title:
The Multimarginal Optimal Transport Formulation of Adversarial
  Multiclass Classification

abstract:
  We study a family of adversarial multiclass classification problems and
provide equivalent reformulations in terms of: 1) a family of generalized
barycenter problems introduced in the paper and 2) a family of multimarginal
optimal transport problems where the number of marginals is equal to the number
of classes in the original classification problem. These new theoretical
results reveal a rich geometric structure of adversarial learning problems in
multiclass classification and extend recent results restricted to the binary
classification setting. A direct computational implication of our results is
that by solving either the barycenter problem and its dual, or the MOT problem
and its dual, we can recover the optimal robust classification rule and the
optimal adversarial strategy for the original adversarial problem. Examples
with synthetic and real data illustrate our results.


title:
Simulating fusion reactions from Coulomb explosions within a transport
  approach

abstract:
  We have studied nuclear fusion reactions from the Coulomb explosion of
deuterium clusters induced by high-intensity laser beams within a transport
approach. By incorporating the D+D $\rightarrow$ n + He$^3$ channel as
inelastic collisions based on the stochastic method, we have calibrated the
neutron yield from the simulation in a box system with that from the reaction
rate equation, and then have investigated the dynamics from Coulomb explosions
of systems of different sizes and with different deuteron numbers in clusters.
We have found that the kinetic energy spectrum of deuterons at final stages,
which depends on the system size and deuteron number in clusters, is different
from that when neutrons are abundantly produced, corresponding to significantly
different reaction rates. We have also extrapolated the neutron yield result
from small systems to large systems based on an intuitive parameterized form.
The present framework can be extended by incorporating more channels, and
useful for further studies of nuclear fusion reactions in plasma systems at
higher energies reached in more recent experiments.


title:
Optimized latent-code selection for explainable conditional
  text-to-image GANs

abstract:
  The task of text-to-image generation has achieved remarkable progress due to
the advances in the conditional generative adversarial networks (GANs).
However, existing conditional text-to-image GANs approaches mostly concentrate
on improving both image quality and semantic relevance but ignore the
explainability of the model which plays a vital role in real-world
applications. In this paper, we present a variety of techniques to take a deep
look into the latent space and semantic space of the conditional text-to-image
GANs model. We introduce pairwise linear interpolation of latent codes and
`linguistic' linear interpolation to study what the model has learned within
the latent space and `linguistic' embeddings. Subsequently, we extend linear
interpolation to triangular interpolation conditioned on three corners to
further analyze the model. After that, we build a Good/Bad data set containing
unsuccessfully and successfully synthetic samples and corresponding latent
codes for the image-quality research. Based on this data set, we propose a
framework for finding good latent codes by utilizing a linear SVM. Experimental
results on the recent DiverGAN generator trained on two benchmark data sets
qualitatively prove the effectiveness of our presented techniques, with a
better than 94\% accuracy in predicting ${Good}$/${Bad}$ classes for latent
vectors. The Good/Bad data set is publicly available at
https://zenodo.org/record/5850224#.YeGMwP7MKUk.


title:
Document-Level Relation Extraction with Sentences Importance Estimation
  and Focusing

abstract:
  Document-level relation extraction (DocRE) aims to determine the relation
between two entities from a document of multiple sentences. Recent studies
typically represent the entire document by sequence- or graph-based models to
predict the relations of all entity pairs. However, we find that such a model
is not robust and exhibits bizarre behaviors: it predicts correctly when an
entire test document is fed as input, but errs when non-evidence sentences are
removed. To this end, we propose a Sentence Importance Estimation and Focusing
(SIEF) framework for DocRE, where we design a sentence importance score and a
sentence focusing loss, encouraging DocRE models to focus on evidence
sentences. Experimental results on two domains show that our SIEF not only
improves overall performance, but also makes DocRE models more robust.
Moreover, SIEF is a general framework, shown to be effective when combined with
a variety of base DocRE models.


title:
Improving the Transferability of Adversarial Examples with Restructure
  Embedded Patches

abstract:
  Vision transformers (ViTs) have demonstrated impressive performance in
various computer vision tasks. However, the adversarial examples generated by
ViTs are challenging to transfer to other networks with different structures.
Recent attack methods do not consider the specificity of ViTs architecture and
self-attention mechanism, which leads to poor transferability of the generated
adversarial samples by ViTs. We attack the unique self-attention mechanism in
ViTs by restructuring the embedded patches of the input. The restructured
embedded patches enable the self-attention mechanism to obtain more diverse
patches connections and help ViTs keep regions of interest on the object.
Therefore, we propose an attack method against the unique self-attention
mechanism in ViTs, called Self-Attention Patches Restructure (SAPR). Our method
is simple to implement yet efficient and applicable to any self-attention based
network and gradient transferability-based attack methods. We evaluate attack
transferability on black-box models with different structures. The result show
that our method generates adversarial examples on white-box ViTs with higher
transferability and higher image quality. Our research advances the development
of black-box transfer attacks on ViTs and demonstrates the feasibility of using
white-box ViTs to attack other black-box models.


title: $G^2$: Enhance Knowledge Grounded Dialogue via Ground Graph

abstract:
  Knowledge grounded dialogue system is designed to generate responses that
convey information from given knowledge documents. However, it's a challenge
for the current Seq2Seq model to acquire knowledge from complex documents and
integrate it to perform correct responses without the aid of an explicit
semantic structure. To address these issues, we present a novel graph
structure, Ground Graph ($G^2$), which models the semantic structure of both
dialogue contexts and knowledge documents to facilitate knowledge selection and
integration for the task. Besides, a Ground Graph Aware Transformer ($G^2AT$)
is proposed to enhance knowledge grounded response generation. Empirical
results show that our proposed model outperforms previous state-of-the-art
methods with more than 10\% and 20\% gains on response generation and factual
consistency. Furthermore, our structure-aware approach shows excellent
generalization ability in resource-limited situations.


title: A Practical Two-stage Ranking Framework for Cross-market Recommendation

abstract:
  Cross-market recommendation aims to recommend products to users in a
resource-scarce target market by leveraging user behaviors from similar
rich-resource markets, which is crucial for E-commerce companies but receives
less research attention. In this paper, we present our detailed solution
adopted in the cross-market recommendation contest, i.e., WSDM CUP 2022. To
better utilize collaborative signals and similarities between target and source
markets, we carefully consider multiple features as well as stacking learning
models consisting of deep graph recommendation models (Graph Neural Network,
DeepWalk, etc.) and traditional recommendation models (ItemCF, UserCF, Swing,
etc.). Furthermore, We adopt tree-based ensembling methods, e.g., LightGBM,
which show superior performance in prediction task to generate final results.
We conduct comprehensive experiments on the XMRec dataset, verifying the
effectiveness of our model. The proposed solution of our team WSDM_Coggle_ is
selected as the second place submission.


title: 11/4-colorability of subcubic triangle-free graphs

abstract:
  We prove that up to two exceptions, every connected subcubic triangle-free
graph has fractional chromatic number at most 11/4. This is tight unless
further exceptional graphs are excluded, and improves the known bound on the
fractional chromatic number of subcubic triangle-free planar graphs.


title: Density-preserving Deep Point Cloud Compression

abstract:
  Local density of point clouds is crucial for representing local details, but
has been overlooked by existing point cloud compression methods. To address
this, we propose a novel deep point cloud compression method that preserves
local density information. Our method works in an auto-encoder fashion: the
encoder downsamples the points and learns point-wise features, while the
decoder upsamples the points using these features. Specifically, we propose to
encode local geometry and density with three embeddings: density embedding,
local position embedding and ancestor embedding. During the decoding, we
explicitly predict the upsampling factor for each point, and the directions and
scales of the upsampled points. To mitigate the clustered points issue in
existing methods, we design a novel sub-point convolution layer, and an
upsampling block with adaptive scale. Furthermore, our method can also compress
point-wise attributes, such as normal. Extensive qualitative and quantitative
results on SemanticKITTI and ShapeNet demonstrate that our method achieves the
state-of-the-art rate-distortion trade-off.


title: Robust Face Anti-Spoofing with Dual Probabilistic Modeling

abstract:
  The field of face anti-spoofing (FAS) has witnessed great progress with the
surge of deep learning. Due to its data-driven nature, existing FAS methods are
sensitive to the noise in the dataset, which will hurdle the learning process.
However, very few works consider noise modeling in FAS. In this work, we
attempt to fill this gap by automatically addressing the noise problem from
both label and data perspectives in a probabilistic manner. Specifically, we
propose a unified framework called Dual Probabilistic Modeling (DPM), with two
dedicated modules, DPM-LQ (Label Quality aware learning) and DPM-DQ (Data
Quality aware learning). Both modules are designed based on the assumption that
data and label should form coherent probabilistic distributions. DPM-LQ is able
to produce robust feature representations without overfitting to the
distribution of noisy semantic labels. DPM-DQ can eliminate data noise from
`False Reject' and `False Accept' during inference by correcting the prediction
confidence of noisy data based on its quality distribution. Both modules can be
incorporated into existing deep networks seamlessly and efficiently.
Furthermore, we propose the generalized DPM to address the noise problem in
practical usage without the need of semantic annotations. Extensive experiments
demonstrate that this probabilistic modeling can 1) significantly improve the
accuracy, and 2) make the model robust to the noise in real-world datasets.
Without bells and whistles, our proposed DPM achieves state-of-the-art
performance on multiple standard FAS benchmarks.


title:
Liouville type theorems for the stationary Hall-MHD equations in local
  Morrey spaces

abstract:
  This paper is concerned with the Liouville type theorems for the 3D
stationary incompressible Hall-MHD equations. We establish that under some
sufficient conditions in local Morrey spaces, solutions of the stationary
Hall-MHD equations are identically zero. In particular, we also prove Liouville
type results for the stationary incompressible MHD equations on $\mathbb{R}^3$.
Our theorems extend and generalize the classical results for the stationary
incompressible Navier-Stokes equations.


title: Multiresolution community analysis of international trade networks

abstract:
  The international trade network is a complex system where multiple trade
blocs with varying sizes coexist and overlap with each other. However, the
resulting structures of community detection in trade networks are often
inconsistent and fails to capture the complex landscape of international trade.
To address these problems, we propose a multiresolution framework that
aggregates all the configuration information from a range of resolutions. This
allows us to consider trade communities of different sizes and illuminate the
underlying hierarchical structure of trade networks and its constituting
blocks. Furthermore, by measuring membership inconsistency (MeI) of each
country and conducting multiple regression analysis with various economic and
political indicators, we demonstrate that there exists a positive correlation
between the external instability of countries and their structural
inconsistency in terms of network topology.


title:
Protostellar disks subject to infall: a one-dimensional inviscid model
  and comparison with ALMA observations

abstract:
  A new one-dimensional, inviscid, and vertically integrated disk model with
prescribed infall is presented. The flow is computed using a second-order
shock-capturing scheme. Included are vertical infall, radial infall at the
outer radial boundary, radiative cooling, stellar irradiation, and heat
addition at the disk-surface shock. Simulation parameters are chosen to target
the L1527 IRS disk which has been observed using ALMA (Atacama Large Millimeter
Array). The results give an outer envelope of radial infall and $u_\phi \propto
1/r$ which encounters a radial shock at $r_\mathrm{shock} \sim 1.5\ \times$ the
centrifugal radius ($r_\mathrm{c}$) across which the radial velocity is greatly
reduced and the temperature rises from a pre-shock value of $20$ K to $\approx
60$ K. At $r_\mathrm{c}$, the azimuthal velocity $u_\phi$ transitions from
being $\propto 1/r$ to being nearly Keplerian. These results qualitatively
agree with recent ALMA observations which indicate a radial shock where SO is
sublimated as well as a transition from a $u_\phi \sim 1/r$ region to a
Keplerian inner disk. However, the observed position-velocity map of
cyclic-C$_3$H$_2$, together with a certain ballistic maximum velocity relation,
has suggested that the radial shock coincides with a ballistic centrifugal
barrier, which places the shock at $r_\mathrm{shock} = 0.5 r_\mathrm{c}$, i.e,
inward of $r_\mathrm{c}$, rather than outward as given by our simulations, as
well as previous magnetic rotating-collapse simulations. This discrepancy is
analyzed and discussed, but remains unresolved.


title:
Improved performance in quantum transport calculations: A
  divide-and-conquer method based on S-matrices

abstract:
  We propose a divide-and-conquer algorithm to find recursively the Scattering
matrix of general tight-binding structures. The Scattering matrix allows a
direct calculation of transport properties in mesoscopic systems by using the
Landauer formula. The method is exact, and by analyzing the performance of the
algorithm in square, triangular and honeycomb lattices, we show a significant
improvement in comparison to other state-of-the-art recursive and non-recursive
methods.


title: Multimodal Pipeline for Collection of Misinformation Data from Telegram

abstract:
  The paper presents the outcomes of AI-COVID19, our project aimed at better
understanding of misinformation flow about COVID-19 across social media
platforms. The specific focus of the study reported in this paper is on
collecting data from Telegram groups which are active in promotion of
COVID-related misinformation. Our corpus collected so far contains around 28
million words, from almost one million messages. Given that a substantial
portion of misinformation flow in social media is spread via multimodal means,
such as images and video, we have also developed a mechanism for utilising such
channels via producing automatic transcripts for videos and automatic
classification for images into such categories as memes, screenshots of posts
and other kinds of images. The accuracy of the image classification pipeline is
around 87%.


title: McMillan map and nonlinear Twiss parameters

abstract:
  In this article we consider two dynamical systems: the McMillan sextupole and
octupole integrable mappings originally introduced by Edwin McMillan; the
second one is also known as canonical McMillan map. Both of them are simplest
symmetric McMillan maps with only one intrinsic parameter, the trace of the
Jacobian at the fixed point. While these dynamical systems have numerous of
applications and are used in many areas of math and physics, some of their
dynamical properties have not been described yet. We fulfill the gap and
provide complete description of all stable trajectories including
parametrization of invariant curves, Pioncar\'e rotation numbers and canonical
action-angle variables.
  In the second part we relate these maps with general chaotic map in
McMillan-Turaev form. We show that McMillan sextupole and octupole mappings are
first order approximations of dynamics around the fixed point, in a similar way
as linear map and quadratic invariant (Courant-Snyder invariant in accelerator
physics) is the zeroth order approximation (known as linearization). Finally we
suggest the new formalism of nonlinear Twiss parameters which incorporate
dependence of rotation number as a function of amplitude, in contrast to e.g.
betatron phase advance used in accelerator physics which is independent of
amplitude. Specifically in application to accelerator physics this new
formalism is capable of predicting dynamical aperture around 1-st, 2-nd, 3-rd
and 4-th order resonances for flat beams, which is critical for beam
injection/extraction.


title:
Bound states in and out of the continuum in nanoribbons with wider
  sections: A novel recursive S-matrix method

abstract:
  We report a novel method to find bound states in general tight-binding
Hamiltonians with semi-infinite leads. The method is based on the recursive
S-matrix method, which allows us to compute iteratively the S-matrix of a
general system in terms of the S-matrices of its subsystems. We establish the
condition that the S-matrices of the subsystems must accomplish to have a bound
state at energy E. Energies that accomplish this relation, can be determined
with high accuracy and efficiency by using the Taylor series of the S-matrices.
The method allows us to find bound states energies and wavefunctions in (BIC)
and out (BOC) of the continuum, including degenerate ones. Bound states in
nanoribbons with wider sections are computed for square and honeycomb lattices.
Using this method, we verify the bound states in a graphene nanoribbon with two
quantum-dot-like structures which has been reported to have BICs by using
another technique. However, this new analysis reveals that such BICs are
double, one with even and the other with odd wavefunction, with slightly
separated energies. In this way, the new method can be used to efficiently find
new BICs and to improve precision in previously reported ones.


title: Distant finetuning with discourse relations for stance classification

abstract:
  Approaches for the stance classification task, an important task for
understanding argumentation in debates and detecting fake news, have been
relying on models which deal with individual debate topics. In this paper, in
order to train a system independent from topics, we propose a new method to
extract data with silver labels from raw text to finetune a model for stance
classification. The extraction relies on specific discourse relation
information, which is shown as a reliable and accurate source for providing
stance information. We also propose a 3-stage training framework where the
noisy level in the data used for finetuning decreases over different stages
going from the most noisy to the least noisy. Detailed experiments show that
the automatically annotated dataset as well as the 3-stage training help
improve model performance in stance classification. Our approach ranks 1st
among 26 competing teams in the stance classification track of the NLPCC 2021
shared task Argumentative Text Understanding for AI Debater, which confirms the
effectiveness of our approach.


title:
Model predictive control of agro-hydrological systems based on a
  two-layer neural network modeling framework

abstract:
  Water scarcity is an urgent issue to be resolved and improving irrigation
water-use efficiency through closed-loop control is essential. The complex
agro-hydrological system dynamics, however, often pose challenges in
closed-loop control applications. In this work, we propose a two-layer neural
network (NN) framework to approximate the dynamics of the agro-hydrological
system. To minimize the prediction error, a linear bias correction is added to
the proposed model. The model is employed by a model predictive controller with
zone tracking (ZMPC), which aims to keep the root zone soil moisture in the
target zone while minimizing the total amount of irrigation. The performance of
the proposed approximation model framework is shown to be better compared to a
benchmark long-short-term-memory (LSTM) model for both open-loop and
closed-loop applications. Significant computational cost reduction of the ZMPC
is achieved with the proposed framework. To handle the tracking offset caused
by the plant-model-mismatch of the proposed NN framework, a shrinking target
zone is proposed for the ZMPC. Different hyper-parameters of the shrinking zone
in the presence of noise and weather disturbances are investigated, of which
the control performance is compared to a ZMPC with a time-invariant target
zone.


title:
Nondominated-Solution-based Multiobjective-Greedy Sensor Selection for
  Optimal Design of Experiments

abstract:
  In this study, a nondominated-solution-based multiobjective-greedy sensor
selection method for the optimal design of experiments is proposed and its
performance is investigated. The proposed method simultaneously considers D-,
A- and E-optimality and applies the idea of Pareto ranking to select the sensor
set. With the proposed method, a new sensor is iteratively added to the
nondominated solutions of sensor sets, and the multiobjective functions are
evaluated for new sets of sensors based on increments in the relevant
optimality index. The nondominated solutions are selected from the examined
solutions, and the next sensor sets are then considered. With this procedure,
the multiobjective optimization of sensor selection can be conducted with
reasonable computational costs. The results show that the proposed method not
only gives the Pareto-optimal front of the multiobjective optimization problem
but also produces sets of sensors in terms of D-, A- and E-optimality, that are
superior to the sets selected by pure-greedy methods that consider only a
single objective function.


title:
Grasping the Arrow of Time from the Singularity: Decoding Micromotion in
  Low-dimensional Latent Spaces from StyleGAN

abstract:
  The disentanglement of StyleGAN latent space has paved the way for realistic
and controllable image editing, but does StyleGAN know anything about temporal
motion, as it was only trained on static images? To study the motion features
in the latent space of StyleGAN, in this paper, we hypothesize and demonstrate
that a series of meaningful, natural, and versatile small, local movements
(referred to as "micromotion", such as expression, head movement, and aging
effect) can be represented in low-rank spaces extracted from the latent space
of a conventionally pre-trained StyleGAN-v2 model for face generation, with the
guidance of proper "anchors" in the form of either short text or video clips.
Starting from one target face image, with the editing direction decoded from
the low-rank space, its micromotion features can be represented as simple as an
affine transformation over its latent feature. Perhaps more surprisingly, such
micromotion subspace, even learned from just single target face, can be
painlessly transferred to other unseen face images, even those from vastly
different domains (such as oil painting, cartoon, and sculpture faces). It
demonstrates that the local feature geometry corresponding to one type of
micromotion is aligned across different face subjects, and hence that
StyleGAN-v2 is indeed "secretly" aware of the subject-disentangled feature
variations caused by that micromotion. We present various successful examples
of applying our low-dimensional micromotion subspace technique to directly and
effortlessly manipulate faces, showing high robustness, low computational
overhead, and impressive domain transferability. Our codes are available at
https://github.com/wuqiuche/micromotion-StyleGAN.


title:
The Bolometric Luminosity Correction of Radio-Quiet and Radio-Loud
  Quasars at 1<z<2

abstract:
  To understand the impact of active galactic nuclei (AGN) on their host
galaxies and large scale environment it is crucial to determine their total
radiative power across all wavelengths (i.e., bolometric luminosity). In this
contribution we describe how quasar accretion disk spectral energy distribution
(SED) templates, parameterized by the black hole (BH) mass, Eddington ratio,
and spin can be used to estimate their total radiated luminosity. To estimate
the bolometric luminosity of AGN, we integrate the accretion disk SEDs from
1$\mu$m to 10keV. Our approach self-consistently covers any gaps in
observations and does not include reprocessed emission from the torus. The
accretion disk SED, and consequently the bolometric correction inferred from
it, strongly depend on the BH mass, the Eddington ratio, and spin. In
particular, the bolometric correction in the visible bands
(5100$\,\mathring{A}$ and 3000$\,\mathring{A}$) strongly depends on BH mass,
and at X-ray strongly depends on the Eddington ratio. At wavelengths closer to
the peak of the accretion disk SED the dependence becomes weaker. Additionally,
maximally-rotating (spin = 1) quasars require a higher bolometric correction
than their non-rotating (spin = 0) counterparts at all wavelengths. The SEDs
and the bolometric correction presented in this work can determine the
radiative power of any sample of radio-quiet to radio-loud Type 1 AGN with
observations in the range from 1$\mu$m to 10$\,$keV provided the observations
are corrected for extinction.


title: Multi-task Learning-based CSI Feedback Design in Multiple Scenarios

abstract:
  For frequency division duplex systems, the essential downlink channel state
information (CSI) feedback includes the links of compression, feedback,
decompression and reconstruction to reduce the feedback overhead. One efficient
CSI feedback method is the Auto-Encoder (AE) structure based on deep learning,
yet facing problems in actual deployments, such as selecting the deployment
mode when deploying in a cell with multiple complex scenarios. Rather than
designing an AE network with huge complexity to deal with CSI of all scenarios,
a more realistic mode is to divide the CSI dataset by region/scenario and use
multiple relatively simple AE networks to handle subregions' CSI. However, both
require high memory capacity for user equipment (UE) and are not suitable for
low-level devices. In this paper, we propose a new user-friendly-designed
framework based on the latter multi-tasking mode. Via Multi-Task Learning, our
framework, Single-encoder-to-Multiple-decoders (S-to-M), designs the multiple
independent AEs into a joint architecture: a shared encoder corresponds to
multiple task-specific decoders. We also complete our framework with GateNet as
a classifier to enable the base station autonomously select the right
task-specific decoder corresponding to the subregion. Experiments on the
simulating multi-scenario CSI dataset demonstrate our proposed S-to-M's
advantages over the other benchmark modes, i.e., significantly reducing the
model complexity and the UE's memory consumption


title:
Randomness and Statistical Inference of Shapes via the Smooth Euler
  Characteristic Transform

abstract:
  In this paper, we provide the mathematical foundations for the randomness of
shapes and the distributions of smooth Euler characteristic transform. Based on
these foundations, we propose an approach for testing hypotheses on random
shapes. Simulation studies are provided to support our mathematical derivations
and show the performance of our proposed hypothesis testing framework. Our
discussions connect the following fields: algebraic and computational topology,
probability theory and stochastic processes, Sobolev spaces and functional
analysis, statistical inference, and medical imaging.


title: Alfv\'en waves: To the 80th anniversary of discovery

abstract:
  The paper is dedicated to the anniversary of the discovery of Alfv\'en waves.
The concept of Alfv\'en waves has played an outstanding role in the formation
and development of cosmical electrodynamics. A distinctive feature of Alfv\'en
waves is that at each point in space the group velocity vector and the external
magnetic field vector are collinear to each other. As a result, Alfv\'en waves
can carry momentum, energy, and information over long distances. We briefly
describe two Alfv\'en resonators, one of which is formed in the ionosphere, and
the second presumably exists in the Earth's radiation belt. The existence of an
ionospheric resonator is justified theoretically and confirmed by numerous
observations. The second resonator is located between reflection points located
high above the Earth symmetrically with respect to the plane of the geomagnetic
equator. Keywords: Alfv\'en velocity, dispersion law, group velocity, geometric
optics, heavy ions.


title:
Mapping suburban bicycle lanes using street scene images and deep
  learning

abstract:
  On-road bicycle lanes improve safety for cyclists, and encourage
participation in cycling for active transport and recreation. With many local
authorities responsible for portions of the infrastructure, official maps and
datasets of bicycle lanes may be out-of-date and incomplete. Even
"crowdsourced" databases may have significant gaps, especially outside popular
metropolitan areas. This thesis presents a method to create a map of bicycle
lanes in a survey area by taking sample street scene images from each road, and
then applying a deep learning model that has been trained to recognise bicycle
lane symbols. The list of coordinates where bicycle lane markings are detected
is then correlated to geospatial data about the road network to record bicycle
lane routes. The method was applied to successfully build a map for a survey
area in the outer suburbs of Melbourne. It was able to identify bicycle lanes
not previously recorded in the official state government dataset,
OpenStreetMap, or the "biking" layer of Google Maps.


title:
Machine learning in the study of phase transition of two-dimensional
  complex plasmas

abstract:
  Machine learning is applied to investigate the phase transition of
two-dimensional complex plasmas. The Langevin dynamics simulation is employed
to prepare particle suspensions in various thermodynamic states. Based on the
resulted particle positions in two extreme conditions, bitmap images are
synthesized and imported to a convolutional neural network (ConvNet) as
training sample. As a result, a phase diagram is obtained. This trained ConvNet
model can be directly applied to the sequence of the recorded images using
video microscopy in the experiments to study the melting.


title:
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in
  Federated Learning

abstract:
  Federated learning (FL) enables edge-devices to collaboratively learn a model
without disclosing their private data to a central aggregating server. Most
existing FL algorithms require models of identical architecture to be deployed
across the clients and server, making it infeasible to train large models due
to clients' limited system resources. In this work, we propose a novel ensemble
knowledge transfer method named Fed-ET in which small models (different in
architecture) are trained on clients, and used to train a larger model at the
server. Unlike in conventional ensemble learning, in FL the ensemble can be
trained on clients' highly heterogeneous data. Cognizant of this property,
Fed-ET uses a weighted consensus distillation scheme with diversity
regularization that efficiently extracts reliable consensus from the ensemble
while improving generalization by exploiting the diversity within the ensemble.
We show the generalization bound for the ensemble of weighted models trained on
heterogeneous datasets that supports the intuition of Fed-ET. Our experiments
on image and language tasks show that Fed-ET significantly outperforms other
state-of-the-art FL algorithms with fewer communicated parameters, and is also
robust against high data-heterogeneity.


title:
Discovering Representative Attribute-stars via Minimum Description
  Length

abstract:
  Graphs are a popular data type found in many domains. Numerous techniques
have been proposed to find interesting patterns in graphs to help understand
the data and support decision-making. However, there are generally two
limitations that hinder their practical use: (1) they have multiple parameters
that are hard to set but greatly influence results, (2) and they generally
focus on identifying complex subgraphs while ignoring relationships between
attributes of nodes.Graphs are a popular data type found in many domains.
Numerous techniques have been proposed to find interesting patterns in graphs
to help understand the data and support decision-making. However, there are
generally two limitations that hinder their practical use: (1) they have
multiple parameters that are hard to set but greatly influence results, (2) and
they generally focus on identifying complex subgraphs while ignoring
relationships between attributes of nodes. To address these problems, we
propose a parameter-free algorithm named CSPM (Compressing Star Pattern Miner)
which identifies star-shaped patterns that indicate strong correlations among
attributes via the concept of conditional entropy and the minimum description
length principle. Experiments performed on several benchmark datasets show that
CSPM reveals insightful and interpretable patterns and is efficient in runtime.
Moreover, quantitative evaluations on two real-world applications show that
CSPM has broad applications as it successfully boosts the accuracy of graph
attribute completion models by up to 30.68\% and uncovers important patterns in
telecommunication alarm data.


title: On the compatible sets expansion of the Tutte polynomial

abstract:
  Kochol (2021) recently gave a new expansion formula for the Tutte polynomial
of a matroid using the notion of compatible sets, and asked how this expansion
relates to the internal-external activities formula. Here, we provide an
answer, which is obtained as a special case of our generalization of the
expansion formula to Las Vergnas's trivariate Tutte polynomials of matroid
perspectives.


title: Estimates for generalized Bohr radii in one and higher dimensions

abstract:
  The generalized Bohr radius $R_{p, q}(X), p, q\in[1, \infty)$ for a complex
Banach space $X$ was introduced by Blasco in 2010. In this article, we
determine the exact value of $R_{p, q}(\mathbb{C})$ for the cases (i) $p,
q\in[1, 2]$, (ii) $p\in (2, \infty), q\in [1, 2]$ and (iii) $p, q\in [2,
\infty)$. Moreover, we consider an $n$-variable version $R_{p, q}^n(X)$ of the
quantity $R_{p, q}(X)$ and determine (i) $R_{p, q}^n(\mathcal{H})$ for an
infinite dimensional complex Hilbert space $\mathcal{H}$, (ii) the precise
asymptotic value of $R_{p, q}^n(X)$ as $n\to\infty$ for finite dimensional $X$.
We also study the multidimensional analogue of a related concept called the
$p$-Bohr radius, introduced by Djakov and Ramanujan in 2000. In particular, we
obtain the asymptotic value of the $n$-dimensional $p$-Bohr radius for bounded
complex-valued functions, and in the vector-valued case we provide a lower
estimate for the same, which is independent of $n$. In a similar vein, we
investigate in detail the multidimensional $p$-Bohr radius problem for
functions with positive real part. Towards the end of this article, we pose one
more generalization $R_{p, q}(Y, X)$ of $R_{p, q}(X)$-considering functions
that map the open unit ball of another complex Banach space $Y$ inside the unit
ball of $X$, and show that the existence of nonzero $R_{p, q}(Y, X)$ is
governed by the geometry of $X$ alone.


title:
Accelerated Continuous-Time Approximate Dynamic Programming via
  Data-Assisted Hybrid Control

abstract:
  We introduce a new closed-loop architecture for the online solution of
approximate optimal control problems in the context of continuous-time systems.
Specifically, we introduce the first algorithm that incorporates dynamic
momentum in actor-critic structures to control continuous-time dynamic plants
with an affine structure in the input. By incorporating dynamic momentum in our
algorithm, we are able to accelerate the convergence properties of the
closed-loop system, achieving superior transient performance compared to
traditional gradient-descent based techniques. In addition, by leveraging the
existence of past recorded data with sufficiently rich information properties,
we dispense with the persistence of excitation condition traditionally imposed
on the regressors of the critic and the actor. Given that our continuous-time
momentum-based dynamics also incorporate periodic discrete-time resets that
emulate restarting techniques used in the machine learning literature, we
leverage tools from hybrid dynamical systems theory to establish asymptotic
stability properties for the closed-loop system. We illustrate our results with
a numerical example.


title:
On the Limitations of Dataset Balancing: The Lost Battle Against
  Spurious Correlations

abstract:
  Recent work has shown that deep learning models in NLP are highly sensitive
to low-level correlations between simple features and specific output labels,
leading to overfitting and lack of generalization. To mitigate this problem, a
common practice is to balance datasets by adding new instances or by filtering
out "easy" instances (Sakaguchi et al., 2020), culminating in a recent proposal
to eliminate single-word correlations altogether (Gardner et al., 2021). In
this opinion paper, we identify that despite these efforts,
increasingly-powerful models keep exploiting ever-smaller spurious
correlations, and as a result even balancing all single-word features is
insufficient for mitigating all of these correlations. In parallel, a truly
balanced dataset may be bound to "throw the baby out with the bathwater" and
miss important signal encoding common sense and world knowledge. We highlight
several alternatives to dataset balancing, focusing on enhancing datasets with
richer contexts, allowing models to abstain and interact with users, and
turning from large-scale fine-tuning to zero- or few-shot setups.


title: Toxicity in the Decentralized Web and the Potential for Model Sharing

abstract:
  The "Decentralised Web" (DW) is an evolving concept, which encompasses
technologies aimed at providing greater transparency and openness on the web.
The DW relies on independent servers (aka instances) that mesh together in a
peer-to-peer fashion to deliver a range of services (e.g. micro-blogs, image
sharing, video streaming). However, toxic content moderation in this
decentralised context is challenging. This is because there is no central
entity that can define toxicity, nor a large central pool of data that can be
used to build universal classifiers. It is therefore unsurprising that there
have been several high-profile cases of the DW being misused to coordinate and
disseminate harmful material. Using a dataset of 9.9M posts from 117K users on
Pleroma (a popular DW microblogging service), we quantify the presence of toxic
content. We find that toxic content is prevalent and spreads rapidly between
instances. We show that automating per-instance content moderation is
challenging due to the lack of sufficient training data available and the
effort required in labelling. We therefore propose and evaluate ModPair, a
model sharing system that effectively detects toxic content, gaining an average
per-instance macro-F1 score 0.89.


title:
CREER: A Large-Scale Corpus for Relation Extraction and Entity
  Recognition

abstract:
  We describe the design and use of the CREER dataset, a large corpus annotated
with rich English grammar and semantic attributes. The CREER dataset uses the
Stanford CoreNLP Annotator to capture rich language structures from Wikipedia
plain text. This dataset follows widely used linguistic and semantic
annotations so that it can be used for not only most natural language
processing tasks but also scaling the dataset. This large supervised dataset
can serve as the basis for improving the performance of NLP tasks in the
future.


title:
Towards Exploring the Code Reuse from Stack Overflow during Software
  Development

abstract:
  As one of the most well-known programmer Q&A websites, Stack Overflow (i.e.,
SO) is serving tens of thousands of developers every day. Previous work has
shown that many developers reuse the code snippets on SO when they find an
answer (from SO) that functionally matches the programming problem they
encounter in their development activities. To study how programmers reuse code
on SO during project development, we conduct a comprehensive empirical study.
First, to capture the development activities of programmers, we collect 342,148
modified code snippets in commits from 793 open-source Java projects, and these
modified code can reflect the programming problems encountered during
development. We also collect the code snippets from 1,355,617 posts on SO.
Then, we employ CCFinder to detect the code clone between the modified code
from commits and the code from SO, and further analyze the code reuse when
programmer solves a programming problem during development. We count the code
reuse ratios of the modified code snippets in the commits of each project in
different years, the results show that the average code reuse ratio is 6.32%,
and the maximum is 8.38%. The code reuse ratio in project commits has increased
year by year, and the proportion of code reuse in the newly established project
is higher than that of old projects. We also find that some projects reuse the
code snippets from many years ago. Additionally, we find that experienced
developers seem to be more likely to reuse the knowledge on SO. Moreover, we
find that the code reuse ratio in bug-related commits (6.67%) is slightly
higher than that of in non-bug-related commits (6.59%). Furthermore, we also
find that the code reuse ratio (14.44%) in Java class files that have undergone
multiple modifications is more than double the overall code reuse ratio
(6.32%).


title:
Release as a Contract: A Concept of Meta-Maintenance for the Entire
  FLOSS Ecosystem

abstract:
  We advocate for a paradigm shift in supporting free/libre and open source
software (FLOSS) ecosystem maintenance, from focusing on individual projects to
monitoring a whole organic system of the entire FLOSS ecosystem, which we call
software meta-maintenance. We discuss challenges of building a global source
code management system, a global issue management system, and FLOSS human
capital index, based on the blockchain technologies.


title:
Modeling the Adoption of Good Tech: Extending TAM3 to Incorporate the
  Phenomenon of Warm-Glow

abstract:
  In this paper, we extend the third evolution of the Technology Acceptance
Model (TAM3) to in-corporate warm-glow in order to understand the role this
phenomenon plays on consumer adoption of 'good tech'. Warm-glow is the feeling
of satisfaction, pleasure, or both, which is experienced by individuals after
they do something 'good' for their fellow human. Two constructs, perceived
extrinsic warm-glow (PEWG) and perceived intrinsic warm-glow (PIWG), were
incorporated into the TAM3 model to measure the two dimensions of
user-experienced warm-glow, forming what we refer to as the TAM3+WG model. An
experimental approach was taken to evaluate the suitability of the proposed
model (i.e., TAM3+WG). A vignette was used to describe a hypothetical internet
search engine solution designed to evoke in participants warm-glow. Our TAM3+WG
model was found to be superior to the TAM3 model within our context.
Furthermore, the PIWG and PEWG constructs were found to be unique within the
original TAM3 model. Our findings indicate that the factors that have the
greatest influence on consumer decisions are (in decreasing order) PU, PIWG,
SN, and PEWG. In other words, both extrinsic and intrinsic warm-glow play a
prominent role in user decisions as to whether or not to adopt technology.


title:
Mapping the dispersion of the occupied and unoccupied band structure in
  photoexcited 1T-TiSe$_2$

abstract:
  Charge density waves (CDW) are states of broken symmetry with a periodic
modulation of charge and lattice typically leading to the opening of a gap in
the band structure. In the model CDW system 1T-TiSe$_2$ such a gap opens up
between its Se$_{4p}$ valence and Ti$_{3d}$ conduction band, accompanied by a
change of dispersion. These changes are crucial in understanding the CDW phase,
as they provide a measure of the Se$_{4p}$-Ti$_{3d}$ hybridization strength and
characteristic mechanistic features. Using time- and angle-resolved
photoelectron spectroscopy (trARPES), the unoccupied band structure is
populated with near-infrared (NIR) pump pulses which allows to to directly
visualize the parabolically-shaped Ti$_{3d}$ conduction band. Furthermore, we
observe a transient change of effective mass in the Se$_{4p}$ valence band
following photoexcitation. This occurs alongside an overall reduction due to
weakening of the CDW phase and is accompanied by an oscillating component with
the frequency of the characteristic A$_{1g}$ phonon. These observations,
enabled by trAPRES, highlight the importance of the lattice contributions in
establishing the CDW order in 1T-TiSe$_2$.


title: Functional Theory for Excitations in Boson Systems

abstract:
  We propose, work out and initiate a one-particle reduced density matrix
functional theory (RDMFT) for targeting excitation energies of bosonic quantum
systems. In analogy to its recently established fermionic counterpart, this
bosonic $\boldsymbol{w}$-ensemble RDMFT is based on a generalization of the
Rayleigh-Ritz variational principle to ensemble states with spectrum
$\boldsymbol{w}$ used within the constrained search formalism. Most
importantly, an exact convex relaxation is implemented to turn
$\boldsymbol{w}$-ensemble RDMFT into a practically feasibly method: The
corresponding convex-relaxed $1$-body $\boldsymbol{w}$-ensemble
$N$-representability problem can be solved, revealing a complete hierarchy of
exclusion principle constraints in analogy to Pauli's famous principle for
fermions. To initiate $\boldsymbol{w}$-ensemble RDMFT, we systematically derive
universal functionals for the symmetric Bose-Hubbard dimer and for
Bose-Einstein condensates in the Bogoliubov regime.


title:
UBERT: A Novel Language Model for Synonymy Prediction at Scale in the
  UMLS Metathesaurus

abstract:
  The UMLS Metathesaurus integrates more than 200 biomedical source
vocabularies. During the Metathesaurus construction process, synonymous terms
are clustered into concepts by human editors, assisted by lexical similarity
algorithms. This process is error-prone and time-consuming. Recently, a deep
learning model (LexLM) has been developed for the UMLS Vocabulary Alignment
(UVA) task. This work introduces UBERT, a BERT-based language model, pretrained
on UMLS terms via a supervised Synonymy Prediction (SP) task replacing the
original Next Sentence Prediction (NSP) task. The effectiveness of UBERT for
UMLS Metathesaurus construction process is evaluated using the UMLS Vocabulary
Alignment (UVA) task. We show that UBERT outperforms the LexLM, as well as
biomedical BERT-based models. Key to the performance of UBERT are the synonymy
prediction task specifically developed for UBERT, the tight alignment of
training data to the UVA task, and the similarity of the models used for
pretrained UBERT.


title: Dataset for Robust and Accurate Leading Vehicle Velocity Recognition

abstract:
  Recognition of the surrounding environment using a camera is an important
technology in Advanced Driver-Assistance Systems and Autonomous Driving, and
recognition technology is often solved by machine learning approaches such as
deep learning in recent years. Machine learning requires datasets for learning
and evaluation. To develop robust recognition technology in the real world, in
addition to normal driving environment, data in environments that are difficult
for cameras such as rainy weather or nighttime are essential. We have
constructed a dataset that one can benchmark the technology, targeting the
velocity recognition of the leading vehicle. This task is an important one for
the Advanced Driver-Assistance Systems and Autonomous Driving. The dataset is
available at https://signate.jp/competitions/657


title:
Evaluation of radiative depolarization in the future circular
  electron-positron collider

abstract:
  Polarized lepton beams are an important design aspect of the future 100
\si{km}-scale Circular Electron-Positron Collider (CEPC). Precision beam energy
calibration using resonant depolarization, as well as longitudinally polarized
colliding beams are being actively investigated. The achievable beam
polarization level for different beam energies and application scenarios depend
on the radiative depolarization in the collider rings. In this paper the
radiative depolarization effects are evaluated for a the CEPC collider ring
lattice with detailed machine imperfections and corrections. Simulations with
the SLIM and Monte-Carlo approaches using the BMAD/PTC codes are compared with
the theory of spin diffusion effects for ultra-high beam energies.


title: On locally concave functions on simplest non-convex domains

abstract:
  We prove that certain Bellman functions of several variables are the minimal
locally concave functions. This generalizes earlier results about Bellman
functions of two variables.


title: Large sets avoiding affine copies of infinite sequences

abstract:
  A conjecture of Erd\H{o}s states that for any infinite set $A \subseteq
\mathbb R$, there exists $E \subseteq \mathbb R$ of positive Lebesgue measure
that does not contain any nontrivial affine copy of $A$. The conjecture remains
open for most fast-decaying sequences, including the geometric sequence $A =
\{2^{-k} : k \geq 1\}$. In this article, we consider infinite decreasing
sequences $A = \{a_k: k \geq 1\}$ in ${\mathbb R}$ that converge to zero at a
prescribed rate; namely $\log (a_n/a_{n+1}) = e^{\varphi(n)} $, where
$\varphi(n)/n\to 0$ as $n\to\infty$. This condition is satisfied by sequences
whose logarithm has polynomial decay, and in particular by the geometric
sequence. For any such sequence $A$, we construct a Borel set ${\mathcal
O}\subseteq \mathbb R$ of Hausdorff dimension 1, but Lebesgue measure zero,
that avoids all nontrivial affine copies of $A\cup\{0\}$.


title: Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching

abstract:
  Box-simplex games are a family of bilinear minimax objectives which
encapsulate graph-structured problems such as maximum flow [She17], optimal
transport [JST19], and bipartite matching [AJJ+22]. We develop efficient
near-linear time, high-accuracy solvers for regularized variants of these
games. Beyond the immediate applications of such solvers for computing Sinkhorn
distances, a prominent tool in machine learning, we show that these solvers can
be used to obtain improved running times for maintaining a (fractional)
$\epsilon$-approximate maximum matching in a dynamic decremental bipartite
graph against an adaptive adversary. We give a generic framework which reduces
this dynamic matching problem to solving regularized graph-structured
optimization problems to high accuracy. Through our reduction framework, our
regularized box-simplex game solver implies a new algorithm for dynamic
decremental bipartite matching in total time $\tilde{O}(m \cdot
\epsilon^{-3})$, from an initial graph with $m$ edges and $n$ nodes. We further
show how to use recent advances in flow optimization [CKL+22] to improve our
runtime to $m^{1 + o(1)} \cdot \epsilon^{-2}$, thereby demonstrating the
versatility of our reduction-based approach. These results improve upon the
previous best runtime of $\tilde{O}(m \cdot \epsilon^{-4})$ [BGS20] and
illustrate the utility of using regularized optimization problem solvers for
designing dynamic algorithms.


title: Elastic deformations of loaded core-shell systems

abstract:
  Macroscopic elastic core-shell systems can be generated as toy models to be
deformed and haptically studied by hand. On the mesoscale, colloidal core-shell
particles and microgels are fabricated and investigated by different types of
microscopy. We analyse, using linear elasticity theory, the response of
spherical core-shell systems under the influence of a line density of force
that is oriented radially and acts along the equator of the outer surface.
Interestingly, deformational coupling of the shell to the core can determine
the resulting overall appearance in response to the forces. We address various
combinations of radii, stiffness, and Poisson ratio of core and shell and
illustrate the resulting deformations. Macroscopically, the situation could be
realized by wrapping a cord around the equator of a macroscopic model system
and pulling it tight. On the mesoscale, colloidal microgel particles
symmetrically confined to the interface between two immiscible fluids are
pulled radially outward by surface tension.


title:
Data-based price discrimination: information theoretic limitations and a
  minimax optimal strategy

abstract:
  This paper studies the gap between the classical pricing theory and the
data-based pricing theory. We focus on the problem of price discrimination with
a continuum of buyer types based on a finite sample of observations. Our first
set of results provides sharp lower bounds in the worst-case scenario for the
discrepancy between any data-based pricing strategies and the theoretical
optimal third-degree price discrimination (3PD) strategy (respectively, uniform
pricing strategy) derived from the distribution (where the sample is drawn)
ranging over a large class of distributions. Consequently, there is an
inevitable gap between revenues based on any data-based pricing strategy and
the revenue based on the theoretical optimal 3PD (respectively, uniform
pricing) strategy. We then propose easy-to-implement data-based 3PD and uniform
pricing strategies and show each strategy is minimax optimal in the sense that
the gap between their respective revenue and the revenue based on the
theoretical optimal 3PD (respectively, uniform pricing) strategy matches our
worst-case lower bounds up to constant factors (that are independent of the
sample size $n$). We show that 3PD strategies are revenue superior to uniform
pricing strategies if and only if the sample size $n$ is large enough. In other
words, if $n$ is below a threshold, uniform pricing strategies are revenue
superior to 3PD strategies. We further provide upper bounds for the gaps
between the welfare generated by our minimax optimal 3PD (respectively, uniform
pricing) strategy and the welfare based on the theoretical optimal 3PD
(respectively, uniform pricing) strategy.

categories: cs.GT cs.IT cs.LG econ.EM econ.TH math.IT


title:
Semiparametric transformation Model with measurement error in
  Covariates: An Instrumental variable approach

abstract:
  Linear transformation model provides a general framework for analyzing
censored survival data with covariates. The proportional hazards and
proportional odds models are special cases of the linear transformation model.
In biomedical studies, covariates with measurement error may occur in survival
data. In this work, we propose a method to obtain estimators of the regression
coefficients in the linear transformation model when the covariates are subject
to measurement error. In the proposed method, we assume that instrumental
variables are available. We develop counting process based estimating equations
for finding the estimators of regression coefficients.
  We prove the large sample properties of the estimators using the martingale
representation of the regression estimators. The finite sample performance of
the estimators are evaluated through an extensive Monte Carlo simulation study.
Finally, we illustrate the proposed method using an AIDS clinical trial (ACTG
175) data.


title:
The low-entropy hydration shell at the binding site of spike RBD
  determines the contagiousness of SARS-CoV-2 variants

abstract:
  The infectivity of SARS-CoV-2 depends on the binding affinity of the
receptor-binding domain (RBD) of the spike protein with the angiotensin
converting enzyme 2 (ACE2) receptor. The calculated RBD-ACE2 binding energies
indicate that the difference in transmission efficiency of SARS-CoV-2 variants
cannot be fully explained by electrostatic interactions, hydrogen-bond
interactions, van der Waals interactions, internal energy, and nonpolar
solvation energies. Here, we demonstrate that low-entropy regions of hydration
shells around proteins drive hydrophobic attraction between shape-matched
low-entropy regions of the hydration shells, which essentially coordinates
protein-protein binding in rotational-configurational space of mutual
orientations and determines the binding affinity. An innovative method was used
to identify the low-entropy regions of the hydration shells of the RBDs of
multiple SARS-CoV-2 variants and the ACE2. We observed integral low-entropy
regions of hydration shells covering the binding sites of the RBDs and matching
in shape to the low-entropy region of hydration shell at the binding site of
the ACE2. The RBD-ACE2 binding is thus found to be guided by hydrophobic
collapse between the shape-matched low-entropy regions of the hydration shells.
A measure of the low-entropy of the hydration shells can be obtained by
counting the number of hydrophilic groups expressing hydrophilicity within the
binding sites. The low-entropy level of hydration shells at the binding site of
a spike protein is found to be an important indicator of the contagiousness of
the coronavirus.


title: PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search

abstract:
  Neural architecture search (NAS) aims to automate architecture engineering in
neural networks. This often requires a high computational overhead to evaluate
a number of candidate networks from the set of all possible networks in the
search space during the search. Prediction of the networks' performance can
alleviate this high computational overhead by mitigating the need for
evaluating every candidate network. Developing such a predictor typically
requires a large number of evaluated architectures which may be difficult to
obtain. We address this challenge by proposing a novel evolutionary-based NAS
strategy, Predictor-assisted E-NAS (PRE-NAS), which can perform well even with
an extremely small number of evaluated architectures. PRE-NAS leverages new
evolutionary search strategies and integrates high-fidelity weight inheritance
over generations. Unlike one-shot strategies, which may suffer from bias in the
evaluation due to weight sharing, offspring candidates in PRE-NAS are
topologically homogeneous, which circumvents bias and leads to more accurate
predictions. Extensive experiments on NAS-Bench-201 and DARTS search spaces
show that PRE-NAS can outperform state-of-the-art NAS methods. With only a
single GPU searching for 0.6 days, competitive architecture can be found by
PRE-NAS which achieves 2.40% and 24% test error rates on CIFAR-10 and ImageNet
respectively.


title:
Photovoltaic effect in noncentrosymmetric material without optical
  absorption

abstract:
  We theoretically propose that the direct current is produced by the light
without any optical absorption in metals that break both inversion
$\mathcal{P}$ and $\mathcal{T}$ symmetries. This process can be regarded as the
Raman process leaving the current in the material. Candidate materials for the
present photovoltaic effect include multiferroics breaking both $\mathcal{P}$
and $\mathcal{T}$ symmetries. Furthermore, $\mathcal{T}$ symmetric
ferroelectrics with slight doping also support similar photocurrent under
illumination of circularly polarized light, i.e., the circular photogalvanic
effect (CPGE).


title: A cost-effective quantum eraser demonstration

abstract:
  The quantum eraser is a variation of the celebrated Young's interference
experiment that can be used to demonstrate the elusive complementarity
principle in quantum physics. Here we show the construction of its classical
analogue for deployment in classrooms in a simple, cost-effective yet robust
manner by employing a laser pointer, double-slits, and polarizers.


title:
Human-Centered Prior-Guided and Task-Dependent Multi-Task Representation
  Learning for Action Recognition Pre-Training

abstract:
  Recently, much progress has been made for self-supervised action recognition.
Most existing approaches emphasize the contrastive relations among videos,
including appearance and motion consistency. However, two main issues remain
for existing pre-training methods: 1) the learned representation is neutral and
not informative for a specific task; 2) multi-task learning-based pre-training
sometimes leads to sub-optimal solutions due to inconsistent domains of
different tasks. To address the above issues, we propose a novel action
recognition pre-training framework, which exploits human-centered prior
knowledge that generates more informative representation, and avoids the
conflict between multiple tasks by using task-dependent representations.
Specifically, we distill knowledge from a human parsing model to enrich the
semantic capability of representation. In addition, we combine knowledge
distillation with contrastive learning to constitute a task-dependent
multi-task framework. We achieve state-of-the-art performance on two popular
benchmarks for action recognition task, i.e., UCF101 and HMDB51, verifying the
effectiveness of our method.


title:
Observation of interaction-induced mobility edge in a disordered atomic
  wire

abstract:
  Mobility edge, a critical energy separating localized and extended
excitations, is a key concept for understanding quantum localization.
Aubry-Andr\'{e} (AA) model, a paradigm for exploring quantum localization, does
not naturally allow mobility edges due to self-duality. Using the
momentum-state lattice of quantum gas of Cs atoms to synthesize a nonlinear AA
model, we provide experimental evidence for mobility edge induced by
interactions. By identifying the extended-to-localized transition of different
energy eigenstates, we construct a mobility-edge phase diagram. The location of
mobility edge in the low- or high-energy region is tunable via repulsive or
attractive interactions. Our observation is in good agreement with the theory,
and supports an interpretation of such interaction-induced mobility edge via a
generalized AA model. Our work also offers new possibilities to engineer
quantum transport and phase transitions in disordered systems.


title:
ALMA Survey of Orion Planck Galactic Cold Clumps (ALMASOP): Deriving
  Inclination Angle and Velocity of the Protostellar Jets from their SiO Knots

abstract:
  We have selected six sources (G209.55-19.68S2, G205.46-14.56S1$_{-}$A,
G203.21-11.20W2, G191.90-11.21S, G205.46-14.56S3, and G206.93-16.61W2) from the
Atacama Large Millimeter/submillimeter Array Survey of Orion Planck Galactic
Cold Clumps (ALMASOP), in which these sources have been mapped in the CO
(J=2-1), SiO (J=5-4), and C$^{18}$O (J=2-1) lines. These sources have
high-velocity SiO jets surrounded by low-velocity CO outflows. The SiO jets
consist of a chain of knots. These knots have been thought to be produced by
semi-periodical variations in jet velocity. Therefore, we adopt a shock-forming
model, which uses such variations to estimate the inclination angle and
velocity of the jets. We also derive the inclination angle of the CO outflows
using the wide-angle wind-driven shell model, and find it to be broadly
consistent with that of the associated SiO jets. In addition, we apply this
shock-forming model to another three protostellar sources with SiO jets in the
literature -- HH 211, HH 212, and L1448C(N) -- and find that their inclination
angle and jet velocity are consistent with those previously estimated from
proper motion and radial velocity studies.


title:
Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named
  Entity Recognition

abstract:
  Nested named entity recognition (nested NER) is a fundamental task in natural
language processing. Various span-based methods have been proposed to detect
nested entities with span representations. However, span-based methods do not
consider the relationship between a span and other entities or phrases, which
is helpful in the NER task. Besides, span-based methods have trouble predicting
long entities due to limited span enumeration length. To mitigate these issues,
we present the Propose-and-Refine Network (PnRNet), a two-stage set prediction
network for nested NER. In the propose stage, we use a span-based predictor to
generate some coarse entity predictions as entity proposals. In the refine
stage, proposals interact with each other, and richer contextual information is
incorporated into the proposal representations. The refined proposal
representations are used to re-predict entity boundaries and classes. In this
way, errors in coarse proposals can be eliminated, and the boundary prediction
is no longer constrained by the span enumeration length limitation.
Additionally, we build multi-scale sentence representations, which better model
the hierarchical structure of sentences and provide richer contextual
information than token-level representations. Experiments show that PnRNet
achieves state-of-the-art performance on four nested NER datasets and one flat
NER dataset.


title: Modeling complex measurement error in microbiome experiments

abstract:
  The relative abundances of species in a microbiome is a scientifically
important parameter to estimate given the critical role that microbiomes play
in human and environmental health. However, data from artificially constructed
microbiomes shows that measurement error may induce substantial bias in common
estimators of this quantity. To address this, we propose a semiparametric model
that accounts for common forms of measurement error in microbiome experiments.
Notably, our model allows relative abundances to lie on the boundary of the
simplex. We present a stable algorithm for computing parameter estimates,
asymptotically valid procedures for inference in this nonstandard problem, and
examples of the utility of the method. Our approach can be used to select or
compare experimental protocols, design experiments with appropriate control
data, analyze mixed-specimen samples, and remove across-sample contamination.


title: A new finite element paradigm to solve contact problems with roughness

abstract:
  This article's main scope is the presentation of a computational method for
the simulation of contact problems within the finite element method involving
complex and rough surfaces. The approach relies on the MPJR (eMbedded Profile
for Joint Roughness) interface finite element proposed in [arXiv:1805.07207],
which is nominally flat but can embed at the nodal level any arbitrary height
to reconstruct the displacement field due to contact in the presence of
roughness. Here, the formulation is generalized to handle 3D surface height
fields and any arbitrary nonlinear interface constitutive relation, including
friction and adhesion. The methodology is herein validated with BEM solutions
for linear elastic contact problems. Then, a selection of nonlinear contact
problems prohibitive to be simulated by BEM and by standard contact algorithms
in FEM are detailed, to highlight the promising aspects of the proposed method
for tribology.


title:
Uncertainty Quantification for nonparametric regression using Empirical
  Bayesian neural networks

abstract:
  We propose a new, two-step empirical Bayes-type of approach for neural
networks. We show in context of the nonparametric regression model that the
procedure (up to a logarithmic factor) provides optimal recovery of the
underlying functional parameter of interest and provides Bayesian credible sets
with frequentist coverage guarantees. The approach requires fitting the neural
network only once, hence it is substantially faster than Bootstrapping type
approaches. We demonstrate the applicability of our method over synthetic data,
observing good estimation properties and reliable uncertainty quantification.


title:
A Multi-Head Convolutional Neural Network With Multi-path Attention
  improves Image Denoising

abstract:
  Recently, convolutional neural networks (CNNs) and attention mechanisms have
been widely used in image denoising and achieved satisfactory performance.
However, the previous works mostly use a single head to receive the noisy
image, limiting the richness of extracted features. Therefore, a novel CNN with
multiple heads (MH) named MHCNN is proposed in this paper, whose heads will
receive the input images rotated by different rotation angles. MH makes MHCNN
simultaneously utilize features of rotated images to remove noise. We also
present a novel multi-path attention mechanism (MPA) to integrate these
features effectively. Unlike previous attention mechanisms that handle
pixel-level, channel-level, and patch-level features, MPA focuses on features
at the image level. Experiments show MHCNN surpasses other state-of-the-art CNN
models on additive white Gaussian noise (AWGN) denoising and real-world image
denoising. Its peak signal-to-noise ratio (PSNR) results are higher than other
networks, such as DnCNN, BRDNet, RIDNet, PAN-Net, and CSANN. It is also
demonstrated that the proposed MH with MPA mechanism can be used as a pluggable
component.


title: A stochastic analysis approach to lattice Yang--Mills at strong coupling

abstract:
  We develop a new stochastic analysis approach to the lattice Yang--Mills
model at strong coupling in any dimension $d>1$, with t' Hooft scaling $\beta
N$ for the inverse coupling strength. We study their Langevin dynamics,
ergodicity, functional inequalities, large $N$ limits, and mass gap.
  Assuming $|\beta| < \frac{N-2}{32(d-1)N}$ for the structure group $SO(N)$, or
$|\beta| < \frac{1}{16(d-1)}$ for $SU(N)$, we prove the following results. The
invariant measure for the corresponding Langevin dynamic is unique on the
entire lattice, and the dynamic is exponentially ergodic under a Wasserstein
distance. The finite volume Yang--Mills measures converge to this unique
invariant measure in the infinite volume limit, for which Log-Sobolev and
Poincar\'e inequalities hold. These functional inequalities imply that the
suitably rescaled Wilson loops for the infinite volume measure has factorized
correlations and converges in probability to deterministic limits in the large
$N$ limit, and correlations of a large class of observables decay
exponentially, namely the infinite volume measure has a strictly positive mass
gap. Our method improves earlier results or simplifies the proofs, and provides
some new perspectives to the study of lattice Yang--Mills model.


title:
Smoothing distributions for conditional Fleming-Viot and Dawson-Watanabe
  diffusions

abstract:
  We study the distribution of the unobserved states of two measure-valued
diffusions of Fleming-Viot and Dawson-Watanabe type, conditional on
observations from the underlying populations collected at past, present and
future times. If seen as nonparametric hidden Markov models, this amounts to
finding the smoothing distributions of these processes, which we show can be
explicitly described in recursive form as finite mixtures of laws of Dirichlet
and gamma random measures respectively. We characterize the time-dependent
weights of these mixtures, accounting for potentially different time intervals
between data collection times, and fully describe the implications of assuming
a discrete or a nonatomic distribution for the underlying process that drives
mutations. In particular, we show that with a non atomic mutation offspring
distribution, the inference automatically upweights mixture components that
carry, as atoms, observed types shared at different collection times. The
predictive distributions for further samples from the population conditional on
the data are also identified and shown to be mixtures of generalized Polya
urns, conditionally on a latent variable in the Dawson-Watanabe case.

categories: math.ST math.PR q-bio.PE stat.CO stat.TH


title: Analysis of the data from photoelectric gas polarimeters

abstract:
  We review the tools and procedures for the analysis of the data collected by
X-ray photoelectric gas polarimeters, like the ones on-board the Imaging X-ray
Polarimetry Explorer (IXPE). Although many of such tools are in principle
common with polarimeters working at other energy bands, the peculiar
characteristics and performance of these devices require a specific approach.
We will start from the analysis of the raw data read-out from this kind of
instruments, that is, the image of the track of the photoelectron. We will
briefly present how such images are processed with highly-specialized
algorithms to extract all the information collected by the instrument. These
include energy, time of arrival and, possibly, absorption point of the photon,
in addition to the initial direction of emission of the photoelectron. The last
is the quantity relevant for polarimetry, and we will present different methods
to obtain the polarization degree and angle from it. A simple method, used
extensively especially during the development phase of X-ray photoelectric gas
polarimeters, is based on the construction and fitting of the azimuthal
distribution of the photoelectrons. We will discuss that there are several
reasons to prefer an analysis based on Stokes parameters, especially when one
wants to analyze measurements of real, i.e., not laboratory, sources. These are
quantities commonly used at all wavelengths because they are additive, and then
operations like background subtraction or the application of calibration are
trivial to apply. We will summarize how Stokes parameters can be used to adapt
current spectroscopy software based on forward folding fitting to perform
spectro-polarimetry. Moreover, we will derive how to properly associate the
statistical uncertainty on a polarimetry measurement and the relation with
another statistical indicator, which is in the minimum detectable polarization.


title: Minimum Displacement Motion Planning for Movable Obstacles

abstract:
  This paper presents a minimum displacement motion planning problem wherein
obstacles are displaced by a minimum amount to find a feasible path. We define
a metric for robot-obstacle intersection that measures the extent of the
intersection and use this to penalize robot-obstacle overlaps. Employing the
actual robot dynamics, the planner first finds a path through the obstacles
that minimizes the robot-obstacle intersections. The metric is then used to
iteratively displace the obstacles to achieve a feasible path. Several examples
are provided that successfully demonstrates the proposed problem.


title:
Multi-scale membrane process optimization with high-fidelity ion
  transport models through machine learning

abstract:
  Innovative membrane technologies optimally integrated into large separation
process plants are essential for economical water treatment and disposal.
However, the mass transport through membranes is commonly described by
nonlinear differential-algebraic mechanistic models at the nano-scale, while
the process and its economics range up to large-scale. Thus, the optimal design
of membranes in process plants requires decision making across multiple scales,
which is not tractable using standard tools. In this work, we embed artificial
neural networks~(ANNs) as surrogate models in the deterministic global
optimization to bridge the gap of scales. This methodology allows for
deterministic global optimization of membrane processes with accurate transport
models -- avoiding the utilization of inaccurate approximations through
heuristics or short-cut models. The ANNs are trained based on data generated by
a one-dimensional extended Nernst-Planck ion transport model and extended to a
more accurate two-dimensional distribution of the membrane module, that
captures the filtration-related decreasing retention of salt. We simultaneously
design the membrane and plant layout yielding optimal membrane module synthesis
properties along with the optimal plant design for multiple objectives, feed
concentrations, filtration stages, and salt mixtures. The developed process
models and the optimization solver are available open-source, enabling
computational resource-efficient multi-scale optimization in membrane science.


title:
Discrete energy analysis of the third-order variable-step BDF
  time-stepping for diffusion equations

abstract:
  This is one of our series works on discrete energy analysis of the
variable-step BDF schemes. In this part, we present stability and convergence
analysis of the third-order BDF (BDF3) schemes with variable steps for linear
diffusion equations, see e.g. [SIAM J. Numer. Anal., 58:2294-2314] and [Math.
Comp., 90: 1207-1226] for our previous works on the BDF2 scheme. To this aim,
we first build up a discrete gradient structure of the variable-step BDF3
formula under the condition that the adjacent step ratios are less than 1.4877,
by which we can establish a discrete energy dissipation law. Mesh-robust
stability and convergence analysis in the $L^2$ norm are then obtained. Here
the mesh robustness means that the solution errors are well controlled by the
maximum time-step size but independent of the adjacent time-step ratios. We
also present numerical tests to support our theoretical results.


title: Self-Supervised Text Erasing with Controllable Image Synthesis

abstract:
  Recent efforts on scene text erasing have shown promising results. However,
existing methods require rich yet costly label annotations to obtain robust
models, which limits the use for practical applications. To this end, we study
an unsupervised scenario by proposing a novel Self-supervised Text Erasing
(STE) framework that jointly learns to synthesize training images with erasure
ground-truth and accurately erase texts in the real world. We first design a
style-aware image synthesis function to generate synthetic images with diverse
styled texts based on two synthetic mechanisms. To bridge the text style gap
between the synthetic and real-world data, a policy network is constructed to
control the synthetic mechanisms by picking style parameters with the guidance
of two specifically designed rewards. The synthetic training images with
erasure ground-truth are then fed to train a coarse-to-fine erasing network. To
produce better erasing outputs, a triplet erasure loss is designed to enforce
the refinement stage to recover background textures. Moreover, we provide a new
dataset (called PosterErase), which contains 60K high-resolution posters with
texts and is more challenging for the text erasing task. The proposed method
has been extensively evaluated with both PosterErase and the widely-used
SCUT-Enstext dataset. Notably, on PosterErase, our unsupervised method achieves
5.07 in terms of FID, with a relative performance of 20.9% over existing
supervised baselines.


title: A Bernstein type result of translating solitons

abstract:
  By using a new test function and the gradient estimate technique, we obtain a
better Bernstein type result of translating solitons.


title:
Optical microscope based universal parameter for identifying layer
  number in two-dimensional materials

abstract:
  Optical contrast is the most common preliminary method to identify layer
number of two-dimensional (2D) materials, but is seldom used as a confirmatory
technique. We explain the reason for variation of optical contrast between
imaging systems. We introduce a universal method to quantify the layer number
using the RGB (red-green-blue) and RAW optical images. For RGB images, the
slope of 2D flake (MoS2, WSe2, graphene) intensity vs. substrate intensity is
extracted from optical images with varying lamp power. The intensity slope
identifies layer number and is system independent. For RAW images, intensity
slopes and intensity ratios are completely system and intensity independent.
Intensity slope (for RGB) and intensity ratio (for RAW) are thus universal
parameters for identifying layer number. A Fresnel-reflectance-based optical
model provides an excellent match with experiments. Further, we have created a
MATLAB-based graphical user interface that can identify layer number rapidly.
This technique is expected to accelerate the preparation of heterostructures,
and fulfil a prolonged need for universal optical contrast method.


title:
Single-beam double-pass miniaturized atomic magnetometer for
  bio-magnetic imaging systems

abstract:
  Miniaturized atomic magnetometers, particularly spin-exchange relaxation-free
atomic magnetometers, have been emerging in clinical imaging applications such
magnetocardiography and magnetoencephalography. Miniaturization, portability,
and low cost are primary development targets for bio-magnetic imaging
technologies, as well as high sensitivity and spatial and time resolution. In
this paper, we propose a low-cost solution for a bio-magnetic imaging system
based on atomic magnetometers, in which one laser source is used for a
multi-channel atomic magnetometer sensor array. A novel design is demonstrated
for a miniaturized spin-exchange relaxation-free atomic magnetometer,
consisting of a single-beam double-pass configuration based on an optical fiber
circulator. The effects of temperature and laser power on the zero-field
magnetic resonance line-width are characterized, and the experimental results
show that the present design achieves better performance, than a traditional
single-beam single-pass configuration. The noise power spectrum shows that the
closed-loop miniaturized atomic magnetometer reaches a sensitivity of
approximately 120 fT/Hz$^{1/2}$ at a bandwidth of 10 Hz. This design is
especially suitable for atomic magnetometers operating in arrays as a basic
building element for low-cost bio-magnetic imaging systems.


title:
Spectrum of the wave equation with Dirac damping on a non-compact star
  graph

abstract:
  We consider the wave equation on non-compact star graphs, subject to a
distributional damping defined through a Robin-type vertex condition with
complex coupling. It is shown that the non-self-adjoint generator of the
evolution problem admits an abrupt change in its spectral properties for a
special coupling related to the number of graph edges. As an application, we
show that the evolution problem is highly unstable for the critical couplings.
The relationship with the Dirac equation in non-relativistic quantum mechanics
is also mentioned.


title:
Self-Driving Car Steering Angle Prediction: Let Transformer Be a Car
  Again

abstract:
  Self-driving vehicles are expected to be a massive economic influence over
the coming decades. Udacity https://www.udacity.com/ has been working on a
completely open-source self driving car. Thus, it regularly organizes various
competitions, one of which was dedicated to steering angle prediction task. In
this work, we perform an extensive study on this particular task by exploring
the Udacity Self-driving Car Challenge 2. We provide insights on the previous
teams' solutions. Moreover, we propose our new architecture that is inspired by
some of the teams. We report our performance and compare it with multiple
baseline architectures as well as other teams' solutions. We make our work
available on GitHub and hope it is useful for the Udacity community and brings
insights for future works https://github.com/chingisooinar/AI_self-driving-car


title:
Control Globally, Understand Locally: A Global-to-Local Hierarchical
  Graph Network for Emotional Support Conversation

abstract:
  Emotional support conversation aims at reducing the emotional distress of the
help-seeker, which is a new and challenging task. It requires the system to
explore the cause of help-seeker's emotional distress and understand their
psychological intention to provide supportive responses. However, existing
methods mainly focus on the sequential contextual information, ignoring the
hierarchical relationships with the global cause and local psychological
intention behind conversations, thus leads to a weak ability of emotional
support. In this paper, we propose a Global-to-Local Hierarchical Graph Network
to capture the multi-source information (global cause, local intentions and
dialog history) and model hierarchical relationships between them, which
consists of a multi-source encoder, a hierarchical graph reasoner, and a
global-guide decoder. Furthermore, a novel training objective is designed to
monitor semantic information of the global cause. Experimental results on the
emotional support conversation dataset, ESConv, confirm that the proposed GLHG
has achieved the state-of-the-art performance on the automatic and human
evaluations.


title:
DraftRec: Personalized Draft Recommendation for Winning in Multi-Player
  Online Battle Arena Games

abstract:
  This paper presents a personalized character recommendation system for
Multiplayer Online Battle Arena (MOBA) games which are considered as one of the
most popular online video game genres around the world. When playing MOBA
games, players go through a draft stage, where they alternately select a
virtual character to play. When drafting, players select characters by not only
considering their character preferences, but also the synergy and competence of
their team's character combination. However, the complexity of drafting induces
difficulties for beginners to choose the appropriate characters based on the
characters of their team while considering their own champion preferences. To
alleviate this problem, we propose DraftRec, a novel hierarchical model which
recommends characters by considering each player's champion preferences and the
interaction between the players. DraftRec consists of two networks: the player
network and the match network. The player network captures the individual
player's champion preference, and the match network integrates the complex
relationship between the players and their respective champions. We train and
evaluate our model from a manually collected 280,000 matches of League of
Legends and a publicly available 50,000 matches of Dota2. Empirically, our
method achieved state-of-the-art performance in character recommendation and
match outcome prediction task. Furthermore, a comprehensive user survey
confirms that DraftRec provides convincing and satisfying recommendations. Our
code and dataset are available at https://github.com/dojeon-ai/DraftRec.


title:
Analysis of lowest-order characteristics-mixed FEMs for incompressible
  miscible flow in porous media

abstract:
  The time discrete scheme of characteristics type is especially effective for
convection-dominated diffusion problems. The scheme has been used in various
engineering areas with different approximations in spatial direction. The
lowest-order mixed method is the most popular one for miscible flow in porous
media. The method is based on a linear Lagrange approximation to the
concentration and the zero-order Raviart-Thomas approximation to the
pressure/velocity. However, the optimal error estimate for the lowest-order
characteristics-mixed FEM has not been presented although numerous effort has
been made in last several decades. In all previous works, only first-order
accuracy in spatial direction was proved under certain time-step and mesh size
restrictions. The main purpose of this paper is to establish optimal error
estimates, $i.e.$, the second-order in $L^2$-norm for the concentration and the
first-order for the pressure/velocity, while the concentration is more
important physical component for the underlying model. For this purpose, an
elliptic quasi-projection is introduced in our analysis to clean up the
pollution of the numerical velocity through the nonlinear dispersion-diffusion
tensor and the concentration-dependent viscosity. Moreover, the numerical
pressure/velocity of the second-order accuracy can be obtained by re-solving
the (elliptic) pressure equation at a given time level with a higher-order
approximation. Numerical results are presented to confirm our theoretical
analysis.


title:
Categorifications of Non-Integer Quivers: Types $H_4$, $H_3$ and
  $I_2(2n+1)$

abstract:
  We define the notion of a weighted unfolding of quivers with real weights,
and use this to provide a categorification of mutations of quivers of finite
types $H_4$, $H_3$ and $I_2(2n+1)$. In particular, the (un)folding induces a
semiring action on the categories associated to the unfolded quivers of types
$E_8$, $D_6$ and $A_{2n}$ respectively. We then define the tropical seed
pattern on the folded quivers, which includes $c$- and $g$-vectors, and show
its compatibility with the unfolding.


title:
A Comprehensive Understanding of Code-mixed Language Semantics using
  Hierarchical Transformer

abstract:
  Being a popular mode of text-based communication in multilingual communities,
code-mixing in online social media has became an important subject to study.
Learning the semantics and morphology of code-mixed language remains a key
challenge, due to scarcity of data and unavailability of robust and
language-invariant representation learning technique. Any morphologically-rich
language can benefit from character, subword, and word-level embeddings, aiding
in learning meaningful correlations. In this paper, we explore a hierarchical
transformer-based architecture (HIT) to learn the semantics of code-mixed
languages. HIT consists of multi-headed self-attention and outer product
attention components to simultaneously comprehend the semantic and syntactic
structures of code-mixed texts. We evaluate the proposed method across 6 Indian
languages (Bengali, Gujarati, Hindi, Tamil, Telugu and Malayalam) and Spanish
for 9 NLP tasks on 17 datasets. The HIT model outperforms state-of-the-art
code-mixed representation learning and multilingual language models in all
tasks. We further demonstrate the generalizability of the HIT architecture
using masked language modeling-based pre-training, zero-shot learning, and
transfer learning approaches. Our empirical results show that the pre-training
objectives significantly improve the performance on downstream tasks.


title:
Instability of Multi-Solitons for Derivative Nonlinear Schr{\"o}dinger
  Equations

abstract:
  In [19] and [26], the authors proved the stability of multi-solitons for
derivative nonlinear Schr{\"o}dinger equations. Roughly speaking, sum of finite
stable solitons is stable. We predict that if there is one unstable solition
then multi-soliton is unstable. This prediction is proved in [7] for classical
nonlinear Schr{\"o}dinger equations. In this paper, we proved this prediction
for derivative nonlinear Schr{\"o}dinger equations by using the method of
C{\^o}te-Le Coz [7] with the help of Gauge transformation.


title: A Thorough Examination on Zero-shot Dense Retrieval

abstract:
  Recent years have witnessed the significant advance in dense retrieval (DR)
based on powerful pre-trained language models (PLM). DR models have achieved
excellent performance in several benchmark datasets, while they are shown to be
not as competitive as traditional sparse retrieval models (e.g., BM25) in a
zero-shot retrieval setting. However, in the related literature, there still
lacks a detailed and comprehensive study on zero-shot retrieval. In this paper,
we present the first thorough examination of the zero-shot capability of DR
models. We aim to identify the key factors and analyze how they affect
zero-shot retrieval performance. In particular, we discuss the effect of
several key factors related to source training set, analyze the potential bias
from the target dataset, and review and compare existing zero-shot DR models.
Our findings provide important evidence to better understand and develop
zero-shot DR models.


title:
Talking Head Generation Driven by Speech-Related Facial Action Units and
  Audio- Based on Multimodal Representation Fusion

abstract:
  Talking head generation is to synthesize a lip-synchronized talking head
video by inputting an arbitrary face image and corresponding audio clips.
Existing methods ignore not only the interaction and relationship of
cross-modal information, but also the local driving information of the mouth
muscles. In this study, we propose a novel generative framework that contains a
dilated non-causal temporal convolutional self-attention network as a
multimodal fusion module to promote the relationship learning of cross-modal
features. In addition, our proposed method uses both audio- and speech-related
facial action units (AUs) as driving information. Speech-related AU information
can guide mouth movements more accurately. Because speech is highly correlated
with speech-related AUs, we propose an audio-to-AU module to predict
speech-related AU information. We utilize pre-trained AU classifier to ensure
that the generated images contain correct AU information. We verify the
effectiveness of the proposed model on the GRID and TCD-TIMIT datasets. An
ablation study is also conducted to verify the contribution of each component.
The results of quantitative and qualitative experiments demonstrate that our
method outperforms existing methods in terms of both image quality and lip-sync
accuracy.


title:
Conductivity of Two-dimensional Dirac Electrons Close to Merging in
  Organic Conductor $\alpha$-STF$_2$I$_3$ at Ambient Pressure

abstract:
  The electric conductivity of Dirac electrons in the organic conductor
$\alpha$-STF$_2$I$_3$ (STF = bis(ethylenedithio)diselenadithiafulvalene), which
has an isostructure of ET(=bis(ethylenedithio)tetrathiafulvalene), has been
theoretically studied using a two-dimensional tight-binding model in the
presence of both impurity and electron-phonon (e-p) scatterings.In contrast to
ET, which has a Dirac cone with almost isotropic velocity, STF provides a large
anisotropy owing to a Dirac point that is close to merging. As a result,
$\sigma_{x}$ becomes much larger than $\sigma_{y}$, where $\sigma_y$ and
$\sigma_x$ are diagonal conductivities parallel and perpendicular to a stacking
axis of molecules, respectively. With increasing temperature ($T$), $\sigma_x$
takes a broad maximum because of e-p scattering and $\sigma_y$ remains almost
constant. The ratio $\sigma_x/\sigma_y$ is analyzed in terms of the band
structure. Such an exotic conductivity of STF is compared with that of an
experiment showing a good correspondence. Finally, $\sigma_x/\sigma_y$ values
of ET and BETS(= bis(ethylenedithio)tetraselenafulvalene) are shown to
demonstrate the dissimilarity with STF.


title:
Advantages of maintaining a multi-task project-specific bot: an
  experience report

abstract:
  Bots are becoming a popular method for automating basic everyday tasks in
many software projects. This is true in particular because of the availability
of many off-the-shelf task-specific bots that teams can quickly adopt (which
are sometimes completed with additional task-specific custom bots). Based on
our experience in the Coq project, where we have developed and maintained a
multi-task project-specific bot, we argue that this alternative approach to
project automation should receive more attention because it strikes a good
balance between productivity and adaptibility. In this article, we describe the
kind of automation that our bot implements, what advantages we have gained by
maintaining a project-specific bot, and the technology and architecture choices
that have made it possible. We draw conclusions that should generalize to other
medium-sized software teams willing to invest in project automation without
disrupting their workflows.


title:
Li-rich Giants in LAMOST Survey. III. The statistical analysis of
  Li-rich giants

abstract:
  The puzzle of Li-rich giant is still unsolved, contradicting the prediction
of the standard stellar models. Although the exact evolutionary stages play a
key role in the knowledge of Li-rich giants, a limited number of Li-rich giants
have been taken with high-quality asteroseismic parameters to clearly
distinguish the stellar evolutionary stages. Based on the LAMOST Data Release 7
(DR7), we applied a data-driven neural network method to derive the parameters
for giant stars, which contain the largest number of Li-rich giants. The red
giant stars are classified into three stages of Red Giant Branch (RGB), Primary
Red Clump (PRC), and Secondary Red Clump (SRC) relying on the estimated
asteroseismic parameters. In the statistical analysis of the properties (i.e.
stellar mass, carbon, nitrogen, Li-rich distribution, and frequency) of Li-rich
giants, we found that: (1) Most of the Li-rich RGB stars are suggested to be
the descendants of Li-rich pre-RGB stars and/or the result of engulfment of
planet or substellar companions; (2) The massive Li-rich SRC stars could be the
natural consequence of Li depletion from the high-mass Li-rich RGB stars. (3)
Internal mixing processes near the helium flash can account for the phenomenon
of Li-rich on PRC that dominated the Li-rich giants. Based on the comparison of
[C/N] distributions between Li-rich and normal PRC stars, the Li-enriched
processes probably depend on the stellar mass.


title:
The integrality of the Genocchi numbers obtained through a new identity
  and other results

abstract:
  In this note, we investigate some properties of the integer sequence of
general term $a_n := \sum_{k = 0}^{n - 1} k! (n - k - 1)!$ ($\forall n \geq 1$)
to derive a new identity of the Genocchi numbers $G_n$ ($n \in \mathbb{N}$),
which immediately shows that $G_n \in \mathbb{Z}$ for any $n \in \mathbb{N}$.
In another direction, we obtain nontrivial lower bounds for the $2$-adic
valuations of the rational numbers $\sum_{k = 1}^{n} \frac{2^k}{k}$.