title:
Retrieving cosmological information from small-scale CMB foregrounds II.
  The kinetic Sunyaev Zel'dovich effect

abstract:
  Recent results of ground-based telescopes, giving high-quality measurements
of the CMB temperature power spectrum on small scales motivate the need for an
accurate model of foregrounds, which dominate the primary signal at these
multipoles. In a previous work, we have shown that cosmological information
could be retrieved from the power spectrum of the thermal Sunyaev Zel'dovich
(SZ) effect. In this work, we introduce a physically-motivated model of the
Epoch of Reionisation (EoR) in the cosmological analysis of CMB data, coherent
on all scales. In particular, at high multipoles, the power spectrum of the
kinetic SZ (kSZ) effect is inferred from a set of cosmological parameters by a
machine-learning algorithm. First including an asymmetric parameterisation of
the EoR history in the Planck 2018 data analysis, we retrieve a value of the
CMB optical depth consistent with previous results but giving a completely
different history of the high-redshift Universe, in which the first luminous
sources light up as early as $z=15$. Considering the latest small-scale data
from the South Pole Telescope (SPT) and letting the cosmology free to vary, we
find that including the new cosmology-dependent tSZ and kSZ spectra help
tighten the constraints on the amplitudes of these two spectra by breaking the
degeneracy between their amplitudes. We report a $5\sigma$ measurement of the
kSZ signal at $\ell=3000$ ($\mathcal{D}_{3000}^\mathrm{kSZ} = 3.3 \pm
0.7~\mu\mathrm{K}^2$, $1\sigma$), marginalised over cosmology, as well as an
upper limit on the patchy signal
$\mathcal{D}_{3000}^\mathrm{pkSZ}<1.58~\mu\mathrm{K}^2$ (95\% C.L.).
Additionally, we find that the SPT data favours slightly earlier reionisation
scenarios than Planck, leading to $\tau = 0.062 \pm 0.012$ and a reionisation
midpoint $z_{\rm re} = 7.9\pm1.1$ ($1\sigma$), in line with constraints from
high-redshift quasars and galaxies.


title: How Do Smart Contracts Benefit Security Protocols?

abstract:
  Smart contracts have recently been adopted by many security protocols.
However, existing studies lack satisfactory theoretical support on how
contracts benefit security protocols. This paper aims to give a systematic
analysis of smart contract (SC)-based security protocols to fulfill the gap of
unclear arguments and statements. We firstly investigate \textit{state of the
art studies} and establish a formalized model of smart contract protocols with
well-defined syntax and assumptions. Then, we apply our formal framework to two
concrete instructions to explore corresponding advantages and desirable
properties. Through our analysis, we abstract three generic properties
(\textit{non-repudiation, non-equivocation, and non-frameability}) and
accordingly identify two patterns. (1) a smart contract can be as an autonomous
subscriber to assist the trusted third party (TTP); (2) a smart contract can
replace traditional TTP. To the best of our knowledge, this is the first study
to provide in-depth discussions of SC-based security protocols from a strictly
theoretical perspective.


title: Detecting and Learning the Unknown in Semantic Segmentation

abstract:
  Semantic segmentation is a crucial component for perception in automated
driving. Deep neural networks (DNNs) are commonly used for this task and they
are usually trained on a closed set of object classes appearing in a closed
operational domain. However, this is in contrast to the open world assumption
in automated driving that DNNs are deployed to. Therefore, DNNs necessarily
face data that they have never encountered previously, also known as anomalies,
which are extremely safety-critical to properly cope with. In this work, we
first give an overview about anomalies from an information-theoretic
perspective. Next, we review research in detecting semantically unknown objects
in semantic segmentation. We demonstrate that training for high entropy
responses on anomalous objects outperforms other recent methods, which is in
line with our theoretical findings. Moreover, we examine a method to assess the
occurrence frequency of anomalies in order to select anomaly types to include
into a model's set of semantic categories. We demonstrate that these anomalies
can then be learned in an unsupervised fashion, which is particularly suitable
in online applications based on deep learning.


title: Revisiting reopened bugs in open source software systems

abstract:
  Reopened bugs can degrade the overall quality of a software system since they
require unnecessary rework by developers. Moreover, reopened bugs also lead to
a loss of trust in the end-users regarding the quality of the software. Thus,
predicting bugs that might be reopened could be extremely helpful for software
developers to avoid rework. Prior studies on reopened bug prediction focus only
on three open source projects (i.e., Apache, Eclipse, and OpenOffice) to
generate insights. We observe that one out of the three projects (i.e., Apache)
has a data leak issue -- the bug status of reopened was included as training
data to predict reopened bugs. In addition, prior studies used an outdated
prediction model pipeline (i.e., with old techniques for constructing a
prediction model) to predict reopened bugs. Therefore, we revisit the reopened
bugs study on a large scale dataset consisting of 47 projects tracked by JIRA
using the modern techniques such as SMOTE, permutation importance together with
7 different machine learning models. We study the reopened bugs using a mixed
methods approach (i.e., both quantitative and qualitative study). We find that:
1) After using an updated reopened bug prediction model pipeline, only 34%
projects give an acceptable performance with AUC >= 0.7. 2) There are four
major reasons for a bug getting reopened, that is, technical (i.e.,
patch/integration issues), documentation, human (i.e., due to incorrect bug
assessment), and reasons not shown in the bug reports. 3) In projects with an
acceptable AUC, 94% of the reopened bugs are due to patch issues (i.e., the
usage of an incorrect patch) identified before bug reopening. Our study
revisits reopened bugs and provides new insights into developer's bug reopening
activities.


title: A Two-Stage U-Net for High-Fidelity Denoising of Historical Recordings

abstract:
  Enhancing the sound quality of historical music recordings is a long-standing
problem. This paper presents a novel denoising method based on a
fully-convolutional deep neural network. A two-stage U-Net model architecture
is designed to model and suppress the degradations with high fidelity. The
method processes the time-frequency representation of audio, and is trained
using realistic noisy data to jointly remove hiss, clicks, thumps, and other
common additive disturbances from old analog discs. The proposed model
outperforms previous methods in both objective and subjective metrics. The
results of a formal blind listening test show that real gramophone recordings
denoised with this method have significantly better quality than the baseline
methods. This study shows the importance of realistic training data and the
power of deep learning in audio restoration.


title:
Robust Frequency Constrained UC Using Data Driven Logistic Regression
  for Island Power Systems

abstract:
  In the current practice of short-term power scheduling, online power reserves
are used to address generation mismatches and contingencies. Neither online
inertia nor the speed of the committed units is considered in the scheduling
process. With the increasing injection of uncertain renewable energy sources,
this practice is starting to fall short especially in island power systems,
where the primary frequency response is already scarce, and any contingency
leads to potentially poor frequency response. This paper introduces a data
driven linear constraint to improve the post-fault frequency quality in island
power systems. A coherent initial data-set is obtained by simulating the system
frequency response of single outages. Then logistic regression is employed as a
predictive analytic procedure to differentiate the acceptable and unacceptable
incidents. To compare the conventional methods with the proposed approach and
also to handle the uncertain nature of renewable energy generation, an adaptive
robust unit commitment formulation is utilized. Results for the island power
system of La Palma show that depending on the chosen cut-point on the logistic
regression estimation the proposed method can improve the frequency response
quality of the system while reducing the operation costs.


title:
A Bi-Criteria FPTAS for Scheduling with Memory Constraints on Graph with
  Bounded Tree-width

abstract:
  In this paper we study a scheduling problem arising from executing numerical
simulations on HPC architectures. With a constant number of parallel machines,
the objective is to minimize the makespan under memory constraints for the
machines. Those constraints come from a neighborhood graph G for the jobs.
Motivated by a previous result on graphs G with bounded path-width, our focus
is on the case when the neighborhood graph G has bounded tree-width. Our result
is a bi-criteria fully polynomial time approximation algorithm based on a
dynamic programming algorithm. It allows to find a solution within a factor of
1 + epsilon of the optimal makespan, where the memory capacity of the machines
may be exceeded by a factor at most 1 + epsilon. This result relies on the use
of a nice tree decomposition of G and its traversal in a specific way which may
be useful on its own. The case of unrelated machines is also tractable with
minor modifications.


title:
Effects of isoscalar- and isovector-scalar meson mixing on neutron star
  structure

abstract:
  Based on the accurately calibrated interaction FSUGold, we show that
including isovector scalar $\delta$ meson and its coupling to isoscalar scalar
$\sigma$ meson in the relativistic mean field (RMF) model can soften the
symmetry energy $E_{\rm{sym}}(n)$ at intermediate densities while stiffen the
$E_{\rm{sym}}(n)$ at high densities. We find this new RMF model can be
simultaneously compatible with (1) the constraints on the equation of state of
symmetric nuclear matter at suprasaturation densities from flow data in
heavy-ion collisions, (2) the neutron skin thickness of $^{208}$Pb from the
PREX-II experiment, (3) the largest mass of neutron star (NS) reported so far
from PSR J0740+6620, (4) the limit of $\Lambda_{1.4}\leq580$ for the
dimensionless tidal deformability of the canonical 1.4$M_{\odot}$ NS from the
gravitational wave signal GW170817, (5) the mass-radius relation of PSR
J0030+0451 and PSR J0740+6620 measured by NICER, and thus remove the tension
between PREX-II and GW170817 observed in the conventional RMF model.


title:
Enabling sub-THz Cloud RANs: Distributed Machine-Learning for Early HARQ
  Feedback Prediction

abstract:
  We propose novel HARQ prediction schemes for Cloud RANs (C-RANs) that use
feedback over a rate-limited feedback channel (4 and 8 bits) from the Remote
Radio Heads (RRHs) to predict at the User Equipment (UE) the decoding outcome
at the BaseBand Unit (BBU) ahead of actual decoding. In particular, we propose
a novel dual-input denoising autoencoder that is trained in a joint end-to-end
fashion over the whole C-RAN setup. In realistic link-level simulations at 100
GHz in the sub-THz band, we show that a combination of the novel dual-input
denoising autoencoder and state-of-the-art SNR-based HARQ feedback prediction
achieves the overall best performance in all scenarios compared to other
proposed and state-of-the-art single prediction schemes. At very low target
error rates down to $1.6 \cdot 10^{-5}$, this combined approach reduces the
number of required transmission rounds by up to 50\% compared to always
transmitting all redundancy.


title: Synchronization induced by directed higher-order interactions

abstract:
  Non-reciprocal interactions play a crucial role in many social and biological
complex systems. While directionality has been thoroughly accounted for in
networks with pairwise interactions, its effects in systems with higher-order
interactions have not yet been explored as deserved. Here, we introduce the
concept of M-directed hypergraphs, a general class of directed higher-order
structures, which allow to investigate dynamical systems coupled through
directed group interactions. As an application we study the synchronization of
nonlinear oscillators on 1-directed hypergraphs, finding that directed
higher-order interactions can destroy synchronization, but also stabilize
otherwise unstable synchronized states.


title:
Learning stochastic dynamics and predicting emergent behavior using
  transformers

abstract:
  We show that a neural network originally designed for language processing can
learn the dynamical rules of a stochastic system by observation of a single
dynamical trajectory of the system, and can accurately predict its emergent
behavior under conditions not observed during training. We consider a lattice
model of active matter undergoing continuous-time Monte Carlo dynamics,
simulated at a density at which its steady state comprises small, dispersed
clusters. We train a neural network called a transformer on a single trajectory
of the model. The transformer, which we show has the capacity to represent
dynamical rules that are numerous and nonlocal, learns that the dynamics of
this model consists of a small number of processes. Forward-propagated
trajectories of the trained transformer, at densities not encountered during
training, exhibit motility-induced phase separation and so predict the
existence of a nonequilibrium phase transition. Transformers have the
flexibility to learn dynamical rules from observation without explicit
enumeration of rates or coarse-graining of configuration space, and so the
procedure used here can be applied to a wide range of physical systems,
including those with large and complex dynamical generators.


title:
Explicitly correlated electronic structure calculations with
  transcorrelated matrix product operators

abstract:
  In this work, we present the first implementation of the transcorrelated
electronic Hamiltonian in an optimization procedure for matrix product states
by the density matrix renormalization group (DMRG) algorithm. In the
transcorrelation ansatz, the electronic Hamiltonian is similarity-transformed
with a Jastrow factor to describe the cusp in the wave function at
electron-electron coalescence. As a result, the wave function is easier to
approximate accurately with the conventional expansion in terms of one-particle
basis functions and Slater determinants. The transcorrelated Hamiltonian in
first quantization comprises up to three-body interactions, which we deal with
in the standard way by applying robust density fitting to two- and three-body
integrals entering the second-quantized representation of this Hamiltonian. The
lack of hermiticity of the transcorrelated Hamiltonian is taken care of along
the lines of the first work on transcorrelated DMRG [J. Chem. Phys. 153, 164115
(2020)] by encoding it as a matrix product operator and optimizing the
corresponding ground state wave function with imaginary-time time-dependent
DMRG. We demonstrate our quantum chemical transcorrelated DMRG approach at the
example of several atoms and first-row diatomic molecules. We show that
transcorrelation improves the convergence rate to the complete basis set limit
in comparison to conventional DMRG. Moreover, we study extensions of our
approach that aim at reducing the cost of handling the matrix product operator
representation of the transcorrelated Hamiltonian.

categories: physics.chem-ph cond-mat.str-el physics.atom-ph physics.comp-ph


title: Sampling and Reconstructing Angular Domains with Uniform Arrays

abstract:
  The surge of massive antenna arrays in wireless networks calls for the
adoption of analog/hybrid array solutions, where multiple antenna elements are
driven by a common radio front end to form a beam along a specific angle in
order to maximize the beamforming gain. Many heuristics have been proposed to
sample the angular domain by trading off between sampling overhead and angular
scanning step size, where arbitrarily small angular step size is only
attainable with infinite sampling overhead. In this work we show that, for
uniform linear and rectangular arrays, loss-less reconstruction of the array's
angular response at arbitrary angular precision is possible using finite number
of samples without resorting to assumptions of angular sparsity. The proposed
method, sampling and reconstructing angular domain (SARA), defines how many and
which angles to be sampled and the corresponding reconstruction. This general
solution to scan the angular domain can therefore be applied not only to beam
acquisition and channel estimation, but also to radio imaging techniques,
making it a candidate for future integrated sensing and communications (ISAC).
We evaluate our proposal by numerical evaluations, which provide clear
advantages versus the other considered baselines both in terms of angular
reconstruction performance and computational complexity.


title: The Iterates of the Frank-Wolfe Algorithm May Not Converge

abstract:
  The Frank-Wolfe algorithm is a popular method for minimizing a smooth convex
function $f$ over a compact convex set $\mathcal{C}$. While many convergence
results have been derived in terms of function values, hardly nothing is known
about the convergence behavior of the sequence of iterates
$(x_t)_{t\in\mathbb{N}}$. Under the usual assumptions, we design several
counterexamples to the convergence of $(x_t)_{t\in\mathbb{N}}$, where $f$ is
$d$-time continuously differentiable, $d\geq2$, and
$f(x_t)\to\min_\mathcal{C}f$. Our counterexamples cover the cases of open-loop,
closed-loop, and line-search step-size strategies. We do not assume
\emph{misspecification} of the linear minimization oracle and our results thus
hold regardless of the points it returns, demonstrating the fundamental
pathologies in the convergence behavior of $(x_t)_{t\in\mathbb{N}}$.


title:
Mining On Alzheimer's Diseases Related Knowledge Graph to Identity
  Potential AD-related Semantic Triples for Drug Repurposing

abstract:
  To date, there are no effective treatments for most neurodegenerative
diseases. Knowledge graphs can provide comprehensive and semantic
representation for heterogeneous data, and have been successfully leveraged in
many biomedical applications including drug repurposing. Our objective is to
construct a knowledge graph from literature to study relations between
Alzheimer's disease (AD) and chemicals, drugs and dietary supplements in order
to identify opportunities to prevent or delay neurodegenerative progression. We
collected biomedical annotations and extracted their relations using SemRep via
SemMedDB. We used both a BERT-based classifier and rule-based methods during
data preprocessing to exclude noise while preserving most AD-related semantic
triples. The 1,672,110 filtered triples were used to train with knowledge graph
completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict
candidates that might be helpful for AD treatment or prevention. Among three
knowledge graph completion models, TransE outperformed the other two (MR =
13.45, Hits@1 = 0.306). We leveraged the time-slicing technique to further
evaluate the prediction results. We found supporting evidence for most highly
ranked candidates predicted by our model which indicates that our approach can
inform reliable new knowledge. This paper shows that our graph mining model can
predict reliable new relationships between AD and other entities (i.e., dietary
supplements, chemicals, and drugs). The knowledge graph constructed can
facilitate data-driven knowledge discoveries and the generation of novel
hypotheses.


title:
Algorithmic Fair Allocation of Indivisible Items: A Survey and New
  Questions

abstract:
  The theory of algorithmic fair allocation is within the center of multi-agent
systems and economics in the last decade due to its industrial and social
importance. At a high level, the problem is to assign a set of items that are
either goods or chores to a set of agents so that every agent is happy with
what she obtains. Particularly, in this survey, we focus on indivisible items,
for which absolute fairness such as envy-freeness and proportionality cannot be
guaranteed. One main theme in the recent research agenda is about designing
algorithms that approximately achieve the fairness criteria. We aim at
presenting a comprehensive survey of recent progresses through the prism of
algorithms, highlighting the ways to relax fairness notions and common
techniques to design algorithms, as well as the most interesting questions for
future research.


title:
Direct measurement of the distribution of dark matter with strongly
  lensed gravitational waves

abstract:
  In this Letter, we present a new idea of probing the distribution of dark
matter exhibiting elastic and velocity-independent self-interactions. These
interactions might be revealed in multiple measurements of strongly lensed
gravitational waves, which can be observationally explored to determine the
strength of self-scatterings. Specifically, each individual galactic-scale
strong-lensing system whose source is a coalescing compact binary emitting
gravitational waves will provide a model-independent measurement of the shear
viscosity of dark matter along the line of sight. These individual measurements
could be a probe of large-scale distribution of dark matter and its properties.
Our results indicate that with 10-1000 strongly lensed gravitational waves from
ET and DECIGO, robust constraints on the large-scale distribution of
self-interacting dark matter might be produced. More stringent limits on the
dark matter scattering cross-section per unit mass ($\sigma_{\chi}/m_{\chi}$)
relevant to galaxy and cluster scales are also expected, compared with the
conservative estimates obtained in the electromagnetic domain. Finally, we
discuss the effectiveness of our method in the context of self-interacting dark
matter particle physics.


title:
Primal and mixed finite element formulations for the relaxed
  micromorphic model

abstract:
  The classical Cauchy continuum theory is suitable to model highly homogeneous
materials. However, many materials, such as porous media or metamaterials,
exhibit a pronounced microstructure. As a result, the classical continuum
theory cannot capture their mechanical behaviour without fully resolving the
underlying microstructure. In terms of finite element computations, this can be
done by modelling the entire body, including every interior cell. The relaxed
micromorphic continuum offers an alternative method by instead enriching the
kinematics of the mathematical model. The theory introduces a microdistortion
field, encompassing nine extra degrees of freedom for each material point. The
corresponding elastic energy functional contains the gradient of the
displacement field, the microdistortion field and its Curl (the
micro-dislocation). Therefore, the natural spaces of the fields are
$[\mathit{H}^1]^3$ for the displacement and $[\mathit{H}(\mathrm{curl})]^3$ for
the microdistortion, leading to unusual finite element formulations. In this
work we describe the construction of appropriate finite elements using
N\'ed\'elec and Raviart-Thomas subspaces, encompassing solutions to the
orientation problem and the discrete consistent coupling condition. Further, we
explore the numerical behaviour of the relaxed micromorphic model for both a
primal and a mixed formulation. The focus of our benchmarks lies in the
influence of the characteristic length $L_\mathrm{c}$ and the correlation to
the classical Cauchy continuum theory.


title:
Final results on the $0\nu\beta\beta$ decay half-life limit of
  $^{100}$Mo from the CUPID-Mo experiment

abstract:
  The CUPID-Mo experiment to search for 0$\nu\beta\beta$ decay in $^{100}$Mo
has been recently completed after about 1.5 years of operation at Laboratoire
Souterrain de Modane (France). It served as a demonstrator for CUPID, a next
generation 0$\nu\beta\beta$ decay experiment. CUPID-Mo was comprised of 20
enriched Li$_2$$^{100}$MoO$_4$ scintillating calorimeters, each with a mass of
$\sim$ 0.2 kg, operated at $\sim$20 mK. We present here the final analysis with
the full exposure of CUPID-Mo ($^{100}$Mo exposure of 1.47 kg$\times$yr) used
to search for lepton number violation via 0$\nu\beta\beta$ decay. We report on
various analysis improvements since the previous result on a subset of data,
reprocessing all data with these new techniques. We observe zero events in the
region of interest and set a new limit on the $^{100}$Mo 0$\nu\beta\beta$ decay
half-life of $T^{0\nu}_{1/2} > 1.8 \times 10^{24}$ year (stat.+syst.) at 90%
C.I. Under the light Majorana neutrino exchange mechanism this corresponds to
an effective Majorana neutrino mass of $\left<m_{\beta \beta}\right> <
(0.28$--$0.49)$ eV, dependent upon the nuclear matrix element utilized.


title:
Level set based particle filter driven by optical flow: an application
  to track the salt boundary from X-ray CT time-series

abstract:
  Image-based computational fluid dynamics have long played an important role
in leveraging knowledge and understanding of several physical phenomena. In
particular, probabilistic computational methods have opened the way to
modelling the complex dynamics of systems in purely random turbulent motion. In
the field of structural geology, a better understanding of the deformation and
stress state both within the salt and the surrounding rocks is of great
interest to characterize all kinds of subsurface long-terms energy-storage
systems. The objective of this research is to determine the non-linear
deformation of the salt boundary over time using a parallelized, stochastic
filtering approach from x-ray computed tomography (CT) image time series
depicting the evolution of salt structures triggered by gravity and under
differential loading. This work represents a first step towards bringing
together physical modeling and advanced stochastic image processing methods
where model uncertainty is taken into account.


title:
A Deterministic Pathogen Transmission Model Based on High-Fidelity
  Physics

abstract:
  A deterministic pathogen transmission model based on high-fidelity physics
has been developed. The model combines computational fluid dynamics and
computational crowd dynamics in order to be able to provide accurate tracing of
viral matter that is exhaled, transmitted and inhaled via aerosols. The
examples shown indicate that even with modest computing resources, the
propagation and transmission of viral matter can be simulated for relatively
large areas with thousands of square meters, hundreds of pedestrians and
several minutes of physical time. The results obtained and insights gained from
these simulations can be used to inform global pandemic propagation models,
increasing substantially their accuracy.

categories: math.NA cs.NA physics.bio-ph physics.flu-dyn


title: Differential Geometry of Contextuality

abstract:
  Contextuality has been related for a long time as a topological phenomenon.
In this work, such a relationship is exposed in the more general framework of
generalized contextuality. The main idea is to identify states, effects, and
transformations as vectors living in a tangent space, and the non-contextual
conditions as discrete closed paths implying null vertical phases. Two
equivalent interpretations hold. The geometrical or realistic view, where flat
space is imposed, implies that the contextual behavior becomes equivalent to
the curvature (non-trivial holonomy) of the probabilistic functions, in analogy
with the electromagnetic tensor; as a modification of the valuation function,
it can be used to connect contextuality with interference, non-commutativity,
and signed measures. The topological or anti-realistic view, where the
valuation functions must be preserved, implies that the contextual behavior can
be translated as topological failures (non-trivial monodromy); it can be used
to connect contextuality with non-embeddability and a generalized Voroby'ev
theorem. Both views can be related to contextual fraction, and the disturbance
in ontic models can be presented as non-trivial transition maps.


title:
Exact densities of loops in O(1) dense loop model and of clusters in
  critical percolation on a cylinder II: rotated lattice

abstract:
  This work continues the study started in \cite{Povolotsky2021}, where the
exact densities of loops in the O(1) dense loop model on an infinite strip of
the square lattice with periodic boundary conditions were obtained. These
densities are also equal to the densities of critical percolation clusters on
forty five degree rotated square lattice rolled into a cylinder. Here, we
extend those results to the square lattice with a tilt. This in particular
allow us to obtain the densities of critical percolation clusters on the
cylinder of the square lattice of standard orientation extensively studied
before. We obtain exact densities of contractible and non-contractible loops or
equivalently the densities of critical percolation clusters, which do not and
do wrap around the cylinder respectively. The solution uses the mapping of O(1)
dense loop model to the six-vertex model in the Razumov-Stroganov point, while
the effective tilt is introduced via the the inhomogeneous transfer matrix
proposed by Fujimoto. The further solution is based on the Bethe ansatz and
Fridkin-Stroganov-Zagier's solution of the Baxter's T-Q equation. The results
are represented in terms of the solution of two explicit systems of linear
algebraic equations, which can be performed either analytically for small
circumferences of the cylinder or numerically for larger ones. We present exact
rational values of the densities on the cylinders of small circumferences and
several lattice orientations and use the results of high precision numerical
calculations to study the finite-size corrections to the densities, in
particular their dependence on the tilt of the lattice.

categories: math-ph cond-mat.stat-mech math.MP math.PR


title: Game Theoretic Models for Profit-Sharing in Multi-fleet Platoons

abstract:
  Profit-sharing is needed within platoons in order for competing
transportation companies to collaborate in forming platoons. In this paper, we
propose distribution models of the profit designed for vehicles that are
located at the same origin and are operated by competing transportation
companies. The vehicles have default departure times, but can decide to depart
at other times in order to benefit from platooning. We model the strategic
interaction among vehicles with game theory and consider pure Nash equilibria
as the solution concept. In a numerical evaluation we compare the outcomes of
the games associated with different distribution models of the profit.


title:
Dimension Reduction via Supervised Clustering of Regression
  Coefficients: A Review

abstract:
  The development and use of dimension reduction methods is prevalent in modern
statistical literature. This paper reviews a class of dimension reduction
techniques which aim to simultaneously select relevant predictors and find
clusters within them which share a common effect on the response. Such methods
have been shown to have superior performance relative to OLS estimates and the
lasso [Tibshirani, 1996] especially when multicollinearity in the predictors is
present. Their applications, which include genetics, epidemiology, and fMRI
studies, are also discussed.


title:
Local exact controllability of the 1D nonlinear Schr\"odinger equation
  in the case of Dirichlet boundary conditions

abstract:
  We consider the 1D nonlinear Schr\"odinger equation with bilinear control. In
the case of Neumann boundary conditions, local exact controllability of this
equation near the ground state has been proved by Beauchard and Laurent in
arXiv:1001.3288. In this paper, we study the case of Dirichlet boundary
conditions. To establish the controllability of the linearised equation, we use
a bilinear control acting through four directions: three Fourier modes and one
generic direction. The Fourier modes are appropriately chosen so that they
satisfy a saturation property. These modes allow to control approximately the
linearised Schr\"odinger equation. We show that the reachable set for the
linearised equation is closed. This is achieved by representing the resolving
operator as a sum of two linear continuous mappings: one is surjective (here
the control in generic direction is used) and the other is compact. A mapping
with dense and closed image is surjective, so the linearised Schr\"odinger
equation is exactly controllable. Then local exact controllability of the
nonlinear equation is derived using the inverse mapping theorem.


title: Real-Time Cross-Fleet Pareto-Improving Truck Platoon Coordination

abstract:
  This paper studies a multi-fleet platoon coordination system in transport
networks that deploy hubs to form trucks into platoons. The trucks belong to
different fleets that are interested in increasing their profits by platooning
across fleets. The profit of each fleet incorporates platooning rewards and
costs for waiting at hubs. Each truck has a fixed route and a waiting time
budget to spend at the hubs along its route. To ensure that all fleets are
willing to participate in the system, we develop a cross-fleet Pareto-improving
coordination strategy that guarantees higher fleet profits than a coordination
strategy without cross-fleet platoons. By leveraging multiple hubs for platoon
formation, the coordination strategy can be implemented in a real-time and
distributed fashion while largely reducing the amount of travel information to
be shared for system-wide coordination. We evaluate the proposed strategy in a
simulation study over the Swedish transportation network. The cross-fleet
platooning strategy significantly improves fleets' profits compared with
single-fleet platooning, especially the profits from smaller fleets. The
cross-fleet platooning strategy also shows strong competitiveness in terms of
the system-wide profit compared to the case when a system planner optimizes all
fleets' total profit.


title:
A Completeness Result for Inequational Reasoning in a Full Higher-Order
  Setting

abstract:
  This paper obtains a completeness result for inequational reasoning with
applicative terms without variables in a setting where the intended semantic
models are the full structures, the full type hierarchies over preorders for
the base types. The syntax allows for the specification that a given symbol be
interpreted as a monotone function, or an antitone function, or both. There is
a natural set of five rules for inequational reasoning. One can add variables
and also add a substitution rule, but we observe that this logic would be
incomplete for full structures. This is why the completeness result in this
paper pertains to terms without variables. Since the completeness is already
known for the class of general (Henkin) structures, we are interested in full
structures. We present a completeness theorem. Our result is not optimal
because we restrict to base preorders which have a weak completeness property:
every pair of elements has an upper bound and a lower bound. To compensate we
add several rules to the logic. We also present extensions and variations of
our completeness result.


title: Calorimetry of a Quantum Phase Slip

abstract:
  In a Josephson junction, which is the central element in superconducting
quantum technology, irreversibility arises from abrupt slips of the
gauge-invariant quantum phase difference across the contact. A quantum phase
slip (QPS) is often visualized as the tunneling of a flux quantum in the
transverse direction to the superconducting weak link, which produces
dissipation. In this work, we detect the instantaneous heat release caused by a
QPS in a Josephson junction using time-resolved electron thermometry on a
nanocalorimeter, signaled by an abrupt increase of the local electronic
temperature in the weak link and subsequent relaxation back to equilibrium.
Beyond providing a cornerstone in experimental quantum thermodynamics in form
of observation of heat in an elementary quantum process, this result sets the
ground for experimentally addressing the ubiquity of dissipation, including
that in superconducting quantum sensors and qubits.


title:
The porous medium equation with large data on Cartan-Hadamard manifolds
  under general curvature bounds

abstract:
  We consider very weak solutions of the Cauchy problem for the porous medium
equation on Cartan-Hadamard manifolds, that are assumed to satisfy general
curvature bounds and to be stochastically complete. We identify a class of
initial data that can grow at infinity at a prescribed rate, which depends on
the assumed curvature bounds through an integral function, such that the
corresponding solution exists at least on $[0,T]$ for a suitable $T>0$. The
maximal existence time $T$ is estimated in terms of a suitable weighted norm of
the initial datum. Our results are sharp, in the sense that slower growth rates
yield global existence, whereas one can construct data with critical growth for
which the corresponding solutions blow up in finite time. Under further
assumptions, uniqueness of very weak solutions is also proved, in the same
growth class.


title: Locally private nonparametric confidence intervals and sequences

abstract:
  This work derives methods for performing nonparametric, nonasymptotic
statistical inference for population parameters under the constraint of local
differential privacy (LDP). Given observations $(X_1, \dots, X_n)$ with mean
$\mu^\star$ that are privatized into $(Z_1, \dots, Z_n)$, we introduce
confidence intervals (CI) and time-uniform confidence sequences (CS) for
$\mu^\star \in \mathbb R$ when only given access to the privatized data. We
introduce a nonparametric and sequentially interactive generalization of
Warner's famous "randomized response" mechanism, satisfying LDP for arbitrary
bounded random variables, and then provide CIs and CSs for their means given
access to the resulting privatized observations. We extend these CSs to capture
time-varying (non-stationary) means, and conclude by illustrating how these
methods can be used to conduct private online A/B tests.


title: Gate-tunable kinetic inductance in proximitized nanowires

abstract:
  We report the detection of a gate-tunable kinetic inductance in a hybrid
InAs/Al nanowire. For this purpose, we have embedded the nanowire into a
quarter-wave coplanar waveguide resonator and measured the resonance frequency
of the circuit. We find that the resonance frequency can be changed via the
gate voltage that controls the electron density of the proximitized
semiconductor and thus the nanowire inductance. Applying Mattis-Bardeen theory,
we extract the gate dependence of the normal state conductivity of the
nanowire, as well as its superconducting gap. Our measurements complement
existing characterization methods for hybrid nanowires and provide a new and
useful tool for gate-controlled superconducting electronics.


title: Colonoscopy polyp detection with massive endoscopic images

abstract:
  We improved an existing end-to-end polyp detection model with better average
precision validated by different data sets with trivial cost on detection
speed. Our previous work on detecting polyps within colonoscopy provided an
efficient end-to-end solution to alleviate doctor's examination overhead.
However, our later experiments found this framework is not as robust as before
as the condition of polyp capturing varies. In this work, we conducted several
studies on data set, identifying main issues that causes low precision rate in
the task of polyp detection. We used an optimized anchor generation methods to
get better anchor box shape and more boxes are used for detection as we believe
this is necessary for small object detection. A alternative backbone is used to
compensate the heavy time cost introduced by dense anchor box regression. With
use of the attention gate module, our model can achieve state-of-the-art polyp
detection performance while still maintain real-time detection speed.


title: Tractable semidefinite bounds of positive maximal singular values

abstract:
  We focus on computing certified upper bounds for the positive maximal
singular value (PMSV) of a given matrix. The PMSV problem boils down to
maximizing a quadratic polynomial on the intersection of the unit sphere and
the nonnegative orthant. We provide a hierarchy of tractable semidefinite
relaxations to approximate the value of the latter polynomial optimization
problem as closely as desired. This hierarchy is based on an extension of
P\'olya's representation theorem. Doing so, positive polynomials can be
decomposed as weighted sums of squares of $s$-nomials, where $s$ can be a
priori fixed ($s=1$ corresponds to monomials, $s=2$ corresponds to binomials,
etc.). This in turn allows us to control the size of the resulting semidefinite
relaxations.


title:
In-medium hadronization of heavy quarks and its effect on charmed meson
  and baryon distributions in heavy-ion collisions

abstract:
  We present a new model for the description of heavy-quark hadronization in
relativistic heavy-ion collisions in the presence of a reservoir of lighter
thermal particles with which recombination can occur leading to the formation
of color-singlet clusters. Color neutralization is assumed to occur locally,
within the same fluid cell occupied by the heavy quark and it proceeds via the
recombination of the latter with light antiquarks (quarks) or diquarks
(anti-diquarks), which are assumed to be present in the medium with thermal
abundance as effective degrees of freedom around the QCD crossover temperature
$T_c$. Typically the resulting color-singlet clusters have quite low invariant
mass, in most of the cases below 4 GeV, and in this case they are assumed to
undergo an isotropic 2-body decay in their local rest-frame. Heavier clusters
are instead fragmented as Lund strings. The possibility of recombination with
light diquarks enhances the yields of charmed baryons, in qualitative agreement
with recent measurements. The assumption of local color neutralization leads to
a strong space-momentum correlation, which provides a substantial enhancement
of the collective flow of the final-state charmed hadrons, affecting both their
momentum and their angular distributions


title:
Geometric structure guided model and algorithms for complete
  deconvolution of gene expression data

abstract:
  Complete deconvolution analysis for bulk RNAseq data is important and helpful
to distinguish whether the difference of disease-associated GEPs (gene
expression profiles) in tissues of patients and normal controls are due to
changes in cellular composition of tissue samples, or due to GEPs changes in
specific cells. One of the major techniques to perform complete deconvolution
is nonnegative matrix factorization (NMF), which also has a wide-range of
applications in the machine learning community. However, the NMF is a
well-known strongly ill-posed problem, so a direct application of NMF to RNAseq
data will suffer severe difficulties in the interpretability of solutions. In
this paper we develop an NMF-based mathematical model and corresponding
computational algorithms to improve the solution identifiability of
deconvoluting bulk RNAseq data. In our approach, we combine the biological
concept of marker genes with the solvability conditions of the NMF theories,
and develop a geometric structured guided optimization model. In this strategy,
the geometric structure of bulk tissue data is first explored by the spectral
clustering technique. Then, the identified information of marker genes is
integrated as solvability constraints, while the overall correlation graph is
used as manifold regularization. Both synthetic and biological data are used to
validate the proposed model and algorithms, from which solution
interpretability and accuracy are significantly improved.


title: Conjugate priors and bias reduction for logistic regression models

abstract:
  Logistic regression models for binomial responses are routinely used in
statistical practice. However, the maximum likelihood estimate may not exist
due to data separability. We address this issue by considering a conjugate
prior penalty which always produces finite estimates. Such a specification has
a clear Bayesian interpretation and enjoys several invariance properties,
making it an appealing prior choice. We show that the proposed method leads to
an accurate approximation of the reduced-bias approach of Firth (1993),
resulting in estimators with smaller asymptotic bias than the
maximum-likelihood and whose existence is always guaranteed. Moreover, the
considered penalized likelihood can be expressed as a genuine likelihood, in
which the original data are replaced with a collection of pseudo-counts. Hence,
our approach may leverage well established and scalable algorithms for logistic
regression. We compare our estimator with alternative reduced-bias methods,
vastly improving their computational performance and achieving appealing
inferential results.


title:
New self-consistent wind parameters fitting optical spectra of O-type
  stars observed with HERMES spectrograph

abstract:
  We perform spectral fitting for a set of O-type stars based on
self-consistent wind solutions, which provide mass-loss rate and velocity
profiles directly derived from the initial stellar parameters. The great
advantage of this self-consistent spectral fitting is therefore the reduction
of the number of free parameters to be tuned.
  Self-consistent values for the line-force parameters (k,alpha,delta) and
subsequently for the mass-loss rate and terminal velocity are provided by the
m-CAK prescription introduced in Paper I, updated in this work with
improvements such as a temperature structure for the wind, self-consistently
evaluated from the line-acceleration. Synthetic spectra are calculated using
the radiative transfer code FASTWIND, replacing the classical beta-law for our
new calculated velocity profiles.
  We found that self-consistent m-CAK solutions provide values for theoretical
mass-loss rates on the order of the most recent predictions of other studies.
From here, we generate synthetic spectra with self-consistent hydrodynamics to
fit and obtain a new set of stellar and wind parameters for our sample of
O-type stars whose spectra was taken with the high resolution echelle
spectrograph HERMES (R=85000). We find a satisfactory global fit for our
observations, with good accuracy for photospheric He I and He II lines and a
quite acceptable fit for H lines. Although this self-consistent spectral
analysis is currently constrained in the optical wavelength range only, this is
an important step towards the determination of stellar and wind parameters
without using a beta-law. Given these results, we expect that the values
introduced here should be helpful for future studies about the stars
constituting this sample, together with the prospective that the m-CAK
self-consistent prescription be extended to numerous studies about massive
stars in future.


title: Magic radius of AA bilayer graphene quantum dot

abstract:
  We study analytically and numerically electronic properties of a circular
quantum dot made from AA bilayer graphene. We observe a discrete set of dot
radii for which the low-energy electron states are degenerate with respect to
the layer parity. By analogy with the "magic angles" in the twisted bilayer
graphene we refer to these radii as "magic". Such a feature is unique for the
AA structures and is related to a specific layer-symmetry of the AA graphene
bilayer: the parity of the highest occupied level changes from
layer-antisymmetric to layer-symmetric when the radius of the AA dot is equal
to its magic value. We explore an analogy in the electronic structure between
twisted bilayer graphene at the magic twist angle and the AA quantum dot with
magic radius. We argue that this analogy can be helpful for theoretical
description of the electronic properties of the twisted bilayer graphene.


title: Listing Maximal k-Plexes in Large Real-World Graphs

abstract:
  Listing dense subgraphs in large graphs plays a key task in varieties of
network analysis applications like community detection. Clique, as the densest
model, has been widely investigated. However, in practice, communities rarely
form as cliques for various reasons, e.g., data noise. Therefore, $k$-plex, --
graph with each vertex adjacent to all but at most $k$ vertices, is introduced
as a relaxed version of clique. Often, to better simulate cohesive communities,
an emphasis is placed on connected $k$-plexes with small $k$. In this paper, we
continue the research line of listing all maximal $k$-plexes and maximal
$k$-plexes of prescribed size. Our first contribution is algorithm ListPlex
that lists all maximal $k$-plexes in $O^*(\gamma^D)$ time for each constant
$k$, where $\gamma$ is a value related to $k$ but strictly smaller than 2, and
$D$ is the degeneracy of the graph that is far less than the vertex number $n$
in real-word graphs. Compared to the trivial bound of $2^n$, the improvement is
significant, and our bound is better than all previously known results. In
practice, we further use several techniques to accelerate listing $k$-plexes of
a given size, such as structural-based prune rules, cache-efficient data
structures, and parallel techniques. All these together result in a very
practical algorithm. Empirical results show that our approach outperforms the
state-of-the-art solutions by up to orders of magnitude.


title: On a Conjecture of Sun Zhi-Wei and Related Diophantine Equations

abstract:
  For any integer $m\ge0$, we recall that triangular numbers are those
$\mathbf{T}(m)=\frac{m(m+1)}{2}$. A conjecture of Sun Zhi-Wei states that an
integer $2^n\pm n$ with any $n>2$ can not be a triangular number. The
motivation of this work is to confirm this conjecture.


title: Computing Euler characteristics using quantum field theory

abstract:
  This paper explains how to use quantum field theory techniques to find formal
power series that encode the virtual Euler characteristics of
$\mathrm{Out}(F_n)$ and related graph complexes. Finding such power series was
a necessary step in the asymptotic analysis of $\chi(\mathrm{Out}(F_n))$
carried out in the authors' previous paper.


title:
On H1, H(curl) and H(sym Curl) finite elements for matrix-valued Curl
  problems

abstract:
  In this work we test the numerical behaviour of matrix-valued fields
approximated by finite element subspaces of $[\mathit{H}^1]^{3\times 3}$,
$[\mathit{H}(\mathrm{curl})]^3$ and $\mathit{H}(\mathrm{sym}\mathrm{Curl})$ for
a linear abstract variational problem connected to the relaxed micromorphic
model. The formulation of the corresponding finite elements is introduced,
followed by numerical benchmarks and our conclusions. The relaxed micromorphic
continuum model reduces the continuity assumptions of the classical
micromorphic model by replacing the full gradient of the microdistortion in the
free energy functional with the Curl. This results in a larger solution space
for the microdistortion, namely $[\mathit{H}(\mathrm{curl})]^3$ in place of the
classical $[\mathit{H}^1]^{3\times 3}$. The continuity conditions on the
microdistortion can be further weakened by taking only the symmetric part of
the Curl. As shown in recent works, the new appropriate space for the
microdistortion is then $\mathit{H}(\mathrm{sym}\mathrm{Curl})$. The newly
introduced space gives rise to a new differential complex for the relaxed
micromorphic continuum theory.


title: Effect of the neutron quadrupole distribution in the TaO$^+$ cation

abstract:
  We estimate the effect of the tensor parity nonconserving (PNC) interaction
in the $^{181}$TaO$^+$ molecular cation. It can be used to probe the unknown
quadrupole distribution of the neutrons inside the Ta nucleus. To this end, we
evaluate the constant which characterizes this interaction using the
relativistic Fock space coupled cluster theory for electronic structure
modelling. The $^3\Delta_1$ state of the TaO$^+$ cation which can be used to
measure the PNC effect is found to be the ground one in agreement with the
previous theoretical study.


title:
Designing a reliable and low-latency LoRaWAN solution for environmental
  monitoring in factories at major accident risk

abstract:
  In this article, we propose a reliable and low-latency Long Range Wide Area
Network (LoRaWAN) solution for environmental monitoring in factories at major
accident risk (FMAR). In particular, a low power wearable device for sensing
the toxic inflammable gases inside an industrial plant is designed with the
purpose of avoiding peculiar risks and unwanted accidents to occur. Moreover,
the detected data have to be urgently and reliably delivered to remote server
to trigger preventive immediate actions so as to improve the machine operation.
In these settings, LoRaWAN has been identified as the most proper
communications technology to the needs owing to the availability of off the
shelf devices and software. Hence, we assess the technological limits of
LoRaWAN in terms of latency and reliability and we propose a fully LoRaWAN
compliant solution to overcome these limits. The proposed solution envisages
coordinated end device (ED) transmissions through the use of Downlink Control
Packets (DCPs). Experimental results validate the proposed method in terms of
service requirements for the considered FMAR scenario.


title: Evolving Constructions for Balanced, Highly Nonlinear Boolean Functions

abstract:
  Finding balanced, highly nonlinear Boolean functions is a difficult problem
where it is not known what nonlinearity values are possible to be reached in
general. At the same time, evolutionary computation is successfully used to
evolve specific Boolean function instances, but the approach cannot easily
scale for larger Boolean function sizes. Indeed, while evolving smaller Boolean
functions is almost trivial, larger sizes become increasingly difficult, and
evolutionary algorithms perform suboptimally. In this work, we ask whether
genetic programming (GP) can evolve constructions resulting in balanced Boolean
functions with high nonlinearity. This question is especially interesting as
there are only a few known such constructions. Our results show that GP can
find constructions that generalize well, i.e., result in the required functions
for multiple tested sizes. Further, we show that GP evolves many equivalent
constructions under different syntactic representations. Interestingly, the
simplest solution found by GP is a particular case of the well-known indirect
sum construction.


title:
Verwey transition as evolution from electronic nematicity to trimerons
  via electron-phonon coupling

abstract:
  Understanding the driving mechanisms behind metal-insulator transitions
(MITs) is a critical step towards controlling material's properties. Since the
proposal of charge-order-induced MIT in magnetite Fe3O4 in 1939 by Verwey, the
nature of the charge order and its role in the transition have remained
elusive-a longstanding challenge in the studies of complex oxides. Recently, a
trimeron order was discovered in the low-temperature monoclinic structure of
Fe3O4; however, this novel charge order was argued not to be the cause but the
result of the Verwey transition in Fe3O4. Here we use electron diffraction to
unveil that a nematic charge order on particular Fe sites emerges in the
high-temperature cubic structure of bulk Fe3O4, and that upon cooling, a
competitive intertwining of charge and lattice orders leads to the emergence of
the Verwey transition. Moreover, MeV ultrafast electron diffraction (UED)
provides a dynamic measure of the strong coupling between photoexcited
electrons and the X3 phonon modes. Our findings discover a new type of
electronic nematicity in correlated materials and offer novel insights into the
Verwey transition mechanism in Fe3O4 via the electron-phonon coupling.


title: Pure gauge theories and spatial periodicity

abstract:
  Properties of pure gauge theories in thermal equilibrium as calculated via
standard functional integral treatments are mathematically identical to ground
state properties of a theory with spatially-periodic boundary conditions
imposed on the gauge fields. Such a theory has states that have no analog in a
theory in which only physical observables associated with gauge-invariant
operators are required to be periodic, rather than the gauge fields themselves;
these states are in topological sectors that do not exist in the unconstrained
theory. The topology arises because the boundary conditions in the functional
integral are gauge invariant on a cylinder but not in the unconstrained theory.


title:
Detection of low-energy X-rays with 1/2 and 1 inch LaBr3:Ce crystals
  read by SIPM arrays

abstract:
  LaBr3:Ce crystals, with photomultiplier or single SiPM readout (up to 3x3
mm$^2$) have been introduced for radiation imaging in medical physics. An R&D
was pursued with 1/2" and 1" LaBr3:Ce crystals, from different producers, to
realize compact large area detectors (up to some cm $^2$ area) with SiPM array
readout, aiming at high light yields, good energy resolution,good detector
linearity and fast time response for low-energy X-rays. The study was triggered
by the FAMU experiment at the RIKEN-RAL muon facility, aiming at a precise
measurement of the proton Zemach radius to solve the so-called "proton radius
puzzle". The goal is the detection of characteristic X-rays around 130 KeV.
Other applications may be foreseen for homeland security and $\gamma$-ray
astronomy. Results were obtained with a direct readout based on a CAEN V1730
FADC, with no need for an amplification stage. At the Cs$^{137}$ peak, energy
resolutions up to $\sim 3 \%$ were obtained, using a readout with Hamamatsu
SiPM arrays. These results compare well with best available results obtained
with a PMT readout.


title: Finding a Battleship of Uncertain Shape

abstract:
  Motivated by a game of Battleship, we consider the problem of efficiently
hitting a ship of an uncertain shape within a large playing board. Formally, we
fix a dimension $d\in\{1,2\}$. A ship is a subset of $\mathbb{Z}^d$. Given a
family $F$ of ships, we say that an infinite subset $X\subset\mathbb{Z}^d$ of
the cells pierces $F$, if it intersects each translate of each ship in $F$ (by
a vector in $\mathbb{Z}^d$). In this work, we study the lowest possible
(asymptotic) density $\pi(F)$ of such a piercing subset. To our knowledge, this
problem has previously been studied only in the special case $|F|=1$ (a single
ship). As our main contribution, we present a formula for $\pi(F)$ when $F$
consists of 2 ships of size 2 each, and we identify the toughest families in
several other cases. We also implement an algorithm for finding $\pi(F)$ in 1D.


title: Multi-Fleet Platoon Matching: A Game-Theoretic Approach

abstract:
  We consider the platoon matching problem for a set of trucks with the same
origin, but different destinations. It is assumed that the vehicles benefit
from traveling in a platoon for instance through reduced fuel consumption. The
vehicles belong to different fleet owners and their strategic interaction is
modeled as a non-cooperative game where the vehicle actions are their departure
times. Each truck has a preferred departure time and its utility function is
defined as the difference between its benefit from platooning and the cost of
deviating from its preferred departure time. We show that the platoon matching
game is an exact potential game. An algorithm based on best response dynamics
is proposed for finding a Nash equilibrium of the game. At a Nash equilibrium,
vehicles with the same departure time are matched to form a platoon. Finally,
the total fuel reduction at the Nash equilibrium is studied and compared with
that of a cooperative matching solution where a common utility function for all
vehicles is optimized.


title: Frame-related Sequences in Chains and Scales of Hilbert Spaces

abstract:
  Frames for Hilbert spaces are interesting for mathematicians but also
important for applications e.g. in signal analysis and in physics. Both in
mathematics and physics it is natural to consider a full scale of spaces, and
not only a single one. In this paper, we study how certain frame-related
properties, as completeness or the property of being a (semi-)frame, of a
certain sequence in one of the spaces propagate to other spaces in a scale. We
link that to the properties of the respective frame-related operators, like
analysis or synthesis. We start with a detailed survey of the theory of Hilbert
chains. Using a canonical isomorphism the properties of frame sequences are
naturally preserved between different spaces. We also show that some results
can be transferred if the original sequence is considered, in particular that
the upper semi-frame property is kept in larger spaces, while the lower one to
smaller ones. This leads to a negative result: a sequence can never be a frame
for two Hilbert spaces of the scale if the scale is non-trivial, i.e. spaces
are not equal.


title: The Strong Homotopy Structure of BRST Reduction

abstract:
  In this paper we propose a reduction scheme for polydifferential operators
phrased in terms of $L_\infty$-morphisms. The desired reduction
$L_\infty$-morphism has been obtained by applying an explicit version of the
homotopy transfer theorem. Finally, we prove that the reduced star product
induced by this reduction $L_\infty$-morphism and the reduced star product
obtained via the formal Koszul complex are equivalent.


title: Improving Rating and Relevance with Point-of-Interest Recommender System

abstract:
  The recommendation of points of interest (POIs) is essential in
location-based social networks. It makes it easier for users and locations to
share information. Recently, researchers tend to recommend POIs by treating
them as large-scale retrieval systems that require a large amount of training
data representing query-item relevance. However, gathering user feedback in
retrieval systems is an expensive task. Existing POI recommender systems make
recommendations based on user and item (location) interactions solely. However,
there are numerous sources of feedback to consider. For example, when the user
visits a POI, what is the POI is about and such. Integrating all these
different types of feedback is essential when developing a POI recommender. In
this paper, we propose using user and item information and auxiliary
information to improve the recommendation modelling in a retrieval system. We
develop a deep neural network architecture to model query-item relevance in the
presence of both collaborative and content information. We also improve the
quality of the learned representations of queries and items by including the
contextual information from the user feedback data. The application of these
learned representations to a large-scale dataset resulted in significant
improvements.


title: OmniSyn: Synthesizing 360 Videos with Wide-baseline Panoramas

abstract:
  Immersive maps such as Google Street View and Bing Streetside provide
true-to-life views with a massive collection of panoramas. However, these
panoramas are only available at sparse intervals along the path they are taken,
resulting in visual discontinuities during navigation. Prior art in view
synthesis is usually built upon a set of perspective images, a pair of
stereoscopic images, or a monocular image, but barely examines wide-baseline
panoramas, which are widely adopted in commercial platforms to optimize
bandwidth and storage usage. In this paper, we leverage the unique
characteristics of wide-baseline panoramas and present OmniSyn, a novel
pipeline for 360{\deg} view synthesis between wide-baseline panoramas. OmniSyn
predicts omnidirectional depth maps using a spherical cost volume and a
monocular skip connection, renders meshes in 360{\deg} images, and synthesizes
intermediate views with a fusion network. We demonstrate the effectiveness of
OmniSyn via comprehensive experimental results including comparison with the
state-of-the-art methods on CARLA and Matterport datasets, ablation studies,
and generalization studies on street views. We envision our work may inspire
future research for this unheeded real-world task and eventually produce a
smoother experience for navigating immersive maps.


title: Strong spatial mixing for repulsive point processes

abstract:
  We prove that a Gibbs point process interacting via a finite-range, repulsive
potential $\phi$ exhibits a strong spatial mixing property for activities
$\lambda < e/\Delta_{\phi}$, where $\Delta_{\phi}$ is the potential-weighted
connective constant of $\phi$, defined recently in [MP21]. Using this we derive
several analytic and algorithmic consequences when $\lambda$ satisfies this
bound: (1) We prove new identities for the infinite volume pressure and surface
pressure of such a process (and in the case of the surface pressure establish
its existence). (2) We prove that local block dynamics for sampling from the
model on a box of volume $N$ in $\mathbb R^d$ mixes in time $O(N \log N)$,
giving efficient randomized algorithms to approximate the partition function of
and approximately sample from these models. (3) We use the above identities and
algorithms to give efficient approximation algorithms for the pressure and
surface pressure.


title: Simulation of the propagation of CR air shower cores in ice

abstract:
  Currently new radio detection techniques are being explored to detect
astrophysical neutrinos beyond the PeV scale interacting in polar ice. Due to
the long attenuation length of radio waves in a medium, it can be expected that
such instruments will also be sensitive to the radio emission of cosmic ray air
showers. Furthermore, cosmic ray air showers hitting a high-altitude layer of
ice will initiate an in-ice particle cascade, also leading to radio emission.
We present the first results of detailed simulations of the in-ice continuation
of these cosmic-ray-induced particle cascades, using a combination of the
CORSIKA Monte Carlo code and the Geant4 simulation toolkit. We give an overview
of the general features of such particle cascades and present a
parameterization in terms of Xmax of the longitudinal and lateral particle
distributions. We discuss the feasibility of observing the in-ice particle
cascades, both through the detection of the Askaryan radio emission as well as
by using the RADAR reflection technique. Based on these results we find that
the expected signals from the continuation of in-ice cosmic-ray induced
particle cascades will be very similar to neutrino signals. This means a
thorough understanding of these events is necessary in the search for neutrino
candidates, while it also promises an interesting in-situ natural calibration
source.


title:
Bridging Koopman Operator and time-series auto-correlation based
  Hilbert-Schmidt operator

abstract:
  Given a stationary continuous-time process $f(t)$, the Hilbert-Schmidt
operator $A_{\tau}$ can be defined for every finite
$\tau$\cite{Vautard1989SingularSA}. Let $\lambda_{\tau,i}$ be the eigenvalues
of $A_{\tau}$ with descending order. In this article, a Hilbert space
$\mathcal{H}_f$ and the (time-shift) continuous one-parameter semigroup of
isometries $\mathcal{K}^s$ are defined. Let $\{v_i, i\in\mathbb{N}\}$ be the
eigenvectors of $\mathcal{K}^s$ for all $s\geq 0$. Let $f =
\displaystyle\sum_{i=1}^{\infty}a_iv_i + f^{\perp}$ be the orthogonal
decomposition with descending $|a_i|$. We prove that
$\displaystyle\lim_{\tau\to\infty}\lambda_{\tau,i} = |a_i|^2$. The continuous
one-parameter semigroup $\{\mathcal{K}^s: s\geq 0\}$ is equivalent, almost
surely, to the classical Koopman one-parameter semigroup defined on
$L^2(X,\nu)$, if the dynamical system is ergodic and has invariant measure
$\nu$ on the phase space $X$.


title:
Ensemble Conformalized Quantile Regression for Probabilistic Time Series
  Forecasting

abstract:
  This paper presents a novel probabilistic forecasting method called ensemble
conformalized quantile regression (EnCQR). EnCQR constructs distribution-free
and approximately marginally valid prediction intervals (PIs), is suitable for
nonstationary and heteroscedastic time series data, and can be applied on top
of any forecasting model, including deep learning architectures that are
trained on long data sequences. EnCQR exploits a bootstrap ensemble estimator,
which enables the use of conformal predictors for time series by removing the
requirement of data exchangeability. The ensemble learners are implemented as
generic machine learning algorithms performing quantile regression, which allow
the length of the PIs to adapt to local variability in the data. In the
experiments, we predict time series characterized by a different amount of
heteroscedasticity. The results demonstrate that EnCQR outperforms models based
only on quantile regression or conformal prediction, and it provides sharper,
more informative, and valid PIs.


title: Physical Layer Authentication for LEO Satellite Constellations

abstract:
  Physical layer authentication (PLA) is the process of claiming identity of a
node based on its physical layer characteristics such as channel fading or
hardware imperfections. In this work, we propose a novel PLA method for the
inter-satellite communication links (ISLs) of the LEO satellites. In the
proposed PLA method, multiple receiving satellites validate the identity of the
transmitter by comparing the Doppler frequency measurements with the reference
mobility information of the legitimate transmitter and then fuse their decision
considering the selected decision rule. Analytical expressions are obtained for
the spoofing detection probability and false alarm probability of the fusion
methods. Numerically obtained high authentication performance results pave the
way to a novel and easily integrable authentication mechanism for the LEO
satellite networks.


title: A Wavelet-based Dual-stream Network for Underwater Image Enhancement

abstract:
  We present a wavelet-based dual-stream network that addresses color cast and
blurry details in underwater images. We handle these artifacts separately by
decomposing an input image into multiple frequency bands using discrete wavelet
transform, which generates the downsampled structure image and detail images.
These sub-band images are used as input to our dual-stream network that
incorporates two sub-networks: the multi-color space fusion network and the
detail enhancement network. The multi-color space fusion network takes the
decomposed structure image as input and estimates the color corrected output by
employing the feature representations from diverse color spaces of the input.
The detail enhancement network addresses the blurriness of the original
underwater image by improving the image details from high-frequency sub-bands.
We validate the proposed method on both real-world and synthetic underwater
datasets and show the effectiveness of our model in color correction and blur
removal with low computational complexity.


title: Geometric Bounds on the Power of Adiabatic Thermal Machines

abstract:
  We analyze the performance of slowly driven meso- and micro-scale
refrigerators and heat engines that operate between two thermal baths with
small temperature difference. Using a general scaling argument, we show that
such devices can work arbitrarily close to their Carnot limit only if
heat-leaks between the baths are fully suppressed. Their power output is then
subject to a universal geometric bound that decays quadratically to zero at the
Carnot limit. This bound can be asymptotically saturated in the quasi-static
limit if the driving protocols are suitably optimized and the temperature
difference between the baths goes to zero with the driving frequency. These
results hold under generic conditions for any thermodynamically consistent
dynamics admitting a well-defined adiabatic-response regime and a generalized
Onsager symmetry. For illustration, we work out models of a qubit-refrigerator
and a coherent charge pump operating as a cooling device.


title: On generalized cyclotomic derivations

abstract:
  In this article we study the field of rational constants and Darboux
polynomials of a generalized cyclotomic $K$-derivation $d$ of $K[X]$. It is
shown that $d$ is without Darboux polynomials if and only if $K(X)^d=K$. Result
is also studied in the tensor product of polynomial algebras.


title:
QuerTCI: A Tool Integrating GitHub Issue Querying with Comment
  Classification

abstract:
  Issue tracking systems enable users and developers to comment on problems
plaguing a software system. Empirical Software Engineering (ESE) researchers
study (open-source) project issues and the comments and threads within to
discover -- among others -- challenges developers face when, e.g.,
incorporating new technologies, platforms, and programming language constructs.
However, issue discussion threads accumulate over time and thus can become
unwieldy, hindering any insight that researchers may gain. While existing
approaches alleviate this burden by classifying issue thread comments, there is
a gap between searching popular open-source software repositories (e.g., those
on GitHub) for issues containing particular keywords and feeding the results
into a classification model. In this paper, we demonstrate a research
infrastructure tool called QuerTCI that bridges this gap by integrating the
GitHub issue comment search API with the classification models found in
existing approaches. Using queries, ESE researchers can retrieve GitHub issues
containing particular keywords, e.g., those related to a certain programming
language construct, and subsequently classify the kinds of discussions
occurring in those issues. Using our tool, our hope is that ESE researchers can
uncover challenges related to particular technologies using certain keywords
through popular open-source repositories more seamlessly than previously
possible. A tool demonstration video may be found at:
https://youtu.be/fADKSxn0QUk.


title: Tutorial: Macroscopic QED and vacuum forces

abstract:
  This tutorial introduces the theory of macroscopic QED, where a Hamiltonian
is found that represents the electromagnetic field interacting with a
dispersive, dissipative material. Using a one dimensional theory as motivation,
we build up the more cumbersome three dimensional theory. Then considering the
extension of this theory to moving materials, where the material response
changes due to both the Doppler effect and the mixing of electric and magnetic
responses, it is shown that one gets the theory of quantum electromagnetic
forces for free. We finish by applying macroscopic QED to reproduce Pendry's
expression for the quantum friction force between sliding plates.


title:
Residual-based error estimation and adaptivity for stabilized immersed
  isogeometric analysis using truncated hierarchical B-splines

abstract:
  We propose an adaptive mesh refinement strategy for immersed isogeometric
analysis, with application to steady heat conduction and viscous flow problems.
The proposed strategy is based on residual-based error estimation, which has
been tailored to the immersed setting by the incorporation of appropriately
scaled stabilization and boundary terms. Element-wise error indicators are
elaborated for the Laplace and Stokes problems, and a THB-spline-based local
mesh refinement strategy is proposed. The error estimation .and adaptivity
procedure is applied to a series of benchmark problems, demonstrating the
suitability of the technique for a range of smooth and non-smooth problems. The
adaptivity strategy is also integrated in a scan-based analysis workflow,
capable of generating reliable, error-controlled, results from scan data,
without the need for extensive user interactions or interventions.


title: Linear Tur\'{a}n numbers of acyclic quadruple systems

abstract:
  A linear $r$-uniform hypergraph is called acycilc if it can be constructed
starting from one single edge then at each step adding a new edge that
intersect the union of the vertices of the previous edges in at most one
vertex. Recently, Gy\'{a}rf\'{a}s, Ruszink\'{o} and S\'{a}rk\''{o}zy initiated
the study of the linear Tur\'{a}n numbers of acyclic linear triple systems. In
this paper, we extend their results to linear quadruple systems. Here, we
concentrate on small trees, paths and matchings. For the case of small trees,
we find that for a linear tree $T$, $ex^{lin}_{4}(n,T)$ relates to difficult
problems on Steiner system $S(2,4,n)$ For example, we show that
$ex^{lin}_{4}(n, P_4)\le \frac{5n}{4}$ with equality holds if and only if the
linear quadruple system is the disjoint union of $S(2,4,16)$. Denote by
$E^{+}_{4}$ the linear tree consisting of three pairwise disjoint quadruples
and a fourth one intersecting all of them. We prove that
$12\lfloor\frac{n-4}{9}\rfloor\le ex^{lin}_{4}(n, E^{+}_4)\le 2n$. Denote by
$M_k$ and $P_k$ the set of $k$ pairwise disjoint quadruples and the linear path
with $k$ quadruples, respectively. For the case of paths, we show that
$ex^{lin}_{4}(n, P_k)\le 2.5kn$. For the case of matchings, we prove that for
fixed $k$ and sufficiently large $n$, $ex^{lin}_{4}(n, M_k)=g(n,k)$ where
$g(n,k$) denotes the maximum number of quadruples that can intersect $k-1$
vertices in a linear quadruple system on $n$ vertices.


title:
Mapping molecular complexes with Super-Resolution Microscopy and
  Single-Particle Analysis

abstract:
  Understanding the structure of supramolecular complexes provides insight into
their functional capabilities and how they can be modulated in the context of
disease. Super-resolution microscopy (SRM) excels in performing this task by
resolving ultrastructural details at the nanoscale with molecular specificity.
However, technical limitations, such as underlabelling, preclude its ability to
provide complete structures. Single-particle analysis (SPA) overcomes this
limitation by combining information from multiple images of identical
structures and producing an averaged model, effectively enhancing the
resolution and coverage of image reconstructions. This review highlights
important studies using SRM-SPA, demonstrating how it broadens our knowledge by
elucidating features of key biological structures with unprecedented detail.


title:
Can DtN and GenEO coarse spaces be sufficiently robust for heterogeneous
  Helmholtz problems?

abstract:
  Numerical solution of heterogeneous Helmholtz problems presents various
computational challenges, with descriptive theory remaining out of reach for
many popular approaches. Robustness and scalability are key for practical and
reliable solvers in large-scale applications, especially for large wave number
problems. In this work we explore the use of a GenEO-type coarse space to build
a two-level additive Schwarz method applicable to highly indefinite Helmholtz
problems. Through a range of numerical tests on a 2D model problem, discretised
by finite elements on pollution-free meshes, we observe robust convergence,
iteration counts that do not increase with the wave number, and good
scalability of our approach. We further provide results showing a favourable
comparison with the DtN coarse space. Our numerical study shows promise that
our solver methodology can be effective for challenging heterogeneous
applications.


title: On the random Chowla conjecture

abstract:
  We show that for a Steinhaus random multiplicative function
$f:\mathbb{N}\to\mathbb{D}$ and any polynomial $P(x)\in\mathbb{Z}[x]$ of
$\text{deg}\ P\ge 2$ which is not of the form $w(x+c)^{d}$ for some $w\in
\mathbb{Z}$, $c\in \mathbb{Q}$, we have \[\frac{1}{\sqrt{x}}\sum_{n\le x}
f(P(n)) \xrightarrow{d} \mathcal{CN}(0,1),\] where $\mathcal{CN}(0,1)$ is the
standard complex Gaussian distribution with mean $0$ and variance $1.$ This
confirms a conjecture of Najnudel in a strong form. We further show that there
almost surely exist arbitrary large values of $x\ge 1,$ such that $$|\sum_{n\le
x} f(P(n))| \gg_{\text{deg}\ P} \sqrt{x} (\log \log x)^{1/2},$$ for any
polynomial $P(x)\in\mathbb{Z}[x]$ with $\text{deg}\ P\ge 2,$ which is not a
product of linear factors (over $\mathbb{Q}$). This matches the bound predicted
by the law of the iterated logarithm. Both of these results are in contrast
with the well-known case of linear phase $P(n)=n,$ where the partial sums are
known to behave in a non-Gaussian fashion and the corresponding sharp
fluctuations are speculated to be $O(\sqrt{x}(\log \log
x)^{\frac{1}{4}+\varepsilon})$ for any $\varepsilon>0$.


title:
Asymptotic symmetries in Carrollian theories of gravity with a negative
  cosmological constant

abstract:
  Asymptotic symmetries of electric and magnetic Carrollian gravitational
theories with a negative cosmological constant $\Lambda$ are analyzed in 3+1
space-time dimensions. In the magnetic theory, the asymptotic symmetry algebra
is given by the conformal Carroll algebra in three dimensions, which is
infinite-dimensional and isomorphic to the BMS$_{4}$ algebra. These results are
in full agreement with holographic expectations, providing a new framework for
the study of "Carrollian holography." On the contrary, in the case of the
electric theory, the presence of a negative $\Lambda$ turns out to be
incompatible with a consistent set of asymptotic conditions, that can be traced
back to the absence of a sensible ground state configuration. This can be
improved if the Carrollian theory obtained from an electric contraction of
Euclidean General Relativity is considered. In this case, asymptotic conditions
can be constructed with an asymptotic symmetry algebra given by
$so\left(1,4\right)$. However, it is shown that the space of spherically
symmetric solutions of this theory is degenerate.


title: Neutron interference from a split-crystal interferometer

abstract:
  We report the first successful operation of a neutron interferometer having a
separate beam recombining crystal. We achieved this result at the neutron
interferometry setup S18 at the ILL in Grenoble by a collaboration between TU
Wien, ILL Grenoble and INRIM Torino. While previous interferometers were
machined out of a single crystal block, we managed to align two crystals on
nanoradian and picometer scales, as required to obtain neutron interference. As
a decisive proof of principle demonstration, it opens the door to a new
generation of neutron interferometers and exciting applications.


title:
Proposal for transduction between microwave and optical photons using
  $\mathrm{^{167}Er}$:YSO

abstract:
  Efficient transduction devices that reversibly convert optical and microwave
quantum signals into each other are essential for integrating different
technologies. Rare-earth ions in solids, and in particular Erbium ions, with
both optical and microwave addressable transitions are promising candidates for
designing transducers. We propose a microwave-to-optical quantum transducer
scheme based on the dark state protocol in $\mathrm{^{167}Er}$ doped into
yttrium orthosilicate (YSO) at zero external magnetic fields. Zero-field
operation is beneficial for superconducting resonators that can incur extra
losses in magnetic fields. By calculating the fidelity and efficiency of the
transducer, considering the most important imperfections, we show that an
efficient conversion is possible with a high fidelity. We also investigate the
microwave transitions of $\mathrm{^{167}Er}$:YSO that can be used for the
transducer protocol.


title: Realistic Blur Synthesis for Learning Image Deblurring

abstract:
  Training learning-based deblurring methods demands a tremendous amount of
blurred and sharp image pairs. Unfortunately, existing synthetic datasets are
not realistic enough, and deblurring models trained on them cannot handle real
blurred images effectively. While real datasets have recently been proposed,
they provide limited diversity of scenes and camera settings, and capturing
real datasets for diverse settings is still challenging. This paper analyzes
various factors that introduce differences between real and synthetic blurred
images, and presents a novel blur synthesis pipeline to synthesize more
realistic blur. We also present RSBlur, a novel dataset with real blurred
images and the corresponding sharp image sequences to enable detailed analysis
on the differences between real and synthetic blur. With our blur synthesis
pipeline and RSBlur dataset, we reveal the effects of different factors in blur
synthesis. We also show that our synthesis method can improve the deblurring
performance on real blurred images.


title: A Survey of Knowledge-Intensive NLP with Pre-Trained Language Models

abstract:
  With the increasing of model capacity brought by pre-trained language models,
there emerges boosting needs for more knowledgeable natural language processing
(NLP) models with advanced functionalities including providing and making
flexible use of encyclopedic and commonsense knowledge. The mere pre-trained
language models, however, lack the capacity of handling such
knowledge-intensive NLP tasks alone. To address this challenge, large numbers
of pre-trained language models augmented with external knowledge sources are
proposed and in rapid development. In this paper, we aim to summarize the
current progress of pre-trained language model-based knowledge-enhanced models
(PLMKEs) by dissecting their three vital elements: knowledge sources,
knowledge-intensive NLP tasks, and knowledge fusion methods. Finally, we
present the challenges of PLMKEs based on the discussion regarding the three
elements and attempt to provide NLP practitioners with potential directions for
further research.


title:
Experimental observations on interaction between a root and droplets in
  relation to aeroponic agriculture

abstract:
  Aeroponics or Soil-less agriculture is a relatively new and recent type of
practice, where plants are grown without soil while nutrient-rich water is
provided via an atomized spray system to the suspended roots. Spray nozzles are
easy-to-use in supplying water (and fertilizers) to (mainly) the roots and root
hairs of the desired crop (or plant) for production. We characterize a spray
nozzle delivering water vertically above against the gravity by measuring,
experimentally, its (a) spray drift, (b) spray height, (c) maximum spray angle,
(d) spray width, and (e) droplets sizes. Experiments were carried out at
different inlet pressures and a majority of the above mentioned parameters were
obtained by processing the images captured using optical (or high speed)
camera, sometimes along a plane lighted by a high-power laser source. We also
studied the spray (or jet) behaviour at different vertical heights and
different horizontal planes using a unique polythene sponge method. We studied
the mass flow rate, the absorption rate, and droplet size dynamics (as a
function of time and pressure) using this method. The water drop/droplet
interaction was also studied in the case of simpler porous and impervious
surfaces as well. We believe that this study can be extrapolated to other
nozzles (especially sprays) to obtain similar characteristic parameters. This
study, hence, is critical in selecting the desired spray system for a given
canopy and is also expected to be of some use in controlled agricultural
practices such as in greenhouses and apartment rooms.


title:
mmWave Communications for Indoor Dense Spaces: Ray-Tracing Based Channel
  Characterization and Performance Comparison

abstract:
  In this paper, the indoor dense space (IDS) channel at 28 GHz is
characterized through extensive Ray-Tracing (RT) simulations. We consider IDS
as a specific type of indoor environment with confined geometry and packed with
humans, such as aircraft cabins and train wagons. Based on RT simulations, we
characterize path loss, shadow fading, root-mean-square delay spread, Rician
K-factor, azimuth/elevation angular spread of arrival/departure considering
different RT simulation scenarios of the fuselage geometry, material, and human
presence. While the large-scale fading parameters are similar to the
state-of-the-art channel models, the small-scale fading parameters demonstrate
richer multipath scattering in IDS, resulting in poorer bit error rate
performance in comparison to the 3GPP indoor channel model.


title: Almost-Riemannian manifolds do not satisfy the $\mathsf{CD}$ condition

abstract:
  The Lott-Sturm-Villani curvature-dimension condition $\mathsf{CD}(K,N)$
provides a synthetic notion for a metric-measure space to have curvature
bounded from below by $K$ and dimension bounded from above by $N$. It was
proved by Juillet that a large class of \sr manifolds do not satisfy the
$\mathsf{CD}(K,N)$ condition, for any $K\in\mathbb R$ and $N\in(1,\infty)$.
However, his result does not cover the case of almost-Riemannian manifolds. In
this paper, we address the problem of disproving the $\mathsf{CD}$ condition in
this setting, providing a new strategy which allows us to contradict the
$1$-dimensional version of the $\mathsf{CD}$ condition. In particular, we prove
that $2$-dimensional almost-Riemannian manifolds and strongly regular
almost-Riemannian manifolds do not satisfy the $\mathsf{CD}(K,N)$ condition for
any $K\in\mathbb R$ and $N\in(1,\infty)$.


title:
Machine Learning Approaches to Solar-Flare Forecasting: Is Complex
  Better?

abstract:
  Recently, there has been growing interest in the use of machine-learning
methods for predicting solar flares. Initial efforts along these lines employed
comparatively simple models, correlating features extracted from observations
of sunspot active regions with known instances of flaring. Typically, these
models have used physics-inspired features that have been carefully chosen by
experts in order to capture the salient features of such magnetic field
structures. Over time, the sophistication and complexity of the models involved
has grown. However, there has been little evolution in the choice of feature
sets, nor any systematic study of whether the additional model complexity is
truly useful. Our goal is to address these issues. To that end, we compare the
relative prediction performance of machine-learning-based, flare-forecasting
models with varying degrees of complexity. We also revisit the feature set
design, using topological data analysis to extract shape-based features from
magnetic field images of the active regions. Using hyperparameter training for
fair comparison of different machine-learning models across different feature
sets, we show that simpler models with fewer free parameters \textit{generally
perform better than more-complicated models}, ie., powerful machinery does not
necessarily guarantee better prediction performance. Secondly, we find that
\textit{abstract, shape-based features contain just as much useful
information}, for the purposes of flare prediction, as the set of hand-crafted
features developed by the solar-physics community over the years. Finally, we
study the effects of dimensionality reduction, using principal component
analysis, to show that streamlined feature sets, overall, perform just as well
as the corresponding full-dimensional versions.


title:
The Column Densities of Molecular Gas across Cosmic Time: Bridging
  Observations and Simulations

abstract:
  Observations of the cosmic evolution of different gas phases across time
indicate a marked increase in the molecular gas mass density towards $z\sim
2-3$. Such a transformation implies an accompanied change in the global
distribution of molecular hydrogen column densities ($N_{\rm{H_2}}$). Using
observations by PHANGS-ALMA/SDSS and simulations by GRIFFIN/IllustrisTNG we
explore the evolution of this H$_2$ column density distribution function
[$f(N_{\rm{H}_2})$]. The H$_2$ (and HI) column density maps for TNG50 and
TNG100 are derived in post-processing and are made available through the
IllustrisTNG online API. The shape and normalization of $f(N_{\rm{H}_2})$ of
individual main-sequence star-forming galaxies are correlated with the star
formation rate (SFR), stellar mass (${M_*}$), and H$_2$ mass ($M_{\rm{H}_2}$)
in both observations and simulations. TNG100, combined with H$_2$
post-processing models, broadly reproduces observations, albeit with
differences in slope and normalization. Also, an analytically modelled $f(N)$,
based on exponential gas disks, matches well with the simulations. The GRIFFIN
simulation gives first indications that the slope of $f(N_{\rm{H}_2})$ might
not majorly differ when including non-equilibrium chemistry in simulations. The
$f(N_{\rm{H}_2})$ by TNG100 implies that higher molecular gas column densities
are reached at $z=3$ than at $z=0$. Further, denser regions contribute more to
the molecular mass density at $z=3$. Finally, H$_2$ starts dominating compared
to HI only at column densities above log($N_{\rm{H}_2} / \rm{cm}^{-2}) \sim
21.8-22$ at both redshifts. These results imply that neutral atomic gas is an
important contributor to the overall cold gas mass found in the ISM of galaxies
including at densities typical for molecular clouds at $z=0$ and $z=3$.


title:
VETTAM: A scheme for radiation hydrodynamics with adaptive mesh
  refinement using the variable Eddington tensor method

abstract:
  We present Variable Eddington Tensor-closed Transport on Adaptive Meshes
(\texttt{VETTAM}), a new algorithm to solve the equations of radiation
hydrodynamics (RHD) with support for adaptive mesh refinement (AMR) in a
frequency-integrated, two-moment formulation. The method is based on a
non-local Variable Eddington Tensor (VET) closure computed with a hybrid
characteristics scheme for ray tracing. We use a Godunov method for the
hyperbolic transport of radiation with an implicit backwards-Euler temporal
update to avoid the explicit timestep constraint imposed by the light-crossing
time, and a fixed-point Picard iteration scheme to handle the nonlinear
gas-radiation exchange term, with the two implicit update stages jointly
iterated to convergence. We also develop a modified wave-speed correction
method for AMR, which we find to be crucial for obtaining accurate results in
the diffusion regime. We demonstrate the robustness of our scheme with a suite
of pure radiation and RHD tests, and show that it successfully captures the
streaming, static diffusion, and dynamic diffusion regimes and the spatial
transitions between them, casts sharp shadows, and yields accurate results for
rates of momentum and energy exchange between radiation and gas. A comparison
between different closures for the radiation moment equations, with the
Eddington approximation (0th-moment closure) and the $M_1$ approximation
(1st-moment closure), demonstrates the advantages of the VET method (2nd-moment
closure) over the simpler closure schemes. \texttt{VETTAM} has been coupled to
the AMR \texttt{FLASH} (magneto-)hydrodynamics code and we summarize by
reporting performance features and bottlenecks of our implementation.


title: Spiral Trajectories for Building Inspection with Quadrotors

abstract:
  Inspection of large building is an important task since it can prevent
material and human losses. A cheap and fast way to do the inspections is by
sensors mounted on quadrotor vehicles. The challenge here is to compute a
trajectory so that the building is completely observed while this same
trajectory can be followed by the quadrotor in a smooth way. To address the
problem, we propose a method that receives a 2.5D model of the target building
and computes a smooth trajectory that can be followed by the quadrotor
controller. The computed trajectory is a Fourier series that matches the
desired behaviour. Our method has been tested in simulation and we have
compared it against polynomial trajectories. Our result show that the method is
efficient and can be applied to different building shapes.


title: Selling Information in Competitive Environments

abstract:
  We consider a setting where data buyers compete in a game of incomplete
information, about which a data seller owns some payoff relevant information.
We formulate the problem facing the seller as a joint information and mechanism
design problem: deciding which information to sell, while at the same time
eliciting the private value types of the buyers and collecting payments. We
derive the welfare- and revenue-optimal mechanisms for a class of binary games.
Our results reveal some important features of selling information in
competitive environments: (i) the negative externalities arising from
competition among buyers increase the profitability of selling information to
one of the buyers exclusively; (ii) in order for the buyers to follow the
seller's action recommendations, the degree of information exclusivity must be
limited; (iii) the same obedience constraints also limit the distortions in the
allocation of information that can be introduced by a monopolist seller; (iv)
as competition across buyers becomes fiercer, these limitations become more
severe, weakening the impact of market power on the equilibrium allocation of
information.


title:
Commentary: analyzing binary data using MCPMod when zero counts are
  expected

abstract:
  Bretz et al (2005) proposed multiple Comparison Procedure and Modeling
(MCPMod) method to design and analyze dose-finding study. Pinheiro (2014) then
generalized it to various types of endpoint, including but not limited to
binary endpoint, survival endpoint, count data, and longitudinal data. Pinheiro
(2013) recommended to use the estimated covariance matrix from the observed
data to recalculate the optimal contrast and the critical value of the test For
many phase II studies it is common to have small sample sizes per arm with low
placebo response rates jointly. Under such circumstances, it cannot be excluded
to have a zero count observed. For example, when the placebo response rate is
10%, there is about 4% chance to observe zero responders in the placebo group,
or other dose group(s), which has a similar response rate as placebo. In this
manuscript, we would like to illustrate the potential problem of Pinheiro
(2013) using a case study and simulations. An alternative method using Firth's
logistic regression was evaluated to get a stable estimate of response for each
dose group. In addition, we evaluated two options to address the issue with
problematic contrast coefficients.