title: Differentiable N-gram Objective on Abstractive Summarization

abstract:
  ROUGE is a standard automatic evaluation metric based on n-grams for
sequence-to-sequence tasks, while cross-entropy loss is an essential objective
of neural network language model that optimizes at a unigram level. We present
differentiable n-gram objectives, attempting to alleviate the discrepancy
between training criterion and evaluating criterion. The objective maximizes
the probabilistic weight of matched sub-sequences, and the novelty of our work
is the objective weights the matched sub-sequences equally and does not ceil
the number of matched sub-sequences by the ground truth count of n-grams in
reference sequence. We jointly optimize cross-entropy loss and the proposed
objective, providing decent ROUGE score enhancement over abstractive
summarization dataset CNN/DM and XSum, outperforming alternative n-gram
objectives.


title:
Full rotational symmetry from reflections or rotational symmetries in
  finitely many subspaces

abstract:
  Two related questions are discussed. The first is when reflection symmetry in
a finite set of $i$-dimensional subspaces, $i\in \{1,\dots,n-1\}$, implies full
rotational symmetry, i.e., the closure of the group generated by the
reflections equals $O(n)$. For $i=n-1$, this has essentially been solved by
Burchard, Chambers, and Dranovski, but new results are obtained for $i\in
\{1,\dots,n-2\}$. The second question, to which an essentially complete answer
is given, is when (full) rotational symmetry with respect to a finite set of
$i$-dimensional subspaces, $i\in \{1,\dots,n-2\}$, implies full rotational
symmetry, i.e., the closure of the group generated by all the rotations about
each of the subspaces equals $SO(n)$. The latter result also shows that a
closed set in $\mathbb{R}^n$ that is invariant under rotations about more than
one axis must be a union of spheres with their centers at the origin.


title:
Improved Convergence Rates for Sparse Approximation Methods in
  Kernel-Based Learning

abstract:
  Kernel-based models such as kernel ridge regression and Gaussian processes
are ubiquitous in machine learning applications for regression and
optimization. It is well known that a serious downside for kernel-based models
is the high computational cost; given a dataset of $n$ samples, the cost grows
as $\mathcal{O}(n^3)$. Existing sparse approximation methods can yield a
significant reduction in the computational cost, effectively reducing the real
world cost down to as low as $\mathcal{O}(n)$ in certain cases. Despite this
remarkable empirical success, significant gaps remain in the existing results
for the analytical confidence bounds on the error due to approximation. In this
work, we provide novel confidence intervals for the Nystr\"om method and the
sparse variational Gaussian processes approximation method. Our confidence
intervals lead to improved error bounds in both regression and optimization. We
establish these confidence intervals using novel interpretations of the
approximate (surrogate) posterior variance of the models.


title:
VC-density and abstract cell decomposition for edge relation in graphs
  of bounded twin-width

abstract:
  We study set systems formed by neighborhoods in graphs of bounded twin-width.
In particular, we prove that such classes of graphs admit linear neighborhood
complexity, in analogy to previous results concerning classes with bounded
expansion and classes of bounded clique-width. Additionally, we show how, for a
given graph from a class of graphs of bounded twin-width, to efficiently encode
the neighborhood of a vertex in a given set of vertices $A$ of the graph. For
the encoding we use only a constant number of vertices from $A$. The obtained
encoding can be decoded using FO formulas. This proves that the edge relation
in graphs of bounded twin-width, seen as first-order structures, admits a
definable distal cell decomposition. From this fact we derive that we can apply
to such classes combinatorial tools based on the Distal cutting lemma and the
Distal regularity lemma.


title: Results and findings of the 2021 Image Similarity Challenge

abstract:
  The 2021 Image Similarity Challenge introduced a dataset to serve as a new
benchmark to evaluate recent image copy detection methods. There were 200
participants to the competition. This paper presents a quantitative and
qualitative analysis of the top submissions. It appears that the most difficult
image transformations involve either severe image crops or hiding into
unrelated images, combined with local pixel perturbations. The key algorithmic
elements in the winning submissions are: training on strong augmentations,
self-supervised learning, score normalization, explicit overlay detection, and
global descriptor matching followed by pairwise image comparison.


title: Lochs-type theorems beyond positive entropy

abstract:
  Lochs' theorem and its generalizations are conversion theorems that relate
the number of digits determined in one expansion of a real number as a function
of the number of digits given in some other expansion. In its original version,
Lochs' theorem related decimal expansions with continued fraction expansions.
Such conversion results can also be stated for sequences of interval partitions
under suitable assumptions, with results holding almost everywhere, or in
measure, involving the entropy. This is the viewpoint we develop here. In order
to deal with sequences of partitions beyond positive entropy, this paper
introduces the notion of log-balanced sequences of partitions, together with
their weight functions. These are sequences of interval partitions such that
the logarithms of the measures of their intervals at each depth are roughly the
same. We then state Lochs-type theorems which work even in the case of zero
entropy, in particular for several important log-balanced sequences of
partitions of a number-theoretic nature.


title: The ECH capacities for the rotating Kepler problem

abstract:
  In this paper, I am going to compute the ECH-capacities of the rotating
Kepler problem when the energy is less than or equal to the critical energy
value $-\dfrac{3}{2}$. To compute the ECH-capacities, I will use the special
concave toric domain of the rotating Kepler problem, that is explained in
[arXiv:2108.04581] and obtain the weights of the special concave toric domain.
I will use a new method to compute the weights via the new tree
[arXiv:2108.04581] and then give an order to them that is necessary to do the
computation. The weights are continuous function belongs to the energy
parameter. So we can use the computation for all energy level below the
critical energy. Finally, we will prove that the first weight is the biggest
weight for all energy $c \leq -\dfrac{3}{2}$ and will see a numerical example
of the ECH-capacities computation for the critical energy.


title:
Multilevel Binary Polar-Coded Modulation Achieving the Capacity of
  Asymmetric Channels

abstract:
  A multilevel coded modulation scheme is studied that uses binary polar codes
and Honda-Yamamoto probabilistic shaping. The scheme is shown to achieve the
capacity of discrete memoryless channels with input alphabets of cardinality a
power of two. The performance of finite-length implementations is compared to
polar-coded probabilistic amplitude shaping and constant composition
distribution matching.


title:
Counting statistics of energy transport across squeezed thermal
  reservoirs

abstract:
  A general formalism for computing the full counting statistics of energy
exchanged between 'N' squeezed thermal photon reservoirs weakly coupled to a
cavity with 'M' photon modes is presented. The formalism is based on the
two-point measurement scheme and is applied to two simple special cases, the
relaxation dynamics of a single mode cavity in contact with a single squeezed
thermal photon reservoir and the steady-state energy transport between two
squeezed thermal photon reservoirs coupled to a single cavity mode. Using
analytical results, it is found that the short time statistics is significantly
affected by noncommutivity of the initial energy measurements with the
reservoirs squeezed states, and may lead to negative probabilities if not
accounted properly. Furthermore, it is found that for the single reservoir
setup, generically there is no transient or steady-state fluctuation theorems
for energy transport. In contrast, for the two reservoir case, although there
is no generic transient fluctuation theorem, steady-state fluctuation theorem
with a non-universal affinity is found to be valid. Statistics of energy
currents are further discussed.

categories: cond-mat.stat-mech cond-mat.mes-hall cond-mat.other physics.optics quant-ph


title: Positive-Definite Matrices over Finite Fields

abstract:
  The study of positive-definite matrices has focused on Hermitian matrices,
that is, square matrices with complex (or real) entries that are equal to their
own conjugate transposes. In the classical setting, positive-definite matrices
enjoy a multitude of equivalent definitions and properties. In this paper, we
investigate when a square, symmetric matrix with entries coming from a finite
field can be called "positive-definite" and discuss which of the classical
equivalences and implications carry over.


title: Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot

abstract:
  Non-Fungible Token (NFT) markets are one of the fastest growing digital
markets today, with the sales during the third quarter of 2021 exceeding $10
billions! Nevertheless, these emerging markets - similar to traditional
emerging marketplaces - can be seen as a great opportunity for illegal
activities (e.g., money laundering, sale of illegal goods etc.). In this study
we focus on a specific marketplace, namely NBA TopShot, that facilitates the
purchase and (peer-to-peer) trading of sports collectibles. Our objective is to
build a framework that is able to label peer-to-peer transactions on the
platform as anomalous or not. To achieve our objective we begin by building a
model for the profit to be made by selling a specific collectible on the
platform. We then use RFCDE - a random forest model for the conditional density
of the dependent variable - to model the errors from the profit models. This
step allows us to estimate the probability of a transaction being anomalous. We
finally label as anomalous any transaction whose aforementioned probability is
less than 1%. Given the absence of ground truth for evaluating the model in
terms of its classification of transactions, we analyze the trade networks
formed from these anomalous transactions and compare it with the full trade
network of the platform. Our results indicate that these two networks are
statistically different when it comes to network metrics such as, edge density,
closure, node centrality and node degree distribution. This network analysis
provides additional evidence that these transactions do not follow the same
patterns that the rest of the trades on the platform follow. However, we would
like to emphasize here that this does not mean that these transactions are also
illegal. These transactions will need to be further audited from the
appropriate entities to verify whether or not they are illicit.


title:
Remarks on the cosmological constant appearing as an initial condition
  for Milne-like spacetimes

abstract:
  Milne-like spacetimes are a class of $k = -1$ FLRW spacetimes which admit
continuous spacetime extensions through the big bang. In a previous paper [29],
it was shown that the cosmological constant appears as an initial condition for
Milne-like spacetimes. In this paper, we generalize this statement to
spacetimes which share similar geometrical properties with Milne-like
spacetimes but without the strong isotropy assumption associated with them. We
show how our results could have applications to inflationary scenarios.


title: NEWSKVQA: Knowledge-Aware News Video Question Answering

abstract:
  Answering questions in the context of videos can be helpful in video
indexing, video retrieval systems, video summarization, learning management
systems and surveillance video analysis. Although there exists a large body of
work on visual question answering, work on video question answering (1) is
limited to domains like movies, TV shows, gameplay, or human activity, and (2)
is mostly based on common sense reasoning. In this paper, we explore a new
frontier in video question answering: answering knowledge-based questions in
the context of news videos. To this end, we curate a new dataset of 12K news
videos spanning across 156 hours with 1M multiple-choice question-answer pairs
covering 8263 unique entities. We make the dataset publicly available. Using
this dataset, we propose a novel approach, NEWSKVQA (Knowledge-Aware News Video
Question Answering) which performs multi-modal inferencing over textual
multiple-choice questions, videos, their transcripts and knowledge base, and
presents a strong baseline.


title: Ontology-based Attack Graph Enrichment

abstract:
  Attack graphs provide a representation of possible actions that adversaries
can perpetrate to attack a system. They are used by cybersecurity experts to
make decisions, e.g., to decide remediation and recovery plans. Different
approaches can be used to build such graphs. We focus on logical attack graphs,
based on predicate logic, to define the causality of adversarial actions. Since
networks and vulnerabilities are constantly changing (e.g., new applications
get installed on system devices, updated services get publicly exposed, etc.),
we propose to enrich the attack graph generation approach with a semantic
augmentation post-processing of the predicates. Graphs are now mapped to
monitoring alerts confirming successful attack actions and updated according to
network and vulnerability changes. As a result, predicates get periodically
updated, based on attack evidences and ontology enrichment. This allows to
verify whether changes lead the attacker to the initial goals or to cause
further damage to the system not anticipated in the initial graphs. We
illustrate the approach under the specific domain of cyber-physical security
affecting smart cities. We validate the approach using existing tools and
ontologies.


title: Supersymmetry in QCD$_2$ coupled to fermions

abstract:
  We consider $1+1$ dimensional Yang-Mills theory with gauge group $G$ coupled
to a massive Majorana fermion field in an adjoint representation and a number
of massless Dirac or Majorana fermions transforming in arbitrary
representations of the gauge group $G$. It is shown that the spectrum of the
massive sector of this theory becomes supersymmetric at particular mass of
adjoint fermion. This mass is independent of the detailed structure of the
massless sector of the model and depends only on the gauge group $G$ and
integer $k$ measuring the total anomaly. The massless sector of the model is
shown to be not necessarily supersymmetric.


title:
Nucleon-nucleon scattering up to next-to-next-to-leading order in
  manifestly Lorentz-invariant chiral effective field theory: peripheral phases

abstract:
  We study the nucleon-nucleon interaction up to next-to-next-to-leading order
using time-ordered perturbation theory in the framework of manifestly
Lorentz-invariant chiral effective field theory. We present the two-pion
exchange contribution at one-loop level, which is consistent with the
corresponding non-relativistic expressions in the large-nucleon-mass limit.
Using the Born series truncated at one-loop order, we calculate the phase
shifts and mixing angles of the partial waves with the angular momentum $l\geq
2$. Comparing with the results of non-relativistic formulation, we find an
improved description of the phase shifts for some $D$ waves such as the $^3D_3$
one. For the other partial waves, both approaches show the globally similar
results.


title:
Energetic nuclear transients in luminous and ultraluminous infrared
  galaxies

abstract:
  We present our findings on the occurrence of energetic nuclear transients in
luminous and ultraluminous infrared galaxies (U/LIRGs). We used mid-infrared
(IR) data from the Wide-field Infrared Survey Explorer (WISE) satellite and its
NEOWISE survey to detect luminous and smoothly evolving transients in a sample
of 215 U/LIRGs. Three new transients are reported, all with $\Delta L >
10^{43}$ erg s$^{-1}$, in addition to two known transients. We performed
radiative transfer model fitting of their host galaxies to investigate their
starburst properties and the contribution of an active galactic nucleus (AGN)
to their luminosities. All the hosts are part of major galaxy mergers and have
to have a significant contribution from an AGN. We characterised the transients
through measurements of their luminosities and resulting energetics, all
between 10$^{50.9}$ erg and 10$^{52.2}$ erg. The IR emission of the transients
was found to be consistent with re-radiation by hot dust of emission at shorter
wavelengths originating from an accretion event onto the supermassive black
hole (SMBH). The corresponding transient rate of (1.6-5.0)$\times$10$^{-3}$ /
yr / galaxy is over an order of magnitude higher than the rate of large
amplitude flares shown by AGN in the optical. We suggest that the observed
transients are part of a dust-obscured population of tidal disruption events
(TDEs) that have remained out of reach of optical or X-ray surveys due to large
column densities of obscuring dust and gas. The observed rate of events is
significantly higher than the TDE rates within relatively dust-free galaxies.
This can be expected in U/LIRG hosts undergoing a major galaxy merger with
increased stellar densities in the nuclear regions due to recent starburst
episodes. Future searches for such transients and multi-wavelength follow-up of
their evolution is required to constrain their rate and nature.


title:
Efficient Algorithms for High-Dimensional Convex Subspace Optimization
  via Strict Complementarity

abstract:
  We consider optimization problems in which the goal is find a $k$-dimensional
subspace of $\reals^n$, $k<<n$, which minimizes a convex and smooth loss. Such
problemsgeneralize the fundamental task of principal component analysis (PCA)
to include robust and sparse counterparts, and logistic PCA for binary data,
among others. While this problem is not convex it admits natural algorithms
with very efficient iterations and memory requirements, which is highly desired
in high-dimensional regimes however, arguing about their fast convergence to a
global optimal solution is difficult. On the other hand, there exists a simple
convex relaxation for which convergence to the global optimum is
straightforward, however corresponding algorithms are not efficient when the
dimension is very large. In this work we present a natural deterministic
sufficient condition so that the optimal solution to the convex relaxation is
unique and is also the optimal solution to the original nonconvex problem.
Mainly, we prove that under this condition, a natural highly-efficient
nonconvex gradient method, which we refer to as \textit{gradient orthogonal
iteration}, when initialized with a "warm-start", converges linearly for the
nonconvex problem. We also establish similar results for the nonconvex
projected gradient method, and the Frank-Wolfe method when applied to the
convex relaxation. We conclude with empirical evidence on synthetic data which
demonstrate the appeal of our approach.


title:
On the Hilbert function of Artinian local complete intersections of
  codimension three

abstract:
  In singularity theory or algebraic geometry, it is natural to investigate
possible Hilbert functions for special algebras $A$ such as local complete
intersections or more generally Gorenstein algebras. The sequences that occur
as {the} Hilbert functions of standard graded complete intersections are well
understood classically thanks to Macaulay and Stanley. Very little is known in
the local case except in codimension two. In this paper we characterise the
Hilbert functions of quadratic Artinian complete intersections of codimension
three. Interestingly we prove that a Hilbert function is admissible for such a
Gorenstein ring if and only if is admissible for such a complete intersection.
We provide an effective construction of a local complete intersection for a
given Hilbert function. {We prove that the symmetric decomposition of such a
complete intersection ideal is determined by its Hilbert function.}


title: The mathematical research-data repository MathRepo

abstract:
  MathRepo, located at https://mathrepo.mis.mpg.de, is an online repository for
mathematical research data. In mathematics, research data comes in many
different flavours. For instance, in computer algebra it most often takes the
form of mathematical documents, notebooks, research-software packages and
libraries, computer algebra systems, algorithms, and collections of
mathematical objects. In this work we give an overview of the type of
contributions hosted by MathRepo, we discuss the principles that should be
followed to make a research-data repository good, and the current challenges we
are facing as MathRepo's maintainers.


title: Ax-Schanuel and exceptional integrability

abstract:
  When can a primitive of a given algebraic function be con-structed by
iteratively solving algebraic equations and composing withthe primitives of
some other given algebraic functions or their inverses? We establish some
results in this direction. Specifically, we establishdecision procedures for
determining whether a given primitive can beexpressed in terms of finitely many
others, or in terms of elliptic integrals.


title: Truncated mass divergence in a Mott metal

abstract:
  Metal-insulator transitions in clean, crystalline solids can be driven by two
distinct mechanisms. In a conventional insulator, the charge carrier
concentration vanishes, when an energy gap separates filled and unfilled
electronic states. In the established picture of a Mott insulator, by contrast,
electronic interactions cause coherent charge carriers to slow down and
eventually stop in electronic grid-lock without materially affecting the
carrier concentration itself. This description has so far escaped experimental
verification by quantum oscillation measurements, which directly probe the
velocity distribution of the coherent charge carriers. By extending this
technique to high pressure we were able to examine the evolution of carrier
concentration and velocity in the strongly correlated metallic state of the
clean, crystalline material NiS$_2$, while tuning the system towards the Mott
insulating phase. Our results confirm that pronounced electronic slowing down
indeed governs the approach to the insulating state. However, the critical
point itself, at which the carrier velocity would reach zero and the effective
carrier mass diverge, is concealed by the insulating sector of the phase
diagram. In the resulting, more nuanced view of Mott localisation, the
inaccessibility of the low temperature Mott critical point resembles that of
the threshold of magnetic order in clean metallic systems, where criticality is
almost universally interrupted by first order transitions, tricritical
behaviour or novel emergent phases such as unconventional superconductivity.


title: Topological Control of Liquid-Metal-Dealloyed Structures

abstract:
  The past few years have witnessed the rapid development of liquid metal
dealloying to fabricate nano-/meso-scale porous and composite structures with
ultra-high interfacial area for diverse materials applications. However, this
method currently has two important limitations. First, it produces bicontinuous
structures with high-genus topologies for a limited range of alloy
compositions. Second, structures have a large ligament size due to substantial
coarsening during dealloying at high temperature. Here we demonstrate
computationally and experimentally that those limitations can be overcome by
adding to the metallic melt an element that promotes high-genus topologies by
limiting the leakage of the immiscible element during dealloying. We further
interpret this finding by showing that bulk diffusive transport of the
immiscible element in the liquid melt strongly influences the evolution of the
solid fraction and topology of the structure during dealloying. The results
shed light on fundamental differences in liquid metal and electrochemical
dealloying and establish a new approach to produce liquid-metal-dealloyed
structures with desired size and topologies.


title:
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix
  Optimization Problems

abstract:
  Low-rank and nonsmooth matrix optimization problems capture many fundamental
tasks in statistics and machine learning. While significant progress has been
made in recent years in developing efficient methods for \textit{smooth}
low-rank optimization problems that avoid maintaining high-rank matrices and
computing expensive high-rank SVDs, advances for nonsmooth problems have been
slow paced.
  In this paper we consider standard convex relaxations for such problems.
Mainly, we prove that under a natural \textit{generalized strict
complementarity} condition and under the relatively mild assumption that the
nonsmooth objective can be written as a maximum of smooth functions, the
\textit{extragradient method}, when initialized with a "warm-start" point,
converges to an optimal solution with rate $O(1/t)$ while requiring only two
\textit{low-rank} SVDs per iteration. We give a precise trade-off between the
rank of the SVDs required and the radius of the ball in which we need to
initialize the method. We support our theoretical results with empirical
experiments on several nonsmooth low-rank matrix recovery tasks, demonstrating
that using simple initializations, the extragradient method produces exactly
the same iterates when full-rank SVDs are replaced with SVDs of rank that
matches the rank of the (low-rank) ground-truth matrix to be recovered.


title: Commensurators of normal subgroups of lattices

abstract:
  We study a question of Shalom concerning arithmeticity of discrete subgroups
of semisimple Lie groups with dense commensurators. We answer Shalom's question
positively for normal subgroups of lattices. This generalizes a result of the
second author and T. Koberda.


title: Recognition of objects through symplectic capacities

abstract:
  We prove that the generalized symplectic capacities recognize objects in
symplectic categories whose objects are of the form $(M,\omega)$, such that $M$
is a compact and 1-connected manifold, $\omega$ is an exact symplectic form on
$M$, and there exists a boundary component of $M$ with negative helicity. The
set of generalized symplectic capacities is thus a complete invariant for such
categories. This answers a question by Cieliebak, Hofer, Latschev, and Schlenk.
It appears to be the first result concerning this question, except for
recognition results for manifolds of dimension 2, ellipsoids, and polydiscs in
$\mathbb{R}^4$. We also prove a version of the result for normalized
capacities.


title: Inflation and the cosmological (not-so) constant in unimodular gravity

abstract:
  We propose a mechanism for generating an inflationary phase in the early
universe without resorting to any type of scalar field(s). Instead, this
accelerated expansion is driven by a dynamical "cosmological constant" in the
framework of unimodular gravity. The time dependent cosmological constant can
be related to an energy diffusion term that arises naturally in unimodular
gravity due to its restrictive diffeomorphism invariance. We derive the generic
conditions required for any type of diffusion to generate a realistic
inflationary epoch. Furthermore, for a given parameterization of inflation (in
terms of the Hubble flow functions), we show how to construct the corresponding
diffusion term in such a way that a smooth transition occurs between inflation
and the subsequent radiation dominated era, hence reheating proceeds naturally.
The primordial spectrum is obtained during the inflationary phase by
considering inhomogeneous perturbations associated to standard hydrodynamical
matter (modeled as a single ultra-relativistic fluid). We demonstrate that the
resulting spectrum is equivalent to that obtained in traditional inflationary
models, and is also independent of the particular form of the diffusion term.
In addition, we analyze the feasibility of identifying the variable
cosmological constant, responsible for the inflationary expansion, with the
current observed value.


title: Self-supervised Contrastive Learning for Volcanic Unrest Detection

abstract:
  Ground deformation measured from Interferometric Synthetic Aperture Radar
(InSAR) data is considered a sign of volcanic unrest, statistically linked to a
volcanic eruption. Recent studies have shown the potential of using Sentinel-1
InSAR data and supervised deep learning (DL) methods for the detection of
volcanic deformation signals, towards global volcanic hazard mitigation.
However, detection accuracy is compromised from the lack of labelled data and
class imbalance. To overcome this, synthetic data are typically used for
finetuning DL models pre-trained on the ImageNet dataset. This approach suffers
from poor generalisation on real InSAR data. This letter proposes the use of
self-supervised contrastive learning to learn quality visual representations
hidden in unlabeled InSAR data. Our approach, based on the SimCLR framework,
provides a solution that does not require a specialized architecture nor a
large labelled or synthetic dataset. We show that our self-supervised pipeline
achieves higher accuracy with respect to the state-of-the-art methods, and
shows excellent generalisation even for out-of-distribution test data. Finally,
we showcase the effectiveness of our approach for detecting the unrest episodes
preceding the recent Icelandic Fagradalsfjall volcanic eruption.


title:
Plasmons in semiconductor and topological insulator wires with large
  dielectric constant

abstract:
  The dispersion law of plasmons running along thin wires with radius $a$ is
known to be practically linear. We show that in a wire with a dielectric
constant $\kappa$ much larger than that of its environment $\kappa_e$, such
dispersion law crosses over to a dispersionless three-dimensional-like law when
the plasmon wavelength becomes shorter than the length $(a/2)
\sqrt{(\kappa/\kappa_e)\ln(\kappa/2\kappa_e)}$ at which the electric field
lines of a point charge exit from the wire to the environment. This happens
both in trivial semiconductor wires and wires of three-dimensional topological
insulators.


title:
Exponential stochastic compression of one-dimensional space and 146
  percent

abstract:
  Exponential stochastic compression is the process when every second cell of
an infinite chain may increase its weight merging randomly with left, right, or
both neighboring cells. The total mass conservation is assumed. After that,
merged cells fill the empty space, compressing the chain twice. They may fill
empty spaces in two different ways: (I) using shifts only, i.e. preserving the
order; (II) using shifts and random permutations. Compressing the initial
homogeneous chain with cell weights $1$ many times, we compute final densities
$\rho_i$ of cells with weight $i=1,2,3,...$. The main result is that
$\rho_i/\rho_1=i$ in the ordered case (I), and
$\rho_i/\rho_1\approx1.464910...(i-1/4)$ in the disordered case (II). The
multiplier in the disordered case has a fractal nature. The compression of
initially inhomogeneous chains and rescaled continuous densities are also
discussed.


title:
Domain variations of the first eigenvalue via a strict Faber-Krahn type
  inequality

abstract:
  For $d\geq 2$ and $\frac{2d+2}{d+2} < p < \infty $, we prove a strict
Faber-Krahn type inequality for the first eigenvalue $\lambda _1(\Omega )$ of
the $p$-Laplace operator on a bounded Lipschitz domain $\Omega \subset
\mathbb{R}^d$ (with mixed boundary conditions) under the polarizations. We
apply this inequality to the obstacle problems on the domains of the form
$\Omega \setminus \mathscr{O}$, where $\mathscr{O}\subset \subset \Omega $ is
an obstacle. Under some geometric assumptions on $\Omega $ and $\mathscr{O}$,
we prove the strict monotonicity of $\lambda _1 (\Omega \setminus \mathscr{O})$
with respect to certain translations and rotations of $\mathscr{O}$ in $\Omega
$.


title: Affine Vogan Diagrams and Symmetric Pairs

abstract:
  We introduce the affine Vogan diagrams of complex simple Lie algebras. These
are generalizations of Vogan diagrams, and we study the involutions represented
by them. We apply these diagrams to study the symmetric pairs, in particular
the associated and symplectic symmetric pairs.


title:
One-loop Corrections to the Higgs Boson Invisible Decay in a Complex
  Singlet Extension of the SM

abstract:
  The search for dark matter (DM) at colliders is founded on the idea of
looking for something invisible. There are searches based on production and
decay processes where DM may reveal itself as missing energy. If nothing is
found, our best tool to constrain the parameter space of many extensions of the
Standard Model (SM) with a DM candidate is the Higgs boson. As the measurements
of the Higgs couplings become increasingly precise, higher-order corrections
will start to play a major role. The tree-level contribution to the invisible
decay width provides information about the portal coupling. Higher-order
corrections also gives us access to other parameters from the dark sector of
the Higgs potential that are not present in the tree-level amplitude. In this
work we will focus on the complex singlet extension of the SM in the phase with
a DM candidate. We calculate the one-loop electroweak corrections to the decay
of the Higgs boson into two DM particles. We find that the corrections are
stable and of the order of a few percent. The present measurement of the Higgs
invisible branching ratio, BR$(H \to$ invisible $) < 0.11$, already constrains
the parameter space of the model at leading order. We expect that by the end of
the LHC the experimental measurement will require the inclusion of the
electroweak corrections to the decay in order to match the experimental
accuracy. Furthermore, the only competing process, which is direct detection,
is shown to have a cross section below the neutrino floor.


title: Residual Aligned: Gradient Optimization for Non-Negative Image Synthesis

abstract:
  In this work, we address an important problem of optical see through (OST)
augmented reality: non-negative image synthesis. Most of the image generation
methods fail under this condition, since they assume full control over each
pixel and cannot create darker pixels by adding light. In order to solve the
non-negative image generation problem in AR image synthesis, prior works have
attempted to utilize optical illusion to simulate human vision but fail to
preserve lightness constancy well under situations such as high dynamic range.
In our paper, we instead propose a method that is able to preserve lightness
constancy at a local level, thus capturing high frequency details. Compared
with existing work, our method shows strong performance in image-to-image
translation tasks, particularly in scenarios such as large scale images, high
resolution images, and high dynamic range image transfer.


title:
Unsupervised Bayesian classification for models with scalar and
  functional covariates

abstract:
  We consider unsupervised classification by means of a latent multinomial
variable which categorizes a scalar response into one of L components of a
mixture model. This process can be thought as a hierarchical model with first
level modelling a scalar response according to a mixture of parametric
distributions, the second level models the mixture probabilities by means of a
generalised linear model with functional and scalar covariates. The traditional
approach of treating functional covariates as vectors not only suffers from the
curse of dimensionality since functional covariates can be measured at very
small intervals leading to a highly parametrised model but also does not take
into account the nature of the data. We use basis expansion to reduce the
dimensionality and a Bayesian approach to estimate the parameters while
providing predictions of the latent classification vector. By means of a
simulation study we investigate the behaviour of our approach considering
normal mixture model and zero inflated mixture of Poisson distributions. We
also compare the performance of the classical Gibbs sampling approach with
Variational Bayes Inference.


title: Exponent equations in HNN-extensions

abstract:
  We consider exponent equations in finitely generated groups. These are
equations, where the variables appear as exponents of group elements and take
values from the natural numbers. Solvability of such (systems of) equations has
been intensively studied for various classes of groups in recent years. In many
cases, it turns out that the set of all solutions on an exponent equation is a
semilinear set that can be constructed effectively. Such groups are called
knapsack semilinear. Examples of knapsack semilinear groups are hyperbolic
groups, virtually special groups, co-context-free groups and free solvable
groups. Moreover, knapsack semilinearity is preserved by many group theoretic
constructions, e.g., finite extensions, graph products, wreath products,
amalgamated free products with finite amalgamated subgroups, and HNN-extensions
with finite associated subgroups. On the other hand, arbitrary HNN-extensions
do not preserve knapsack semilinearity. In this paper, we consider the knapsack
semilinearity of HNN-extensions, where the stable letter $t$ acts trivially by
conjugation on the associated subgroup $A$ of the base group $G$. We show that
under some additional technical conditions, knapsack semilinearity transfers
from base group $G$ to the HNN-extension $H$. These additional technical
conditions are satisfied in many cases, e.g., when $A$ is a centralizer in $G$
or $A$ is a quasiconvex subgroup of the hyperbolic group $G$.


title: Using Genetic Programming to Predict and Optimize Protein Function

abstract:
  Protein engineers conventionally use tools such as Directed Evolution to find
new proteins with better functionalities and traits. More recently,
computational techniques and especially machine learning approaches have been
recruited to assist Directed Evolution, showing promising results. In this
paper, we propose POET, a computational Genetic Programming tool based on
evolutionary computation methods to enhance screening and mutagenesis in
Directed Evolution and help protein engineers to find proteins that have better
functionality. As a proof-of-concept we use peptides that generate MRI contrast
detected by the Chemical Exchange Saturation Transfer contrast mechanism. The
evolutionary methods used in POET are described, and the performance of POET in
different epochs of our experiments with Chemical Exchange Saturation Transfer
contrast are studied. Our results indicate that a computational modelling tool
like POET can help to find peptides with 400% better functionality than used
before.


title: Self-Conditioned Generative Adversarial Networks for Image Editing

abstract:
  Generative Adversarial Networks (GANs) are susceptible to bias, learned from
either the unbalanced data, or through mode collapse. The networks focus on the
core of the data distribution, leaving the tails - or the edges of the
distribution - behind. We argue that this bias is responsible not only for
fairness concerns, but that it plays a key role in the collapse of
latent-traversal editing methods when deviating away from the distribution's
core. Building on this observation, we outline a method for mitigating
generative bias through a self-conditioning process, where distances in the
latent-space of a pre-trained generator are used to provide initial labels for
the data. By fine-tuning the generator on a re-sampled distribution drawn from
these self-labeled data, we force the generator to better contend with rare
semantic attributes and enable more realistic generation of these properties.
We compare our models to a wide range of latent editing methods, and show that
by alleviating the bias they achieve finer semantic control and better identity
preservation through a wider range of transformations. Our code and models will
be available at https://github.com/yzliu567/sc-gan


title:
Physics-informed neural networks for solving parametric magnetostatic
  problems

abstract:
  The optimal design of magnetic devices becomes intractable using current
computational methods when the number of design parameters is high. The
emerging physics-informed deep learning framework has the potential to
alleviate this curse of dimensionality. The objective of this paper is to
investigate the ability of physics-informed neural networks to learn the
magnetic field response as a function of design parameters in the context of a
two-dimensional (2-D) magnetostatic problem. Our approach is as follows. We
derive the variational principle for 2-D parametric magnetostatic problems, and
prove the existence and uniqueness of the solution that satisfies the equations
of the governing physics, i.e., Maxwell's equations. We use a deep neural
network (DNN) to represent the magnetic field as a function of space and a
total of ten parameters that describe geometric features and operating point
conditions. We train the DNN by minimizing the physics-informed loss function
using a variant of stochastic gradient descent. Subsequently, we conduct
systematic numerical studies using a parametric EI-core electromagnet problem.
In these studies, we vary the DNN architecture trying more than one hundred
different possibilities. For each study, we evaluate the accuracy of the DNN by
comparing its predictions to those of finite element analysis. In an exhaustive
non-parametric study, we observe that sufficiently parameterized dense networks
result in relative errors of less than 1%. Residual connections always improve
relative errors for the same number of training iterations. Also, we observe
that Fourier encoding features aligned with the device geometry do improve the
rate of convergence, albeit higher-order harmonics are not necessary. Finally,
we demonstrate our approach on a ten-dimensional problem with parameterized
geometry.


title:
Outer Automorphism Groups of Simple Lie Algebras and Symmetries of
  Painted Diagrams

abstract:
  For a real semisimple Lie algebra, we consider its automorphism group
quotient by its identity component. This is known as the outer automorphism
group. In this article, we compute the outer automorphism groups of all real
semisimple Lie algebras.


title: Bounded meromorphic functions on the complex 2-disc

abstract:
  We describe bounded, holomorphic functions on the complex 2-disc, that admit
meromorphic extension to a larger 2-disc. This solves a conjecture of Bickel,
Knese, Pascoe and Sola. The key technical ingredient is an old theorem of
Zariski about integrally closed ideals in 2-dimensional regular rings.


title: Aging Scientists and Slowed Advance

abstract:
  What is the relationship between aging and the character of scientific
advance? Prior research focuses on star scientists, their changing dates, and
rates of breakthrough success through history. Analyzing more than 244 million
scholars across 241 million articles over the last two centuries, we show that
for all fields, periods, and impact levels, scientists research ideas and
references age over time, their research is less likely to disrupt the state of
science and more likely to criticize emerging work. Early success accelerates
scientist aging; while changing institutions and fields and collaborating with
young scientists slows it. These patterns aggregate within fields such that
those with a higher proportion of older scientists experience a lower churn of
ideas and more rapid individual aging, suggesting a universal link between
aging, activity, and advance.


title:
Study of charmonium and charmonium-like contributions in $B^+
  \rightarrow J/\psi \eta K^+$ decays

abstract:
  A study of $B^+ \rightarrow J/\psi \eta K^+$ decays, followed by $J/\psi
\rightarrow \mu^+ \mu^-$ and $\eta \rightarrow \gamma \gamma$, is performed
using a dataset collected with the LHCb detector in proton-proton collisions at
centre-of-mass energies of 7, 8 and 13 TeV, corresponding to an integrated
luminosity of 9 fb$^{-1}$. The $J/psi\eta$ mass spectrum is investigated for
contributions from charmonia and charmonium-like states. Evidence is found for
the $B^+\rightarrow \left( \psi_2(3823) \rightarrow J/\psi \eta \right) K^+$
and $B^+\rightarrow \left( \psi(4040) \rightarrow J/\psi \eta \right) K^+$
decays with significance of 3.4 and 4.7~standard deviations, respectively. This
constitutes the~first~evidence for the $\psi_2(3823) \rightarrow J/\psi \eta$
decay.


title: Indecomposable entanglement witnesses from symmetric measurements

abstract:
  We propose a family of positive maps constructed from a recently introduced
class of symmetric measurements. These maps are used to define entanglement
witnesses, which include other popular approaches with mutually unbiased bases
and mutually unbiased measurements. A particular interest is given to
indecomposable witnesses that can be used to detect entanglement of quantum
states with positive partial transposition. We present several examples for
different number of measurements.


title:
An exact quantum hidden subgroup algorithm and applications to solvable
  groups

abstract:
  We present a polynomial time exact quantum algorithm for the hidden subgroup
problem in $Z_{m^k}^n$. The algorithm uses the quantum Fourier transform modulo
m and does not require factorization of m. For smooth m, i.e., when the prime
factors of m are of size poly(log m), the quantum Fourier transform can be
exactly computed using the method discovered independently by Cleve and
Coppersmith, while for general m, the algorithm of Mosca and Zalka is
available. Even for m=3 and k=1 our result appears to be new. We also present
applications to compute the structure of abelian and solvable groups whose
order has the same (but possibly unknown) prime factors as m. The applications
for solvable groups also rely on an exact version of a technique proposed by
Watrous for computing the uniform superposition of elements of subgroups.


title: Integrating question answering and text-to-SQL in Portuguese

abstract:
  Deep learning transformers have drastically improved systems that
automatically answer questions in natural language. However, different
questions demand different answering techniques; here we propose, build and
validate an architecture that integrates different modules to answer two
distinct kinds of queries. Our architecture takes a free-form natural language
text and classifies it to send it either to a Neural Question Answering
Reasoner or a Natural Language parser to SQL. We implemented a complete system
for the Portuguese language, using some of the main tools available for the
language and translating training and testing datasets. Experiments show that
our system selects the appropriate answering method with high accuracy (over
99\%), thus validating a modular question answering strategy.


title:
A 120dB Programmable-Range On-Chip Pulse Generator for Characterizing
  Ferroelectric Devices

abstract:
  Novel non-volatile memory devices based on ferroelectric thin films represent
a promising emerging technology that is ideally suited for neuromorphic
applications. The physical switching mechanism in such films is the nucleation
and growth of ferroelectric domains. Since this has a strong dependence on both
pulse width and voltage amplitude, it is important to use precise pulsing
schemes for a thorough characterization of their behaviour. In this work, we
present an on-chip 120 dB programmable range pulse generator, that can generate
pulse widths ranging from 10ns to 10ms $\pm$2.5% which eliminates the RLC
bottleneck in the device characterisation setup. We describe the pulse
generator design and show how the pulse width can be tuned with high accuracy,
using Digital to Analog converters. Finally, we present experimental results
measured from the circuit, fabricated using a standard 180nm CMOS technology.


title: Age of Information in the Presence of an Adversary

abstract:
  We consider a communication system where a base station serves $N$ users, one
user at a time, over a wireless channel. We consider the timeliness of the
communication of each user via the age of information metric. A constrained
adversary can block at most a given fraction, $\alpha$, of the time slots over
a horizon of $T$ slots, i.e., it can block at most $\alpha T$ slots. We show
that an optimum adversary blocks $\alpha T$ consecutive time slots of a
randomly selected user. The interesting consecutive property of the blocked
time slots is due to the cumulative nature of the age metric.


title: Towards automated Capability Assessment leveraging Deep Learning

abstract:
  Aiming for a higher economic efficiency in manufacturing, an increased degree
of automation is a key enabler. However, assessing the technical feasibility of
an automated assembly solution for a dedicated process is difficult and often
determined by the geometry of the given product parts. Among others, decisive
criterions of the automation feasibility are the ability to separate and
isolate single parts or the capability of component self-alignment in final
position. To assess the feasibility, a questionnaire based evaluation scheme
has been developed and applied by Fraunhofer researchers. However, the results
strongly depend on the implicit knowledge and experience of the single engineer
performing the assessment. This paper presents NeuroCAD, a software tool that
automates the assessment using voxelization techniques. The approach enables
the assessment of abstract and production relevant geometries features through
deep-learning based on CAD files.


title:
Decision boundaries and convex hulls in the feature space that deep
  learning functions learn from images

abstract:
  The success of deep neural networks in image classification and learning can
be partly attributed to the features they extract from images. It is often
speculated about the properties of a low-dimensional manifold that models
extract and learn from images. However, there is not sufficient understanding
about this low-dimensional space based on theory or empirical evidence. For
image classification models, their last hidden layer is the one where images of
each class is separated from other classes and it also has the least number of
features. Here, we develop methods and formulations to study that feature space
for any model. We study the partitioning of the domain in feature space,
identify regions guaranteed to have certain classifications, and investigate
its implications for the pixel space. We observe that geometric arrangements of
decision boundaries in feature space is significantly different compared to
pixel space, providing insights about adversarial vulnerabilities, image
morphing, extrapolation, ambiguity in classification, and the mathematical
understanding of image classification models.


title:
DALL-Eval: Probing the Reasoning Skills and Social Biases of
  Text-to-Image Generative Transformers

abstract:
  Generating images from textual descriptions has gained a lot of attention.
Recently, DALL-E, a multimodal transformer language model, and its variants
have shown high-quality text-to-image generation capabilities with a simple
architecture and training objective, powered by large-scale training data and
computation. However, despite the interesting image generation results, there
has not been a detailed analysis on how to evaluate such models. In this work,
we investigate the reasoning capabilities and social biases of such
text-to-image generative transformers in detail. First, we measure four visual
reasoning skills: object recognition, object counting, color recognition, and
spatial relation understanding. For this, we propose PaintSkills, a diagnostic
dataset and evaluation toolkit that measures these four visual reasoning
skills. Second, we measure the text alignment and quality of the generated
images based on pretrained image captioning, image-text retrieval, and image
classification models. Third, we assess social biases in the models. For this,
we suggest evaluation of gender and racial biases of text-to-image generation
models based on a pretrained image-text retrieval model and human evaluation.
In our experiments, we show that recent text-to-image models perform better in
recognizing and counting objects than recognizing colors and understanding
spatial relations, while there exists a large gap between model performances
and oracle accuracy on all skills. Next, we demonstrate that recent
text-to-image models learn specific gender/racial biases from web image-text
pairs. We also show that our automatic evaluations of visual reasoning skills
and gender bias are highly correlated with human judgments. We hope our work
will help guide future progress in improving text-to-image models on visual
reasoning skills and social biases. Code and data at:
https://github.com/j-min/DallEval


title:
The evolution of energy, momentum, and spin parameter in
  self-gravitating collisionless dark matter flow and integral constants on
  large and small scales

abstract:
  N-body equations of motion in comoving system and expanding background is
recast in a transformed system with static background. The energy and momentum
evolution in dark matter flow is formulated for both systems. In transformed
system, 1) energy equation is identical to that of a damped harmonic oscillator
and consistent with cosmic energy equation; 2) two-body collapse model (TBCM)
predicts an exponential evolution of energy. Combining both leads to a
power-law energy evolution in comoving system and suggests an effective
potential exponent $n_e$=-10/7 for virial theorem (-1.38 from simulation) that
deviates from -1. With $n_e$=-10/7, kinetic/potential energy (K$_p$ and P$_y$)
increase linearly with time t such that K$_p$=$\epsilon_u$t and
P$_y$=-7$\epsilon_u$t/5, where $\epsilon_u$ is a constant rate of energy
production. On halo scale, halo size, energy and mass are related by two
constants $\alpha_s^*$ and $\beta_s^*$, whose mean values are independent of
time and mass. Their dispersion decreases with halo mass due to short lifespan
of large halos, whose effective potential exponent $n_s^*$=-1.3. Simulation
suggests scaling $\propto a^{3/2}$ and $\propto a^{5/2}$ for radial and angular
momentum of entire system, and $\propto a^{3/2}$ for momentum of halos. Halo
momentum can be modeled by two mass-dependent coefficients $\tau_s^*$ and
$\eta_s^*$. Halo spin $\lambda_p$ is determined by $\alpha_s^*$, $\eta_s^*$,
and $n_s^*$ and decreases with halo mass with $\lambda_p$=0.09 and 0.031 for
small and large halos. The radial/angular momentum (G and H) are closely
related to constant $I_m$ that is defined as integral of velocity correlation
or $m$th derivative of energy spectrum at small k. On large scale, H is
negligible, $I_2$=0 reflects conservation of linear momentum, while $I_4$
reflects fluctuation of G. On halo scale, $I_4$ is determined by both G and H
that are comparable.


title:
Simulations of surface charge density changes during the untreated solid
  tumor growth

abstract:
  Understanding the untreated tumor growth kinetics and its intrinsic findings
is interesting and intriguing. The aim of this study is to propose an
approximate analytical expression that allows to simulate changes in surface
charge density changes at cancer-surrounding healthy tissue interface during
the untreated solid tumor growth. For this, the Gompertz and Poisson equations
are used. Simulations reveal that the unperturbed solid tumor growth is closely
related to changes in the surface charge density over time between the tumor
and the surrounding healthy tissue. Furthermore, the unperturbed solid tumor
growth is governed by temporal changes in this surface charge density. It is
concluded that graphic strategies corroborate the correspondence between the
electrical and physiological parameters in the untreated cancer, which may have
an essential role in its growth, progression, metastasis and protection against
immune system attack and anti-cancer therapies. In addition, knowledge of
surface charge density changes at cancer-surrounding healthy tissue interface
may be relevant in the redesign of chemotherapy and immunotherapy that have
into account the polarity of the substances or in the design completely novel
therapies.


title: Spectroscopic orbits of subsystems in multiple stars. VIII

abstract:
  Periods, eccentricities, and masses in hierarchical stellar systems inform us
on the formation and early evolution of these fascinating objects. To
complement the multiplicity statistics of nearby solar-type stars, 19 new
spectroscopic orbits of inner subsystems in 15 hierarchies (10 triples and 5
quadruples) are determined based on high-resolution echelle spectra collected
during several years. While previous papers of this series contained mostly
short-period orbits, here most periods are on the order of a year. The main
components of these hierarchies are HIP 7852, 9148, 12548, 21079, 24320, 27970,
34212, 56282, 57860, 76400, 76816, 81394, 96284, 100420, and HD 108938.
Noteworthy systems are HIP 12548 and 24230 (hierarchies of 2+2 architecture
with low-mass spectroscopic secondaries), HIP 56282 (a planetary-type 3+1
hierarchy), and HIP 27970 (a compact triple with periods of 15 and 1049 days).


title: On a Conjecture of Sun about sums of restricted squares

abstract:
  In this paper, we investigate sums of four squares of integers whose prime
factorizations are restricted, making progress towards a conjecture of Sun that
states that two of the integers may be restricted to the forms $2^a3^b$ and
$2^c5^d$. We obtain an ineffective generalization of results of Gauss and
Legendre on sums of three squares and an effective generalization of Lagrange's
four-square theorem.


title: PrivFair: a Library for Privacy-Preserving Fairness Auditing

abstract:
  Machine learning (ML) has become prominent in applications that directly
affect people's quality of life, including in healthcare, justice, and finance.
ML models have been found to exhibit discrimination based on sensitive
attributes such as gender, race, or disability. Assessing if an ML model is
free of bias remains challenging to date, and by definition has to be done with
sensitive user characteristics that are subject of anti-discrimination and data
protection law. Existing libraries for fairness auditing of ML models offer no
mechanism to protect the privacy of the audit data. We present PrivFair, a
library for privacy-preserving fairness audits of ML models. Through the use of
Secure Multiparty Computation (MPC), PrivFair protects the confidentiality of
the model under audit and the sensitive data used for the audit, hence it
supports scenarios in which a proprietary classifier owned by a company is
audited using sensitive audit data from an external investigator. We
demonstrate the use of PrivFair for group fairness auditing with tabular data
or image data, without requiring the investigator to disclose their data to
anyone in an unencrypted manner, or the model owner to reveal their model
parameters to anyone in plaintext.


title:
Taming quantum noise for efficient low temperature simulations of open
  quantum systems

abstract:
  The hierarchical equations of motion (HEOM), derived from the exact
Feynman-Vernon path integral, is one of the most powerful numerical methods to
simulate the dynamics of open quantum systems that are embedded in thermal
environments. However, its applicability is restricted to specific forms of
spectral reservoir distributions and relatively elevated temperatures. Here we
solve this problem and introduce an effective treatment of quantum noise in
frequency space by systematically clustering higher order Matsubara poles
equivalent to an optimized rational decomposition. This leads to an elegant
extension of the HEOM to arbitrary temperatures and very general reservoirs in
combination with efficiency, high accuracy and long-time stability. Moreover,
the technique can directly be implemented in alternative approaches such as
Greens-function, stochastic, and pseudo-mode formulations. As one highly
non-trivial application, for the sub-ohmic spin-boson model at vanishing
temperature the Shiba relation is quantitatively verified which predicts the
long-time decay of correlation functions.


title: Streaming word problems

abstract:
  We study deterministic and randomized streaming algorithms for word problems
of finitely generated groups. For finitely generated linear groups, metabelian
groups and free solvable groups we show the existence of randomized streaming
algorithms with logarithmic space complexity for their word problems. We also
show that the class of finitely generated groups with a logspace randomized
streaming algorithm for the word problem is closed under several group
theoretical constructions: finite extensions, direct products, free products
and wreath products by free abelian groups. We contrast these results with
several lower bound. An example of a finitely presented group, where the word
problem has only a linear space randomized streaming algorithm, is Thompson's
group $F$.


title: Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms

abstract:
  Sparse Principal Component Analysis (PCA) is a prevalent tool across a
plethora of subfields of applied statistics. While several results have
characterized the recovery error of the principal eigenvectors, these are
typically in spectral or Frobenius norms. In this paper, we provide entrywise
$\ell_{2,\infty}$ bounds for Sparse PCA under a general high-dimensional
subgaussian design. In particular, our results hold for any algorithm that
selects the correct support with high probability, those that are sparsistent.
Our bound improves upon known results by providing a finer characterization of
the estimation error, and our proof uses techniques recently developed for
entrywise subspace perturbation theory.


title: Study of Solutions of Certain Kind of Non-Linear Differential Equations

abstract:
  In this paper we study about the existence of solutions of certain kind of
non-linear differential and differential-difference equations. We give partial
answer to a problem which was asked by chen et al. in [13].


title:
Improved estimator for numerical renormalization group calculations of
  the self-energy

abstract:
  We present a new estimator for the self-energy based on a combination of two
equations of motion and discuss its benefits for numerical renormalization
group (NRG) calculations. In challenging regimes, NRG results from the standard
estimator, a ratio of two correlators, often suffer from artifacts: the
imaginary part of the retarded self-energy is not properly normalized and, at
low energies, overshoots to unphysical values and displays wiggles. We show
that the new estimator resolves the artifacts in these properties as they can
be determined directly from the imaginary parts of auxiliary correlators and do
not involve real parts obtained by Kramers-Kronig transform. Furthermore, we
find that the new estimator yields converged results with reduced numerical
effort (for a lower number of kept states) and thus is highly valuable when
applying NRG to multiorbital systems. Our analysis is targeted at NRG
treatments of quantum impurity models, especially those arising within
dynamical mean-field theory, but most results can be straightforwardly
generalized to other impurity or cluster solvers.


title:
Parallel Contests for Crowdsourcing Reviews: Existence and Quality of
  Equilibria

abstract:
  Motivated by the intricacies of allocating treasury funds in blockchain
settings, we study the problem of crowdsourcing reviews for many different
proposals, in parallel. During the reviewing phase, every reviewer can select
the proposals to write reviews for, as well as the quality of each review. The
quality levels follow certain very coarse community guidelines and can have
values such as 'excellent' or 'good'. Based on these scores and the
distribution of reviews, every reviewer will receive some reward for their
efforts. In this paper, we design a reward scheme and show that it always has
pure Nash equilibria, for any set of proposals and reviewers. In addition, we
show that these equilibria guarantee constant factor approximations for two
natural metrics: the total quality of all reviews, as well as the fraction of
proposals that received at least one review, compared to the optimal outcome.


title: Computational Indentation in Highly Cross-linked Polymer Networks

abstract:
  Indentation is a common experimental technique to study the mechanics of
polymeric materials. The main advantage of using indentation is because this
provides a direct correlation between the microstructure and the small-scale
mechanical response, which is otherwise difficult within the standard tensile
testing. Here, majority of studies have investigated hydrogels, microgels
and/or elastomers. However, a lesser investigated system is the indentation in
highly cross-linked polymer (HCP) networks, where the complex network structure
plays a key role in dictating their physical properties. In this work, we
investigate the structure-property relationship in HCP networks using the
computational indentation of a generic model. We establish a correlation
between the local bond breaking, the network rearrangement, and the small-scale
mechanics. The results are compared with the elastic-plastic deformation model.
HCPs harden upon indentation.


title:
Self-pulsing and chaos in the asymmetrically-driven dissipative photonic
  Bose-Hubbard dimer: A bifurcation analysis

abstract:
  We perform a systematic study of the temporal dynamics emerging in the
asymmetrically driven dissipative Bose-Hubbard dimer model. This model
successfully describes the nonlinear dynamics of photonic diatomic molecules in
linearly coupled Kerr resonators coherently excited by a single laser beam.
Such temporal dynamics include self-pulsing oscillations, period doubled
oscillatory states, chaotic dynamics, and spikes. The different states and
dynamical regimes have been thoroughly characterized using bifurcation
analysis. This analysis has allowed us to identify the main instabilities, i.e.
bifurcations, responsible for the appearance of the previously stated dynamics.


title: Time Series Anomaly Detection by Cumulative Radon Features

abstract:
  Detecting anomalous time series is key for scientific, medical and industrial
tasks, but is challenging due to its inherent unsupervised nature. In recent
years, progress has been made on this task by learning increasingly more
complex features, often using deep neural networks. In this work, we argue that
shallow features suffice when combined with distribution distance measures. Our
approach models each time series as a high dimensional empirical distribution
of features, where each time-point constitutes a single sample. Modeling the
distance between a test time series and the normal training set therefore
requires efficiently measuring the distance between multivariate probability
distributions. We show that by parameterizing each time series using cumulative
Radon features, we are able to efficiently and effectively model the
distribution of normal time series. Our theoretically grounded but
simple-to-implement approach is evaluated on multiple datasets and shown to
achieve better results than established, classical methods as well as complex,
state-of-the-art deep learning methods. Code is provided.


title:
Test Automation Maturity Improves Product Quality -- Quantitative Study
  of Open Source Projects Using Continuous Integration

abstract:
  The popularity of continuous integration (CI) is increasing as a result of
market pressure to release product features or updates frequently. The ability
of CI to deliver quality at speed depends on reliable test automation. In this
paper, we present an empirical study to observe the effect of test automation
maturity (assessed by standard best practices in the literature) on product
quality, test automation effort, and release cycle in the CI context of open
source projects. We run our test automation maturity survey and got responses
from 37 open source java projects. We also mined software repositories of the
same projects. The main results of regression analysis reveal that, higher
levels of test automation maturity are positively associated with higher
product quality (p-value=0.000624) and shorter release cycle (p-value=0.01891);
There is no statistically significant evidence of increased test automation
effort due to higher levels of test automation maturity and product quality.
Thus, we conclude that, a potential benefit of improving test automation
maturity (using standard best practices) is product quality improvement and
release cycle acceleration in the CI context of open source projects. We
encourage future research to extend our findings by adding more datasets with
different programming languages and CI tools, closed source projects, and
large-scale industrial projects. Our recommendation to practitioners (in the
similar CI context) is to utilize standard best practices to improve test
automation maturity.


title: Detecting and Localizing Copy-Move and Image-Splicing Forgery

abstract:
  In the world of fake news and deepfakes, there have been an alarmingly large
number of cases of images being tampered with and published in newspapers, used
in court, and posted on social media for defamation purposes. Detecting these
tampered images is an important task and one we try to tackle. In this paper,
we focus on the methods to detect if an image has been tampered with using both
Deep Learning and Image transformation methods and comparing the performances
and robustness of each method. We then attempt to identify the tampered area of
the image and predict the corresponding mask. Based on the results, suggestions
and approaches are provided to achieve a more robust framework to detect and
identify the forgeries.


title:
Joint user association and power allocation in ultra-dense mmWave
  networks: a multi-connectivity approach

abstract:
  In ultra-dense millimeter wave (mmWave) networks, mmWave signals suffer from
severe path losses and are easily blocked by obstacles. Meanwhile, ultra-dense
deployment causes excessive handovers, which reduces the data link reliability.
To alleviate the above issues, the novel technology, known as
multi-connectivity enabled user association (MCUA) is incorporated in this
letter. We aim to jointly optimize MCUAs and downlink (DL) power allocations
(PAs) to maximize the DL rate of each user simultaneously, rather than total.
This is a non-convex nonlinear 0-1 mixed integer multi-objective optimization
problem and quite complicated. To solve it, we first use the weighted sum
method to scalarize it as a single-objective optimization problem (SOOP), and
then relax the binary association variables to real ones. Considering that the
relaxed SOOP is still non-convex, we perform a series of transformations upon
it and make it a differential of convex programming. Finally, we develop an
iterative algorithm based on the convex-concave procedure to solve the SOOP.
Numerical results are presented to demonstrate the effectiveness of the
proposed algorithms.


title:
Negative Chemical Pressure Effect on Superconductivity and Charge
  Density Wave of Cu0.5Ir1-xZrxTe2

abstract:
  This study demonstrates the design and synthesis of Cu0.5Ir1-xZrxTe2 system
by partial substitution of Ir with Zr acting as a negative chemical pressure.
With the doping of Zr, the cell parameters significantly expand, signifying an
effective negative chemical pressure. The experimental results found evidence
that the charge density wave (CDW)-like order is immediately quenched by subtle
Zr substitution for Ir and a classical dome-shape Tc(x) that peaked at 2.80 K
can be observed. The optimal Cu0.5Ir0.95Zr0.05Te2 compound is a BCS-type
superconductor and exhibits type-II SC. However, high Zr concentration can
provoke disorder, inducing the reappearance of CDW order. The present study
shows that the Cu0.5Ir1-xZrxTe2 system may provide a new platform for further
understanding of multiple electronic orders in transition metal
dichalcogenides.


title: Latent gaze information in highly dynamic decision-tasks

abstract:
  Digitization is penetrating more and more areas of life. Tasks are
increasingly being completed digitally, and are therefore not only fulfilled
faster, more efficiently but also more purposefully and successfully. The rapid
developments in the field of artificial intelligence in recent years have
played a major role in this, as they brought up many helpful approaches to
build on. At the same time, the eyes, their movements, and the meaning of these
movements are being progressively researched. The combination of these
developments has led to exciting approaches. In this dissertation, I present
some of these approaches which I worked on during my Ph.D.
  First, I provide insight into the development of models that use artificial
intelligence to connect eye movements with visual expertise. This is
demonstrated for two domains or rather groups of people: athletes in
decision-making actions and surgeons in arthroscopic procedures. The resulting
models can be considered as digital diagnostic models for automatic expertise
recognition. Furthermore, I show approaches that investigate the
transferability of eye movement patterns to different expertise domains and
subsequently, important aspects of techniques for generalization. Finally, I
address the temporal detection of confusion based on eye movement data. The
results suggest the use of the resulting model as a clock signal for possible
digital assistance options in the training of young professionals. An
interesting aspect of my research is that I was able to draw on very valuable
data from DFB youth elite athletes as well as on long-standing experts in
arthroscopy. In particular, the work with the DFB data attracted the interest
of radio and print media, namely DeutschlandFunk Nova and SWR DasDing. All
resulting articles presented here have been published in internationally
renowned journals or at conferences.


title:
The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular
  Dataset of 3.5M Screening and Diagnostic Mammograms

abstract:
  Developing and validating artificial intelligence models in medical imaging
requires datasets that are large, granular, and diverse. To date, the majority
of publicly available breast imaging datasets lack in one or more of these
areas. Models trained on these data may therefore underperform on patient
populations or pathologies that have not previously been encountered. The EMory
BrEast imaging Dataset (EMBED) addresses these gaps by providing 3650,000 2D
and DBT screening and diagnostic mammograms for 116,000 women divided equally
between White and African American patients. The dataset also contains 40,000
annotated lesions linked to structured imaging descriptors and 61 ground truth
pathologic outcomes grouped into six severity classes. Our goal is to share
this dataset with research partners to aid in development and validation of
breast AI models that will serve all patients fairly and help decrease bias in
medical AI.


title:
Cross-level Contrastive Learning and Consistency Constraint for
  Semi-supervised Medical Image Segmentation

abstract:
  Semi-supervised learning (SSL), which aims at leveraging a few labeled images
and a large number of unlabeled images for network training, is beneficial for
relieving the burden of data annotation in medical image segmentation.
According to the experience of medical imaging experts, local attributes such
as texture, luster and smoothness are very important factors for identifying
target objects like lesions and polyps in medical images. Motivated by this, we
propose a cross-level contrastive learning scheme to enhance representation
capacity for local features in semi-supervised medical image segmentation.
Compared to existing image-wise, patch-wise and point-wise contrastive learning
algorithms, our devised method is capable of exploring more complex similarity
cues, namely the relational characteristics between global and local patch-wise
representations. Additionally, for fully making use of cross-level semantic
relations, we devise a novel consistency constraint that compares the
predictions of patches against those of the full image. With the help of the
cross-level contrastive learning and consistency constraint, the unlabelled
data can be effectively explored to improve segmentation performance on two
medical image datasets for polyp and skin lesion segmentation respectively.
Code of our approach is available.


title:
Joint-bone Fusion Graph Convolutional Network for Semi-supervised
  Skeleton Action Recognition

abstract:
  In recent years, graph convolutional networks (GCNs) play an increasingly
critical role in skeleton-based human action recognition. However, most
GCN-based methods still have two main limitations: 1) They only consider the
motion information of the joints or process the joints and bones separately,
which are unable to fully explore the latent functional correlation between
joints and bones for action recognition. 2) Most of these works are performed
in the supervised learning way, which heavily relies on massive labeled
training data. To address these issues, we propose a semi-supervised
skeleton-based action recognition method which has been rarely exploited
before. We design a novel correlation-driven joint-bone fusion graph
convolutional network (CD-JBF-GCN) as an encoder and use a pose prediction head
as a decoder to achieve semi-supervised learning. Specifically, the CD-JBF-GC
can explore the motion transmission between the joint stream and the bone
stream, so that promoting both streams to learn more discriminative feature
representations. The pose prediction based auto-encoder in the self-supervised
training stage allows the network to learn motion representation from unlabeled
data, which is essential for action recognition. Extensive experiments on two
popular datasets, i.e. NTU-RGB+D and Kinetics-Skeleton, demonstrate that our
model achieves the state-of-the-art performance for semi-supervised
skeleton-based action recognition and is also useful for fully-supervised
methods.


title: K-ST: A Formal Executable Semantics of PLC Structured Text Language

abstract:
  Programmable Logic Controllers (PLCs) are responsible for automating process
control in many industrial systems (e.g. in manufacturing and public
infrastructure), and thus it is critical to ensure that they operate correctly
and safely. The majority of PLCs are programmed in languages such as Structured
Text (ST). However, a lack of formal semantics makes it difficult to ascertain
the correctness of their translators and compilers, which vary from
vendor-to-vendor. In this work, we develop K-ST, a formal executable semantics
for ST in the K framework. Defined with respect to the IEC 61131-3 standard and
PLC vendor manuals, K-ST is a high-level reference semantics that can be used
to evaluate the correctness and consistency of different ST implementations. We
validate K-ST by executing 509 ST programs extracted from Github and comparing
the results against existing commercial compilers (i.e., CODESYS,
CX-Programmer, and GX Works2). We then apply K-ST to validate the
implementation of the open source OpenPLC platform, comparing the executions of
several test programs to uncover five bugs and nine functional defects in the
compiler.


title: The Pantheon+ Analysis: Cosmological Constraints

abstract:
  We present constraints on cosmological parameters from the Pantheon+ analysis
of 1701 light curves of 1550 distinct Type Ia supernovae (SNe Ia) ranging in
redshift from $z=0.001$ to 2.26. This work features an increased sample size,
increased redshift span, and improved treatment of systematic uncertainties in
comparison to the original Pantheon analysis and results in a factor of two
improvement in cosmological constraining power. For a Flat$\Lambda$CDM model,
we find $\Omega_M=0.338\pm0.018$ from SNe Ia alone. For a Flat$w_0$CDM model,
we measure $w_0=-0.89\pm0.13$ from SNe Ia alone, H$_0=72.86^{+0.94}_{-1.06}$ km
s$^{-1}$ Mpc$^{-1}$ when including the Cepheid host distances and covariance
(SH0ES), and $w_0=-0.978^{+0.024}_{-0.031}$ when combining the SN likelihood
with constraints from the cosmic microwave background (CMB) and baryon acoustic
oscillations (BAO); both $w_0$ values are consistent with a cosmological
constant. We also present the most precise measurements to date on the
evolution of dark energy in a Flat$w_0w_a$CDM universe, and measure
$w_a=-0.4^{+1.0}_{-1.8}$ from Pantheon+ alone, H$_0=73.40^{+0.99}_{-1.22}$ km
s$^{-1}$ Mpc$^{-1}$ when including SH0ES, and $w_a=-0.65^{+0.28}_{-0.32}$ when
combining Pantheon+ with CMB and BAO data. Finally, we find that systematic
uncertainties in the use of SNe Ia along the distance ladder comprise less than
one third of the total uncertainty in the measurement of H$_0$ and cannot
explain the present "Hubble tension" between local measurements and
early-Universe predictions from the cosmological model.


title:
Systematic errors on optical-SED stellar mass estimates for galaxies
  across cosmic time and their impact on cosmology

abstract:
  Studying galaxies at different cosmic epochs entails several observational
effects that need to be taken into account to compare populations across a
large time span in a consistent manner. We use a sample of 166 nearby galaxies
that hosted type Ia supernovae (SNe Ia) and have been observed with the
integral field spectrograph MUSE through the AMUSING survey. Here, we present a
study of the systematic errors and bias in the host stellar mass with
increasing redshifts that are generally overlooked in SNe Ia cosmological
analyses. We simulate observations at different redshifts (0.1<z<2.0) using
four photometric bands (griz, similar to the Dark Energy Survey-SN program) to
then estimate the host galaxy properties across cosmic time. We find that
stellar masses are systematically underestimated as we move towards higher
redshifts, due mostly to different rest-frame wavelength coverage, with
differences reaching 0.3 dex at z~1. We have used the newly derived corrections
as a function of redshift to correct the stellar masses of a known sample of SN
Ia hosts and derive cosmological parameters. We show that these corrections
have a small impact on the derived cosmological parameters. The most affected
is the value of the mass step $\Delta_M$, which is reduced by $\sim$0.004 (6%
lower). The dark energy equation of state parameter $w$ changes by $\Delta w
\sim $0.006 (0.6% higher) and the value of $\Omega_m$ increases at most by
0.001 ($\sim$0.3%), all within the derived uncertainties of the model. While
the systematic error found in the estimate of the host stellar mass does not
significantly affect the derived cosmological parameters, it is an important
source of a systematic error that one should correct for as we enter a new era
of precision cosmology.


title:
The timbre of Hawking gravitons: an effective description of energy
  transport from holography

abstract:
  Planar black holes in AdS, which are holographically dual to compressible
relativistic fluids, have a long-lived phonon mode that captures the physics of
attenuated sound propagation and transports energy in the plasma. We describe
the open effective field theory of this fluctuating phonon degree of freedom.
The dynamics of the phonon is encoded in a single scalar field whose
gravitational coupling has non-trivial spatial momentum dependence. This
description fits neatly into the paradigm of classifying gravitational modes by
their Markovianity index, depending on whether they are long-lived. The sound
scalar is a non-Markovian field with index (3-d) for a d-dimensional fluid. We
reproduce (and extend) the dispersion relation of the holographic sound mode to
quartic order in derivatives, constructing in the process the effective field
theory governing its attenuated dynamics and associated stochastic
fluctuations. We also remark on the presence of additional spatially
homogeneous zero modes in the gravitational problem, which remain disconnected
from the phonon Goldstone mode.


title:
ALMA as a Redshift Machine: Using [CII] to Efficiently Confirm Galaxies
  in the Epoch of Reionization

abstract:
  The [CII]$_{158\mu m}$ line has long been proposed as a promising line to
spectroscopically confirm galaxies in the epoch of reionization. In this paper
we present the results of new ALMA observations spectral scanning for [CII] in
six particularly luminous Lyman Break Galaxies at $z\sim7$. The six sources
were drawn from a sample of bright $z\sim7$ galaxies identified using the
wide-area optical, near-IR, and Spitzer/IRAC data over the COSMOS/UltraVISTA
field and were targeted on the basis of tight constraints on their redshifts
from their IRAC [3.6]-[4.5] colors. We detect significant ($>9\sigma$) [CII]
lines in three of our six targets ($50\%$) co-spatial with the rest-$UV$
emission from the ground/space-based near-IR imaging. The luminosities of the
[CII] lines lie in the range $5.6$ to $8.8\times10^{8}L_{\odot}$, consistent
with the local [CII]-SFR relation. Meanwhile, their
[CII]/$L_{IR}\sim1-3\times10^{-3}$ ratios are slightly elevated compared to
local (U)LIRGS. This could be due to lower dust-to-gas or dust-to-metal ratios.
We also find that our sources display a large kinematic diversity, with one
source showing signs of rotation, one source a likely major merger and one
dispersion dominated source that might contain a bright star-forming clump. Our
results highlight the effectiveness of spectral scans with ALMA in
spectroscopically confirming luminous galaxies in the epoch of reionization,
something that is being be applied on a significantly larger sample in the
on-going REBELS large program.


title:
The Star Formation Burstiness and Ionizing Efficiency of Low-mass
  Galaxies

abstract:
  We investigate the burstiness of star formation and the ionizing efficiency
of a large sample of galaxies at $0.7 < z < 1.5$ using HST grism spectroscopy
and deep ultraviolet (UV) imaging in the GOODS-N and GOODS-S fields. The star
formation history (SFH) in these strong emission line low-mass galaxies
indicates an elevated star formation rate (SFR) based on the H$\alpha$ emission
line at a given stellar mass when compared to the standard main sequence.
Moreover, when comparing the H$\alpha$ and UV SFR indicators, we find that an
excess in SFR(H$\alpha$) compared to SFR(UV) is preferentially observed in
lower-mass galaxies below $10^{9}$ M$\odot$, which are also the highest-EW
galaxies. These findings suggest that the burstiness parameters of these strong
emission line galaxies may differ from those inferred from hydrodynamical
simulations and previous observations. For instance, a larger burstiness duty
cycle would explain the observed SFR(H$\alpha$) excess. We also estimate the
ionizing photon production efficiency $\xi_{ion}$, finding a median value of
Log($\xi_{ion}$/erg$^{-1}$ Hz)$=24.80 \pm 0.26$ when adopting a Galactic dust
correction for H$\alpha$ and an SMC one for the stellar component. We observe
an increase of $\xi_{ion}$ with redshift, further confirming similar results at
higher redshifts. We also find that $\xi_{ion}$ is strongly correlated with
EW(H$\alpha$), which provides an approach for deriving $\xi_{ion}$ in early
galaxies. Lower-mass, lower-luminosity galaxies have a higher $\xi_{ion}$.
Overall, these results provide further support for faint galaxies playing a
major role in the reionization of the Universe.


title:
The MaNGA FIREFLY Value-Added-Catalogue: resolved stellar populations of
  10,010 nearby galaxies

abstract:
  We present the MaNGA FIREFLY Value-Added-Catalogue (VAC) - a catalogue of
~3.7 million spatially resolved stellar population properties across 10,010
nearby galaxies from the final data release of the MaNGA survey. The full
spectral fitting code firefly is employed to derive parameters such as stellar
ages, metallicities, stellar and remnant masses, star formation histories, star
formation rates and dust attenuation. In addition to Voronoi-binned
measurements, our VAC also provides global properties, such as central values
and radial gradients. Two variants of the VAC are available: presenting the
results from fits using the M11-MILES and the novel MaStar stellar population
models. MaStar allows to constrain the fit over the whole MaNGA wavelength
range, extends the age-metallicity parameter space, and uses empirical spectra
from the same instrument as MaNGA. The fits employing MaStar models find on
average slightly younger ages, higher mass-weighted metallicities and smaller
colour excesses. These differences are reduced when matching wavelength range
and converging template grids. We further report that FIREFLY stellar masses
are systematically lower by ~0.3 dex than masses from the MaNGA PCA and Pipe3D
VACs, but match masses from the NSA best with only ~0.1 dex difference.
Finally, we show that FIREFLY stellar ages correlate with spectral index age
indicators H$\delta_A$ and $D_n$(4000), though with a clear additional
metallicity dependence.


title:
RAMSES-RTZ: Non-Equilibrium Metal Chemistry and Cooling Coupled to
  On-The-Fly Radiation Hydrodynamics

abstract:
  Emission and absorption lines from elements heavier than helium (metals)
represent one of our strongest probes of galaxy formation physics across nearly
all redshifts accessible to observations. The vast majority of simulations that
model these metal lines often assume either collisional or photoionisation
equilibrium, or a combination of the two. For the few simulations that have
relaxed these assumptions, a redshift-dependent meta-galactic UV background or
fixed spectrum is often used in the non-equilibrium photoionisation
calculation, which is unlikely to be accurate in the interstellar medium where
the gas can self-shield as well as in the high-redshift circumgalactic medium
where locally emitted radiation may dominate over the UV background. In this
work, we relax this final assumption by coupling the ionisation states of
individual metals to the radiation hydrodynamics solver present in RAMSES-RT.
Our chemical network follows radiative recombination, dielectronic
recombination, collisional ionisation, photoionisation, and charge transfer and
we use the ionisation states to compute non-equilibrium optically-thin
metal-line cooling. The fiducial model solves for the ionisation states of C,
N, O, Mg, Si, S, Fe, and Ne in addition to H, He, and H$_2$, but can be easily
extended for other ions. We provide interfaces to two different ODE solvers
that are competitive in both speed and accuracy. The code has been benchmarked
across a variety of gas conditions to reproduce results from CLOUDY when
equilibrium is reached. We show an example isolated galaxy simulation with
on-the-fly radiative transfer that demonstrates the utility of our code for
translating between simulations and observations without the use of idealised
photoionisation models.


title:
Unruh-DeWitt detector in dimensionally-reduced static spherically
  symmetric spacetimes

abstract:
  We study the dynamics of an Unruh-DeWitt detector interacting with a massless
scalar field in an arbitrary static spherically symmetric spacetimes whose
metric is characterised by a single metric function $f(r)$. In order to obtain
clean physical insights, we employ the derivative coupling variant of the
Unruh-DeWitt model in (1+1) dimensions where powerful conformal techniques
enable closed-form expressions for the vacuum two-point functions. Due to the
generality of the formalism, we will be able to study a very general class of
static spherically symmetric (SSS) background. We pick three examples to
illustrate our method: (1) non-singular Hayward black holes, (2) the recently
discovered $D\to 4$ limit of Gauss-Bonnet black holes, and (3) the "black
bounce" metric that interpolates Schwarzschild black holes and traversable
wormholes. We also show that the derivative coupling Wightman function
associated with the generalized Hartle-Hawking vacuum satisfies the KMS
property with the well-known temperature $f'(r_\text{H})/(4\pi)$, where
$r_\text{H}$ is the horizon radius.


title:
Celestial Blocks and Transverse Spin in the Three-Point Energy
  Correlator

abstract:
  Quantitative theoretical techniques for understanding the substructure of
jets at the LHC enable new insights into the dynamics of QCD, and novel
approaches to search for new physics. Recently, there has been a program to
reformulate jet substructure in terms of correlation functions, $\langle
\mathcal{E}(\vec n_1) \mathcal{E}(\vec n_2) \cdots \mathcal{E}(\vec n_k)
\rangle$, of light-ray operators, $\mathcal{E}(\vec n)$, allowing the
application of techniques developed in the study of Conformal Field Theories
(CFTs). In this paper we further develop these techniques in the particular
context of the three-point correlator $\langle \mathcal{E}(\vec n_1)
\mathcal{E}(\vec n_2) \mathcal{E}(\vec n_3) \rangle$, using recently computed
perturbative data in both QCD and $\mathcal{N}=4$ sYM. We derive the celestial
blocks appearing in the light-ray operator product expansion (OPE) of the
three-point correlator, and use the Lorentzian inversion formula to extract the
spectrum of light-ray operators appearing in the expansion, showing, in
particular, that the OPE data is analytic in transverse spin. Throughout our
presentation, we highlight the relation between the OPE approach, and more
standard splitting function based approaches of perturbative QCD, emphasizing
the utility of the OPE approach for incorporating symmetries in jet
substructure calculations. We hope that our presentation introduces a number of
new techniques to the jet substructure community, and also illustrates the
phenomenological relevance of the study of light-ray operators in the OPE limit
to the CFT community.


title:
Stellar population gradients at cosmic noon as a constraint to the
  evolution of passive galaxies

abstract:
  Context: The radial variations of the stellar populations properties within
passive galaxies at high redshift contain information about their assembly
mechanisms, based on which galaxy formation and evolution scenarios may be
distinguished.
  Aims: The aim of this work is to give constraints on massive galaxy formation
scenarios through one of the first analyses of age and metallicity gradients of
the stellar populations in a sample of passive galaxies at $z > 1.6$ based on
spectroscopic data from the Hubble Space Telescope.
  Methods: We combined G$141$ deep slitless spectroscopic data and F$160$W
photometric data of the spectroscopically passive galaxies at $1.6< z < 2.4$
with $H_{160} < 22.0$ in the field of view of the cluster JKCS $041$. We
extracted spectra from different zones of the galaxies, and we analysed them by
fitting them with a library of synthetic templates of stellar population models
to obtain estimates of the age and metallicity gradients.
  Results: We obtained reliable measurements of age and metallicity parameters
in different spatial zones of $\text{four}$ galaxies. We performed spatially
resolved measurements in individual high-redshift galaxies without the need of
peculiar situations (i.e. gravitational lensing) for the first time. All four
galaxies exhibit negative metallicity gradients. Their amplitude, similar to
that measured in galaxies in the local Universe, suggests that the stellar
populations of passive galaxies from $z \sim 2$ to $z = 0$ are not
redistributed.
  Conclusions: Although the sample we analysed is small, the results we
obtained suggest that the main mechanism that determines the spatial
distribution of the stellar population properties within passive galaxies is
constrained in the first $3$ Gyr of the Universe. This is consistent with the
revised monolithic scenario.


title: The LVS Parametric Tadpole Constraint

abstract:
  The large volume scenario (LVS) for de Sitter compactifications of the type
IIB string is, at least in principle, well protected from various unknown
corrections. The reason is that, by construction, the Calabi-Yau volume is
exponentially large. However, as has recently been emphasised, in practice the
most explicit models are rather on the border of parametric control. We
identify and quantify parametrically what we believe to be the main issue
behind this difficulty. Namely, a large volume implies a shallow AdS minimum
and hence a small uplift. The latter, if it relies on an anti-D3 in a throat,
requires a large negative tadpole. As our main result, we provide a simple and
explicit formula for what this tadpole has to be in order to control the most
dangerous corrections. The fundamental ingredients are parameters specifying
the desired quality of control. We comment on the interplay between our
constraint and the tadpole conjecture. We also discuss directions for future
work which could lead to LVS constructions satisfying the tadpole constraint
with better control, as well as further challenges that may exist for the LVS.
Our formula then represents a very concrete challenge for future searches for
and the understanding of relevant geometries.