title:
Balancing Expert Utilization in Mixture-of-Experts Layers Embedded in
  CNNs

abstract:
  This work addresses the problem of unbalanced expert utilization in
sparsely-gated Mixture of Expert (MoE) layers, embedded directly into
convolutional neural networks. To enable a stable training process, we present
both soft and hard constraint-based approaches. With hard constraints, the
weights of certain experts are allowed to become zero, while soft constraints
balance the contribution of experts with an additional auxiliary loss. As a
result, soft constraints handle expert utilization better and support the
expert specialization process, hard constraints mostly maintain generalized
experts and increase the model performance for many applications. Our findings
demonstrate that even with a single dataset and end-to-end training, experts
can implicitly focus on individual sub-domains of the input space. Experts in
the proposed models with MoE embeddings implicitly focus on distinct domains,
even without suitable predefined datasets. As an example, experts trained for
CIFAR-100 image classification specialize in recognizing different domains such
as sea animals or flowers without previous data clustering. Experiments with
RetinaNet and the COCO dataset further indicate that object detection experts
can also specialize in detecting objects of distinct sizes.


title: On solvability of dissipative partial differential-algebraic equations

abstract:
  In this article we investigate the solvability of infinite-dimensional
differential algebraic equations. Such equations often arise as partial
differential-algebraic equations (PDAEs). A decomposition of the state-space
that leads to an extension of the Hille-Yosida Theorem on Hilbert spaces for
these equations is described. For dissipative partial differential equations
the famous Lumer-Phillips generation theorem characterizes solvability and also
boundedness of the associated semigroup. An extension of the Lumer-Phillips
generation theorem to dissipative differential-algebraic equations is given.
The results is illustrated by coupled systems and the Dzektser equation.


title:
Mergers prompted by dynamical resonances in compact, multiple-star
  systems: a stellar-reduction case for the massive triple TIC 470710327

abstract:
  Eisner et al. (2022) reported the discovery of TIC 470710327, a massive
compact hierarchical triple star system. TIC 470710327 is comprised of a
compact (1.10 d) circular eclipsing binary, with total mass $\approx 10.9-13.2\
\rm{M_{\odot}}$, and a more massive ($\approx 14-17\ \rm{M_{\odot}}$) eccentric
non-eclipsing tertiary in a $52.04$ d orbit. Here we present a progenitor
scenario for TIC 470710327 in which '2+2' quadruple dynamics result in
Zeipel-Lidov-Kozai (ZLK) resonances that lead to a contact phase of the more
massive binary. In this scenario, the two binary systems should form in a very
similar manner, and dynamics trigger the merger of the more massive binary
either during late phases of star formation or several Myr after the zero-age
main sequence (ZAMS), when the stars begin to expand. Any evidence that the
tertiary is a highly-magnetised ($\sim 1-10$ kG), slowly-rotating blue
main-sequence star would hint towards a quadruple origin. Finally, our scenario
suggests that the population of inclined, compact multiple-stellar systems is
reduced into co-planar systems, via mergers, late during star formation or
early in the main sequence. The elucidation of the origin of TIC 470710327 is
crucial in our understanding of multiple massive-star formation and evolution.


title:
Generalized Rogers Ramanujan Expressions for Some Non--Singlet Twisted
  Affine Algebras

abstract:
  Hatayama et al. described generalized Rogers--Ramanujan (GRR) expressions for
the string functions of the singlet representation of twisted affine algebras.
We give here such GRR expressions for some non-singlet string functions. In the
case of the algebra $A_2^{(2)}$ this gives all the string functions. We verify
these expressions using Freudenthal--Kac formula.


title: Arnold diffusion in Hamiltonian systems on infinite lattices

abstract:
  We consider a system of infinitely many penduli on an $m$-dimensional lattice
with a weak coupling. For any prescribed path in the lattice, for suitable
couplings, we construct orbits for this Hamiltonian system of infinite degrees
of freedom which transfer energy between nearby penduli along the path. We
allow the weak coupling to be next-to-nearest neighbor or long range as long as
it is strongly decaying. The transfer of energy is given by an Arnold diffusion
mechanism which relies on the original V. I Arnold approach: to construct a
sequence of hyperbolic invariant quasiperiodic tori with transverse
heteroclinic orbits. We implement this approach in an infinite dimensional
setting, both in the space of bounded $\mathbb{Z}^m$-sequences and in spaces of
decaying $\mathbb{Z}^m$-sequences. Key steps in the proof are an invariant
manifold theory for hyperbolic tori and a Lambda Lemma for infinite dimensional
coupled map lattices with decaying interaction.


title: Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors

abstract:
  It is an important task to reconstruct surfaces from 3D point clouds. Current
methods are able to reconstruct surfaces by learning Signed Distance Functions
(SDFs) from single point clouds without ground truth signed distances or point
normals. However, they require the point clouds to be dense, which dramatically
limits their performance in real applications. To resolve this issue, we
propose to reconstruct highly accurate surfaces from sparse point clouds with
an on-surface prior. We train a neural network to learn SDFs via projecting
queries onto the surface represented by the sparse point cloud. Our key idea is
to infer signed distances by pushing both the query projections to be on the
surface and the projection distance to be the minimum. To achieve this, we
train a neural network to capture the on-surface prior to determine whether a
point is on a sparse point cloud or not, and then leverage it as a
differentiable function to learn SDFs from unseen sparse point cloud. Our
method can learn SDFs from a single sparse point cloud without ground truth
signed distances or point normals. Our numerical evaluation under widely used
benchmarks demonstrates that our method achieves state-of-the-art
reconstruction accuracy, especially for sparse point clouds.


title: Deformations of Theta Integrals and A Conjecture of Gross-Zagier

abstract:
  In this paper, we complete the proof of Gross-Zagier's conjecture concerning
algebraicity of higher Green functions at a single CM point on the product of
modular curves. The new ingredient is an analogue of the incoherent Eisenstein
series over a real quadratic field, which is constructed as the Doi-Naganuma
theta lift of a deformed theta integral on hyperbolic space.


title:
Distributed stochastic projection-free solver for constrained
  optimization

abstract:
  This paper proposes a distributed stochastic projection-free algorithm for
large-scale constrained finite-sum optimization whose constraint set is
complicated such that the projection onto the constraint set can be expensive.
The global cost function is allocated to multiple agents, each of which
computes its local stochastic gradients and communicates with its neighbors to
solve the global problem. Stochastic gradient methods enable low computational
cost, while they are hard and slow to converge due to the variance caused by
random sampling. To construct a convergent distributed stochastic
projection-free algorithm, this paper incorporates a variance reduction
technique and gradient tracking technique in the Frank-Wolfe update. We develop
a sampling rule for the variance reduction technique to reduce the variance
introduced by stochastic gradients. Complete and rigorous proofs show that the
proposed distributed projection-free algorithm converges with a sublinear
convergence rate and enjoys superior complexity guarantees for both convex and
non-convex objective functions. By comparative simulations, we demonstrate the
convergence and computational efficiency of the proposed algorithm.


title: Enhancing the Transferability via Feature-Momentum Adversarial Attack

abstract:
  Transferable adversarial attack has drawn increasing attention due to their
practical threaten to real-world applications. In particular, the feature-level
adversarial attack is one recent branch that can enhance the transferability
via disturbing the intermediate features. The existing methods usually create a
guidance map for features, where the value indicates the importance of the
corresponding feature element and then employs an iterative algorithm to
disrupt the features accordingly. However, the guidance map is fixed in
existing methods, which can not consistently reflect the behavior of networks
as the image is changed during iteration. In this paper, we describe a new
method called Feature-Momentum Adversarial Attack (FMAA) to further improve
transferability. The key idea of our method is that we estimate a guidance map
dynamically at each iteration using momentum to effectively disturb the
category-relevant features. Extensive experiments demonstrate that our method
significantly outperforms other state-of-the-art methods by a large margin on
different target models.


title: Federated Learning via Inexact ADMM

abstract:
  One of the crucial issues in federated learning is how to develop efficient
optimization algorithms. Most of the current ones require full devices
participation and/or impose strong assumptions for convergence. Different from
the widely-used gradient descent-based algorithms, this paper develops an
inexact alternating direction method of multipliers (ADMM), which is both
computation and communication-efficient, capable of combating the stragglers'
effect, and convergent under mild conditions.


title:
Nuclear modification factor of neutral pions in the forward and backward
  regions in $p$Pb collisions

abstract:
  The nuclear modification factor of neutral pions is measured in proton-lead
collisions collected at a center-of-mass energy per nucleon of $8.16~{\rm TeV}$
with the LHCb detector. The $\pi^0$ production cross section is measured
differentially in transverse momentum ($p_{\rm T}$) for $1.5<p_{\rm
T}<10.0~{\rm GeV}$ and in center-of-mass pseudorapidity ($\eta_{\rm CM}$)
regions $2.5<\eta_{\rm CM}<3.5$ (forward) and $-4.0<\eta_{\rm CM}<-3.0$
(backward) defined relative to the proton beam direction. The forward
measurement shows a sizable suppression of $\pi^0$ production, while the
backward measurement shows the first evidence of $\pi^0$ enhancement in
proton-lead collisions at the LHC. Together, these measurements provide precise
constraints on models of nuclear structure and particle production in
high-energy nuclear collisions.


title: Quantum time dilation in a gravitational field

abstract:
  According to relativity, the reading of an ideal clock is interpreted as the
elapsed proper time along its single classical trajectory. In contrast, quantum
theory allows the association of many simultaneous paths with a single quantum
clock. Here, we investigate how the superposition principle affects the
gravitational time dilation observed by a simple clock -- a decaying two-level
atom. Placing such an atom in a superposition of positions enables us to
analyze a quantum contribution to a classical time dilation, manifested in the
process of spontaneous emission. In particular, we show that the emission rate
of an atom prepared in a coherent superposition of separated wave packets in a
gravitational field is different from the transition rate of the atom in a
classical mixture of these packets, which gives rise to a
nonclassical-gravitational time dilation effect. In addition, we show the
effect of spatial coherence on the atom's emission spectrum.


title: Fast Autonomous Robotic Exploration Using the Underlying Graph Structure

abstract:
  In this work, we fully define the existing relationships between traditional
optimality criteria and the connectivity of the underlying pose-graph in Active
SLAM, characterizing, therefore, the connection between Graph Theory and the
Theory Optimal Experimental Design. We validate the proposed relationships in
2D and 3D graph SLAM datasets, showing a remarkable relaxation of the
computational load when using the graph structure. Furthermore, we present a
novel Active SLAM framework which outperforms traditional methods by
successfully leveraging the graphical facet of the problem so as to
autonomously explore an unknown environment.


title: Bridging Sapling: Private Cross-Chain Transfers

abstract:
  Interoperability is one of the main challenges of blockchain technologies,
which are generally designed as self-contained systems. Interoperability
schemes for privacy-focused blockchains are particularly hard to design: they
must integrate with the unique privacy features of the underlying blockchain so
as to prove statements about specific transactions in protocols designed to
obfuscate them. This has led to users being forced to weaken their privacy,
e.g. by using centralised exchanges, to move assets from one chain to another.
We present ZCLAIM, a framework for trustless cross-chain asset migration based
on the Zcash privacy-protecting protocol. ZCLAIM integrates with an
implementation of the Sapling version of Zcash on a smart-contract capable
issuing chain in order to attain private cross-chain transfers. We show that a
tokenised representation can be created via a set of collateralised
intermediaries without relying on or revealing the total amount to any third
party.


title:
Simulating active agents under confinement with Dissipative Particles
  (hydro)Dynamics

abstract:
  We study active agents embedded in bulk or in confinement explicitly
considering hydrodynamics and simulating the swimmers via an implementation
inspired by the squirmer model. We develop a Dissipative Particle Dynamics
scheme for the solvent. This approach allows us to properly deal not only with
hydrodynamics but also with thermal fluctuations. On the other side, this
approach enables us to study active agents with complex shapes, ranging from
spherical colloids to polymers. To start with, we study a simple spherical
colloid. We analyze the features of the velocity fields of the surrounding
solvent, when the colloid is a pusher, a puller or a neutral swimmer either in
bulk or confined in a cylindrical channel. Next, we characterise its dynamical
behaviour by computing the mean square displacement and the long time diffusion
when the active colloid is in bulk or in a channel (varying its radius) and
analyze the orientation autocorrelation function in the latter case. While the
three studied squirmer types are characterised by the same bulk diffusion, the
cylindrical confinement considerably modulates the diffusion and the
orientation autocorrelation function. Finally, we focus our attention on a more
complex shape: an active polymer. We first characterise the structural features
computing its radius of gyration when in bulk or in cylindrical confinement,
and compare to known results obtained without hydrodynamics. Next, we
characterise the dynamical behaviour of the active polymer by computing its
mean square displacement and the long time diffusion. On the one hand, both
diffusion and radius of gyration decrease due to the hydrodynamic interaction
when the system is in bulk. On the other hand, the effect of confinement is to
decrease the radius of gyration, disturbing the motion of the polymer and thus
reducing its diffusion.


title: Zero-shot Query Contextualization for Conversational Search

abstract:
  Current conversational passage retrieval systems cast conversational search
into ad-hoc search by using an intermediate query resolution step that places
the user's question in context of the conversation. While the proposed methods
have proven effective, they still assume the availability of large-scale
question resolution and conversational search datasets. To waive the dependency
on the availability of such data, we adapt a pre-trained token-level dense
retriever on ad-hoc search data to perform conversational search with no
additional fine-tuning. The proposed method allows to contextualize the user
question within the conversation history, but restrict the matching only
between question and potential answer. Our experiments demonstrate the
effectiveness of the proposed approach. We also perform an analysis that
provides insights of how contextualization works in the latent space, in
essence introducing a bias towards salient terms from the conversation.


title: Modelling graph dynamics in fraud detection with "Attention"

abstract:
  At online retail platforms, detecting fraudulent accounts and transactions is
crucial to improve customer experience, minimize loss, and avoid unauthorized
transactions. Despite the variety of different models for deep learning on
graphs, few approaches have been proposed for dealing with graphs that are both
heterogeneous and dynamic. In this paper, we propose DyHGN (Dynamic
Heterogeneous Graph Neural Network) and its variants to capture both temporal
and heterogeneous information. We first construct dynamic heterogeneous graphs
from registration and transaction data from eBay. Then, we build models with
diachronic entity embedding and heterogeneous graph transformer. We also use
model explainability techniques to understand the behaviors of DyHGN-* models.
Our findings reveal that modelling graph dynamics with heterogeneous inputs
need to be conducted with "attention" depending on the data structure,
distribution, and computation cost.


title:
Generalized Quantifiers as a Source of Error in Multilingual NLU
  Benchmarks

abstract:
  Logical approaches to representing language have developed and evaluated
computational models of quantifier words since the 19th century, but today's
NLU models still struggle to capture their semantics. We rely on Generalized
Quantifier Theory for language-independent representations of the semantics of
quantifier words, to quantify their contribution to the errors of NLU models.
We find that quantifiers are pervasive in NLU benchmarks, and their occurrence
at test time is associated with performance drops. Multilingual models also
exhibit unsatisfying quantifier reasoning abilities, but not necessarily worse
for non-English languages. To facilitate directly-targeted probing, we present
an adversarial generalized quantifier NLI task (GQNLI) and show that
pre-trained language models have a clear lack of robustness in generalized
quantifier reasoning.


title:
The eight-port homodyne detector: the effect of imperfections on quantum
  number generation and on detection of quadratures

abstract:
  The eight-port homodyne detector is an optical circuit designed to perform
the monitoring of two quadratures of an optical field, the signal. By using
quantum Bose fields and quantum stochastic calculus, we give a complete quantum
description of this apparatus, when used as quadrature detector in continuous
time. The analysis includes imperfections, such as not perfectly balanced beam
splitters, detector efficiency, electronic noise, phase and intensity noise in
the laser acting as local oscillator; this last noise is modelled by using
mixtures of field coherent states as statistical operator of the laser
component. When the output process is sampled at discrete times, the quantum
description can be reduced to discrete mode operators, but at the price of
having random operators, which contain also the noise of the local oscillator.
As an application, the problem of secure random number generation is
considered, based on the local oscillator shot noise. The rate of random bits
that can be generated is quantified by the min-entropy; the possibility of side
information is taken into account by suitable conditional min-entropies. The
final rate depends on which parts of the apparatus are considered to be secure
and on which ones are considered to be exposed to the intervention of an
intruder. In some experimentally possible situations, the entropy losses are
computed, depending on the values of the parameters quantifying the
imperfections.


title: Torsion codimension 2 cycles on supersingular abelian varieties

abstract:
  We prove that torsion codimension 2 algebraic cycles modulo rational
equivalence on supersingular abelian varieties are algebraically equivalent to
zero. As a consequence, we prove that homological equivalence coincides with
algebraic equivalence for algebraic cycles of codimension 2 on supersingular
abelian varieties over the algebraic closure of finite fields.


title:
An Upper Bound of the Information Flow From Children to Parent Node on
  Trees

abstract:
  We consider the transmission of a state from the root of a tree towards its
leaves, assuming that each transmission occurs through a noisy channel. The
states at the leaves are observed, while at deeper nodes we can compute the
likelihood of each state given the observation. In this sense, information
flows from child nodes towards the parent node. Here we find an upper bound of
this children-to-parent information flow. To do so, first we introduce a new
measure of information, the memory vector, whose norm quantifies whether all
states have the same likelihood. Then we find conditions such that the norm of
the memory vector at the parent node can be linearly bounded by the sum of
norms at the child nodes. We also describe the reconstruction problem of
estimating the ancestral state at the root given the observation at the leaves.
We infer sufficient conditions under which the original state at the root
cannot be confidently reconstructed using the observed leaves, assuming that
the number of levels from the root to the leaves is large.


title:
Real-time HOG+SVM based object detection using SoC FPGA for a UHD video
  stream

abstract:
  Object detection is an essential component of many vision systems. For
example, pedestrian detection is used in advanced driver assistance systems
(ADAS) and advanced video surveillance systems (AVSS). Currently, most
detectors use deep convolutional neural networks (e.g., the YOLO -- You Only
Look Once -- family), which, however, due to their high computational
complexity, are not able to process a very high-resolution video stream in
real-time, especially within a limited energy budget. In this paper we present
a hardware implementation of the well-known pedestrian detector with HOG
(Histogram of Oriented Gradients) feature extraction and SVM (Support Vector
Machine) classification. Our system running on AMD Xilinx Zynq UltraScale+
MPSoC (Multiprocessor System on Chip) device allows real-time processing of 4K
resolution (UHD -- Ultra High Definition, 3840 x 2160 pixels) video for 60
frames per second. The system is capable of detecting a pedestrian in a single
scale. The results obtained confirm the high suitability of reprogrammable
devices in the real-time implementation of embedded vision systems.


title:
A Birman-Schwinger Principle in General Relativity: Linearly Stable
  Shells of Collisionless Matter Surrounding a Black Hole

abstract:
  We develop a Birman-Schwinger principle for the spherically symmetric,
asymptotically flat Einstein-Vlasov system. It characterizes stability
properties of steady states such as the positive definiteness of an
Antonov-type operator or the existence of exponentially growing modes in terms
of a one-dimensional variational problem for a Hilbert-Schmidt operator. This
requires a refined analysis of the operators arising from linearizing the
system, which uses action-angle type variables. For the latter, a single-well
structure of the effective potential for the particle flow of the steady state
is required. This natural property can be verified for a broad class of
singularity-free steady states. As a particular example for the application of
our Birman-Schwinger principle we consider steady states where a Schwarzschild
black hole is surrounded by a shell of Vlasov matter. We prove the existence of
such steady states and derive linear stability if the mass of the Vlasov shell
is small compared to the mass of the black hole.

categories: math.AP astro-ph.GA gr-qc math-ph math.MP


title: Out-of-Domain Evaluation of Finnish Dependency Parsing

abstract:
  The prevailing practice in the academia is to evaluate the model performance
on in-domain evaluation data typically set aside from the training corpus.
However, in many real world applications the data on which the model is applied
may very substantially differ from the characteristics of the training data. In
this paper, we focus on Finnish out-of-domain parsing by introducing a novel UD
Finnish-OOD out-of-domain treebank including five very distinct data sources
(web documents, clinical, online discussions, tweets, and poetry), and a total
of 19,382 syntactic words in 2,122 sentences released under the Universal
Dependencies framework. Together with the new treebank, we present extensive
out-of-domain parsing evaluation utilizing the available section-level
information from three different Finnish UD treebanks (TDT, PUD, OOD). Compared
to the previously existing treebanks, the new Finnish-OOD is shown include
sections more challenging for the general parser, creating an interesting
evaluation setting and yielding valuable information for those applying the
parser outside of its training domain.


title:
Non-interpenetration conditions in the passage from nonlinear to
  linearized Griffith fracture

abstract:
  We characterize the passage from nonlinear to linearized Griffith-fracture
theories under non-interpenetration constraints. In particular, sequences of
deformations satisfying a Ciarlet-Ne\v{c}as condition in SBV^2 and for which a
convergence of the energies is ensured, are shown to admit asymptotic
representations in GSBD^2 satisfying a suitable contact condition. With an
explicit counterexample, we prove that this result fails if convergence of the
energies does not hold. We further prove that each limiting displacement
satisfying the contact condition can be approximated by an energy-convergent
sequence of deformations fulfilling a Ciarlet-Ne\v{c}as condition. The proof
relies on a piecewise Korn-Poincar\'e inequality in GSBD2, on a careful blow-up
analysis around jump points, as well as on a refined GSBD^2-density result
guaranteeing enhanced contact conditions for the approximants.


title:
Large Sample Properties of Entropy Balancing Estimators of Average
  Causal Effects

abstract:
  Weighting methods are used in observational studies to adjust for covariate
imbalances between treatment and control groups. Entropy balancing (EB) is an
alternative to inverse probability weighting with an estimated propensity
score. The EB weights are constructed to satisfy balance constraints and
optimized towards stability. We describe large sample properties of EB
estimators of the average causal treatment effect, based on the
Kullback-Leibler and quadratic R\'enyi relative entropies. Additionally, we
propose estimators of their asymptotic variances. Even though the objective of
EB is to reduce model dependence, the estimators are generally not consistent
unless implicit parametric assumptions for the propensity score or conditional
outcomes are met. The finite sample properties of the estimators are
investigated through a simulation study. In an application with observational
data from the Swedish Childhood Diabetes Register, we estimate the average
effect of school achievements on hospitalization due to acute complications of
type 1 diabetes mellitus.


title: Learning Functional Distributional Semantics with Visual Data

abstract:
  Functional Distributional Semantics is a recently proposed framework for
learning distributional semantics that provides linguistic interpretability. It
models the meaning of a word as a binary classifier rather than a numerical
vector. In this work, we propose a method to train a Functional Distributional
Semantics model with grounded visual data. We train it on the Visual Genome
dataset, which is closer to the kind of data encountered in human language
acquisition than a large text corpus. On four external evaluation datasets, our
model outperforms previous work on learning semantics from Visual Genome.


title: Weak and Strong Extremal Biquadratics

abstract:
  We study quasiconvex quadratic forms on $n \times m$ matrices which
correspond to nonnegative biquadratic forms in $(n,m)$ variables. We disprove a
conjecture stated by Harutyunyan--Milton (Comm. Pure Appl. Math. 70(11), 2017)
as well as Harutyunyan--Hovsepyan (Arch. Ration. Mech. Anal. 244, 2022) that
extremality in the cone of quasiconvex quadratic forms on $3\times 3$ matrices
can follow only from the extremality of the determinant of its acoustic tensor,
using previous work by Buckley--\v{S}ivic (Linear Algebra Appl. 598, 2020). Our
main result is to establish a conjecture of Harutyunyan--Milton (Comm. Pure
Appl. Math. 70(11), 2017) that weak extremal quasiconvex quadratics on $3
\times 3$ matrices are strong extremal. Our main technical ingredient is a
generalization of the work of Kunert--Scheiderer on extreme nonnegative ternary
sextics (Trans. Amer. Math. Soc. 370(6), 2018). Specifically, we show that a
nonnegative ternary sextic, which is not a square, is extremal if and only if
its variety (over the complex numbers) is a rational curve and all its
singularities are real.


title:
Log-Sobolev inequality and proof of Hypothesis of the Gaussian
  Maximizers for the capacity of quantum noisy homodyning

abstract:
  In the present paper we give proof that the information-transmission capacity
of the approximate position measurement with the oscillator energy constraint,
which underlies noisy Gaussian homodyning in quantum optics, is attained on
Gaussian encoding. The proof is based on general principles of convex
programming. Rather remarkably, for this particular model the method reduces
the solution of the optimization problem to a generalization of the celebrated
log-Sobolev inequality. We hope that this method should work also for other
models lying out of the scope of the "threshold condition" ensuring that the
upper bound for the capacity as a difference between the maximum and the
minimum output entropies is attainable.


title:
Solutions of a charged scalar field in five-dimensional helicoid
  solution with electromagnetic field

abstract:
  We study a charged and massive scalar field in the background of the
Nutku-Ghezelbash-Kumar metric which is obtained by the addition of a time
coordinate to the Nutku helicoid metric in a non-trivial way. The angular part
of the Klein-Gordon equation can be written as a double confluent Heun
equation. The radial equation cannot be solved in terms of a known function in
its general form. However, in some special cases, the radial equation can also
be written explicitly as a double confluent Heun equation. We study the full
radial equation numerically and observe that the electromagnetic field
parameter defines an effective cut-off on the range of the radial coordinate.
Finally, we obtain a quasi-exact solution with an approximation.


title:
Autoregressive Search Engines: Generating Substrings as Document
  Identifiers

abstract:
  Knowledge-intensive language tasks require NLP systems to both provide the
correct answer and retrieve supporting evidence for it in a given corpus.
Autoregressive language models are emerging as the de-facto standard for
generating answers, with newer and more powerful systems emerging at an
astonishing pace. In this paper we argue that all this (and future) progress
can be directly applied to the retrieval problem with minimal intervention to
the models' architecture. Previous work has explored ways to partition the
search space into hierarchical structures and retrieve documents by
autoregressively generating their unique identifier. In this work we propose an
alternative that doesn't force any structure in the search space: using all
ngrams in a passage as its possible identifiers. This setup allows us to use an
autoregressive model to generate and score distinctive ngrams, that are then
mapped to full passages through an efficient data structure. Empirically, we
show this not only outperforms prior autoregressive approaches but also leads
to an average improvement of at least 10 points over more established retrieval
solutions for passage-level retrieval on the KILT benchmark, establishing new
state-of-the-art downstream performance on some datasets, while using a
considerably lighter memory footprint than competing systems. Code and
pre-trained models at https://github.com/facebookresearch/SEAL.


title:
MEKER: Memory Efficient Knowledge Embedding Representation for Link
  Prediction and Question Answering

abstract:
  Knowledge Graphs (KGs) are symbolically structured storages of facts. The KG
embedding contains concise data used in NLP tasks requiring implicit
information about the real world. Furthermore, the size of KGs that may be
useful in actual NLP assignments is enormous, and creating embedding over it
has memory cost issues. We represent KG as a 3rd-order binary tensor and move
beyond the standard CP decomposition by using a data-specific generalized
version of it. The generalization of the standard CP-ALS algorithm allows
obtaining optimization gradients without a backpropagation mechanism. It
reduces the memory needed in training while providing computational benefits.
We propose a MEKER, a memory-efficient KG embedding model, which yields
SOTA-comparable performance on link prediction tasks and KG-based Question
Answering.


title: On the the missing diagrams in Category Theory (first-person version)

abstract:
  Most texts on Category Theory are written in a very terse style, in which
people pretend a) that all concepts are visualizable, and b) that the readers
can reconstruct the diagrams that the authors had in mind based on only the
most essential cues. As an outsider I spent years believing that the techniques
for drawing diagrams were part of the oral culture of the field, and that the
insiders could read texts on CT reconstructing the "missing diagrams" in them
line by line and paragraph by paragraph, and drawing for each page of text a
page of diagrams with all the diagrams that the authors had omitted. My belief
was wrong: there are lots of conventions for drawing diagrams scattered through
the literature, but that unified diagrammatic language did not exist. In this
chapter I will show an attempt to reconstruct that (imaginary) language for
missing diagrams: we will see an extensible diagrammatic language, called DL,
that follows the conventions of the diagrams in the literature of CT whenever
possible and that seems to be adequate for drawing "missing diagrams" for
Category Theory. Our examples include the "missing diagrams" for adjunctions,
for the Yoneda Lemma, for Kan extensions, and for geometric morphisms, and how
to formalize them in Agda.


title:
Enough is Enough: Towards Autonomous Uncertainty-driven Stopping
  Criteria

abstract:
  Autonomous robotic exploration has long attracted the attention of the
robotics community and is a topic of high relevance. Deploying such systems in
the real world, however, is still far from being a reality. In part, it can be
attributed to the fact that most research is directed towards improving
existing algorithms and testing novel formulations in simulation environments
rather than addressing practical issues of real-world scenarios. This is the
case of the fundamental problem of autonomously deciding when exploration has
to be terminated or changed (stopping criteria), which has not received any
attention recently. In this paper, we discuss the importance of using
appropriate stopping criteria and analyse the behaviour of a novel criterion
based on the evolution of optimality criteria in active graph-SLAM.


title: KdV stationary systems and their St\"ackel representations

abstract:
  The notion of KdV stationary systems is introduced. Taking advantage of the
Lax formalism, it is proved that these systems have two different
representations by means of the particular St\"ackel systems of Benenti type.
Besides, the Miura map between both representations of the KdV stationary
systems is derived.


title: Positivity of permutation pattern character polynomials

abstract:
  Let $N_\sigma(\pi)$ denote the number of occurrences of a permutation pattern
$\sigma\in S_k$ in a permutation $\pi\in S_n$. Gaetz and Ryba showed using
partition algebras that the $d$-th moment $M_{\sigma,d,n}(\pi)$ of $N_\sigma$
on the conjugacy class of $\pi$ is given by a polynomial in
$n,m_1,\dots,m_{dk}$, where $m_i$ denotes the number of $i$-cycles of $\pi$.
They also show that the coefficient $\langle \chi^{\lambda[n]},
M_{\sigma,d,n}\rangle$ agrees with a polynomial $a_{\sigma,d}^\lambda(n)$ in
$n$. This work is motivated by our conjecture that when $\sigma=12...k$ is the
identity permutation, all of these coefficients $a_{12...k}^{\lambda}(n)$ are
nonnegative. We directly compute closed forms for these polynomials in the
cases $\lambda=(1),(1,1),$ and $(2)$, and use this to verify our positivity
conjecture for those cases by showing that the polynomials are real-rooted with
all roots less than $k$. We also study the case $a_{\sigma}^{(1)}(n)$, for
which we give a formula for the polynomials and their leading coefficients.


title:
Existence and asymptotic behavior of normalized ground states for
  Sobolev critical Schr\"odinger systems

abstract:
  The paper is concerned with the existence and asymptotic properties of
normalized ground states of the following nonlinear Schr\"odinger system with
critical exponent: \begin{equation*}
  \left\{\begin{aligned}
  &-\delta u+\lambda_1 u=|u|^{2^*-2}u+{\nu\alpha} |u|^{\alpha-2}|v|^\beta
u,\quad \text{in }\mathbb{R}^N,
  &-\delta v+\lambda_2 v=|v|^{2^*-2}v+{\nu\beta} |u|^\alpha
|v|^{\beta-2}v,\quad \text{in }\mathbb{R}^N,
  &\int u^2=a^2,\;\;\; \int v^2=b^2,
  \end{aligned} \right. \end{equation*} where $N=3,4$, $\alpha,\beta>1$,
$2<\alpha+\beta<2^*=\frac{2N}{N-2}$. We prove that a normalized ground state
does not exist for $\nu<0$. When $\nu>0$ and $\alpha+\beta\le 2+\frac{4}{N}$,
we show that the system has a normalized ground state solution for
$0<\nu<\nu_0$, the constant $\nu_0$ will be explicitly given. In the case
$\alpha+\beta>2+\frac{4}{N}$ we prove the existence of a threshold $\nu_1\ge 0$
such that a normalized ground state solution exists for $\nu>\nu_1$, and does
not exist for $\nu<\nu_1$. We also give conditions for $\nu_1=0$. Finally we
obtain the asymptotic behavior of the minimizers as $\nu\to0^+$ or
$\nu\to+\infty$.


title:
On critical variable-order Kirchhoff type problems with variable
  singular exponent

abstract:
  We establish a continuous embedding $W^{s(\cdot),2}(\Omega)\hookrightarrow
L^{\alpha(\cdot)}(\Omega)$, where the variable exponent $\alpha(x)$ can be
close to the critical exponent $2_{s}^*(x)=\frac{2N}{N-2\bar{s}(x)}$, with
$\bar{s}(x)=s(x,x)$ for all $x\in\bar{\Omega}$. Subsequently, this continuous
embedding is used to prove the multiplicity of solutions for critical nonlocal
degenerate Kirchhoff problems with a variable singular exponent. Moreover, we
also obtain the uniform $L^{\infty}$-estimate of these infinite solutions by a
bootstrap argument.


title:
Ontology-based system to support industrial system design for aircraft
  assembly

abstract:
  The development of an aircraft industrial system is a complex process which
faces the challenge of digital discontinuity in multidisciplinary engineering
due to various interfaces between different digital tools, leading to extra
development time and costs. This paper proposes an ontology-based system,
aiming at functionality integration and design process automation, by Models
for Manufacturing methodology principles. A tool-agnostic modelling, simulation
and validation platform with Discrete Event Simulation and 3D simulation is
enabled and demonstrated in a real case study. An ontology layer collecting the
domain knowledge enables integration of the proposed system, accelerating the
design process and enhancing design quality.


title: Ramification theory for reciprocity sheaves, III, Abbes-Saito formula

abstract:
  We give a new geometric characterization of the motivic ramification
filtration of reciprocity sheaves, by imitating a method used by Abbes and
(Takeshi) Saito to study the ramification of torsors under finite \'etale
groups. This new characterization is used to define characteristic forms for
reciprocity sheaves. We obtain applications on pseudo-rational singularities
and on questions regarding the representability of certain cohomology groups of
reciprocity sheaves in the triangulated category of motives with modulus
introduced by Kahn-Miyazaki-Saito-Yamazaki.


title: Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation

abstract:
  The key challenge for few-shot semantic segmentation (FSS) is how to tailor a
desirable interaction among support and query features and/or their prototypes,
under the episodic training scenario. Most existing FSS methods implement such
support-query interactions by solely leveraging plain operations - e.g., cosine
similarity and feature concatenation - for segmenting the query objects.
However, these interaction approaches usually cannot well capture the intrinsic
object details in the query images that are widely encountered in FSS, e.g., if
the query object to be segmented has holes and slots, inaccurate segmentation
almost always happens. To this end, we propose a dynamic prototype convolution
network (DPCN) to fully capture the aforementioned intrinsic details for
accurate FSS. Specifically, in DPCN, a dynamic convolution module (DCM) is
firstly proposed to generate dynamic kernels from support foreground, then
information interaction is achieved by convolution operations over query
features using these kernels. Moreover, we equip DPCN with a support activation
module (SAM) and a feature filtering module (FFM) to generate pseudo mask and
filter out background information for the query images, respectively. SAM and
FFM together can mine enriched context information from the query features. Our
DPCN is also flexible and efficient under the k-shot FSS setting. Extensive
experiments on PASCAL-5i and COCO-20i show that DPCN yields superior
performances under both 1-shot and 5-shot settings.


title:
Simulating all multipartite non-signalling channels via
  quasiprobabilistic mixtures of local channels in generalised probabilistic
  theories

abstract:
  Non-signalling quantum channels -- relevant in, e.g., the study of Bell and
Einstein-Podolsky-Rosen scenarios -- may be simulated via affine combinations
of local operations in bipartite scenarios. Moreover, when these channels
correspond to stochastic maps between classical variables, such simulation is
possible even in multipartite scenarios. These two results have proven useful
when studying the properties of these channels, such as their communication and
information processing power, and even when defining measures of the
non-classicality of physical phenomena (such as Bell non-classicality and
steering). In this paper we show that such useful quasi-stochastic
characterizations of channels may be unified and applied to the broader class
of multipartite non-signalling channels. Moreover, we show that this holds for
non-signalling channels in quantum theory, as well as in a larger family of
generalised probabilistic theories. More precisely, we prove that
non-signalling channels can always be simulated by affine combinations of
corresponding local operations, provided that the underlying physical theory is
locally tomographic -- a property that quantum theory satisfies. Our results
then can be viewed as a generalisation of Refs.~[Phys. Rev. Lett. 111, 170403]
and [Phys. Rev. A 88, 022318 (2013)] to the multipartite scenario for arbitrary
tomographically local generalised probabilistic theories (including quantum
theory). Our proof technique leverages Hardy's duotensor formalism,
highlighting its utility in this line of research.


title:
W$^\pm$-boson production in p$-$Pb collisions at $\sqrt{s_{NN}} = 8.16$
  TeV and PbPb collisions at $\sqrt{s_{NN}} = 5.02$ TeV

abstract:
  The production of the W$^\pm$ bosons measured in p$-$Pb collisions at a
centre-of-mass energy per nucleon$-$nucleon collision $\sqrt{s_{NN}} = 8.16$
TeV and Pb$-$Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV with ALICE at the LHC
is presented. The W$^\pm$ bosons are measured via their muonic decay channel,
with the muon reconstructed in the pseudorapidity region $-4 < \eta^\mu_{\rm
lab} < -2.5$ with transverse momentum $p_{\rm T}^\mu > 10$ GeV/$c$. While in
Pb$-$Pb collisions the measurements are performed in the forward ($2.5 <
y^\mu_{\rm cms} < 4$) rapidity region, in p$-$Pb collisions, where the
centre-of-mass frame is boosted with respect to the laboratory frame, the
measurements are performed in the backward ($-4.46 < y^\mu_{\rm cms} < -2.96$)
and forward ($2.03 < y^\mu_{\rm cms} < 3.53$) rapidity regions. The W$^{-}$ and
W$^{+}$ production cross sections, lepton-charge asymmetry, and nuclear
modification factors are evaluated as a function of the muon rapidity. In order
to study the production as a function of the p$-$Pb collision centrality, the
production cross sections of the W$^{-}$ and W$^{+}$ bosons are combined and
normalised to the average number of binary nucleon$-$nucleon collision $\langle
N_\mathrm{coll} \rangle$. In Pb$-$Pb collisions, the same measurements are
presented as a function of the collision centrality. Study of the binary
scaling of the W$^\pm$-boson cross sections in p$-$Pb and Pb$-$Pb collisions is
also reported. The results are compared with perturbative QCD (pQCD)
calculations, with and without nuclear modifications of the Parton Distribution
Functions (PDFs), as well as with available data at the LHC. Significant
deviations from the theory expectations are found in the two collision systems,
indicating that the measurements can provide additional constraints for the
determination of nuclear PDF (nPDFs) and in particular of the light-quark
distributions.


title:
Pre-train a Discriminative Text Encoder for Dense Retrieval via
  Contrastive Span Prediction

abstract:
  Dense retrieval has shown promising results in many information retrieval
(IR) related tasks, whose foundation is high-quality text representation
learning for effective search. Some recent studies have shown that
autoencoder-based language models are able to boost the dense retrieval
performance using a weak decoder. However, we argue that 1) it is not
discriminative to decode all the input texts and, 2) even a weak decoder has
the bypass effect on the encoder. Therefore, in this work, we introduce a novel
contrastive span prediction task to pre-train the encoder alone, but still
retain the bottleneck ability of the autoencoder. % Therefore, in this work, we
propose to drop out the decoder and introduce a novel contrastive span
prediction task to pre-train the encoder alone. The key idea is to force the
encoder to generate the text representation close to its own random spans while
far away from others using a group-wise contrastive loss. In this way, we can
1) learn discriminative text representations efficiently with the group-wise
contrastive learning over spans and, 2) avoid the bypass effect of the decoder
thoroughly. Comprehensive experiments over publicly available retrieval
benchmark datasets show that our approach can outperform existing pre-training
methods for dense retrieval significantly.


title: Quantum Unique Ergodicity for Cayley graphs of quasirandom groups

abstract:
  A finite group $G$ is called $C$-quasirandom (by Gowers) if all non-trivial
irreducible complex representations of $G$ have dimension at least $C$. For any
unit $\ell^{2}$ function on a finite group we associate the quantum probability
measure on the group given by the absolute value squared of the function. We
show that if a group is highly quasirandom, in the above sense, then any Cayley
graph of this group has an orthonormal eigenbasis of the adjacency operator
such that the quantum probability measures of the eigenfunctions put close to
the correct proportion of their mass on subsets of the group that are not too
small.


title: Quantum Proof of Work with Parametrized Quantum Circuits

abstract:
  Despite all the progress in quantum technologies over the last decade, there
is still a dearth of practical applications for quantum computers with a small
number of noisy qubits. The effort to show quantum supremacy has been largely
focused on demonstrating computations that cannot be accomplished on a
classical computer at all, a difficult and controversial target. Quantum
advantage (a speedup over classical computers) is a more practical milestone
for today's modest quantum processors. In this work, we proposed a scheme for
quantum-computer compatible proof of work (cryptographic mechanism used in
Bitcoin mining) and verified it on a 4-qubit superconducting quantum node.


title:
Dense random packing with a power-law size distribution: the structure
  factor, mass-radius relation, and pair distribution function

abstract:
  We consider dense random packing of disks with a power-law distribution of
radii and investigate their correlation properties. We study the corresponding
structure factor, mass-radius relation and pair distribution function of the
disk centers. A toy model of dense segments in one dimension (1d) is solved
exactly. It is shown theoretically in 1d and numerically in 1d and 2d that such
packing exhibits fractal properties. It is found that the exponent of the
power-law distribution and the fractal dimension coincide. An approximate
relation for the structure factor in arbitrary dimension is derived, which can
be used as a fitting formula in small-angle scattering. The findings can be
useful for understanding microstructural properties of various systems like
ultra-high performance concrete, high-internal-phase ratio emulsions or
biological systems.


title:
A robust Bayesian bias-adjusted random effects model for consideration
  of uncertainty about bias terms in evidence synthesis

abstract:
  Meta-analysis is a statistical method used in evidence synthesis for
combining, analyzing and summarizing studies that have the same target endpoint
and aims to derive a pooled quantitative estimate using fixed and random
effects models or network models. Differences among included studies depend on
variations in target populations (i.e. heterogeneity) and variations in study
quality due to study design and execution (i.e. bias). The risk of bias is
usually assessed qualitatively using critical appraisal, and quantitative bias
analysis can be used to evaluate the influence of bias on the quantity of
interest. We propose a way to consider ignorance or ambiguity in how to
quantify bias terms in a bias analysis by characterizing bias with imprecision
(as bounds on probability) and use robust Bayesian analysis to estimate the
overall effect. Robust Bayesian analysis is here seen as Bayesian updating
performed over a set of coherent probability distributions, where the set
emerges from a set of bias terms. We show how the set of bias terms can be
specified based on judgments on the relative magnitude of biases (i.e., low,
unclear and high risk of bias) in one or several domains of the Cochrane's risk
of bias table. For illustration, we apply a robust Bayesian bias-adjusted
random effects model to an already published meta-analysis on the effect of
Rituximab for rheumatoid arthritis from the Cochrane Database of Systematic
Reviews.


title: Measuring the Salad Bowl: Superdiversity on Twitter

abstract:
  Superdiversity refers to large cultural diversity in a population due to
immigration. In this paper, we introduce a superdiversity index based on the
changes in the emotional content of words used by a multi-cultural community,
compared to the standard language. To compute our index we use Twitter data and
we develop an algorithm to extend a dictionary for lexicon-based sentiment
analysis. We validate our index by comparing it with official immigration
statistics available from the European Commission's Joint Research Center,
through the D4I data challenge. We show that, in general, our measure
correlates with immigration rates, at various geographical resolutions. Our
method produces very good results across languages, being tested here both on
English and Italian tweets. We argue that our index has predictive power in
regions where exact data on immigration is not available, paving the way for a
nowcasting model of immigration rates.


title:
Log-based Sparse Nonnegative Matrix Factorization for Data
  Representation

abstract:
  Nonnegative matrix factorization (NMF) has been widely studied in recent
years due to its effectiveness in representing nonnegative data with
parts-based representations. For NMF, a sparser solution implies better
parts-based representation.However, current NMF methods do not always generate
sparse solutions.In this paper, we propose a new NMF method with log-norm
imposed on the factor matrices to enhance the sparseness.Moreover, we propose a
novel column-wisely sparse norm, named $\ell_{2,\log}$-(pseudo) norm to enhance
the robustness of the proposed method.The $\ell_{2,\log}$-(pseudo) norm is
invariant, continuous, and differentiable.For the $\ell_{2,\log}$ regularized
shrinkage problem, we derive a closed-form solution, which can be used for
other general problems.Efficient multiplicative updating rules are developed
for the optimization, which theoretically guarantees the convergence of the
objective value sequence.Extensive experimental results confirm the
effectiveness of the proposed method, as well as the enhanced sparseness and
robustness.


title: Exposure Correction Model to Enhance Image Quality

abstract:
  Exposure errors in an image cause a degradation in the contrast and low
visibility in the content. In this paper, we address this problem and propose
an end-to-end exposure correction model in order to handle both under- and
overexposure errors with a single model. Our model contains an image encoder,
consecutive residual blocks, and image decoder to synthesize the corrected
image. We utilize perceptual loss, feature matching loss, and multi-scale
discriminator to increase the quality of the generated image as well as to make
the training more stable. The experimental results indicate the effectiveness
of proposed model. We achieve the state-of-the-art result on a large-scale
exposure dataset. Besides, we investigate the effect of exposure setting of the
image on the portrait matting task. We find that under- and overexposed images
cause severe degradation in the performance of the portrait matting models. We
show that after applying exposure correction with the proposed model, the
portrait matting quality increases significantly.
https://github.com/yamand16/ExposureCorrection


title: Choice of mixture Poisson models based on Extreme value theory

abstract:
  Count data are omnipresent in many applied fields, often with overdispersion
due to an excess of zeroes or extreme values. With mixtures of Poisson
distributions representing an elegant and appealing modelling strategy, we
focus here on the challenging problem of identifying a suitable mixing
distribution and study how extreme value theory can be used. We propose an
original strategy to select the most appropriate candidate among three
categories: Fr{\'e}chet, Gumbel and pseudo-Gumbel. Such an approach is
presented with the aid of a decision tree and evaluated with numerical
simulations.


title: How Friedmann Shod Einstein

abstract:
  Exactly one hundred years ago, in May 1922, Alexander Friedmann discovered
that General Relativity (GR) predicts non-stationary Universe. His equation
describing the evolution of the Universe is now named after him. In this paper
we briefly recall the human and scientific aspects of this revolutionary change
in our picture of the world. We also recall that immediately after finishing
his seminal paper Friedmann wrote a book on GR for a wide audience. In that
book he presented an impressive picture of the expanding Universe created from
nothing.


title:
Natural separation of two primordial planetary reservoirs in an
  expanding solar protoplanetary disk

abstract:
  Meteorites display an isotopic composition dichotomy between non-carbonaceous
(NC) and carbonaceous (CC) groups, indicating that planetesimal formation in
the solar protoplanetary disk occurred in two distinct reservoirs. The
prevailing view is that a rapidly formed Jupiter acted as a barrier between
these reservoirs. We show a fundamental inconsistency in this model: if Jupiter
is an efficient blocker of drifting pebbles, then the interior NC reservoir is
depleted by radial drift within a few hundred thousand years. If Jupiter lets
material pass it, then the two reservoirs will be mixed. Instead, we
demonstrate that the arrival of the CC pebbles in the inner disk is delayed for
several million years by the viscous expansion of the protoplanetary disk. Our
results support that Jupiter formed in the outer disk (>10 AU) and allowed a
considerable amount of CC material to pass it and become accreted by the
terrestrial planets.


title:
Design and Implementation of EEG-Mechatronic System Interface for
  Computational Intelligence

abstract:
  The devices that can read Electroencephalography (EEG) signals have been
widely used for Brain-Computer Interfaces (BCIs). Popularity in the field of
BCIs has increased in recent years with the development of several
consumer-grade EEG devices that can detect human cognitive states in real-time
and deliver feedback to enhance human performance. Several studies are
conducted to understand the fundamentals and essential aspects of EEG in BCIs.
However, the significant issue of how can consumer-grade EEG devices be used to
control mechatronic systems effectively has been given less attention. In this
paper, we have designed and implemented an EEG BCI system using the OpenBCI
Cyton headset and a user interface running a game. We employ real-world
participants to play a game to gather training data that was later put into
multiple machine learning models, including a linear discriminant analysis
(LDA), k-nearest neighbours (KNN), and a convolutional neural network (CNN).
After training the machine learning models, a validation phase of the
experiment took place where participants tried to play the same game but
without direct control, utilising the outputs of the machine learning models to
determine how the game moved. We find that a CNN trained to the specific user
playing the game performed with the highest activation accuracy from the
machine learning models tested, allowing for future implementation with a
mechatronic system.


title:
On systems of particles in singular repulsive interaction in dimension
  one : log and Riesz gas

abstract:
  In this article, we prove the first quantitative uniform in time propagation
of chaos for a class of systems of particles in singular repulsive interaction
in dimension one that contains the Dyson Brownian motion. We start by
establishing existence and uniqueness for the Riesz gases, before proving
propagation of chaos with an original approach to the problem, namely coupling
with a Cauchy sequence type argument. We also give a general argument to turn a
result of weak propagation of chaos into a strong and uniform in time result
using the long time behavior and some bounds on moments, in particular enabling
us to get a uniform in time version of the result of C\'epa-L\'epingle.


title:
On functional limit theorems for branching processes with dependent
  immigration

abstract:
  In this paper we consider a triangular array of branching processes with
non-stationary immigration. We prove a weak convergence of properly normalized
branching processes with immigration to deterministic function under assumption
that immigration is rowwise $\psi-$mixing and the offspring mean tends to its
critical value 1, immigration mean and variance controlled by regularly varying
functions. Moreover, we obtain a fluctuation limit theorem for branching
process with immigration when immigration is $m-$dependent where $m$ may tend
to infinity with the row index at a certain rate. In this case the limiting
process is a time-changed Wiener process. Our results extend and improve the
previous known results in the literature.


title:
Use of Context in Data Quality Management: a Systematic Literature
  Review

abstract:
  The importance of context in data quality (DQ) was shown many years ago and
nowadays is widely accepted. Early approaches and surveys defined DQ as
\textit{fitness for use} and showed the influence of context on DQ. This paper
presents a Systematic Literature Review (SLR) for investigating how context is
taken into account in recent proposals for DQ management. We specifically
present the planning and execution of the SLR, the analysis criteria and our
results reflecting the relationship between context and DQ in the state of the
art and, particularly, how that context is defined and used for DQ management.


title:
Shear viscosity for finitely extensible chains with fluctuating internal
  friction and hydrodynamic interactions

abstract:
  An exact solution of coarse-grained polymer models with fluctuating internal
friction and hydrodynamic interactions has not been proposed so far due to a
one-to-all coupling between the connector vector velocities that precludes the
formulation of the governing stochastic differential equations. A methodology
for the removal of this coupling is presented, and the governing stochastic
differential equations, obtained by attaching a kinetic interpretation to the
Fokker-Planck equation for the system, are integrated numerically using
Brownian dynamics simulations. The proposed computational route eliminates the
calculation of the divergence of the diffusion tensor which appears in models
with internal friction, and is about an order of magnitude faster than the
recursion-based decoupling algorithm previously developed [J. Rheol., 65, 903
(2021)] for the solution of freely draining models with internal friction. The
effects of the interplay of various combinations of finite extensibility,
internal friction and hydrodynamic interactions on the steady-shear-viscosity
is examined. While finite extensibility leads solely to shear-thinning, both
internal friction and hydrodynamic interactions result in shear-thinning
followed by shear-thickening. The shear-thickening induced by internal friction
effects are more pronounced than that due to hydrodynamic interactions.


title: Signatures of topotactic hydrogen in nickelate superconductors

abstract:
  Superconductivity has entered the nickel age marked by enormous experimental
and theoretical efforts. Notwithstanding, synthesizing nickelate
superconductors remains extremely challenging, not least due to incomplete
oxygen reduction and topotactic hydrogen. Here, we present density-functional
theory calculations, identify a phonon mode as a possible indication for
topotactic hydrogen and discuss the charge redistribution patterns around
oxygen and hydrogen impurities.


title:
Sequential Learning and Economic Benefits from Dynamic Term Structure
  Models

abstract:
  This paper explores the statistical and economic importance of restrictions
on the dynamics of risk compensation, from the perspective of a real-time
Bayesian learner who predicts bond excess returns using a dynamic term
structure model (DTSM). We propose a novel methodological framework that
successfully handles sequential model search and parameter estimation over the
restriction space landscape in real time, allowing investors to revise their
beliefs when new information arrives, thus informing their asset allocation and
maximizing their expected utility. Our setup provides the entire predictive
density of returns, allowing us to revisit the evident puzzling behaviour
between statistical predictability and meaningful out-of-sample economic
benefits for bond investors. Empirical results reveal the importance of
different sets of restrictions across market conditions and monetary policy
actions. Furthermore, our results reinforce the argument of sparsity in the
market price of risk specification since we find strong evidence of
out-of-sample predictability only for those models that allow for level risk to
be priced. Most importantly, such statistical evidence is turned into
economically significant utility gains, across prediction horizons. The
sequential version of the stochastic search variable selection (SSVS) scheme
developed offers an important diagnostic as it monitors potential changes in
the importance of different risk prices over time and provides further
improvement during periods of macroeconomic uncertainty, where results are more
pronounced.


title: Evaluating the Reliability of Air Temperature from ERA5 Reanalysis Data

abstract:
  The reliability of ERA5 satellite-based air temperature data is under
investigation in this paper. To evaluate this, the ERA5 data will be compared
with land-based data obtained from weather stations on the Global Historical
Climatology Network. Two climate regions are taken into consideration,
temperate and tropical. Five years' worth of data is collected and compared
through box plots, regression models and statistical metrics. The results show
that the satellite temperature performs better in the temperate region than the
tropical region. This suggests that the time of year and climate region have an
impact on the accuracy of the satellite data as milder temperatures produce
better approximations.


title:
On $\mathcal{I}$-convergence of sequences of functions and uniform
  conjugacy

abstract:
  In this paper we introduce the notion of
$\mathcal{I^*}\text{-}\alpha$-uniform equal convergence and
$\mathcal{I^*}\text{-}\alpha$-strong uniform equal convergence of sequences of
functions and then investigate some lattice properties of $\Phi
^{\mathcal{I^*}\text{-}\alpha\text{-}u.e.}$ and $\Phi
^{\mathcal{I^*}\text{-}\alpha \text{-}s.u.e.}$, the classes of all functions
which are $\mathcal{I^*}\text{-}\alpha$-uniform equal limits and
$\mathcal{I^*}\text{-}\alpha$-strong uniform equal limits of sequences of
functions respectively obtained from a class of functions $\Phi$. We have also
shown that $\mathcal{I}$-exhaustiveness, $\mathcal{I}$-uniform and
$\mathcal{I}\text{-}\alpha$- convergence of sequences of functions are
preserved under uniform conjugacy


title: Complex Langevin approach to interacting Bose gases

abstract:
  Quantitative numerical analyses of interacting dilute Bose-Einstein
condensates are most often based on semiclassical approximations. Since the
complex-valued field-theoretic action of the Bose gas does not offer itself to
standard Monte Carlo techniques, simulations beyond their scope almost
exclusively rely on quantum-mechanical techniques, which are inherently limited
to small particle numbers. Here we explore an alternative approach based on a
Langevin-type sampling in an extended state space, known as complex Langevin
(CL) algorithm. While the use of the CL technique has a long-standing history
in high-energy physics, in particular in the simulation of QCD at finite baryon
density, applications to ultracold atoms are still in their infancy. Here we
examine the applicability of the CL approach for a one- and two-component,
three-dimensional non-relativistic Bose gas in thermal equilibrium, below and
above the Bose-Einstein phase transition. By comparison with analytic
descriptions at the Gaussian level, including Bogoliubov and Hartree-Fock
theory, we find that the method allows computing reliably and efficiently
observables in the regime of experimentally accessible parameters. Close to the
transition, quantum corrections lead to a shift of the critical temperature
which we reproduce within the numerical range known in the literature. With
this work, we aim to provide a first test of CL as a potential out-of-the-box
tool for the simulation of experimentally realistic situations, including
trapping geometries and multicomponent/-species models.


title: OPerA: Object-Centric Performance Analysis

abstract:
  Performance analysis in process mining aims to provide insights on the
performance of a business process by using a process model as a formal
representation of the process. Such insights are reliably interpreted by
process analysts in the context of a model with formal semantics. Existing
techniques for performance analysis assume that a single case notion exists in
a business process (e.g., a patient in healthcare process). However, in
reality, different objects might interact (e.g., order, item, delivery, and
invoice in an O2C process). In such a setting, traditional techniques may yield
misleading or even incorrect insights on performance metrics such as waiting
time. More importantly, by considering the interaction between objects, we can
define object-centric performance metrics such as synchronization time, pooling
time, and lagging time. In this work, we propose a novel approach to
performance analysis considering multiple case notions by using object-centric
Petri nets as formal representations of business processes. The proposed
approach correctly computes existing performance metrics, while supporting the
derivation of newly-introduced object-centric performance metrics. We have
implemented the approach as a web application and conducted a case study based
on a real-life loan application process.


title:
3D pride without 2D prejudice: Bias-controlled multi-level generative
  models for structure-based ligand design

abstract:
  Generative models for structure-based molecular design hold significant
promise for drug discovery, with the potential to speed up the hit-to-lead
development cycle, while improving the quality of drug candidates and reducing
costs. Data sparsity and bias are, however, two main roadblocks to the
development of 3D-aware models. Here we propose a first-in-kind training
protocol based on multi-level contrastive learning for improved bias control
and data efficiency. The framework leverages the large data resources available
for 2D generative modelling with datasets of ligand-protein complexes. The
result are hierarchical generative models that are topologically unbiased,
explainable and customizable. We show how, by deconvolving the generative
posterior into chemical, topological and structural context factors, we not
only avoid common pitfalls in the design and evaluation of generative models,
but furthermore gain detailed insight into the generative process itself. This
improved transparency significantly aids method development, besides allowing
fine-grained control over novelty vs familiarity.


title:
i-GSI: A Fast and Reliable Grasp-type Switching Interface based on
  Augmented Reality and Eye-tracking

abstract:
  The control of multi-fingered dexterous prosthetics hand remains challenging
due to the lack of an intuitive and efficient Grasp-type Switching Interface
(GSI). We propose a new GSI (i-GSI) t hat integrates the manifold power of
eye-tracking and augmentced reality technologies to solve this problem. It runs
entirely in a HoloLens2 helmet, where users can glance at icons on the
holographic interface to switch between six daily grasp types quickly. Compared
to traditional GSIs (FSM-based, PR-based, and mobile APP-based), i-GSI achieved
the best results in the experiment with eight healthy subjects, achieving a
switching time of 0.84 s, a switching success rate of 99.0%, and learning
efficiency of 93.50%. By verifying on one patient with a congenital upper limb
deficiency, i-GSI achieved an equivalent great outcome as on healthy people,
with a switching time of 0.78 s and switching success rate of 100%. The new
i-GSI, as a standalone module, can be combined with traditional proportional
myoelectric control to form a hybrid-controlled prosthetic system that can help
patients accomplish dexterous operations in various daily-life activities.


title: Manifestation of Majorana modes overlap in the Aharonov-Bohm effect

abstract:
  One of the key features of the Majorana bound states emerging in topological
superconducting (SC) wires is increasing oscillations of their energy under the
growth of magnetic field or chemical potential due to concomitant enhancement
of hybridization of the Majorana mode wave functions initially localized at the
opposite edges of the structure. In this study we found that the other
consequence of it is a shift of Aharonov-Bohm (AB) oscillations of
linear-response conductance in an interference device where two ends of the SC
wire connected with a normal contact via non-SC wires (arms). In addition, it
is accompanied by an oscillation period doubling. The numerical calculations
for the spinful system are supported by the analytical results for different
spinless models allowing to track the conductance evolution as the
hybridization of the Majorana modes increases. It is shown that since the
coupling between the different arms and normal contact is implemented only via
the different-type Majoranas the AB oscillations acquire a fundamental {\pi}/2
shift in comparison with the effect for an analogous system of zero-energy
quantum dots.


title: Some results on the super domination number of a graph

abstract:
  Let $G=(V,E)$ be a simple graph. A dominating set of $G$ is a subset
$S\subseteq V$ such that every vertex not in $S$ is adjacent to at least one
vertex in $S$. The cardinality of a smallest dominating set of $G$, denoted by
$\gamma(G)$, is the domination number of $G$. A dominating set $S$ is called a
super dominating set of $G$, if for every vertex $u\in \overline{S}=V-S$, there
exists $v\in S$ such that $N(v)\cap \overline{S}=\{u\}$. The cardinality of a
smallest super dominating set of $G$, denoted by $\gamma_{sp}(G)$, is the super
domination number of $G$. In this paper, we study super domination number of
some graph classes and present sharp bounds for some graph operations.


title:
DFAM-DETR: Deformable feature based attention mechanism DETR on slender
  object detection

abstract:
  Object detection is one of the most significant aspects of computer vision,
and it has achieved substantial results in a variety of domains. It is worth
noting that there are few studies focusing on slender object detection. CNNs
are widely employed in object detection, however it performs poorly on slender
object detection due to the fixed geometric structure and sampling points. In
comparison, Deformable DETR has the ability to obtain global to specific
features. Even though it outperforms the CNNs in slender objects detection
accuracy and efficiency, the results are still not satisfactory. Therefore, we
propose Deformable Feature based Attention Mechanism (DFAM) to increase the
slender object detection accuracy and efficiency of Deformable DETR. The DFAM
has adaptive sampling points of deformable convolution and attention mechanism
that aggregate information from the entire input sequence in the backbone
network. This improved detector is named as Deformable Feature based Attention
Mechanism DETR (DFAM- DETR). Results indicate that DFAM-DETR achieves
outstanding detection performance on slender objects.


title:
The Hubble constant from two sibling Type Ia supernovae in the nearby
  galaxy NGC 4414: SN 1974G and SN 2021J

abstract:
  Having two "sibling" Type Ia supernovae (SNe Ia) in the same galaxy offers
additional advantages in reducing a variety of systematic errors involved in
estimating the Hubble constant, $H_{0}$. NGC 4414 is a nearby galaxy included
in the Hubble Space Telescope Key Project to measure its distance using Cepheid
variables. It hosts two sibling SNe Ia: SN 2021J and SN 1974G. This provides
the opportunity to improve the precision of the previous estimate of $H_{0}$,
which was based solely on SN 1974G. Here we present new optical $BVRI$
photometry obtained at the Observatorio de Sierra Nevada and complement it with
Swift UVOT $UBV$ data, which cover the first 70 days of emission of SN 2021J. A
first look at SN 2021J optical spectra obtained with the Gran Telescopio
Canarias (GTC) reveals typical SN type Ia features. The main SN luminosity
parameters for the two sibling SNe are obtained by using SNooPy, a light curve
fitting code based on templates. Using a hierarchical bayesian approach, we
build the Hubble diagram with a sample of 95 SNe Ia obtained from the Combined
Pantheon Sample in the redshift range $z = 0.02-0.07$, and calibrate the zero
point with the two sibling type-Ia SNe in NGC 4414. We report a value of the
Hubble constant $H_{0}$ $= 72.19 \pm 2.32$ (stat.) km s$^{-1}$Mpc$^{-1}$, with
a systematic error of 5.44 km s$^{-1}$Mpc$^{-1}$ due mostly to the pre-Gaia LMC
zero-point systematics adopted in the HST photometry calibration of the NGC
4414 Cepheids which were used to measure the distance to NGC 4414. We expect a
significant reduction of this systematic error after a new analysis of the
Cepheids period-luminosity relation and photometry calibration using the
upcoming Gaia DR4


title: Risk Awareness in HTN Planning

abstract:
  Actual real-world domains are characterised by uncertain situations in which
acting and use of resources require embracing risk. Performing actions in such
domains always entails costs of consuming some resource, such as time, money,
or energy, where the knowledge about these costs can range from totally known
to totally unknown and even unknowable probabilities of costs. Think of robotic
domains, where actions and their costs are non-deterministic due to uncertain
factors like obstacles. Choosing which action to perform considering its cost
on the available resource requires taking a stance on risk. Thus, these domains
call for not only planning under uncertainty but also planning while embracing
risk. Taking Hierarchical Task Network (HTN) planning as a widely used planning
technique in real-world applications, one can observe that existing approaches
do not account for risk. That is, computing most probable or optimal plans
using actions with single-valued costs is only enough to express risk
neutrality. In this work, we postulate that HTN planning can become risk aware
by considering expected utility theory, a representative concept of decision
theory that enables choosing actions considering a probability distribution of
their costs and a given risk attitude expressed using a utility function. In
particular, we introduce a general framework for HTN planning that allows
modelling risk and uncertainty using a probability distribution of action costs
upon which we define risk-aware HTN planning as an approach that accounts for
the different risk attitudes and allows computing plans that go beyond risk
neutrality. In fact, we layout that computing risk-aware plans requires finding
plans with the highest expected utility. Finally, we argue that it is possible
for HTN planning agents to solve specialised risk-aware HTN planning problems
by adapting some existing HTN planning approaches.


title:
Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better
  than Dot-Product Self-Attention

abstract:
  Self-Attention is a widely used building block in neural modeling to mix
long-range data elements. Most self-attention neural networks employ pairwise
dot-products to specify the attention coefficients. However, these methods
require $O(N^2)$ computing cost for sequence length $N$. Even though some
approximation methods have been introduced to relieve the quadratic cost, the
performance of the dot-product approach is still bottlenecked by the low-rank
constraint in the attention matrix factorization. In this paper, we propose a
novel scalable and effective mixing building block called Paramixer. Our method
factorizes the interaction matrix into several sparse matrices, where we
parameterize the non-zero entries by MLPs with the data elements as input. The
overall computing cost of the new building block is as low as $O(N \log N)$.
Moreover, all factorizing matrices in Paramixer are full-rank, so it does not
suffer from the low-rank bottleneck. We have tested the new method on both
synthetic and various real-world long sequential data sets and compared it with
several state-of-the-art attention networks. The experimental results show that
Paramixer has better performance in most learning tasks.


title:
Exponential Separation between Quantum and Classical Ordered Binary
  Decision Diagrams, Reordering Method and Hierarchies

abstract:
  In this paper, we study quantum Ordered Binary Decision Diagrams($OBDD$)
model; it is a restricted version of read-once quantum branching programs, with
respect to "width" complexity. It is known that the maximal gap between
deterministic and quantum complexities is exponential. But there are few
examples of functions with such a gap. We present a new technique
("reordering") for proving lower bounds and upper bounds for OBDD with an
arbitrary order of input variables if we have similar bounds for the natural
order. Using this transformation, we construct a total function $REQ$ such that
the deterministic $OBDD$ complexity of it is at least $2^{\Omega(n / \log n)}$,
and the quantum $OBDD$ complexity of it is at most $O(n^2/\log n)$. It is the
biggest known gap for explicit functions not representable by $OBDD$s of a
linear width. Another function(shifted equality function) allows us to obtain a
gap $2^{\Omega(n)}$ vs $O(n^2)$.
  Moreover, we prove the bounded error quantum and probabilistic $OBDD$ width
hierarchies for complexity classes of Boolean functions. Additionally, using
"reordering" method we extend a hierarchy for read-$k$-times Ordered Binary
Decision Diagrams ($k$-$OBDD$) of polynomial width, for $k = o(n / \log^3 n)$.
We prove a similar hierarchy for bounded error probabilistic $k$-$OBDD$s of
polynomial, superpolynomial and subexponential width.
  The extended abstract of this work was presented on International
  Computer Science Symposium in Russia, CSR 2017, Kazan, Russia, June
  8 -- 12, 2017


title:
A systematic review of guidelines for the use of race, ethnicity, and
  ancestry reveals widespread consensus but also points of ongoing disagreement

abstract:
  The use of population descriptors like race, ethnicity, and ancestry in
science, medicine and public health has a long, complicated, and at times dark
history, particularly for genetics, given the field's perceived importance for
understanding between-group differences. The historical and potential harms
that come with irresponsible use of these categories suggests a clear need for
definitive guidance about when and how they can be used appropriately. However,
while many prior authors have provided such guidance, no established consensus
exists, and the extant literature has not been examined for implied consensus
and sources of disagreement. Here we present the results of a systematic review
of published normative recommendations regarding the use of population
categories, particularly in genetics research. Following PRISMA guidelines, we
extracted recommendations from n=121 articles matching inclusion criteria.
Articles were published consistently throughout the time period examined and in
a broad range of journals, demonstrating an ongoing and interdisciplinary
perceived need for guidance. Examined recommendations fall under one of eight
themes identified during analysis. Seven are characterized by broad agreement
across articles; one, Appropriate definitions of population categories and
contexts for use, revealed substantial fundamental disagreement among articles.
While many articles focus on the inappropriate use of race, none fundamentally
problematize ancestry. This work can be a resource to researchers looking for
normative guidance on the use of population descriptors, and can orient authors
of future guidelines to this complex field, contributing to the development of
more effective future guidelines for genetics research.


title: Generative De Novo Protein Design with Global Context

abstract:
  The linear sequence of amino acids determines protein structure and function.
Protein design, known as the inverse of protein structure prediction, aims to
obtain a novel protein sequence that will fold into the defined structure.
Recent works on computational protein design have studied designing sequences
for the desired backbone structure with local positional information and
achieved competitive performance. However, similar local environments in
different backbone structures may result in different amino acids, indicating
that protein structure's global context matters. Thus, we propose the
Global-Context Aware generative de novo protein design method (GCA), consisting
of local and global modules. While local modules focus on relationships between
neighbor amino acids, global modules explicitly capture non-local contexts.
Experimental results demonstrate that the proposed GCA method outperforms
state-of-the-arts on de novo protein design. Our code and pretrained model will
be released.


title:
Proton-Electron Mass Ratio from Laser Spectroscopy of HD$^+$ at the
  Part-Per-Trillion Level

abstract:
  Accepted values of the masses of several subatomic particles have been under
debate since recent measurements in Penning traps produced more precise yet
incompatible results, implying possible inconsistencies in closely related
physical constants like the proton-electron and deuteron-proton mass ratios.
These quantities also influence the predicted vibrational spectrum of the
deuterated molecular hydrogen ion in its electronic ground state, of which we
measured the v=0 - 9 overtone transition frequency with an uncertainty of 2.9
parts-per-trillion through Doppler-free two-photon laser spectroscopy.
Leveraging high-precision ab initio calculations we convert our measurement to
tight constraints on the proton-electron and deuteron-proton mass ratios,
consistent with the most recent Penning-trap determinations of these
quantities, and yielding a new value of the proton-electron mass ratio with an
unprecedented precision of 21 parts-per-trillion.


title:
Unknown Face Presentation Attack Detection via Localised Learning of
  Multiple Kernels

abstract:
  The paper studies face spoofing, a.k.a. presentation attack detection (PAD)
in the demanding scenarios of unknown types of attack. While earlier studies
have revealed the benefits of ensemble methods, and in particular, a multiple
kernel learning approach to the problem, one limitation of such techniques is
that they typically treat the entire observation space similarly and ignore any
variability and local structure inherent to the data. This work studies this
aspect of the face presentation attack detection problem in relation to
multiple kernel learning in a one-class setting to benefit from intrinsic local
structure in bona fide face samples. More concretely, inspired by the success
of the one-class Fisher null formalism, we formulate a convex localised
multiple kernel learning algorithm by imposing a joint matrix-norm constraint
on the collection of local kernel weights and infer locally adaptive weights
for zero-shot one-class unseen attack detection.
  We present a theoretical study of the proposed localised MKL algorithm using
Rademacher complexities to characterise its generalisation capability and
demonstrate the advantages of the proposed technique over some other options.
An assessment of the proposed approach on general object image datasets
illustrates its efficacy for abnormality and novelty detection while the
results of the experiments on face PAD datasets verifies its potential in
detecting unknown/unseen face presentation attacks.


title: Bayesian mixed-effect models for independent dynamic social network data

abstract:
  Relational event or time-stamped social network data have become increasingly
available over the years. Accordingly, statistical methods for such data have
also surfaced. These techniques are based on log-linear models of the rates of
interactions in a social network via actor covariates and network statistics.
Particularly, the use of survival analysis concepts has stimulated the
development of powerful methods over the past decade. These models mainly focus
on the analysis of single networks. To date, there are few models that can deal
with multiple relational event networks jointly. In this paper, we propose a
new Bayesian hierarchical model for multiple relational event sequences. This
approach allows inferences at the actor level, which are useful in
understanding which effects guide actors' preferences in social interactions.
We also present Bayes factors for hypothesis testing in this class of models.
In addition, a new Bayes factor to test random-effect structures is developed.
In this test, we let the prior be determined by the data, alleviating the issue
of employing improper priors in Bayes factors and thus preventing the use of
ad-hoc choices in absence of prior information. We use data of classroom
interactions among high school students to illustrate the proposed methods.


title: Improving tracking with a tracklet associator

abstract:
  Multiple object tracking (MOT) is a task in computer vision that aims to
detect the position of various objects in videos and to associate them to a
unique identity. We propose an approach based on Constraint Programming (CP)
whose goal is to be grafted to any existing tracker in order to improve its
object association results. We developed a modular algorithm divided into three
independent phases. The first phase consists in recovering the tracklets
provided by a base tracker and to cut them at the places where uncertain
associations are spotted, for example, when tracklets overlap, which may cause
identity switches. In the second phase, we associate the previously constructed
tracklets using a Belief Propagation Constraint Programming algorithm, where we
propose various constraints that assign scores to each of the tracklets based
on multiple characteristics, such as their dynamics or the distance between
them in time and space. Finally, the third phase is a rudimentary interpolation
model to fill in the remaining holes in the trajectories we built. Experiments
show that our model leads to improvements in the results for all three of the
state-of-the-art trackers on which we tested it (3 to 4 points gained on HOTA
and IDF1).


title:
Sequential monitoring using the Second Generation P-Value with Type I
  error controlled by monitoring frequency

abstract:
  Many adaptive monitoring schemes adjust the required evidence toward a
hypothesis to control Type I error. This shifts focus away from determining
scientific relevance with an uncompromised degree of evidence. We propose
sequentially monitoring the Second Generation P-Value (SGPV) on repeated
intervals until establishing evidence for scientific relevance (SeqSGPV).
SeqSGPV encompasses existing strategies to monitor Region of Practical
Equivalence (ROPE) or Region of Equivalence (ROE) hypotheses. Hence, our focus
is to formalize sequential SGPV monitoring; establish a novel set of scientific
hypotheses, called PRISM, which is a ROE with a ROPE surrounding the null
hypothesis; and use monitoring frequency and a novel affirmation step to
control Type I error. Under immediate and delayed outcomes, we assess finite
and limiting SeqSGPV operating characteristics when monitoring PRISM, ROPE, and
null-bound ROE hypotheses. In extensive simulations, SeqSGPV PRISM monitoring
reduced wait time for fully sequential monitoring, average sample size, and
reversals of null hypothesis conclusions under the null. With real-world data,
we design a SeqSGPV-monitored randomized trial. SeqSGPV is method-agnostic and
easy to implement. Adjusting monitoring frequency/affirmation and monitoring a
one-sided PRISM synergistically control Type I error. PRISM monitoring and
adjusting monitoring frequency to control Type I error may have application
beyond SeqSGPV.


title:
Deterministic Sensitivity Oracles for Diameter, Eccentricities and All
  Pairs Distances

abstract:
  We construct data structures for extremal and pairwise distances in directed
graphs in the presence of transient edge failures. Henzinger et al. [ITCS 2017]
initiated the study of fault-tolerant (sensitivity) oracles for the diameter
and vertex eccentricities. We extend this with a special focus on space
efficiency. We present several new data structures, among them the first
fault-tolerant eccentricity oracle for dual failures in subcubic space. We
further prove lower bounds that show limits to approximation vs. space and
diameter vs. space trade-offs for fault-tolerant oracles. They highlight key
differences between data structures for undirected and directed graphs.
  Initially, our oracles are randomized leaning on a sampling technique
frequently used in sensitivity analysis. Building on the work of Alon, Chechik,
and Cohen [ICALP 2019] as well as Karthik and Parter [SODA 2021], we develop a
hierarchical framework to derandomize fault-tolerant data structures. We first
apply it to our own diameter and eccentricity oracles and then show its
versatility by derandomizing algorithms from the literature: the distance
sensitivity oracle of Ren [JCSS 2022] and the Single-Source Replacement Path
algorithm of Chechik and Magen [ICALP 2020]. This way, we obtain the first
deterministic distance sensitivity oracle with subcubic preprocessing time.


title:
TYC~2990-127-1: an Algol-type SB2 binary system of subgiant and red
  giant with a probable ongoing mass-transfer

abstract:
  We present a study of the spectroscopic binary TYC 2990-127-1 from the LAMOST
survey. We use full-spectrum fitting to derive radial velocities and spectral
parameters. The high mass ratio indicates that the system underwent mass
transfer in the past. We compute the orbital solution and find that it is a
very close sub-giant/red giant pair on circular orbit, slightly inclined to the
sky-plane. Fitting of the TESS photometrical data confirms this and suggests an
inclination of i \sim 39.8 deg. The light curve and spectrum around H_alpha
show signs of irregular variability, which supports ongoing mass transfer. The
binary evolution simulations suggest that the binary may experience
non-conservative mass transfer with accretion efficiency 0.3, and the binary
will enter into common envelope phase in the subsequent evolution. The remnant
product after the ejection of common envelope may be a detached double helium
white dwarf (He WD) or a merger.


title: A Weak Law of Large Numbers for Dependent Random Variables

abstract:
  Every sequence $f_1, f_2, \cdots \, $ of random variables with $ \, \lim_{M
\to \infty} \big( M \sup_{k \in \mathbb{N}} \mathbb{P} ( |f_k| > M ) \big)=0\,$
contains a subsequence $ f_{k_1}, f_{k_2} , \cdots \,$ that satisfies, along
with all its subsequences, the weak law of large numbers: $ \, \lim_{N \to
\infty} \big( (1/N) \sum_{n=1}^N f_{k_n} - D_N \big) =0\,,$ in probability.
Here $\, D_N\, $ is a "corrector" random variable with values in $[-N,N]$, for
each $N \in \mathbb{N} $; these correctors are all equal to zero if, in
addition, $\, \liminf_{k \to \infty} \mathbb{E} \big( f_k^2 \, \mathbf{ 1}_{ \{
|f_k| \le M \} } \big) =0\,$ holds for every $M \in (0, \infty)\,.$


title:
Spin Density Wave vs. Fractional Magnetization Plateau in a Triangular
  Antiferromagnet

abstract:
  We report an excellent realization of the highly non-classical incommensurate
spin-density wave (SDW) state in the quantum frustrated antiferromagnetic
insulator Cs$_2$CoBr$_4$. In contrast to the well-known Ising spin chain case,
here the SDW is stabilized by virtue of competing planar in-chain anisotropies
and frustrated interchain exchange. Adjacent to the SDW phase is a broad $m =
1/3$ magnetization plateau that can be seen as a commensurate locking of the
SDW state into the up-up-down spin structure. This represents the first example
of long-sought SDW-UUD transition in triangular-type quantum magnets.


title:
Finite-disorder critical point in the yielding transition of
  elasto-plastic models

abstract:
  Upon loading, amorphous solids can exhibit brittle yielding, with the abrupt
formation of macroscopic shear bands leading to fracture, or ductile yielding,
with a multitude of plastic events leading to homogeneous flow. It has been
recently proposed, and subsequently questioned, that the two regimes are
separated by a sharp critical point, as a function of some control parameter
characterizing the intrinsic disorder strength and the degree of stability of
the solid. In order to resolve this issue, we have performed extensive
numerical simulations of athermally driven elasto-plastic models with realistic
interaction kernels in two and three dimensions. Our results provide clear
evidence for a finite-disorder critical point separating brittle and ductile
yielding, and we provide an estimate of the critical exponents in 2D and 3D.


title:
Photoproduction of low-$p_{\rm T}$ J/$\psi$ from peripheral to central
  Pb$-$Pb collisions at 5.02 TeV

abstract:
  An excess of J/$\psi$ yield at very low transverse momentum ($p_{\rm T} <
0.3$ GeV/$c$), originating from coherent photoproduction, is observed in
peripheral and semicentral hadronic Pb$-$Pb collisions at a center-of-mass
energy per nucleon pair of $\sqrt{s_{\rm NN}} = 5.02$ TeV. The measurement is
performed with the ALICE detector via the dimuon decay channel at forward
rapidity ($2.5<y<4$). The nuclear modification factor at very low $p_{\rm T}$
and the coherent photoproduction cross section are measured as a function of
centrality down to the 10% most central collisions. These results extend the
previous study at $\sqrt{s_{\rm NN}} = 2.76$ TeV, confirming the clear excess
over hadronic production in the $p_{\rm T}$ range 0$-$0.3 GeV/$c$ and the
centrality range 70$-$90%, and establishing an excess with a significance
greater than 5$\sigma$ also in the 50$-$70% and 30$-$50% centrality ranges. The
results are compared with earlier measurements at $\sqrt{s_{\rm NN}} = 2.76$
TeV and with different theoretical predictions aiming at describing how
coherent photoproduction occurs in hadronic interactions with nuclear overlap.