title: Duality pairs, phantom maps, and definability in triangulated categories

abstract:
  We define duality triples and duality pairs in compactly generated
triangulated categories and investigate their properties. This enables us to
give an elementary way to determine whether a class is closed under pure
subobjects, pure quotients and pure extensions, as well as providing a way to
show the existence of approximations. One key ingredient is a new
characterisation of phantom maps. We pay particular attention to symmetric
(co)product closed duality pairs, which are shown to be nothing other than
(dual) definable categories. Localisations of duality pairs are considered,
alongside many examples that naturally arise in representation theory and
homotopy theory. It is shown that duality pairs abound; for example every class
of objects is shown to generate a duality pair.


title: Using Navigational Information to Learn Visual Representations

abstract:
  Children learn to build a visual representation of the world from
unsupervised exploration and we hypothesize that a key part of this learning
ability is the use of self-generated navigational information as a similarity
label to drive a learning objective for self-supervised learning. The goal of
this work is to exploit navigational information in a visual environment to
provide performance in training that exceeds the state-of-the-art
self-supervised training. Here, we show that using spatial and temporal
information in the pretraining stage of contrastive learning can improve the
performance of downstream classification relative to conventional contrastive
learning approaches that use instance discrimination to discriminate between
two alterations of the same image or two different images. We designed a
pipeline to generate egocentric-vision images from a photorealistic ray-tracing
environment (ThreeDWorld) and record relevant navigational information for each
image. Modifying the Momentum Contrast (MoCo) model, we introduced spatial and
temporal information to evaluate the similarity of two views in the pretraining
stage instead of instance discrimination. This work reveals the effectiveness
and efficiency of contextual information for improving representation learning.
The work informs our understanding of the means by which children might learn
to see the world without external supervision.


title: Unusual ferrimagnetic ground state in rhenium ferrite

abstract:
  Through comprehensive density functional calculations, we predict the
stability of a rhenium-based ferrite


title: A new approach to odd perfect numbers via GCDs

abstract:
  Let $q^k n^2$ be an odd perfect number with special prime $q$. Define the
GCDs $$G = \gcd\bigg(\sigma(q^k),\sigma(n^2)\bigg)$$ $$H =
\gcd\bigg(n^2,\sigma(n^2)\bigg)$$ and $$I = \gcd\bigg(n,\sigma(n^2)\bigg).$$ We
prove that $G \times H = I^2$. (Note that it is trivial to show that $G \mid I$
and $I \mid H$ both hold.) We then compute expressions for $G, H,$ and $I$ in
terms of $\sigma(q^k)/2, n,$ and $\gcd\bigg(\sigma(q^k)/2,n\bigg)$. Afterwards,
we prove that if $G = H = I$, then $\sigma(q^k)/2$ is not squarefree. Other
natural and related results are derived further. Lastly, we conjecture that the
set $$\mathscr{A} = \{m : \gcd(m,\sigma(m^2))=\gcd(m^2,\sigma(m^2))\}$$ has
asymptotic density zero.


title: Fair Division with Money and Prices

abstract:
  We must divide a finite number of indivisible goods and cash transfers
between agents with quasi-linear but otherwise arbitrary utilities over the
subsets of goods. We compare two division rules with cognitively feasible and
privacy preserving individual messages. In Sell&Buy agents bid for the role of
Seller or Buyer: with two agents the smallest bid defines the Seller who then
charges any a price constrained only by her winning bid. In Divide&Choose
agents bid for the role of Divider, then everyone bids on the shares of the
Divider's partition. S&B dominates D&C on two counts: its guaranteed utility in
the worst case rewards (resp. penalises) more subadditive (resp. superadditive)
utilities; playing safe is never ambiguous and is also better placed to collect
a larger share of the efficient surplus.


title:
Smart Cities, Smart Libraries and Smart Knowledge Managers: Ushering in
  the neo-Knowledge Society

abstract:
  The emergence of smart cities as a specific concept is not very old. In
simple terms, it refers to cities which are sustainable and driven
predominantly by their Information and Communication Technology (ICT)
infrastructure. Smart libraries and smart knowledge managers, alongside its
other smart component-entities, are vital for their emergence, sustenance and
progress. The paper attempts at deducing a symbiosis amongst smart cities,
smart libraries and smart knowledge managers. It further elaborates on how
these will usher in the neo-knowledge society, and the opportunities it'll
offer vis-\`a-vis Library and Information Science (LIS). Finally, it concludes
on an optimistic note, mentioning possible future research activities in this
regard.


title: The Parameterized Complexity of Quantum Verification

abstract:
  We initiate the study of parameterized complexity of $\textsf{QMA}$ problems
in terms of the number of non-Clifford gates in the problem description. We
show that for the problem of parameterized quantum circuit satisfiability,
there exists a classical algorithm solving the problem with a runtime scaling
exponentially in the number of non-Clifford gates but only polynomially with
the system size. This result follows from our main result, that for any
Clifford + $t$ $T$-gate quantum circuit satisfiability problem, the search
space of optimal witnesses can be reduced to a stabilizer subspace isomorphic
to at most $t$ qubits (independent of the system size). Furthermore, we derive
new lower bounds on the $T$-count of circuit satisfiability instances and the
$T$-count of the $W$-state assuming the classical exponential time hypothesis
($\textsf{ETH}$). Lastly, we explore the parameterized complexity of the
quantum non-identity check problem.


title: Descent and \'etale-Brauer obstructions for 0-cycles

abstract:
  We fill a gap in the theory of obstructions for 0-cycles by defining, in this
context, an analogue of the classical descent set for rational points on
varieties over number fields. This leads to, among other things, a definition
of the \'etale-Brauer obstruction set for 0-cycles, which we show is contained
in the Brauer-Manin set. We then transfer some tools and techniques used to
study the arithmetic of rational points into the setting of 0-cycles. For
example, we extend the strategy developed by Y. Liang, relating the arithmetic
of rational points over finite extensions of the base field to that of
0-cycles, to torsors. We give applications of our results to study the
arithmetic behaviour of 0-cycles for Enriques surfaces, torsors given by
(twisted) Kummer varieties, universal torsors, and torsors under tori.


title: Notes about collapse in magnetohydrodynamics

abstract:
  We discuss a problem about magnetic collapse as a possible process for
singularity formation of the magnetic field in a finite time within ideal
magneto-hydrodynamics for incompressible fluids. This process is very important
from the point of view of various astrophysical applications, in particular, as
a mechanism of magnetic filaments formation in the convective zone of the Sun.
The collapse possibility is connected with compressibility of continuously
distributed magnetic field lines. A well-known example of the formation of
magnetic filaments in the kinematic dynamo approximation with a given velocity
field, first considered by Parker in 1963, rather indicates that the increase
in the magnetic field is exponential in time. In the case of the kinematic
approximation for the induction equation, the magnetic filaments formation is
shown to occur in areas with a hyperbolic velocity profile.


title: Irreducibility of generalized Fibonacci polynomials

abstract:
  A second order polynomial sequence is of Fibonacci-type $\mathcal{F}_{n}$
(Lucas-type $\mathcal{L}_{n}$) if its Binet formula has a structure similar to
that for Fibonacci (Lucas) numbers. Under certain conditions these polynomials
are irreducible if and only if $n$ is a prime number. For example, the
Fibonacci polynomials, Pell polynomials, Fermat polynomials, Lucas polynomials,
Pell-Lucas polynomials, Fermat-Lucas polynomials are irreducible when $n$ is a
prime number; and Chebyshev polynomials (second kind), Morgan-Voyce polynomials
(Fibonacci type), and Vieta polynomials are reducible when $n$ is a prime
number.
  In this paper we give some theorems to determine whether the Fibonacci type
polynomials and Lucas type polynomials are irreducible when $n$ is prime.


title: Graph Partitions Under Average Degree Constraint

abstract:
  In this paper, we prove that every graph with average degree at least $s+t+2$
has a vertex partition into two parts, such that one part has average degree at
least $s$, and the other part has average degree at least $t$. This solves a
conjecture of Cs\'{o}ka, Lo, Norin, Wu and Yepremyan.


title: XFBoost: Improving Text Generation with Controllable Decoders

abstract:
  Multimodal conditionality in transformer-based natural language models has
demonstrated state-of-the-art performance in the task of product description
generation. Recent approaches condition a language model on one or more images
and other textual metadata to achieve near-human performance for describing
products from e-commerce stores. However, generated descriptions may exhibit
degrees of inaccuracy or even contradictory claims relative to the inputs of a
given product. In this paper, we propose a controllable language generation
framework called Extract-Finetune-Boost (XFBoost), which addresses the problem
of inaccurate low-quality inference. By using visual semantic attributes as
constraints at the decoding stage of the generation process and finetuning the
language model with policy gradient techniques, the XFBoost framework is found
to produce significantly more descriptive text with higher image relevancy,
outperforming baselines and lowering the frequency of factually inaccurate
descriptions. We further demonstrate the application of XFBoost to online
learning wherein human-in-the-loop critics improve language models with active
feedback.


title:
Processing the structure of documents: Logical Layout Analysis of
  historical newspapers in French

abstract:
  Background. In recent years, libraries and archives led important
digitisation campaigns that opened the access to vast collections of historical
documents. While such documents are often available as XML ALTO documents, they
lack information about their logical structure. In this paper, we address the
problem of Logical Layout Analysis applied to historical documents in French.
We propose a rule-based method, that we evaluate and compare with two
Machine-Learning models, namely RIPPER and Gradient Boosting. Our data set
contains French newspapers, periodicals and magazines, published in the first
half of the twentieth century in the Franche-Comt\'e Region. Results. Our
rule-based system outperforms the two other models in nearly all evaluations.
It has especially better Recall results, indicating that our system covers more
types of every logical label than the other two models. When comparing RIPPER
with Gradient Boosting, we can observe that Gradient Boosting has better
Precision scores but RIPPER has better Recall scores. Conclusions. The
evaluation shows that our system outperforms the two Machine Learning models,
and provides significantly higher Recall. It also confirms that our system can
be used to produce annotated data sets that are large enough to envisage
Machine Learning or Deep Learning approaches for the task of Logical Layout
Analysis. Combining rules and Machine Learning models into hybrid systems could
potentially provide even better performances. Furthermore, as the layout in
historical documents evolves rapidly, one possible solution to overcome this
problem would be to apply Rule Learning algorithms to bootstrap rule sets
adapted to different publication periods.


title: Admissible family for binary perfect polynomials

abstract:
  The paper is about an arithmetic problem in $\F_2[x]$. We give
\emph{admissible} (necessary) conditions satisfied by a set of odd prime
divisors of perfect polynomials over $\F_2$. This allows us to prove a new
characterization of \emph{all} known perfect polynomials, and to open a way of
finding more of them (if they exist).


title:
Baikov representations, intersection theory, and canonical Feynman
  integrals

abstract:
  The method of canonical differential equations is an important tool in the
calculation of Feynman integrals in quantum field theories. It has been
realized that the canonical bases are closely related to $d$-dimensional
$d\log$-form integrands. In this work, we introduce the concept of generalized
loop-by-loop Baikov representation, and clarify its relation and difference
with Feynman integrals using the language of intersection theory. We then
utilize the generalized Baikov representation to construct $d$-dimensional
$d\log$-form integrands, and discuss how to convert them to Feynman integrals.
We describe the technical details of our method, in particular how to deal with
the difficulties encountered in the construction procedure. Our method provides
a constructive approach to the problem of finding canonical bases of Feynman
integrals, and we demonstrate its applicability to complicated scattering
amplitudes involving multiple physical scales.


title:
Polymer translocation into an attractive channel: Iso-flux tension
  propagation theory and Langevin dynamics simulations

abstract:
  Iso-flux tension propagation (IFTP) theory and Langevin dynamics (LD)
simulations are employed to study the dynamics of channel-driven polymer
translocation in which a polymer translocates into a narrow channel and the
monomers in the channel experience a driving force $f_{\rm c}$. In the high
driving force limit, regardless of the channel width, IFTP theory predicts
$\tau\propto f_{\textrm{c}}^{\beta}$ for the translocation time, where
$\beta=-1$ is the force scaling exponent. Moreover, LD data show that for a
very narrow channel fitting only a single file of monomers, the entropic force
due to the subchain inside the channel does not play a significant role in the
translocation dynamics, and the force exponent $\beta= -1$ regardless of the
force magnitude. As the channel width increases the number of possible spatial
configurations of the subchain inside the channel becomes significant, and the
resulting entropic force causes the force exponent to drop below unity.


title:
Measures Determined by the Restriction of Convolution Powers to the
  Proper Concave Cone

abstract:
  Let $\mu$ and $\nu$ be two non-degenerate finite signed Borel measures
defined on a proper convex cone of $\mathbb{R}^n$. We prove that if all
convolution powers of $\mu$ and $\nu$ are appropriately equal (and non-zero) on
a proper concave cone of $\mathbb{R}^n$, the measures are equal. A similar but
more general result for measures defined on $\mathbb{R}$ can be found in [2].
We also provide an example of two-dimensional measures, which indicates that
equality of measures and their appropriate convolution powers on a half-plane
is not enough for equality of measures.


title: On strict extensional reflexivity in compact closed categories

abstract:
  This article studies the categorical setting of Abramsky, Haghverdi, and
Scott's untyped linear combinatory algebras, and relates this to more recent
work of Abramsky and Heunen on Frobenius algebras in the infinitary setting.
The key to this is extensional reflexivity (the property of an object being
isomorphic to its own internal hom. $N\cong [N\rightarrow N]$). We characterise
extensional reflexivity in compact closed categories, and consider how this may
be `strictified' to give monoidally equivalent categories where the
isomorphisms exhibiting reflexivity are identity arrows.
  This results in two-object compact closed categories consisting of a unit
object, and a non-unit extensionally reflexive object.
  We study the endomorphism monoids of such `strictly extensionally reflexive'
objects from an algebraic viewpoint. They necessarily contain an interesting
monoid that may be thought of as Richard Thompson's iconic group F together
with the equally iconic bicyclic monoid of semigroup theory, with non-trivial
interactions between the two derived from the Frobenius algebra identity. We
claim this as a significant example of the (unitless) Frobenius algebras of
Abramsky and Heunen.
  We then give concrete examples, based on the algebra and category theory
behind the Geometry of Interaction program. These are based on the traced
monoidal category of partial injections, and reflexive objects in the compact
closed category that results from applying the Int or GoI construction. We give
compact closed categories, monoidally equivalent to compact closed
subcategories of Int(pInj), where this reflexivity is exhibited by identity
arrows, and show how the above algebraic structures (Thompson's F, the bicyclic
monoid, and Frobenius algebras) arise in a fundamental manner.


title:
Natural Language Proof Checking in Introduction to Proof Classes --
  First Experiences with Diproche

abstract:
  We present and analyze the employment of the Diproche system, a natural
language proof checker, within a one-semester mathematics beginners lecture
with 228 participants. The system is used to check the students' solution
attempts to proving exercises in Boolean set theory and elementary number
theory and to give them immediate feedback. The benefits of the employment of
the system are assessed via a questionnaire at the end of the semester and via
analyzing the solution attempts of a subgroup of the students. Based on our
results we develop approaches for future improvements.


title:
Prospect Pruning: Finding Trainable Weights at Initialization using
  Meta-Gradients

abstract:
  Pruning neural networks at initialization would enable us to find sparse
models that retain the accuracy of the original network while consuming fewer
computational resources for training and inference. However, current methods
are insufficient to enable this optimization and lead to a large degradation in
model performance. In this paper, we identify a fundamental limitation in the
formulation of current methods, namely that their saliency criteria look at a
single step at the start of training without taking into account the
trainability of the network. While pruning iteratively and gradually has been
shown to improve pruning performance, explicit consideration of the training
stage that will immediately follow pruning has so far been absent from the
computation of the saliency criterion. To overcome the short-sightedness of
existing methods, we propose Prospect Pruning (ProsPr), which uses
meta-gradients through the first few steps of optimization to determine which
weights to prune. ProsPr combines an estimate of the higher-order effects of
pruning on the loss and the optimization trajectory to identify the trainable
sub-network. Our method achieves state-of-the-art pruning performance on a
variety of vision classification tasks, with less data and in a single shot
compared to existing pruning-at-initialization methods.


title: Quantum Off-Shell Recursion Relation

abstract:
  We construct off-shell recursion relations for arbitrary loop-level
scattering amplitudes beyond the conventional tree-level recursion relations
for $\phi^{4}$-theory and the Yang-Mills theory. We define a quantum
perturbiner expansion that includes loop corrections from the quantum effective
action formalism by identifying the external source. Our method clearly shows
how the perturbiner expansion becomes an off-shell current generating function.
Instead of using the classical equations of motion in the conventional
perturbiner method, we exploit the Dyson-Schwinger equation to derive the
quantum off-shell recursion relation to arbitrary order of loop-level
scattering amplitudes. We solve the recursion relation and reproduce the
results which agree up to one-loop six-point scattering amplitudes for
$\phi^{4}$-theory. Furthermore, we construct the recursions for computing
loop-level correlation functions by replacing the choice of the external
source.


title:
GrADyS-SIM -- A OMNET++/INET simulation framework for Internet of Flying
  things

abstract:
  This technical report describes GrADyS-SIM, a framework for simulating
cooperating swarms of UAVs in joint mission in hypothetical landscape and
communicating through RF radios. The framework was created to aid and verify
the communication, coordination and context-awareness protocols being developed
in the GrADyS project. GrADyS-SIM uses the OMNeT++ simulation library and its
INET model suite and and allows for addition of modified or customized versions
of some simulated components, network configurations and vehicle coordination,
so that new coordination protocols can be developed and tested through the
framework. The framework simulates UAV movement dictated by file containing
some MAVLink instructions and affected on the fly by different network
situations. The UAV swarm coordination protocol emerges from individual
interactions between UAVs and has the objective of optimizing the collection of
sensor data over an area. It also allows for the simulation of some types of
failures to test the protocol adaptability. Every node in the simulation is
highly configurable making testing different network opographies, coordination
protocols, node hardware configurations and more a quick task.


title:
Vector Magnetic Current Imaging of an 8 nm Process Node Chip and 3D
  Current Distributions Using the Quantum Diamond Microscope

abstract:
  The adoption of 3D packaging technology necessitates the development of new
approaches to failure electronic device analysis. To that end, our team is
developing a tool called the quantum diamond microscope (QDM) that leverages an
ensemble of nitrogen vacancy (NV) centers in diamond, achieving vector magnetic
imaging with a wide field-of-view and high spatial resolution under ambient
conditions. Here, we present the QDM measurement of 2D current distributions in
an 8-nm flip chip IC and 3D current distributions in a multi-layer PCB.
Magnetic field emanations from the C4 bumps in the flip chip dominate the QDM
measurements, but these prove to be useful for image registration and can be
subtracted to resolve adjacent current traces in the die at the micron scale.
Vias in 3D ICs display only Bx and By magnetic fields due to their vertical
orientation and are difficult to detect with magnetometers that only measure
the Bz component (orthogonal to the IC surface). Using the multi-layer PCB, we
show that the QDM's ability to simultaneously measure Bx, By, and Bz is
advantageous for resolving magnetic fields from vias as current passes between
layers. We also show how spacing between conducting layers is determined by
magnetic field images and how it agrees with the design specifications of the
PCB. In our initial efforts to provide further z-depth information for current
sources in complex 3D circuits, we show how magnetic field images of individual
layers can be subtracted from the magnetic field image of the total structure.
This allows for isolation of signal layers and can be used to map embedded
current paths via solution of the 2D magnetic inverse. In addition, the paper
also discusses the use of neural networks to identify 2D current distributions
and its potential for analyzing 3D structures.


title: On BV Supermanifolds and the Super Atiyah Class

abstract:
  We study global and local geometry of forms on odd symplectic BV
supermanifolds, constructed from the total space of the bundle of 1-forms on a
base supermanifold. We show that globally 1-forms are an extension of vector
bundles defined on the base supermanifold. In the holomorphic category, we
prove that this extension is split if and only if the super Atiyah class of the
base supermanifold vanishes, which is equivalent to the existence of a
holomorphic superconnection and we show how this condition is related to the
characteristic non-split geometry of complex supermanifolds. From a local point
of view, we prove that the deformed de Rham double complex naturally arises as
a de-quantization of the de Rham/Spencer double complex of the base
supermanifold. Following \v{S}evera, we show that the associated spectral
sequence yields integral forms on the base supermanifold disguised as
semidensities, together with their differential in the form of a super BV
Laplacian.


title: A data-driven approach for learning to control computers

abstract:
  It would be useful for machines to use computers as humans do so that they
can aid us in everyday tasks. This is a setting in which there is also the
potential to leverage large-scale expert demonstrations and human judgements of
interactive behaviour, which are two ingredients that have driven much recent
success in AI. Here we investigate the setting of computer control using
keyboard and mouse, with goals specified via natural language. Instead of
focusing on hand-designed curricula and specialized action spaces, we focus on
developing a scalable method centered on reinforcement learning combined with
behavioural priors informed by actual human-computer interactions. We achieve
state-of-the-art and human-level mean performance across all tasks within the
MiniWob++ benchmark, a challenging suite of computer control problems, and find
strong evidence of cross-task transfer. These results demonstrate the
usefulness of a unified human-agent interface when training machines to use
computers. Altogether our results suggest a formula for achieving competency
beyond MiniWob++ and towards controlling computers, in general, as a human
would.


title:
When Did It Happen? Duration-informed Temporal Localization of Narrated
  Actions in Vlogs

abstract:
  We consider the task of temporal human action localization in lifestyle
vlogs. We introduce a novel dataset consisting of manual annotations of
temporal localization for 13,000 narrated actions in 1,200 video clips. We
present an extensive analysis of this data, which allows us to better
understand how the language and visual modalities interact throughout the
videos. We propose a simple yet effective method to localize the narrated
actions based on their expected duration. Through several experiments and
analyses, we show that our method brings complementary information with respect
to previous methods, and leads to improvements over previous work for the task
of temporal action localization.


title:
Asymptotic behavior of 2D Wave-Klein-Gordon coupled system under null
  condition

abstract:
  We study the 2D coupled wave-Klein-Gordon systems with semi-linear null
nonlinearities $Q_0$ and $Q_{\alpha\beta}$. The main result states that the
solution to the 2D coupled systems exists globally provided that the initial
data are small in some weighted Sobolev space, which do not necessarily have
compact support, and we also show the optimal time decay of the solution.
  The major difficulties lie in the slow decay nature of the wave and the
Klein-Gordon components in two space dimensions, in addition, extra
difficulties arise due to the presence of the null form $Q_0$ which is not of
divergence form and is not compatible with the Klein-Gordon equations. To
overcome the difficulties, a new observation for the structure of the null form
$Q_0$ is required.


title: A projection--less approach to Rickart Jordan structures

abstract:
  The main goal of this paper is to introduce and explore an appropriate notion
of weakly Rickart JB$^*$-triples. We introduce weakly order Rickart
JB$^*$-triples, and we show that a C$^*$-algebra $A$ is a weakly (order)
Rickart JB$^*$-triple precisely when it is a weakly Rickart C$^*$-algebra. We
also prove that the Peirce-2 subspace associated with a tripotent in a weakly
order Rickart JB$^*$-triple is a Rickart JB$^*$-algebra in the sense of Ayupov
and Arzikulov. By extending a classical property of Rickart C$^*$-algebras, we
prove that every weakly order Rickart JB$^*$-triple is generated by its
tripotents.


title:
FUN-SIS: a Fully UNsupervised approach for Surgical Instrument
  Segmentation

abstract:
  Automatic surgical instrument segmentation of endoscopic images is a crucial
building block of many computer-assistance applications for minimally invasive
surgery. So far, state-of-the-art approaches completely rely on the
availability of a ground-truth supervision signal, obtained via manual
annotation, thus expensive to collect at large scale. In this paper, we present
FUN-SIS, a Fully-UNsupervised approach for binary Surgical Instrument
Segmentation. FUN-SIS trains a per-frame segmentation model on completely
unlabelled endoscopic videos, by solely relying on implicit motion information
and instrument shape-priors. We define shape-priors as realistic segmentation
masks of the instruments, not necessarily coming from the same dataset/domain
as the videos. The shape-priors can be collected in various and convenient
ways, such as recycling existing annotations from other datasets. We leverage
them as part of a novel generative-adversarial approach, allowing to perform
unsupervised instrument segmentation of optical-flow images during training. We
then use the obtained instrument masks as pseudo-labels in order to train a
per-frame segmentation model; to this aim, we develop a
learning-from-noisy-labels architecture, designed to extract a clean
supervision signal from these pseudo-labels, leveraging their peculiar noise
properties. We validate the proposed contributions on three surgical datasets,
including the MICCAI 2017 EndoVis Robotic Instrument Segmentation Challenge
dataset. The obtained fully-unsupervised results for surgical instrument
segmentation are almost on par with the ones of fully-supervised
state-of-the-art approaches. This suggests the tremendous potential of the
proposed method to leverage the great amount of unlabelled data produced in the
context of minimally invasive surgery.


title: On Generalized Theories of Varying Fine Structure Constant

abstract:
  We work with a class of scalar extended theory of gravity that can drive the
present cosmic acceleration as well as accommodate a mild cosmic variation of
the fine structure constant $\alpha$. The motivation comes from a vintage
theory developed by Bekenstein, Sandvik, Barrow and Magueijo. The $\alpha$
variation is introduced by a real scalar field interacting with charged matter.
We execute a cosmological reconstruction based on a parametrization of the
present matter density of the Universe. Observational consistency is ensured by
comparing the theoretical estimates with JLA + OHD + BAO data sets, using a
Markov chain Monte Carlo simulation. An analysis of molecular absorption lines
from HIRES and UVES spectrographs is considered as a reference for the
variation of $\alpha$ at different redshifts. Two examples are discussed. The
first explores a field-dependent kinetic coupling of the scalar field
interacting with charged matter. The second example is a generalized
Brans-Dicke formalism where the varying $\alpha$ is fitted in as an effective
matter field. This generates a simultaneous variation of the Newtonian constant
$G$ and $\alpha$. The pattern of this variation can have a crucial role in
cosmic expansion history.


title: Bias in Automated Image Colorization: Metrics and Error Types

abstract:
  We measure the color shifts present in colorized images from the ADE20K
dataset, when colorized by the automatic GAN-based DeOldify model. We introduce
fine-grained local and regional bias measurements between the original and the
colorized images, and observe many colorization effects. We confirm a general
desaturation effect, and also provide novel observations: a shift towards the
training average, a pervasive blue shift, different color shifts among image
categories, and a manual categorization of colorization errors in three
classes.


title: The RADIOSTAR Project

abstract:
  Radioactive nuclei are the key to understanding the circumstances of the
birth of our Sun because meteoritic analysis has proven that many of them were
present at that time. Their origin, however, has been so far elusive. The
ERC-CoG-2016 RADIOSTAR project is dedicated to investigating the production of
radioactive nuclei by nuclear reactions inside stars, their evolution in the
Milky Way Galaxy, and their presence in molecular clouds. So far, we have
discovered that: (i) radioactive nuclei produced by slow ($^{107}$Pd and
$^{182}$Hf) and rapid ($^{129}$I and $^{247}$Cm) neutron captures originated
from stellar sources - asymptotic giant branch (AGB) stars and compact binary
mergers, respectively - within the galactic environment that predated the
formation of the molecular cloud where the Sun was born; (ii) the time that
elapsed from the birth of the cloud to the birth of the Sun was of the order of
10$^7$ years, and (iii) the abundances of the very short-lived nuclei
$^{26}$Al, $^{36}$Cl, and $^{41}$Ca can be explained by massive star winds in
single or binary systems, if these winds directly polluted the early Solar
System. Our current and future work, as required to finalise the picture of the
origin of radioactive nuclei in the Solar System, involves studying the
possible origin of radioactive nuclei in the early Solar System from
core-collapse supernovae, investigating the production of $^{107}$Pd in massive
star winds, modelling the transport and mixing of radioactive nuclei in the
galactic and molecular cloud medium, and calculating the galactic chemical
evolution of $^{53}$Mn and $^{60}$Fe and of the p-process isotopes $^{92}$Nb
and $^{146}$Sm.


title: A multivariate extension of the Erd\"os-Taylor theorem

abstract:
  The Erd\"os-Taylor theorem [Acta Math. Acad. Sci. Hungar, 1960] states that
if $\mathsf{L}_N$ is the local time at zero, up to time $2N$, of a
two-dimensional simple, symmetric random walk, then $\tfrac{\pi}{\log N}
\,\mathsf{L}_N$ converges in distribution to an exponential random variable
with parameter one. This can be equivalently stated in terms of the total
collision time of two independent simple random walks on the plane. More
precisely, if $\mathsf{L}_N^{(1,2)}=\sum_{n=1}^N 1_{\{S_n^{(1)}= S_n^{(2)}\}}$,
then $\tfrac{\pi}{\log N}\, \mathsf{L}^{(1,2)}_N$ converges in distribution to
an exponential random variable of parameter one. We prove that for every $h
\geq 3$, the family $ \big\{ \frac{\pi}{\log N} \,\mathsf{L}_N^{(i,j)}
\big\}_{1\leq i<j\leq h}$, of logarithmically rescaled, two-body collision
local times between $h$ independent simple, symmetric random walks on the plane
converges jointly to a vector of independent exponential random variables with
parameter one, thus providing a multivariate version of the Erd\"os-Taylor
theorem. We also discuss connections to directed polymers in random
environments.


title:
A Prospective Approach for Human-to-Human Interaction Recognition from
  Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural
  Network with GUI Application Implementation

abstract:
  Recent advances in 5G wireless technology and socioeconomic transformation
have brought a paradigm shift in sensor applications. Wi-Fi signal demonstrates
a strong correlation between its temporal variation and body movements, which
can be leveraged to recognize human activity. In this article, we demonstrate
the cognitive ability of device free mutual human-to-human interaction
recognition method based on the time scale Wi-Fi channel state information. The
mutual activities examined are steady-state, approaching, departing,
handshaking, high-five, hugging, kicking (left-leg), kicking (right-leg),
pointing (left-hand), pointing (right-hand), punching(left-hand), punching
(right-hand), and pushing. We explore and propose a Self-Attention furnished
Bidirectional Gated Recurrent Neural Network model to classify the 13
interaction types from the time-series data through automated temporal feature
extraction. Our proposed model can recognize a two subject pair mutual
interaction with a maximum benchmark accuracy of 94%. This has been expanded
for ten subject pairs, which secured a benchmark accuracy of 88% with improved
classification around the interaction-transition region. Also, an executable
graphical user interface (GUI) is developed, using the PyQt5 python module, to
subsequently display the overall mutual human-interaction recognition procedure
in real-time. Finally, we conclude with a brief discourse regarding the
possible solutions to the handicaps that resulted in curtailments observed
during the study. Such, Wi-Fi channel perturbation pattern analysis is believed
to be an efficient, economical & privacy-friendly approach to be potentially
utilized in mutual human-interaction recognition for indoor activity
monitoring, surveillance system, smart health monitoring systems & independent
assisted living.


title: Modular multi-source prediction of drug side-effects with DruGNN

abstract:
  Drug Side-Effects (DSEs) have a high impact on public health, care system
costs, and drug discovery processes. Predicting the probability of
side-effects, before their occurrence, is fundamental to reduce this impact, in
particular on drug discovery. Candidate molecules could be screened before
undergoing clinical trials, reducing the costs in time, money, and health of
the participants. Drug side-effects are triggered by complex biological
processes involving many different entities, from drug structures to
protein-protein interactions. To predict their occurrence, it is necessary to
integrate data from heterogeneous sources. In this work, such heterogeneous
data is integrated into a graph dataset, expressively representing the
relational information between different entities, such as drug molecules and
genes. The relational nature of the dataset represents an important novelty for
drug side-effect predictors. Graph Neural Networks (GNNs) are exploited to
predict DSEs on our dataset with very promising results. GNNs are deep learning
models that can process graph-structured data, with minimal information loss,
and have been applied on a wide variety of biological tasks. Our experimental
results confirm the advantage of using relationships between data entities,
suggesting interesting future developments in this scope. The experimentation
also shows the importance of specific subsets of data in determining
associations between drugs and side-effects.


title:
Optimal market completion through financial derivatives with
  applications to volatility risk

abstract:
  This paper investigates the optimal choices of financial derivatives to
complete a financial market in the framework of stochastic volatility (SV)
models. We introduce an efficient and accurate simulation-based method,
applicable to generalized diffusion models, to approximate the optimal
derivatives-based portfolio strategy. We build upon the double optimization
approach (i.e. expected utility maximization and risk exposure minimization)
proposed in Escobar-Anel et al. (2022); demonstrating that strangle options are
the best choices for market completion within equity options. Furthermore, we
explore the benefit of using volatility index derivatives and conclude that
they could be more convenient substitutes when only long-term maturity equity
options are available.


title: Self-Supervised Class-Cognizant Few-Shot Classification

abstract:
  Unsupervised learning is argued to be the dark matter of human intelligence.
To build in this direction, this paper focuses on unsupervised learning from an
abundance of unlabeled data followed by few-shot fine-tuning on a downstream
classification task. To this aim, we extend a recent study on adopting
contrastive learning for self-supervised pre-training by incorporating
class-level cognizance through iterative clustering and re-ranking and by
expanding the contrastive optimization loss to account for it. To our
knowledge, our experimentation both in standard and cross-domain scenarios
demonstrate that we set a new state-of-the-art (SoTA) in (5-way, 1 and 5-shot)
settings of standard mini-ImageNet benchmark as well as the (5-way, 5 and
20-shot) settings of cross-domain CDFSL benchmark. Our code and experimentation
can be found in our GitHub repository: https://github.com/ojss/c3lr.


title: Dynamical Stability Analysis of Accelerating f(T) Gravity Models

abstract:
  In this paper, we have emphasized on the stability analysis of the
accelerating cosmological models obtained through f(T) gravity. The behavior of
the models based on the evolution of equation of state parameter shows
phantom-like behavior at present epoch. The scalar perturbation technique is
used to create the perturbed evolution equations, and the stability of the
models has been demonstrated. Also, we have incorporated the method of
dynamical system analysis in the model. In the two specific models of f(T)
gravity, three critical points are obtained in each model. In each model, at
least one critical point has been observed to be stable.


title:
Emission of high-energy gamma quanta by ultrarelativistic electrons on
  nuclei in strong x-ray fields

abstract:
  The possibility of radiation of high-energy gamma quanta with energies of the
order of 100 GeV by ultrarelativistic electrons on nuclei in strong X-ray
fields with intensities up to $\sim 10^{27}\ \text{Wcm}^{-2}$ has been
theoretically studied. It is shown that this effect can be realized under
special experimental conditions in the process of resonant spontaneous
bremsstrahlung radiation of ultrarelativistic electrons on nuclei in an
external electromagnetic field. These special experimental conditions determine
the characteristic energy of the electrons. This characteristic energy should
be significantly less than the energy of the initial electrons. Under these
conditions, spontaneous gamma quanta are emitted in a narrow cone with energies
close to the energy of the initial electrons. Moreover, the resonant
differential cross-sections of such processes can exceed the corresponding
differential cross-section without an external field by twenty orders of
magnitude. The results obtained can explain the occurrence of high-energy gamma
quanta near pulsars and magnetars.


title: Cell-Free MIMO Systems Powered by Intelligent Reflecting Surfaces

abstract:
  Cell-free massive multiple-input multiple-output (MIMO) and intelligent
reflecting surface (IRS) are considered as the prospective multiple antenna
technologies for beyond the fifth-generation (5G) networks. Cell-free MIMO
systems powered by IRSs, combining both technologies, can further improve the
performance of cell-free MIMO systems at low cost and energy consumption. Prior
works focused on instantaneous performance metrics and relied on alternating
optimization algorithms, which impose huge computational complexity and
signaling overhead. To address these challenges, we propose a novel two-step
algorithm that provides the long-term passive beamformers at the IRSs using
statistical channel state information (S-CSI) and short-term active precoders
and long-term power allocation at the access points (APs) to maximize the
minimum achievable rate. Simulation results verify that the proposed scheme
outperforms benchmark schemes and brings a significant performance gain to the
cell-free MIMO systems powered by IRSs.


title: IoT Smart Plant Monitoring, Watering and Security System

abstract:
  Interest in home gardening has burgeoned since governments around the
world-imposed lockdowns to suppress the spread of COVID-19. Nowadays, most
families start to do gardening during this lockdown season because they can
grow vegetables and fruits or any other plants that they want in their
day-to-day life. So, they can survive without spending money on online grocery
shopping for fruits and vegetables during this lockdown season. In Sri Lanka,
home gardening was a trend during the past couple of months due to this
pandemic. Most of the families were trying to do gardening for their needs. But
the problem is, nowadays the government is trying to release those restrictions
to start day-to-day work in Sri Lanka. With this situation, people are starting
to do their jobs and they do not have time to spend in their gardens continuing
their gardening. We thought about this problem and tried to find a solution to
continue the gardening work while doing their jobs. The major concern is people
cannot monitor their plants every time and protect their garden. So, we decided
to automate the garden work. With our new solution, gardeners can monitor some
important factors like the plant's healthiness, soil moisture level, air
humidity level, and the surrounding temperature and water their garden from
anywhere in the world at any time by using our app. Plant health has a
significant impact on plant development, production, and quality of
agricultural goods. The goal of this study is to create an automated system
that can identify the presence of illness in plants based on variations in
plant leaf health state is created utilizing sensors such as temperature,
humidity, and color....


title: Supersymmetric Lattice Theories: Contribution to Snowmass 2022

abstract:
  In this white paper we summarise the construction and applications of lattice
theories possessing exact supersymmetry focusing, in particular, on N=4
Yang-Mills theory. Lattice formulations of this theory allow for numerical
simulation of the theory at strong coupling and hence give a window on
non-perturbative physics away from the planar limit. This has important
applications to our understanding of holographic approaches to quantum gravity
and conformal field theories.


title: Koopmans spectral functionals in periodic-boundary conditions

abstract:
  Koopmans spectral functionals aim to describe simultaneously ground state
properties and charged excitations of atoms, molecules, nanostructures and
periodic crystals. This is achieved by augmenting standard density functionals
with simple but physically motivated orbital-density-dependent corrections.
These corrections act on a set of localized orbitals that, in periodic systems,
resemble maximally localized Wannier functions. At variance with the original,
direct supercell implementation [Phys. Rev. X 8, 021051 (2018)], we discuss
here i) the complex but efficient formalism required for a periodic-boundary
code using explicit Brillouin zone sampling, and ii) the calculation of the
screened Koopmans corrections with density-functional perturbation theory. The
implementation in the QUANTUM ESPRESSO distribution and the application to
prototypical insulating and semiconducting systems are presented and discussed.


title: A novel public key cryptography based on generalized Lucas matrices

abstract:
  In this article, we have proposed a generalized Lucas matrix (recursive
matrix of higher order) having relation with generalized Fibonacci sequences
and established many special properties in addition to that usual matrix
algebra. Further, we have proposed a modified public key cryptography using
these matrices as keys in Affine cipher and key agreement for
encryption-decryption with the combination of terms of generalized Lucas
sequences under residue operations. In this scheme, instead of exchanging the
whole key matrix, only a pair of numbers(parameters) need to be exchanged,
which reduces the time complexity as well as space complexity of the key
transmission and has a large key-space.


title: Sympathetic Quiet and Active Region Filament Eruptions

abstract:
  We present the observations of three sympathetic filament eruptions occurring
on 19 July 2015 namely F1, F2, and F3. The events were observed in UV/EUV
wavelengths by Atmospheric Imaging Assembly onboard the Solar Dynamics
Observatory and by Global Oscillation Network Group telescope in H{\alpha}
line. As filament F1 starts to erupt, a part of it falls close to the location
of the F2 and F3 filaments. This causes the eruption of F2 and F3 during which
the two filaments merge together and trigger a medium-class CME and a
long-duration GOES C2.1 class flare. We discuss the dynamics and kinematics of
these three filament eruptions and related phenomena.


title:
Measurement of Direct-Photon Cross Section and Double-Helicity Asymmetry
  at $\sqrt{s}=510$ GeV in $\vec{p}+\vec{p}$ Collisions

abstract:
  We present the measurement of the cross section and double-helicity asymmetry
$A_{LL}$ of direct-photon production in $\vec{p}+\vec{p}$ collisions at
$\sqrt{s}=510$ GeV. The measurement has been performed at midrapidity
($|\eta|<0.25$) with the PHENIX detector at the Relativistic Heavy Ion
Collider. Direct photons are dominantly produced by the quark-gluon scattering
at relativistic energies. Direct photons are produced from the initial partonic
hard scattering and do not interact via the strong force. Therefore, this
measurement provides a clean and direct access to the gluons in the polarized
proton in the gluon-momentum-fraction range $0.02<x<0.08$.


title: Domain Adaptive Fake News Detection via Reinforcement Learning

abstract:
  With social media being a major force in information consumption, accelerated
propagation of fake news has presented new challenges for platforms to
distinguish between legitimate and fake news. Effective fake news detection is
a non-trivial task due to the diverse nature of news domains and expensive
annotation costs. In this work, we address the limitations of existing
automated fake news detection models by incorporating auxiliary information
(e.g., user comments and user-news interactions) into a novel reinforcement
learning-based model called \textbf{RE}inforced \textbf{A}daptive
\textbf{L}earning \textbf{F}ake \textbf{N}ews \textbf{D}etection (REAL-FND).
REAL-FND exploits cross-domain and within-domain knowledge that makes it robust
in a target domain, despite being trained in a different source domain.
Extensive experiments on real-world datasets illustrate the effectiveness of
the proposed model, especially when limited labeled data is available in the
target domain.


title: Inflation driven by Barrow Holographic dark energy

abstract:
  In this work we have investigated the inflation mechanism driven by the
Barrow Holographic dark energy (BHDE) in the early universe. BHDE is based on
the Barrow relation for horizon entropy, which in turn is inspired from the
shape of the COVID-19 virus. It was shown by Barrow that the quantum
gravitational effects may instigate complex fractal features in the structure
of a black hole. Since the length scale during the inflation is expected to be
small, the energy density obtained from the application of the holographic
principle in the early universe will be large enough to support the
inflationary scenario. Using the Granda-Oliveros IR cut-off we have studied the
inflationary scenario with the universe filled with BHDE. Various analytic
solutions for the model were found out including the slow-roll parameters,
scalar spectral index and tensor-to-scalar ratio. Since inflation is generally
attributed to the presence of scalar fields, we have explored a correspondence
between BHDE and scalar field models. Both canonical scalar field and the
Tachyonic scalar field have been considered for this purpose. The evolution of
the potential generated from the fields are plotted and found to be consistent
with the observations. From the work we see that BHDE can be a model of dark
energy that can successfully drive the early time inflation.


title: A generalized marriage theorem

abstract:
  We consider a set-valued mapping on a simple graph and ask for the existence
of a disparate selection. The term disparate is defined in the paper and we
present a sufficient and necessary condition for the existence of a disparate
selection. This approach generalizes the classical marriage theorem of Hall. We
define the disparate kernel of the set-valued mapping and provide calculation
methods for the disparate kernel and a disparate selection. Our main theorem is
applied to a result of Ryser on the completion of partially prepopulated Latin
squares and we derive Hall's marriage theorem.


title: Completeness of Bethe ansatz for Gaudin models associated with gl(1|1)

abstract:
  We study the Gaudin models associated with $\mathfrak{gl}(1|1)$. We give an
explicit description of the algebra of Hamiltonians (Gaudin Hamiltonians)
acting on tensor products of polynomial evaluation
$\mathfrak{gl}(1|1)[t]$-modules. It follows that there exists a bijection
between common eigenvectors (up to proportionality) of the algebra of
Hamiltonians and monic divisors of an explicit polynomial written in terms of
the highest weights and evaluation parameters. In particular, our result
implies that each common eigenspace of the algebra of Hamiltonians has
dimension one. Therefore, we confirm Conjecture 8.3 from arXiv:1809.01279. We
also give dimensions of the generalized eigenspaces. Moreover, we express the
generating pseudo-differential operator of Gaudin transfer matrices associated
to antisymmetrizers in terms of the quadratic Gaudin transfer matrix and the
center of $\mathrm{U}(\mathfrak{gl}(1|1)[t])$.


title:
Propagation of Chaos of Forward-Backward Stochastic Differential
  Equations with Graphon Interactions

abstract:
  In this paper, we study graphon mean field games using a system of
forward-backward stochastic differential equations. We establish the existence
and uniqueness of solutions under two different assumptions and prove the
stability with respect to the interacting graphons which are necessary to show
propagation of chaos results. As an application of propagation of chaos, we
prove the convergence of n-player game Nash equilibrium for a general model,
which is new in the theory of graphon mean field games.


title:
Voice Filter: Few-shot text-to-speech speaker adaptation using voice
  conversion as a post-processing module

abstract:
  State-of-the-art text-to-speech (TTS) systems require several hours of
recorded speech data to generate high-quality synthetic speech. When using
reduced amounts of training data, standard TTS models suffer from speech
quality and intelligibility degradations, making training low-resource TTS
systems problematic. In this paper, we propose a novel extremely low-resource
TTS method called Voice Filter that uses as little as one minute of speech from
a target speaker. It uses voice conversion (VC) as a post-processing module
appended to a pre-existing high-quality TTS system and marks a conceptual shift
in the existing TTS paradigm, framing the few-shot TTS problem as a VC task.
Furthermore, we propose to use a duration-controllable TTS system to create a
parallel speech corpus to facilitate the VC task. Results show that the Voice
Filter outperforms state-of-the-art few-shot speech synthesis techniques in
terms of objective and subjective metrics on one minute of speech on a diverse
set of voices, while being competitive against a TTS model built on 30 times
more data.


title: Dependence of pulsation mode of Cepheids on metallicity

abstract:
  The Cepheid variables in SMC, LMC, the Milky Way, M33 and M31 are used to
examine the dependence of pulsation mode on metallicity which was previously
found in red supergiants. The initial samples of Cepheids are collected from
the Cepheid catalogs identified from the OGLE, PS1, DIRECT, WISE and ZTF
surveys. The contaminants are removed with the help of the Gaia/EDR3
astrometric information for extra galaxies or by comparing the geometric
distance and the distance from the P-L relation for the Milky Way. The division
of fundamental and first-overtone mode is refined according to the gap between
the two modes in the P-L diagram of the objects in each galaxy. The ratio of
FU/(FU+1O) is found to be 0.59, 0.60, 0.69, 0.83 and 0.85 for SMC, LMC, the
Milky Way, M33 and M31 respectively in order of metallicity, which confirms
that the pulsation mode depends on metallicity in the way that the ratio of
FU/(FU+1O) increases with metallicity. This dependence is not changed if the
incompleteness of the samples is taken into account.


title: Novel Muon Tomography Detector for the Pyramids

abstract:
  Cosmic-ray muons which impinge upon the Earth's surface can be used to image
the density of geological and man-made materials located above a muon detector.
The detectors used for these measurements must be capable of determining the
muon rate as a function of the angle of incidence. Applications of this
capability include geological carbon storage, natural gas storage, enhanced oil
recovery, compressed air storage, oil and gas production, tunnel detection, and
detection of hidden rooms in man-made structures such as the pyramids. For
these applications the detector must be small, rugged, and have operational
characteristics which enable its use in remote locations, such as low power
requirements. A new muon detector design is now being constructed to make
measurements on the Khafre pyramid, in Egypt, to look for unknown voids that
might exist in the structure. The new detector design uses monolithic plates of
scintillator with wavelength shifting fiber optic readout to obtain location
information. This design will meet the operational requirements, while also
providing a geometry which can be modified for different measurement
conditions.


title:
Effective Lifetime of Non-Equilibrium Carriers in Semiconductors from
  Non-Adiabatic Molecular Dynamics Simulations

abstract:
  The lifetime of non-equilibrium electrons and holes in semiconductors is
crucial for solar cell and optoelectronic applications. Non-adiabatic molecular
dynamics (NAMD) simulations based on time-dependent density functional theory
(TDDFT) are widely used to study excited-state carrier dynamics. However, the
calculated carrier lifetimes are often different from experimental results by
orders of magnitude. In this work, by revisiting the definition of carrier
lifetime and considering different recombination mechanisms, we report a
systematic procedure for calculating the effective carrier lifetime in
realistic semiconductor crystals that can be compared directly to experimental
measurements. The procedure shows that considering all recombination mechanisms
and using reasonable densities of carriers and defects are crucial in
calculating the effective lifetime. When NAMD simulations consider only
Shockey-Read-Hall (SRH) defect-assisted and band-to-band non-radiative
recombination while neglect band-to-band radiative recombination, and the
densities of non-equilibrium carriers and defects in supercell simulations are
much higher than those in realistic semiconductors under solar illumination,
the calculated lifetimes are ineffective and thus differ from experiments.
Using our procedure, the calculated effective lifetime of the halide perovskite
CH3NH3PbI3 agrees with experiments. It is mainly determined by band-to-band
radiative and defect-assisted non-radiative recombination, while band-to-band
non-radiative recombination is negligible. These results indicate that it is
possible to calculate carrier lifetimes accurately based on NAMD simulations,
but the directly calculated values should be converted to effective lifetimes
for comparison to experiments. The revised procedure can be widely applied in
future carrier lifetime simulations.


title:
Small defects reconstruction in waveguides from multifrequency one-side
  scattering data

abstract:
  Localization and reconstruction of small defects in acoustic or
electromagnetic waveguides is of crucial interest in nondestructive evaluation
of structures. The aim of this work is to present a new multi-frequency
inversion method to reconstruct small defects in a 2D waveguide. Given one-side
multi-frequency wave field measurements of propagating modes, we use a Born
approximation to provide a L2-stable reconstruction of three types of defects:
a local perturbation inside the waveguide, a bending of the waveguide, and a
localized defect in the geometry of the waveguide. This method is based on a
mode-by-mode spacial Fourier inversion from the available partial data in the
Fourier domain. Indeed, in the available data, some high and low spatial
frequency information on the defect are missing. We overcome this issue using
both a compact support hypothesis and a minimal smoothness hypothesis on the
defects. We also provide a suitable numerical method for efficient
reconstruction of such defects and we discuss its applications and limits.


title:
On the virtual and residual properties of a generalization of
  Bestvina-Brady groups

abstract:
  Previously one of us introduced a family of groups $G^M_L(S)$, parametrized
by a finite flag complex $L$, a regular covering $M$ of $L$, and a set $S$ of
integers. We give conjectural descriptions of when $G^M_L(S)$ is either
residually finite or virtually torsion-free. In the case that $M$ is a finite
cover and $S$ is periodic, there is an extension with kernel $G_L^M(S)$ and
infinite cyclic quotient that is a CAT(0) cubical group. We conjecture that
this group is virtually special. We relate these three conjectures to each
other and prove many cases of them.


title: Faithfulness of generalised Verma modules for Iwasawa algebras

abstract:
  We prove faithfulness of infinite-dimensional generalised Verma modules for
Iwasawa algebras corresponding to split simple Lie algebras with a Chevalley
basis. We use this to prove faithfulness of all infinite-dimensional
highest-weight modules in the case of type $A_{2}$. In this case we also show
that all prime ideals of the corresponding Iwasawa algebras are annihilators of
finite-dimensional simple modules.


title:
Capitalization Normalization for Language Modeling with an Accurate and
  Efficient Hierarchical RNN Model

abstract:
  Capitalization normalization (truecasing) is the task of restoring the
correct case (uppercase or lowercase) of noisy text. We propose a fast,
accurate and compact two-level hierarchical word-and-character-based recurrent
neural network model. We use the truecaser to normalize user-generated text in
a Federated Learning framework for language modeling. A case-aware language
model trained on this normalized text achieves the same perplexity as a model
trained on text with gold capitalization. In a real user A/B experiment, we
demonstrate that the improvement translates to reduced prediction error rates
in a virtual keyboard application. Similarly, in an ASR language model fusion
experiment, we show reduction in uppercase character error rate and word error
rate.


title: Classifying Galaxy Morphologies with Few-Shot Learning

abstract:
  The taxonomy of galaxy morphology is critical in astrophysics as the
morphological properties are powerful tracers of galaxy evolution. With the
upcoming Large-scale Imaging Surveys, billions of galaxy images challenge
astronomers to accomplish the classification task by applying traditional
methods or human inspection. Consequently, machine learning, in particular
supervised deep learning, has been widely employed to classify galaxy
morphologies recently due to its exceptional automation, efficiency, and
accuracy. However, supervised deep learning requires extensive training sets,
which causes considerable workloads; also, the results are strongly dependent
on the characteristics of training sets, which leads to biased outcomes
potentially. In this study, we attempt Few-shot Learning to bypass the two
issues. Our research adopts the dataset from Galaxy Zoo Challenge Project on
Kaggle, and we divide it into five categories according to the corresponding
truth table. By classifying the above dataset utilizing few-shot learning based
on Siamese Networks and supervised deep learning based on AlexNet, VGG_16, and
ResNet_50 trained with different volumes of training sets separately, we find
that few-shot learning achieves the highest accuracy in most cases, and the
most significant improvement is $21\%$ compared to AlexNet when the training
sets contain 1000 images. In addition, to guarantee the accuracy is no less
than 90\%, few-shot learning needs $\sim$6300 images for training, while
ResNet_50 requires 13000 images. Considering the advantages stated above,
foreseeably, few-shot learning is suitable for the taxonomy of galaxy
morphology and even for identifying rare astrophysical objects, despite limited
training sets consisting of observational data only.


title: Distributed k-Means with Outliers in General Metrics

abstract:
  Center-based clustering is a pivotal primitive for unsupervised learning and
data analysis. A popular variant is undoubtedly the k-means problem, which,
given a set $P$ of points from a metric space and a parameter $k<|P|$, requires
to determine a subset $S$ of $k$ centers minimizing the sum of all squared
distances of points in $P$ from their closest center. A more general
formulation, known as k-means with $z$ outliers, introduced to deal with noisy
datasets, features a further parameter $z$ and allows up to $z$ points of $P$
(outliers) to be disregarded when computing the aforementioned sum. We present
a distributed coreset-based 3-round approximation algorithm for k-means with
$z$ outliers for general metric spaces, using MapReduce as a computational
model. Our distributed algorithm requires sublinear local memory per reducer,
and yields a solution whose approximation ratio is an additive term $O(\gamma)$
away from the one achievable by the best known sequential (possibly bicriteria)
algorithm, where $\gamma$ can be made arbitrarily small. An important feature
of our algorithm is that it obliviously adapts to the intrinsic complexity of
the dataset, captured by the doubling dimension $D$ of the metric space. To the
best of our knowledge, no previous distributed approaches were able to attain
similar quality-performance tradeoffs for general metrics.


title:
Towards Battery-Free Machine Learning and Inference in Underwater
  Environments

abstract:
  This paper is motivated by a simple question: Can we design and build
battery-free devices capable of machine learning and inference in underwater
environments? An affirmative answer to this question would have significant
implications for a new generation of underwater sensing and monitoring
applications for environmental monitoring, scientific exploration, and
climate/weather prediction.
  To answer this question, we explore the feasibility of bridging advances from
the past decade in two fields: battery-free networking and low-power machine
learning. Our exploration demonstrates that it is indeed possible to enable
battery-free inference in underwater environments. We designed a device that
can harvest energy from underwater sound, power up an ultra-low-power
microcontroller and on-board sensor, perform local inference on sensed
measurements using a lightweight Deep Neural Network, and communicate the
inference result via backscatter to a receiver. We tested our prototype in an
emulated marine bioacoustics application, demonstrating the potential to
recognize underwater animal sounds without batteries. Through this exploration,
we highlight the challenges and opportunities for making underwater
battery-free inference and machine learning ubiquitous.


title:
GraphNLI: A Graph-based Natural Language Inference Model for Polarity
  Prediction in Online Debates

abstract:
  Online forums that allow participatory engagement between users have been
transformative for public discussion of important issues. However, debates on
such forums can sometimes escalate into full blown exchanges of hate or
misinformation. An important tool in understanding and tackling such problems
is to be able to infer the argumentative relation of whether a reply is
supporting or attacking the post it is replying to. This so called polarity
prediction task is difficult because replies may be based on external context
beyond a post and the reply whose polarity is being predicted. We propose
GraphNLI, a novel graph-based deep learning architecture that uses graph walk
techniques to capture the wider context of a discussion thread in a principled
fashion. Specifically, we propose methods to perform root-seeking graph walks
that start from a post and captures its surrounding context to generate
additional embeddings for the post. We then use these embeddings to predict the
polarity relation between a reply and the post it is replying to. We evaluate
the performance of our models on a curated debate dataset from Kialo, an online
debating platform. Our model outperforms relevant baselines, including S-BERT,
with an overall accuracy of 83%.


title:
Bias and unfairness in machine learning models: a systematic literature
  review

abstract:
  One of the difficulties of artificial intelligence is to ensure that model
decisions are fair and free of bias. In research, datasets, metrics,
techniques, and tools are applied to detect and mitigate algorithmic unfairness
and bias. This study aims to examine existing knowledge on bias and unfairness
in Machine Learning models, identifying mitigation methods, fairness metrics,
and supporting tools. A Systematic Literature Review found 40 eligible articles
published between 2017 and 2022 in the Scopus, IEEE Xplore, Web of Science, and
Google Scholar knowledge bases. The results show numerous bias and unfairness
detection and mitigation approaches for ML technologies, with clearly defined
metrics in the literature, and varied metrics can be highlighted. We recommend
further research to define the techniques and metrics that should be employed
in each case to standardize and ensure the impartiality of the machine learning
model, thus, allowing the most appropriate metric to detect bias and unfairness
in a given context.


title: Generative modeling with projected entangled-pair states

abstract:
  We argue and demonstrate that projected entangled-pair states (PEPS)
outperform matrix product states significantly for the task of generative
modeling of datasets with an intrinsic two-dimensional structure such as
images. Our approach builds on a recently introduced algorithm for sampling
PEPS, which allows for the efficient optimization and sampling of the
distributions.


title:
Strict dissipativity for generalized linear-quadratic problems in
  infinite dimensions

abstract:
  We analyze strict dissipativity of generalized linear quadratic optimal
control problems on Hilbert spaces. Here, the term ``generalized'' refers to
cost functions containing both quadratic and linear terms. We characterize
strict pre-dissipativity with a quadratic storage function via coercivity of a
particular Lyapunov-like quadratic form. Further, we show that under an
additional algebraic assumption, strict pre-dissipativity can be strengthened
to strict dissipativity. Last, we relate the obtained characterizations of
dissipativity with exponential detectability.


title:
The Helmholtz problem in slowly varying waveguides at locally resonant
  frequencies

abstract:
  This article aims to present a general study of the Helmholtz problem in
slowly varying waveguides. This work is of particular interest at locally
resonant frequencies, where a phenomenon close to the tunnel effect for
Schr\"odinger equation in quantum mechanics can be observed. In this situation,
locally resonant modes propagate in the waveguide under the form of Airy
functions. Using previous mathematical results on the Schr\"odinger equation,
we prove the existence of a unique solution to the Helmholtz source problem
with outgoing conditions in such waveguides. We provide an explicit modal
approximation of this solution, as well as a control of the approximation error
in H1loc. The main theorem is proved in the case of a waveguide with a
monotonously varying profile and then generalized using a matching strategy. We
finally validate the modal approximation by comparing it to numerical solutions
based on the finite element method.


title: Geometry of the Minimum Volume Confidence Sets

abstract:
  Computation of confidence sets is central to data science and machine
learning, serving as the workhorse of A/B testing and underpinning the
operation and analysis of reinforcement learning algorithms. This paper studies
the geometry of the minimum-volume confidence sets for the multinomial
parameter. When used in place of more standard confidence sets and intervals
based on bounds and asymptotic approximation, learning algorithms can exhibit
improved sample complexity. Prior work showed the minimum-volume confidence
sets are the level-sets of a discontinuous function defined by an exact
p-value. While the confidence sets are optimal in that they have minimum
average volume, computation of membership of a single point in the set is
challenging for problems of modest size. Since the confidence sets are
level-sets of discontinuous functions, little is apparent about their geometry.
This paper studies the geometry of the minimum volume confidence sets by
enumerating and covering the continuous regions of the exact p-value function.
This addresses a fundamental question in A/B testing: given two multinomial
outcomes, how can one determine if their corresponding minimum volume
confidence sets are disjoint? We answer this question in a restricted setting.


title:
Global derivation of a Boussinesq-Navier-Stokes type system from
  fluid-kinetic equations

abstract:
  We study a hydrodynamic limit of the Vlasov-Navier-Stokes system with
external gravity force, following the framework introduced by Han-Kwan and
Michel in [Han-Kwan - Michel, arXiv:2103.06668]. We answer a question raised in
the latter concerning the derivation of a Boussinesq-Navier-Stokes type system
for arbitrarily large times, starting from the previous fluid-kinetic system.
To do so, we consider a particular spatial geometric setting corresponding to
the half-space case. Our proof is based on an absorption effect at the boundary
which leads to crucial decay in time estimates.


title:
An Intrusion Response System utilizing Deep Q-Networks and System
  Partitions

abstract:
  Intrusion Response is a relatively new field of research. Recent approaches
for the creation of Intrusion Response Systems (IRSs) use Reinforcement
Learning (RL) as a primary technique for the optimal or near-optimal selection
of the proper countermeasure to take in order to stop or mitigate an ongoing
attack. However, most of them do not consider the fact that systems can change
over time or, in other words, that systems exhibit a non-stationary behavior.
Furthermore, stateful approaches, such as those based on RL, suffer the curse
of dimensionality, due to a state space growing exponentially with the size of
the protected system.
  In this paper, we introduce and develop an IRS software prototype, named
irs-partition. It leverages the partitioning of the protected system and Deep
Q-Networks to address the curse of dimensionality by supporting a multi-agent
formulation. Furthermore, it exploits transfer learning to follow the evolution
of non-stationary systems.


title:
Increasing loudness in audio signals: a perceptually motivated approach
  to preserve audio quality

abstract:
  We present a method to maintain the subjective perception of volume of audio
signals and, at the same time, reduce their absolute peak value. We focus on
achieving this without compromising the perceived audio quality. This is
specially useful, for example, to maximize the perceived reproduction level of
loudspeakers where simply amplifying the signal amplitude, and hence their peak
value, is limited due to already constrained physical designs. In particular,
we minimize the absolute peak value subject to a constraint based on auditory
masking. This limits the perceptual difference between the original and the
modified signals. Moreover, this constraint can be tuned and allows to control
the resulting audio quality. We show results comparing loudness and audio
quality as a function of peak reduction. These results suggest that our method
presents the best trade-off between loudness and audio quality when compared
against classical methods based on compression and clipping.


title: Tomographic Muon Imaging of the Great Pyramid of Giza

abstract:
  The pyramids of the Giza plateau have fascinated visitors since ancient times
and are the last of the Seven Wonders of the ancient world still standing. It
has been half a century since Luiz Alvarez and his team used cosmic-ray muon
imaging to look for hidden chambers in Khafres Pyramid. Advances in
instrumentation for High-Energy Physics (HEP) allowed a new survey,
ScanPyramids, to make important new discoveries at the Great Pyramid (Khufu)
utilizing the same basic technique that the Alvarez team used, but now with
modern instrumentation. The Exploring the Great Pyramid Mission plans to field
a very-large muon telescope system that will be transformational with respect
to the field of cosmic-ray muon imaging. We plan to field a telescope system
that has upwards of 100 times the sensitivity of the equipment that has
recently been used at the Great Pyramid, will image muons from nearly all
angles and will, for the first time, produce a true tomographic image of such a
large structure.

categories: physics.ins-det astro-ph.IM eess.IV physics.acc-ph


title:
The Adversarial Security Mitigations of mmWave Beamforming Prediction
  Models using Defensive Distillation and Adversarial Retraining

abstract:
  The design of a security scheme for beamforming prediction is critical for
next-generation wireless networks (5G, 6G, and beyond). However, there is no
consensus about protecting the beamforming prediction using deep learning
algorithms in these networks. This paper presents the security vulnerabilities
in deep learning for beamforming prediction using deep neural networks (DNNs)
in 6G wireless networks, which treats the beamforming prediction as a
multi-output regression problem. It is indicated that the initial DNN model is
vulnerable against adversarial attacks, such as Fast Gradient Sign Method
(FGSM), Basic Iterative Method (BIM), Projected Gradient Descent (PGD), and
Momentum Iterative Method (MIM), because the initial DNN model is sensitive to
the perturbations of the adversarial samples of the training data. This study
also offers two mitigation methods, such as adversarial training and defensive
distillation, for adversarial attacks against artificial intelligence
(AI)-based models used in the millimeter-wave (mmWave) beamforming prediction.
Furthermore, the proposed scheme can be used in situations where the data are
corrupted due to the adversarial examples in the training data. Experimental
results show that the proposed methods effectively defend the DNN models
against adversarial attacks in next-generation wireless networks.


title: Quantum speedups for treewidth

abstract:
  In this paper, we study quantum algorithms for computing the exact value of
the treewidth of a graph. Our algorithms are based on the classical algorithm
by Fomin and Villanger (Combinatorica 32, 2012) that uses $O(2.616^n)$ time and
polynomial space. We show three quantum algorithms with the following
complexity, using QRAM in both exponential space algorithms: $\bullet$
$O(1.618^n)$ time and polynomial space; $\bullet$ $O(1.554^n)$ time and
$O(1.452^n)$ space; $\bullet$ $O(1.538^n)$ time and space. In contrast, the
fastest known classical algorithm for treewidth uses $O(1.755^n)$ time and
space. The first two speed-ups are obtained in a fairly straightforward way.
The first version uses additionally only Grover's search and provides a
quadratic speedup. The second speedup is more time-efficient and uses both
Grover's search and the quantum exponential dynamic programming by Ambainis et
al. (SODA '19). The third version uses the specific properties of the classical
algorithm and treewidth, with a modified version of the quantum dynamic
programming on the hypercube. Lastly, as a small side result, we also give a
new classical time-space tradeoff for computing treewidth in $O^*(2^n)$ time
and $O^*(\sqrt{2^n})$ space.


title:
Differential Privacy and Fairness in Decisions and Learning Tasks: A
  Survey

abstract:
  This paper surveys recent work in the intersection of differential privacy
(DP) and fairness. It reviews the conditions under which privacy and fairness
may have aligned or contrasting goals, analyzes how and why DP may exacerbate
bias and unfairness in decision problems and learning tasks, and describes
available mitigation measures for the fairness issues arising in DP systems.
The survey provides a unified understanding of the main challenges and
potential risks arising when deploying privacy-preserving machine-learning or
decisions-making tasks under a fairness lens.


title:
Ultrathin plasma polymer passivation of perovskite solar cells for
  improved stability and reproducibility

abstract:
  Despite the youthfulness of hybrid halide perovskite solar cells, their
efficiencies are currently comparable to commercial silicon and have surpassed
quantum-dots solar cells. Yet, the scalability of these devices is a challenge
due to their low reproducibility and stability under environmental conditions.
However, the methods reported to date to tackle such issues recurrently involve
the use of solvent methods that would further complicate their transfer to
industry. Herein we present a reliable alternative relaying in the
implementation of an ultrathin plasma polymer as passivation interface between
the electron transport material and the hybrid perovskite layer. Such
nanoengineering interface provides solar devices with increased long-term
stability under ambient conditions. Thus, without consideringr any additional
encapsulation step, the cells retain more than 80 % of their efficiency after
being exposed to the ambient atmosphere for more than 1000 h. Moreover, this
plasma polymer passivation strategy significantly improves the coverage of the
mesoporous scaffold by the perovskite layer, providing the solar cells with
enhanced performance as well as improved reproducibility.


title: Inversion Formulas for the $j$-function Around Elliptic Points

abstract:
  Recently, Hong, Mertens, Ono and Zhang proved a conjecture of
C\u{a}ld\u{a}raru, He, and Huang that expresses the Taylor series of the
modular $j$-function around the elliptic points $i$ and $\rho=e^{\pi i/3}$ as
rational functions arising from the signature 2 and 3 cases of Ramanujan's
theory of elliptic functions to alternative bases. We extend these results and
give inversion formulas for the $j$-function around $i$ and $\rho$ arising from
Gauss' hypergeometric functions and Ramanujan's theory in signatures 4 and 6.


title: On the $h$-adic quantum vertex algebras associated with Hecke symmetries

abstract:
  We study the quantum vertex algebraic framework for the Yangians of RTT-type
and the braided Yangians associated with Hecke symmetries, introduced by
Gurevich and Saponov. First, we construct several families of modules for the
aforementioned Yangian-like algebras which, in the RTT-type case, lead to a
certain $h$-adic quantum vertex algebra $\mathcal{V}_c (R)$ via the
Etingof-Kazhdan construction, while, in the braided case, they produce
($\phi$-coordinated) $\mathcal{V}_c (R)$-modules. Next, we show that the
coefficients of suitably defined quantum determinant can be used to obtain
central elements of $\mathcal{V}_c (R)$, as well as the invariants of such
($\phi$-coordinated) $\mathcal{V}_c (R)$-modules. Finally, we investigate a
certain algebra which is closely connected with the representation theory of
$\mathcal{V}_c (R)$.


title:
Sampling The Lowest Eigenfunction to Recover the Potential in a
  One-Dimensional Schr\"odinger Equation

abstract:
  We consider the BVP $-y" + qy = \lambda y$ with $y(0)=y(1)=0$. The inverse
spectral problems asks one to recover $q$ from spectral information. In this
paper, we present a very simple method to recover a potential by sampling one
eigenfunction. The spectral asymptotics imply that for larger modes, more and
more information is lost due to imprecise measurements (i.e. relative errors
\textit{increases}) and so it is advantageous to use data from lower modes. Our
method also allows us to recover "any" potential from \textit{one} boundary
condition.


title: Flexible-Modal Face Anti-Spoofing: A Benchmark

abstract:
  Face anti-spoofing (FAS) plays a vital role in securing face recognition
systems from presentation attacks. Benefitted from the maturing camera sensors,
single-modal (RGB) and multi-modal (e.g., RGB+Depth) FAS has been applied in
various scenarios with different configurations of sensors/modalities. Existing
single- and multi-modal FAS methods usually separately train and deploy models
for each possible modality scenario, which might be redundant and inefficient.
Can we train a unified model, and flexibly deploy it under various modality
scenarios? In this paper, we establish the first flexible-modal FAS benchmark
with the principle `train one for all'. To be specific, with trained
multi-modal (RGB+Depth+IR) FAS models, both intra- and cross-dataset testings
are conducted on four flexible-modal sub-protocols (RGB, RGB+Depth, RGB+IR, and
RGB+Depth+IR). We also investigate prevalent deep models and feature fusion
strategies for flexible-modal FAS. We hope this new benchmark will facilitate
the future research of the multi-modal FAS. The protocols and codes are
available at https://github.com/ZitongYu/Flex-Modal-FAS.


title:
The intra-group baryons in the LEO I pair from the VST Early-type GAlaxy
  Survey

abstract:
  In this paper we present the deep, wide-field and multi-band imaging of the
LEO I pair NGC~3379-NGC~3384, from the VST Early-type GAlaxy Survey (VEGAS).
The main goal of this study is to map the intra-group baryons in the pair, in
the form of diffuse light and globular clusters (GCs). Taking advantage from
the large covered area, which extends for $\sim$ 3.9 square degrees around the
pair, and the long integration time, we can map the light distribution out to
$\sim$ 63 kpc and down to $\sim$ 30 mag/arcsec$^2$ in the $g$ band and $\sim$
29 mag/arcsec$^2$ in the $r$ band, deeper than previous data available for this
target. The map of the intra-group light (IGL) presents two very faint ($\mu_g
\sim$ 28-29 mag/arcsec$^2$) streams protruding from the brightest group member
NGC~3379 and elongated toward North-West and South. We estimate that the
fraction of the stellar halo around NGC~3379 plus the IGL is $\sim 17 \pm 2\%$
in both $g$ and $r$ bands, with an average color $g$-$r$= $0.75 \pm 0.04$~mag.
The color distribution of the GCs appears multi-modal, with two dominant peaks
at (u-r) = 1.8 mag and (u-r) = 2.1 mag, respectively. The GC population
stretches from North-East to South-West and from North-West to South of the
pair, in the last case overlapping with the streams of IGL, as well as the PNe
distribution found by \citet{Hartke2020} and \citet{Hartke2022arXiv220108710H}.
Since these structures are elongated in the direction of the two nearby
galaxies M96 and NGC~3338, they could be the remnant of a past gravitational
interactions with the pair.


title:
Deep Contextual Bandits for Orchestrating Multi-User MISO Systems with
  Multiple RISs

abstract:
  The emergent technology of Reconfigurable Intelligent Surfaces (RISs) has the
potential to transform wireless environments into controllable systems, through
programmable propagation of information-bearing signals. Techniques stemming
from the field of Deep Reinforcement Learning (DRL) have recently gained
popularity in maximizing the sum-rate performance in multi-user communication
systems empowered by RISs. Such approaches are commonly based on Markov
Decision Processes (MDPs). In this paper, we instead investigate the sum-rate
design problem under the scope of the Multi-Armed Bandits (MAB) setting, which
is a relaxation of the MDP framework. Nevertheless, in many cases, the MAB
formulation is more appropriate to the channel and system models under the
assumptions typically made in the RIS literature. To this end, we propose a
simpler DRL approach for orchestrating multiple metasurfaces in RIS-empowered
multi-user Multiple-Input Single-Output (MISO) systems, which we numerically
show to perform equally well with a state-of-the-art MDP-based approach, while
being less demanding computationally.


title:
Label Propagation for Annotation-Efficient Nuclei Segmentation from
  Pathology Images

abstract:
  Nuclei segmentation is a crucial task for whole slide image analysis in
digital pathology. Generally, the segmentation performance of fully-supervised
learning heavily depends on the amount and quality of the annotated data.
However, it is time-consuming and expensive for professional pathologists to
provide accurate pixel-level ground truth, while it is much easier to get
coarse labels such as point annotations. In this paper, we propose a
weakly-supervised learning method for nuclei segmentation that only requires
point annotations for training. The proposed method achieves label propagation
in a coarse-to-fine manner as follows. First, coarse pixel-level labels are
derived from the point annotations based on the Voronoi diagram and the k-means
clustering method to avoid overfitting. Second, a co-training strategy with an
exponential moving average method is designed to refine the incomplete
supervision of the coarse labels. Third, a self-supervised visual
representation learning method is tailored for nuclei segmentation of pathology
images that transforms the hematoxylin component images into the H\&E stained
images to gain better understanding of the relationship between the nuclei and
cytoplasm. We comprehensively evaluate the proposed method using two public
datasets. Both visual and quantitative results demonstrate the superiority of
our method to the state-of-the-art methods, and its competitive performance
compared to the fully-supervised methods. The source codes for implementing the
experiments will be released after acceptance.


title:
Off-shell cubic hypermultiplet couplings to $\mathcal{N}=2$ higher spin
  gauge superfields

abstract:
  We construct manifestly $4D, \mathcal{N}=2$ supersymmetric and gauge
invariant off-shell cubic couplings of matter hypermultiplets to the higher
integer spin gauge $\mathcal{N}=2$ multiplets introduced in arXiv:2109.07639
[hep-th]. The hypermultiplet is described by an analytic harmonic $4D,
\mathcal{N}=2$ superfield $q^{+}$ with the physical component spins ${\bf s} =
(\frac{1}{2}\,, \;0)$ and an infinite number of auxiliary fields. The cubic
coupling constructed has the schematic structure $q^+ \hat{{\cal H}}^{++}_{(s)}
q^+$, where $\hat{{\cal H}}^{++}_{(s)}$ is a differential analytic operator of
the highest degree $({\bf s} - 1)$ accommodating the massless gauge
$\mathcal{N}=2$ multiplet with the highest spin ${\bf s}$. For odd ${\bf s}$
the gauge group generators and couplings are proportional to ${\rm U}(1)_{PG}$
generator of the internal ${\rm SU}(2)_{PG}$ symmetry of the hypermultiplet and
so do not exist if ${\rm SU}(2)_{PG}$ is unbroken. If this ${\rm U}(1)_{PG}$ is
identified with the central charge of $ 4D, \mathcal{N}=2$ supersymmetry, a
mass for the hypermultiplet is generated and the odd ${\bf s}$ couplings vanish
in the proper massless limit. For even ${\bf s}$ the higher-spin gauge
transformations and cubic superfield couplings can be defined for both massive
and massless (central-charge neutral) hypermultiplets without including ${\rm
U}(1)_{PG}$ generator. All these features directly extend to the case of $n$
hypermultiplets with the maximal internal symmetry ${\rm USp}(2n) \times {\rm
SU}(2)$.


title:
Automated Generation of Arbitrarily Many Kochen-Specker and Other
  Contextual Sets in Odd Dimensional Hilbert Spaces

abstract:
  Development of quantum computation and communication, recently shown to be
supported by contextuality, arguably asks for an abundant supply of contextual
sets. While that has been achieved in even dimensional spaces, in odd
dimensional spaces only a dozen contextual critical Kochen-Specker (KS) sets
have been found so far. In this paper we obtain millions of KS and other
contextual sets in dimensions 3, 5, 7, and 9 by three different methods.


title:
Analysis of Random Sequential Message Passing Algorithms for Approximate
  Inference

abstract:
  We analyze the dynamics of a random sequential message passing algorithm for
approximate inference with large Gaussian latent variable models in a
student-teacher scenario. To model nontrivial dependencies between the latent
variables, we assume random covariance matrices drawn from rotation invariant
ensembles. Moreover, we consider a model mismatching setting, where the teacher
model and the one used by the student may be different. By means of dynamical
functional approach, we obtain exact dynamical mean-field equations
characterizing the dynamics of the inference algorithm. We also derive a range
of model parameters for which the sequential algorithm does not converge. The
boundary of this parameter range coincides with the de Almeida Thouless (AT)
stability condition of the replica symmetric ansatz for the static
probabilistic model.


title: Less is More: Surgical Phase Recognition from Timestamp Supervision

abstract:
  Surgical phase recognition is a fundamental task in computer-assisted surgery
systems. Most existing works require expensive frame-wise annotations, which is
very time-consuming. In this paper, we introduce timestamp supervision to
surgical phase recognition for the first time, which only requires randomly
labeling one frame for each phase in a video. With timestamp supervision,
current methods in natural videos aim to generate pseudo labels of full frames.
However, due to the surgical videos containing ambiguous boundaries, these
methods would generate many noisy and inconsistent pseudo labels, leading to
limited performance. We argue that less is more in surgical phase
recognition,~\ie, less but discriminative pseudo labels outperform full but
ambiguous frames. To this end, we propose a novel method called
uncertainty-aware temporal diffusion to generate trustworthy pseudo labels. Our
approach evaluates the confidence of generated pseudo labels based on
uncertainty estimation. Then, we treat the annotated frames as anchors and make
pseudo labels diffuse to both sides, starting from anchors and stopping at the
high-uncertainty frames. In this way, our proposed method can generate
contiguous confident pseudo labels while discarding the uncertain ones.
Extensive experiments demonstrate that our method not only significantly save
annotation cost, but also outperforms fully supervised methods. Moreover, our
proposed approach can be used to clean noisy labels near boundaries and improve
the performance of the current surgical phase recognition methods.


title: Modelling multi-cell edge video analytics

abstract:
  Edge intelligence is a scalable solution for analyzing distributed data, but
it cannot provide reliable services in large-scale cellular networks unless the
inherent aspects of fading and interference are also taken into consideration.
In this paper, we present the first mathematical framework for modelling edge
video analytics in multi-cell cellular systems. We derive the expressions for
the coverage probability, the ergodic capacity, the probability of successfully
completing the video analytics within a target delay requirement, and the
effective frame rate. We also analyze the effect of the system parameters on
the accuracy of the detection algorithm, the supported frame rate at the edge
server, and the system fairness.


title:
Time-resolved hadronic particle acceleration in the recurrent Nova RS
  Ophiuchi

abstract:
  Recurrent Novae are repeating thermonuclear explosions in the outer layers of
white dwarfs, due to the accretion of fresh material from a binary companion.
The shock generated by ejected material slamming into the companion star's
wind, accelerates particles to very-high-energies. We report very-high-energy
(VHE, $\gtrsim100$\,GeV) gamma rays from the recurrent nova RS\,Ophiuchi up to
a month after its 2021 outburst, using the High Energy Stereoscopic System. The
VHE emission has a similar temporal profile to lower-energy GeV emission,
indicating a common origin, with a two-day delay in peak flux. These
observations constrain models of time-dependent particle energization,
favouring a hadronic emission scenario over the leptonic alternative. This
confirms that shocks in dense winds provide favourable environments for
efficient cosmic-ray acceleration to very-high-energies.


title: Efficient generation of high-order harmonics in gases

abstract:
  High-order harmonic generation (HHG) in gases leads to short-pulse extreme
ultraviolet (XUV) radiation useful in a number of applications, for example,
attosecond science and nanoscale imaging. However, this process depends on many
parameters and there is still no consensus on how to choose the target geometry
to optimize the source efficiency. Here, we review the physics of HHG with
emphasis on the macroscopic aspects of the nonlinear interaction. We analyze
the influence of medium length, pressure, position of the medium and intensity
of the driving laser on the HHG conversion efficiency (CE), using both
numerical modelling and analytical expressions. We find that efficient
high-order harmonic generation can be realized over a large range of pressures
and medium lengths, if these follow a certain hyperbolic equation. The spatial
and temporal properties of the generated radiation are, however, strongly
dependent on the choice of pressure and medium length. Our results explain the
large versatility in gas target design for efficient HHG and provide design
guidance for future high-flux XUV sources.


title:
Fast inverse transform sampling of non-Gaussian distribution functions
  in space plasmas

abstract:
  Non-Gaussian distributions are commonly observed in collisionless space
plasmas. Generating samples from non-Gaussian distributions is critical for the
initialization of particle-in-cell simulations that investigate their driven
and undriven dynamics. To this end, we report a computationally efficient,
robust tool, Chebsampling, to sample general distribution functions in one and
two dimensions. This tool is based on inverse transform sampling with function
approximation by Chebyshev polynomials. We demonstrate practical uses of
Chebsampling through sampling typical distribution functions in space plasmas.


title:
Special Relativity, Einstein Velocity Addition, and Gyrogroups: An
  Introduction

abstract:
  In these notes we give an introductory unified treatment to the topics of
special relativity, Lorentz transformations and the Lorentz group, Einstein
velocitiy addition, and gyrogroups and gyrovector spaces. An effort has been
made to present the material in a manner that is accessible to non-specialists
and graduate students, and may even serve as the basis for a graduate course or
seminar.