title:
$\phi$ meson properties in nuclear matter from QCD sum rules with
  chirally separated four-quark condensates

abstract:
  The modification of the $\phi$ meson spectrum in nuclear matter is studied in
an updated QCD sum rule analysis, taking into account recent improvements in
properly treating the chiral invariant and breaking components of four-quark
condensates. Allowing both mass and decay width to change at finite density,
the QCD sum rule analysis determines certain combinations of changes for these
parameters that satisfy the sum rules equally well. A comprehensive error
analysis, including uncertainties related to the behavior of various
condensates at linear order in density, the employed renormalization scale and
perturbative corrections of the Wilson coefficients, is used to compute the
allowed ranges of these parameter combinations. We find that the $\phi$ meson
mass shift in nuclear matter is especially sensitive to the strange sigma term
$\sigma_{sN}$, which determines the decrease of the strange quark condensate in
nuclear matter. Specifically, we obtain a linear relation between the width
$\Gamma_{\phi}$ and mass shift $\Delta m_{\phi}$ given as $ \Gamma_{\phi} =
a\Delta m_{\phi} + b\sigma_{sN}+c$ with $a = (3.947^{+0.139}_{-0.130})$, $b =
(0.936^{+0.180}_{-0.177} )$ and $c = -(7.707^{+4.791}_{-5.679}) \mathrm{MeV}$.


title:
Multiscale modelling in nuclear ferritic steels: from nano-sized defects
  to embrittlement

abstract:
  Radiation-induced embrittlement of nuclear steels is one of the main limiting
factors for safe long-term operation of nuclear power plants. In support of
accurate and safe reactor pressure vessel (RPV) lifetime assessments, we
developed a physics-based model that predicts RPV steel hardening and
subsequent embrittlement as a consequence of the formation of nano-sized
clusters of minor alloying elements. This model is shown to provide reliable
assessments of embrittlement for a very wide range of materials, with higher
accuracy than industrial correlations. The core of our model is a multiscale
modelling tool that predicts the kinetics of solute clustering, given the steel
chemical composition and its irradiation conditions. It is based on the
observation that the formation of solute clusters ensues from atomic transport
driven by radiation-induced mechanisms, differently from classical
nucleation-and-growth theories. We then show that the predicted information
about solute clustering can be translated into a reliable estimate for
radiation-induced embrittlement, via standard hardening laws based on the
dispersed barrier model. We demonstrate the validity of our approach by
applying it to hundreds of nuclear reactors vessels from all over the world.


title:
Generalized Cram\'er's coefficient via $f$-divergence for contingency
  tables

abstract:
  This study proposes measures describing the strength of association between
the row and column variables via the $f$-divergence. Cram\'er's coefficient is
a possible mechanism for the analysis of two-way contingency tables. Tomizawa
et al. (2004) proposed more general measures, including Cram\'er's coefficient,
using the power-divergence. In this paper, we propose more general measures and
show some of their properties, demonstrating that the proposed measures are
beneficial for comparing the strength of association in several tables.


title: On the monotonicity of the generalized Markov numbers

abstract:
  Using the Markov distance and Ptolemy inequality introduced by
Lee-Li-Rabideau-Schiffler \cite{LLRS}, we completely determine the monotonicity
of the generalized Markov numbers along the lines of a given slope.


title: Fine-tuning Pruned Networks with Linear Over-parameterization

abstract:
  Structured pruning compresses neural networks by reducing channels (filters)
for fast inference and low footprint at run-time. To restore accuracy after
pruning, fine-tuning is usually applied to pruned networks. However, too few
remaining parameters in pruned networks inevitably bring a great challenge to
fine-tuning to restore accuracy. To address this challenge, we propose a novel
method that first linearly over-parameterizes the compact layers in pruned
networks to enlarge the number of fine-tuning parameters and then
re-parameterizes them to the original layers after fine-tuning. Specifically,
we equivalently expand the convolution/linear layer with several consecutive
convolution/linear layers that do not alter the current output feature maps.
Furthermore, we utilize similarity-preserving knowledge distillation that
encourages the over-parameterized block to learn the immediate data-to-data
similarities of the corresponding dense layer to maintain its feature learning
ability. The proposed method is comprehensively evaluated on CIFAR-10 and
ImageNet which significantly outperforms the vanilla fine-tuning strategy,
especially for large pruning ratio.


title:
Aspect-based Analysis of Advertising Appeals for Search Engine
  Advertising

abstract:
  Writing an ad text that attracts people and persuades them to click or act is
essential for the success of search engine advertising. Therefore, ad creators
must consider various aspects of advertising appeals (A$^3$) such as the price,
product features, and quality. However, products and services exhibit unique
effective A$^3$ for different industries. In this work, we focus on exploring
the effective A$^3$ for different industries with the aim of assisting the ad
creation process. To this end, we created a dataset of advertising appeals and
used an existing model that detects various aspects for ad texts. Our
experiments demonstrated that different industries have their own effective
A$^3$ and that the identification of the A$^3$ contributes to the estimation of
advertising performance.


title: Imitation Learning from Observations under Transition Model Disparity

abstract:
  Learning to perform tasks by leveraging a dataset of expert observations,
also known as imitation learning from observations (ILO), is an important
paradigm for learning skills without access to the expert reward function or
the expert actions. We consider ILO in the setting where the expert and the
learner agents operate in different environments, with the source of the
discrepancy being the transition dynamics model. Recent methods for scalable
ILO utilize adversarial learning to match the state-transition distributions of
the expert and the learner, an approach that becomes challenging when the
dynamics are dissimilar. In this work, we propose an algorithm that trains an
intermediary policy in the learner environment and uses it as a surrogate
expert for the learner. The intermediary policy is learned such that the state
transitions generated by it are close to the state transitions in the expert
dataset. To derive a practical and scalable algorithm, we employ concepts from
prior work on estimating the support of a probability distribution. Experiments
using MuJoCo locomotion tasks highlight that our method compares favorably to
the baselines for ILO with transition dynamics mismatch.


title: Evaluating Extrapolation Performance of Dense Retrieval

abstract:
  A retrieval model should not only interpolate the training data but also
extrapolate well to the queries that are rather different from the training
data. While dense retrieval (DR) models have been demonstrated to achieve
better retrieval performance than the traditional term-based retrieval models,
we still know little about whether they can extrapolate. To shed light on the
research question, we investigate how DR models perform in both the
interpolation and extrapolation regimes. We first investigate the distribution
of training and test data on popular retrieval benchmarks and identify a
considerable overlap in query entities, query intent, and relevance labels.
This finding implies that the performance on these test sets is biased towards
interpolation and cannot accurately reflect the extrapolation capacity.
Therefore, to evaluate the extrapolation performance of DR models, we propose
two resampling strategies for existing retrieval benchmarks and comprehensively
investigate how DR models perform. Results show that DR models may interpolate
as well as complex interaction-based models (e.g., BERT and ColBERT) but
extrapolate substantially worse. Among various DR training strategies,
text-encoding pretraining and target-domain pretraining are particularly
effective for improving the extrapolation capacity. Finally, we compare the
extrapolation capacity with domain transfer ability. Despite its simplicity and
ease of use, the extrapolation performance can reflect the domain transfer
ability in some domains of the BEIR dataset, further highlighting the
feasibility of our approaches in evaluating the generalizability of DR models.


title:
High-Efficiency Lossy Image Coding Through Adaptive Neighborhood
  Information Aggregation

abstract:
  Questing for lossy image coding (LIC) with superior efficiency on both
compression performance and computation throughput is challenging. The vital
factor behind is how to intelligently explore Adaptive Neighborhood Information
Aggregation (ANIA) in transform and entropy coding modules. To this aim,
Integrated Convolution and Self-Attention (ICSA) unit is first proposed to form
content-adaptive transform to dynamically characterize and embed neighborhood
information conditioned on the input. Then a Multistage Context Model (MCM) is
developed to stagewisely execute context prediction using necessary
neighborhood elements for accurate and parallel entropy probability estimation.
Both ICSA and MCM are stacked under a Variational Auto-Encoder (VAE)
architecture to derive rate-distortion optimized compact representation of
input image via end-to-end training. Our method reports the superior
compression performance surpassing the VVC Intra with $\approx$15% BD-rate
improvement averaged across Kodak, CLIC and Tecnick datasets; and also
demonstrates $\approx$10$\times$ speedup of image decoding when compared with
other notable learned LIC approaches. All materials are made publicly
accessible at https://njuvision.github.io/TinyLIC for reproducible research.


title: OCFormer: One-Class Transformer Network for Image Classification

abstract:
  We propose a novel deep learning framework based on Vision Transformers (ViT)
for one-class classification. The core idea is to use zero-centered Gaussian
noise as a pseudo-negative class for latent space representation and then train
the network using the optimal loss function. In prior works, there have been
tremendous efforts to learn a good representation using varieties of loss
functions, which ensures both discriminative and compact properties. The
proposed one-class Vision Transformer (OCFormer) is exhaustively experimented
on CIFAR-10, CIFAR-100, Fashion-MNIST and CelebA eyeglasses datasets. Our
method has shown significant improvements over competing CNN based one-class
classifier approaches.


title:
Flavor symmetric origin of texture zeros in minimal inverse seesaw and
  impacts on leptogenesis

abstract:
  We study the prediction of maximal zeros of the Dirac mass matrix on neutrino
phenomenology and baryon asymmetry of the universe (BAU) within the framework
of an inverse seesaw ISS $(2,3)$. We try to find the origin of the allowed two
zero textures of the Dirac mass matrix from $S_{4}$ flavor symmetry. ISS
$(2,3)$ model contains two pairs of quasi-Dirac particles and one sterile state
in the keV scale along with three active neutrinos. The decays of the
quasi-Dirac pairs create lepton asymmetry that can be converted to baryon
asymmetry of the universe by the sphaleron process. Thus, BAU can be explained
in this framework through leptogenesis. We study BAU in all the two zero
textures of the Dirac mass matrix. The viabilities of the textures within the
framework have been verified with the latest cosmology data on BAU.


title:
Safe Delivery of Critical Services in Areas with Volatile Security
  Situation via a Stackelberg Game Approach

abstract:
  Vaccine delivery in under-resourced locations with security risks is not just
challenging but also life threatening. The current COVID pandemic and the need
to vaccinate have added even more urgency to this issue. Motivated by this
problem, we propose a general framework to set-up limited temporary
(vaccination) centers that balance physical security and desired (vaccine)
service coverage with limited resources. We set-up the problem as a Stackelberg
game between the centers operator (defender) and an adversary, where the set of
centers is not fixed a priori but is part of the decision output. This results
in a mixed combinatorial and continuous optimization problem. As part of our
scalable approximation of this problem, we provide a fundamental contribution
by identifying general duality conditions of switching max and min when both
discrete and continuous variables are involved. We perform detailed experiments
to show that the solution proposed is scalable in practice.


title:
Modeling the Effect of Observational Social Learning on Vaccination and
  Diseases using Household Networks

abstract:
  In this paper, we study observational social learning and vaccine-preventable
childhood disease spread over a bi-layer household network. The two overlapping
networks are either Erd\H{o}s-R\'{e}nyi (random) network or Barab\'{a}si-Albert
network. We introduce a new Bayesian rule of observational social learning over
the social network of which other decision models, like voting and DeGroot
models, are special cases. We show how social learning can lead to convergence
of opinions and affect levels of vaccine uptake. We also study the effect of
the existence of two cultures of social learning on the establishment of social
norms and levels of vaccine uptake and so on the disease spread. The mutual
influence between the opinion change through observational social learning and
the disease spread shows dependence on the network's topology and on the
vaccine safety and availability.


title: Semisimple random walks on the torus

abstract:
  We study linear random walks on the torus and show a quantitative
equidistribution statement, under the assumption that the Zariski closure of
the acting group is semisimple.


title: Natural Language to Code Translation with Execution

abstract:
  Generative models of code, pretrained on large corpora of programs, have
shown great success in translating natural language to code (Chen et al., 2021;
Austin et al., 2021; Li et al., 2022, inter alia). While these models do not
explicitly incorporate program semantics (i.e., execution results) during
training, they are able to generate correct solutions for many problems.
However, choosing a single correct program from among a generated set for each
problem remains challenging. In this work, we introduce execution result--based
minimum Bayes risk decoding (MBR-EXEC) for program selection and show that it
improves the few-shot performance of pretrained code models on
natural-language-to-code tasks. We select output programs from a generated
candidate set by marginalizing over program implementations that share the same
semantics. Because exact equivalence is intractable, we execute each program on
a small number of test inputs to approximate semantic equivalence. Across
datasets, execution or simulated execution significantly outperforms the
methods that do not involve program semantics. We find that MBR-EXEC
consistently improves over all execution-unaware selection methods, suggesting
it as an effective approach for natural language to code translation.


title:
Lord Rayleigh's Conjecture for Vibrating Clamped Plates in Positively
  Curved Spaces

abstract:
  We affirmatively solve the analogue of Lord Rayleigh's conjecture on
Riemannian manifolds with positive Ricci curvature for any clamped plates in 2
and 3 dimensions, and for sufficiently large clamped plates in dimensions
beyond 3. These results complement those from the flat (M. Ashbaugh & R.
Benguria, 1995, and N. Nadirashvili, 1995) and negatively curved (A.
Krist\'aly, 2020) cases that are valid only in 2 and 3 dimensions, and at the
same time also provide the first positive answer to Lord Rayleigh's conjecture
in higher dimensions. The proofs rely on an
Ashbaugh-Benguria-Nadirashvili-Talenti nodal-decomposition argument, on the
L\'evy-Gromov isoperimetric inequality, on fine properties of Gaussian
hypergeometric functions and on sharp spectral gap estimates of fundamental
tones for both small and large clamped spherical caps. Our results show that
positive curvature enhances genuine differences between low- and
high-dimensional settings, a tacitly accepted paradigm in the theory of
vibrating clamped plates. In the limit case - when the Ricci curvature is
non-negative - we establish a Lord Rayleigh-type isoperimetric inequality that
involves the asymptotic volume ratio of the non-compact complete Riemannian
manifold; moreover, the inequality is strongly rigid in 2 and 3 dimensions,
i.e., if equality holds for a given clamped plate then the manifold is
isometric to the Euclidean space.


title: Sparse optimization problems in fractional order Sobolev spaces

abstract:
  We consider optimization problems in the fractional order Sobolev spaces
$H^s(\Omega)$, $s\in (0,1)$, with sparsity promoting objective functionals
containing $L^p$-pseudonorms, $p\in (0,1)$. Existence of solutions is proven.
By means of a smoothing scheme, we obtain first-order optimality conditions. An
algorithm based on this smoothing scheme is developed. Weak limit points of
iterates are shown to satisfy a stationarity system that is slightly weaker
than that given by the necessary condition.


title: Islander: A Real-Time News Monitoring and Analysis System

abstract:
  With thousands of news articles from hundreds of sources distributed and
shared every day, news consumption and information acquisition have been
increasingly difficult for readers. Additionally, the content of news articles
is becoming catchy or even inciting to attract readership, harming the accuracy
of news reporting. We present Islander, an online news analyzing system. The
system allows users to browse trending topics with articles from multiple
sources and perspectives. We define several metrics as proxies for news
quality, and develop algorithms for automatic estimation. The quality
estimation results are delivered through a web interface to newsreaders for
easy access to news and information. The website is publicly available at
https://islander.cc/


title:
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking
  Inference

abstract:
  State-of-the-art neural models typically encode document-query pairs using
cross-attention for re-ranking. To this end, models generally utilize an
encoder-only (like BERT) paradigm or an encoder-decoder (like T5) approach.
These paradigms, however, are not without flaws, i.e., running the model on all
query-document pairs at inference-time incurs a significant computational cost.
This paper proposes a new training and inference paradigm for re-ranking. We
propose to finetune a pretrained encoder-decoder model using in the form of
document to query generation. Subsequently, we show that this encoder-decoder
architecture can be decomposed into a decoder-only language model during
inference. This results in significant inference time speedups since the
decoder-only architecture only needs to learn to interpret static encoder
embeddings during inference. Our experiments show that this new paradigm
achieves results that are comparable to the more expensive cross-attention
ranking approaches while being up to 6.8X faster. We believe this work paves
the way for more efficient neural rankers that leverage large pretrained
models.


title: Zero entropy actions of amenable groups are not dominant

abstract:
  A probability measure preserving action of a discrete amenable group $G$ is
said to be dominant if it is isomorphic to a generic extension of itself.
Recently, it was shown that for $G = \mathbb{Z}$, an action is dominant if and
only if it has positive entropy and that for any $G$, positive entropy implies
dominance. In this paper, we show that the converse also holds for any $G$,
i.e. that zero entropy implies non-dominance.


title:
Error Performance Analysis of Multi-user Detection in Uplink-NOMA with
  Adaptive $\mathcal{M}$-QAM

abstract:
  This work provides a generalized performance analysis for the multi-user
uplink-NOMA system with adaptive square quadrature amplitude modulation (QAM)
over Rayleigh fading channels. Motivated by the massive IoT connections and
unavailability of orthogonal resources for each node, we consider a
multi-access scheme where multi-users with single-antenna transmit data to a
multiple-antenna base station through the same resource block. By taking
advantage of combining diversity paths with the proposed joint
maximum-likelihood detector (JMLD), a closed form expression for the upper
bound of bit error rate (BER) is obtained. Despite the number of users or the
order of modulation, the analytical results endorsed via computer simulations
reveal the ability of the MRC-JMLD detector to discard the error floor
completely. Moreover, the simulation results show that the MRC-JMLD surpasses
its counterparts significantly and ensures a full diversity order.


title:
A Review of Research on Civic Technology: Definitions, Theories, History
  and Insights

abstract:
  There have been initiatives that take advantage of information and
communication technologies to serve civic purposes, referred to as civic
technologies (Civic Tech). In this paper, we present a review of 224 papers
from the ACM Digital Library focusing on Computer Supported Cooperative Work
and Human-Computer Interaction, the key fields supporting the building of Civic
Tech. Through this review, we discuss the concepts, theories and history of
civic tech research and provide insights on the technological tools, social
processes and participation mechanisms involved. Our work seeks to direct
future civic tech efforts to the phase of by the citizens.


title:
Probing the presence and absence of metal-fullerene electron transfer
  reactions in helium nanodroplets by deflection measurements

abstract:
  Metal-fullerene compounds are characterized by significant electron transfer
to the fullerene cage, giving rise to an electric dipole moment. We use the
method of electrostatic beam deflection to verify whether such reactions take
place within superfluid helium nanodroplets between an embedded C$_{60}$
molecule and either alkali (heliophobic) or rare-earth (heliophilic) atoms. The
two cases lead to distinctly different outcomes: C$_{60}$Na$_n$ ($n$=1-4)
display no discernible dipole moment, while C$_{60}$Yb is strongly polar. This
suggests that the fullerene and small alkali clusters fail to form a
charge-transfer bond in the helium matrix despite their strong van der Waals
attraction. The C$_{60}$Yb dipole moment, on the other hand, is in agreement
with the value expected for an ionic complex.


title: IMDeception: Grouped Information Distilling Super-Resolution Network

abstract:
  Single-Image-Super-Resolution (SISR) is a classical computer vision problem
that has benefited from the recent advancements in deep learning methods,
especially the advancements of convolutional neural networks (CNN). Although
state-of-the-art methods improve the performance of SISR on several datasets,
direct application of these networks for practical use is still an issue due to
heavy computational load. For this purpose, recently, researchers have focused
on more efficient and high-performing network structures. Information
multi-distilling network (IMDN) is one of the highly efficient SISR networks
with high performance and low computational load. IMDN achieves this efficiency
with various mechanisms such as Intermediate Information Collection (IIC),
working in a global setting, Progressive Refinement Module (PRM), and Contrast
Aware Channel Attention (CCA), employed in a local setting. These mechanisms,
however, do not equally contribute to the efficiency and performance of IMDN.
In this work, we propose the Global Progressive Refinement Module (GPRM) as a
less parameter-demanding alternative to the IIC module for feature aggregation.
To further decrease the number of parameters and floating point operations
persecond (FLOPS), we also propose Grouped Information Distilling Blocks
(GIDB). Using the proposed structures, we design an efficient SISR network
called IMDeception. Experiments reveal that the proposed network performs on
par with state-of-the-art models despite having a limited number of parameters
and FLOPS. Furthermore, using grouped convolutions as a building block of GIDB
increases room for further optimization during deployment. To show its
potential, the proposed model was deployed on NVIDIA Jetson Xavier AGX and it
has been shown that it can run in real-time on this edge device


title:
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning
  Methods

abstract:
  In recent years, a growing number of deep model-based reinforcement learning
(RL) methods have been introduced. The interest in deep model-based RL is not
surprising, given its many potential benefits, such as higher sample efficiency
and the potential for fast adaption to changes in the environment. However, we
demonstrate, using an improved version of the recently introduced Local Change
Adaptation (LoCA) setup, that well-known model-based methods such as PlaNet and
DreamerV2 perform poorly in their ability to adapt to local environmental
changes. Combined with prior work that made a similar observation about the
other popular model-based method, MuZero, a trend appears to emerge, suggesting
that current deep model-based methods have serious limitations. We dive deeper
into the causes of this poor performance, by identifying elements that hurt
adaptive behavior and linking these to underlying techniques frequently used in
deep model-based RL. We empirically validate these insights in the case of
linear function approximation by demonstrating that a modified version of
linear Dyna achieves effective adaptation to local changes. Furthermore, we
provide detailed insights into the challenges of building an adaptive nonlinear
model-based method, by experimenting with a nonlinear version of Dyna.


title:
Active orbital degree of freedom and potential spin-orbit-entangled
  moments in Kitaev magnet candidate BaCo$_2$(AsO$_4$)$_2$

abstract:
  Candidate materials for Kitaev spin liquid phase have been intensively
studied recently because of their potential applications in fault-tolerant
quantum computing. Although most of the studies on Kitaev spin liquid have been
done in 4$d$ and 5$d$ based transition metal compounds, recently there has been
a growing research interest in Co-based quasi-two-dimensional honeycomb
magnets, such as BaCo$_2$(AsO$_4$)$_2$ because of formation of
spin-orbit-entangled $J_{\rm eff}$ = 1/2 pseudospin moments at Co$^{2+}$ sites
and potential realizations of Kitaev-like magnetism therein. Here, we obtain
high-accuracy crystal and electronic structure of BaCo$_2$(AsO$_4$)$_2$ by
employing a combined density functional and dynamical mean-field theory
calculations, which correctly capture the Mott-insulating nature of the target
system. We show that Co$^{2+}$ ions form a high spin configuration, $S=3/2$,
with an active $L_{\rm eff}=1$ orbital degree of freedom, in the absence of
spin-orbit coupling. The size of trigonal distortion within CoO$_6$ octahedra
is found to be not strong enough to completely quench the orbital degree of
freedom, so that the presence of spin-orbit coupling can give rise to the
formation of spin-orbit-entangled moments and the Kitaev exchange interaction.
Our finding supports recent studies on potential Kitaev magnetism in this
compound and other Co-based layered honeycomb systems.


title:
Fourier-transform spectroscopy of $^{13}$C$^{17}$O and deperturbation
  analysis of the A$^1\Pi$ ($\nu$ = 0 - 3) levels

abstract:
  The high-resolution B$^1\Sigma^+$ - A$^1\Pi$ (0, 0) and (0, 3) emission bands
of the less-abundant $^{13}$C$^{17}$O isotopologue have been investigated by
Fourier-transform spectroscopy in the visible region using a Bruker IFS 125HR
spectrometer at an accuracy 0.003 cm$^{-1}$. These spectra are combined with
high-resolution photoabsorption measurements of the $^{13}$C$^{17}$O
B$^1\Sigma^+$ $\leftarrow$ X$^1\Sigma^+$ (0, 0), B$^1\Sigma^+$ $\leftarrow$
X$^1\Sigma^+$ (1, 0) and C$^1\Sigma^+$ $\leftarrow$ X$^1\Sigma^+$ (0, 0) bands
recorded with an accuracy of 0.01 cm$^{-1}$ using the vacuum ultraviolet
Fourier-transform spectrometer, installed on the DESIRS beamline at the SOLEIL
synchrotron. In the studied 17,950 - 22,500 cm$^{-1}$ and 86,800 - 92,100
cm$^{-1}$ regions, 480 transitions have been measured. These new experimental
data were combined with data from the C $\to$ A and B $\to$ A systems,
previously analyzed in 13C17O. The frequencies of 1003 transitions derived from
12 bands were used to analyze the perturbations between the A$^1\Pi$ ($\nu$ = 0
- 3) levels and rovibrational levels of the d$^3\Delta_i$, e$^3\Sigma^-$,
a$'{}^3\Sigma^+$, I$^1\Sigma^-$ and D$^1\Delta$ states as well as to a
preliminary investigation of weak irregularities that appear in the
B$^1\Sigma^+$ ($\nu$ = 0) level. Deperturbed molecular constants and term
values of the A$^1\Pi$ state were obtained. The spin-orbit and L-uncoupling
interaction parameters as well as isotopologue-independent spin-orbit and
rotation-electronic perturbation parameters were derived.


title:
Local Hypergraph-based Nested Named Entity Recognition as Query-based
  Sequence Labeling

abstract:
  There has been a growing academic interest in the recognition of nested named
entities in many domains. We tackle the task with a novel local
hypergraph-based method: We first propose start token candidates and generate
corresponding queries with their surrounding context, then use a query-based
sequence labeling module to form a local hypergraph for each candidate. An end
token estimator is used to correct the hypergraphs and get the final
predictions. Compared to span-based approaches, our method is free of the high
computation cost of span sampling and the risk of losing long entities.
Sequential prediction makes it easier to leverage information in word order
inside nested structures, and richer representations are built with a local
hypergraph. Experiments show that our proposed method outperforms all the
previous hypergraph-based and sequence labeling approaches with large margins
on all four nested datasets. It achieves a new state-of-the-art F1 score on the
ACE 2004 dataset and competitive F1 scores with previous state-of-the-art
methods on three other nested NER datasets: ACE 2005, GENIA, and KBP 2017.


title: On the empty balls of a critical super-Brownian motion

abstract:
  Let $\{X_t\}_{t\geq0}$ be a $d$-dimensional critical super-Brownian motion
started from a Poisson random measure whose intensity is the Lebesgue measure.
Denote by $R_t:=\sup\{u>0: X_t(\{x\in\mathbb{R}^d:|x|< u\})=0\}$ the radius of
the largest empty ball centered at the origin of $X_t$. In this work, we prove
that for $r>0$,
$$\lim_{t\to\infty}\mathbb{P}\left(\frac{R_t}{t^{(1/d)\wedge(3-d)^+}}\geq
r\right)=e^{-A_d(r)},$$ where $A_d(r)$ satisfies
$\lim_{r\to\infty}\frac{A_d(r)}{r^{|d-2|+d\ind_{\{d=2\}}}}=C$ for some
$C\in(0,\infty)$ depending only on $d$.


title: Explicit Lower Bounds Against $\Omega(n)$-Rounds of Sum-of-Squares

abstract:
  We construct an explicit family of 3-XOR instances hard for
$\Omega(n)$-levels of the Sum-of-Squares (SoS) semi-definite programming
hierarchy. Not only is this the first explicit construction to beat brute force
search (beyond low-order improvements (Tulsiani 2021, Pratt 2021)), combined
with standard gap amplification techniques it also matches the (optimal)
hardness of random instances up to imperfect completeness (Grigoriev TCS 2001,
Schoenebeck FOCS 2008).
  Our result is based on a new form of small-set high dimensional expansion
(SS-HDX) inspired by recent breakthroughs in locally testable and quantum LDPC
codes. Adapting the recent framework of Dinur, Filmus, Harsha, and Tulsiani
(ITCS 2021) for SoS lower bounds from the Ramanujan complex to this setting, we
show any (bounded-degree) SS-HDX can be transformed into a highly unsatisfiable
3-XOR instance that cannot be refuted by $\Omega(n)$-levels of SoS. We then
show Leverrier and Z\'emor's (Arxiv 2022) recent qLDPC construction gives the
desired explicit family of bounded-degree SS-HDX. Incidentally, this gives the
strongest known form of bi-directional high dimensional expansion to date.


title: Undoping Strained Ge Quantum Well with Two Million Highly Hole Mobility

abstract:
  We fabricate an undoped Ge quantum well under 30 nm Ge0.8Si0.2 shallow
barrier with reverse grading technology. The under barrier is deposited by
Ge0.8Si0.2 followed by Ge0.9Si0.1 so that the variation of Ge content forms a
sharp interface which can suppress the threading dislocation density
penetrating into undoped Ge quantum well. And the Ge0.8Si0.2 barrier introduces
enough in-plane parallel strain -0.41% in the Ge quantum well. The
heterostructure field-effect transistors with a shallow buried channel get a
high two-dimensional hole gas (2DHG) mobility over 2E6 cm2/Vs at a low
percolation density of 2.51 E-11 cm2. We also discover a tunable fractional
quantum Hall effect at high densities and high magnetic fields. This approach
defines strained germanium as providing the material basis for tuning the
spin-orbit coupling strength for fast and coherent quantum computation.


title: From no-signalling to quantum states

abstract:
  Characterising quantum correlations from physical principles is a central
problem in the field of quantum information theory. Entanglement breaks bounds
on correlations put by Bell's theorem, thus challenging the notion of local
causality as a physical principle. A natural relaxation is to study
no-signalling as a constraint on joint probability distributions. It has been
shown that when considered with respect to so-called locally quantum
observables, bipartite non-signalling correlations never exceed their quantum
counterparts; still, such correlations generally do not derive from quantum
states. This leaves open the search for additional principles which identify
quantum states within the larger set of (collections of) non-signalling joint
probability distributions over locally quantum observables. Here, we suggest a
natural generalisation of no-signalling in the form of no-disturbance to
dilated systems. We prove that non-signalling joint probability distributions
satisfying this extension correspond with bipartite quantum states up to a
choice of time orientation in subsystems.


title: Maximization of Neumann eigenvalues

abstract:
  This paper is motivated by the maximization of the $k$-th eigenvalue of the
Laplace operator with Neumann boundary conditions among domains of ${\mathbb
R}^N$ with prescribed measure. We relax the problem to the class of (possibly
degenerate) densities in ${\mathbb R}^N$ with prescribed mass and prove the
existence of an optimal density. For $k=1,2$ the two problems are equivalent
and the maximizers are known to be one and two equal balls, respectively. For
$k \ge 3$ this question remains open, except in one dimension of the space
where we prove that the maximal densities correspond to a union of $k$ equal
segments. This result provides sharp upper bounds for Sturm-Liouville
eigenvalues and proves the validity of the P\'olya conjecture in the class of
densities in ${\mathbb R}$. Based on the relaxed formulation, we provide
numerical approximations of optimal densities for $k=1, \dots, 8$ in ${\mathbb
R}^2$.


title:
Mitigation of Cyberattacks through Battery Storage for Stable Microgrid
  Operation

abstract:
  In this paper, we present a mitigation methodology that leverages battery
energy storage system (BESS) resources in coordination with microgrid (MG)
ancillary services to maintain power system operations during cyberattacks. The
control of MG agents is achieved in a distributed fashion, and once a
misbehaving agent is detected, the MG's mode supervisory controller (MSC)
isolates the compromised agent and initiates self-healing procedures to support
the power demand and restore the compromised agent. Our results demonstrate the
practicality of the proposed attack mitigation strategy and how grid resilience
can be improved using BESS synergies. Simulations are performed on a modified
version of the Canadian urban benchmark distribution model.


title:
Application of deep learning to the evaluation of goodness in the
  waveform processing of transition-edge sensor calorimeters

abstract:
  Optimal filtering is the crucial technique for the data analysis of
transition-edge-sensor (TES) calorimeters to achieve their state-of-the-art
energy resolutions. Filtering out the `bad' data from the dataset is important
because it otherwise leads to the degradation of energy resolutions, while it
is not a trivial task. We propose a neural network-based technique for the
automatic goodness tagging of TES pulses, which is fast and automatic and does
not require bad data for training.


title:
Adaptive actuation of magnetic soft robots using deep reinforcement
  learning

abstract:
  Magnetic soft robots have attracted growing interest due to their unique
advantages in terms of untethered actuation and excellent controllability.
However, finding the required magnetization patterns or magnetic fields to
achieve the desired functions of these robots is quite challenging in many
cases. No unified framework for design has been proposed yet, and existing
methods mainly rely on manual heuristics, which are hard to satisfy the high
complexity level of the desired robotic motion. Here, we develop an intelligent
method to solve the related inverse-design problems, implemented by introducing
a novel simulation platform for magnetic soft robots based on Cosserat rod
models and a deep reinforcement learning framework based on TD3. We demonstrate
that magnetic soft robots with different magnetization patterns can learn to
move without human guidance in simulations, and effective magnetic fields can
be autonomously generated that can then be applied directly to real magnetic
soft robots in an open-loop way.


title:
A Time-Triggered Dimension Reduction Algorithm for the Task Assignment
  Problem

abstract:
  The task assignment problem is fundamental in combinatorial optimisation,
aiming at allocating one or more tasks to a number of agents while minimizing
the total cost or maximizing the overall assignment benefit. This problem is
known to be computationally hard since it is usually formulated as a
mixed-integer programming problem. In this paper, we consider a novel
time-triggered dimension reduction algorithm (TTDRA). We propose
convexification approaches to convexify both the constraints and the cost
function for the general non-convex assignment problem. The computational speed
is accelerated via our time-triggered dimension reduction scheme, where the
triggering condition is designed based on the optimality tolerance and the
convexity of the cost function. Optimality and computational efficiency are
verified via numerical simulations on benchmark examples.


title:
Deep Reinforcement Learning for Online Routing of Unmanned Aerial
  Vehicles with Wireless Power Transfer

abstract:
  The unmanned aerial vehicle (UAV) plays an vital role in various applications
such as delivery, military mission, disaster rescue, communication, etc., due
to its flexibility and versatility. This paper proposes a deep reinforcement
learning method to solve the UAV online routing problem with wireless power
transfer, which can charge the UAV remotely without wires, thus extending the
capability of the battery-limited UAV. Our study considers the power
consumption of the UAV and the wireless charging process. Unlike the previous
works, we solve the problem by a designed deep neural network. The model is
trained using a deep reinforcement learning method offline, and is used to
optimize the UAV routing problem online. On small and large scale instances,
the proposed model runs from four times to 500 times faster than Google
OR-tools, the state-of-the-art combinatorial optimization solver, with
identical solution quality. It also outperforms different types of heuristic
and local search methods in terms of both run-time and optimality. In addition,
once the model is trained, it can scale to new generated problem instances with
arbitrary topology that are not seen during training. The proposed method is
practically applicable when the problem scale is large and the response time is
crucial.


title: Automatic Datapath Optimization using E-Graphs

abstract:
  Manual optimization of Register Transfer Level (RTL) datapath is commonplace
in industry but holds back development as it can be very time consuming. We
utilize the fact that a complex transformation of one RTL into another
equivalent RTL can be broken down into a sequence of smaller, localized
transformations. By representing RTL as a graph and deploying modern graph
rewriting techniques we can automate the circuit design space exploration,
allowing us to discover functionally equivalent but optimized architectures. We
demonstrate that modern rewriting frameworks can adequately capture a wide
variety of complex optimizations performed by human designers on bit-vector
manipulating code, including significant error-prone subtleties regarding the
validity of transformations under complex interactions of bitwidths. The
proposed automated optimization approach is able to reproduce the results of
typical industrial manual optimization, resulting in a reduction in circuit
area by up to 71%. Not only does our tool discover optimized RTL, but also
correctly identifies that the optimal architecture to implement a given
arithmetic expression can depend on the width of the operands, thus producing a
library of optimized designs rather than the single design point typically
generated by manual optimization. In addition, we demonstrate that prior
academic work on maximally exploiting carry-save representation and on multiple
constant multiplication are both generalized and extended, falling out as
special cases of this paper.


title:
End-to-End Audio Strikes Back: Boosting Augmentations Towards An
  Efficient Audio Classification Network

abstract:
  While efficient architectures and a plethora of augmentations for end-to-end
image classification tasks have been suggested and heavily investigated,
state-of-the-art techniques for audio classifications still rely on numerous
representations of the audio signal together with large architectures,
fine-tuned from large datasets. By utilizing the inherited lightweight nature
of audio and novel audio augmentations, we were able to present an efficient
end-to-end network with strong generalization ability. Experiments on a variety
of sound classification sets demonstrate the effectiveness and robustness of
our approach, by achieving state-of-the-art results in various settings. Public
code will be available.


title:
Magnetocaloric particles of the Laves phase compound HoAl2 prepared by
  electrode induction melting gas atomization

abstract:
  Processing magnetocaloric materials into magnetic refrigerant particles is an
essential issue in developing high-performance magnetic refrigerators. Here, we
succeed in stably producing magnetocaloric particles of the promising material
HoAl2 by a newly devised method based on electrode induction melting gas
atomization process. The particle size range is on the order of submillimeter,
which is suitable for practical refrigeration systems. The resulting particles
with less contamination have good morphological, magnetic, and magnetocaloric
properties: (i) almost spherical shapes with few internal pores, (ii) a sharp
ferromagnetic transition around 30 K, and (iii) a large magnetocaloric effect
comparable to the bulk counterpart. These features suggest the HoAl$_{2}$
gas-atomized particles have the potential of use as a magnetic refrigerant. The
presented method can be applied not only to HoAl2 but also to other brittle
magnetocaloric materials with high melting points, facilitating the production
of various magnetic refrigerants needed to develop magnetic refrigerators for
hydrogen liquefaction.


title:
"Think Before You Speak": Improving Multi-Action Dialog Policy by
  Planning Single-Action Dialogs

abstract:
  Multi-action dialog policy (MADP), which generates multiple atomic dialog
actions per turn, has been widely applied in task-oriented dialog systems to
provide expressive and efficient system responses. Existing MADP models usually
imitate action combinations from the labeled multi-action dialog samples. Due
to data limitations, they generalize poorly toward unseen dialog flows. While
interactive learning and reinforcement learning algorithms can be applied to
incorporate external data sources of real users and user simulators, they take
significant manual effort to build and suffer from instability. To address
these issues, we propose Planning Enhanced Dialog Policy (PEDP), a novel
multi-task learning framework that learns single-action dialog dynamics to
enhance multi-action prediction. Our PEDP method employs model-based planning
for conceiving what to express before deciding the current response through
simulating single-action dialogs. Experimental results on the MultiWOZ dataset
demonstrate that our fully supervised learning-based method achieves a solid
task success rate of 90.6%, improving 3% compared to the state-of-the-art
methods.


title:
A short proof of Timashev's theorem on the real component group of a
  real reductive group

abstract:
  Using results of Cartan, Matsumoto, and Casselman, we give a short proof of
Timashev's theorem computing the real component group \pi_0 G(R) of a connected
reductive real algebraic group G in terms of a maximal torus of G containing a
maximal split torus.


title:
Estimation of Strong Structural Controllable Subspace of Network:
  Equitable Partition Method

abstract:
  In this paper, the strong structural controllability of the network is
analyzed. Based on the unified definition of equitable partition for kinds of
scene, the upper bound of the strong structural controllable subspace in
different scenarios is given, and the strong structural observability is
analyzed by using the characteristics of the dual system. Finally, the
practical significance when the dimension of the strong structural controllable
subspace is less than the number of individuals is given, and an invariant
attribute of strong structural controllability analysis is proposed.


title:
AQuaMoHo: Localized Low-Cost Outdoor Air Quality Sensing over a
  Thermo-Hygrometer

abstract:
  Efficient air quality sensing serves as one of the essential services
provided in any recent smart city. Mostly facilitated by sparsely deployed Air
Quality Monitoring Stations (AQMSs) that are difficult to install and maintain,
the overall spatial variation heavily impacts air quality monitoring for
locations far enough from these pre-deployed public infrastructures. To
mitigate this, we in this paper propose a framework named AQuaMoHo that can
annotate data obtained from a low-cost thermo-hygrometer (as the sole physical
sensing device) with the AQI labels, with the help of additional publicly
crawled Spatio-temporal information of that locality. At its core, AQuaMoHo
exploits the temporal patterns from a set of readily available spatial features
using an LSTM-based model and further enhances the overall quality of the
annotation using temporal attention. From a thorough study of two different
cities, we observe that AQuaMoHo can significantly help annotate the air
quality data on a personal scale.


title: Correlation in Momentum Space of Tonks-Girardeau Gas

abstract:
  We investigate the correlation properties of the ground state of
Tonks-Gigrardeal gases in the momentum space. With Bose-Fermi mapping method
the exact ground state wavefunction in coordinate space can be obtained basing
on the wavefunction of spin-polarized Fermions. By Fourier transformation we
obtain the ground state wavefunction in momentum space, and therefore the
momentum distribution, pair correlation and the reduced one-body density matrix
(ROBDM) in momentum space. The ROBDM in momentum space is the Fourier
transformation of the ROBDM in coordinate space and the pair correlation in
momentun space is the Fourier transformation of the reduced two-body density
matrix in coordinate space. The correlations in momentum space display larger
values only in small momentum region and vanish in most other regions. The
lowest natural orbital and occupation distribution are also obtained.


title: Provenance of astronomical data

abstract:
  In the context of Open Science, provenance has become a decisive piece of
information to provide along with astronomical data. Provenance is explicitly
cited in the FAIR principles, that aims to make research data Findable,
Accessible, Interoperable and Reusable. The IVOA Provenance Data Model,
published in 2020, puts in place the foundations for structuring and managing
detailed provenance information, from the acquisition of raw data, to the
dissemination of final products. The ambition is to provide for each
astronomical dataset a sufficiently fine grained and detailed provenance
information so that end-users understand the quality, reliability and
trustworthiness of the data. This would ensure that the Reusable principle is
respected.


title:
Linear magnetoelastic coupling and magnetic phase diagrams of the
  buckled-kagom\'{e} antiferromagnet Cu$_3$Bi(SeO$_3$)$_2$O$_2$Cl

abstract:
  Single crystals of Cu$_3$Bi(SeO$_3$)$_2$O$_2$Cl were investigated using
high-resolution capacitance dilatometry in magnetic fields up to 15 T.
Pronounced magnetoelastic coupling is found upon evolution of long-range
antiferromagnetic order at T$_{\mathrm{N}} = 26.4(3)$ K. Gr\"{u}neisen analysis
reveals moderate effects of uniaxial pressure on T$_{\mathrm{N}}$, of $1.8(4)$
K/GPa, $-0.62(15)$ K/GPa and $0.33(10)$ K/GPa for $p \parallel a$, $b$, and
$c$, respectively. Below 22 K, Gr\"{u}neisen scaling fails which implies the
presence of competing interactions. The structural phase transition at
T$_{\mathrm{S}} = 120.7(5)$ K is much more sensitive to uniaxial pressure than
T$_{\mathrm{N}}$, with strong effects of up to $27(3)$ K/GPa ($p \parallel c$).
Magnetostriction and magnetization measurements reveal a linear magnetoelastic
coupling for $B\parallel c$ below T$_{\mathrm{N}}$, as well as a mixed phase
behavior above the tricritical point around 0.4 T. An analysis of the critical
behavior in zero-field points to three-dimensional (3D) Ising-like magnetic
ordering. In addition, the magnetic phase diagrams for fields along the main
crystalline axes are reported.


title:
Mining and searching association relation of scientific papers based on
  deep learning

abstract:
  There is a complex correlation among the data of scientific papers. The
phenomenon reveals the data characteristics, laws, and correlations contained
in the data of scientific and technological papers in specific fields, which
can realize the analysis of scientific and technological big data and help to
design applications to serve scientific researchers. Therefore, the research on
mining and searching the association relationship of scientific papers based on
deep learning has far-reaching practical significance.


title: Groupwise Query Performance Prediction with BERT

abstract:
  While large-scale pre-trained language models like BERT have advanced the
state-of-the-art in IR, its application in query performance prediction (QPP)
is so far based on pointwise modeling of individual queries. Meanwhile, recent
studies suggest that the cross-attention modeling of a group of documents can
effectively boost performances for both learning-to-rank algorithms and
BERT-based re-ranking. To this end, a BERT-based groupwise QPP model is
proposed, in which the ranking contexts of a list of queries are jointly
modeled to predict the relative performance of individual queries. Extensive
experiments on three standard TREC collections showcase effectiveness of our
approach. Our code is available at https://github.com/VerdureChen/Group-QPP.


title: Experimental Periodic Korteweg--de Vries Solitons along a Torus of Fluid

abstract:
  We report on the experimental observation of solitons propagating along a
torus of fluid. We show that such a periodic system leads to significant
differences compared to the classical plane geometry. In particular, we
highlight the observation of subsonic elevation solitons, and a nonlinear
dependence of the soliton velocity on its amplitude. The soliton profile,
velocity, collision, and dissipation are characterized using high resolution
space--time measurements. By imposing {\em periodic boundary conditions} onto
Korteweg--de Vries (KdV) equation, we recover these observations. A nonlinear
spectral analysis of solitons (periodic inverse scattering transform) is also
implemented and experimentally validated in this periodic geometry. Our work
thus reveals the importance of periodicity for studying solitons and could be
applied to other fields involving periodic systems governed by a KdV equation.


title:
On a Fast Solution Strategy for a Surface-Wire Integral Formulation of
  the Anisotropic Forward Problem in Electroencephalography

abstract:
  This work focuses on a quasi-linear-in-complexity strategy for a hybrid
surface-wire integral equation solver for the electroencephalography forward
problem. The scheme exploits a block diagonally dominant structure of the wire
self block -- that models the neuronal fibers self interactions -- and of the
surface self block -- modeling interface potentials. This structure leads to
two Neumann iteration schemes further accelerated with adaptive integral
methods. The resulting algorithm is linear up to logarithmic factors. Numerical
results confirm the performance of the method in biomedically relevant
scenarios.


title: Strongly Aperiodic SFTs on Generalized Baumslag-Solitar groups

abstract:
  We look at constructions of aperiodic SFTs on fundamental groups of graph of
groups. In particular, we prove that all generalized Baumslag-Solitar groups
(GBS) admit a strongly aperiodic SFT. Our proof is based on first a structural
theorem by Whyte and second two constructions of strongly aperiodic SFTs on
$\mathbb{F}_n\times \mathbb{Z}$ and $BS(m,n)$ of our own. Our two constructions
rely on a path-folding technique that lifts an SFT on $\mathbb{Z}^2$ inside an
SFT on $\mathbb{F}_n\times \mathbb{Z}$ or an SFT on the hyperbolic plane inside
an SFT on $BS(m,n)$. In the case of $ \mathbb{F}_n\times \mathbb{Z}$ the path
folding technique also preserves minimality, so that we get minimal strongly
aperiodic SFTs on unimodular GBS.


title:
Self-supervision versus synthetic datasets: which is the lesser evil in
  the context of video denoising?

abstract:
  Supervised training has led to state-of-the-art results in image and video
denoising. However, its application to real data is limited since it requires
large datasets of noisy-clean pairs that are difficult to obtain. For this
reason, networks are often trained on realistic synthetic data. More recently,
some self-supervised frameworks have been proposed for training such denoising
networks directly on the noisy data without requiring ground truth. On
synthetic denoising problems supervised training outperforms self-supervised
approaches, however in recent years the gap has become narrower, especially for
video. In this paper, we propose a study aiming to determine which is the best
approach to train denoising networks for real raw videos: supervision on
synthetic realistic data or self-supervision on real data. A complete study
with quantitative results in case of natural videos with real motion is
impossible since no dataset with clean-noisy pairs exists. We address this
issue by considering three independent experiments in which we compare the two
frameworks. We found that self-supervision on the real data outperforms
supervision on synthetic data, and that in normal illumination conditions the
drop in performance is due to the synthetic ground truth generation, not the
noise model.


title:
Energy decay estimates for the wave equation with supercritical
  nonlinear damping

abstract:
  We consider a damped wave equation in a bounded domain. The damping is
nonlinear and is homogeneous with degree p -- 1 with p > 2. First, we show that
the energy of the strong solution in the supercritical case decays as a
negative power of t; the rate of decay is the same as in the subcritical or
critical cases, provided that the space dimension does not exceed ten. Next,
relying on a new differential inequality, we show that if the initial
displacement is further required to lie in L p , then the energy of the
corresponding weak solution decays logarithmically in the supercritical case.
Those new results complement those in the literature and open an important
breach in the unknown land of super-critical damping mechanisms.


title: Graph Product Structure for h-Framed Graphs

abstract:
  Graph product structure theory expresses certain graphs as subgraphs of the
strong product of much simpler graphs. In particular, an elegant formulation
for the corresponding structural theorems involves the strong product of a path
and of a bounded treewidth graph, and allows to lift combinatorial results for
bounded treewidth graphs to graph classes for which the product structure
holds, such as to planar graphs [Dujmovi\'c et al., J. ACM, 67(4), 22:1-38,
2020].
  In this paper, we join the search for extensions of this powerful tool beyond
planarity by considering the h-framed graphs, a graph class that includes
1-planar, optimal 2-planar, and k-map graphs (for appropriate values of h). We
establish a graph product structure theorem for h-framed graphs stating that
the graphs in this class are subgraphs of the strong product of a path, of a
planar graph of treewidth at most 3, and of a clique of size $3\lfloor h/2
\rfloor +\lfloor h/3 \rfloor -1$. This allows us to improve over the previous
structural theorems for 1-planar and k-map graphs. Our results constitute
significant progress over the previous bounds on the queue number,
non-repetitive chromatic number, and p-centered chromatic number of these graph
classes, e.g., we lower the currently best upper bound on the queue number of
1-planar graphs and k-map graphs from 495 to 81 and from 32225k(k-3) to 61k,
respectively. We also employ the product structure machinery to improve the
current upper bounds of twin-width of planar and 1-planar graphs from 183 to
37, and from O(1) to 80, respectively. All our structural results are
constructive and yield efficient algorithms to obtain the corresponding
decompositions.


title: Dennis Sullivan: Mathematics and physics; manifold and space

abstract:
  This article is a personal overview of the work of Dennis Sullivan who was
awarded the 2022 Abel prize. It was commissioned by the Bulletin of the
(Indian) Mathematics Consortium, and it will appear there.


title: Collective dynamics and rheology of confined phoretic suspensions

abstract:
  Similarly to their biological counterparts, suspensions of chemically-active
autophoretic swimmers exhibit nontrivial dynamics involving self-organization
processes as a result of inter-particle interactions. Using a kinetic model for
a dilute suspension of autochemotactic Janus particles, we analyse the effect
of a confined pressure-driven flow on these collective behaviours and the
impact of chemotactic aggregation on the effective viscosity of the active
fluid. Four dynamic regimes are identified when increasing the strength of the
imposed pressure-driven flow, each associated with a different collective
behaviour resulting from the competition of flow- and chemically-induced
reorientation of the swimmers together with the constraints of confinement.
Interestingly, we observe that the effect of the pusher (resp. puller)
hydrodynamic signature, which is known to reduce (resp. increase) the effective
viscosity of a sheared suspension, is inverted upon the emergence of
autochemotactic aggregation. Our results provide new insights on the role of
collective dynamics in complex environments, which are relevant to synthetic as
well as biological systems.


title:
Reduced effective magnetization and damping by slowly-relaxing
  impurities in strained $\gamma$-$\mathrm{Fe_2O_3}$ thin films

abstract:
  We study the static and dynamic magnetic properties of epitaxially strained
$\gamma$-$\mathrm{Fe_2O_3}$ (maghemite) thin films grown via pulsed-laser
deposition on MgO substrates by SQUID magnetometry and cryogenic broadband
ferromagnetic resonance experiments. SQUID magnetometry measurements reveal
hysteretic magnetization curves for magnetic fields applied both in- and out of
the sample plane. From the magnetization dynamics of our thin films, we find a
small negative effective magnetization in agreement with a strain induced
perpendicular magnetic anisotropy. Moreover, we observe a non-linear evolution
of the ferromagnetic resonance-linewidth as function of the microwave frequency
and explain this finding with a model based on slowly relaxing impurities, the
so-called slow relaxor model. By investigating the magnetization dynamics in
our maghemite thin films as a function of frequency and temperature, we can
isolate the temperature dependent contribution of the slowly relaxing
impurities to the resonance linewidth and, in particular, observe a sign change
in the effective magnetization. This finding provides evidence for a transition
of the magnetic anisotropy from a perpendicular easy axis to an easy in-plane
anisotropy for reduced temperatures.


title: Numerical calculation of RPC time resolution

abstract:
  Resistive Plate Chamber (RPC) is a gaseous detector, known for its good
spatial resolution and excellent time resolution. Due to its fast response and
excellent time resolution, it is used for both triggering and timing purpose.
But the time resolution of RPC is dependent on the detector geometry, applied
voltage and the gas mixture used for detector operation. In this work, we have
tried to develop a numerical model to estimate the time resolution of the
detector. The model is developed using COMSOL Multiphysics, a commercially
available finite element method solver. Using the primary ionization
information from HEED and the electron transport properties from MAGBOLTZ, the
model solves the Boltzmann equations to simulate the avalanche in the detector
and finds the time to cross a previously determined threshold current, which is
used to measure the time resolution of the detector.


title:
Quantifying Unknown Quantum Entanglement via a Hybrid Quantum-Classical
  Machine Learning Framework

abstract:
  Quantifying unknown quantum entanglement experimentally is a difficult task,
but also becomes more and more necessary because of the fast development of
quantum engineering. Machine learning provides practical solutions to this
fundamental problem, where one has to train a proper machine learning model to
predict entanglement measures of unknown quantum states based on experimentally
measurable data, say moments or correlation data produced by local
measurements. In this paper, we compare the performance of these two different
machine learning approaches systematically. Particularly, we first show that
the approach based on moments enjoys a remarkable advantage over that based on
correlation data, though the cost of measuring moments is much higher. Next,
since correlation data is much easier to obtain experimentally, we try to
better its performance by proposing a hybrid quantum-classical machine learning
framework for this problem, where the key is to train optimal local
measurements to generate more informative correlation data. Our numerical
simulations show that the new framework brings us comparable performance with
the approach based on moments to quantify unknown entanglement. Our work
implies that it is already practical to fulfill such tasks on near-term quantum
devices.


title:
Graph Convolutional Network Based Semi-Supervised Learning on
  Multi-Speaker Meeting Data

abstract:
  Unsupervised clustering on speakers is becoming increasingly important for
its potential uses in semi-supervised learning. In reality, we are often
presented with enormous amounts of unlabeled data from multi-party meetings and
discussions. An effective unsupervised clustering approach would allow us to
significantly increase the amount of training data without additional costs for
annotations. Recently, methods based on graph convolutional networks (GCN) have
received growing attention for unsupervised clustering, as these methods
exploit the connectivity patterns between nodes to improve learning
performance. In this work, we present a GCN-based approach for semi-supervised
learning. Given a pre-trained embedding extractor, a graph convolutional
network is trained on the labeled data and clusters unlabeled data with
"pseudo-labels". We present a self-correcting training mechanism that
iteratively runs the cluster-train-correct process on pseudo-labels. We show
that this proposed approach effectively uses unlabeled data and improves
speaker recognition accuracy.


title:
An overview of the Eight International Olympiad in Cryptography
  "Non-Stop University CRYPTO"

abstract:
  Non-Stop University CRYPTO is the International Olympiad in Cryptography that
was held for the eight time in 2021. Hundreds of university and school
students, professionals from 33 countries worked on mathematical problems in
cryptography during a week. The aim of the Olympiad is to attract attention to
curious and even open scientific problems of modern cryptography. In this
paper, problems and their solutions of the Olympiad'2021 are presented. We
consider 19 problems of varying difficulty and topics: ciphers, online
machines, passwords, binary strings, permutations, quantum circuits, historical
ciphers, elliptic curves, masking, implementation on a chip, etc. We discuss
several open problems on quantum error correction, finding special permutations
and s-Boolean sharing of a function, obtaining new bounds on the distance to
affine vectorial functions.


title:
Brain-Computer Interfaces: Investigating the Transition from Visually
  Evoked to Purely Imagined Steady-State Potentials

abstract:
  Brain-Computer Interfaces (BCIs) based on Steady State Visually Evoked
Potentials (SSVEPs) have proven effective and provide significant accuracy and
information-transfer rates. This family of strategies, however, requires
external devices that provide the frequency stimuli required by the technique.
This limits the scenarios in which they can be applied, especially when
compared to other BCI approaches. In this work, we have investigated the
possibility of obtaining frequency responses in the EEG output based on the
pure visual imagination of SSVEP-eliciting stimuli. Our results show that not
only that EEG signals present frequency-specific peaks related to the frequency
the user is focusing on, but also that promising classification accuracy can be
achieved, paving the way for a robust and reliable visual imagery BCI modality.


title:
Trajectory Optimization and Tracking for a Fixed-Wing UAV in Deep Stall
  with Perch Landing

abstract:
  This paper presents a novel recovery technique based on constrained
optimization for a fixed-wing UAV (Uninhabited Aerial Vehicle). The fixed-wing
UAV first reduces its altitude by deep stalling, then perches on a recovery
net. Compared to nominal net recovery procedures, this technique greatly
reduces the landing time and the final airspeed of the UAV. Simulation results
and comparisons with existing approaches demonstrate the feasibility of the
idea.


title:
Offline and online monitoring of scattered uncertain logs using
  uncertain linear dynamical systems

abstract:
  Monitoring the correctness of distributed cyber-physical systems is
essential. We address the analysis of the log of a black-box cyber-physical
system. Detecting possible safety violations can be hard when some samples are
uncertain or missing. In this work, the log is made of values known with some
uncertainty; in addition, we make use of an over-approximated yet expressive
model, given by a non-linear extension of dynamical systems. Given an offline
log, our approach is able to monitor the log against safety specifications with
a limited number of false alarms. As a second contribution, we show that our
approach can be used online to minimize the number of sample triggers, with the
aim at energetic efficiency. We apply our approach to two benchmarks, an
anesthesia model and an adaptive cruise controller.


title:
Collapse of a hemicatenoid bounded by a solid wall: instability and
  dynamics driven by surface Plateau border friction

abstract:
  The collapse of a catenoidal soap film when the rings supporting it are moved
beyond a critical separation is a classic problem in interface motion in which
there is a balance between surface tension and the inertia of the surrounding
air, with film viscosity playing only a minor role. Recently [Goldstein, et
al., Phys. Rev. E 104, 035105 (2021)], we introduced a variant of this problem
in which the catenoid is bisected by a glass plate located in a plane of
symmetry perpendicular to the rings, producing two identical hemicatenoids,
each with a surface Plateau border (SPB) on the glass plate. Beyond the
critical ring separation, the hemicatenoids collapse in a manner qualitatively
similar to the bulk problem, but their motion is governed by the frictional
forces arising from viscous dissipation in the SPBs. Here we present numerical
studies of a model that includes classical friction laws for SPB motion on wet
surfaces and show consistency with our experimental measurements of the
temporal evolution of this process. This study can help explain the
fragmentation of bubbles inside very confined geometries such as porous
materials or microfluidic devices.


title:
Rescuing constraints on modified gravity through relativistic
  distortions in large-scale structure

abstract:
  The distribution of galaxies provides an ideal laboratory to test for
deviations from General Relativity. In particular, redshift-space distortions
are commonly used to constrain modifications to the Poisson equation, which
governs the strength of dark matter clustering. Here, we show that these
constraints rely on the validity of the weak equivalence principle, which has
never been tested for the dark matter component. Relaxing this restrictive
assumption leads to modifications in the growth of structure that are fully
degenerate with modifications induced by the Poisson equation. This in turns
strongly degrades the constraining power of redshift-space distortions. Such
degeneracies can however be broken and tight constraints on modified gravity
can be recovered using relativistic distortions in the galaxy distribution,
which will be observable by the coming generation of large-scale structure
surveys.


title: The Performance of MBFGS with Different Inexact Line Search Rule

abstract:
  The modified BFGS optimization algorithm is generally used when the objective
function is non-convex. In this method, one has to move in a specific direction
such that the value of the objective function reduces. Therefore, the different
inexact line search or exact line search plays an important role in
optimization. Here, we have studied Modified BFGS with different inexact line
searches methods and compared them in some test problems. Numerical results
show that MBFGS with Armijo line search methods is efficient for solving
non-convex non-linear unconstrained optimization problems.


title: Streaming vs. Functions: A Cost Perspective on Cloud Event Processing

abstract:
  In cloud event processing, data generated at the edge is processed in
real-time by cloud resources. Both distributed stream processing (DSP) and
Function-as-a-Service (FaaS) have been proposed to implement such event
processing applications. FaaS emphasizes fast development and easy operation,
while DSP emphasizes efficient handling of large data volumes. Despite their
architectural differences, both can be used to model and implement
loosely-coupled job graphs.
  In this paper, we consider the selection of FaaS and DSP from a cost
perspective. We implement stateless and stateful workflows from the Theodolite
benchmarking suite using cloud FaaS and DSP. In an extensive evaluation, we
show how application type, cloud service provider, and runtime environment can
influence the cost of application deployments and derive decision guidelines
for cloud engineers.


title: MLP4Rec: A Pure MLP Architecture for Sequential Recommendations

abstract:
  Self-attention models have achieved state-of-the-art performance in
sequential recommender systems by capturing the sequential dependencies among
user-item interactions. However, they rely on positional embeddings to retain
the sequential information, which may break the semantics of item embeddings.
In addition, most existing works assume that such sequential dependencies exist
solely in the item embeddings, but neglect their existence among the item
features. In this work, we propose a novel sequential recommender system
(MLP4Rec) based on the recent advances of MLP-based architectures, which is
naturally sensitive to the order of items in a sequence. To be specific, we
develop a tri-directional fusion scheme to coherently capture sequential,
cross-channel and cross-feature correlations. Extensive experiments demonstrate
the effectiveness of MLP4Rec over various representative baselines upon two
benchmark datasets. The simple architecture of MLP4Rec also leads to the linear
computational complexity as well as much fewer model parameters than existing
self-attention methods.


title: A Spatio-Temporal Multilayer Perceptron for Gesture Recognition

abstract:
  Gesture recognition is essential for the interaction of autonomous vehicles
with humans. While the current approaches focus on combining several modalities
like image features, keypoints and bone vectors, we present neural network
architecture that delivers state-of-the-art results only with body skeleton
input data. We propose the spatio-temporal multilayer perceptron for gesture
recognition in the context of autonomous vehicles. Given 3D body poses over
time, we define temporal and spatial mixing operations to extract features in
both domains. Additionally, the importance of each time step is re-weighted
with Squeeze-and-Excitation layers. An extensive evaluation of the TCG and
Drive&Act datasets is provided to showcase the promising performance of our
approach. Furthermore, we deploy our model to our autonomous vehicle to show
its real-time capability and stable execution.


title:
Spaces of Positive Scalar Curvature metrics on totally nonspin Manifolds
  with spin boundary

abstract:
  In this article we study the space of positive scalar curvature metrics on
totally nonspin manifolds with spin boundary. We prove that for such manifolds
of certain dimensions, those spaces are not connected and have nontrivial
fundamental group. Furthermore we show that a well-known propagation technique
for detection results on spaces of positive scalar curvature metrics on spin
manifolds ceases to work in the totally nonspin case.


title:
Go Wide or Go Deep: Levering Watermarking Performance with Computational
  Cost for Specific Images

abstract:
  Digital watermarking has been widely studied for the protection of
intellectual property. Traditional watermarking schemes often design in a
"wider" rule, which applies one general embedding mechanism to all images. But
this will limit the scheme into a robustness-invisibility trade-off, where the
improvements of robustness can only be achieved by the increase of embedding
intensity thus causing the visual quality decay. However, a new scenario comes
out at this stage that many businesses wish to give high level protection to
specific valuable images, which requires high robustness and high visual
quality at the same time. Such scenario makes the watermarking schemes should
be designed in a "deeper" way which makes the embedding mechanism customized to
specific images. To achieve so, we break the robustness-invisibility trade-off
by introducing computation cost in, and propose a novel auto-decoder-like
image-specified watermarking framework (ISMark). Based on ISMark, the strong
robustness and high visual quality for specific images can be both achieved. In
detail, we apply an optimization procedure (OPT) to replace the traditional
embedding mechanism. Unlike existing schemes that embed watermarks using a
learned encoder, OPT regards the cover image as the optimizable parameters to
minimize the extraction error of the decoder, thus the features of each
specified image can be effectively exploited to achieve superior performance.
Extensive experiments indicate that ISMark outperforms the state-of-the-art
methods by a large margin, which improves the average bit error rate by 4.64%
(from 4.86% to 0.22%) and PSNR by 2.20dB (from 32.50dB to 34.70dB).


title:
Monopoles of the Dirac type and color confinement in QCD -- Gauge
  invariant mechansim

abstract:
  As a color confinement mechanism, a dual Meissner effect due to Abelian
monopoles involved in QCD has been discussed so far in various ways. But still
there is an important problem unsolved. It is gauge invariance of the schemes
or, in other words, the reason why non-Abelian color confinement is explained
by means of the Abelian dual Meissner effect. Here it is shown that a random
Abelian method based on the violation of non-Abelian Bianchi identity (VNABI)
could prove SU(3) invariance and explain why color-singlets alone can survive
in the confinement phase of QCD in the framework of the Abelian dual Meissner
effect due to Abelian monopole condensation. This is completely different from
the 't Hooft's Abelian projection scheme which introduces an additional partial
gauge-fixing or the idea of Bonati et al. which is based also on VNABI. Bonati
et al. say that VNABI is related to the 't Hooft tensor and the relation can
prove the gauge invariance of the 't Hooft's Abelian projection schemes. But
the last idea is found to be incorrect. Existence of the relation between VNABI
and the 't Hooft tensor alone can not account for gauge invariance of 't
Hooft's Abelian projection schemes.


title:
Multimodal Dual Emotion with Fusion of Visual Sentiment for Rumor
  Detection

abstract:
  In recent years, rumors have had a devastating impact on society, making
rumor detection a significant challenge. However, the studies on rumor
detection ignore the intense emotions of images in the rumor content. This
paper verifies that the image emotion improves the rumor detection efficiency.
A Multimodal Dual Emotion feature in rumor detection, which consists of visual
and textual emotions, is proposed. To the best of our knowledge, this is the
first study which uses visual emotion in rumor detection. The experiments on
real datasets verify that the proposed features outperform the state-of-the-art
sentiment features, and can be extended in rumor detectors while improving
their performance.


title:
Randomly Initialized Alternating Least Squares: Fast Convergence for
  Matrix Sensing

abstract:
  We consider the problem of reconstructing rank-one matrices from random
linear measurements, a task that appears in a variety of problems in signal
processing, statistics, and machine learning. In this paper, we focus on the
Alternating Least Squares (ALS) method. While this algorithm has been studied
in a number of previous works, most of them only show convergence from an
initialization close to the true solution and thus require a carefully designed
initialization scheme. However, random initialization has often been preferred
by practitioners as it is model-agnostic. In this paper, we show that ALS with
random initialization converges to the true solution with
$\varepsilon$-accuracy in $O(\log n + \log (1/\varepsilon)) $ iterations using
only a near-optimal amount of samples, where we assume the measurement matrices
to be i.i.d. Gaussian and where by $n$ we denote the ambient dimension. Key to
our proof is the observation that the trajectory of the ALS iterates only
depends very mildly on certain entries of the random measurement matrices.
Numerical experiments corroborate our theoretical predictions.


title: Instantons on flat space: Explicit constructions

abstract:
  In this note, we revisit some well-known examples of instantons on flat space
that were originally discovered in the physics literature. In particular, we
explain how the basic instanton on $\mathbb{R}^4$, with its flat hyperkaehler
structure, has natural generalisations to $\mathbb{R}^7$ and $\mathbb{R}^8$
viewed as flat $G_2$- and Spin(7)-manifolds, respectively. We also provide the
details of an arguably less known construction of ASD instantons on
$\mathbb{H}^n$, in the sense of quaternionic geometry.


title:
Explicit computations with cubic fourfolds, Gushel-Mukai fourfolds, and
  their associated K3 surfaces

abstract:
  We present some applications of the Macaulay2 software package
SpecialFanoFourfolds, a package for working with Hodge-special cubic fourfolds
and Hodge-special Gushel--Mukai fourfolds. In particular, we show how to
construct new examples of such fourfolds, some of which turn out to be
rational. We also describe how to calculate K3 surfaces associated with cubic
or Gushel-Mukai fourfolds, which relies on an explicit unirationality of some
moduli spaces of K3 surfaces.


title:
Quantum kinetic theory for dynamical spin polarization from QED-type
  interaction

abstract:
  We investigate the dynamical spin polarization of a massless electron probing
an electron plasma in locally thermal equilibrium via the Moller scattering
from the quantum kinetic theory. We derive an axial kinetic equation
delineating the dynamical spin evolution in the presence of the collision term
with quantum corrections up to $\mathcal{O}(\hbar)$ and the leading-logarithmic
order in coupling by using the hard-thermal-loop (HTL) approximation, from
which we extract the spin-polarization rate induced by the spacetime gradients
of the medium. When the electron probe approaches local equilibrium, we further
simplify the collision term into a relaxation-time expression. Our kinetic
equation may be implemented in the future numerical simulations for dynamical
spin polarization.


title:
Impact of charge-symmetry breaking on gluon and sea-quark distributions
  in the pion and kaon

abstract:
  In this exploratory study I present, for the first time, the implications of
the charge-symmetry breaking (CSB)\emdash arising from the $u$ and $d$ quark
mass difference\emdash on gluon and sea-quark distribution functions of both
the pion and kaon, with the pion and kaon partonic structure functions are
described by the Nambu--Jona-Lasino (NJL) model\emdash a quark-level chiral
effective theory of QCD\emdash with the help of the proper-time regularization
scheme\emdash which is a fabrication of the color confinement of QCD. From the
analysis, one finds that the gluon distribution for the pion has a good
agreement with the prediction results obtained from the recent lattice QCD
simulation and JAM global fit QCD analysis at higher scale of $Q^2 =$ 5
GeV$^2$. The size of the CSB effects on gluon and sea-quark distributions for
the pion with the realistic ratios of $m_u/m_d = 0.5$ at $Q^2 =$ 5 GeV$^2$ are
respectively estimated by 1.3\% and 2.0\% at $x \simeq 1$ in comparison with
those for $m_u/m_d = 1.0$, whereas those for the kaon are approximately about
0.3\% and 0.5\% at $ x \simeq 1$, respectively. A remarkable result is found
that the CSB effects on gluon distribution for the kaon is smaller than that
for the pion\emdash which has a similar prediction result as that for the
chiral symmetry (CS) case.


title:
On a geometric combination of functions related to Pr\'ekopa-Leindler
  inequality

abstract:
  We introduce a new operation between nonnegative integrable functions on
$\mathbb{R} ^n$, that we call geometric combination; it is obtained via a mass
transportation approach, playing with inverse distribution functions. The main
feature of this operation is that the Lebesgue integral of the geometric
combination equals the geometric mean of the two separate integrals; as a
natural consequence, we derive a new functional inequality of
Pr\'ekopa-Leindler type. When applied to the characteristic functions of two
measurable sets, their geometric combination provides a set whose volume equals
the geometric mean of the two separate volumes. In the framework of convex
bodies, by comparing the geometric combination with the $0$-sum, we get an
alternative proof of the log-Brunn-Minkowski inequality for unconditional
convex bodies and for convex bodies with $n$ symmetries.


title:
On construction of splitting contraction algorithms in a
  prediction-correction framework for separable convex optimization

abstract:
  In the past decade, we had developed a series of splitting contraction
algorithms for separable convex optimization problems, at the root of the
alternating direction method of multipliers. Convergence of these algorithms
was studied under specific model-tailored conditions, while these conditions
can be conceptually abstracted as two generic conditions when these algorithms
are all unified as a prediction-correction framework. In this paper, in turn,
we showcase a constructive way for specifying the generic
convergence-guaranteeing conditions, via which new splitting contraction
algorithms can be generated automatically. It becomes possible to design more
application-tailored splitting contraction algorithms by specifying the
prediction-correction framework, while proving their convergence is a routine.


title:
Spin Transport Hydrodynamics of Polarized Deuterium-Tritium Fusion
  Plasma

abstract:
  The spin transport equations for polarized deuterium-tritium (DT) fusion
plasma are derived with the density matrix formulation, which are used to
investigate the hydrodynamics of polarized DT-gas-filled targets during
indirectly driven inertial confinement fusion implosions. The depolarization of
DT ions by strong self-generated magnetic fields can be captured by the spin
transport equation. The neutron yield, angular distribution and neutron beam
polarization are obtained from three-dimensional spin transport hydrodynamics
simulations of the target implosions. The simulation results indicate that an
optimized spin alignment of the polarized target can reduce the depolarization
of DT ions and the neutron beams induced by polar mode asymmetries in
indirectly driven implosions.


title:
Multi-UE Multi-AP Beam Alignment in User-Centric Cell-Free Massive MIMO
  Systems Operating at mmWave

abstract:
  This paper considers the problem of beam alignment in a cell-free massive
MIMO deployment with multiple access points (APs) and multiple user equipments
(UEs) simultaneously operating in the same millimeter wave frequency band.
Assuming the availability of a control channel at sub-6 GHz frequencies, a
protocol is developed that permits estimating, for each UE, the strongest
propagation path from each of the surrounding APs, and to perform user-centric
association between the UEs and the APs. Estimation of the strongest paths from
nearby APs is realized at the UE in a one-phase procedure, during which all the
APs simultaneously transmit on pseudo-randomly selected channels with
pseudo-random transmit beamformers. An algorithm for orthogonal channels
assignment to the APs is also proposed, with the aim of minimizing the mutual
interference between APs that transmit on the same channels. The performance of
the proposed strategy is evaluated both in terms of probability of correct
detection of the directions of arrival and of departure associated to the
strongest beam from nearby APs, and in terms of downlink and uplink
signal-to-interference-plus-noise ratio. Numerical results show that the
proposed approach is effective and capable of efficiently realizing beam
alignment in a multi-UE multi-AP wireless scenario.


title:
A Polynomial-Time Algorithm for 1/3-Approximate Nash Equilibria in
  Bimatrix Games

abstract:
  Since the celebrated PPAD-completeness result for Nash equilibria in bimatrix
games, a long line of research has focused on polynomial-time algorithms that
compute $\varepsilon$-approximate Nash equilibria. Finding the best possible
approximation guarantee that we can have in polynomial time has been a
fundamental and non-trivial pursuit on settling the complexity of approximate
equilibria. Despite a significant amount of effort, the algorithm of Tsaknakis
and Spirakis \cite{TS08}, with an approximation guarantee of $(0.3393+\delta)$,
remains the state of the art over the last 15 years. In this paper, we propose
a new refinement of the Tsaknakis-Spirakis algorithm, resulting in a
polynomial-time algorithm that computes a $(\frac{1}{3}+\delta)$-Nash
equilibrium, for any constant $\delta>0$. The main idea of our approach is to
go beyond the use of convex combinations of primal and dual strategies, as
defined in the optimization framework of \cite{TS08}, and enrich the pool of
strategies from which we build the strategy profiles that we output in certain
bottleneck cases of the algorithm.


title: Faculty Distillation with Optimal Transport

abstract:
  Knowledge distillation (KD) has shown its effectiveness in improving a
student classifier given a suitable teacher. The outpouring of diverse and
plentiful pre-trained models may provide abundant teacher resources for KD.
However, these models are often trained on different tasks from the student,
which requires the student to precisely select the most contributive teacher
and enable KD across different label spaces. These restrictions disclose the
insufficiency of standard KD and motivate us to study a new paradigm called
faculty distillation. Given a group of teachers (faculty), a student needs to
select the most relevant teacher and perform generalized knowledge reuse. To
this end, we propose to link teacher's task and student's task by optimal
transport. Based on the semantic relationship between their label spaces, we
can bridge the support gap between output distributions by minimizing Sinkhorn
distances. The transportation cost also acts as a measurement of teachers'
adaptability so that we can rank the teachers efficiently according to their
relatedness. Experiments under various settings demonstrate the succinctness
and versatility of our method.


title:
SELECTOR: Selecting a Representative Benchmark Suite for Reproducible
  Statistical Comparison

abstract:
  Fair algorithm evaluation is conditioned on the existence of high-quality
benchmark datasets that are non-redundant and are representative of typical
optimization scenarios. In this paper, we evaluate three heuristics for
selecting diverse problem instances which should be involved in the comparison
of optimization algorithms in order to ensure robust statistical algorithm
performance analysis. The first approach employs clustering to identify similar
groups of problem instances and subsequent sampling from each cluster to
construct new benchmarks, while the other two approaches use graph algorithms
for identifying dominating and maximal independent sets of nodes. We
demonstrate the applicability of the proposed heuristics by performing a
statistical performance analysis of five portfolios consisting of three
optimization algorithms on five of the most commonly used optimization
benchmarks. The results indicate that the statistical analyses of the
algorithms' performance, conducted on each benchmark separately, produce
conflicting outcomes, which can be used to give a false indication of the
superiority of one algorithm over another. On the other hand, when the analysis
is conducted on the problem instances selected with the proposed heuristics,
which uniformly cover the problem landscape, the statistical outcomes are
robust and consistent.


title:
Multiple orthogonal polynomials, $d$-orthogonal polynomials, production
  matrices, and branched continued fractions

abstract:
  I analyze an unexpected connection between multiple orthogonal polynomials,
$d$-orthogonal polynomials, production matrices and branched continued
fractions. This work can be viewed as a partial extension of Viennot's
combinatorial theory of orthogonal polynomials to the case where the production
matrix is lower-Hessenberg but is not necessarily tridiagonal.


title:
Filling space with hypercubes of two sizes -- The pythagorean tiling in
  higher dimensions

abstract:
  We construct a unilateral lattice tiling of $\mathbb{R}^n$ into hypercubes of
two differnet side lengths $p$ or $q$. This generalizes the Pythagorean tiling
in $\mathbb{R}^2$. We also show that this tiling is unique up to symmetries,
which proves a variation of a conjecture by B\"olcskei from 2001. For positive
integers $p$ and $q$ this tiling also provides a tiling of
$(\mathbb{Z}/(p^n+q^n)\mathbb{Z})^n$.


title: From evolved Long-Period-Variable stars to the evolution of M31

abstract:
  One of the ways to understand the genesis and evolution of the universe is to
know how galaxies have formed and evolved. In this regard, the study of star
formation history (SFH) plays an important role in the accurate understanding
of galaxies. In this paper, we used long-period variable stars (LPVs) to
estimate the SFH in the Andromeda galaxy (M31). These cool stars reach their
peak luminosity in the final stage of their evolution; their birth mass is
directly related to their luminosity. Therefore, we construct the mass function
and the star formation history using stellar evolution models.


title:
VITA: A Multi-Source Vicinal Transfer Augmentation Method for
  Out-of-Distribution Generalization

abstract:
  Invariance to diverse types of image corruption, such as noise, blurring, or
colour shifts, is essential to establish robust models in computer vision. Data
augmentation has been the major approach in improving the robustness against
common corruptions. However, the samples produced by popular augmentation
strategies deviate significantly from the underlying data manifold. As a
result, performance is skewed toward certain types of corruption. To address
this issue, we propose a multi-source vicinal transfer augmentation (VITA)
method for generating diverse on-manifold samples. The proposed VITA consists
of two complementary parts: tangent transfer and integration of multi-source
vicinal samples. The tangent transfer creates initial augmented samples for
improving corruption robustness. The integration employs a generative model to
characterize the underlying manifold built by vicinal samples, facilitating the
generation of on-manifold samples. Our proposed VITA significantly outperforms
the current state-of-the-art augmentation methods, demonstrated in extensive
experiments on corruption benchmarks.


title: On Discrete Age of Information of Infinite Size Status Updating System

abstract:
  In this paper, for the discrete time status updating system with infinite
size, we derive the explicit expression of average age of information (AoI),
and the AoI's stationary distribution. Based on the discussions of difference
between finite and infinite size status updating systems, we successfully
characterize the random dynamics of system's AoI using a two-dimensional state
vector, which simultaneously tracks the real time AoI and the age of packet
currently under service. We constitute the two-dimensional age process and
completely solve all the stationary probabilities. Then, as one of the marginal
distributions, the stationary distribution of system's AoI is derived, and with
which the mean of the AoI is also determined.


title:
Fusionize: Improving Serverless Application Performance through
  Feedback-Driven Function Fusion

abstract:
  Serverless computing increases developer productivity by removing operational
concerns such as managing hardware or software runtimes. Developers, however,
still need to partition their application into functions, which can be
error-prone and adds complexity: Using a small function size where only the
smallest logical unit of an application is inside a function maximizes
flexibility and reusability. Yet, having small functions leads to invocation
overheads, additional cold starts, and may increase cost due to busy waiting.
  In this paper we present Fusionize, a framework that removes these concerns
from developers by automatically fusing the application code into a
multi-function orchestration with varying function size. Developers only need
to write the application code following a lightweight programming model and do
not need to worry how the application is turned into functions. Our framework
automatically fuses different parts of the application into functions and
manages their interactions. Leveraging monitoring data, the framework optimizes
the distribution of application parts to functions to optimize deployment goals
such as end-to-end latency and cost. Using two example applications, we show
that Fusionize can automatically and iteratively improve the deployment
artifacts of the application.


title:
On Identifiable Polytope Characterization for Polytopic Matrix
  Factorization

abstract:
  Polytopic matrix factorization (PMF) is a recently introduced matrix
decomposition method in which the data vectors are modeled as linear
transformations of samples from a polytope. The successful recovery of the
original factors in the generative PMF model is conditioned on the
"identifiability" of the chosen polytope. In this article, we investigate the
problem of determining the identifiability of a polytope. The identifiability
condition requires the polytope to be permutation-and/or-sign-only invariant.
We show how this problem can be efficiently solved by using a graph
automorphism algorithm. In particular, we show that checking only the
generating set of the linear automorphism group of a polytope, which
corresponds to the automorphism group of an edge-colored complete graph, is
sufficient. This property prevents checking all the elements of the permutation
group, which requires factorial algorithm complexity. We demonstrate the
feasibility of the proposed approach through some numerical experiments.