title:
LUCE: A Blockchain-based data sharing platform for monitoring data
  license accountability and compliance

abstract:
  Easy access to data is one of the main avenues to accelerate scientific
research. As a key element of scientific innovations, data sharing allows the
reproduction of results, helps prevent data fabrication, falsification, and
misuse. Although the research benefits from data reuse are widely acknowledged,
the data collections existing today are still kept in silos. Indeed, monitoring
what happens to data once they have been handed to a third party is currently
not feasible within the current data-sharing practices. We propose a
blockchain-based system to trace data collections, and potentially create a
more trustworthy data sharing process. In this paper, we present the LUCE
(License accoUntability and CompliancE) architecture as a decentralized
blockchain-based platform supporting data sharing and reuse. LUCE is designed
to provide full transparency on what happens to the data after they are shared
with third parties. The contributions of this work are: the definition of a
generic model and an implementation for decentralized data sharing
accountability and compliance and to incorporates dynamic consent and legal
compliance mechanisms. We test the scalability of the platform in a real-time
environment where a growing number of users access and reuse different
datasets. Compared to existing data-sharing solutions, LUCE provides
transparency over data sharing practices, enables data reuse and supports
regulatory requirements. The experimentation shows that the platform can be
scaled for a large number of users.


title: An application of sums of triple products of binomials

abstract:
  We prove that a certain family of sums of products of three binomials has
alternating behavior modulo a prime $p$. To accomplish this we rewrite these
sums as signed sums of products of three binomials, the better to handle $p$,
and we give closed-form expressions for two related sums of signed products of
three binomials.


title:
Optical Theorem, Crossing Property and Derivative Dispersion Relations:
  Implications on the Asymptotic Behavior of $\sigma_{tot}(s)$ and $\rho(s)$

abstract:
  In this paper, one presents some results concerning the behavior of the total
cross section and $\rho$-parameter at asymptotic energies in proton--proton
($pp$) and antiproton--proton ($\bar{p}p$) collisions. For this intent, we
consider three of the main theoretical results in high energy physics: the
crossing property, the derivative dispersion relation, and the optical theorem.
The use of such machinery allows the analytic formulas for wide set of the
measured global scattering parameters and some important relations between
them. The suggested parameterizations approximate simultaneously the energy
dependence for total cross section and $\rho$-parameter for $pp$ and $\bar{p}p$
with statistically acceptable quality in multi-TeV region. Also the qualitative
description is obtained for important interrelations, namely difference, sum
and ratio of the antiparticle--particle and particle--particle total cross
sections. Despite the reduced number of experimental data for the total cross
section and $\rho$-parameter in TeV-scale, which turns any prediction for the
beginning of the asymptotic domain a hard task, the fitting procedures
indicates that asymptotia lies in the energy range 25.5--130 TeV. Moreover, in
the asymptotic regime, one obtains $\alpha_{\mathbb{P}}=1$. Detailed
quantitative study of energy behavior of measured scattering parameters and
their combinations in ultra--high energy domain indicates that the scenario
with the generalized formulation of the Pomeranchuk theorem is more favorable
with respect of the original formulation of this theorem.


title:
On the Stability of Unconditionally Positive and Linear Invariants
  Preserving Time Integration Schemes

abstract:
  Higher-order time integration methods that unconditionally preserve the
positivity and linear invariants of the underlying differential equation system
cannot belong to the class of general linear methods. This poses a major
challenge for the stability analysis of such methods since the new iterate
depends nonlinearly on the current iterate. Moreover, for linear systems, the
existence of linear invariants is always associated with zero eigenvalues, so
that steady states of the continuous problem become non-hyperbolic fixed points
of the numerical time integration scheme. Altogether, the stability analysis of
such methods requires the investigation of non-hyperbolic fixed points for
general nonlinear iterations. Based on the center manifold theory for maps we
present a theorem for the analysis of the stability of non-hyperbolic fixed
points of time integration schemes applied to problems whose steady states form
a subspace. This theorem provides sufficient conditions for both the stability
of the method and the local convergence of the iterates to the steady state of
the underlying initial value problem. This theorem is then used to prove the
unconditional stability of the MPRK22($\alpha$)-family of modified
Patankar-Runge-Kutta schemes when applied to arbitrary positive and
conservative linear systems of differential equations. The theoretical results
are confirmed by numerical experiments.


title:
Inflationary phenomenology of quadratic gravity in the Palatini
  formulation

abstract:
  The theory of General Relativity was established on a spacetime manifold
equipped with a metric tensor, $(\mathcal{M}_4,\text{g})$, and the connection
on $\mathcal{M}_4$ identified with the Levi-Civita one. Even though there are
valid reasons to assume a torsionless manifold that preserves the metric, it
was shown that dealing away with these assumptions the Levi-Civita condition
can be reproduced at the level of equations of motion of GR. It was not long
before the equivalence of General Relativity between the two descriptions,
known as the Palatini or first-order formalism in which the connection is
independent of the metric, and the conventional metric or second-order
formalism, was broken for more complicated actions involving higher-order
curvature invariants and/or nonminimal couplings between the gravitational and
matter sector. Nowadays these types of theories are prominent in modeling
inflation where they have found major success. Since the paradigm of inflation
is fused with the gravitational degrees of freedom and thus their
parametrisation, it is interesting to understand how the predictions of these
models differ between the two formulations. One of the outstanding models of
inflation is the Starobinsky or quadratic gravity model. However in the
Palatini formalism the scalar degree of freedom sourced by the $R^2$ term is
nondynamical and is unable to drive an inflationary phase. In order for
inflation to be realised in the first-order formalism the Starobinsky model has
to be coupled with a fundamental scalar field that will assume the role of the
inflaton. In this thesis we investigate different inflationary scenarios,
starting with previously ruled-out models, where we find that the $R^2$ term
has a significant role in flattening the Einstein-frame inflaton potential and
thus giving the opportunity for these models to come in contact with
observations.


title: Which galaxy property is the best gauge of the oxygen abundance?

abstract:
  We present an extensive exploration of the impact of 29 physical parameters
in the oxygen abundance for a sample of 299 star-forming galaxies extracted
from the extended CALIFA sample. We corroborate that the stellar mass is the
physical parameter that better traces the observed oxygen abundance (i.e., the
mass-metallicity relation, MZR), while other physical parameters could play a
potential role in shaping this abundance, but with a lower significant impact.
We find that the functional form that best describes the MZR is a third-order
polynomial function. From the residuals between this best functional form and
the MZR, we find that once considered the impact of the mass in the oxygen
abundance, the other physical parameters do not play a significant secondary
role in shaping the oxygen abundance in these galaxies (including the gas
fraction or the star formation rate). Our analysis suggests that the origin of
the MZR is related to the chemical enrichment evolution of the interstellar
medium due, most likely, to the build-up of stellar mass in these star-forming
galaxies.


title:
Pseudodifferential arithmetic and the Riemann hypothesis: a short
  version

abstract:
  A criterion for the validity of the Riemann hypothesis reduced the problem to
the search for a certain estimate, for a hermitian form associated by means of
the Weyl symbolic calculus of operators to a distribution in the plane of an
arithmetic nature. One can reduce the question further to an algebraic
question, which leads to a proof of the conjecture.


title:
Nonergodicity of $d$-dimensional generalized L\'evy walks and their
  relation to other space-time coupled models

abstract:
  We investigate the nonergodicity of the generalized L\'evy walk introduced by
Shlesinger et al. [Phys. Rev. Lett. 58, 1100 (1987)] with respect to the
squared displacements. We present detailed analytical derivations of our
previous findings outlined in a recent Letter [Phys. Rev. Lett. 120, 104501
(2018)], give profound interpretations, and especially emphasize three
surprising results: First, we find that the mean-squared displacements can
diverge for a certain range of parameter values. Second, we show that an
ensemble of trajectories can spread subdiffusively, whereas individual
time-averaged squared displacements show superdiffusion. Third, we recognize
that the fluctuations of the time-averaged squared displacements can become so
large that the ergodicity breaking parameter diverges, what we call infinitely
strong ergodicity breaking. The latter phenomenon can also occur for paramter
values where the lag-time dependence of the mean-squared displacements is
linear indicating normal diffusion. In order to numerically determine the full
distribution of time-averaged squared displacements, we use importance
sampling. For an embedding of our new findings into existing results in the
literature, we define a more general model which we call variable speed
generalized L\'evy walk and which includes well known models from the
literature as special cases such as the space-time coupled L\'evy flight or the
anomalous Drude model. We discuss and interpret our findings regarding the
generalized L\'evy walk in detail and compare them with the nonergodicity of
the other space-time coupled models following from the more general model.


title:
$Rheomergy$: Collective behavior mediated by active flow-based
  recruitment

abstract:
  The physics of signal propagation in a collection of organisms that
communicate with each other both enables and limits how active excitations at
the individual level reach, recruit and lead to collective patterning. Inspired
by the patterns in a planar swarm of bees that release pheromones, and use
fanning flows to recruit additional bees, we develop a theoretical framework
for patterning via active flow-based recruitment. Our model generalizes the
well-known Patlak-Keller-Segel model of diffusion-dominated aggregation and
leads to more complex phase space of patterns spanned by two dimensionless
parameters that measure the scaled stimulus/activity and the scaled chemotactic
response. Together these determine the efficacy of signal communication that
leads to a variety of migration and aggregation patterns consistent with
observations.


title:
TEE-based decentralized recommender systems: The raw data sharing
  redemption

abstract:
  Recommenders are central in many applications today. The most effective
recommendation schemes, such as those based on collaborative filtering (CF),
exploit similarities between user profiles to make recommendations, but
potentially expose private data. Federated learning and decentralized learning
systems address this by letting the data stay on user's machines to preserve
privacy: each user performs the training on local data and only the model
parameters are shared. However, sharing the model parameters across the network
may still yield privacy breaches. In this paper, we present REX, the first
enclave-based decentralized CF recommender. REX exploits Trusted execution
environments (TEE), such as Intel software guard extensions (SGX), that provide
shielded environments within the processor to improve convergence while
preserving privacy. Firstly, REX enables raw data sharing, which ultimately
speeds up convergence and reduces the network load. Secondly, REX fully
preserves privacy. We analyze the impact of raw data sharing in both deep
neural network (DNN) and matrix factorization (MF) recommenders and showcase
the benefits of trusted environments in a full-fledged implementation of REX.
Our experimental results demonstrate that through raw data sharing, REX
significantly decreases the training time by 18.3x and the network load by 2
orders of magnitude over standard decentralized approaches that share only
parameters, while fully protecting privacy by leveraging trustworthy hardware
enclaves with very little overhead.


title:
Nonlinear mechanisms in Al and Ti superconducting travelling-wave
  parametric amplifiers

abstract:
  The underlying nonlinear mechanisms behind the operation of travelling-wave
parametric amplifiers (TWPAs) are important in determining their performance in
terms of added noise, maximum gain, and bandwidth. We describe a method of
characterising the underlying nonlinearity of a superconducting material in
terms of its dissipative-reactive ratio and the response time of the underlying
microscopic processes. We describe and calculate the different behaviour
arising from the equilibrium supercurrent nonlinearity, which has low
dissipation and fast response time, and the non-equilibrium heating
nonlinearity, which has high dissipation and slow response time. We have
fabricated TWPAs based on Al and Ti, and characterised their nonlinearities
using our analysis. For both Al and Ti, the measured dissipative-reactive
ratios and response times are quantitatively similar to predictions for the
non-equilibrium heating nonlinearity. In line with this, we were able to obtain
more than 20 dB of peak power gain, although only over a narrow bandwidth of a
few kilohertz.


title:
Enhanced superconducting pairing strength near a nonmagnetic nematic
  quantum critical point

abstract:
  The quest for high-temperature superconductivity at ambient pressure is a
central issue in physics. In this regard, the relationship between
unconventional superconductivity and the quantum critical point (QCP)
associated with the suppression of some form of symmetry-breaking order to zero
temperature has received particular attention. The key question is how the
strength of the electron pairs changes near the QCP, and this can be verified
by high-field experiments. However, such studies are limited mainly to
superconductors with magnetic QCPs, and the possibility of unconventional
mechanisms by which nonmagnetic QCP promotes strong pairing remains a
nontrivial issue. Here, we report systematic measurements of the upper critical
field $H_{{\rm c2}}$ in nonmagnetic FeSe$_{1-x}$Te$_{x}$ superconductors, which
exhibit a QCP of electronic nematicity characterized by spontaneous
rotational-symmetry breaking. As the magnetic field increases, the
superconducting phase of FeSe$_{1-x}$Te$_{x}$ shrinks to a narrower dome
surrounding the nematic QCP. The analysis of $H_{{\rm c2}}$ reveals that the
Pauli-limiting field is enhanced toward the QCP, implying that the pairing
interaction is significantly strengthened via nematic fluctuations emanated
from the QCP. Remarkably, this nonmagnetic nematic QCP is not accompanied by a
divergent effective mass, distinct from the magnetically mediated pairing. Our
observation opens up a nonmagnetic route to high-temperature superconductivity.


title: The honeycomb and hyperhoneycomb polymorphs of IrI$_3$

abstract:
  The synthesis of IrI$_3$ at high pressure in its layered honeycomb polymorph
is reported. Its crystal structure is refined by single crystal X-ray
diffraction. Faults in the honeycomb layer stacking are observed by single
crystal diffraction, synchrotron powder diffraction, and transmission electron
microscopy. A previously unreported hyperhoneycomb polymorph of IrI$_3$
($\beta$-IrI$_3$), is also described. Its structure in space group Fddd is
determined by single crystal XRD. Both materials are highly-resistive
diamagnetic semiconductors, consistent with a low spin d$^6$ configuration for
Ir(III). The two- and three-dimensional Ir arrays in these polymorphs of
IrI$_3$ are analogous to those found in the $\alpha$- and $\beta$- polymorphs
of Li$_2$IrO$_3$, although the Ir electron configurations are different.


title:
Globally Convergent Policy Search over Dynamic Filters for Output
  Estimation

abstract:
  We introduce the first direct policy search algorithm which provably
converges to the globally optimal $\textit{dynamic}$ filter for the classical
problem of predicting the outputs of a linear dynamical system, given noisy,
partial observations. Despite the ubiquity of partial observability in
practice, theoretical guarantees for direct policy search algorithms, one of
the backbones of modern reinforcement learning, have proven difficult to
achieve. This is primarily due to the degeneracies which arise when optimizing
over filters that maintain internal state.
  In this paper, we provide a new perspective on this challenging problem based
on the notion of $\textit{informativity}$, which intuitively requires that all
components of a filter's internal state are representative of the true state of
the underlying dynamical system. We show that informativity overcomes the
aforementioned degeneracy. Specifically, we propose a $\textit{regularizer}$
which explicitly enforces informativity, and establish that gradient descent on
this regularized objective - combined with a ``reconditioning step'' -
converges to the globally optimal cost a $\mathcal{O}(1/T)$ rate. Our analysis
relies on several new results which may be of independent interest, including a
new framework for analyzing non-convex gradient descent via convex
reformulation, and novel bounds on the solution to linear Lyapunov equations in
terms of (our quantitative measure of) informativity.


title: Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors

abstract:
  We present a new method for the unsupervised detection of geometric anomalies
in high-resolution 3D point clouds. In particular, we propose an adaptation of
the established student-teacher anomaly detection framework to three
dimensions. A student network is trained to match the output of a pretrained
teacher network on anomaly-free point clouds. When applied to test data,
regression errors between the teacher and the student allow reliable
localization of anomalous structures. To construct an expressive teacher
network that extracts dense local geometric descriptors, we introduce a novel
self-supervised pretraining strategy. The teacher is trained by reconstructing
local receptive fields and does not require annotations. Extensive experiments
on the comprehensive MVTec 3D Anomaly Detection dataset highlight the
effectiveness of our approach, which outperforms the next-best method by a
large margin. Ablation studies show that our approach meets the requirements of
practical applications regarding performance, runtime, and memory consumption.


title:
On-shell versus curvature mass parameter fixing schemes in the
  quark-meson model and its phase diagrams

abstract:
  We compute and compare the effective potential and phase structure for the
quark-meson model in an extended mean-field approximation (e-MFA) when vacuum
one loop quark fluctuations are included and the model parameters are fixed
using different renormalization prescriptions.When the quark one loop vacuum
divergence is regularized under the minimal subtraction scheme,the model
setting of the parameter fixing using the curvature masses of the scalar and
pseudo-scalar mesons, has been termed as the quark-meson model with the vacuum
term(QMVT).However,this prescription becomes inconsistent when we notice that
the curvature mass is akin to defining the meson mass by the self-energy
evaluation at vanishing momentum.In this work,we apply the recently reported
exact prescription of the on-shell parameter fixing,to that version of
quark-meson (QM) model where the two quark flavors are coupled to the eight
mesons of the $ SU(2)_{L} \times SU(2)_{R} $ linear sigma model with
iso-singlet $ \sigma $ ($\eta$),iso-triplet $ \vec{a_{0}} $ ($ \vec{\pi} $)
scalar(pseudo-scalar) mesons.The model then becomes, the renormalized
quark-meson (RQM) model where physical (pole) masses of mesons and pion decay
constant, are put into the relation of the running mass parameter and couplings
by using the on-shell and the minimal subtraction renormalization schemes.The
vacuum effective potential plots,the phase diagrams and the order parameter
temperature variations for both the RQM model and QMVT model,are exactly
identical for the $m_\sigma=$616 MeV.The effective potential is deepest in the
QMVT model for $m_\sigma < $ 616 MeV and it becomes deepest in the RQM model
when $m_\sigma > $616 MeV.


title: Asymptotic expansion of the nonlocal heat content

abstract:
  Let $\mathbf{X}=\{X_t\}_{t\geq 0}$ be a L\'evy process in $\mathbb{R}^d$ and
$\Omega$ be an open subset of $\mathbb{R}^d$ with finite Lebesgue measure. In
this article we consider the quantity $H(t)=\int_{\Omega} \mathbb{P}^x
(X_t\in\Omega^c) \, \mathrm{d}x$ which is called the heat content. We study its
asymptotic expansion for isotropic $\alpha$-stable L\'evy processes and more
general L\'evy processes, under mild assumptions on the characteristic
exponent.


title: Fast Reconfiguration for Programmable Matter

abstract:
  The concept of programmable matter envisions a very large number of tiny and
simple robot particles forming a smart material that can change its physical
properties and shape based on the outcome of computation and movement performed
by the individual particles in a concurrent manner. Shape reconfiguration of
particle systems is the fundamental task in the algorithmic research of
programmable matter. In this paper we describe a new approach for shape
reconfiguration in the amoebot model. The amoebot model is a distributed model
which significantly restricts memory, computing, and communication capacity of
the individual particles. Thus the challenge lies in coordinating local actions
of particles to produce the desired behavior of the global system.
  We use geometric insight to develop new primitives for programmable matter
systems, which facilitate reconfiguration tasks. Among others, we introduce a
new primitive, feather trees, whose geometry allows particles to traverse a
tree in the presence of other overlapping trees. Using the graph structure of
overlapping feather trees as an underlying network, we show how to reconfigure
a particle system in a linear number of activation rounds. Our method is
sensitive to the symmetric difference between input and output shape; it
minimizes unnecessary disassembly and reassembly of the particle system when
the symmetric difference between the initial and the target shapes is small.


title: Comparative analysis of machine learning methods for active flow control

abstract:
  Machine learning frameworks such as Genetic Programming (GP) and
Reinforcement Learning (RL) are gaining popularity in flow control. This work
presents a comparative analysis of the two, bench-marking some of their most
representative algorithms against global optimization techniques such as
Bayesian Optimization (BO) and Lipschitz global optimization (LIPO). First, we
review the general framework of the flow control problem, linking optimal
control theory with model-free machine learning methods. Then, we test the
control algorithms on three test cases. These are (1) the stabilization of a
nonlinear dynamical system featuring frequency cross-talk, (2) the wave
cancellation from a Burgers' flow and (3) the drag reduction in a cylinder wake
flow. Although the control of these problems has been tackled in the recent
literature with one method or the other, we present a comprehensive comparison
to illustrate their differences in exploration versus exploitation and their
balance between `model capacity' in the control law definition versus `required
complexity'. We believe that such a comparison opens the path towards
hybridization of the various methods, and we offer some perspective on their
future development in the literature of flow control problems.


title:
Chaotic Diffusion in Delay Systems: Giant Enhancement by Time Lag
  Modulation

abstract:
  We consider a typical class of systems with delayed nonlinearity, which we
show to exhibit chaotic diffusion. It is demonstrated that a periodic
modulation of the time-lag can lead to an enhancement of the diffusion constant
by several orders of magnitude. This effect is the largest if the circle map
defined by the modulation shows mode locking and more specifically, fulfills
the conditions for laminar chaos. Thus we establish for the first time a
connection between Arnold tongue structures in parameter space and diffusive
properties of a delay system. Counterintuitively, the enhancement of diffusion
is accompanied by a strong reduction of the effective dimensionality of the
system.


title: Matrix models for cyclic monotone and monotone independences

abstract:
  Cyclic monotone independence is an algebraic notion of noncommutative
independence, introduced in the study of multi-matrix random matrix models with
small rank. Its algebraic form turns out to be surprisingly close to monotone
independence, which is why it was named cyclic monotone independence. This
paper conceptualizes this notion by showing that the same random matrix model
is also a model for the monotone convergence with an appropriately chosen
state. This observation provides a unified nonrandom matrix model for both
types of monotone independences.


title:
Human Motion Detection Using Sharpened Dimensionality Reduction and
  Clustering

abstract:
  Sharpened dimensionality reduction (SDR), which belongs to the class of
multidimensional projection techniques, has recently been introduced to tackle
the challenges in the exploratory and visual analysis of high-dimensional data.
SDR has been applied to various real-world datasets, such as human activity
sensory data and astronomical datasets. However, manually labeling the samples
from the generated projection are expensive. To address this problem, we
propose here to use clustering methods such as k-means, Hierarchical
Clustering, Density-Based Spatial Clustering of Applications with Noise
(DBSCAN), and Spectral Clustering to easily label the 2D projections of
high-dimensional data. We test our pipeline of SDR and the clustering methods
on a range of synthetic and real-world datasets, including two different public
human activity datasets extracted from smartphone accelerometer or gyroscope
recordings of various movements. We apply clustering to assess the visual
cluster separation of SDR, both qualitatively and quantitatively. We conclude
that clustering SDR results yields better labeling results than clustering
plain DR, and that k-means is the recommended clustering method for SDR in
terms of clustering accuracy, ease-of-use, and computational scalability.


title: Kummer quartic double solids

abstract:
  We study equivariant birational geometry of (rational) quartic double solids
ramified over (singular) Kummer surfaces.


title:
Refining the state-of-the-art in Machine Translation, optimizing NMT for
  the JA <-> EN language pair by leveraging personal domain expertise

abstract:
  Documenting the construction of an NMT (Neural Machine Translation) system
for En/Ja based on the Transformer architecture leveraging the OpenNMT
framework. A systematic exploration of corpora pre-processing, hyperparameter
tuning and model architecture is carried out to obtain optimal performance. The
system is evaluated using standard auto-evaluation metrics such as BLEU, and my
subjective opinion as a Japanese linguist.


title: Wide Mean-Field Bayesian Neural Networks Ignore the Data

abstract:
  Bayesian neural networks (BNNs) combine the expressive power of deep learning
with the advantages of Bayesian formalism. In recent years, the analysis of
wide, deep BNNs has provided theoretical insight into their priors and
posteriors. However, we have no analogous insight into their posteriors under
approximate inference. In this work, we show that mean-field variational
inference entirely fails to model the data when the network width is large and
the activation function is odd. Specifically, for fully-connected BNNs with odd
activation functions and a homoscedastic Gaussian likelihood, we show that the
optimal mean-field variational posterior predictive (i.e., function space)
distribution converges to the prior predictive distribution as the width tends
to infinity. We generalize aspects of this result to other likelihoods. Our
theoretical results are suggestive of underfitting behavior previously
observered in BNNs. While our convergence bounds are non-asymptotic and
constants in our analysis can be computed, they are currently too loose to be
applicable in standard training regimes. Finally, we show that the optimal
approximate posterior need not tend to the prior if the activation function is
not odd, showing that our statements cannot be generalized arbitrarily.


title: Distributional Counterfactual Analysis in High-Dimensional Setup

abstract:
  In the context of treatment effect estimation, this paper proposes a new
methodology to recover the counterfactual distribution when there is a single
(or a few) treated unit and possibly a high-dimensional number of potential
controls observed in a panel structure. The methodology accommodates, albeit
does not require, the number of units to be larger than the number of time
periods (high-dimensional setup). As opposed to model only the conditional
mean, we propose to model the entire conditional quantile function (CQF) in the
absence of intervention and estimate it using the pre-intervention period using
a penalized regression. We derive non-asymptotic bounds for the estimated CQF
valid uniformly over the quantiles, allowing the practitioner to re-construct
the entire contractual distribution. Moreover, we bound the probability
coverage of this estimated CQF which can be used to construct valid confidence
intervals for the (possibly random) treatment effect for every
post-intervention period or simultaneously. We also propose a new hypothesis
test for the sharp null of no-effect based on the $\mathcal{L}^p$ norm of
deviation of the estimated CQF to the population one. Interestingly, the null
distribution is quasi-pivotal in the sense that it only depends on the
estimated CQF, $\mathcal{L}^p$ norm, and the number of post-intervention
periods, but not on the size of the post-intervention period. For that reason,
critical values can then be easily simulated. We illustrate the methodology is
by revisiting the empirical study in Acemoglu et al (2016).


title: Learning Fast and Slow for Online Time Series Forecasting

abstract:
  The fast adaptation capability of deep neural networks in non-stationary
environments is critical for online time series forecasting. Successful
solutions require handling changes to new and recurring patterns. However,
training deep neural forecaster on the fly is notoriously challenging because
of their limited ability to adapt to non-stationary environments and the
catastrophic forgetting of old knowledge. In this work, inspired by the
Complementary Learning Systems (CLS) theory, we propose Fast and Slow learning
Networks (FSNet), a holistic framework for online time-series forecasting to
simultaneously deal with abrupt changing and repeating patterns. Particularly,
FSNet improves the slowly-learned backbone by dynamically balancing fast
adaptation to recent changes and retrieving similar old knowledge. FSNet
achieves this mechanism via an interaction between two complementary components
of an adapter to monitor each layer's contribution to the lost, and an
associative memory to support remembering, updating, and recalling repeating
events. Extensive experiments on real and synthetic datasets validate FSNet's
efficacy and robustness to both new and recurring patterns. Our code will be
made publicly available.


title: Extremal Characteristics of Conditional Models

abstract:
  Conditionally specified models are often used to describe complex
multivariate data. Such models assume implicit structures on the extremes. So
far, no methodology exists for calculating extremal characteristics of
conditional models since the copula and marginals are not expressed in closed
forms. We consider bivariate conditional models that specify the distribution
of $X$ and the distribution of $Y$ conditional on $X$. We provide tools to
quantify implicit assumptions on the extremes of this class of models. In
particular, these tools allow us to approximate the distribution of the tail of
$Y$ and the coefficient of asymptotic independence $\eta$ in closed forms. We
apply these methods to a widely used conditional model for wave height and wave
period. Moreover, we introduce a new condition on the parameter space for the
conditional extremes model of Heffernan and Tawn (2004), and prove that the
conditional extremes model does not capture $\eta$, when $\eta<1$.


title:
Pure nematic quantum critical point accompanied by a superconducting
  dome

abstract:
  When a symmetry-breaking phase of matter is suppressed to a quantum critical
point (QCP) at absolute zero, quantum-mechanical fluctuations proliferate. Such
fluctuations can lead to unconventional superconductivity, as evidenced by the
superconducting domes often found near magnetic QCPs in correlated materials.
However, it remains unclear whether this superconductivity mechanism holds for
QCPs of the electronic nematic phase, characterized by rotational symmetry
breaking. Here, we demonstrate from systematic elastoresistivity measurements
that nonmagnetic FeSe$_{1-x}$Te$_{x}$ exhibits an electronic nematic QCP
showing diverging nematic susceptibility. This finding establishes two nematic
QCPs in FeSe-based superconductors with contrasting accompanying phase
diagrams. In FeSe$_{1-x}$Te$_{x}$, a superconducting dome is centered at the
QCP, whereas FeSe$_{1-x}$S$_{x}$ shows no QCP-associated enhancement of
superconductivity. We find that this difference is related to the relative
strength of nematic and spin fluctuations. Our results in FeSe$_{1-x}$Te$_{x}$
present the first case in support of the superconducting dome being associated
with the pure nematic QCP.


title:
Photon-ALP oscillations inducing modification on $\gamma$-ray
  polarization

abstract:
  Axion-like particles (ALPs) are very light, neutral, spin zero bosons
predicted by many theories which try to complete the standard model of
elementary particles. ALPs interact primarily with two photons and can generate
photon-ALP oscillations in the presence of an external magnetic field. They are
attracting increasing interest since photon-ALP oscillations produce deep
consequences in astrophysics particularly in the very-high-energy (VHE) band.
Two hints for the existence of an ALP have recently been proposed. In this
paper, we study another effect of the photon-ALP interaction: the change of the
polarization state of photons. In particular, we study the propagation of the
photon-ALP beam starting where photons are produced - we consider photons
generated in a galaxy cluster or in the jet of a blazar - crossing several
magnetized media (blazar jet, host galaxy, galaxy cluster, extragalactic space,
Milky Way) up to their arrival at the Earth, where photons can be detected. In
the presence of photon-ALP interaction, we analyze the photon survival
probability $P_{\gamma \to \gamma}$ and the corresponding photon degree of
linear polarization $\Pi_L$ for observed energies in the range $(1-10^{15}) \,
\rm eV$. We observe that photons, which are expected as unpolarized in the
absence of ALPs, are made partially polarized by photon-ALP interaction. Our
findings about the X-ray and high-energy bands can be tested by current and
planned observatories like IXPE, Polstar, COSI, e-ASTROGAM and AMEGO. We also
discover a peculiar feature in the VHE band, where photons at energies above $
\sim 1 \, \rm TeV$ are fully polarized because of photon-ALP interaction. A
possible detection of this feature would represent a proof for the existence of
an ALP, but, unfortunately, current technologies do not allow yet to detect
photon polarization up to so high energies.


title: On a question of Nori and it's applications

abstract:
  This article concerns a question asked by M. Nori on homotopy of sections of
Projective modules. In this article we improve the bound imposed by S. Mandal
and a similar question on Laurent polynomial rings over the base field
$\overline{\mathbb{F}}_p$. As an application we define the $n$-th Euler class
group $E^n(A)$, where $A$ is an affine algebra over $\overline{\mathbb{F}}_p$
of dimension $d\ge 3$, for all $n$ satisfying $2n\ge d+2.$ Moreover if $A$ is
smooth we assign a unimodular row of length $n+1$ to $E^n(A)$ and show that the
corresponding stably free module has a unimodular element if and only if the
image vanishes in $E^n(A)$.


title:
Metalated Porous-Organic-Polymer Renders Mustard-Gas Simulant Harmless:
  Core Planarity Matters

abstract:
  The presence of open active metal sites in Metal-Organic Frameworks (MOFs)
exhibit higher catalytic activity. However, rational accomplishment of MOFs in
heterogeneous catalysis is limited due to coordination bonds. Recently balanced
characteristic feature with combination of both the covalent bonds (structural
stability) and open metal sites (single site catalysis) introduced an entirely
organic alternative architecture named as Metalated Porous-Organic-Polymers
(M-POPs). In this contribution, we demonstrate successful construction of two
Fe-POPs (Fe-Tt-POP & Fe-Rb-POP) by ternary copolymerization approach for
catalytic oxidative decontamination of different sulfur-based mustard gas
simulants. Fe-Tt-POP exhibited superior catalytic performance for oxidation of
the thioanisole (TA) studied in terms of conversion (99% after 13 h) in
comparison with Fe-Rb-POP (43% after 13h). The remarkable difference in the
mechanistic pathways towards catalytic performance for oxidation of TA was
investigated by in situ operando Diffuse Reflectance Infrared Fourier Transform
Spectroscopy (DRIFTS) analysis, complemented by Density Functional Theory (DFT)
computational study.


title: Bayesian Model Selection, the Marginal Likelihood, and Generalization

abstract:
  How do we compare between hypotheses that are entirely consistent with
observations? The marginal likelihood (aka Bayesian evidence), which represents
the probability of generating our observations from a prior, provides a
distinctive approach to this foundational question, automatically encoding
Occam's razor. Although it has been observed that the marginal likelihood can
overfit and is sensitive to prior assumptions, its limitations for
hyperparameter learning and discrete model comparison have not been thoroughly
investigated. We first revisit the appealing properties of the marginal
likelihood for learning constraints and hypothesis testing. We then highlight
the conceptual and practical issues in using the marginal likelihood as a proxy
for generalization. Namely, we show how marginal likelihood can be negatively
correlated with generalization, with implications for neural architecture
search, and can lead to both underfitting and overfitting in hyperparameter
learning. We provide a partial remedy through a conditional marginal
likelihood, which we show is more aligned with generalization, and practically
valuable for large-scale hyperparameter learning, such as in deep kernel
learning.


title: Magnetized supersonic downflows in the chromosphere

abstract:
  The chromosphere above active regions (ARs) on the Sun hosts magnetized
supersonic downflows. Studies of these supersonic downflows help to decipher
the magnetic fine structure and dynamics of the chromosphere. We perform a
statistical analysis of the magnetized supersonic downflows in a number of ARs
and survey their characteristics. We analyze spectro-polarimetric scans of
parts of 13 ARs obtained in the infrared He I 10830 \r{A} triplet formed in the
upper chromosphere recorded with the GREGOR Infrared Spectrograph (GRIS)
mounted at the GREGOR solar telescope. We retrieve the line-of-sight velocities
and the magnetic field vector using the HeLIx+ inversion code that assumes
Milne-Eddington atmospheres. We find magnetized supersonic downflows in all the
ARs, with larger area coverage by such flows in ARs observed during their
emerging phase. The fact that supersonic downflows were detected in all scans,
though they cover only a small fraction, 0.2--6.4%, of the observed
field-of-view, suggests that they are a common phenomenon in the upper
chromospheres of ARs. The supersonic downflows are found to be associated with
many AR features such as pores, sunspot umbrae, sunspot penumbrae, light
bridges, plages, He I loops as part of arch filament systems characteristic of
emerging fields, and filaments. Although several mechanisms are identified to
be causing the supersonic downflows, by far the most common one appears to be
the draining of plasma along the legs of rising magnetic loops. The loops
mainly drain into forming pores. The line-of-sight velocities of the supersonic
downflows reach up to 49 kms-1 and the velocity distribution shows multiple
populations. Almost 92% of these supersonic downflows coexist with a subsonic
flow component. The weaker, more horizontal fields associated with the
supersonic component suggests that it is formed above the subsonic component.


title:
Differences in the attitudes and beliefs about science of students in
  the physics-mathematics and life sciences areas and their impact on teaching

abstract:
  For this study, we compared the attitudes and beliefs about science of
physical science (physics and mathematics) and life science (biochemistry and
biology) students at the beginning of their university degrees using the CLASS
(Colorado Learning Attitudes about Science Survey) tool. It is worth noting
that both groups of students received similar physics courses during their
high-school education. Through a detailed analysis of the different categories
of the test, we examined the differences in performance in each of the areas
that make up the questionnaire. Among other aspects, we found that a
considerable percentage of life science students (higher than that of physical
science students) adopted a novice type of behavior in problem solving.
Finally, we discussed the possible causes of the differences found and their
implications for teaching.


title:
On the behavior of formal neighborhoods in the Nash sets associated with
  toric valuations: a comparison theorem

abstract:
  We show that there exists a strong connection between the generic formal
neighborhood at a rational arc lying in the Nash set associated with a toric
divisorial valuation on a toric variety and the formal neighborhood at the
generic point of the same Nash set. This may be interpreted as the fact that,
analytically along such a Nash set, the arc scheme of a toric variety is a
product of a finite dimensional singularity and an infinite dimensional affine
space.


title: State Amplification and Masking While Timely Updating

abstract:
  In status update systems, multiple features carried by the status updating
process require pursuit of objectives beyond timeliness measured by the age of
information of updates. We consider such a problem where the transmitter sends
status update messages through a noiseless binary energy harvesting channel
that is equivalent to a timing channel. The transmitter aims to amplify or mask
the energy state information that is carried in the updating process. The
receiver extracts encoded information, infers the energy state sequence while
maintaining timeliness of status updates. Consequently, the timings of the
updates must be designed to control the message rate, the energy state
uncertainty, and the age of information. We investigate this three-way
trade-off between the achievable rate, the reduction in energy arrival state
uncertainty, and the age of information, for zero and infinite battery cases.


title: Polynomial representations of classical Lie algebras and flag varieties

abstract:
  Recently we have started a program to describe the action of Lie algebras
associated with Dynkin-type diagrams on generic Verma modules in terms of
polynomial vector fields. In this paper we explain that the results for the
classical ABCD series of Lie algebras coincide with the more conventional
approach, based on the knowledge of the entire algebra, not only the simple
roots. We apply the coset description, starting with a large representation and
then reducing it with the help of the algebra, commuting with the original one.
The irreducible representations are then obtained by gauge fixing this residual
symmetry.


title:
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation
  Social Network Dataset

abstract:
  Misinformation is becoming increasingly prevalent on social media and in news
articles. It has become so widespread that we require algorithmic assistance
utilising machine learning to detect such content. Training these machine
learning models require datasets of sufficient scale, diversity and quality.
However, datasets in the field of automatic misinformation detection are
predominantly monolingual, include a limited amount of modalities and are not
of sufficient scale and quality. Addressing this, we develop a data collection
and linking system (MuMiN-trawl), to build a public misinformation graph
dataset (MuMiN), containing rich social media data (tweets, replies, users,
images, articles, hashtags) spanning 21 million tweets belonging to 26 thousand
Twitter threads, each of which have been semantically linked to 13 thousand
fact-checked claims across dozens of topics, events and domains, in 41
different languages, spanning more than a decade. The dataset is made available
as a heterogeneous graph via a Python package (mumin). We provide baseline
results for two node classification tasks related to the veracity of a claim
involving social media, and demonstrate that these are challenging tasks, with
the highest macro-average F1-score being 62.55% and 61.45% for the two tasks,
respectively. The MuMiN ecosystem is available at
https://mumin-dataset.github.io/, including the data, documentation, tutorials
and leaderboards.


title:
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and
  Optimality

abstract:
  We study the problem of transfer learning, observing that previous efforts to
understand its information-theoretic limits do not fully exploit the geometric
structure of the source and target domains. In contrast, our study first
illustrates the benefits of incorporating a natural geometric structure within
a linear regression model, which corresponds to the generalized eigenvalue
problem formed by the Gram matrices of both domains. We next establish a
finite-sample minimax lower bound, propose a refined model interpolation
estimator that enjoys a matching upper bound, and then extend our framework to
multiple source domains and generalized linear models. Surprisingly, as long as
information is available on the distance between the source and target
parameters, negative-transfer does not occur. Simulation studies show that our
proposed interpolation estimator outperforms state-of-the-art transfer learning
methods in both moderate- and high-dimensional settings.


title:
Nonsymmorphic Luttinger liquids in the generalized antiferromagnetic
  Kitaev spin-1/2 chain

abstract:
  We perform a detailed study on the consequences of the nonsymmorphic
symmetries in the Luttinger phase of the one-dimensional (1D) spin-1/2
Kitaev-Heisenberg-Gamma model with an antiferromagnetic Kitaev interaction.
Although the low energy Hamiltonian has an emergent U(1) symmetry, the
bosonization formulas for the spin operators contain ten non-universal
coefficients which only respect the discrete nonsymmorphic symmetries and can
be determined by our density matrix renormalization group simulations to a high
degree of accuracy. Using the nonsymmorphic bosonization formulas, the response
to weak magnetic field is analyzed, and the zigzag phase in the 2D
Kitaev-Heisenberg-Gamma model is recovered by weakly coupling an infinite
number of 1D chains. Furthermore, we find a line of critical points with an
emergent SU(2)$_1$ conformal symmetry located on the boundary of the Luttinger
liquid phase. Our work reveals the rich physics related to nonsymmorphic
symmetries in strongly correlated quantum magnetic models, and demonstrates the
usefulness of 1D analysis for understanding the 2D Kitaev physics.


title:
Fluctuations of the process of moduli for the Ginibre and hyperbolic
  ensembles

abstract:
  We investigate the point process of moduli of the Ginibre and hyperbolic
ensembles. We show that far from the origin and at an appropriate scale, these
processes exhibit Gaussian and Poisson fluctuations. Among the possible
Gaussian fluctuations, we can find white noise but also fluctuations with
non-trivial covariance at a particular scale.


title: Bounding quantum capacities via partial orders and complementarity

abstract:
  Quantum capacities are fundamental quantities that are notoriously hard to
compute and can exhibit surprising properties such as superadditivity. Thus, a
vast amount of literature is devoted to finding tight and computable bounds on
these capacities. We add a new viewpoint by giving operationally motivated
bounds on several capacities, including the quantum capacity and private
capacity of a quantum channel and the one-way distillable entanglement and
private key of a quantum state. These bounds are generally phrased in terms of
capacity quantities involving the complementary channel or state. As a tool to
obtain these bounds, we discuss partial orders on quantum channels and states,
such as the less noisy and the more capable order. Our bounds help to further
understand the interplay between different capacities, as they give operational
limitations on superadditivity and the difference between capacities in terms
of the information-theoretic properties of the complementary channel or state.
They can also be used as a new approach towards numerically bounding
capacities, as discussed with some examples.


title:
Interval Observer Synthesis for Locally Lipschitz Nonlinear Dynamical
  Systems via Mixed-Monotone Decompositions

abstract:
  This paper proposes a novel unified interval-valued observer synthesis
approach for locally Lipschitz nonlinear continuous-time (CT) and discrete-time
(DT) systems with nonlinear observations. A key feature of our proposed
observer, which is derived using mixed-monotone decompositions, is that it is
correct by construction (i.e., the true state trajectory of the system is
framed by the states of the observer) without the need for imposing additional
constraints and assumptions such as global Lipschitz continuity or contraction,
as is done in existing approaches in the literature. Furthermore, we derive
sufficient conditions for designing stabilizing observer gains in the form of
Linear Matrix Inequalities (LMIs). Finally, we compare the performance of our
observer design with some benchmark CT and DT observers in the literature.


title: Brain Structural Saliency Over The Ages

abstract:
  Brain Age (BA) estimation via Deep Learning has become a strong and reliable
bio-marker for brain health, but the black-box nature of Neural Networks does
not easily allow insight into the features of brain ageing. We trained a ResNet
model as a BA regressor on T1 structural MRI volumes from a small
cross-sectional cohort of 524 individuals. Using Layer-wise Relevance
Propagation (LRP), we analysed the trained model to determine the most
revealing structures over the course of brain ageing for the network. We show
the change in attribution of relevance to different brain regions through the
course of ageing. A tripartite pattern of relevance attribution to brain
regions emerges. Some regions increase in relevance with age (e.g. the right
Transverse Temporal Gyrus); some decrease in relevance with age (e.g. the right
Fourth Ventricle); and others remained consistently relevant across ages. We
also examine the effect of Brain Age Gap (BAG) on the distribution of
LRP-assigned relevance within the brain volume. It is hoped that these findings
will provide clinically relevant region-wise trajectories for normal brain
ageing, and a baseline against which to compare brain ageing trajectories.


title:
Asymptotic Critical Transmission Radii in Wireless Networks over a
  Convex Region

abstract:
  Critical transmission ranges (or radii) in wireless ad-hoc and sensor
networks have been extensively investigated for various performance metrics
such as connectivity, coverage, power assignment and energy consumption.
However, the regions on which the networks are distributed are typically either
squares or disks in existing works, which seriously limits the usage in
real-life applications. In this article, we consider a convex region (i.e., a
generalisation of squares and disks) on which wireless nodes are uniformly
distributed. We have investigated two types of critical transmission radii,
defined in terms of k-connectivity and the minimum vertex degree, respectively,
and have also established their precise asymptotic distributions. These make
the previous results obtained under the circumstance of squares or disks
special cases of this work. More importantly, our results reveal how the region
shape impacts on the critical transmission ranges: it is the length of the
boundary of the (fixed-area) region that completely determines the transmission
ranges. Furthermore, by isodiametric inequality, the smallest critical
transmission ranges are achieved when regions are disks only.


title:
An Analytical Model for SNR Prediction in Coherent Systems after generic
  Jones Matrices

abstract:
  In this letter, we propose the extension of a previously presented analytical
model for the estimation of the signal-to-noise ratio (SNR) at the output of an
adaptive equalizer in coherent optical transmission systems when transmission
is modeled as a generic 2x2 matrix transfer functions, to be applied to
polarization multiplexed communications based on advanced modulation formats
(PM-QAM). We present the model and then we extensively test its accuracy on an
possible application environment. Our findings show a remarkable agreement,
with maximum SNR discrepancies of about 0.5 $dB$ between time-domain
simulations and analytical results.


title:
Hybrid Mechanical and Electronic Beam Steering for Maximizing OAM
  Channel Capacity

abstract:
  Radio frequency-orbital angular momentum (RF-OAM) is a novel approach of
multiplexing a set of orthogonal modes on the same frequency channel to achieve
high spectrum efficiencies. Since OAM requires precise alignment of the
transmit and the receive antennas, the electronic beam steering approach has
been proposed for the uniform circular array (UCA)-based OAM communication
system to circumvent large performance degradation induced by small antenna
misalignment in practical environment. However, in the case of large-angle
misalignment, the OAM channel capacity can not be effectively compensated only
by the electronic beam steering. To solve this problem, we propose a hybrid
mechanical and electronic beam steering scheme, in which mechanical rotating
devices controlled by pulse width modulation (PWM) signals as the execution
unit are utilized to eliminate the large misalignment angle, while electronic
beam steering is in charge of the remaining small misalignment angle caused by
perturbations. Furthermore, due to the interferometry, the receive
signal-to-noise ratios (SNRs) are not uniform at the elements of the receive
UCA. Therefore, a rotatable UCA structure is proposed for the OAM receiver to
maximize the channel capacity, in which the simulated annealing algorithm is
adopted to obtain the optimal rotation angle at first, then the servo system
performs mechanical rotation, at last the electronic beam steering is adjusted
accordingly. Both mathematical analysis and simulation results validate that
the proposed hybrid mechanical and electronic beam steering scheme can
effectively eliminate the effect of diverse misalignment errors of any
practical OAM channel and maximize the OAM channel capacity.


title: An information-theoretic proof of the Erd\H{o}s-Kac theorem

abstract:
  In this article we show that the Erd\H{o}s-Kac theorem, which informally
states that the number of prime divisors of very large integers converges to a
normal distribution, has an elegant proof via Algorithmic Information Theory.


title: On the Arithmetic Complexity of the Bandwidth of Bandlimited Signals

abstract:
  The bandwidth of a signal is an important physical property that is of
relevance in many signal- and information-theoretic applications. In this paper
we study questions related to the computability of the bandwidth of computable
bandlimited signals. To this end we employ the concept of Turing computability,
which exactly describes what is theoretically feasible and can be computed on a
digital computer. Recently, it has been shown that there exist computable
bandlimited signals with finite energy, the actual bandwidth of which is not a
computable number, and hence cannot be computed on a digital computer. In this
work, we consider the most general class of band-limited signals, together with
different computable representations thereof. Among other things, our analysis
includes a characterization of the arithmetic complexity of the bandwidth of
such signals and yields a negative answer to the question of whether it is at
least possible to compute non-trivial upper or lower bounds for the bandwidth
of a bandlimited signal. Furthermore, we relate the problem of bandwidth
computation to the theory of oracle machines. In particular, we consider
halting and totality oracles, which belong to the most frequently investigated
oracle machines in the theory of computation.


title:
Double Threshold based Optimal Device Selection Scheme for D2D or
  Sidelink Network

abstract:
  Device-to-device (D2D) or Sidelink aided communication is regarded as one of
the most promising technologies to improve the spectral efficiency of the 5G
and beyond communication system. However, two main challenges exist: 1) the
selection of the optimal number of devices for improving the spectral
efficiency, and 2) improving the physical layer security of such a
communication system. The optimal device improves the secrecy capacity, and the
selection of optimal devices enhances the physical layer security. Therefore,
in this paper, we propose a double threshold-based optimal device selection
(ODS) scheme for a cooperative wireless network with amplify and forward (AF)
protocol in the presence and absence of an eavesdropper to enhance the physical
layer security for the D2D network. The proposed scheme is analyzed with
different distance cases, device scenarios, and modulation schemes. The bit
error rate (BER) performance analysis concludes that a performance gain of more
than 4 dB is achieved at the BER of 0:004 for the proposed double
threshold-based ODS scheme compared to the existing ODS scheme at a high signal
to noise ratio (SNR). Furthermore, the proposed scheme enhances the physical
layer security.


title:
Stochastic Coded Offloading Scheme for Unmanned Aerial Vehicle-Assisted
  Edge Computing

abstract:
  Unmanned aerial vehicles (UAVs) have gained wide research interests due to
their technological advancement and high mobility. The UAVs are equipped with
increasingly advanced capabilities to run computationally intensive
applications enabled by machine learning techniques. However, because of both
energy and computation constraints, the UAVs face issues hovering in the sky
while performing computation due to weather uncertainty. To overcome the
computation constraints, the UAVs can partially or fully offload their
computation tasks to the edge servers. In ordinary computation offloading
operations, the UAVs can retrieve the result from the returned output.
Nevertheless, if the UAVs are unable to retrieve the entire result from the
edge servers, i.e., straggling edge servers, this operation will fail. In this
paper, we propose a coded distributed computing approach for computation
offloading to mitigate straggling edge servers. The UAVs can retrieve the
returned result when the number of returned copies is greater than or equal to
the recovery threshold. There is a shortfall if the returned copies are less
than the recovery threshold. To minimize the cost of the network, energy
consumption by the UAVs, and prevent over and under subscription of the
resources, we devise a two-phase Stochastic Coded Offloading Scheme (SCOS). In
the first phase, the appropriate UAVs are allocated to the charging stations
amid weather uncertainty. In the second phase, we use the $z$-stage Stochastic
Integer Programming (SIP) to optimize the number of computation subtasks
offloaded and computed locally, while taking into account the computation
shortfall and demand uncertainty. By using a real dataset, the simulation
results show that our proposed scheme is fully dynamic, and minimizes the cost
of the network and UAV energy consumption amid stochastic uncertainties.


title: Signal reconstruction from noisy multichannel samples

abstract:
  We consider the signal reconstruction problem under the case of the signals
sampled in the multichannel way and with the presence of noise. Observing that
if the samples are inexact, the rigorous enforcement of multichannel
interpolation is inappropriate. Thus the reasonable smoothing and regularized
corrections are indispensable. In this paper, we propose several alternative
methods for the signal reconstruction from the noisy multichannel samples under
different smoothing and regularization principles. We compare these signal
reconstruction methods theoretically and experimentally in the various
situations. To demonstrate the effectiveness of the proposed methods, the
probability interpretation and the error analysis for these methods are
provided. Additionally, the numerical simulations as well as some guidelines to
use the methods are also presented.


title:
Exploiting Side Information for Improved Online Learning Algorithms in
  Wireless Networks

abstract:
  In wireless networks, the rate achieved depends on factors like level of
interference, hardware impairments, and channel gain. Often, instantaneous
values of some of these factors can be measured, and they provide useful
information about the instantaneous rate achieved. For example, higher
interference implies a lower rate. In this work, we treat any such measurable
quality that has a non-zero correlation with the rate achieved as
side-information and study how it can be exploited to quickly learn the channel
that offers higher throughput (reward). When the mean value of the
side-information is known, using control variate theory we develop algorithms
that require fewer samples to learn the parameters and can improve the learning
rate compared to cases where side-information is ignored. Specifically, we
incorporate side-information in the classical Upper Confidence Bound (UCB)
algorithm and quantify the gain achieved in the regret performance. We show
that the gain is proportional to the amount of the correlation between the
reward and associated side-information. We discuss in detail various
side-information that can be exploited in cognitive radio and air-to-ground
communication in $L-$band. We demonstrate that correlation between the reward
and side-information is often strong in practice and exploiting it improves the
throughput significantly.


title:
Gaussian Process-Driven History Matching for Physical Layer Parameter
  Estimation in Optical Fiber Communication Networks

abstract:
  We present a methodology for the estimation of optical network physical layer
parameters from signal to noise ratio via history matching. An expensive
network link simulator is emulated by a Gaussian process surrogate model, which
is used to estimate a set of physical layer parameters from simulated ground
truth data. The a priori knowledge assumed consists of broad parameter bounds
obtained from the literature and specification sheets of typical network
components, and the physics-based model of the simulator. Accurate estimation
of the physical layer parameters is demonstrated with a signal to noise ratio
penalty of 1~dB or greater, using only 3 simulated measurements. The proposed
approach is highly flexible, allowing for the calibration of any unknown
simulator input from broad a priori bounds. The role of this method in the
improvement of optical network modeling is discussed.


title: Super-resolution GANs of randomly-seeded fields

abstract:
  Reconstruction of field quantities from sparse measurements is a problem
arising in a broad spectrum of applications. This task is particularly
challenging when mapping between point sparse measurements and field quantities
shall be performed in an unsupervised manner. Further complexity is added for
moving sensors and/or random on-off status. Under such conditions, the most
straightforward solution is to interpolate the scattered data onto a regular
grid. However, the spatial resolution achieved with this approach is ultimately
limited by the mean spacing between the sparse measurements. In this work, we
propose a novel super-resolution generative adversarial network (GAN) framework
to estimate field quantities from random sparse sensors without needing any
full-resolution field for training. The algorithm exploits random sampling to
provide incomplete views of the high-resolution underlying distributions. It is
hereby referred to as RAndomly-SEEDed super-resolution GAN (RaSeedGAN). The
proposed technique is tested on synthetic databases of fluid flow simulations,
ocean surface temperature distributions measurements, and particle image
velocimetry data of a zero-pressure-gradient turbulent boundary layer. The
results show an excellent performance of the proposed methodology even in cases
with a high level of gappyness (>50\%) or noise conditions. To our knowledge,
this is the first super-resolution GANs algorithm for full-field estimation
from randomly-seeded fields with no need of a full-field high-resolution
representation during training nor of a library of training examples.


title:
Deep Reinforcement Learning based Joint Active and Passive Beamforming
  Design for RIS-Assisted MISO Systems

abstract:
  Owing to the unique advantages of low cost and controllability,
reconfigurable intelligent surface (RIS) is a promising candidate to address
the blockage issue in millimeter wave (mmWave) communication systems,
consequently has captured widespread attention in recent years. However, the
joint active beamforming and passive beamforming design is an arduous task due
to the high computational complexity and the dynamic changes of wireless
environment. In this paper, we consider a RIS-assisted multi-user
multiple-input single-output (MU-MISO) mmWave system and aim to develop a deep
reinforcement learning (DRL) based algorithm to jointly design active hybrid
beamformer at the base station (BS) side and passive beamformer at the RIS
side. By employing an advanced soft actor-critic (SAC) algorithm, we propose a
maximum entropy based DRL algorithm, which can explore more stochastic policies
than deterministic policy, to design active analog precoder and passive
beamformer simultaneously. Then, the digital precoder is determined by minimum
mean square error (MMSE) method. The experimental results demonstrate that our
proposed SAC algorithm can achieve better performance compared with
conventional optimization algorithm and DRL algorithm.


title:
Paying U-Attention to Textures: Multi-Stage Hourglass Vision Transformer
  for Universal Texture Synthesis

abstract:
  We present a novel U-Attention vision Transformer for universal texture
synthesis. We exploit the natural long-range dependencies enabled by the
attention mechanism to allow our approach to synthesize diverse textures while
preserving their structures in a single inference. We propose a multi-stage
hourglass backbone that attends to the global structure and performs patch
mapping at varying scales in a coarse-to-fine-to-coarse stream. Further
completed by skip connection and convolution designs that propagate and fuse
information at different scales, our U-Attention architecture unifies attention
to microstructures, mesostructures and macrostructures, and progressively
refines synthesis results at successive stages. We show that our method
achieves stronger 2$\times$ synthesis than previous work on both stochastic and
structured textures while generalizing to unseen textures without fine-tuning.
Ablation studies demonstrate the effectiveness of each component of our
architecture.


title:
Reduced modeling and global instability of finite-Reynolds-number flow
  in compliant rectangular channels

abstract:
  Experiments have shown that flow in compliant microchannels can become
unstable at a much lower Reynolds number than the corresponding flow in a rigid
conduit. Therefore, it has been suggested that the wall's elastic compliance
can be exploited towards new modalities of microscale mixing. While previous
studies mainly focused on the local instability induced by the fluid--structure
interactions (FSIs) in the system, we derive a new one-dimensional (1D) model
to study the FSI's effect on the global instability. The proposed 1D FSI model
is tailored to long, shallow rectangular microchannels with a deformable top
wall, similar to the experiments. Going beyond the usual lubrication flows
analyzed in these geometries, we include finite fluid inertia and couple the
reduced flow equations to a reduced 1D wall deformation equation. Although a
quantitative comparison to previous experiments is quite difficult, the
behaviors of the proposed derived model show qualitatively agreement with the
experimental observations, and capture several key effects. Specifically, we
find the critical conditions under which the inflated base state of the 1D FSI
model is linearly unstable to infinitesimal perturbations. The critical
Reynolds numbers predicted are in agreement with experimental observations. The
unstable modes are highly oscillatory, with frequencies close to the natural
frequency of the wall, suggesting that the observed instabilities are resonance
phenomena. Furthermore, during the start-up from an undeformed initial state,
self-sustained oscillations can be triggered due to FSIs. Our proposed modeling
framework can be applied to other microfluidic systems with similar geometric
scale separation, even under different operation conditions.


title:
COLD Decoding: Energy-based Constrained Text Generation with Langevin
  Dynamics

abstract:
  Many applications of text generation require incorporating different
constraints to control the semantics or style of generated text. These
constraints can be hard (e.g., ensuring certain keywords are included in the
output) and soft (e.g., contextualizing the output with the left- or right-hand
context). In this paper, we present Energy-based Constrained Decoding with
Langevin Dynamics (COLD), a decoding framework which unifies constrained
generation as specifying constraints through an energy function, then
performing efficient differentiable reasoning over the constraints through
gradient-based sampling. COLD decoding is a flexible framework that can be
applied directly to off-the-shelf left-to-right language models without the
need for any task-specific fine-tuning, as demonstrated through three
challenging text generation applications: lexically-constrained generation,
abductive reasoning, and counterfactual reasoning. Our experiments on these
constrained generation tasks point to the effectiveness of our approach, both
in terms of automatic and human evaluation.


title:
Traveling waves of a generalized Rotation-Camassa-Holm equation with the
  Coriolis effect

abstract:
  In this paper, we analyze the dynamics of a generalized Rotation-Camassa-Holm
equation, which is the $\theta$-equation augmented with the Coriolis effect,
induced by the earth rotation. The generalized Rotation-Camassa-Holm equation
(named as Rotation-$\theta$ equation) is a generalization of a family of models
(including the Rotation-Camassa-Holm equation for $\theta=\frac{1}{3}$,
asymptotic Rotation-Camassa-Holm equation for $\theta=1$ and the
Rotation-Degasperis-Procesi (DP) equation for $\theta=\frac{1}{4}$). Our study
is conducted via the bifurcation method and qualitative theory of dynamical
systems. The existence of not only smooth solitary wave solutions, periodic
wave solutions, but also peakons and periodic peakon solutions is shown. The
chosen values $\theta$, allow us to assess the difference in behavior between
the classical $\theta$-equation and the Rotation-$\theta$ equation. We conclude
that the Coriolis effect does affect the traveling wave solutions. We summarize
the bifurcations and explicit expressions of waves solutions in three theorems
in Section 4. A conclusion ends the paper.


title:
Types of electromagnetic fields around black holes in Schwarzschild,
  Kerr and Kerr-Newmann spacetimes

abstract:
  We consider electromagnetic fields having an angular momentum density in a
locally non-rotating reference frame in Schwarzschild, Kerr, and Kerr-Newman
spacetimes. The nature of such fields is assessed with two families of
observers, the locally non-rotating ones and those of vanishing Poynting flux.
The velocity fields of the vanishing-Poynting observers in the locally
non-rotating reference frames are determined using the 3+1 decomposition
formalism. From a methodological point of view, and considering a
classification of the electromagnetic field based on its invariants, it is
convenient to separate the consideration of the vanishing-Poynting observers
into two cases corresponding to the pure and non-pure fields, additionally if
there are regions where the field rotates with the speed of light (light
surfaces) it becomes necessary to split these observers into two subfamilies.
We present several examples of relevance in astrophysics and general
relativity, such as pure rotating dipolar-like magnetic fields and the
electromagnetic field of the Kerr-Newman solution. For the latter example, we
see that vanishing-Poynting observers also measure a vanishing super-Poynting
vector, confirming recent results in the literature. Finally, for all non-null
electromagnetic fields, we present the 4-velocity fields of vanishing Poynting
observers in an arbitrary spacetime.


title:
Reply to "Comment on 'Greybody radiation and quasinormal modes of
  Kerr-like black hole in Bumblebee gravity model' "

abstract:
  In our recent study Ref. [1]: Eur. Phys. J. C 81, 501 (2021) , the main goal
was to reveal the Lorentz symmetry breaking (LSB) effect in the rotating black
holes via the greybody factor and quasinormal mode analyzes. Ding et al. [2]
Kerr-like black hole solution of the bumblebee gravity model used in our paper
[1] has unquestionably proven by Maluf and Muniz [3] that it is wrong. We now
explain under which condition the greybody factor and quasinormal mode analyzes
performed in [1] become valid. We re-make the calculations according to the
condition in question and present the new results in this reply.


title:
Co-occurring Diseases Heavily Influence the Performance of Weakly
  Supervised Learning Models for Classification of Chest CT

abstract:
  Despite the potential of weakly supervised learning to automatically annotate
massive amounts of data, little is known about its limitations for use in
computer-aided diagnosis (CAD). For CT specifically, interpreting the
performance of CAD algorithms can be challenging given the large number of
co-occurring diseases. This paper examines the effect of co-occurring diseases
when training classification models by weakly supervised learning, specifically
by comparing multi-label and multiple binary classifiers using the same
training data. Our results demonstrated that the binary model outperformed the
multi-label classification in every disease category in terms of AUC. However,
this performance was heavily influenced by co-occurring diseases in the binary
model, suggesting it did not always learn the correct appearance of the
specific disease. For example, binary classification of lung nodules resulted
in an AUC of < 0.65 when there were no other co-occurring diseases, but when
lung nodules co-occurred with emphysema, the performance reached AUC > 0.80. We
hope this paper revealed the complexity of interpreting disease classification
performance in weakly supervised models and will encourage researchers to
examine the effect of co-occurring diseases on classification performance in
the future.


title: On Some Applications of the Sagnac Effect

abstract:
  Considering exact spacetimes representing rotating black holes and naked
singularities, we study the possibility that the Sagnac effect detects $i$)
higher dimensions, $ii$) rotation of black holes in higher dimension, and also,
$iii$) distinguishes black holes from naked singularities. The results indicate
that the Sagnac time delay gets affected by the presence of extra-dimension or
its associated angular momentum. This time delay is also different in the
spacetime of a naked singularity compared to that of a black hole. Hence, the
Sagnac effect may be used as an experiment for better understanding of
spacetimes with higher dimensions or those that admit naked singularities.


title:
ALMA's view of the M-dwarf GSC 07396-00759's edge-on debris disc: AU
  Mic's coeval twin

abstract:
  We present new ALMA Band 7 observations of the edge-on debris disc around the
M1V star GSC 07396-00759. At ~20 Myr old and in the beta Pictoris Moving Group
along with AU Mic, GSC 07396-00759 joins it in the handful of low mass M-dwarf
discs to be resolved in the sub-mm. With previous VLT/SPHERE scattered light
observations we present a multi-wavelength view of the dust distribution within
the system under the effects of stellar wind forces. We find the mm dust grains
to be well described by a Gaussian torus at 70 au with a FWHM of 48 au and we
do not detect the presence of CO in the system. Our ALMA model radius is
significantly smaller than the radius derived from polarimetric scattered light
observations, implying complex behaviour in the scattering phase function. The
brightness asymmetry in the disc observed in scattered light is not recovered
in the ALMA observations, implying that the physical mechanism only affects
smaller grain sizes. High resolution follow-up observations of the system would
allow investigation into its unique dust features as well as provide a true
coeval comparison for its smaller sibling AU Mic, singularly well observed
amongst M-dwarfs systems.


title: Flow-based sampling in the lattice Schwinger model at criticality

abstract:
  Recent results suggest that flow-based algorithms may provide efficient
sampling of field distributions for lattice field theory applications, such as
studies of quantum chromodynamics and the Schwinger model. In this work, we
provide a numerical demonstration of robust flow-based sampling in the
Schwinger model at the critical value of the fermion mass. In contrast, at the
same parameters, conventional methods fail to sample all parts of configuration
space, leading to severely underestimated uncertainties.


title:
Measurement of Galactic $^{26}$Al with the Compton Spectrometer and
  Imager

abstract:
  The Compton Spectrometer and Imager (COSI) is a balloon-borne compact Compton
telescope designed to survey the 0.2-5 MeV sky. COSI's energy resolution of
$\sim$0.2% at 1.8 MeV, single-photon reconstruction, and wide field of view
make it capable of studying astrophysical nuclear lines, particularly the 1809
keV $\gamma$-ray line from decaying Galactic $^{26}$Al. Most $^{26}$Al
originates in massive stars and core-collapse supernova nucleosynthesis, but
the path from stellar evolution models to Galaxy-wide emission remains
unconstrained. In 2016, COSI had a successful 46-day flight on a NASA
superpressure balloon. Here, we detail the first search for the 1809 keV
$^{26}$Al line in the COSI 2016 balloon flight using a maximum likelihood
analysis. We find a Galactic $^{26}$Al flux of $(8.6 \pm 2.5) \times 10^{-4}$
ph cm$^{-2}$ s$^{-1}$ within the Inner Galaxy ($|\ell| \leq 30^{\circ}$, $|b|
\leq 10^{\circ}$) with 3.7$\sigma$ significance above background. Within
uncertainties, this flux is consistent with expectations from previous
measurements by SPI and COMPTEL. This analysis demonstrates COSI's powerful
capabilities for studies of $\gamma$-ray lines and underscores the scientific
potential of future compact Compton telescopes. In particular, the next
iteration of COSI as a NASA Small Explorer satellite has recently been approved
for launch in 2025.


title:
Near or Far Detectors? Optimizing Long-Lived Particle Searches at
  Electron-Positron Colliders

abstract:
  We explore the discovery potential for long-lived particles at the 250-GeV
ILC. The goal is to investigate possible gains of a dedicated far detector over
the main detector ILD. For concreteness, we perform our study for sub-GeV
axion-like particles $a$ produced via $e^+e^- \to a \gamma$ or $e^+e^- \to Z
\gamma \to (a\gamma)\gamma$ and decaying into pairs of charged leptons. In the
ideal case of zero background and perfect detection efficiency, we find that
far detectors placed in the planned underground cavities or a large cuboid on
the ground can enhance the sensitivity to long-lived pseudo-scalars at best
moderately. On the other hand, the ILD itself is a perfect environment to
search for long-lived particles, due to its excellent angular coverage and
radial thickness. For long-lived particles produced with cross sections of a
few picobarns, the ILD could probe lifetimes up to 300$\,$ns, or proper decay
lengths up to 100$\,$m, in 250$\,$fb$^{-1}$ of data. For axion-like particles
produced through weak interactions, the ILC can reach an even higher
sensitivity than searches for displaced vertices in meson decays at Belle II.
Our findings apply similarly to other proposed electron-positron experiments
with a high angular coverage, such as the FCC-ee and CEPC.


title: Analyticity constraints bound the decay of the spectral form factor

abstract:
  Quantum chaos cannot develop faster than $\lambda \leq 2 \pi/(\hbar \beta)$
for systems in thermal equilibrium [Maldacena et. al. JHEP (2016)]. This `MSS
bound' on the Lyapunov exponent is set by the width of the strip on which the
regularized out-of-time-order-correlator is analytic. We show that similar
analyticity constraints also bound the evolution of other dynamical quantities.
We first find a family of functions that admit a universal bound inspired by
the MSS bound, and then detail the case of the spectral form factor (SFF),
which is the Fourier transform of the two-level correlation function and can be
understood as the survival probability of the coherent Gibbs state.
Specifically, the inflection exponent $\eta$ that we introduce here to
characterize the decay of the SFF, is bounded as $\eta\leq \pi/(2\hbar\beta)$.
The bound that we derive is universal and exists outside of the chaotic regime.
We illustrate the results in systems with regular, chaotic, and tunable
dynamics, namely the single-particle harmonic oscillator, the many-particle
Calogero-Sutherland model, a random matrix ensemble, and the quantum kicked
top. The relation of the derived bound with known quantum speed limits is
discussed.


title: Dark Matter Direct Detection in Materials with Spin-Orbit Coupling

abstract:
  Semiconductors with $\mathcal{O}(\text{meV})$ band gaps have been shown to be
promising targets to search for sub-MeV mass dark matter (DM). In this paper we
focus on a class of materials where such narrow band gaps arise naturally as a
consequence of spin-orbit coupling (SOC). Specifically, we are interested in
computing DM-electron scattering and absorption rates in these materials using
state-of-the-art density functional theory (DFT) techniques. To do this, we
extend the DM interaction rate calculation to include SOC effects which
necessitates a generalization to spin-dependent wave functions. We apply our
new formalism to calculate limits for several DM benchmark models using an
example ZrTe$_{5}$ target and show that the inclusion of SOC can substantially
alter projected constraints.


title: Berry phases, wormholes and factorization in AdS/CFT

abstract:
  For two-dimensional holographic CFTs, we demonstrate the role of Berry phases
for relating the non-factorization of the Hilbert space to the presence of
wormholes. The wormholes are characterized by a non-exact symplectic form that
gives rise to the Berry phase. For wormholes connecting two spacelike regions
in gravitational spacetimes, we find that the non-exactness is linked to a
variable appearing in the phase space of the boundary CFT. This variable
corresponds to a loop integral in the bulk. Through this loop integral,
non-factorization becomes apparent in the dual entangled CFTs. Furthermore, we
classify Berry phases in holographic CFTs based on the type of dual bulk
diffeomorphism involved. We distinguish between Virasoro, gauge and modular
Berry phases, each corresponding to a spacetime wormhole geometry in the bulk.
Using kinematic space, we extend a relation between the modular Hamiltonian and
the Berry curvature to the finite temperature case. We find that the Berry
curvature, given by the Crofton form, characterizes the topological transition
of the entanglement entropy in presence of a black hole.


title: Sailing past the End of the World and discovering the Island

abstract:
  Large black holes in anti-de Sitter space have positive specific heat and do
not evaporate. In order to mimic the behavior of evaporating black holes, one
may couple the system to an external bath. In this paper we explore a rich
family of such models, namely ones obtained by coupling two holographic CFTs
along a shared interface (ICFTs). We focus on the limit where the bulk solution
is characterized by a thin brane separating the two individual duals. These
systems may be interpreted in a double holographic way, where one integrates
out the bath and ends up with a lower-dimensional gravitational braneworld dual
to the interface degrees of freedom. Our setup has the advantage that all
observables can be defined and calculated by only relying on standard rules of
AdS/CFT. We exploit this to establish a number of general results, relying on a
detailed analysis of the geodesics in the bulk. Firstly, we prove that the
entropy of Hawking radiation in the braneworld is obtained by extremizing the
generalized entropy, and moreover that at late times a so-called `island
saddle' gives the dominant contribution. We also derive Takayanagi's
prescription for calculating entanglement entropies in BCFTs as a limit of our
ICFT results.


title:
Sculpting the circumbinary planet size distribution through resonant
  interactions with companion planets

abstract:
  Resonant locking of two planets is an expected outcome of convergent disc
migration. The planets subsequently migrate together as a resonant pair. In the
context of circumbinary planets, the disc is truncated internally by the
binary. If there were only a single planet, then this inner disc edge would
provide a natural parking location. However, for two planets migrating together
in resonance there will be a tension between the inner planet stopping at the
disc edge, and the outer planet continuing to be torqued inwards. In this paper
we study this effect, showing that the outcome is a function of the
planet-planet mass ratio. Smaller outer planets tend to be parked in a stable
exterior 2:1 or 3:2 resonance with the inner planet, which itself remains near
the disc edge. Equal or larger mass outer planets tend to push the inner planet
past the disc edge and too close to the binary, causing it to be ejected or
sometimes flipped to an exterior orbit. Our simulations show that this process
may explain an observed dearth of small (< 3 Earth radii) circumbinary planets,
since small planets are frequently ejected or left on long-period orbits, for
which transit detection is less likely. This may also be an efficient mechanism
for producing free-floating planets and interstellar interlopers like
`Oumuamua.


title:
The Planck clusters in the LOFAR sky. I. LoTSS-DR2: new detections and
  sample overview

abstract:
  Relativistic electrons and magnetic fields permeate the intra-cluster medium
(ICM) and manifest themselves as diffuse sources of synchrotron emission
observable at radio wavelengths, namely radio halos and radio relics. Although
there is broad consensus that the formation of these sources is connected to
turbulence and shocks in the ICM, the details of the required particle
acceleration, the strength and morphology of the magnetic field in the cluster
volume, and the influence of other sources of high-energy particles are poorly
known. Sufficiently large samples of radio halos and relics, which would allow
us to examine the variation among the source population and pinpoint their
commonalities and differences, are still missing. At present, large numbers of
these sources are easiest to detect at low radio frequencies, where they shine
brightly. We examined the low-frequency radio emission from all 309 clusters in
the second catalog of Planck Sunyaev Zel'dovich detected sources that lie
within the 5634 deg$^2$ covered by the Second Data Release of the LOFAR
Two-meter Sky Survey (LoTSS-DR2). We produced LOFAR images at different
resolutions, with and without discrete sources subtracted, and created overlays
with optical and X-ray images before classifying the diffuse sources in the
ICM, guided by a decision tree. Overall, we found 83 clusters that host a radio
halo and 26 that host one or more radio relics (including candidates). About
half of them are new discoveries. The detection rate of clusters hosting a
radio halo and one or more relics in our sample is $30\pm11$% and $10\pm6$%,
respectively. Extrapolating these numbers, we anticipate that once LoTSS covers
the entire northern sky it will provide the detection of $251\pm92$ clusters
hosting a halo and $83\pm50$ clusters hosting at least one relic from Planck
clusters alone.


title: PATOKA: Simulating Electromagnetic Observables of Black Hole Accretion

abstract:
  The Event Horizon Telescope (EHT) has released analyses of reconstructed
images of horizon-scale millimeter emission near the supermassive black hole at
the center of the M87 galaxy. Parts of the analyses made use of a large library
of synthetic black hole images and spectra, which were produced using numerical
general relativistic magnetohydrodynamics fluid simulations and polarized ray
tracing. In this article, we describe the PATOKA pipeline, which was used to
generate the Illinois contribution to the EHT simulation library. We begin by
describing the relevant accretion systems and radiative processes. We then
describe the details of the three numerical codes we use, iharm, ipole, and
igrmonty, paying particular attention to differences between the current
generation of the codes and the originally published versions. Finally, we
provide a brief overview of simulated data as produced by PATOKA and conclude
with a discussion of limitations and future directions.


title:
Search for High-Energy Neutrino Emission from Galactic X-ray Binaries
  with IceCube

abstract:
  We present the first comprehensive search for high-energy neutrino emission
from high- and low-mass X-ray binaries conducted by IceCube. Galactic X-ray
binaries are long-standing candidates for the source of Galactic hadronic
cosmic rays and neutrinos. The compact object in these systems can be the site
of cosmic-ray acceleration, and neutrinos can be produced by interactions of
cosmic rays with radiation or gas, in the jet of a microquasar, in the stellar
wind, or in the atmosphere of the companion star. We study X-ray binaries using
7.5 years of IceCube data with three separate analyses. In the first, we search
for periodic neutrino emission from 55 binaries in the Northern Sky with known
orbital periods. In the second, the X-ray light curves of 102 binaries across
the entire sky are used as templates to search for time-dependent neutrino
emission. Finally, we search for time-integrated emission of neutrinos for a
list of 4 notable binaries identified as microquasars. In the absence of a
significant excess, we place upper limits on the neutrino flux for each
hypothesis and compare our results with theoretical predictions for several
binaries. In addition, we evaluate the sensitivity of the next generation
neutrino telescope at the South Pole, IceCube-Gen2, and demonstrate its power
to identify potential neutrino emission from these binary sources in the
Galaxy.


title: The ALPINE-ALMA [CII] survey: dust attenuation curves at z=4.4-5.5

abstract:
  There is now ample evidence that dust is already present in abundance at high
z. However, given the faintness of distant galaxies in the optical and the NIR,
datasets are still limited and how the dust affects the emerging radiation of
galaxies at very high redshift is not yet fully understood. Using the ALPINE
survey, our objective is to quantify the dust attenuation properties in
galaxies at z=4.4-5.5, and in particular the shape of their attenuation curve.
Using the CIGALE code, we model the stellar populations and their interaction
with the dust in order to measure some of the physical properties of a
subsample of 23 main-sequence ALPINE galaxies. We find that the attenuation
curves span a broad range of properties, from curves that are much steeper than
the SMC extinction curve, to shallower than the starburst attenuation curve.
The shape of the attenuation curves strongly depends on the V-band attenuation.
Galaxies with the lowest attenuation also present the steepest curves. The
steepness of such curves is probably the consequence of the combination of the
intrinsic physical properties of the dust, the relative distribution of stars
and dust in the interstellar medium, and the differential reddening. The broad
range of attenuation curves found at z~5 shows that no single attenuation curve
is appropriate for main sequence galaxies and that assuming a fixed curve can
lead to large errors, for instance in the interpretation and use of the
IRX-beta diagram, if SED modeling is not feasible. Great caution should be
exercised when correcting high redshift galaxies for the presence of dust using
the UV slope beta as it can affect the estimation of both SFR and stellar mass
even at low V-band attenuation due to the steepness of the attenuation curve.
However, when SED modeling can be used, the impact of the choice of the
attenuation curve on the SFR and the stellar mass is limited.


title: Skewing the CMB$\times$LSS: a Fast Method for Bispectrum Analysis

abstract:
  Upcoming cosmic microwave background (CMB) lensing measurements and
tomographic galaxy surveys are expected to provide us with high-precision data
sets in the coming years, thus paving the way for fruitful cross-correlation
analyses. In this paper we study the information content of the weighted
skew-spectrum, a nearly-optimal estimator of the angular bispectrum amplitude,
as a means to extract non-Gaussian information on both bias and cosmological
parameters from the bispectra of galaxies cross-correlated with CMB lensing,
while gaining significantly on speed. Our results suggest that for the
combination of Planck satellite and the Dark Energy Spectroscopic Instrument
(DESI), the skew-spectrum achieves almost equivalent information for both bias
and cosmological parameters as the bispectrum, where the difference in the
constraints is at most $17\%$. We further compare and find agreement between
our theoretical skew-spectra and those estimated from N-body simulations, and
show that it is crucial to include gravitational non-linearities beyond
perturbation theory and the post-Born effect for CMB lensing. We define an
algorithm to apply the skew-spectrum estimator to the data and, as a
preliminary step, we use the skew-spectra to constrain bias parameters and the
amplitude of shot noise from the simulations through a Markov chain Monte Carlo
likelihood analysis, finding that it may be possible to reach percent-level
estimates for the linear bias parameter $b_1$.


title: Surface-dominated finite size effects in nanoconfined superfluid helium

abstract:
  Superfluid $^4$He (He-II) is a widely studied model system for exploring
finite-size effects in strongly confined geometries. Here we study He-II
confined into mm-scale channels of 25 and 50 nm height, at arbitrarily high
pressures using a nanofluidic Helmholtz resonator. We find that the superfluid
density is measurably suppressed in the confined geometry from the transition
temperature down to 0.6 K. Importantly, this suppression can be accounted for
by roton-like thermal excitation with an energy gap of 5 K. This finding sheds
light on the unexplained lack of finite-size scaling of suppression of the
superfluid density. Additionally, the strong confinement allows for observation
of the Kosterlitz-Thouless transition for the first time at arbitrary pressure.


title: Arresting classical many-body chaos by kinetic constraints

abstract:
  We investigate the effect of kinetic constraints on classical many-body chaos
in a translationally-invariant Heisenberg spin chain using a classical
counterpart of the out-of-time-ordered correlator (OTOC). The strength of the
constraint drives a 'dynamical phase transition' separating a delocalised
phase, where the classical OTOC propagates ballistically, from a localised
phase, where the OTOC does not propagate at all and the entire system freezes.
This is unexpected given that all spins configurations are dynamically
connected to each other. We show that localisation arises due to the dynamical
formation of frozen islands, contiguous segments of spins immobile due to the
constraints, dominating over the melting of such islands.