title:
Perturbative renormalization of the supercurrent operator in lattice
  ${\cal N}{=}1$ supersymmetric Yang-Mills theory

abstract:
  In this work we perform a perturbative study of the Noether supercurrent
operator in the context of Supersymmetric ${\cal N}{=}1$ Yang-Mills (SYM)
theory on the lattice. The supercurrent mixes with several other operators,
some of which are not gauge invariant, having the same quantum numbers. We
determine, to one loop order, the renormalization and all corresponding mixing
coefficients by computing relevant Green's functions of each one of the mixing
operators with external elementary fields. Our calculations are performed both
in dimensional and lattice regularization. From the first regularization we
obtain the $\bar{MS}$-renormalized Green's functions; comparison of the latter
with the corresponding Green's functions in the lattice regularization leads to
the extraction of the lattice renormalization factors and mixing coefficients
in the $\bar{MS}$ scheme. The lattice calculations are performed to lowest
order in the lattice spacing, using Wilson gluons and clover improved gluinos.
The lattice results can be used in nonperturbative studies of supersymmetric
Ward identities.


title: A simple nonconforming tetrahedral element for the Stokes equations

abstract:
  In this paper we apply a nonconforming rotated bilinear tetrahedral element
to the Stokes problem in $\mathbb{R}^3$. We show that the element is stable in
combination with a piecewise linear, continuous, approximation of the pressure.
This gives an approximation similar to the well known continuous $P^2-P^1$
Taylor$-$Hood element, but with fewer degrees of freedom. The element is a
stable non-conforming low order element which fulfils Korn's inequality,
leading to stability also in the case where the Stokes equations are written on
stress form for use in the case of free surface flow.


title: On Continual Model Refinement in Out-of-Distribution Data Streams

abstract:
  Real-world natural language processing (NLP) models need to be continually
updated to fix the prediction errors in out-of-distribution (OOD) data streams
while overcoming catastrophic forgetting. However, existing continual learning
(CL) problem setups cannot cover such a realistic and complex scenario. In
response to this, we propose a new CL problem formulation dubbed continual
model refinement (CMR). Compared to prior CL settings, CMR is more practical
and introduces unique challenges (boundary-agnostic and non-stationary
distribution shift, diverse mixtures of multiple OOD data clusters,
error-centric streams, etc.). We extend several existing CL approaches to the
CMR setting and evaluate them extensively. For benchmarking and analysis, we
propose a general sampling algorithm to obtain dynamic OOD data streams with
controllable non-stationarity, as well as a suite of metrics measuring various
aspects of online performance. Our experiments and detailed analysis reveal the
promise and challenges of the CMR problem, supporting that studying CMR in
dynamic OOD streams can benefit the longevity of deployed NLP models in
production.


title:
Dynamical excitation processes and correlations of three-body
  two-dimensional mixtures

abstract:
  A scheme is proposed to dynamically excite distinct eigenstate superpositions
in three-body Bose-Fermi mixtures confined in a two-dimensional harmonic trap.
The system is initialized in a non-interacting state with a variable spatial
extent, and the scattering lengths are subsequently quenched. For spatial
widths smaller than the three-body harmonic oscillator length, a superposition
of trimers and atom-dimers is dynamically attained, otherwise trap states are
predominantly populated. Accordingly, the Tan contacts evince the build-up of
short range two- and three-body correlations in the course of the evolution. A
larger spatial extent of the initial state leads to a reduction of few-body
correlations, endowed however with characteristic peaks at the positions of the
avoided-crossings in the energy spectra, thereby signalling the participation
of atom-dimers. Our results expose ways to dynamically excite selectively
trimers, atom-dimers and trapped few-body states characterized by substantial
correlations and likely to be accessible within current experiments.


title:
Locally analytic completed cohomology of Shimura varieties and
  overconvergent $\mathrm{BGG}$ maps

abstract:
  We generalize the work of Lue Pan on locally analytic completed cohomology of
modular curves to Shimura varieties. In the process, we extend the axiomatic
Sen-Tate formalism of Berger-Colmez to a certain class of locally analytic
representations. Using Sen theory over rigid spaces, we define a geometric Sen
operator which is nothing but a pro\'etale Higgs bundle related with the
$p$-adic Simpson correspondence. In the application to Shimura varieties, we
obtain a proof of the rational Calegari-Emerton conjectures without a
perfectoid assumption at infinite level. We also construct the overconvergent
$\mathrm{BGG}$ maps for the derived Jacquet module of the locally analytic
completed cohomology and its dual.


title:
So (2, 1) algebra, local Fermi velocity, and position-dependent mass
  Dirac equation

abstract:
  We investigate the (1+1)-dimensional position-dependent mass Dirac equation
within the confines of so(2,1) potential algebra by utilizing the character of
a spatial varying Fermi velocity. We examine the combined effects of the two
when the Dirac equation is equipped with an external pseuodoscalar potential.
Solutions of the three cases induced by so(2, 1) are explored by profitably
making use of a point canonical transformation.


title:
Measurement of the branching fractions for Cabibbo-suppressed decays
  $D^{+}\to K^{+} K^{-}\pi^{+}\pi^{0}$ and $D_{(s)}^{+}\to
  K^{+}\pi^{-}\pi^{+}\pi^{0}$ at Belle

abstract:
  We present measurements of the branching fractions for the singly
Cabibbo-suppressed decays $D^+\to K^{+}K^{-}\pi^{+}\pi^{0}$ and $D_s^{+}\to
K^{+}\pi^{-}\pi^{+}\pi^{0}$, and the doubly Cabibbo-suppressed decay $D^{+}\to
K^{+}\pi^{-}\pi^{+}\pi^{0}$, based on 980 ${\rm fb}^{-1}$ of data recorded by
the Belle experiment at the KEKB $e^{+}e^{-}$ collider. We measure these modes
relative to the Cabibbo-favored modes $D^{+}\to K^{-}\pi^{+}\pi^{+}\pi^{0}$ and
$D_s^{+}\to K^{+}K^{-}\pi^{+}\pi^{0}$. Our results for the ratios of branching
fractions are $B(D^{+}\to K^{+}K^{-}\pi^{+}\pi^{0})/B(D^{+}\to
K^{-}\pi^{+}\pi^{+}\pi^{0}) = (11.32 \pm 0.11 \pm 0.26)\%$, $B(D^{+}\to
K^{+}\pi^{-}\pi^{+}\pi^{0})/B(D^{+}\to K^{-}\pi^{+}\pi^{+}\pi^{0}) = (1.68 \pm
0.08\pm 0.03)\%$, and $B(D_s^{+}\to K^{+}\pi^{-}\pi^{+}\pi^{0})/B(D_s^{+}\to
K^{+}K^{-}\pi^{+}\pi^{0}) = (17.13 \pm 0.46 \pm 0.51)\%$, where the
uncertainties are statistical and systematic, respectively. The second value
corresponds to $(5.83\pm 0.30)\times\tan^4\theta_C$, where $\theta_C$ is the
Cabibbo angle; this value is larger than other measured ratios of branching
fractions for a doubly Cabibbo-suppressed charm decay to a Cabibbo-favored
decay. Multiplying these results by world average values for $B(D^{+}\to
K^{-}\pi^{+}\pi^{+}\pi^{0})$ and $B(D_s^{+}\to K^{+}K^{-}\pi^{+}\pi^{0})$
yields $B(D^{+}\to K^{+}K^{-}\pi^{+}\pi^{0})= (7.08\pm 0.07\pm 0.16\pm
0.20)\times10^{-3}$, $B(D^{+}\to K^{+}\pi^{-}\pi^{+}\pi^{0})= (1.05\pm 0.05\pm
0.02\pm 0.03)\times10^{-3}$, and $B(D_s^{+}\to K^{+}\pi^{-}\pi^{+}\pi^{0}) =
(9.44\pm 0.25\pm 0.28\pm 0.32)\times10^{-3}$, where the third uncertainty is
due to the branching fraction of the normalization mode. The first two results
are consistent with, but more precise than, the current world averages. The
last result is the first measurement of this branching fraction.


title: Representations of $p$-adic groups over coefficient rings

abstract:
  Motivated by the Langlands program in representation theory, number theory
and geometry, the theory of representations of a reductive $p$-adic group over
a coefficient ring different from the field of complex numbers has been widely
developped during the last two decades. This article provides a survey of basic
results obtained in the 21st century.


title:
The Faraday effect in magnetoplasmonic nanostructures with spatial
  modulation of magnetization

abstract:
  The magneto-optical Faraday effect in the magnetoplasmonic nanostructures
with nonuniform, periodically modulated spatial distribution of the
magnetization is considered. It is shown that in such nanostructures the
Faraday effect can experience the resonant enhancement in the spectral range of
the plasmonic and waveguide optical modes excitation. It happens both for $s$
and $p$-polarized light. Such an effect can serve for the spectrally selective
detection of the short spin waves in the magnetic materials.


title: Max-Min $k$-Dispersion on a Convex Polygon

abstract:
  In this paper, we consider the following $k$-dispersion problem. Given a set
$S$ of $n$ points placed in the plane in a convex position, and an integer $k$
($0<k<n$), the objective is to compute a subset $S'\subset S$ such that
$|S'|=k$ and the minimum distance between a pair of points in $S'$ is
maximized. Based on the bounded search tree method we propose an exact
fixed-parameter algorithm in $O(2^k(n^2\log n+n(\log^2 n)(\log k)))$ time, for
this problem, where $k$ is the parameter. The proposed exact algorithm is
better than the current best exact exponential algorithm
[$n^{O(\sqrt{k})}$-time algorithm by Akagi et al.,(2018)] whenever
$k<c\log^2{n}$ for some constant $c$. We then present an $O(\log{n})$-time
$\frac{1}{2\sqrt{2}}$-approximation algorithm for the problem when $k=3$ if the
points are given in convex position order.


title:
A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models
  for African News Translation

abstract:
  Recent advances in the pre-training of language models leverage large-scale
datasets to create multilingual models. However, low-resource languages are
mostly left out in these datasets. This is primarily because many widely spoken
languages are not well represented on the web and therefore excluded from the
large-scale crawls used to create datasets. Furthermore, downstream users of
these models are restricted to the selection of languages originally chosen for
pre-training. This work investigates how to optimally leverage existing
pre-trained models to create low-resource translation systems for 16 African
languages. We focus on two questions: 1) How can pre-trained models be used for
languages not included in the initial pre-training? and 2) How can the
resulting translation models effectively transfer to new domains? To answer
these questions, we create a new African news corpus covering 16 languages, of
which eight languages are not part of any existing evaluation dataset. We
demonstrate that the most effective strategy for transferring both to
additional languages and to additional domains is to fine-tune large
pre-trained models on small quantities of high-quality translation data.


title:
Same Neurons, Different Languages: Probing Morphosyntax in Multilingual
  Pre-trained Models

abstract:
  The success of multilingual pre-trained models is underpinned by their
ability to learn representations shared by multiple languages even in absence
of any explicit supervision. However, it remains unclear how these models learn
to generalise across languages. In this work, we conjecture that multilingual
pre-trained models can derive language-universal abstractions about grammar. In
particular, we investigate whether morphosyntactic information is encoded in
the same subset of neurons in different languages. We conduct the first
large-scale empirical study over 43 languages and 14 morphosyntactic categories
with a state-of-the-art neuron-level probe. Our findings show that the
cross-lingual overlap between neurons is significant, but its extent may vary
across categories and depends on language proximity and pre-training data size.


title:
Angular Control Charts: A New Perspective for Monitoring Reliability of
  Multi-State Systems

abstract:
  Control charts, as had been used traditionally for quality monitoring, were
applied alternatively to monitor systems' reliability. In other words, they can
be applied to detect changes in the failure behavior of systems. Such purpose
imposed modifying traditional control charts in addition to developing charts
that are more compatible with reliability monitoring. The latter developed
category is known as probability limits control charts. The existing
reliability monitoring control charts were only dedicated to binary-state
systems, and they can't be used to monitor several states simultaneously.
Therefore, this paper develops a design of control charts that accommodates
multi-state systems, called here as the Angular Control Chart, which represents
a new version of the probability limits control charts. This design is able to
monitor state transitions simultaneously and individually in addition.
Illustrative system examples are implemented to explore the monitoring
procedure of the new design and to demonstrate its efficiency, effectiveness,
and limitations.


title:
Gibbs measures for HC-model with a countable set of spin values on a
  Cayley tree

abstract:
  In this paper, we study the HC-model with a countable set $\mathbb Z$ of spin
values on a Cayley tree of order $k\geq 2$. This model is defined by a
countable set of parameters (that is, the activity function $\lambda_i>0$,
$i\in \mathbb Z$). A functional equation is obtained that provides the
consistency condition for finite-dimensional Gibbs distributions. Analyzing
this equation, the following results are obtained:
  - Let $\Lambda=\sum_i\lambda_i$.
  For $\Lambda=+\infty$ there are no translation-invariant Gibbs measures
(TIGM) and no two-periodic Gibbs measures (TPGM);
  - For $\Lambda<+\infty$, the uniqueness of TIGM is proved;
  - Let $\Lambda_{\rm cr}(k)=\frac{k^k}{(k-1)^{k+1}}$. If
$0<\Lambda\leq\Lambda_{\rm cr}$, then there is exactly one TPGM that is TIGM;
  - For $\Lambda>\Lambda_{\rm cr}$, there are exactly three TPGMs, one of which
is TIGM.


title:
Early Solar System instability triggered by dispersal of the gaseous
  disk

abstract:
  The Solar System's orbital structure is thought to have been sculpted by an
episode of dynamical instability among the giant planets. However, the
instability trigger and timing have not been clearly established.
Hydrodynamical modeling has shown that while the Sun's gaseous protoplanetary
disk was present the giant planets migrated into a compact orbital
configuration in a chain of resonances. Here we use dynamical simulations to
show that the giant planets' instability was likely triggered by the dispersal
of the gaseous disk. As the disk evaporated from the inside-out, its inner edge
swept successively across and dynamically perturbed each planet's orbit in
turn. The associated orbital shift caused a dynamical compression of the
exterior part of the system, ultimately triggering instability. The final
orbits of our simulated systems match those of the Solar System for a viable
range of astrophysical parameters. The giant planet instability therefore took
place as the gaseous disk dissipated, constrained by astronomical observations
to be a few to ten million years after the birth of the Solar System.
Terrestrial planet formation would not complete until after such an early giant
planet instability; the growing terrestrial planets may even have been sculpted
by its perturbations, explaining the small mass of Mars relative to Earth.


title:
The degree of commutativity of wreath products with infinite cyclic top
  group

abstract:
  The degree of commutativity of a finite group is the probability that two
uniformly and randomly chosen elements commute. This notion extends naturally
to finitely generated groups $G$: the degree of commutativity $\text{dc}_S(G)$,
with respect to a given finite generating set $S$, results from considering the
fractions of commuting pairs of elements in increasing balls around $1_G$ in
the Cayley graph $\mathcal{C}(G,S)$. We focus on restricted wreath products the
form $G = H \wr \langle t \rangle$, where $H \ne 1$ is finitely generated and
the top group $\langle t \rangle$ is infinite cyclic. In accordance with a more
general conjecture, we show that $\text{dc}_S(G) = 0$ for such groups $G$,
regardless of the choice of $S$.
  This extends results of Cox who considered lamplighter groups with respect to
certain kinds of generating sets. We also derive a generalisation of Cox's main
auxiliary result: in `reasonably large' homomorphic images of wreath products
$G$ as above, the image of the base group has density zero, with respect to
certain types of generating sets.


title:
TransRank: Self-supervised Video Representation Learning via
  Ranking-based Transformation Recognition

abstract:
  Recognizing transformation types applied to a video clip (RecogTrans) is a
long-established paradigm for self-supervised video representation learning,
which achieves much inferior performance compared to instance discrimination
approaches (InstDisc) in recent works. However, based on a thorough comparison
of representative RecogTrans and InstDisc methods, we observe the great
potential of RecogTrans on both semantic-related and temporal-related
downstream tasks. Based on hard-label classification, existing RecogTrans
approaches suffer from noisy supervision signals in pre-training. To mitigate
this problem, we developed TransRank, a unified framework for recognizing
Transformations in a Ranking formulation. TransRank provides accurate
supervision signals by recognizing transformations relatively, consistently
outperforming the classification-based formulation. Meanwhile, the unified
framework can be instantiated with an arbitrary set of temporal or spatial
transformations, demonstrating good generality. With a ranking-based
formulation and several empirical practices, we achieve competitive performance
on video retrieval and action recognition. Under the same setting, TransRank
surpasses the previous state-of-the-art method by 6.4% on UCF101 and 8.3% on
HMDB51 for action recognition (Top1 Acc); improves video retrieval on UCF101 by
20.4% (R@1). The promising results validate that RecogTrans is still a worth
exploring paradigm for video self-supervised learning. Codes will be released
at https://github.com/kennymckormick/TransRank.


title:
CODE-MVP: Learning to Represent Source Code from Multiple Views with
  Contrastive Pre-Training

abstract:
  Recent years have witnessed increasing interest in code representation
learning, which aims to represent the semantics of source code into distributed
vectors. Currently, various works have been proposed to represent the complex
semantics of source code from different views, including plain text, Abstract
Syntax Tree (AST), and several kinds of code graphs (e.g., Control/Data Flow
Graph). However, most of them only consider a single view of source code
independently, ignoring the correspondences among different views. In this
paper, we propose to integrate different views with the natural-language
description of source code into a unified framework with Multi-View contrastive
Pre-training, and name our model as CODE-MVP. Specifically, we first extract
multiple code views using compiler tools, and learn the complementary
information among them under a contrastive learning framework. Inspired by the
type checking in compilation, we also design a fine-grained type inference
objective in the pre-training. Experiments on three downstream tasks over five
datasets demonstrate the superiority of CODE-MVP when compared with several
state-of-the-art baselines. For example, we achieve 2.4/2.3/1.1 gain in terms
of MRR/MAP/Accuracy metrics on natural language code retrieval, code
similarity, and code defect detection tasks, respectively.


title: Exploring phase space with Nested Sampling

abstract:
  We present the first application of a Nested Sampling algorithm to explore
the high-dimensional phase space of particle collision events. We describe the
adaptation of the algorithm, designed to perform Bayesian inference
computations, to the integration of partonic scattering cross sections and the
generation of individual events distributed according to the corresponding
squared matrix element. As a first concrete example we consider gluon
scattering processes into 3-, 4- and 5-gluon final states and compare the
performance with established sampling techniques. Starting from a flat prior
distribution Nested Sampling outperforms the Vegas algorithm and achieves
results comparable to a dedicated multi-channel importance sampler. We outline
possible approaches to combine Nested Sampling with non-flat prior
distributions to further reduce the variance of integral estimates and to
increase unweighting efficiencies.


title:
Self-Supervised Super-Resolution for Multi-Exposure Push-Frame
  Satellites

abstract:
  Modern Earth observation satellites capture multi-exposure bursts of
push-frame images that can be super-resolved via computational means. In this
work, we propose a super-resolution method for such multi-exposure sequences, a
problem that has received very little attention in the literature. The proposed
method can handle the signal-dependent noise in the inputs, process sequences
of any length, and be robust to inaccuracies in the exposure times.
Furthermore, it can be trained end-to-end with self-supervision, without
requiring ground truth high resolution frames, which makes it especially suited
to handle real data. Central to our method are three key contributions: i) a
base-detail decomposition for handling errors in the exposure times, ii) a
noise-level-aware feature encoding for improved fusion of frames with varying
signal-to-noise ratio and iii) a permutation invariant fusion strategy by
temporal pooling operators. We evaluate the proposed method on synthetic and
real data and show that it outperforms by a significant margin existing
single-exposure approaches that we adapted to the multi-exposure case.


title:
Relative Gorenstein flat modules and Foxby classes and their model
  structures

abstract:
  A model structure on a category is a formal way of introducing a homotopy
theory on that category, and if the model structure is abelian and hereditary,
its homotopy category is known to be triangulated. So a good way to both build
and model a triangulated category is to build a hereditary abelian model
structure. Given a ring $R$ and a (non necessarily semidualizing) left
$R$-module $C$, we introduce and study new concepts of relative Gorenstein
cotorsion and cotorsion modules: $\rm G_C$-cotorsion and (strongly)
$\mathcal{C}_C$-cotorsion. As an application, we prove that there is a unique
hereditary abelian model structure on the category of left $R$-modules, in
which the cofibrations are the monomorphisms with $\rm G_C$-flat cokernel and
the fibrations are the epimorphisms with $\mathcal{C}_C$-cotorsion kernel
belonging to the Bass class $\mathcal{B}_C(R)$. In the second part, when $C$ is
a semidualizing $(R,S)$-bimodule, we investigate the existence of abelian model
structures on the category of left (resp., right) $R$-modules where the
cofibrations are the epimorphisms (resp., monomorphisms) with kernel (resp.,
cokernel) belonging to the Bass (resp., Auslander) class $\mathcal{B}_C(R)$
(resp., $\mathcal{A}_C(R)$). We also study the class of $\rm G_C$-flat modules
and the Bass class from the Auslander-Buchweitz approximation theory point of
view. We show that they are part of weak AB-contexts. As the concept of weak
AB-context can be dualized, we also give dual results that involve the class of
$\rm G_C$-cotorsion modules and the Auslander class.


title:
How Does Author Affiliation Affect Preprint Citation Count? Analyzing
  Citation Bias at the Institution and Country Level

abstract:
  Citing is an important aspect of scientific discourse and important for
quantifying the scientific impact quantification of researchers. Previous works
observed that citations are made not only based on the pure scholarly
contributions but also based on non-scholarly attributes, such as the
affiliation or gender of authors. In this way, citation bias is produced.
Existing works, however, have not analyzed preprints with respect to citation
bias, although they play an increasingly important role in modern scholarly
communication. In this paper, we investigate whether preprints are affected by
citation bias with respect to the author affiliation. We measure citation bias
for bioRxiv preprints and their publisher versions at the institution level and
country level, using the Lorenz curve and Gini coefficient. This allows us to
mitigate the effects of confounding factors and see whether or not citation
biases related to author affiliation have an increased effect on preprint
citations. We observe consistent higher Gini coefficients for preprints than
those for publisher versions. Thus, we can confirm that citation bias exists
and that it is more severe in case of preprints. As preprints are on the rise,
affiliation-based citation bias is, thus, an important topic not only for
authors (e.g., when deciding what to cite), but also to people and institutions
that use citations for scientific impact quantification (e.g., funding agencies
deciding about funding based on citation counts).


title: Buckling vs unilateral constraint for a multistable metamaterial element

abstract:
  A structural element is designed and investigated, forming the basis for the
development of an elastic multistable metamaterial. The leitmotif of the
structural design is the implementation of a strut characterized by a
bifurcation occurring at either vanishing tensile or compressive load. It is
shown that buckling at null load leads to a mechanical equivalence with a
unilateral constraint formulation, introducing shocks in dynamics. Towards a
future analysis of the latter, the nonlinear quasi-static response is
investigated, showing the multistable character of the structure, which may
appear as bistable or tetrastable.


title:
Masked Summarization to Generate Factually Inconsistent Summaries for
  Improved Factual Consistency Checking

abstract:
  Despite the recent advances in abstractive summarization systems, it is still
difficult to determine whether a generated summary is factual consistent with
the source text. To this end, the latest approach is to train a factual
consistency classifier on factually consistent and inconsistent summaries.
Luckily, the former is readily available as reference summaries in existing
summarization datasets. However, generating the latter remains a challenge, as
they need to be factually inconsistent, yet closely relevant to the source text
to be effective. In this paper, we propose to generate factually inconsistent
summaries using source texts and reference summaries with key information
masked. Experiments on seven benchmark datasets demonstrate that factual
consistency classifiers trained on summaries generated using our method
generally outperform existing models and show a competitive correlation with
human judgments. We also analyze the characteristics of the summaries generated
using our method. We will release the pre-trained model and the code at
https://github.com/hwanheelee1993/MFMA.


title:
Rate-Splitting Multiple Access for 6G -- Part III: Interplay with
  Reconfigurable Intelligent Surfaces

abstract:
  This letter is the third part of a three-part tutorial that focuses on
rate-splitting multiple access (RSMA) for 6G. As Part III of the tutorial, this
letter provides an overview of integrating RSMA and reconfigurable intelligent
surface (RIS). We first introduce two potential PHY layer techniques, namely,
RSMA and RIS, including the need for integrating RSMA with RIS and how they
could help each other. Next, we provide a general model of an RIS-aided RSMA
system and summarize some key performance metrics. Then, we discuss the major
advantages of RIS-aided RSMA networks, and illustrate the rate region of
RIS-aided RSMA for both perfect and imperfect channel conditions. Finally, we
summarize the research challenges and open problems for RIS-aided RSMA systems.
In conclusion, RSMA is a promising technology for next generation multiple
access (NGMA) and future networks such as 6G and beyond.


title:
Low regularity well-posedness for KP-I equations: the dispersion
  generalised case

abstract:
  We prove new local and global well-posedness results for
dispersion-generalized Kadomtsev--Petviashvili I equations in $\R^2$, which
family links the classical KP-I equation with the fifth order KP-I equation. To
this end, we combine resonance and transversality considerations with
Strichartz estimates and a nonlinear Loomis--Whitney inequality. Moreover, we
prove that for small dispersion, the equations cannot be solved via Picard
iteration. In this case, we use an additional frequency dependent time
localization.


title: Integrable deformations of superintegrable quantum circuits

abstract:
  Superintegrable models are very special dynamical systems: they possess more
conservation laws than what is necessary for complete integrability. This
severely constrains their dynamical processes, and it often leads to their
exact solvability, even in non-equilibrium situations. In this paper we
consider special Hamiltonian deformations of superintegrable quantum circuits.
The deformations break superintegrability, but they preserve integrability. We
focus on a selection of concrete models and show that for each model there is
an (at least) one parameter family of integrable deformations. Our most
interesting example is the so-called Rule54 model. We show that the model is
compatible with a one parameter family of Yang-Baxter integrable spin chains
with six-site interaction. Therefore, the Rule54 model does not have a unique
integrability structure, instead it lies at the intersection of a family of
quantum integrable models.


title: Generic Newton points and cordial elements

abstract:
  We describe the generic $\sigma$-conjugacy classes associated with the
Iwahori-Bruhat decomposition of a reductive group. As an application, we
classify cordial elements as introduced by Mili\'cevi\'c-Viehmann.


title:
A counter example to the theorems of social preference transitivity and
  social choice set existence under the majority rule

abstract:
  I present an example in which the individuals' preferences are strict
orderings, and under the majority rule, a transitive social ordering can be
obtained and thus a non-empty choice set can also be obtained. However, the
individuals' preferences in that example do not satisfy any conditions
(restrictions) of which at least one is required by Inada (1969) for social
preference transitivity under the majority rule. Moreover, the considered
individuals' preferences satisfy none of the conditions of value restriction
(VR), extremal restriction (ER) or limited agreement (LA), some of which is
required by Sen and Pattanaik (1969) for the existence of a non-empty social
choice set. Therefore, the example is an exception to a number of theorems of
social preference transitivity and social choice set existence under the
majority rule. This observation indicates that the collection of the conditions
listed by Inada (1969) is not as complete as might be supposed. This is also
the case for the collection of conditions VR, ER and LA considered by Sen and
Pattanaik (1969). This observation is a challenge to some necessary conditions
in the current social choice theory. In addition to seeking new conditions, one
possible way to deal with this challenge may be, from a theoretical
prospective, to represent the identified conditions (such as the VR, ER and LA)
in terms of a common mathematical tool, and then, people may find more.


title:
Who Will Support My Project? Interactive Search of Potential
  Crowdfunding Investors Through InSearch

abstract:
  Crowdfunding provides project founders with a convenient way to reach online
investors. However, it is challenging for founders to find the most potential
investors and successfully raise money for their projects on crowdfunding
platforms. A few machine learning based methods have been proposed to recommend
investors' interest in a specific crowdfunding project, but they fail to
provide project founders with explanations in detail for these recommendations,
thereby leading to an erosion of trust in predicted investors. To help
crowdfunding founders find truly interested investors, we conducted
semi-structured interviews with four crowdfunding experts and presents
inSearch, a visual analytic system. inSearch allows founders to search for
investors interactively on crowdfunding platforms. It supports an effective
overview of potential investors by leveraging a Graph Neural Network to model
investor preferences. Besides, it enables interactive exploration and
comparison of the temporal evolution of different investors' investment
details.


title: Solution of Tensor Complementarity Problem Using Homotopy Function

abstract:
  The paper aims to propose a suitable method in finding the solution of tensor
complementarity problem. The tensor complementarity problem is a subclass of
nonlinear complementarity problems for which the involved function is defined
by a tensor. We propose a new homotopy function with smooth and bounded
homotopy path to obtain solution of the tensor complementarity problem under
some conditions. A homotopy continuation method is developed based on the
proposed homotopy function. Several numerical examples are provided to show the
effectiveness of the proposed homotopy continuation method.


title:
A Manifold Two-Sample Test Study: Integral Probability Metric with
  Neural Networks

abstract:
  Two-sample tests are important areas aiming to determine whether two
collections of observations follow the same distribution or not. We propose
two-sample tests based on integral probability metric (IPM) for
high-dimensional samples supported on a low-dimensional manifold. We
characterize the properties of proposed tests with respect to the number of
samples $n$ and the structure of the manifold with intrinsic dimension $d$.
When an atlas is given, we propose two-step test to identify the difference
between general distributions, which achieves the type-II risk in the order of
$n^{-1/\max\{d,2\}}$. When an atlas is not given, we propose H\"older IPM test
that applies for data distributions with $(s,\beta)$-H\"older densities, which
achieves the type-II risk in the order of $n^{-(s+\beta)/d}$. To mitigate the
heavy computation burden of evaluating the H\"older IPM, we approximate the
H\"older function class using neural networks. Based on the approximation
theory of neural networks, we show that the neural network IPM test has the
type-II risk in the order of $n^{-(s+\beta)/d}$, which is in the same order of
the type-II risk as the H\"older IPM test. Our proposed tests are adaptive to
low-dimensional geometric structure because their performance crucially depends
on the intrinsic dimension instead of the data dimension.


title:
Deformations of the canonical commutation relation and Lorentz symmetry
  violation

abstract:
  Deformations of the canonical commutation relation and, independently,
Lorentz symmetry violations are two common features in many candidate models
for quantum gravity. Despite that, overlap between both has not been explored
yet. In this brief paper, we take the first steps on this direction. At the
nonrelativistic level and in the realm of commutative spacetime coordinates, a
large class of both isotropic and non isotropic modifications of the canonical
commutation relation is shown to produce signals experimentally
indistinguishable from those predicted by the Standard Model Extension, the
common framework for studying Lorentz-violating phenomena beyond the Standard
Model.


title: Duality in convex stochastic optimization

abstract:
  This paper studies duality and optimality conditions in general convex
stochastic optimization problems introduced by Rockafellar and Wets in 1976. We
derive an explicit dual problem in terms of two dual variables, one of which is
the shadow price of information while the other one gives the marginal cost of
a perturbation much like in classical Lagrangian duality. Existence of primal
solutions and the absence of duality gap are obtained without compactness or
boundedness assumptions. In the context of financial mathematics, the relaxed
assumptions are satisfied under the well-known no-arbitrage condition and the
reasonable asymptotic elasticity condition of the utility function. We extend
classical portfolio optimization duality theory to problems of optimal
semi-static hedging. Besides financial mathematics, we obtain several new
frameworks in stochastic programming and stochastic optimal control.


title: DNA Pre-alignment Filter using Processing Near Racetrack Memory

abstract:
  Recent DNA pre-alignment filter designs employ DRAM for storing the reference
genome and its associated meta-data. However, DRAM incurs increasingly high
energy consumption background and refresh energy as devices scale. To overcome
this problem, this paper explores a design with racetrack memory (RTM)--an
emerging non-volatile memory that promises higher storage density, faster
access latency, and lower energy consumption. Multi-bit storage cells in RTM
are inherently sequential and thus require data placement strategies to
mitigate the performance and energy impacts of shifting during data accesses.
We propose a near-memory pre-alignment filter with a novel data mapping and
several shift reduction strategies designed explicitly for RTM. On a set of
four input genomes from the 1000 Genome Project, our approach improves
performance and energy efficiency by 68% and 52%, respectively, compared to the
state of the art proposed DRAM-based architecture.


title: Hyperbolic Relevance Matching for Neural Keyphrase Extraction

abstract:
  Keyphrase extraction is a fundamental task in natural language processing and
information retrieval that aims to extract a set of phrases with important
information from a source document. Identifying important keyphrase is the
central component of the keyphrase extraction task, and its main challenge is
how to represent information comprehensively and discriminate importance
accurately. In this paper, to address these issues, we design a new hyperbolic
matching model (HyperMatch) to represent phrases and documents in the same
hyperbolic space and explicitly estimate the phrase-document relevance via the
Poincar\'e distance as the important score of each phrase. Specifically, to
capture the hierarchical syntactic and semantic structure information,
HyperMatch takes advantage of the hidden representations in multiple layers of
RoBERTa and integrates them as the word embeddings via an adaptive mixing
layer. Meanwhile, considering the hierarchical structure hidden in the
document, HyperMatch embeds both phrases and documents in the same hyperbolic
space via a hyperbolic phrase encoder and a hyperbolic document encoder. This
strategy can further enhance the estimation of phrase-document relevance due to
the good properties of hyperbolic space. In this setting, the keyphrase
extraction can be taken as a matching problem and effectively implemented by
minimizing a hyperbolic margin-based triplet loss. Extensive experiments are
conducted on six benchmarks and demonstrate that HyperMatch outperforms the
state-of-the-art baselines.


title: Few-Shot Document-Level Relation Extraction

abstract:
  We present FREDo, a few-shot document-level relation extraction (FSDLRE)
benchmark. As opposed to existing benchmarks which are built on sentence-level
relation extraction corpora, we argue that document-level corpora provide more
realism, particularly regarding none-of-the-above (NOTA) distributions.
Therefore, we propose a set of FSDLRE tasks and construct a benchmark based on
two existing supervised learning data sets, DocRED and sciERC. We adapt the
state-of-the-art sentence-level method MNAV to the document-level and develop
it further for improved domain adaptation. We find FSDLRE to be a challenging
setting with interesting new characteristics such as the ability to sample NOTA
instances from the support set. The data, code, and trained models are
available online (https://github.com/nicpopovic/FREDo).


title:
Self-Supervised Learning for Invariant Representations from
  Multi-Spectral and SAR Images

abstract:
  Self-Supervised learning (SSL) has become the new state-of-art in several
domain classification and segmentation tasks. Of these, one popular category in
SSL is distillation networks such as BYOL. This work proposes RSDnet, which
applies the distillation network (BYOL) in the remote sensing (RS) domain where
data is non-trivially different from natural RGB images. Since Multi-spectral
(MS) and synthetic aperture radar (SAR) sensors provide varied spectral and
spatial resolution information, we utilised them as an implicit augmentation to
learn invariant feature embeddings. In order to learn RS based invariant
features with SSL, we trained RSDnet in two ways, i.e., single channel feature
learning and three channel feature learning. This work explores the usefulness
of single channel feature learning from random MS and SAR bands compared to the
common notion of using three or more bands. In our linear evaluation, these
single channel features reached a 0.92 F1 score on the EuroSAT classification
task and 59.6 mIoU on the DFC segmentation task for certain single bands. We
also compared our results with ImageNet weights and showed that the RS based
SSL model outperforms the supervised ImageNet based model. We further explored
the usefulness of multi-modal data compared to single modality data, and it is
shown that utilising MS and SAR data learn better invariant representations
than utilising only MS data.


title: A Perfect Sampler for Hypergraph Independent Sets

abstract:
  The problem of uniformly sampling hypergraph independent sets is revisited.
We design an efficient perfect sampler for the problem under a similar
condition to the asymmetric Lov\'asz Local Lemma. When specialized to
$d$-regular $k$-uniform hypergraphs on $n$ vertices, our sampler terminates in
expected $O(n\log n)$ time provided $d\le c\cdot 2^{\frac{k}{2}}$ where $c>0$
is a constant, matching the rapid mixing condition for Glauber dynamics in
[HSZ19]. The analysis of our algorithm is simple and clean.


title: Weight distribution of random linear codes and Krawchouk polynomials

abstract:
  For $0 < \lambda < 1$ and $n \rightarrow \infty$ pick uniformly at random
$\lambda n$ vectors in $\{0,1\}^n$ and let $C$ be the orthogonal complement of
their span. Given $0 < \gamma < \frac12$ with $0 < \lambda < h(\gamma)$, let
$X$ be the random variable that counts the number of words in $C$ of Hamming
weight $i = \gamma n$ (where $i$ is assumed to be an even integer). Linial and
Mosheiff determined the asymptotics of the moments of $X$ of all orders
$o\left(\frac{n}{\log n}\right)$. In this paper we extend their estimates up to
moments of linear order. Our key observation is that the behavior of the
suitably normalized $k^{th}$ moment of $X$ is essentially determined by the
$k^{th}$ norm of the Krawchouk polynomial $K_i$.


title: Exploring Rawlsian Fairness for K-Means Clustering

abstract:
  We conduct an exploratory study that looks at incorporating John Rawls' ideas
on fairness into existing unsupervised machine learning algorithms. Our focus
is on the task of clustering, specifically the k-means clustering algorithm. To
the best of our knowledge, this is the first work that uses Rawlsian ideas in
clustering. Towards this, we attempt to develop a postprocessing technique
i.e., one that operates on the cluster assignment generated by the standard
k-means clustering algorithm. Our technique perturbs this assignment over a
number of iterations to make it fairer according to Rawls' difference principle
while minimally affecting the overall utility. As the first step, we consider
two simple perturbation operators -- $\mathbf{R_1}$ and $\mathbf{R_2}$ -- that
reassign examples in a given cluster assignment to new clusters; $\mathbf{R_1}$
assigning a single example to a new cluster, and $\mathbf{R_2}$ a pair of
examples to new clusters. Our experiments on a sample of the Adult dataset
demonstrate that both operators make meaningful perturbations in the cluster
assignment towards incorporating Rawls' difference principle, with
$\mathbf{R_2}$ being more efficient than $\mathbf{R_1}$ in terms of the number
of iterations. However, we observe that there is still a need to design
operators that make significantly better perturbations. Nevertheless, both
operators provide good baselines for designing and comparing any future
operator, and we hope our findings would aid future work in this direction.


title:
An Haloscope Amplification Chain based on a Travelling Wave Parametric
  Amplifier

abstract:
  In this paper we will describe the characterisation of a rf detection chain
based on a travelling wave parametric amplifier (TWPA). The detection chain is
meant to be used for dark matter axion searches and thus it is mounted coupled
to a high Q microwave resonant cavity. A system noise temperature $T_{\rm sys}
= (3.3 \pm 0.1$) K has been measured at a frequency of 10.77 GHz, using a novel
scheme allowing measurement of $T_{\rm sys} $ exactly at the cavity output
port.


title:
Measuring and Improving Compositional Generalization in Text-to-SQL via
  Component Alignment

abstract:
  In text-to-SQL tasks -- as in much of NLP -- compositional generalization is
a major challenge: neural networks struggle with compositional generalization
where training and test distributions differ. However, most recent attempts to
improve this are based on word-level synthetic data or specific dataset splits
to generate compositional biases. In this work, we propose a clause-level
compositional example generation method. We first split the sentences in the
Spider text-to-SQL dataset into sub-sentences, annotating each sub-sentence
with its corresponding SQL clause, resulting in a new dataset Spider-SS. We
then construct a further dataset, Spider-CG, by composing Spider-SS
sub-sentences in different combinations, to test the ability of models to
generalize compositionally. Experiments show that existing models suffer
significant performance degradation when evaluated on Spider-CG, even though
every sub-sentence is seen during training. To deal with this problem, we
modify a number of state-of-the-art models to train on the segmented data of
Spider-SS, and we show that this method improves the generalization
performance.


title:
Planning a Cost-Effective Delay-Constrained Passive Optical Network for
  5G Fronthaul

abstract:
  With the rapid growth in the telecommunications industry moving towards 5G
and beyond (5GB) and the emergence of data-hungry and time-sensitive
applications, Mobile Network Operators (MNOs) are faced with a considerable
challenge to keep up with these new demands. Cloud radio access network (CRAN)
has emerged as a cost-effective architecture that improves 5GB performance. The
fronthaul segment of the CRAN necessitates a high-capacity and low-latency
connection. Optical technologies presented by Passive Optical Networks (PON)
have gained attention as a promising technology to meet the fronthaul
challenges. In this paper, we proposed an Integer Linear Program (ILP) that
optimizes the total cost of ownership (TCO) for 5G using CRAN architecture
under different delay thresholds. We considered the Time and Wavelength
Division Multiplexing Passive Optical Network (TWDM-PON) as a fronthaul with
different splitting ratios.


title: On the Complexity of Majority Illusion in Social Networks

abstract:
  Majority illusion occurs in a social network when the majority of the network
nodes belong to a certain type but each node's neighbours mostly belong to a
different type, therefore creating the wrong perception, i.e., the illusion,
that the majority type is different from the actual one. From a system
engineering point of view, we want to devise algorithms to detect and,
crucially, correct this undesirable phenomenon. In this paper we initiate the
computational study of majority illusion in social networks, providing
complexity results for its occurrence and avoidance. Namely, we show that
identifying whether a network can be labelled such that majority illusion is
present, as well as the problem of removing an illusion by adding or deleting
edges of the network, are NP-complete problems.


title:
Reinforcement Learning for Improved Random Access in Delay-Constrained
  Heterogeneous Wireless Networks

abstract:
  In this paper, we for the first time investigate the random access problem
for a delay-constrained heterogeneous wireless network. We begin with a simple
two-device problem where two devices deliver delay-constrained traffic to an
access point (AP) via a common unreliable collision channel. By assuming that
one device (called Device 1) adopts ALOHA, we aim to optimize the random access
scheme of the other device (called Device 2). The most intriguing part of this
problem is that Device 2 does not know the information of Device 1 but needs to
maximize the system timely throughput. We first propose a Markov Decision
Process (MDP) formulation to derive a model-based upper bound so as to quantify
the performance gap of certain random access schemes. We then utilize
reinforcement learning (RL) to design an R-learning-based random access scheme,
called tiny state-space R-learning random access (TSRA), which is subsequently
extended for the tackling of the general multi-device problem. We carry out
extensive simulations to show that the proposed TSRA simultaneously achieves
higher timely throughput, lower computation complexity, and lower power
consumption than the existing baseline--deep-reinforcement learning multiple
access (DLMA). This indicates that our proposed TSRA scheme is a promising
means for efficient random access over massive mobile devices with limited
computation and battery capabilities.


title: SVTS: Scalable Video-to-Speech Synthesis

abstract:
  Video-to-speech synthesis (also known as lip-to-speech) refers to the
translation of silent lip movements into the corresponding audio. This task has
received an increasing amount of attention due to its self-supervised nature
(i.e., can be trained without manual labelling) combined with the ever-growing
collection of audio-visual data available online. Despite these strong
motivations, contemporary video-to-speech works focus mainly on small- to
medium-sized corpora with substantial constraints in both vocabulary and
setting. In this work, we introduce a scalable video-to-speech framework
consisting of two components: a video-to-spectrogram predictor and a
pre-trained neural vocoder, which converts the mel-frequency spectrograms into
waveform audio. We achieve state-of-the art results for GRID and considerably
outperform previous approaches on LRW. More importantly, by focusing on
spectrogram prediction using a simple feedforward model, we can efficiently and
effectively scale our method to very large and unconstrained datasets: To the
best of our knowledge, we are the first to show intelligible results on the
challenging LRS3 dataset.


title: Carleman estimates for sub-Laplacians on Carnot groups

abstract:
  In this note, we establish a new Carleman estimate with singular weights for
the sub-Laplacian on a Carnot group $\mathbb G$ for functions satisfying the
discrepancy assumption in (2.16) below. We use such an estimate to derive a
sharp vanishing order estimate for solutions to stationary Schr\"odinger
equations.


title: Estimation of Standard Auction Models

abstract:
  We provide efficient estimation methods for first- and second-price auctions
under independent (asymmetric) private values and partial observability. Given
a finite set of observations, each comprising the identity of the winner and
the price they paid in a sequence of identical auctions, we provide algorithms
for non-parametrically estimating the bid distribution of each bidder, as well
as their value distributions under equilibrium assumptions. We provide
finite-sample estimation bounds which are uniform in that their error rates do
not depend on the bid/value distributions being estimated. Our estimation
guarantees advance a body of work in Econometrics wherein only identification
results have been obtained, unless the setting is symmetric, parametric, or all
bids are observable. Our guarantees also provide computationally and
statistically effective alternatives to classical techniques from reliability
theory. Finally, our results are immediately applicable to Dutch and English
auctions.


title:
Creating Teams of Simple Agents for Specified Tasks: A Computational
  Complexity Perspective

abstract:
  Teams of interacting and co-operating agents have been proposed as an
efficient and robust alternative to monolithic centralized control for carrying
out specified tasks in a variety of applications. A number of different team
and agent architectures have been investigated, e.g., teams based on single vs
multiple behaviorally-distinct types of agents (homogeneous vs heterogeneous
teams), simple vs complex agents, direct vs indirect agent-to-agent
communication. A consensus is emerging that (1) heterogeneous teams composed of
simple agents that communicate indirectly are preferable and (2) automated
methods for verifying and designing such teams are necessary. In this paper, we
use computational complexity analysis to assess viable algorithmic options for
such automated methods for various types of teams. Building on recent
complexity analyses addressing related questions in swarm robotics, we prove
that automated team verification and design are by large both exact and
approximate polynomial-time intractable in general for the most basic types of
homogeneous and heterogeneous teams consisting of simple agents that
communicate indirectly. Our results suggest that tractability for these
problems must be sought relative to additional restrictions on teams, agents,
operating environments, and tasks.


title:
Mechanical coupling effects of 2D lattices uncovered by decoupled
  micropolar elasticity tensor and symmetry operation

abstract:
  Mechanical couplings such as axial-shear and axial-bending have great
potential in the design of active mechanical metamaterials with directional
control of input and output loads in sensors and actuators. However, the
current ad hoc design of mechanical coupling without theoretical support of
elasticity cannot provide design guidelines for mechanical coupling with
lattice geometries. Moreover, the correlation between mechanical coupling
effects and geometric symmetry is not yet clearly understood. In this work, we
systematically search for all possible mechanical couplings in 2D lattice
structures by determining the non-zero diagonal terms in the decomposed
micropolar elasticity tensor. We also correlate the mechanical couplings with
the point-group symmetry of 2D lattices by applying the symmetry operation to
the decomposed micropolar elasticity tensor. The decoupled micropolar
constitutive equation uncovers eight coupling effects for 2D lattice
structures. The symmetry operation of the decoupled micropolar elasticity
tensor reveals the correlation of the mechanical coupling with the point
groups. Our findings can strengthen the design of mechanical metamaterials with
potential applications in areas including sensors, actuators, soft robots, and
active metamaterials for elastic/acoustic wave guidance and thermal management.


title: Diffusive search with resetting via the Brownian bridge

abstract:
  Fix $D>0$. For $r>0$, let $X^{(r)}(\cdot)$ be a Brownian motion with
diffusion coefficient $D$, equipped with an exponential clock with rate $r$, so
that when the clock rings the process jumps to the origin, where it resets and
begins anew. Denote expectations with respect to this process by $E_0^{(r)}$.
  This process, by now rather well-studied, is called Brownian motion with
resetting.
  For $T>0$, consider also a process $X^{\text{bb};T}(\cdot)$ that performs a
Brownian bridge with diffusion coefficient $D$ and bridge interval $T$, and
then at time $T$ resets and starts anew from the origin. Denote expectations
with respect to this process by $E_0^{\text{bb};T}$. The two resetting
processes, one with jumps and the other continuous, search for a random target
$a\in\mathbb{R}$ that has a known distribution $\mu$. Letting $\tau_a$ denote
the hitting time of $a$, the expected time to locate the target is
$\int_{\mathbb{R}}\big(E_0^{(r)}\tau_a\big)\mu(da)$ for the first process and
$\int_{\mathbb{R}}\big(E_0^{\text{bb};T}\tau_a\big)\mu(da)$ for the second
process. It is known that $E_0^{(r)}\tau_a=\frac{e^{\sqrt{\frac{2r}D}\thinspace
|a|}-1}r$. We calculate $E_0^{\text{bb};T}\tau_a$. Then we calculate
$\int_{\mathbb{R}}\big(E_0^{(r)}\tau_a\big)\mu(da)$ and
$\int_{\mathbb{R}}\big(E_0^{\text{bb};T}\tau_a\big)\mu(da)$ in the case that
$\mu$ is a centered Gaussian distribution with variance $\sigma^2$, which is
the mean-squared distance of the target from the origin. Finally, we compare
$\inf_{r>0}\int_{\mathbb{R}}\big(E_0^{(r)}\tau_a\big)\mu(da)$ to
$\inf_{T>0}\int_{\mathbb{R}}\big(E_0^{\text{bb};T}\tau_a\big)\mu(da)$,
obtaining
$\inf_{r>0}\int_{\mathbb{R}}\big(E_0^{(r)}\tau_a\big)\mu(da)\approx3.548\frac{\sigma^2}D$
and
$\inf_{T>0}\int_{\mathbb{R}}\big(E_0^{\text{bb};T}\tau_a\big)\mu(da)\approx4.847\frac{\sigma^2}D$.


title:
Updated constraints on Georgi-Machacek model, and its electroweak phase
  transition and associated gravitational waves

abstract:
  With theoretical constraints such as perturbative unitarity and vacuum
stability conditions and updated experimental data of Higgs measurements and
direct searches for exotic scalars at the LHC, we perform an updated scan of
the allowed parameter space of the Georgi-Machacek (GM) model. With the refined
global fit, we examine the allowed parameter space for inducing strong
first-order electroweak phase transitions (EWPTs) and find only the one-step
phase transition is phenomenologically viable. Based upon the result, we study
the associated gravitational wave (GW) signals and find most of which can be
detected by several proposed experiments. We also make predictions on processes
that may serve as promising probes to the GM model in the near future at the
LHC, including the di-Higgs productions and several exotic scalar production
channels.


title:
Mobile-URSONet: an Embeddable Neural Network for Onboard Spacecraft Pose
  Estimation

abstract:
  Spacecraft pose estimation is an essential computer vision application that
can improve the autonomy of in-orbit operations. An ESA/Stanford competition
brought out solutions that seem hardly compatible with the constraints imposed
on spacecraft onboard computers. URSONet is among the best in the competition
for its generalization capabilities but at the cost of a tremendous number of
parameters and high computational complexity. In this paper, we propose
Mobile-URSONet: a spacecraft pose estimation convolutional neural network with
178 times fewer parameters while degrading accuracy by no more than four times
compared to URSONet.


title:
Biembeddings of Archdeacon type: their full automorphism group and their
  number

abstract:
  Archdeacon, in his seminal paper $[1]$, defined the concept of Heffter array
in order to provide explicit constructions of $\mathbb{Z}_{v}$-regular
biembeddings of complete graphs $K_v$ into orientable surfaces.
  In this paper, we first introduce the quasi-Heffter arrays as a
generalization of the concept of Heffer array and we show that, in this
context, we can define a $2$-colorable embedding of Archdeacon type of the
complete multipartite graph $K_{\frac{v}{t}\times t}$ into an orientable
surface. Then, our main goal is to study the full automorphism groups of these
embeddings: here we are able to prove, using a probabilistic approach, that,
almost always, this group is exactly $\mathbb{Z}_{v}$.
  As an application of this result, given a positive integer $t\not\equiv
0\pmod{4}$, we prove that there are, for infinitely many pairs of $v$ and $k$,
at least $(1-o(1)) \frac{(\frac{v-t}{2})!}{\phi(v)} $ non-isomorphic
biembeddings of $K_{\frac{v}{t}\times t}$ whose face lengths are multiples of
$k$. Here $\phi(\cdot)$ denotes the Euler's totient function. Moreover, in case
$t=1$ and $v$ is a prime, almost all these embeddings define faces that are all
of the same length $kv$, i.e. we have a more than exponential number of
non-isomorphic $kv$-gonal biembeddings of $K_{v}$.


title:
Topological and homological properties of the orbit space of a simple
  three-dimensional compact linear Lie group

abstract:
  The article is devoted to the question whether the orbit space of a compact
linear group is a topological manifold and a homological manifold. In the
paper, the case of a simple three-dimensional group is considered. An upper
bound is obtained for the sum of the half-dimension integral parts of the
irreducible components of a representation whose quotient space is a
homological manifold, that enhances an earlier result giving the same bound if
the quotient space of a representation is a smooth manifold. The most of the
representations satisfying this bound are also researched before. In the
proofs, standard arguments from linear algebra, theory of Lie groups and
algebras and their representations are used.


title: Compositional Task-Oriented Parsing as Abstractive Question Answering

abstract:
  Task-oriented parsing (TOP) aims to convert natural language into
machine-readable representations of specific tasks, such as setting an alarm. A
popular approach to TOP is to apply seq2seq models to generate linearized parse
trees. A more recent line of work argues that pretrained seq2seq models are
better at generating outputs that are themselves natural language, so they
replace linearized parse trees with canonical natural-language paraphrases that
can then be easily translated into parse trees, resulting in so-called
naturalized parsers. In this work we continue to explore naturalized semantic
parsing by presenting a general reduction of TOP to abstractive question
answering that overcomes some limitations of canonical paraphrasing.
Experimental results show that our QA-based technique outperforms
state-of-the-art methods in full-data settings while achieving dramatic
improvements in few-shot settings.


title:
Dual Branch Neural Network for Sea Fog Detection in Geostationary Ocean
  Color Imager

abstract:
  Sea fog significantly threatens the safety of maritime activities. This paper
develops a sea fog dataset (SFDD) and a dual branch sea fog detection network
(DB-SFNet). We investigate all the observed sea fog events in the Yellow Sea
and the Bohai Sea (118.1{\deg}E-128.1{\deg}E, 29.5{\deg}N-43.8{\deg}N) from
2010 to 2020, and collect the sea fog images for each event from the
Geostationary Ocean Color Imager (GOCI) to comprise the dataset SFDD. The
location of the sea fog in each image in SFDD is accurately marked. The
proposed dataset is characterized by a long-time span, large number of samples,
and accurate labeling, that can substantially improve the robustness of various
sea fog detection models. Furthermore, this paper proposes a dual branch sea
fog detection network to achieve accurate and holistic sea fog detection. The
poporsed DB-SFNet is composed of a knowledge extraction module and a dual
branch optional encoding decoding module. The two modules jointly extracts
discriminative features from both visual and statistical domain. Experiments
show promising sea fog detection results with an F1-score of 0.77 and a
critical success index of 0.63. Compared with existing advanced deep learning
networks, DB-SFNet is superior in detection performance and stability,
particularly in the mixed cloud and fog areas.


title: DeepPortraitDrawing: Generating Human Body Images from Freehand Sketches

abstract:
  Researchers have explored various ways to generate realistic images from
freehand sketches, e.g., for objects and human faces. However, how to generate
realistic human body images from sketches is still a challenging problem. It
is, first because of the sensitivity to human shapes, second because of the
complexity of human images caused by body shape and pose changes, and third
because of the domain gap between realistic images and freehand sketches. In
this work, we present DeepPortraitDrawing, a deep generative framework for
converting roughly drawn sketches to realistic human body images. To encode
complicated body shapes under various poses, we take a local-to-global
approach. Locally, we employ semantic part auto-encoders to construct
part-level shape spaces, which are useful for refining the geometry of an input
pre-segmented hand-drawn sketch. Globally, we employ a cascaded spatial
transformer network to refine the structure of body parts by adjusting their
spatial locations and relative proportions. Finally, we use a global synthesis
network for the sketch-to-image translation task, and a face refinement network
to enhance facial details. Extensive experiments have shown that given roughly
sketched human portraits, our method produces more realistic images than the
state-of-the-art sketch-to-image synthesis techniques.


title:
ANUBIS: Review and Benchmark Skeleton-Based Action Recognition Methods
  with a New Dataset

abstract:
  Skeleton-based action recognition, as a subarea of action recognition, is
swiftly accumulating attention and popularity. The task is to recognize actions
performed by human articulation points. Compared with other data modalities, 3D
human skeleton representations have extensive unique desirable characteristics,
including succinctness, robustness, racial-impartiality, and many more. We aim
to provide a roadmap for new and existing researchers a on the landscapes of
skeleton-based action recognition for new and existing researchers. To this
end, we present a review in the form of a taxonomy on existing works of
skeleton-based action recognition. We partition them into four major
categories: (1) datasets; (2) extracting spatial features; (3) capturing
temporal patterns; (4) improving signal quality. For each method, we provide
concise yet informatively-sufficient descriptions. To promote more fair and
comprehensive evaluation on existing approaches of skeleton-based action
recognition, we collect ANUBIS, a large-scale human skeleton dataset. Compared
with previously collected dataset, ANUBIS are advantageous in the following
four aspects: (1) employing more recently released sensors; (2) containing
novel back view; (3) encouraging high enthusiasm of subjects; (4) including
actions of the COVID pandemic era. Using ANUBIS, we comparably benchmark
performance of current skeleton-based action recognizers. At the end of this
paper, we outlook future development of skeleton-based action recognition by
listing several new technical problems. We believe they are valuable to solve
in order to commercialize skeleton-based action recognition in the near future.
The dataset of ANUBIS is available at:
http://hcc-workshop.anu.edu.au/webs/anu101/home.


title: Gamma-Ray Polarimetry

abstract:
  While the scientific potential of high-energy X-ray and gamma-ray polarimetry
has long been recognized, measuring the polarization of high-energy photons is
challenging. To date, there has been very few significant detections from an
astrophysical source. However, recent technological developments raise the
possibility that this may change in the not-too-distant future. Significant
progress has been made in the development of Gamma-ray Burst (GRB) polarimeters
and polarization sensitive Compton telescopes. A second-generation dedicated
GRB polarimeter, POLAR-2, is under development for launch in 2024, and COSI a
second-generation polarization sensitive Compton Telescope has been selected by
NASA for launch in 2025. This chapter reviews basic concepts and experimental
approaches of scattering polarimetry of hard X-rays to MeV {\gamma}-rays, and
pair production polarimetry of higher-energy photons


title:
Integrable generalized Heisenberg ferromagnet equations in 1+1
  dimensions: reductions and gauge equivalence

abstract:
  The present work addresses the study and characterization of the
integrability of some generalized Heisenberg ferromagnet equations (GHFE) in
1+1 dimensions. Lax representations for these GHFE are successfully obtained.
The gauge equivalent counterparts of these integrable GHFE are presented.


title: Subexponentialiy of densities of infinitely divisible distributions

abstract:
  We show the equivalence of three properties for an infinitely divisible
distribution: the subexponentiality of the density, the subexponentiality of
the density of its L\'evy measure and the tail equivalence between the density
and its L\'evy measure density, under monotonic-type assumptions on the L\'evy
measure. The key assumption is that tail of the L\'evy measure density is
asymptotic to a non-increasing function or is eventually non-increasing. Our
conditions are novel and cover a rather wide class of infinitely divisible
distributions. Several significant properties for analyzing the
subexponentiality of densities have been derived such as closure properties of
[ convolution, convolution roots and asymptotic equivalence ] and the
factorization property. Moreover, we illustrate that the results are applicable
for developing the statistical inference of subexponential infinitely divisible
distributions which are absolutely continuous.


title: Learning, fast and slow

abstract:
  Animals can learn efficiently from a single experience and change their
future behavior in response. However, in other instances, animals learn very
slowly, requiring thousands of experiences. Here I survey tasks involving fast
and slow learning and consider some hypotheses for what differentiates the
underlying neural mechanisms. It has been proposed that fast learning relies on
neural representations that favor efficient Hebbian modification of synapses.
These efficient representations may be encoded in the genome, resulting in a
repertoire of fast learning that differs across species. Alternatively, the
required neural representations may be acquired from experience through a slow
process of unsupervised learning from the environment.


title: Radial oscillations in neutron stars from unified equation of states

abstract:
  We study radial oscillations in non-rotating neutron stars by considering the
unified equation of states (EoSs), which is consistent with finite nuclei and
nuclear matter properties and supports the $\ge 2M_\odot$ star criterion. We
solve the Sturm-Liouville problem to compute oscillation of $f$-mode frequency
and their eigenfunction for neutron star modelled with six selected unified
EoSs from two distinct Skyrme-Hartree Fock and Relativistic Mean-Field models.
The calculated eigenfunction reveals the damped harmonic motion varying with
the frequencies corresponding to EoS and with different central densities. We
check the variation of $f$-mode frequency of oscillation with different
quantities like central density, mass, average density and compactness of the
neutron star. In particular, we derived an empirical relation for the frequency
of $f$-mode scaled with central density as a function of the square of the
average density and discovered a linear trend.


title:
Holographic description of the dissipative model of universe with
  curvature

abstract:
  We investigate the accelerated expansion of the late-time universe in the
Friedmann-Robertson-Walker metric with nonzero curvature, applying a
holographic principle based on a generalized holographic dark energy model
introduced by Nojiri and Odintsov (2005,2006). We describe the evolution of the
universe using a generalized equation of state in the presence of a viscous
fluid. Solutions of the gravitational equation of motions are obtained in
explicit form for a constant value of the thermodynamic parameter, and for
various forms of the bulk viscosity. We calculate analytic expressions for
infrared cut-offs in terms of the particle horizon, and derive the energy
conservation law in the holographic picture. We show that the inclusion of
nonzero curvature in the Friedmann equation leads to the appearance of
additional singularities of type Big Rip in the Universe.


title: One-kpc Expanding Cylinder of HI-Gas around the Galactic Center

abstract:
  We report the discovery of an expanding cylinder of HI gas of radius 1 kpc
and vertical extent 800 pc by analyzing the 21-cm line survey data from the
literature. The cylinder is expanding at 150 km s$^{-1}$ and rotating at 100 km
s$^{-1}$, and is interpreted as due to a high-velocity conical wind at $\sim
180$ km s$^{-1}$ from the Galactic Center. The total mass of the cylinder is
estimated to be $\sim 8.5\times 10^5 M_\odot$ and kinetic energy $\sim 3\times
10^{53}$ ergs.


title: SDF-based RGB-D Camera Tracking in Neural Scene Representations

abstract:
  We consider the problem of tracking the 6D pose of a moving RGB-D camera in a
neural scene representation. Different such representations have recently
emerged, and we investigate the suitability of them for the task of camera
tracking. In particular, we propose to track an RGB-D camera using a signed
distance field-based representation and show that compared to density-based
representations, tracking can be sped up, which enables more robust and
accurate pose estimates when computation time is limited.


title: Formality of cochains on BG

abstract:
  Let $G$ be a compact Lie group with maximal torus $T$. If $|N_G(T)/T|$ is
invertible in the field $k$ then the algebra of cochains $C^*(BG;k)$ is formal
as an $A_\infty$ algebra, or equivalently as a DG algebra.


title:
Impact of the equation of state on $f$- and $p$- mode oscillations of
  neutron stars

abstract:
  We investigate the impact of the neutron-star matter equation of state on the
$f$- and $p_1$-mode oscillations of neutron stars obtained within the Cowling
approximation and linearized general relativity. The $f$- and $p_1$-mode
oscillation frequencies, and their damping times are calculated using
representative sets of Skyrme Hartree-Fock and relativistic mean-field models,
all of which reproduce nuclear systematics and support $2M_\odot$ neutron
stars. Our study shows strong correlations between the frequencies of $f$- and
$p_1$-modes and their damping times with the pressure of $\beta$-equilibrated
matter at densities equal to or slightly higher than the nuclear saturation
density $\rho_0$. Such correlations are found to be almost independent of the
composition of the stars. The frequency of the $p_1$-mode of $1.4M_\odot$ star
is strongly correlated with the slope of the symmetry energy $L_0$ and
$\beta$-equilibrated pressure at density $\rho_0$. Compared to GR calculations,
the error in the Cowling approximation for the $f$-mode is about 30\% for
neutron stars of low mass, whereas it decreases with increasing mass. The
accuracy of the $p_1$-mode is better than 15\% for neutron stars of maximum
mass, and improves for lower masses and higher number of radial nodes.


title: Persistence in Complex Systems

abstract:
  Persistence is an important characteristic of many complex systems in nature,
related to how long the system remains at a certain state before changing to a
different one. The study of complex systems' persistence involves different
definitions and uses different techniques, depending on whether short-term or
long-term persistence is considered. In this paper we discuss the most
important definitions, concepts, methods, literature and latest results on
persistence in complex systems. Firstly, the most used definitions of
persistence in short-term and long-term cases are presented. The most relevant
methods to characterize persistence are then discussed in both cases. A
complete literature review is also carried out. We also present and discuss
some relevant results on persistence, and give empirical evidence of
performance in different detailed case studies, for both short-term and
long-term persistence. A perspective on the future of persistence concludes the
work.


title: Optimization via Rejection-Free Partial Neighbor Search

abstract:
  Simulated Annealing using Metropolis steps at decreasing temperatures is
widely used to solve complex combinatorial optimization problems. To improve
its efficiency, we can use the Rejection-Free version of the Metropolis
algorithm which avoids the inefficiency of rejections by considering all the
neighbors at each step. To avoid the algorithm getting stuck in a local extreme
area, we propose an enhanced version of Rejection-Free called Partial Neighbor
Search, which only considers random part of the neighbors while applying the
Rejection-Free technique. In this paper, we apply these methods to several
examples such as quadratic unconstrained binary optimization (QUBO) problems to
demonstrate superior performance of the Rejection-Free Partial Neighbor Search
algorithm.


title: Video Extrapolation in Space and Time

abstract:
  Novel view synthesis (NVS) and video prediction (VP) are typically considered
disjoint tasks in computer vision. However, they can both be seen as ways to
observe the spatial-temporal world: NVS aims to synthesize a scene from a new
point of view, while VP aims to see a scene from a new point of time. These two
tasks provide complementary signals to obtain a scene representation, as
viewpoint changes from spatial observations inform depth, and temporal
observations inform the motion of cameras and individual objects. Inspired by
these observations, we propose to study the problem of Video Extrapolation in
Space and Time (VEST). We propose a model that leverages the self-supervision
and the complementary cues from both tasks, while existing methods can only
solve one of them. Experiments show that our method achieves performance better
than or comparable to several state-of-the-art NVS and VP methods on indoor and
outdoor real-world datasets.


title:
Does a PESQNet (Loss) Require a Clean Reference Input? The Original PESQ
  Does, But ACR Listening Tests Don't

abstract:
  Perceptual evaluation of speech quality (PESQ) requires a clean speech
reference as input, but predicts the results from (reference-free) absolute
category rating (ACR) tests. In this work, we train a fully convolutional
recurrent neural network (FCRN) as deep noise suppression (DNS) model, with
either a non-intrusive or an intrusive PESQNet, where only the latter has
access to a clean speech reference. The PESQNet is used as a mediator providing
a perceptual loss during the DNS training to maximize the PESQ score of the
enhanced speech signal. For the intrusive PESQNet, we investigate two
topologies, called early-fusion (EF) and middle-fusion (MF) PESQNet, and
compare to the non-intrusive PESQNet to evaluate and to quantify the benefits
of employing a clean speech reference input during DNS training. Detailed
analyses show that the DNS trained with the MF-intrusive PESQNet outperforms
the Interspeech 2021 DNS Challenge baseline and the same DNS trained with an
MSE loss by 0.23 and 0.12 PESQ points, respectively. Furthermore, we can show
that only marginal benefits are obtained compared to the DNS trained with the
non-intrusive PESQNet. Therefore, as ACR listening tests, the PESQNet does not
necessarily require a clean speech reference input, opening the possibility of
using real data for DNS training.


title: Intrinsically Lipschitz sections and applications to metric groups

abstract:
  We introduce the notion of intrinsically Lipschitz graphs to metric spaces.
We generalize the notion of intrinsically Lipschitz graphs in the sense of
Franchi, Serapioni and Serra Cassano. We do this by focusing our attention on
the graphs property instead of the maps. More precisely, we prove some relevant
results as the Ascoli-Arzel\`a compactness Theorem, Ahlfors regularity and the
Extension Theorem for the intrinsically Lipschitz sections.


title: Hypercomplex Image-to-Image Translation

abstract:
  Image-to-image translation (I2I) aims at transferring the content
representation from an input domain to an output one, bouncing along different
target domains. Recent I2I generative models, which gain outstanding results in
this task, comprise a set of diverse deep networks each with tens of million
parameters. Moreover, images are usually three-dimensional being composed of
RGB channels and common neural models do not take dimensions correlation into
account, losing beneficial information. In this paper, we propose to leverage
hypercomplex algebra properties to define lightweight I2I generative models
capable of preserving pre-existing relations among image dimensions, thus
exploiting additional input information. On manifold I2I benchmarks, we show
how the proposed Quaternion StarGANv2 and parameterized hypercomplex StarGANv2
(PHStarGANv2) reduce parameters and storage memory amount while ensuring high
domain translation performance and good image quality as measured by FID and
LPIPS scores. Full code is available at: https://github.com/ispamm/HI2I.


title:
A leptoquark and vector-like quark extended model for the simultaneous
  explanation of the $W$ boson mass and muon $g-2$ anomalies

abstract:
  The CDF collaboration has announced a new measurement result of the $W$ boson
mass, and it is in tension with the standard model (SM) prediction. In this
paper, we will explain this anomaly in a vector-like quark (VLQ) and leptoquark
(LQ) extended model, which is proposed before the CDF result in our previous
work.


title:
A New Dimensionality Reduction Method Based on Hensel's Compression for
  Privacy Protection in Federated Learning

abstract:
  Differential privacy (DP) is considered a de-facto standard for protecting
users' privacy in data analysis, machine, and deep learning. Existing DP-based
privacy-preserving training approaches consist of adding noise to the clients'
gradients before sharing them with the server. However, implementing DP on the
gradient is not efficient as the privacy leakage increases by increasing the
synchronization training epochs due to the composition theorem. Recently
researchers were able to recover images used in the training dataset using
Generative Regression Neural Network (GRNN) even when the gradient was
protected by DP. In this paper, we propose two layers of privacy protection
approach to overcome the limitations of the existing DP-based approaches. The
first layer reduces the dimension of the training dataset based on Hensel's
Lemma. We are the first to use Hensel's Lemma for reducing the dimension (i.e.,
compress) of a dataset. The new dimensionality reduction method allows reducing
the dimension of a dataset without losing information since Hensel's Lemma
guarantees uniqueness. The second layer applies DP to the compressed dataset
generated by the first layer. The proposed approach overcomes the problem of
privacy leakage due to composition by applying DP only once before the
training; clients train their local model on the privacy-preserving dataset
generated by the second layer. Experimental results show that the proposed
approach ensures strong privacy protection while achieving good accuracy. The
new dimensionality reduction method achieves an accuracy of 97%, with only 25 %
of the original data size.


title: Improve Discourse Dependency Parsing with Contextualized Representations

abstract:
  Recent works show that discourse analysis benefits from modeling intra- and
inter-sentential levels separately, where proper representations for text units
of different granularities are desired to capture both the meaning of text
units and their relations to the context. In this paper, we propose to take
advantage of transformers to encode contextualized representations of units of
different levels to dynamically capture the information required for discourse
dependency analysis on intra- and inter-sentential levels. Motivated by the
observation of writing patterns commonly shared across articles, we propose a
novel method that treats discourse relation identification as a sequence
labelling task, which takes advantage of structural information from the
context of extracted discourse trees, and substantially outperforms traditional
direct-classification methods. Experiments show that our model achieves
state-of-the-art results on both English and Chinese datasets.


title:
Gravitational Waves Constraint over Asymptotic Inflation in a Nonminimal
  Derivative Coupling Cosmology

abstract:
  In this paper, we review the nonminimal derivative coupling (NDC) between
gravity and a scalar field, in face of the constraint imposed by the detection
of gravitational waves. We study in detail the relation between the asymptotic
inflationary solution obtained from NDC and its relation with the constraint,
by means of a dynamical system analysis. We thus show that such solutions are
incompatible with the given constraints.


title: Learning Abstract and Transferable Representations for Planning

abstract:
  We are concerned with the question of how an agent can acquire its own
representations from sensory data. We restrict our focus to learning
representations for long-term planning, a class of problems that
state-of-the-art learning methods are unable to solve. We propose a framework
for autonomously learning state abstractions of an agent's environment, given a
set of skills. Importantly, these abstractions are task-independent, and so can
be reused to solve new tasks. We demonstrate how an agent can use an existing
set of options to acquire representations from ego- and object-centric
observations. These abstractions can immediately be reused by the same agent in
new environments. We show how to combine these portable representations with
problem-specific ones to generate a sound description of a specific task that
can be used for abstract planning. Finally, we show how to autonomously
construct a multi-level hierarchy consisting of increasingly abstract
representations. Since these hierarchies are transferable, higher-order
concepts can be reused in new tasks, relieving the agent from relearning them
and improving sample efficiency. Our results demonstrate that our approach
allows an agent to transfer previous knowledge to new tasks, improving sample
efficiency as the number of tasks increases.


title:
A Novel Fully Annotated Thermal Infrared Face Dataset: Recorded in
  Various Environment Conditions and Distances From The Camera

abstract:
  Facial thermography is one of the most popular research areas in infrared
thermal imaging, with diverse applications in medical, surveillance, and
environmental monitoring. However, in contrast to facial imagery in the visual
spectrum, the lack of public datasets on facial thermal images is an obstacle
to research improvement in this area. Thermal face imagery is still a
relatively new research area to be evaluated and studied in different
domains.The current thermal face datasets are limited in regards to the
subjects' distance from the camera, the ambient temperature variation, and
facial landmarks' localization. We address these gaps by presenting a new
facial thermography dataset. This article makes two main contributions to the
body of knowledge. First, it presents a comprehensive review and comparison of
current public datasets in facial thermography. Second, it introduces and
studies a novel public dataset on facial thermography, which we call it
Charlotte-ThermalFace. Charlotte-ThermalFace contains more than10000 infrared
thermal images in varying thermal conditions, several distances from the
camera, and different head positions. The data is fully annotated with the
facial landmarks, ambient temperature, relative humidity, the air speed of the
room, distance to the camera, and subject thermal sensation at the time of
capturing each image. Our dataset is the first publicly available thermal
dataset annotated with the thermal sensation of each subject in different
thermal conditions and one of the few datasets in raw 16-bit format. Finally,
we present a preliminary analysis of the dataset to show the applicability and
importance of the thermal conditions in facial thermography. The full dataset,
including annotations, are freely available for research purpose at
https://github.com/TeCSAR-UNCC/UNCC-ThermalFace


title:
Representatives of similarity classes of matrices over PIDs
  corresponding to ideal classes

abstract:
  For a principal ideal domain $A$, the Latimer--MacDuffee correspondence sets
up a bijection between the similarity classes of matrices in
$\operatorname{M}_{n}(A)$ with irreducible characteristic polynomial $f(x)$ and
the ideal classes of the order $A[x]/(f(x))$. We prove that when $A[x]/(f(x))$
is maximal (i.e., integrally closed, i.e., a Dedekind domain), then every
similarity class contains a representative that is, in a sense, close to being
a companion matrix. The first step in the proof is to show that any similarity
class corresponding to an ideal (not necessarily prime) of degree one contains
a representative of the desired form. The second step is a previously
unpublished result due to Lenstra that implies that when $A[x]/(f(x))$ is
maximal, every ideal class contains an ideal of degree one.


title:
qLEET: Visualizing Loss Landscapes, Expressibility, Entangling Power and
  Training Trajectories for Parameterized Quantum Circuits

abstract:
  We present qLEET, an open-source Python package for studying parameterized
quantum circuits (PQCs), which are widely used in various variational quantum
algorithms (VQAs) and quantum machine learning (QML) algorithms. qLEET enables
computation of properties such as expressibility and entangling power of a PQC
by studying its entanglement spectrum and the distribution of parameterized
states produced by it. Furthermore, it allows users to visualize the training
trajectories of PQCs along with high-dimensional loss landscapes generated by
them for different objective functions. It supports quantum circuits and noise
models built using popular quantum computing libraries such as Qiskit, Cirq,
and Pyquil. In our work, we demonstrate how qLEET provides opportunities to
design and improve hybrid quantum-classical algorithms by utilizing intuitive
insights from the ansatz capability and structure of the loss landscape.


title: Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets

abstract:
  Wearable and IoT devices requiring positioning and localisation services grow
in number exponentially every year. This rapid growth also produces millions of
data entries that need to be pre-processed prior to being used in any indoor
positioning system to ensure the data quality and provide a high Quality of
Service (QoS) to the end-user. In this paper, we offer a novel and
straightforward data cleansing algorithm for WLAN fingerprinting radio maps.
This algorithm is based on the correlation among fingerprints using the
Received Signal Strength (RSS) values and the Access Points (APs)'s identifier.
We use those to compute the correlation among all samples in the dataset and
remove fingerprints with low level of correlation from the dataset. We
evaluated the proposed method on 14 independent publicly-available datasets. As
a result, an average of 14% of fingerprints were removed from the datasets. The
2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with
a slight increase in the floor hit rate by 1.2% on average. Consequently, the
average speed of position prediction was also increased by 14%.


title: Singularities of Frontal Surfaces

abstract:
  We consider singularities of frontal surfaces of corank one and finite
frontal codimension. We look at the classification under left-right-equivalence
and introduce the notion of frontalisation for singularities of fold type. We
define the cuspidal and the transverse double point curves and prove that the
frontal has finite codimension if and only if both curves are reduced. Finally,
we also discuss about the frontal versions of the Marar-Mond formulas and the
Mond's conjecture.


title:
A nonsingular spherically symmetric black-hole model with holonomy
  corrections

abstract:
  We present a covariant model of a spherically symmetric black hole with
corrections motivated by loop quantum gravity. The effective modifications,
parametrized by a positive constant $\lambda$, are implemented through a
canonical transformation and a linear combination of the constraints of general
relativity, in such a way that the theory remains free of anomalies and general
relativity is recovered for $\lambda=0$. In addition, the corresponding metric
is constructed in a fully covariant way to ensure that gauge transformations on
phase space correspond to coordinate changes. The solution for each gauge
choice provides a chart and corresponding line element of a spacetime solution
whose geometry is unambiguously determined in terms of the parameter $\lambda$
and a constant of motion $m$. For positive values of $m$, the solution is
asymptotically flat and contains a globally hyperbolic black-hole/white-hole
region with a minimal spacelike hypersurface that replaces the Schwarzschild
singularity. The corresponding exterior regions are isometric and, in
particular, allow the computation of the ADM mass. The procedure to obtain the
global causal structure of the solution yields, also, its maximal analytic
extension.


title:
Asymptotic Autonomy of Random Attractors in Regular Spaces for
  Non-autonomous Stochastic Navier-Stokes Equations

abstract:
  This article concerns the long-term random dynamics in regular spaces for a
non-autonomous Navier-Stokes equation defined on a bounded smooth domain
$\mathcal{O}$ driven by multiplicative and additive noise. For the two kinds of
noise driven equations, we demonstrate the existence of a unique pullback
attractor which is backward compact and asymptotically autonomous in
$\mathbb{L}^2(\mathcal{O})$ and $\mathbb{H}_0^1(\mathcal{O})$, respectively.
The backward-uniform flattening property of the solution is used to prove the
backward-uniform pullback asymptotic compactness of the non-autonomous random
dynamical systems in the regular space $\mathbb{H}_0^1(\mathcal{O})$.


title:
MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time
  Series

abstract:
  In this paper, we introduce Masked Anomaly Detection (MAD), a general
self-supervised learning task for multivariate time series anomaly detection.
With the increasing availability of sensor data from industrial systems, being
able to detecting anomalies from streams of multivariate time series data is of
significant importance. Given the scarcity of anomalies in real-world
applications, the majority of literature has been focusing on modeling
normality. The learned normal representations can empower anomaly detection as
the model has learned to capture certain key underlying data regularities. A
typical formulation is to learn a predictive model, i.e., use a window of time
series data to predict future data values. In this paper, we propose an
alternative self-supervised learning task. By randomly masking a portion of the
inputs and training a model to estimate them using the remaining ones, MAD is
an improvement over the traditional left-to-right next step prediction (NSP)
task. Our experimental results demonstrate that MAD can achieve better anomaly
detection rates over traditional NSP approaches when using exactly the same
neural network (NN) base models, and can be modified to run as fast as NSP
models during test time on the same hardware, thus making it an ideal upgrade
for many existing NSP-based NN anomaly detection models.


title: Dynamic Sparse R-CNN

abstract:
  Sparse R-CNN is a recent strong object detection baseline by set prediction
on sparse, learnable proposal boxes and proposal features. In this work, we
propose to improve Sparse R-CNN with two dynamic designs. First, Sparse R-CNN
adopts a one-to-one label assignment scheme, where the Hungarian algorithm is
applied to match only one positive sample for each ground truth. Such
one-to-one assignment may not be optimal for the matching between the learned
proposal boxes and ground truths. To address this problem, we propose dynamic
label assignment (DLA) based on the optimal transport algorithm to assign
increasing positive samples in the iterative training stages of Sparse R-CNN.
We constrain the matching to be gradually looser in the sequential stages as
the later stage produces the refined proposals with improved precision. Second,
the learned proposal boxes and features remain fixed for different images in
the inference process of Sparse R-CNN. Motivated by dynamic convolution, we
propose dynamic proposal generation (DPG) to assemble multiple proposal experts
dynamically for providing better initial proposal boxes and features for the
consecutive training stages. DPG thereby can derive sample-dependent proposal
boxes and features for inference. Experiments demonstrate that our method,
named Dynamic Sparse R-CNN, can boost the strong Sparse R-CNN baseline with
different backbones for object detection. Particularly, Dynamic Sparse R-CNN
reaches the state-of-the-art 47.2% AP on the COCO 2017 validation set,
surpassing Sparse R-CNN by 2.2% AP with the same ResNet-50 backbone.


title: Concept Activation Vectors for Generating User-Defined 3D Shapes

abstract:
  We explore the interpretability of 3D geometric deep learning models in the
context of Computer-Aided Design (CAD). The field of parametric CAD can be
limited by the difficulty of expressing high-level design concepts in terms of
a few numeric parameters. In this paper, we use a deep learning architectures
to encode high dimensional 3D shapes into a vectorized latent representation
that can be used to describe arbitrary concepts. Specifically, we train a
simple auto-encoder to parameterize a dataset of complex shapes. To understand
the latent encoded space, we use the idea of Concept Activation Vectors (CAV)
to reinterpret the latent space in terms of user-defined concepts. This allows
modification of a reference design to exhibit more or fewer characteristics of
a chosen concept or group of concepts. We also test the statistical
significance of the identified concepts and determine the sensitivity of a
physical quantity of interest across the dataset.


title:
Efficient Accelerator for Dilated and Transposed Convolution with
  Decomposition

abstract:
  Hardware acceleration for dilated and transposed convolution enables real
time execution of related tasks like segmentation, but current designs are
specific for these convolutional types or suffer from complex control for
reconfigurable designs. This paper presents a design that decomposes input or
weight for dilated and transposed convolutions respectively to skip redundant
computations and thus executes efficiently on existing dense CNN hardware as
well. The proposed architecture can cut down 87.8\% of the cycle counts to
achieve 8.2X speedup over a naive execution for the ENet case.