise, they are not directly
applicable to our general model due to the curse of nonlinearity. In order to
tackle this problem, we propose a multi-level learning framework for the
general bandit model. The core idea of our framework is to partition the
observed data into different levels according to the variance of their
respective reward and perform online learning at each level collaboratively.
Under our framework, we first design an algorithm that constructs the
variance-aware confidence set based on empirical risk minimization and prove a
variance-dependent regret bound. For generalized linear bandits, we further
propose an algorithm based on follow-the-regularized-leader (FTRL) subroutine
and online-to-confidence-set conversion, which can achieve a tighter
variance-dependent regret under certain conditions.


title:
Generalizable task representation learning from human demonstration
  videos: a geometric approach

abstract:
  We study the problem of generalizable task learning from human demonstration
videos without extra training on the robot or pre-recorded robot motions. Given
a set of human demonstration videos showing a task with different objects/tools
(categorical objects), we aim to learn a representation of visual observation
that generalizes to categorical objects and enables efficient controller
design. We propose to introduce a geometric task structure to the
representation learning problem that geometrically encodes the task
specification from human demonstration videos, and that enables generalization
by building task specification correspondence between categorical objects.
Specifically, we propose CoVGS-IL, which uses a graph-structured task function
to learn task representations under structural constraints. Our method enables
task generalization by selecting geometric features from different objects
whose inner connection relationships define the same task in geometric
constraints. The learned task representation is then transferred to a robot
controller using uncalibrated visual servoing (UVS); thus, the need for extra
robot training or pre-recorded robot motions is removed.


title: Quality-aware News Recommendation

abstract:
  News recommendation is a core technique used by many online news platforms.
Recommending high-quality news to users is important for keeping good user
experiences and news platforms' reputations. However, existing news
recommendation methods mainly aim to optimize news clicks while ignoring the
quality of news they recommended, which may lead to recommending news with
uninformative content or even clickbaits. In this paper, we propose a
quality-aware news recommendation method named QualityRec that can effectively
improve the quality of recommended news. In our approach, we first propose an
effective news quality evaluation method based on the distributions of users'
reading dwell time on news. Next, we propose to incorporate news quality
information into user interest modeling by designing a content-quality
attention network to select clicked news based on both news semantics and
qualities. We further train the recommendation model with an auxiliary news
quality prediction task to learn quality-aware recommendation model, and we add
a recommendation quality regularization loss to encourage the model to
recommend higher-quality news. Extensive experiments on two real-world datasets
show that QualityRec can effectively improve the overall quality of recommended
news and reduce the recommendation of low-quality news, with even slightly
better recommendation accuracy.


title:
Asynchronous Decentralized Federated Learning for Collaborative Fault
  Diagnosis of PV Stations

abstract:
  Due to the different losses caused by various photovoltaic (PV) array faults,
accurate diagnosis of fault types is becoming increasingly important. Compared
with a single one, multiple PV stations collect sufficient fault samples, but
their data is not allowed to be shared directly due to potential conflicts of
interest. Therefore, federated learning can be exploited to train a
collaborative fault diagnosis model. However, the modeling efficiency is
seriously affected by the model update mechanism since each PV station has a
different computing capability and amount of data. Moreover, for the safe and
stable operation of the PV system, the robustness of collaborative modeling
must be guaranteed rather than simply being processed on a central server. To
address these challenges, a novel asynchronous decentralized federated learning
(ADFL) framework is proposed. Each PV station not only trains its local model
but also participates in collaborative fault diagnosis by exchanging model
parameters to improve the generalization without losing accuracy. The global
model is aggregated distributedly to avoid central node failure. By designing
the asynchronous update scheme, the communication overhead and training time
are greatly reduced. Both the experiments and numerical simulations are carried
out to verify the effectiveness of the proposed method.


title: Are Big Recommendation Models Fair to Cold Users?

abstract:
  Big models are widely used by online recommender systems to boost
recommendation performance. They are usually learned on historical user
behavior data to infer user interest and predict future user behaviors (e.g.,
clicks). In fact, the behaviors of heavy users with more historical behaviors
can usually provide richer clues than cold users in interest modeling and
future behavior prediction. Big models may favor heavy users by learning more
from their behavior patterns and bring unfairness to cold users. In this paper,
we study whether big recommendation models are fair to cold users. We
empirically demonstrate that optimizing the overall performance of big
recommendation models may lead to unfairness to cold users in terms of
performance degradation. To solve this problem, we propose a BigFair method
based on self-distillation, which uses the model predictions on original user
data as a teacher to regularize predictions on augmented data with randomly
dropped user behaviors, which can encourage the model to fairly capture
interest distributions of heavy and cold users. Experiments on two datasets
show that BigFair can effectively improve the performance fairness of big
recommendation models on cold users without harming the performance on heavy
users.


title:
Semi-supervised Learning on Large Graphs: is Poisson Learning a
  Game-Changer?

abstract:
  We explain Poisson learning on graph-based semi-supervised learning to see if
it could avoid the problem of global information loss problem as Laplace-based
learning methods on large graphs. From our analysis, Poisson learning is simply
Laplace regularization with thresholding, cannot overcome the problem.


title:
Phase separation and multistability of two-component Bose-Einstein
  condensate in an optical cavity

abstract:
  We examine the multistability associated with miscibility-immiscibility
conditions for a two-component Bose-Einstein condensate coupled to the light
field in an optical cavity. For a strongly immiscible condition, the system
exhibits a variety of density structures, including separated state, stripe
state, and their coexistence. The multistability arises from these spatial
structures of the two-component condensate, which significantly alter the
hysteresis curve with respect to the intensity of cavity pumping. We present a
variational approach to confirm our numerical results.


title:
Detecting Stance in Scientific Papers: Did we get more Negative
  Recently?

abstract:
  In this paper, we classify scientific articles in the domain of natural
language processing (NLP) and machine learning (ML) into whether (i) they
extend the current state-of-the-art by introduction of novel techniques which
beat existing models or whether (ii) they mainly criticize the existing
state-of-the-art, i.e., that it is deficient with respect to some property
(e.g., wrong evaluation, wrong datasets, misleading task specification). We
refer to contributions under (i) as having a "positive stance" and
contributions under (ii) as having a "negative stance" to related work. We
annotate over 2k papers from NLP and ML to train a SciBERT based model to
automatically predict the stance of a paper based on its title and abstract. We
then analyze large-scale trends on over 41k papers from the last ~35 years in
NLP and ML, finding that papers have gotten substantially more positive over
time, but negative papers also got more negative and we observe considerably
more negative papers in recent years. Negative papers are also more influential
in terms of citations they receive.


title:
Prepare for Trouble and Make it Double. Supervised and Unsupervised
  Stacking for AnomalyBased Intrusion Detection

abstract:
  In the last decades, researchers, practitioners and companies struggled in
devising mechanisms to detect malicious activities originating security
threats. Amongst the many solutions, network intrusion detection emerged as one
of the most popular to analyze network traffic and detect ongoing intrusions
based on rules or by means of Machine Learners (MLs), which process such
traffic and learn a model to suspect intrusions. Supervised MLs are very
effective in detecting known threats, but struggle in identifying zero-day
attacks (unknown during learning phase), which instead can be detected through
unsupervised MLs. Unfortunately, there are no definitive answers on the
combined use of both approaches for network intrusion detection. In this paper
we first expand the problem of zero-day attacks and motivate the need to
combine supervised and unsupervised algorithms. We propose the adoption of
meta-learning, in the form of a two-layer Stacker, to create a mixed approach
that detects both known and unknown threats. Then we implement and empirically
evaluate our Stacker through an experimental campaign that allows i) debating
on meta-features crafted through unsupervised base-level learners, ii) electing
the most promising supervised meta-level classifiers, and iii) benchmarking
classification scores of the Stacker with respect to supervised and
unsupervised classifiers. Last, we compare our solution with existing works
from the recent literature. Overall, our Stacker reduces misclassifications
with respect to (un)supervised ML algorithms in all the 7 public datasets we
considered, and outperforms existing studies in 6 out of those 7 datasets. In
particular, it turns out to be more effective in detecting zero-day attacks
than supervised algorithms, limiting their main weakness but still maintaining
adequate capabilities in detecting known attacks.


title: Effective Lagrangian Morphing

abstract:
  With the LHC entering the precision era, focus on interpreting the
measurements performed in an effective field theory holds key to testing the
Standard Model. An effective field theory provides a well-defined theoretical
formalism which extends the Standard Model by introduce new terms with free
coefficients that can be measured with experimental data. Constructing models
parametric in these new coefficients is achieved by virtue of combining
predictions from Monte Carlo generators for different interactions. This paper
builds upon earlier works and describes a state-of-the-art approach to build
multidimensional parametric models for new physics using effective Lagrangians.
Documentation and tutorials for an associated toolkit that have been
contributed to the ROOT data analysis software framework is included.


title: ALMA [O III] and [C II] Detections of A1689-zD1 at $z=7.13$

abstract:
  A1689-zD1 is one of the most distant galaxies, discovered with the aid of
gravitational lensing, providing us with an important opportunity to study
galaxy formation in the very early Universe. In this study, we report the
detection of [C II]158$\mu$m and [O III]88$\mu$m emission lines of A1689-zD1 in
the ALMA Bands 6 and 8. We measure the redshift of this galaxy as
$z_{\rm{sys}}=7.133\pm0.005$ based on the [C II] and [O III] emission lines,
consistent with that adopted by Bakx et al. (2021). The observed
$L_{[\rm{O\,III]}}/L_{[\rm{C\,II]}}$ ratio is $2.09\pm0.09$, higher than most
of the local galaxies, but consistent with other $z\sim7$ galaxies. The
moderate-spatial resolution of ALMA data provided us with a precious
opportunity to investigate spatial variation of
$L_{[\rm{O\,III]}}/L_{[\rm{C\,II]}}$. In contrast to the average value of 2.09,
we find a much higher $L_{[\rm{O\,III]}}/L_{[\rm{C\,II]}}$ of $\sim 7$ at the
center of the galaxy. This spatial variation of
$L_{[\rm{O\,III]}}/L_{[\rm{C\,II]}}$ was seldom reported for other high-z
galaxies. It is also interesting that the peak of the ratio does not overlap
with optical peaks. Possible physical reasons include a central AGN, shock
heating from merging, and starburst. Our moderate-spatial resolution data also
reveals that in addition to the observed two clumps shown in previous HST
images, there is a redshifted segment to the west of the northern optical
clump. Such a structure is consistent with previous claims that A1689-zD1 is a
merging galaxy, but with the northern redshifted part being some ejected
materials, or that the northern redshifted materials being from a third more
highly obscured region of the galaxy.


title: Mg-doping and free-hole properties of hot-wall MOCVD GaN

abstract:
  The hot-wall metal-organic chemical vapor deposition (MOCVD), previously
shown to enable superior III-nitride material quality and high performance
devices, has been explored for Mg doping of GaN. We have investigated the Mg
incorporation in a wide doping range ($2.45\times{10}^{18}~cm^{-3}$ up to
$1.10\times{10}^{20}~cm^{-3}$) and demonstrate GaN:Mg with low background
impurity concentrations under optimized growth conditions. Dopant and impurity
levels are discussed in view of Ga supersaturation which provides a unified
concept to explain the complexity of growth conditions impact on Mg acceptor
incorporation and compensation. The results are analysed in relation to the
extended defects, revealed by scanning transmission electron microscopy (STEM),
X-ray diffraction (XRD), and surface morphology, and in correlation with the
electrical properties obtained by Hall effect and capacitance-voltage (C-V)
measurements. This allows to establish a comprehensive picture of GaN:Mg growth
by hot-wall MOCVD providing guidance for growth parameters optimization
depending on the targeted application. We show that substantially lower H
concentration as compared to Mg acceptors can be achieved in GaN:Mg without any
in-situ or post-growth annealing resulting in p-type conductivity in as-grown
material. State-of-the-art $p$-GaN layers with a low-resistivity and a high
free-hole density (0.77 $\Omega$.cm and $8.4\times{10}^{17}~cm^{-3}$,
respectively) are obtained after post-growth annealing demonstrating the
viability of hot-wall MOCVD for growth of power electronic device structures.


title: Short term variability of DS Tuc A observed with TESS

abstract:
  Impulsive short term variations occur in all kinds of solar-type stars. They
are the results of complex phenomena such as the stellar magnetic field
reconnection, low-level variability or in some cases even star-planet
interactions. The radiation arising from these events is often highly energetic
and, in stars hosting planets, may interact with the planetary atmospheres.
Studying the rate of these energetic phenomena is fundamental to understand
their role in modifying the chemical composition or, in some extreme cases, to
the disruption of the planetary atmospheres. Here, we present a new procedure
developed to identify the impulsive events in TESS light curves. Our goal is to
have a simple and effective tool to study the short-term activity of a star
using only its light curve, in order to derive its distribution and energetic.
As our first case, we studied the system DS Tuc. Our technique consists of
fitting the TESS light curves using iteratively Gaussian processes in order to
remove all the long-term stellar activity contributions. Then, we identify the
impulsive events and, derive amplitudes, time scales and the amount of energy
emitted. We validate our procedure using the AU Mic TESS light curves obtaining
results consistent with those presented in the literature. We estimate the
frequency distribution of energetic events for DS Tuc. In particular, we find
that there are approx 2 events per day with energy greater than 2x10^32 erg. We
find evidence for a favoured stellar phase for short term activity on AU Mic,
and also indications of short term activity in phase with the planetary orbit.
For DS Tuc we find that the events distribution is not equally spaced in time
but often grouped. The resulting distribution may be used to estimate the
impact of short term variability on planetary atmosphere chemical compositions.


title:
WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation
  Models

abstract:
  Learning the user-item relevance hidden in implicit feedback data plays an
important role in modern recommender systems. Neural sequential recommendation
models, which formulates learning the user-item relevance as a sequential
classification problem to distinguish items in future behaviors from others
based on the user's historical behaviors, have attracted a lot of interest in
both industry and academic due to their substantial practical value. Though
achieving many practical successes, we argue that the intrinsic {\bf
incompleteness} and {\bf inaccuracy} of user behaviors in implicit feedback
data is ignored and conduct preliminary experiments for supporting our claims.
Motivated by the observation that model-free methods like behavioral
retargeting (BR) and item-based collaborative filtering (ItemCF) hit different
parts of the user-item relevance compared to neural sequential recommendation
models, we propose a novel model-agnostic training approach called WSLRec,
which adopts a three-stage framework: pre-training, top-$k$ mining, and
fine-tuning. WSLRec resolves the incompleteness problem by pre-training models
on extra weak supervisions from model-free methods like BR and ItemCF, while
resolves the inaccuracy problem by leveraging the top-$k$ mining to screen out
reliable user-item relevance from weak supervisions for fine-tuning.
Experiments on two benchmark datasets and online A/B tests verify the
rationality of our claims and demonstrate the effectiveness of WSLRec.


title: Deep learning enhanced Rydberg multifrequency microwave recognition

abstract:
  Recognition of multifrequency microwave (MW) electric fields is challenging
because of the complex interference of multifrequency fields in practical
applications. Rydberg atom-based measurements for multifrequency MW electric
fields is promising in MW radar and MW communications. However, Rydberg atoms
are sensitive not only to the MW signal but also to noise from atomic
collisions and the environment, meaning that solution of the governing Lindblad
master equation of light-atom interactions is complicated by the inclusion of
noise and high-order terms. Here, we solve these problems by combining Rydberg
atoms with deep learning model, demonstrating that this model uses the
sensitivity of the Rydberg atoms while also reducing the impact of noise
without solving the master equation. As a proof-of-principle demonstration, the
deep learning enhanced Rydberg receiver allows direct decoding of the
frequency-division multiplexed (FDM) signal. This type of sensing technology is
expected to benefit Rydberg-based MW fields sensing and communication.


title:
Supervised Machine Learning Algorithm for Detecting Consistency between
  Reported Findings and the Conclusions of Mammography Reports

abstract:
  Objective. Mammography reports document the diagnosis of patients'
conditions. However, many reports contain non-standard terms (non-BI-RADS
descriptors) and incomplete statements, which can lead to conclusions that are
not well-supported by the reported findings. Our aim was to develop a tool to
detect such discrepancies by comparing the reported conclusions to those that
would be expected based on the reported radiology findings. Materials and
Methods. A deidentified data set from an academic hospital containing 258
mammography reports supplemented by 120 reports found on the web was used for
training and evaluation. Spell checking and term normalization was used to
unambiguously determine the reported BI-RADS descriptors. The resulting data
were input into seven classifiers that classify mammography reports, based on
their Findings sections, into seven BI-RADS final assessment categories.
Finally, the semantic similarity score of a report to each BI-RADS category is
reported. Results. Our term normalization algorithm correctly identified 97% of
the BI-RADS descriptors in mammography reports. Our system provided 76%
precision and 83% recall in correctly classifying the reports according to
BI-RADS final assessment category. Discussion. The strength of our approach
relies on providing high importance to BI-RADS terms in the summarization
phase, on the semantic similarity that considers the complex data
representation, and on the classification into all seven BI-RADs categories.
Conclusion. BI-RADS descriptors and expected final assessment categories could
be automatically detected by our approach with fairly good accuracy, which
could be used to make users aware that their reported findings do not match
well with their conclusion.


title:
Homotopy type and homology versus volume for arithmetic locally
  symmetric spaces

abstract:
  We study locally symmetric spaces associated with arithmetic lattices in
semisimple Lie groups. We prove the following results about their topology: the
minimal number of tetrahedra needed for a triangulation is at most linear in
the volume and the Betti numbers are sub-linear in the volume except possibly
in middle degree. The proof of these results uses the geometry of these spaces,
namely the study of their thin parts. In this regard we prove that these spaces
converge in the Benjamini--Schramm sense to their universal covers and give an
explicit bound for the volume of the thin part for trace fields of large
degree. The main technical ingredients for our proofs are new estimates on
orbital integrals, a counting result for elements of small displacement, and a
refined version of the Margulis lemma for arithmetic locally symmetric spaces.


title:
Cutting a tree with Subgraph Complementation is hard, except for some
  small trees

abstract:
  For a property $\Pi$, Subgraph Complementation to $\Pi$ is the problem to
find whether there is a subset $S$ of vertices in the input graph $G$ such that
modifying $G$ by complementing the subgraph induced by $S$ results in a graph
satisfying the property $\Pi$. We prove that, the problem of Subgraph
Complementation to $T$-free graphs is NP-Complete, for $T$ being a tree, except
for 40 trees of at most 13 vertices (a graph is $T$-free if it does not contain
any induced copies of $T$). Further, we prove that these hard problems do not
admit any subexponential-time algorithms, assuming the Exponential Time
Hypothesis. As an additional result, we obtain that Subgraph Complementation to
paw-free graphs can be solved in polynomial-time.


title:
Anomalous single-mode lasing induced by nonlinearity and the
  non-Hermitian skin effect

abstract:
  Single-mode operation is a desirable but elusive property for lasers
operating at high pump powers. Typically, single-mode lasing is attainable
close to threshold, but increasing the pump power gives rise to multiple lasing
peaks due to inter-modal gain competition. We propose a laser with the opposite
behavior: multi-mode lasing occurs at low output powers, but pumping beyond a
certain value produces a single lasing mode, with all other candidate modes
experiencing negative effective gain. This behavior arises in a lattice of
coupled optical resonators with non-fine-tuned asymmetric couplings, and is
caused by an interaction between nonlinear gain saturation and the
non-Hermitian skin effect. The single-mode lasing is observed in both frequency
domain and time domain simulations. It is robust against on-site disorder, and
scales up to large lattice sizes. This finding might be useful for implementing
high-power laser arrays.


title: Tunable two-dimensional laser arrays with zero-phase locking

abstract:
  Two-dimensional (2D) laser arrays are shown to be achievable at a large scale
by exploiting the interplay of higher-order topological insulator (HOTI)
physics and the so-called non-Hermitian skin effect (NHSE). The higher-order
topology allows for the amplification and hence lasing of a single-mode
protected by a band gap; whereas the NHSE, widely known to accumulate
population in a biased direction in non-Hermitian systems, is introduced to
compete with the topological localization of corner modes. By tuning the system
parameters appropriately and pumping at one site only, a single topologically
protected lasing mode delocalized across over two dimensions emerges, with its
power widely tunable by adjusting the pump strength. Computational studies
clearly indicate that the lasing mode thus engineered is stable, and the phase
difference between nearest lasing sites is locked at zero, even after the
disorder is accounted for. The total power of the lasing mode forming a 2D
topological laser array is proportional to the area of the 2D lattice
accommodating a HOTI phase. Based on existing experiments, we further propose
to use coupled optical ring resonators as a promising platform to realize
large-scale 2D laser arrays.


title: Interactive Machine Learning for Image Captioning

abstract:
  We propose an approach for interactive learning for an image captioning
model. As human feedback is expensive and modern neural network based
approaches often require large amounts of supervised data to be trained, we
envision a system that exploits human feedback as good as possible by
multiplying the feedback using data augmentation methods, and integrating the
resulting training examples into the model in a smart way. This approach has
three key components, for which we need to find suitable practical
implementations: feedback collection, data augmentation, and model update. We
outline our idea and review different possibilities to address these tasks.


title:
Weakly Supervised Disentangled Representation for Goal-conditioned
  Reinforcement Learning

abstract:
  Goal-conditioned reinforcement learning is a crucial yet challenging
algorithm which enables agents to achieve multiple user-specified goals when
learning a set of skills in a dynamic environment. However, it typically
requires millions of the environmental interactions explored by agents, which
is sample-inefficient. In the paper, we propose a skill learning framework
DR-GRL that aims to improve the sample efficiency and policy generalization by
combining the Disentangled Representation learning and Goal-conditioned visual
Reinforcement Learning. In a weakly supervised manner, we propose a Spatial
Transform AutoEncoder (STAE) to learn an interpretable and controllable
representation in which different parts correspond to different object
attributes (shape, color, position). Due to the high controllability of the
representations, STAE can simply recombine and recode the representations to
generate unseen goals for agents to practice themselves. The manifold structure
of the learned representation maintains consistency with the physical position,
which is beneficial for reward calculation. We empirically demonstrate that
DR-GRL significantly outperforms the previous methods in sample efficiency and
policy generalization. In addition, DR-GRL is also easy to expand to the real
robot.


title: Enhance transferability of adversarial examples with model architecture

abstract:
  Transferability of adversarial examples is of critical importance to launch
black-box adversarial attacks, where attackers are only allowed to access the
output of the target model. However, under such a challenging but practical
setting, the crafted adversarial examples are always prone to overfitting to
the proxy model employed, presenting poor transferability. In this paper, we
suggest alleviating the overfitting issue from a novel perspective, i.e.,
designing a fitted model architecture. Specifically, delving the bottom of the
cause of poor transferability, we arguably decompose and reconstruct the
existing model architecture into an effective model architecture, namely
multi-track model architecture (MMA). The adversarial examples crafted on the
MMA can maximumly relieve the effect of model-specified features to it and
toward the vulnerable directions adopted by diverse architectures. Extensive
experimental evaluation demonstrates that the transferability of adversarial
examples based on the MMA significantly surpass other state-of-the-art model
architectures by up to 40% with comparable overhead.


title: Improving Response Time of Home IoT Services in Federated Learning

abstract:
  For intelligent home IoT services with sensors and machine learning, we need
to upload IoT data to the cloud server which cannot share private data for
training. A recent machine learning approach, called federated learning, keeps
user data on the device in the distributed computing environment. Though
federated learning is useful for protecting privacy, it experiences poor
performance in terms of the end-to-end response time in home IoT services,
because IoT devices are usually controlled by remote servers in the cloud. In
addition, it is difficult to achieve the high accuracy of federated learning
models due to insufficient data problems and model inversion attacks. In this
paper, we propose a local IoT control method for a federated learning home
service that recognizes the user behavior in the home network quickly and
accurately. We present a federated learning client with transfer learning and
differential privacy to solve data scarcity and data model inversion attack
problems. From experiments, we show that the local control of home IoT devices
for user authentication and control message transmission by the federated
learning clients improves the response time to less than 1 second. Moreover, we
demonstrate that federated learning with transfer learning achieves 97% of
accuracy under 9,000 samples, which is only 2% of the difference from
centralized learning.


title:
Changeable Rate and Novel Quantization for CSI Feedback Based on Deep
  Learning

abstract:
  Deep learning (DL)-based channel state information (CSI) feedback improves
the capacity and energy efficiency of massive multiple-input multiple-output
(MIMO) systems in frequency division duplexing mode. However, multiple neural
networks with different lengths of feedback overhead are required by
time-varying bandwidth resources. The storage space required at the user
equipment (UE) and the base station (BS) for these models increases linearly
with the number of models. In this paper, we propose a DL-based changeable-rate
framework with novel quantization scheme to improve the efficiency and
feasibility of CSI feedback systems. This framework can reutilize all the
network layers to achieve overhead-changeable CSI feedback to optimize the
storage efficiency at the UE and the BS sides. Designed quantizer in this
framework can avoid the normalization and gradient problems faced by
traditional quantization schemes. Specifically, we propose two DL-based
changeable-rate CSI feedback networks CH-CsiNetPro and CH-DualNetSph by
introducing a feedback overhead control unit. Then, a pluggable quantization
block (PQB) is developed to further improve the encoding efficiency of CSI
feedback in an end-to-end way. Compared with existing CSI feedback methods, the
proposed framework saves the storage space by about 50% with changeable-rate
scheme and improves the encoding efficiency with the quantization module.


title:
Factorization of the forward-backward charge asymmetry and measurements
  of the weak mixing angle and proton structure at hadron colliders

abstract:
  The forward-backward charge asymmetry (AFB) at hadron colliders is sensitive
to both the electroweak (EW) symmetry breaking represented by the effective
weak mixing angle, and the proton structure information in the initial state
modeled by the parton distribution functions (PDFs). Due to their strong
correlation, the precisions of the determination on the weak mixing angle and
PDFs using the measured AFB spectrum are limited. In this paper, we define a
set of structure parameters which factorize the proton information in the AFB
observation. These parameters reflect unique information of the relative
difference between quarks and antiquarks, and can be simultaneously measured
along with the weak mixing angle, by fitting to the AFB distributions observed
from one single experiment. It is demonstrated that at the LHC, the precision
of the weak mixing angle determination can be significantly improved, compared
to a conventional measurement of simply using the current PDF predictions. At
the same time, the fitted structure parameters can be used to further reduce
the current PDF uncertainties.


title:
Local strict singular characteristics II: existence for stationary
  equation on $\mathbb{R}^2$

abstract:
  The notion of strict singular characteristics is important in the
wellposedness issue of singular dynamics on the cut locus of the viscosity
solutions. We provide an intuitive and rigorous proof of the existence of the
strict singular characteristics of Hamilton-Jacobi equation $H(x,Du(x),u(x))=0$
in two dimensional case. We also proved if $\mathbf{x}$ is a strict singular
characteristic, then we really have the right-differentiability of $\mathbf{x}$
and the right-continuity of $\dot{\mathbf{x}}^+(t)$ for every $t$. Such a
strict singular characteristic must give a selection $p(t)\in
D^+u(\mathbf{x}(t))$ such that $p(t)=\arg\min_{p\in
D^+u(\mathbf{x}(t))}H(\mathbf{x}(t),p,u(\mathbf{x}(t)))$.


title: Stream Containers for Resource-oriented RDF Stream Processing

abstract:
  We introduce Stream Containers inspired by the Linked Data Platform as an
alternative way to process RDF streams. A Stream Container represents a single
RDF data stream that can be accessed in a resource-oriented way which allows
for better interoperability with the existing Semantic Web infrastructure.
Stream Containers are managed by webservers that are responsible for
implementing the S2R operator, i.e. calculating the window for their clients.
The clients on the other hand can use a standard SPARQL processor in
combination with HTTP requests to do RDF processing. Query results can be
converted back to an RDF stream (R2S operator) by posting the data to a Stream
Container. Our approach of resource-oriented RDF stream processing can lead to
a better distribution of load and thus to better worldwide scalability. We give
a general overview of the proposed architecture as well as the formal semantics
of the overall system.


title:
Koszul property of Ulrich bundles and rationality of moduli spaces of
  stable bundles on del pezzo surfaces

abstract:
  Let $\mathcal{E}$ be a vector bundle on a smooth projective variety
$X\subseteq\mathbb{P}^N$ that is Ulrich with respect to the hyperplane section
$H$. In this article, we study the Koszul property of $\mathcal{E}$, the
slope--semistability of the $k$--th iterated syzygy bundle
$\mathcal{S}_k(\mathcal{E})$ for all $k\geq 0$ and rationality of moduli spaces
of slope--stable bundles on del--Pezzo surfaces. As a consequence of our study,
we show that if $X$ is a del--Pezzo surface of degree $d\geq 4$, then any
Ulrich bundle $\mathcal{E}$ satisfies the Koszul property and is
slope--semistable. We also show that, for infinitely many Chern characters
${\bf v}=(r,c_1, c_2)$, the corresponding moduli spaces of slope--stable
bundles $\mathfrak{M}_H({\bf v})$ when non--empty, are rational, and thereby
produce new evidences for a conjecture of Costa and Mir\'o-Roig. As a
consequence, we show that the iterated syzygy bundles of Ulrich bundles are
dense in these moduli spaces.


title: Stochastic Linear-Quadratic Optimal Control with Partial Observation

abstract:
  The paper studies a class of quadratic optimal control problems for partially
observable linear dynamical systems. In contrast to the full information case,
the control is required to be adapted to the filtration generated by the
observation system, which in turn is influenced by the control. The variation
method fails in this case due to the fact that the filtration is not fixed. To
overcome the difficulty, we use the orthogonal decomposition of the state
process to write the cost functional as the sum of two parts: one is a
functional of the control and the filtering process and the other part is
independent of the choice of the control. The first part possesses a
mathematical structure similar to the full information problem. By completing
the square, it is shown that the optimal control is given by a feedback
representation via the filtering process. The optimal value is also obtained
explicitly.


title: Fantastic Morphisms and Where to Find Them: A Guide to Recursion Schemes

abstract:
  Structured recursion schemes have been widely used in constructing,
optimising, and reasoning about programs over inductive and coinductive
datatypes. Their plain forms, catamorphisms and anamorphisms, are restricted in
expressiveness. Thus many generalisations have been proposed, which further
lead to several unifying frameworks of structured recursion schemes. However,
the existing work on unifying frameworks typically focuses on the categorical
foundation, and thus is perhaps inaccessible to practitioners who are willing
to apply recursion schemes in practice but are not versed in category theory.
To fill this gap, this expository paper introduces structured recursion schemes
from a practical point of view: a variety of recursion schemes are motivated
and explained in contexts of concrete programming examples. The categorical
duals of these recursion schemes are also explained.


title: Multifractal eigenfunctions for quantum star graphs

abstract:
  We prove that the eigenfunctions of quantum star graphs exhibit multifractal
self-similar structure in certain specified circumstances. In the semiclassical
regime, when the spectral parameter and the number of vertices tend to
infinity, we derive an asymptotic condition for the Mellin transform of a
specified function arising from the set of bond lengths which yields an
asymptotic for the Renyi entropy associated with an eigenfunction. We apply
this result to show that one may construct simple quantum star graphs which
satisfy a multifractal scaling law. In the low frequency regime we prove
multifractality by computing the Renyi entropy in terms of a zeta function
associated with the set of bond lengths. In certain arithmetic cases the
fractal exponent D_q satisfies a symmetry relation around q=1/4 which arises
from the functional equation of the zeta function. Our results are, in some
sense, analogous to the multifractal scaling law that the authors recently
proved for arithmetic Seba billiards. However, unlike in that case, we do not
require arithmetic conditions to be satisfied, and nor do we rely on delicate
arithmetic estimates.


title: Parameterised Partially-Predrawn Crossing Number

abstract:
  Inspired by the increasingly popular research on extending partial graph
drawings, we propose a new perspective on the traditional and arguably most
important geometric graph parameter, the crossing number. Specifically, we
define the partially predrawn crossing number to be the smallest number of
crossings in any drawing of a graph, part of which is prescribed on the input
(not counting the prescribed crossings). Our main result - an FPT-algorithm to
compute the partially predrawn crossing number - combines advanced ideas from
research on the classical crossing number and so called partial planarity in a
very natural but intricate way. Not only do our techniques generalise the known
FPT-algorithm by Grohe for computing the standard crossing number, they also
allow us to substantially improve a number of recent parameterised results for
various drawing extension problems.


title: Improving Lexical Embeddings for Robust Question Answering

abstract:
  Recent techniques in Question Answering (QA) have gained remarkable
performance improvement with some QA models even surpassed human performance.
However, the ability of these models in truly understanding the language still
remains dubious and the models are revealing limitations when facing
adversarial examples. To strengthen the robustness of QA models and their
generalization ability, we propose a representation Enhancement via Semantic
and Context constraints (ESC) approach to improve the robustness of lexical
embeddings. Specifically, we insert perturbations with semantic constraints and
train enhanced contextual representations via a context-constraint loss to
better distinguish the context clues for the correct answer. Experimental
results show that our approach gains significant robustness improvement on four
adversarial test sets.


title:
Time-dependent generator coordinate method study of fission: dissipation
  effects

abstract:
  Starting from a quantum theory of dissipation for nuclear collective motion,
the time-dependent generator coordinate method (TDGCM) is extended to allow for
dissipation effects in the description of induced fission dynamics. The
extension is based on a generalization of the GCM generating functions that
includes excited states, and the resulting equation of motion in the collective
coordinates and excitation energy. With the assumption of a narrow hamiltonian
kernel, an expansion in a power series in collective momenta leads to a
Schr\"odinger-like equation that explicitly includes a dissipation term,
proportional to the momentum of the statistical wave function. An illustrative
calculation is performed for induced fission of $^{228}$Th. The
three-dimensional model space includes the axially-symmetric quadrupole and
octupole shape variables, and the nuclear temperature. When compared to data
for photo-induced fission of $^{228}$Th, the calculated fission yields
demonstrate the important role of the additional term in the hamiltonian that
explicitly takes into account the dissipation of energy of collective motion
into intrinsic degrees of freedom.


title:
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation
  of Gaussian Processes for Real-World Control

abstract:
  The ability of Gaussian processes (GPs) to predict the behavior of dynamical
systems as a more sample-efficient alternative to parametric models seems
promising for real-world robotics research. However, the computational
complexity of GPs has made policy search a highly time and memory consuming
process that has not been able to scale to larger problems. In this work, we
develop a policy optimization method by leveraging fast predictive sampling
methods to process batches of trajectories in every forward pass, and compute
gradient updates over policy parameters by automatic differentiation of Monte
Carlo evaluations, all on GPU. We demonstrate the effectiveness of our approach
in training policies on a set of reference-tracking control experiments with a
heavy-duty machine. Benchmark results show a significant speedup over exact
methods and showcase the scalability of our method to larger policy networks,
longer horizons, and up to thousands of trajectories with a sublinear drop in
speed.