title:
Nonlocal transport equations in multiscale media. Modeling,
  dememorization, and discretizations

abstract:
  In this paper, we consider a class of convection-diffusion equations with
memory effects. These equations arise as a result of homogenization or
upscaling of linear transport equations in heterogeneous media and play an
important role in many applications. First, we present a dememorization
technique for these equations. We show that the convection-diffusion equations
with memory effects can be written as a system of standard
convection-diffusion-reaction equations. This allows removing the memory term
and simplifying the computations. We consider a relation between dememorized
equations and micro-scale equations, which do not contain memory terms. We note
that dememorized equations differ from micro-scale equations and constitute a
macroscopic model. Next, we consider both implicit and partially explicit
methods. The latter is introduced for problems in multiscale media with
high-contrast properties. Because of high contrast, explicit methods are
restrictive and require time steps that are very small (scales as the inverse
of the contrast). We show that, by appropriately decomposing the space, we can
treat only a few degrees of freedom implicitly and the remaining degrees of
freedom explicitly. We present a stability analysis. Numerical results are
presented that confirm our theoretical findings of partially explicit schemes
applied to dememorized systems of equations.


title:
1-D CNN based Acoustic Scene Classification via Reducing Layer-wise
  Dimensionality

abstract:
  This paper presents an alternate representation framework to commonly used
time-frequency representation for acoustic scene classification (ASC). A raw
audio signal is represented using a pre-trained convolutional neural network
(CNN) using its various intermediate layers. The study assumes that the
representations obtained from the intermediate layers lie in low-dimensions
intrinsically. To obtain low-dimensional embeddings, principal component
analysis is performed, and the study analyzes that only a few principal
components are significant. However, the appropriate number of significant
components are not known. To address this, an automatic dictionary learning
framework is utilized that approximates the underlying subspace. Further, the
low-dimensional embeddings are aggregated in a late-fusion manner in the
ensemble framework to incorporate hierarchical information learned at various
intermediate layers. The experimental evaluation is performed on publicly
available DCASE 2017 and 2018 ASC datasets on a pre-trained 1-D CNN, SoundNet.
Empirically, it is observed that deeper layers show more compression ratio than
others. At 70% compression ratio across different datasets, the performance is
similar to that obtained without performing any dimensionality reduction. The
proposed framework outperforms the time-frequency representation based methods.


title:
Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers
  and Ordinal Regression

abstract:
  This paper outlines the system using which team Nowruz participated in
SemEval 2022 Task 7 Identifying Plausible Clarifications of Implicit and
Underspecified Phrases for both subtasks A and B. Using a pre-trained
transformer as a backbone, the model targeted the task of multi-task
classification and ranking in the context of finding the best fillers for a
cloze task related to instructional texts on the website Wikihow.
  The system employed a combination of two ordinal regression components to
tackle this task in a multi-task learning scenario. According to the official
leaderboard of the shared task, this system was ranked 5th in the ranking and
7th in the classification subtasks out of 21 participating teams. With
additional experiments, the models have since been further optimised.


title: Cherenkov radiation with massive bosons and quantum friction

abstract:
  This work is devoted to several translation-invariant models in
non-relativistic quantum field theory (QFT), describing a non-relativistic
quantum particle interacting with a quantized relativistic field of bosons. In
this setting, we aim at the rigorous study of Cherenkov radiation or friction
effects at small disorder, which amounts to the metastability of the embedded
mass shell of the free non-relativistic particle when the coupling to the
quantized field is turned on. Although this problem is naturally approached by
means of Mourre's celebrated commutator method, important regularity issues are
known to be inherent to QFT models and restrict the application of this method.
In this perspective, we introduce a novel non-standard construction procedure
for Mourre conjugate operators, which differs from second quantization and
allows to circumvent regularity issues. To show its versatility, we apply this
construction to the Nelson model with massive bosons, to Fr\"ohlich's polaron
model, and to a quantum friction model with massless bosons introduced by
Bruneau and De Bi\`evre: for each of these examples, we improve on previous
results.


title:
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language
  Understanding

abstract:
  End-to-end Spoken Language Understanding (E2E SLU) has attracted increasing
interest due to its advantages of joint optimization and low latency when
compared to traditionally cascaded pipelines. Existing E2E SLU models usually
follow a two-stage configuration where an Automatic Speech Recognition (ASR)
network first predicts a transcript which is then passed to a Natural Language
Understanding (NLU) module through an interface to infer semantic labels, such
as intent and slot tags. This design, however, does not consider the NLU
posterior while making transcript predictions, nor correct the NLU prediction
error immediately by considering the previously predicted word-pieces. In
addition, the NLU model in the two-stage system is not streamable, as it must
wait for the audio segments to complete processing, which ultimately impacts
the latency of the SLU system. In this work, we propose a streamable multi-task
semantic transducer model to address these considerations. Our proposed
architecture predicts ASR and NLU labels auto-regressively and uses a semantic
decoder to ingest both previously predicted word-pieces and slot tags while
aggregating them through a fusion network. Using an industry scale SLU and a
public FSC dataset, we show the proposed model outperforms the two-stage E2E
SLU model for both ASR and NLU metrics.


title: DFNet: Enhance Absolute Pose Regression with Direct Feature Matching

abstract:
  We introduce a camera relocalization pipeline that combines absolute pose
regression (APR) and direct feature matching. Existing photometric-based
methods have trouble on scenes with large photometric distortions, e.g. outdoor
environments. By incorporating an exposure-adaptive novel view synthesis, our
methods can successfully address the challenges. Moreover, by introducing
domain-invariant feature matching, our solution can improve pose regression
accuracy while using semi-supervised learning on unlabeled data. In particular,
the pipeline consists of two components, Novel View Synthesizer and FeatureNet
(DFNet). The former synthesizes novel views compensating for changes in
exposure and the latter regresses camera poses and extracts robust features
that bridge the domain gap between real images and synthetic ones. We show that
domain invariant feature matching effectively enhances camera pose estimation
both in indoor and outdoor scenes. Hence, our method achieves a
state-of-the-art accuracy by outperforming existing single-image APR methods by
as much as 56%, comparable to 3D structure-based methods.


title:
Effects of geomagnetic field perturbations on the power supply of
  transoceanic fiber optic cables

abstract:
  There is a growing concern that a big coronal mass ejection event will induce
perturbations on the power supply of fiber optic transoceanic cables that may
produce a global internet blackout. In this paper we give the expression of the
voltage variations that a transient change of the geomagnetic field induces on
the voltage of the power supply of a transoceanic fiber optic cable. We show
that the transient voltage change is proportional to the magnitude of the
magnetic field deviations and not to its time derivative as a direct
application of Faraday's law would imply, and this suggests design criteria to
protect transoceanic fiber optic systems against big geomagnetic storm events.
The presented analysis also enables the classification of existing systems into
some that are less sensitive to the weakening of the geomagnetic field
occurring during strong geomagnetic storms and others that are more prone to
experience an outage when a weakening of the geomagnetic field occurs.


title: The Tulczyjew triple for gauge field theories

abstract:
  In this article we construct and discuss a new rigorous geometric formalism
for gauge field theories. The basis of our work is the notion of the Tulczyjew
triple, a geometric structure which successfully solved numerous problems in
mathematical description of mechanics and classical field theory. In
particular, we construct a Tulczyjew triple for gauge field theories and reduce
it for systems that depend only on the value of a connection and curvature
instead of the entire first jet of the gauge field. We also introduce new
geometric structures such as the vector-affine product of bundles and analyse
the connection bundle from a new perspective. Finally, we apply the derived
formalism to Yang-Mills theory.


title: Influence of music on empirical brain networks

abstract:
  We analyze the influence of music in a network of FitzHugh-Nagumo oscillators
with empirical structural connectivity measured in healthy human subjects. We
report an increase of coherence between the global dynamics in our network and
the input signal induced by a specific music song. We show that the level of
coherence depends crucially on the frequency band. We compare our results with
experimental data, which also describe global neural synchronization between
different brain regions in the gamma-band range and its increase just before
transitions between different parts of the musical form (musical high-level
events). The results also suggest a split in musical form-related brain
synchronization between high brain frequencies, associated with neocortical
activity, and low frequencies in the range of dance movements, associated with
interactivity between cortical and subcortical regions. Finally, we discuss the
general modalities of the influence of music on the human brain.


title: Arithmetic logical Irreversibility and the Turing's Halt Problem

abstract:
  A new approach to the halting problem of the Turing machine using different
interpretations of the Shannon measure of the information on the computational
process represented as a distribution of events and defining a new concept of
arithmetic logical irreversibility and memory erasure that generate uncertainty
and computational improbability due to loss of information during computation.
Different computational steps (input) can give the same result (next step,
output) introducing thus information entropy in the computing process and
uncertainty about the original step (cause). This means that the same output is
produced by different inputs. Global indeterminism of computation as
distribution but determinism of the computation as current process because the
outputs are the same but the information not. The program or Turing machine as
macro description of the computational states as micro description that they
may be several and different but give the same result when they work .


title: Reach and complementarity of $\mu\to e$ searches

abstract:
  In Effective Field Theory, we describe $\mu\leftrightarrow e$ flavour
changing transitions using an operator basis motivated by experimental
observables. In a six-dimensional subspace probed by $\mu \to e \gamma$, $\mu
\to 3e$ and $\mu\to e$ conversion on nuclei, we derive constraints on the New
Physics scale from past and future experiments, illustrating the
complementarity of the processes in an intuitive way. We also recall that a
precise determination of the scalar quark currents in the nucleon will be
required to distinguish scalar $\mu\to e$ interactions on u-quarks from those
on d-quarks.


title:
What makes useful auxiliary tasks in reinforcement learning:
  investigating the effect of the target policy

abstract:
  Auxiliary tasks have been argued to be useful for representation learning in
reinforcement learning. Although many auxiliary tasks have been empirically
shown to be effective for accelerating learning on the main task, it is not yet
clear what makes useful auxiliary tasks. Some of the most promising results are
on the pixel control, reward prediction, and the next state prediction
auxiliary tasks; however, the empirical results are mixed, showing substantial
improvements in some cases and marginal improvements in others. Careful
investigations of how auxiliary tasks help the learning of the main task is
necessary. In this paper, we take a step studying the effect of the target
policies on the usefulness of the auxiliary tasks formulated as general value
functions. General value functions consist of three core elements: 1) policy 2)
cumulant 3) continuation function. Our focus on the role of the target policy
of the auxiliary tasks is motivated by the fact that the target policy
determines the behavior about which the agent wants to make a prediction and
the state-action distribution that the agent is trained on, which further
affects the main task learning. Our study provides insights about questions
such as: Does a greedy policy result in bigger improvement gains compared to
other policies? Is it best to set the auxiliary task policy to be the same as
the main task policy? Does the choice of the target policy have a substantial
effect on the achieved performance gain or simple strategies for setting the
policy, such as using a uniformly random policy, work as well? Our empirical
results suggest that: 1) Auxiliary tasks with the greedy policy tend to be
useful. 2) Most policies, including a uniformly random policy, tend to improve
over the baseline. 3) Surprisingly, the main task policy tends to be less
useful compared to other policies.


title: Defect Dynamics in Active Smectics Steered by Extreme Confinement

abstract:
  The persistent dynamics in systems out of equilibrium, particularly those
characterized by annihilation and creation of topological defects, is known to
involve complicated spatiotemporal processes and thus deemed difficult, if even
possible, to control. Here the steering of defect dynamics in active smectic
layers exposed to extreme confinements is explored, through self-propulsion of
active particles and a variety of confining geometries with different
topologies. We discover a wealth of dynamical behaviors during the evolution of
complex spatiotemporal defect patterns that can be steered by the confining
shape and topology, particularly a perpetual creation-annihilation dynamical
state with huge fluctuations of topological defects and a transition from
oscillatory to damped time correlation of defect number density via the
mechanism governed by boundary cusps. Our results are obtained by using an
active phase field crystal approach of self-propelled stripes. Possible
experimental realizations are also discussed.


title: Multi-Agent Path Planning with Asymmetric Interactions In Tight Spaces

abstract:
  By starting with the assumption that motion is fundamentally a decision
making problem, we use the world-line concept from Special Relativity as the
inspiration for a novel multi-agent path planning method. We have identified a
particular set of problems that have so far been overlooked by previous works.
We present our solution for the global path planning problem for each agent and
ensure smooth local collision avoidance for each pair of agents in the scene.
We accomplish this by modeling the trajectories of the agents through 2D space
and time as curves in 3D. Global path planning is solved using a modified
Djikstra's algorithm to ensure that initial trajectories for agents do not
intersect. We then solve for smooth local trajectories using a quasi-Newton
interior point solver, providing the trajectory curves with a radius to turn
them into rods. Subsequently, resolving collision of the rods ensures that no
two agents are in the same spatial position at the same time. This space-time
formulation allows us to simulate previously ignored phenomena such as highly
asymmetric interactions in very constrained environments. It also provides a
solution for scenes with unnaturally symmetric agent alignments without the
need for jittering agent positions or velocities.


title: High-resolution Compton spectroscopy using X-ray microcalorimeters

abstract:
  X-ray Compton spectroscopy is one of the few direct probes of the electron
momentum distribution of bulk materials in ambient and operando environments.
We report high-resolution inelastic X-ray scattering experiments with high
momentum and energy transfer performed at a storage-ring-based high-energy
X-ray light source facility using an X-ray microcalorimeter detector. Compton
profiles were measured for lithium and cobalt oxide powders relevant to
lithium-ion battery research. Spectroscopic analysis of the measured Compton
profiles shows high-sensitivity to the low-Z elements and oxidation states. The
lineshape analysis of the measured Compton profiles in comparison with computed
Hartree-Fock profiles is limited by the resolution of the energy-resolving
semiconductor detector. We have characterized an X-ray transition-edge sensor
microcalorimeter detector for high-resolution Compton scattering experiments
using a bending magnet source at the Advanced Photon Source (APS) with a double
crystal monochromator providing monochromatic photon energies near 27.5 keV.
The momentum resolution below 0.16 atomic units was measured yielding an
improvement of more than a factor of 7 over a state-of-the-art silicon drift
detector for the same scattering geometry. Furthermore, the lineshapes of
narrow valence and broad core electron profiles of sealed lithium metal were
clearly resolved using an X-ray microcalorimeter detector compared to smeared
and broadened lineshapes observed when using a silicon drift detector.
High-resolution Compton scattering using the energy-resolving detector shown
here presents new opportunities for spatial imaging of electron momentum
distributions for a wide class of materials with applications ranging from
electrochemistry to condensed matter physics.


title:
Considering the two Spin and the two Angular Momenta String Solutions in
  $AdS_5 \times S^5$

abstract:
  In this paper, we consider two almost opposite sectors of actual string
configuration ans\"atze in $AdS_5\times S^5$, which anyway have almost the same
features: The two spin solution, which has constant angles in $S^5$ and the two
angular momenta solution, which has constant "angels" in $AdS_5$, however for
the two angular momenta solution, we have to take the time coordinate from
$AdS_5$, thus there is a little asymmetry between the two string configurations
in $AdS_5\times S^5$. Without being autistic, there is around $69$ equations
between the similar equations in the two sectors, compare equations (34) and
(104) and also compare equations (64) and (134). Again, without being autistic,
the text after the equations in the two sectors, is almost precisely the same.
In our notation, the difference between the two sectors is as follows;
$\rho\leftrightarrow \theta$, $\phi \leftrightarrow \psi$, $\sinh\rho
\leftrightarrow \sin\theta$, $y_i\leftrightarrow x_i$, $y \leftrightarrow x$
etc. The string configurations of this paper, are both solvable by the Neumann
System. However, our setup in this paper is generally for the
Neumann-Rosochatius System, which is also solvable, since we intend to
generalize our results from the Neumann System to the Neumann-Rosochatius
System and to several types of deformed Neumann-Rosochatius Systems. In the
second part of this paper, which is independent of any string configurations in
$AdS_5\times S^5$ and concerns String Cosmology in $D=10$ dimensions. I will
seriously argue that there was no Big Bang; I truly believe that the Universe
has been there forever, see True Conclusions, Section 10.


title:
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised
  Domain Adaptation

abstract:
  We consider unsupervised domain adaptation (UDA), where labeled data from a
source domain (e.g., photographs) and unlabeled data from a target domain
(e.g., sketches) are used to learn a classifier for the target domain.
Conventional UDA methods (e.g., domain adversarial training) learn
domain-invariant features to improve generalization to the target domain. In
this paper, we show that contrastive pre-training, which learns features on
unlabeled source and target data and then fine-tunes on labeled source data, is
competitive with strong UDA methods. However, we find that contrastive
pre-training does not learn domain-invariant features, diverging from
conventional UDA intuitions. We show theoretically that contrastive
pre-training can learn features that vary subtantially across domains but still
generalize to the target domain, by disentangling domain and class information.
Our results suggest that domain invariance is not necessary for UDA. We
empirically validate our theory on benchmark vision datasets.


title:
Neumann problems for $p$-harmonic functions, and induced nonlocal
  operators in metric measure spaces

abstract:
  Following ideas of Caffarelli and Silvestre in~\cite{CS}, and using recent
progress in hyperbolic fillings, we define fractional $p$-Laplacians
$(-\Delta_p)^\theta$ with $0<\theta<1$ on any compact, doubling metric measure
space $(Z,d,\nu)$, and prove existence, regularity and stability for the
non-homogenous non-local equation $(-\Delta_p)^\theta u =f.$ These results, in
turn, rest on the new existence, global H\"older regularity and stability
theorems that we prove for the Neumann problem for $p$-Laplacians $\Delta_p$,
$1<p<\infty$, in bounded domains of measure metric spaces endowed with a
doubling measure that supports a Poincar\'e inequality. Our work also extends
beyond the compact setting, and includes as special cases much of the previous
results by other authors in the Euclidean, Riemannian and Carnot group
settings. Unlike other recent contributions in the metric measure spaces
context, our work does not rely on the assumption that $(Z,d,\nu)$ supports a
Poincar\'e inequality.


title:
Fractal and multifractal descriptors restore ergodicity broken by
  non-Gaussianity in time series

abstract:
  Ergodicity breaking is a challenge for biological and psychological sciences.
Ergodicity is a necessary condition for linear causal modeling. Long-range
correlations and non-Gaussianity characterizing various biological and
psychological measurements break ergodicity routinely, threatening our capacity
for causal modeling. Long-range correlations (e.g., in fractional Gaussian
noise, a.k.a. "pink noise") break ergodicity--in raw Gaussian series, as well
as in some but not all standard descriptors of variability, i.e., in
coefficient of variation (CV) and root mean square (RMS) but not standard
deviation (SD) for longer series. The present work demonstrates that
progressive increases in non-Gaussianity conspire with long-range correlations
to break ergodicity in SD for all series lengths. Meanwhile, explicitly
encoding the cascade dynamics that can generate temporally correlated
non-Gaussian noise offers a way to restore ergodicity to our causal models.
Specifically, fractal and multifractal properties encode both scale-invariant
power-law correlations and their variety, respectively, both of which features
index the underlying cascade parameters. Fractal and multifractal descriptors
of long-range correlated non-Gaussian processes show no ergodicity breaking and
hence, provide a more stable explanation for the long-range correlated
non-Gaussian form of biological and psychological processes. Fractal and
multifractal descriptors offer a path to restoring ergodicity to causal
modeling in these fields.


title:
Towards a Perron-Frobenius Theorem for Strongly Aperiodic Stochastic
  Chains

abstract:
  Perron-Frobenius theorem is a fundamental tool in matrix analysis that has
applications to complex networks, population dynamics, social learning, and
numerous physical, engineering, and economic phenomena. However, since this
theorem and many of its extensions can be applied only to a single matrix at a
time, their applications in networked dynamical systems are limited to static
networks. To extend the applicability of these results to time-varying
networks, we generalize two assertions of the Perron-Frobenius theorem to
sequences as well as continua of row-stochastic matrices. Our results have
potential applications in areas such as distributed averaging, optimization,
and estimation.


title:
Hyperbolic summation for functions of the GCD and LCM of several
  integers

abstract:
  Let $k\ge 2$ be a fixed integer. We consider sums of type $\sum_{n_1\cdots
n_k\le x} F(n_1,\ldots,n_k)$, taken over the hyperbolic region
$\{(n_1,\ldots,n_k)\in {\Bbb N}^k: n_1\cdots n_k\le x\}$, where $F:{\Bbb
N}^k\to {\Bbb C}$ is a given function. In particular, we deduce asymptotic
formulas with remainder terms for the hyperbolic summations $\sum_{n_1\cdots
n_k\le x} f((n_1,\ldots,n_k))$ and $\sum_{n_1\cdots n_k\le x}
f([n_1,\ldots,n_k])$, involving the GCD and LCM of the integers
$n_1,\ldots,n_k$, where $f:{\Bbb N}\to {\Bbb C}$ belongs to certain classes of
functions. Some of our results generalize those obtained by the authors for
$k=2$.


title: Wavefront Sensing and Control with the Many Headed Hydra Modal Basis

abstract:
  The future of space and ground based telescopes is intimately tied to
technology and algorithm development surrounding wavefront sensing and control.
Only with cutting edge developments and unusual ideas will we be able to build
diffraction limited observatories on the ground that contend with
earth-atmosphere, as well as space-based observatories that sense and control
for optical aberrations and telescope jitter. There exist a variety of
mathematical bases for decomposing wavefront images, including the zonal modal
basis, Zernike polynomials, and Fourier modes. However, previous bases have
neglected the most vital element of astronomical optics: our field is only as
good as the people in it. In this paper we propose a new Many Headed Hydra
Modal Basis (hydra heads) for wavefront decomposition, using physical
representations of the Adaptive Optics community. We find that the first author
makes the best wavefront decomposition, on the order of a correct
reconstruction within $\sim 1\%$ of the original turbulence image. We discuss
engineering implications of the Many Headed Hydra Modal Basis, as applied to
deformable mirror technology and within active and adaptive optics control
loops. Finally, we explore predictive control avenues, by positing that the
first author being the most effective wavefront predicts a successful future
for them as a high contrast imaging scientist.


title: Modular SAT-based techniques for reasoning tasks in team semantics

abstract:
  We study the complexity of reasoning tasks for logics in team semantics. Our
main focus is on the data complexity of model checking but we also derive new
results for logically defined counting and enumeration problems. Our approach
is based on modular reductions of these problems into the corresponding
problems of various classes of Boolean formulas. We illustrate our approach via
several new tractability/intractability results.


title:
The Unlocking of High-Pressure Science with Broadband Neutron
  Spectroscopy at the ISIS Pulsed Neutron & Muon Source

abstract:
  Following significant instrument upgrades and parallel methodological
developments over the past decade, the TOSCA neutron spectrometer at the ISIS
Pulsed Neutron & Muon Source in the United Kingdom has developed a rich and
growing scientific community spanning a broad range of non-traditional areas of
neutron science, including chemical catalysis, gas adsorption & storage, and
new materials for energy and sustainability. High-pressure science, however,
has seen little to no representation to date owing to previous limitations in
capability. Herein, we explore for the first time the viability of rapid
high-pressure measurements in the gigapascal regime, capitalizing from the
orders-of-magnitude increase in incident flux afforded by a recent upgrade of
the primary-beam path. In particular, we show that spectroscopic measurements
up to pressures of 2 GPa over an unprecedented energy-transfer range are now
possible within the hour timescale. In addition, we have designed and
commissioned a dedicated set of high-pressure vessels, with a view to foster
and support the further growth and development of an entirely new user
community on TOSCA.


title:
Inverse-design of high-dimensional quantum optical circuits in a complex
  medium

abstract:
  Programmable optical circuits form a key part of quantum technologies today,
ranging from transceivers for quantum communication to integrated photonic
chips for quantum information processing. As the size of such circuits is
increased, maintaining precise control over every individual component becomes
challenging, leading to a reduction in the quality of the operations performed.
In parallel, minor imperfections in circuit fabrication are amplified in this
regime, dramatically inhibiting their performance. Here we show how embedding
an optical circuit in the higher-dimensional space of a large, ambient
mode-mixer using inverse-design techniques allows us to forgo control over each
individual circuit element, while retaining a high degree of programmability
over the circuit. Using this approach, we implement high-dimensional linear
optical circuits within a complex scattering medium consisting of a commercial
multi-mode fibre placed between two controllable phase planes. We employ these
circuits to manipulate high-dimensional spatial-mode entanglement in up to
seven dimensions, demonstrating their application as fully programmable quantum
gates. Furthermore, we show how their programmability allows us to turn the
multi-mode fibre itself into a generalised multi-outcome measurement device,
allowing us to both transport and certify entanglement within the transmission
channel. Finally, we discuss the scalability of our approach, numerically
showing how a high circuit fidelity can be achieved with a low circuit depth by
harnessing the resource of a high-dimensional mode-mixer. Our work serves as an
alternative yet powerful approach for realising precise control over
high-dimensional quantum states of light, with clear applications in
next-generation quantum communication and computing technologies.


title:
On the X-ray, optical and radio afterglows of the BdHN I GRB 180720B
  generated by the synchrotron emission

abstract:
  Gamma-ray bursts (GRBs) are systems of unprecedented complexity across all
the electromagnetic spectrum, including the radio, optical, X-rays, gamma-rays
in the megaelectronvolt (MeV) and gigaelectronvolt (GeV) regime, as well as
ultrahigh-energy cosmic rays (UHECRs), each manifested in seven specific
physical processes with widely different characteristic evolution timescales
ranging from $10^{-14}$ s to $10^{7}$ s or longer. We here study the long GRB
180720B originating from a binary system composed of a massive CO$_{\rm core}$
of about $10 M_\odot$ and a companion neutron star (NS). The gravitational
collapse of the CO$_{\rm core}$ gives rise to a spinning newborn NS ($\nu$NS),
with an initial period of $P_0=1$ ms that powers the synchrotron radiation in
the radio, optical, and X-ray wavelengths. We here investigate solely the GRB
180720B afterglows and present a detailed treatment of its origin based on the
synchrotron radiation released by the interaction of the $\nu$NS and the SN
ejecta. We show that in parallel to the X-ray afterglow, the spinning $\nu$NS
also powers the optical and radio afterglows and allows to infer the $\nu$NS
and ejecta parameters that fit the observational data.


title: Uniform quasi-convex optimisation via Extremum Seeking

abstract:
  The paper deals with a well-known extremum seeking scheme by proving
uniformity properties with respect to the amplitudes of the dither signal and
of the cost function. Those properties are then used to show that the scheme
guarantees the global minimiser to be semi-global practically stable despite
the presence of local saddle points. To achieve these results, we analyse the
average system associated with the extremum seeking scheme via arguments based
on the Fourier series.


title:
Reference Network and Localization Architecture for Smart Manufacturing
  based on 5G

abstract:
  5G promises to shift Industry 4.0 to the next level by allowing flexible
production. However, many communication standards are used throughout a
production site, which will stay so in the foreseeable future. Furthermore,
localization of assets will be equally valuable in order to get to a higher
level of automation. This paper proposes a reference architecture for a
convergent localization and communication network for smart manufacturing that
combines 5G with other existing technologies and focuses on high-mix low-volume
application, in particular at small and medium-sized enterprises. The
architecture is derived from a set of functional requirements, and we describe
different views on this architecture to show how the requirements can be
fulfilled. It connects private and public mobile networks with local networking
technologies to achieve a flexible setup addressing many industrial use cases.


title:
Female Agency and its Implications on Mental and Physical Health:
  Evidence from the city of Dhaka

abstract:
  Women agency defined as the ability to conceive of purposeful plan and to
carry out action consistent with such a plan can play an important role in
determining health status. Using data from female respondents conducted in a
survey in the city of Dhaka in Bangladesh, this paper explores how women agency
relates to their physical and mental health. The findings indicate women with
high agency to experience significantly lesser mental distress on average.
Counterintuitively, these women are more likely to report poor physical health.
As an explanation, we propose purposeful action among women with high agency as
a potential reason, wherein they conceive purpose in the future and formulate
action that is feasible today. Hence, these women prefer to report illness and
get the required treatment to ensure better future health. This illuminates our
understanding of sustainable development and emphasises the critical role of
women agency for sustainable human development.


title: Beyond Wilson-Cowan dynamics: oscillations and chaos without inhibition

abstract:
  Fifty years ago, Wilson and Cowan developed a mathematical model to describe
the activity of neural populations. In this seminal work, they divided the
cells in three groups: active, sensitive and refractory, and obtained a
dynamical system to describe the evolution of the average firing rates of the
populations. In the present work, we investigate the impact of the often
neglected refractory state and show that taking it into account can introduce
new dynamics. Starting from a continuous-time Markov chain, we perform a
rigorous derivation of a mean-field model that includes the refractory
fractions of populations as dynamical variables. Then, we perform bifurcation
analysis to explain the occurance of periodic solutions in cases where the
classical Wilson-Cowan does not predict oscillations. We also show that our
mean-field model is able to predict chaotic behavior in the dynamics of
networks with as little as two populations.


title: Opinion Dynamics Steering using Stochastic Search

abstract:
  In this paper, we apply the stochastic search dynamic optimization framework
for steering opinion dynamics in a partially active population. The framework
is grounded on stochastic optimization theory and relies on sampling of
candidate solutions from distributions of the exponential family. We derive the
distribution parameter update for an open loop, a feedback, and a novel
adaptive feedback policy. All three policies are tested on two different
opinion dynamics steering scenarios in simulation and compared against a hand
designed feedback policy. The results showcase the effectiveness the framework
for opinion dynamics control and the robustness of the adaptive feedback policy
with respect to the active agent set size.


title:
From Data to Knowledge Graphs: A Multi-Layered Method to Model User's
  Visual Analytics Workflow for Analytical Purposes

abstract:
  The importance of knowledge generation drives much of Visual Analytics (VA).
User-tracking and behavior graphs have shown the value of understanding users'
knowledge generation while performing VA workflows. Works in theoretical
models, ontologies, and provenance analysis have greatly described means to
structure and understand the connection between knowledge generation and VA
workflows. Yet, two concepts are typically intermixed: the temporal aspect,
which indicates sequences of events, and the atemporal aspect, which indicates
the workflow state space. In works where these concepts are separated, they do
not discuss how to analyze the recorded user's knowledge gathering process when
compared to the VA workflow itself. This paper presents Visual Analytic
Knowledge Graph (VAKG), a conceptual framework that generalizes existing
knowledge models and ontologies by focusing on how humans relate to computer
processes temporally and how it relates to the workflow's state space. Our
proposal structures this relationship as a 4-way temporal knowledge graph with
specific emphasis on modeling the human and computer aspect of VA as separate
but interconnected graphs for, among others, analytical purposes. We compare
VAKG with relevant literature to show that VAKG's contribution allows VA
applications to use it as a provenance model and a state space graph, allowing
for analytics of domain-specific processes, usage patterns, and users'
knowledge gain performance. We also interviewed two domain experts to check, in
the wild, whether real practice and our contributions are aligned.


title: Robust and Efficient Aggregation for Distributed Learning

abstract:
  Distributed learning paradigms, such as federated and decentralized learning,
allow for the coordination of models across a collection of agents, and without
the need to exchange raw data. Instead, agents compute model updates locally
based on their available data, and subsequently share the update model with a
parameter server or their peers. This is followed by an aggregation step, which
traditionally takes the form of a (weighted) average. Distributed learning
schemes based on averaging are known to be susceptible to outliers. A single
malicious agent is able to drive an averaging-based distributed learning
algorithm to an arbitrarily poor model. This has motivated the development of
robust aggregation schemes, which are based on variations of the median and
trimmed mean. While such procedures ensure robustness to outliers and malicious
behavior, they come at the cost of significantly reduced sample efficiency.
This means that current robust aggregation schemes require significantly higher
agent participation rates to achieve a given level of performance than their
mean-based counterparts in non-contaminated settings. In this work we remedy
this drawback by developing statistically efficient and robust aggregation
schemes for distributed learning.


title: Exploring canyons in glassy energy landscapes using metadynamics

abstract:
  The complex physics of glass forming systems is controlled by the structure
of the low energy portions of their potential energy landscapes. Here, we
report that a modified metadynamics algorithm efficiently explores and samples
low energy regions of such high-dimensional landscapes. In the energy landscape
for a model foam, metadynamics finds and descends meandering `canyons' in the
landscape, which contain dense clusters of energy minima along their floors.
Similar canyon structures in the energy landscapes of two model glass
formers--hard sphere fluids and the Kob-Andersen glass--allow metadynamics to
reach low energies. In the hard sphere system, fluid configurations are found
to form continuous regions that cover the canyon floors, but only up to a
volume fraction close to that predicted for kinetic arrest. For the
Kob-Andersen glass former, metadynamics reaches the canyons' ends with modest
computational effort; with the lowest energies found approaching the predicted
Kauzmann limit.


title: Prefix-Free Coding for LQG Control

abstract:
  In this work, we develop quantization and variable-length source codecs for
the feedback links in linear-quadratic-Gaussian (LQG) control systems. We prove
that for any fixed control performance, the approaches we propose nearly
achieve lower bounds on communication cost that have been established in prior
work. In particular, we refine the analysis of a classical achievability
approach with an eye towards more practical details. Notably, in the prior
literature the source codecs used to demonstrate the (near) achievability of
these lower bounds are often implicitly assumed to be time-varying. For
single-input single-output (SISO) plants, we prove that it suffices to consider
time-invariant quantization and source coding. This result follows from
analyzing the long-term stochastic behavior of the system's quantized
measurements and reconstruction errors. To our knowledge, this time-invariant
achievability result is the first in the literature.

categories: cs.IT cs.SY eess.SP eess.SY math.IT math.OC


title:
Multispike Solutions for a slightly subcritical elliptic problem with
  non-power nonlinearity

abstract:
  In this paper, we are concerned with the following elliptic equation
$$\left\{\begin{array}{rrl}-\Delta u&=& |u|^{4/(n-2)}u/[\ln
(e+|u|)]^\varepsilon\hbox{ in } \Omega,\\ u&=&0 \hbox{ on }\partial \Omega,
\end{array} \right.$$ where $\Omega$ is a smooth bounded open domain in
$\mathbb{R}^n, \ n\geq 3$ and $\varepsilon>0$. Clapp et al. in Journal of Diff.
Eq. (Vol 275) proved that there exists a single-peak positive solution for
small $\varepsilon$ if $n \geq 4$. Here we construct positive as well as
changing sign solutions concentrated at several points at the same time.


title: Nonlinear VRFT with LASSO

abstract:
  Virtual Reference Feedback Tuning (VRFT) is a well known and very successful
data-driven control design method. It has been initially conceived for linear
plants and this original formulation has been much explored in the literature,
besides having already found many practical applications. A nonlinear version
of VRFT has been proposed early on, but not much explored later on. In this
paper we highlight various issues involved in the application of nonlinear VRFT
and propose the inclusion of L1 regularization in its formulation. We
illustrate by means of two simple examples the critical role played by two
aspects: the L1 regularization and the choice of dictionary used to describe
the nonlinearity of the controller.


title: Cauchy Slice Holography: A New AdS/CFT Dictionary

abstract:
  We investigate a new approach to holography in asymptotically AdS spacetimes,
in which time rather than space is the emergent dimension. By making a
sufficiently large T^2-deformation of a Euclidean CFT, we define a holographic
theory that lives on Cauchy slices of the Lorentzian bulk. (More generally, for
an arbitrary Hamiltonian constraint equation that closes, we show how to obtain
it by an irrelevant deformation from a CFT with suitable anomalies.) The
partition function of this theory defines a natural map between the bulk
canonical quantum gravity theory Hilbert space, and the Hilbert space of the
usual (undeformed) boundary CFT. We argue for the equivalence of the ADM and
CFT Hamiltonians. We also explain how bulk unitarity emerges naturally, even
though the boundary theory is not reflection-positive. This allows us to
reformulate the holographic principle in the language of Wheeler-DeWitt
canonical quantum gravity.
  Along the way, we outline a procedure for obtaining a bulk Hilbert space from
the gravitational path integral with Dirichlet boundary conditions. Following
previous conjectures, we postulate that this finite-cutoff gravitational path
integral agrees with the T^2-deformed theory living on an arbitrary boundary
manifold -- at least near the semiclassical regime. However, the T^2-deformed
theory may be easier to UV complete, in which case it would be natural to take
it as the definition of nonperturbative quantum gravity.


title:
Fashion Style Generation: Evolutionary Search with Gaussian Mixture
  Models in the Latent Space

abstract:
  This paper presents a novel approach for guiding a Generative Adversarial
Network trained on the FashionGen dataset to generate designs corresponding to
target fashion styles. Finding the latent vectors in the generator's latent
space that correspond to a style is approached as an evolutionary search
problem. A Gaussian mixture model is applied to identify fashion styles based
on the higher-layer representations of outfits in a clothing-specific attribute
prediction model. Over generations, a genetic algorithm optimizes a population
of designs to increase their probability of belonging to one of the Gaussian
mixture components or styles. Showing that the developed system can generate
images of maximum fitness visually resembling certain styles, our approach
provides a promising direction to guide the search for style-coherent designs.


title:
Population Games With Erlang Clocks: Convergence to Nash Equilibria For
  Pairwise Comparison Dynamics

abstract:
  The prevailing methodology for analyzing population games and evolutionary
dynamics in the large population limit assumes that a Poisson process (or
clock) inherent to each agent determines when the agent can revise its
strategy. Hence, such an approach presupposes exponentially distributed
inter-revision intervals, and is inadequate for cases where each strategy
entails a sequence of sub-tasks (sub-strategies) that must be completed before
a new revision time occurs. This article proposes a methodology for such cases
under the premise that a sub-strategy's duration is exponentially-distributed,
leading to Erlang distributed inter-revision intervals. We assume that a
so-called pairwise-comparison protocol captures the agents' revision
preferences to render our analysis concrete. The presence of sub-strategies
brings on additional dynamics that is incompatible with existing models and
results. Our main contributions are twofold, both derived for a deterministic
approximation valid for large populations. We prove convergence of the
population's state to the Nash equilibrium set when a potential game generates
a payoff for the strategies. We use system-theoretic passivity to determine
conditions under which this convergence is guaranteed for contractive games.


title: Attractive trion-polariton nonlinearity due to Coulomb scattering

abstract:
  We theoretically investigate the nonlinearity of trion-polaritons in a
two-dimensional material that arises from Coulomb interaction between
quasiparticles. To evaluate the interaction constant, we solve a three-body
Wannier equation precisely by expanding trion wavefunctions into a Gaussian
basis. Using these wavefunctions, we calculate the trion-polariton interaction
energies for the exchange processes, resolving the outstanding question of
trion-trion scattering. We find that the nonlinearity is the result of the
competition between different scattering channels. Such a cancellation effect
is sensitive to wavefunction overlaps and depends on material parameters. Most
importantly, our result shows that the nonlinear interaction between
trion-polaritons is attractive, and is fivefold stronger than exciton-polariton
interaction. Our work thus describes the regime where trion-polaritons offer
the prospects for attractive fluids of light in monolayers of transition metal
dichalcogenides.


title:
Monarch: Expressive Structured Matrices for Efficient and Accurate
  Training

abstract:
  Large neural networks excel in many domains, but they are expensive to train
and fine-tune. A popular approach to reduce their compute or memory
requirements is to replace dense weight matrices with structured ones (e.g.,
sparse, low-rank, Fourier transform). These methods have not seen widespread
adoption (1) in end-to-end training due to unfavorable efficiency--quality
tradeoffs, and (2) in dense-to-sparse fine-tuning due to lack of tractable
algorithms to approximate a given dense weight matrix. To address these issues,
we propose a class of matrices (Monarch) that is hardware-efficient (they are
parameterized as products of two block-diagonal matrices for better hardware
utilization) and expressive (they can represent many commonly used transforms).
Surprisingly, the problem of approximating a dense weight matrix with a Monarch
matrix, though nonconvex, has an analytical optimal solution. These properties
of Monarch matrices unlock new ways to train and fine-tune sparse and dense
models. We empirically validate that Monarch can achieve favorable
accuracy-efficiency tradeoffs in several end-to-end sparse training
applications: speeding up ViT and GPT-2 training on ImageNet classification and
Wikitext-103 language modeling by 2x with comparable model quality, and
reducing the error on PDE solving and MRI reconstruction tasks by 40%. In
sparse-to-dense training, with a simple technique called "reverse
sparsification," Monarch matrices serve as a useful intermediate representation
to speed up GPT-2 pretraining on OpenWebText by 2x without quality drop. The
same technique brings 23% faster BERT pretraining than even the very optimized
implementation from Nvidia that set the MLPerf 1.1 record. In dense-to-sparse
fine-tuning, as a proof-of-concept, our Monarch approximation algorithm speeds
up BERT fine-tuning on GLUE by 1.7x with comparable accuracy.


title: Disorder in AdS$_3$/CFT$_2$

abstract:
  We perturbatively study marginally relevant quenched disorder in
AdS$_3$/CFT$_2$ to second order in the disorder strength. Using the
Chern-Simons formulation of AdS$_3$ gravity for the Poincar\'e patch, we
introduce disorder via the chemical potentials. We discuss the bulk and
boundary properties resulting from the disorder averaged metric. The disorder
generates a small mass and angular momentum. In the bulk and the boundary, we
find unphysical features due to the disorder average. Motivated by these
features, we propose a Poincar\'e-Lindstedt-inspired resummation method. We
discuss how this method enables us to remove all of the unphysical features.


title:
Fast and Automatic Object Registration for Human-Robot Collaboration in
  Industrial Manufacturing

abstract:
  We present an end-to-end framework for fast retraining of object detection
models in human-robot-collaboration. Our Faster R-CNN based setup covers the
whole workflow of automatic image generation and labeling, model retraining
on-site as well as inference on a FPGA edge device. The intervention of a human
operator reduces to providing the new object together with its label and
starting the training process. Moreover, we present a new loss, the
intraspread-objectosphere loss, to tackle the problem of open world
recognition. Though it fails to completely solve the problem, it significantly
reduces the number of false positive detections of unknown objects.


title: Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

abstract:
  Large foundation models can exhibit unique capabilities depending on the
domain of data they are trained on. While these domains are generic, they may
only barely overlap. For example, visual-language models (VLMs) are trained on
Internet-scale image captions, but large language models (LMs) are further
trained on Internet-scale text with no images (e.g. from spreadsheets, to SAT
questions). As a result, these models store different forms of commonsense
knowledge across different domains. In this work, we show that this model
diversity is symbiotic, and can be leveraged to build AI systems with
structured Socratic dialogue -- in which new multimodal tasks are formulated as
a guided language-based exchange between different pre-existing foundation
models, without additional finetuning. In the context of egocentric perception,
we present a case study of Socratic Models (SMs) that can provide meaningful
results for complex tasks such as generating free-form answers to contextual
questions about egocentric video, by formulating video Q&A as short story Q&A,
i.e. summarizing the video into a short story, then answering questions about
it. Additionally, SMs can generate captions for Internet images, and are
competitive with state-of-the-art on zero-shot video-to-text retrieval with
42.8 R@1 on MSR-VTT 1k-A. SMs demonstrate how to compose foundation models
zero-shot to capture new multimodal functionalities, without domain-specific
data collection. Prototypes are available at socraticmodels.github.io.


title:
Hamiltonian of mean force in the weak-coupling and high-temperature
  approximations and refined quantum master equations

abstract:
  The Hamiltonian of mean force is a widely used concept to describe the
modification of the usual canonical Gibbs state for a quantum system whose
coupling strength with the thermal bath is non-negligible. Here we
perturbatively derive general approximate expressions for the Hamiltonians of
mean force in the weak-coupling approximation and in the high-temperature one.
We numerically analyse the accuracy of the corresponding expressions and show
that the precision of the Bloch-Redfield equantum master equation can be
improved if we replace the original system Hamiltonian by the Hamiltonian of
mean force.


title:
Traversability, Reconfiguration, and Reachability in the Gadget
  Framework

abstract:
  Consider an agent traversing a graph of "gadgets", each with local state that
changes with each traversal by the agent. We characterize the complexity of
universal traversal, where the goal is to traverse every gadget at least once,
for DAG gadgets, one-state gadgets, and reversible deterministic gadgets. We
also study the complexity of reconfiguration, where the goal is to bring the
system of gadgets to a specified state, proving many cases PSPACE-complete, and
showing in some cases that reconfiguration can be strictly harder than
reachability (where the goal is for the agent to reach a specified location),
while in other cases, reachability is strictly harder than reconfiguration.


title: Dipole-dipole Interactions Through a Lens

abstract:
  We study the fluctuation-mediated interactions between two atoms in the
presence of an aplanatic lens, demonstrating an enhancement in their resonant
dipole-dipole interaction. We derive the field propagation of the linear
optical system in terms of the electromagnetic Green's tensor for an aplanatic
lens. The collective internal atomic dynamics is analyzed via a Lindblad master
equation, which allows one to characterize the dispersive and dissipative
interactions between atoms. We thus demonstrate that the resonant dipole-dipole
coupling between the atoms can be enhanced in the focal plane of the lens, and
the lens-modified energy exchange between the atoms can create a mutual
trapping potential. Our work opens new avenues for expanding dipole-dipole
interactions to macroscopic scales and the experimental platforms to study
them.


title:
Perceval: A Software Platform for Discrete Variable Photonic Quantum
  Computing

abstract:
  We introduce Perceval, an evolutive open-source software platform for
simulating and interfacing with discrete variable photonic quantum computers,
and describe its main features and components. Its Python front-end allows
photonic circuits to be composed from basic photonic building blocks like
photon sources, beam splitters, phase shifters and detectors. A variety of
computational back-ends are available and optimised for different use-cases.
These use state-of-the-art simulation techniques covering both weak simulation,
or sampling, and strong simulation. We give examples of Perceval in action by
reproducing a variety of photonic experiments and simulating photonic
implementations of a range of quantum algorithms, from Grover's and Shor's to
examples of quantum machine learning. Perceval is intended to be a useful
toolkit both for experimentalists wishing to easily model, design, simulate, or
optimise a discrete variable photonic experiment, and for theoreticians wishing
to design algorithms and applications for discrete-variable photonic quantum
computing platforms.


title:
The OCEAN mailing list data set: Network analysis spanning mailing lists
  and code repositories

abstract:
  Communication surrounding the development of an open source project largely
occurs outside the software repository itself. Historically, large communities
often used a collection of mailing lists to discuss the different aspects of
their projects. Multimodal tool use, with software development and
communication happening on different channels, complicates the study of open
source projects as a sociotechnical system. Here, we combine and standardize
mailing lists of the Python community, resulting in 954,287 messages from 1995
to the present. We share all scraping and cleaning code to facilitate
reproduction of this work, as well as smaller datasets for the Golang (122,721
messages), Angular (20,041 messages) and Node.js (12,514 messages) communities.
To showcase the usefulness of these data, we focus on the CPython repository
and merge the technical layer (which GitHub account works on what file and with
whom) with the social layer (messages from unique email addresses) by
identifying 33% of GitHub contributors in the mailing list data. We then
explore correlations between the valence of social messaging and the structure
of the collaboration network. We discuss how these data provide a laboratory to
test theories from standard organizational science in large open source
projects.


title: Quantized GAN for Complex Music Generation from Dance Videos

abstract:
  We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal
framework that generates complex musical samples conditioned on dance videos.
Our proposed framework takes dance video frames and human body motion as input,
and learns to generate music samples that plausibly accompany the corresponding
input. Unlike most existing conditional music generation works that generate
specific types of mono-instrumental sounds using symbolic audio representations
(e.g., MIDI), and that heavily rely on pre-defined musical synthesizers, in
this work we generate dance music in complex styles (e.g., pop, breakdancing,
etc.) by employing a Vector Quantized (VQ) audio representation, and leverage
both its generality and the high abstraction capacity of its symbolic and
continuous counterparts. By performing an extensive set of experiments on
multiple datasets, and following a comprehensive evaluation protocol, we assess
the generative quality of our approach against several alternatives. The
quantitative results, which measure the music consistency, beats
correspondence, and music diversity, clearly demonstrate the effectiveness of
our proposed method. Last but not least, we curate a challenging dance-music
dataset of in-the-wild TikTok videos, which we use to further demonstrate the
efficacy of our approach in real-world applications - and which we hope to
serve as a starting point for relevant future research.


title: Towards an Acoustic Geometry in Slightly Viscous Fluids

abstract:
  We explore the behaviour of barotropic and irrotational fluids with a small
viscosity under the effect of first-order acoustic perturbations. We discuss,
following the extant literature, the difficulties in gleaning an acoustic
geometry in the presence of viscosity. In order to obviate various technical
encumbrances, when viscosity is present, for an extraction of a possible
acoustic geometry, we adopted a method of double perturbations, whereby
dynamical quantities such as the velocity field and potential undergo a
perturbation both in viscosity and in an external acoustic stimulus. The
resulting perturbation equations yield a solution which can be interpreted in
terms of a generalised acoustic geometry, over and above the one known for
inviscid fluids.


title: Anomalous buoyancy of quantum bubbles in immiscible Bose mixtures

abstract:
  Buoyancy is a well-known effect in immiscible binary Bose-Einstein
condensates. Depending on the differential confinement experienced by the two
components, a bubble of one component sitting at the center of the other
eventually floats to the surface, around which it spreads either totally or
partially. We discuss how quantum fluctuations may significantly change the
volume and position of immiscible bubbles. We consider the particular case of
two miscible components, forming a pseudo-scalar bubble condensate with
enhanced quantum fluctuations (quantum bubble), immersed in a bath provided by
a third component, with which they are immiscible. We show that in such a
peculiar effective binary mixture, quantum fluctuations change the equilibrium
of pressures that define the bubble volume and modify as well the criterion for
buoyancy. Once buoyancy sets in, in contrast to the mean-field case, quantum
fluctuations may place the bubble at an intermediate position between the
center and the surface. At the surface, the quantum bubble may transition into
a floating self-bound droplet.


title: From Statistical to Causal Learning

abstract:
  We describe basic ideas underlying research to build and understand
artificially intelligent systems: from symbolic approaches via statistical
learning to interventional models relying on concepts of causality. Some of the
hard open problems of machine learning and AI are intrinsically related to
causality, and progress may require advances in our understanding of how to
model and infer causality from data.


title: Silting theory under change of rings

abstract:
  The main goal of this paper is to compare the silting theory of an
$R$-algebra $\Lambda$ over a Noetherian ring $R$ with that of its tensor
product $\Lambda \otimes \Gamma$ with another $R$-algebra $\Gamma$. In the case
that the $R$-algebra $\Lambda$ is Noetherian, $R$ a complete local ring and
$\mathfrak{a}$ a certain ideal of the ring $R$, we obtain an isomorphism
between the silting poset of $\Lambda$ and that of its quotient $\Lambda
\otimes R/ \mathfrak{a}$. Furthermore, we study the restrictions of such a
bijection to tilting complexes and deduce silting embedding and descent results
for the algebra $\Lambda$ and a certain family of algebras $(\Lambda \otimes
\Gamma_i)_{i \in I}$.


title:
Iterative location modeling of hand sanitizer deployment based upon
  qualitative interviews

abstract:
  The onset of the COVID-19 pandemic in Spring 2020 forced universities in the
United States to quickly shift to remote learning in order to protect
university students, faculty, and staff. During the summer of 2020, Clemson
University prioritized a safe return to in-person learning by public health
guidance. Because of concerns that COVID-19 was transmissible via physical
contact between individuals and surfaces, adequate hand sanitation for all
university personnel became a top priority. In this study, we examine how the
Clemson University Facilities department team placed hand sanitizer dispensers
across the university campus in order to maximize availability to students,
faculty, and staff through qualitative interviews with the facilities personnel
responsible for placing the dispensers. We present an iterative design process
of how a facility location model was designed to encapsulate the same
considerations of the facilities team. The iterative modeling approach blends
human factors analysis of decision-maker interview data with model development
practices. Through the creation of our mathematical model, we compare the
effectiveness of the initial deployment of sanitizer dispensers with the
optimal allocation based on our model results.


title: Metaplectic group schemes

abstract:
  Given a reductive group scheme $G$, we give a linear algebraic description of
reduced \'etale $4$-cocycles on its classifying stack $\mathrm B(G)$. These
cocycles form a $2$-groupoid, which we interpret as parameters of metaplectic
covers of $G$. We use our linear algebraic description to define the Langlands
dual of a metaplectic cover.


title:
Learning the conditional law: signatures and conditional GANs in
  filtering and prediction of diffusion processes

abstract:
  We consider the filtering and prediction problem for a diffusion process. The
signal and observation are modeled by stochastic differential equations (SDEs)
driven by Wiener processes. In classical estimation theory, measure-valued
stochastic partial differential equations (SPDEs) are derived for the filtering
and prediction measures. These equations can be hard to solve numerically. We
provide an approximation algorithm using conditional generative adversarial
networks (GANs) and signatures, an object from rough path theory. The signature
of a sufficiently smooth path determines the path completely. In some cases,
GANs based on signatures have been shown to efficiently approximate the law of
a stochastic process. In this paper we extend this method to approximate the
prediction measure conditional to noisy observation. We use controlled
differential equations (CDEs) as universal approximators to propose an
estimator for the conditional and prediction law. We show well-posedness in
providing a rigorous mathematical framework. Numerical results show the
efficiency of our algorithm.


title:
First Search for Unstable Sterile Neutrinos with the IceCube Neutrino
  Observatory

abstract:
  We present a search for an unstable sterile neutrino by looking for a
matter-induced signal in eight years of atmospheric $\nu_\mu$ data collected
from 2011 to 2019 at the IceCube Neutrino Observatory. Both the (stable)
three-neutrino and the 3+1 sterile neutrino models are disfavored relative to
the unstable sterile neutrino model, though with $p$-values of 2.5\% and
0.81\%, respectively, we do not observe evidence for 3+1 neutrinos with
neutrino decay. The best-fit parameters for the sterile neutrino with decay
model from this study are $\Delta m_{41}^2=6.7^{+3.9}_{-2.5}\,\rm{eV}^2$,
$\sin^2 2\theta_{24}=0.33^{+0.20}_{-0.17}$, and $g^2=2.5\pi\pm1.5\pi$, where
$g$ is the decay-mediating coupling. The preferred regions from short-baseline
oscillation searches are excluded at 90\% C.L.


title: On the Importance of Asymmetry for Siamese Representation Learning

abstract:
  Many recent self-supervised frameworks for visual representation learning are
based on certain forms of Siamese networks. Such networks are conceptually
symmetric with two parallel encoders, but often practically asymmetric as
numerous mechanisms are devised to break the symmetry. In this work, we conduct
a formal study on the importance of asymmetry by explicitly distinguishing the
two encoders within the network -- one produces source encodings and the other
targets. Our key insight is keeping a relatively lower variance in target than
source generally benefits learning. This is empirically justified by our
results from five case studies covering different variance-oriented designs,
and is aligned with our preliminary theoretical analysis on the baseline.
Moreover, we find the improvements from asymmetric designs generalize well to
longer training schedules, multiple other frameworks and newer backbones.
Finally, the combined effect of several asymmetric designs achieves a
state-of-the-art accuracy on ImageNet linear probing and competitive results on
downstream transfer. We hope our exploration will inspire more research in
exploiting asymmetry for Siamese representation learning.


title: Integrability in the weak noise theory

abstract:
  We consider the variational problem associated with the Freidlin--Wentzell
Large Deviation Principle (LDP) for the Stochastic Heat Equation (SHE). For a
general class of initial-terminal conditions, we show that a minimizer of this
variational problem exists, and any minimizer solves a system of imaginary-time
Nonlinear Schr\"{o}dinger equations. This system is integrable. Utilizing the
integrability, we prove that the formulas from the physics work Krajenbrink and
Le Doussal (2021) hold for every minimizer of the variational problem. As an
application, we consider the Freidlin--Wentzell LDP for the SHE with the delta
initial condition. Under a technical assumption on the poles of the reflection
coefficients, we prove the explicit expression for the one-point rate function
that was predicted in the physics works Le Doussal, Majumdar, Rosso, and Schehr
(2016) and Krajenbrink and Le Doussal (2021). Under the same assumption, we
give detailed pointwise estimates of the most probable shape in the upper-tail
limit.


title: Large-scale Rook Placements

abstract:
  For each certain "nice" piecewise linear function $f:[0,1] \to [0,1]$, we
consider a family of growing Young diagrams $\{\lambda(f,N)\}_{N=1}^{\infty}$
by enlarging the region under the graph of $f$. We compute asymptotic formulas
for the number of rook placements of the shape $\lambda(f,N)$. We prove that
the normalized cumulative X-ray of a uniformly random permutation, as the size
of the permutation grows, exhibits a limit shape phenomenon.


title:
Simplicial Embeddings in Self-Supervised Learning and Downstream
  Classification

abstract:
  We introduce Simplicial Embeddings (SEMs) as a way to constrain the encoded
representations of a self-supervised model to $L$ simplices of $V$ dimensions
each using a Softmax operation. This procedure imposes a structure on the
representations that reduce their expressivity for training downstream
classifiers, which helps them generalize better. Specifically, we show that the
temperature $\tau$ of the Softmax operation controls for the SEM
representation's expressivity, allowing us to derive a tighter downstream
classifier generalization bound than that for classifiers using unnormalized
representations. We empirically demonstrate that SEMs considerably improve
generalization on natural image datasets such as CIFAR-100 and ImageNet.
Finally, we also present evidence of the emergence of semantically relevant
features in SEMs, a pattern that is absent from baseline self-supervised
models.


title:
Visual explanations for polyp detection: How medical doctors assess
  intrinsic versus extrinsic explanations

abstract:
  Deep learning has in recent years achieved immense success in all areas of
computer vision and has the potential of assisting medical doctors in analyzing
visual content for disease and other abnormalities. However, the current state
of deep learning is very much a black box, making medical professionals highly
skeptical about integrating these methods into clinical practice. Several
methods have been proposed in order to shine some light onto these black boxes,
but there is no consensus on the opinion of the medical doctors that will
consume these explanations. This paper presents a study asking medical doctors
about their opinion of current state-of-the-art explainable artificial
intelligence methods when applied to a gastrointestinal disease detection use
case. We compare two different categories of explanation methods, intrinsic and
extrinsic, and gauge their opinion of the current value of these explanations.
The results indicate that intrinsic explanations are preferred and that
explanation.


title: A single speaker is almost all you need for automatic speech recognition

abstract:
  We explore the use of speech synthesis and voice conversion applied to
augment datasets for automatic speech recognition (ASR) systems, in scenarios
with only one speaker available for the target language. Through extensive
experiments, we show that our approach achieves results compared to the
state-of-the-art (SOTA) and requires only one speaker in the target language
during speech synthesis/voice conversion model training. Finally, we show that
it is possible to obtain promising results in the training of an ASR model with
our data augmentation method and only a single real speaker in different target
languages.


title:
CogNGen: Constructing the Kernel of a Hyperdimensional Predictive
  Processing Cognitive Architecture

abstract:
  We present a new cognitive architecture that combines two neurobiologically
plausible, computational models: (1) a variant of predictive processing known
as neural generative coding (NGC) and (2) hyperdimensional, vector-symbolic
models of human memory. We draw inspiration from well-known cognitive
architectures such as ACT-R, Soar, Leabra, and Spaun/Nengo. Our cognitive
architecture, the COGnitive Neural GENerative system (CogNGen), is in broad
agreement with these architectures, but provides a level of detail between
ACT-R's high-level, symbolic description of human cognition and Spaun's
low-level neurobiological description. CogNGen creates the groundwork for
developing agents that learn continually from diverse tasks and model human
performance at larger scales than what is possible with existent cognitive
architectures. We aim to develop a cognitive architecture that has the power of
modern machine learning techniques while retaining long-term memory,
single-trial learning, transfer-learning, planning, and other capacities
associated with high-level cognition. We test CogNGen on a set of maze-learning
tasks, including mazes that test short-term memory and planning, and find that
the addition of vector-symbolic models of memory improves the ability of the
NGC reinforcement learning model to master the maze task. Future work includes
testing CogNGen on more tasks and exploring methods for efficiently scaling
hyperdimensional memory models to lifetime learning.


title: Computation of optimal beams in weak turbulence

abstract:
  When an optical beam propagates through a turbulent medium such as the
atmosphere or ocean, the beam will become distorted. It is then natural to seek
the best or optimal beam that is distorted least, under some metric such as
intensity or scintillation. We seek to maximize the light intensity at the
receiver using the paraxial wave equation with weak-fluctuation as the model.
In contrast to classical results that typically confine original laser beams to
be from a special class, we allow the beam to be general, which leads to an
eigenvalue problem of a large-sized matrix with each entry being a
multi-dimensional integral. This is an expensive and sometimes infeasible
computational task in many practically reasonable settings. To overcome this,
we utilize an asymptotic expansion and transform the derivation to Fourier
space, which allows us to incorporate some optional turbulence assumptions,
such as homogeneous-statistics assumption, small-length-scale cutoff
assumption, and Markov assumption, to reduce the dimension of the numerical
integral. The proposed methods provide a computational strategy that is
numerically feasible, and results are demonstrated in several numerical
examples.

categories: physics.comp-ph cs.NA math.NA physics.optics


title:
Study about a Differential Equation in an Infinite Servers Queue System
  with Poisson Arrivals Busy Cycle Distribution Study

abstract:
  In the infinite servers queue with Poisson arrivals real life practical
applications, the busy period and the busy cycle probabilistic study is of main
importance. But it is a very difficult task. In this text, we show that by
solving a Riccati equation induced by this queue transient probabilities
monotony study as time functions, we obtain a collection of service length
distribution functions, for which both the busy period and the busy cycle have
lengths with quite simple distributions, generally given in terms of
exponential distributions and the degenerate at the origin distribution.


title: Universal Lymph Node Detection in T2 MRI using Neural Networks

abstract:
  Purpose: Identification of abdominal Lymph Nodes (LN) that are suspicious for
metastasis in T2 Magnetic Resonance Imaging (MRI) scans is critical for staging
of lymphoproliferative diseases. Prior work on LN detection has been limited to
specific anatomical regions of the body (pelvis, rectum) in single MR slices.
Therefore, the development of a universal approach to detect LN in full T2 MRI
volumes is highly desirable.
  Methods: In this study, a Computer Aided Detection (CAD) pipeline to
universally identify abdominal LN in volumetric T2 MRI using neural networks is
proposed. First, we trained various neural network models for detecting LN:
Faster RCNN with and without Hard Negative Example Mining (HNEM), FCOS,
FoveaBox, VFNet, and Detection Transformer (DETR). Next, we show that the
state-of-the-art (SOTA) VFNet model with Adaptive Training Sample Selection
(ATSS) outperforms Faster RCNN with HNEM. Finally, we ensembled models that
surpassed a 45% mAP threshold. We found that the VFNet model and one-stage
model ensemble can be interchangeably used in the CAD pipeline.
  Results: Experiments on 122 test T2 MRI volumes revealed that VFNet achieved
a 51.1% mAP and 78.7% recall at 4 false positives (FP) per volume, while the
one-stage model ensemble achieved a mAP of 52.3% and sensitivity of 78.7% at
4FP.
  Conclusion: Our contribution is a CAD pipeline that detects LN in T2 MRI
volumes, resulting in a sensitivity improvement of $\sim$14 points over the
current SOTA method for LN detection (sensitivity of 78.7% at 4 FP vs. 64.6% at
5 FP per volume).


title: Bayesian Image Super-Resolution with Deep Modeling of Image Statistics

abstract:
  Modeling statistics of image priors is useful for image super-resolution, but
little attention has been paid from the massive works of deep learning-based
methods. In this work, we propose a Bayesian image restoration framework, where
natural image statistics are modeled with the combination of smoothness and
sparsity priors. Concretely, firstly we consider an ideal image as the sum of a
smoothness component and a sparsity residual, and model real image degradation
including blurring, downscaling, and noise corruption. Then, we develop a
variational Bayesian approach to infer their posteriors. Finally, we implement
the variational approach for single image super-resolution (SISR) using deep
neural networks, and propose an unsupervised training strategy. The experiments
on three image restoration tasks, \textit{i.e.,} ideal SISR, realistic SISR,
and real-world SISR, demonstrate that our method has superior model
generalizability against varying noise levels and degradation kernels and is
effective in unsupervised SISR. The code and resulting models are released via
\url{https://zmiclab.github.io/projects.html}.


title:
Explainable and Interpretable Diabetic Retinopathy Classification Based
  on Neural-Symbolic Learning

abstract:
  In this paper, we propose an explainable and interpretable diabetic
retinopathy (ExplainDR) classification model based on neural-symbolic learning.
To gain explainability, a highlevel symbolic representation should be
considered in decision making. Specifically, we introduce a human-readable
symbolic representation, which follows a taxonomy style of diabetic retinopathy
characteristics related to eye health conditions to achieve explainability. We
then include humanreadable features obtained from the symbolic representation
in the disease prediction. Experimental results on a diabetic retinopathy
classification dataset show that our proposed ExplainDR method exhibits
promising performance when compared to that from state-of-the-art methods
applied to the IDRiD dataset, while also providing interpretability and
explainability.


title:
An ICT based Solution for Virtual Garment Fitting for Online Market
  Place: A Review of Related Literature

abstract:
  In this paper, we describe various technologies that are being used in
virtual garment fitting and simulation. There, we have focused about the usage
of anthropometry in clothing industry and avatar generation of virtual garment
fitting. Most commonly used technologies for avatar generation in virtual
environment have been discussed in this paper such as generic body model and
laser scanning. Moreover, this paper includes the real-time tracking
technologies used in virtual garment fitting like markers and depth cameras in
various related researches as well as how the virtual cloth generation and
simulation carried out in the related researches. Apart from these, virtual
clothing methods such as geometrical, physical and hybrid based models were
also discussed in this paper. As ease allowance has a major impact on virtual
cloth fitting, it is also considered in this paper related to similar
researches. Within this paper, all the above mentioned areas were described
thoroughly while stating the existing gap of the virtual garment fitting in
online marketplaces.


title:
Stochastic state-transition-change process and time resolved velocity
  spectrometry

abstract:
  Motion of particles (bodies) in presence of random effects can be considered
stochastic process. However, application of widely known stochastic processes
used for description of particle motion is reduced to relatively small class of
particle transport phenomena. Stochastic state-transition-change (STC) process
is suitable for description of many systems. In this paper it is shown under
which assumptions formulae of time resolved velocity spectrometry can be
derived with the help of STC process. It opens up new possibilities of unified
description of particles moving in a force field in presence of random effects.
It extends possibilities of applications of theory of stochastic processes in
physics.


title:
Teaching scenarios and their role in the interdisciplinary approach.
  Case study: The Minkowskian Metric

abstract:
  One of the demands of modern teaching is the interdisciplinary approach of
cognitive subjects and in particular that of the natural sciences, with
simultaneous engagement of teachers and students. At the same time, teaching
scenarios seem to be gaining ground in the methodology of teaching and learning
Mathematics in school. In this paper we analyze the concepts of the teaching
scenario and interdisciplinarity, and we briefly present the basic results of a
teaching experiment using teaching scenario, focused on the teaching of
Minkowskian Metric in two dimensions.


title: Learning Neural Acoustic Fields

abstract:
  Our environment is filled with rich and dynamic acoustic information. When we
walk into a cathedral, the reverberations as much as appearance inform us of
the sanctuary's wide open space. Similarly, as an object moves around us, we
expect the sound emitted to also exhibit this movement. While recent advances
in learned implicit functions have led to increasingly higher quality
representations of the visual world, there have not been commensurate advances
in learning spatial auditory representations. To address this gap, we introduce
Neural Acoustic Fields (NAFs), an implicit representation that captures how
sounds propagate in a physical scene. By modeling acoustic propagation in a
scene as a linear time-invariant system, NAFs learn to continuously map all
emitter and listener location pairs to a neural impulse response function that
can then be applied to arbitrary sounds. We demonstrate that the continuous
nature of NAFs enables us to render spatial acoustics for a listener at an
arbitrary location, and can predict sound propagation at novel locations. We
further show that the representation learned by NAFs can help improve visual
learning with sparse views. Finally, we show that a representation informative
of scene structure emerges during the learning of NAFs.


title: TopTemp: Parsing Precipitate Structure from Temper Topology

abstract:
  Technological advances are in part enabled by the development of novel
manufacturing processes that give rise to new materials or material property
improvements. Development and evaluation of new manufacturing methodologies is
labor-, time-, and resource-intensive expensive due to complex, poorly defined
relationships between advanced manufacturing process parameters and the
resulting microstructures. In this work, we present a topological
representation of temper (heat-treatment) dependent material micro-structure,
as captured by scanning electron microscopy, called TopTemp. We show that this
topological representation is able to support temper classification of
microstructures in a data limited setting, generalizes well to previously
unseen samples, is robust to image perturbations, and captures domain
interpretable features. The presented work outperforms conventional deep
learning baselines and is a first step towards improving understanding of
process parameters and resulting material properties.


title: Extremely Low-light Image Enhancement with Scene Text Restoration

abstract:
  Deep learning-based methods have made impressive progress in enhancing
extremely low-light images - the image quality of the reconstructed images has
generally improved. However, we found out that most of these methods could not
sufficiently recover the image details, for instance, the texts in the scene.
In this paper, a novel image enhancement framework is proposed to precisely
restore the scene texts, as well as the overall quality of the image
simultaneously under extremely low-light images conditions. Mainly, we employed
a self-regularised attention map, an edge map, and a novel text detection loss.
In addition, leveraging synthetic low-light images is beneficial for image
enhancement on the genuine ones in terms of text detection. The quantitative
and qualitative experimental results have shown that the proposed model
outperforms state-of-the-art methods in image restoration, text detection, and
text spotting on See In the Dark and ICDAR15 datasets.


title:
UNetFormer: A Unified Vision Transformer Model and Pre-Training
  Framework for 3D Medical Image Segmentation

abstract:
  Vision Transformers (ViT)s have recently become popular due to their
outstanding modeling capabilities, in particular for capturing long-range
information, and scalability to dataset and model sizes which has led to
state-of-the-art performance in various computer vision and medical image
analysis tasks. In this work, we introduce a unified framework consisting of
two architectures, dubbed UNetFormer, with a 3D Swin Transformer-based encoder
and Convolutional Neural Network (CNN) and transformer-based decoders. In the
proposed model, the encoder is linked to the decoder via skip connections at
five different resolutions with deep supervision. The design of proposed
architecture allows for meeting a wide range of trade-off requirements between
accuracy and computational cost. In addition, we present a methodology for
self-supervised pre-training of the encoder backbone via learning to predict
randomly masked volumetric tokens using contextual information of visible
tokens. We pre-train our framework on a cohort of $5050$ CT images, gathered
from publicly available CT datasets, and present a systematic investigation of
various components such as masking ratio and patch size that affect the
representation learning capability and performance of downstream tasks. We
validate the effectiveness of our pre-training approach by fine-tuning and
testing our model on liver and liver tumor segmentation task using the Medical
Segmentation Decathlon (MSD) dataset and achieve state-of-the-art performance
in terms of various segmentation metrics. To demonstrate its generalizability,
we train and test the model on BraTS 21 dataset for brain tumor segmentation
using MRI images and outperform other methods in terms of Dice score. Code:
https://github.com/Project-MONAI/research-contributions


title:
Emergent $\mathbb{Z}_2$ gauge theories and topological excitations in
  Rydberg atom arrays

abstract:
  Strongly interacting arrays of Rydberg atoms provide versatile platforms for
exploring exotic many-body phases and dynamics of correlated quantum systems.
Motivated by recent experimental advances, we show that the combination of
Rydberg interactions and appropriate lattice geometries naturally leads to
emergent $\mathbb{Z}_2$ gauge theories endowed with matter fields. Based on
this mapping, we describe how Rydberg platforms could realize two distinct
classes of topological $\mathbb{Z}_2$ quantum spin liquids, which differ in
their patterns of translational symmetry fractionalization. We also discuss the
natures of the fractionalized excitations of these $\mathbb{Z}_2$ spin liquid
states using both fermionic and bosonic parton theories, and illustrate their
rich interplay with proximate solid phases.

categories: cond-mat.quant-gas cond-mat.str-el hep-th quant-ph


title:
Images of Embedded Jovian Planet Formation At A Wide Separation Around
  AB Aurigae

abstract:
  Direct images of protoplanets embedded in disks around infant stars provide
the key to understanding the formation of gas giant planets like Jupiter. Using
the Subaru Telescope and Hubble Space Telescope, we find evidence for a jovian
protoplanet around AB Aurigae orbiting at a wide projected separation (93 au),
likely responsible for multiple planet-induced features in the disk. Its
emission is reproducible as reprocessed radiation from an embedded protoplanet.
We also identify two structures located at 430-580 au that are candidate sites
of planet formation. These data reveal planet formation in the embedded phase
and a protoplanet discovery at wide, > 50 au separations characteristic of most
imaged exoplanets. With at least one clump-like protoplanet and multiple spiral
arms, the AB Aur system may also provide the evidence for a long-considered
alternative to the canonical model for Jupiter's formation: disk
(gravitational) instability.


title: Searching for QGP droplets with high-$p_T$ hadrons and heavy flavor

abstract:
  The search for the smallest quark-gluon plasma (QGP) droplets in nature has
motivated recent small collisions system programs at RHIC and LHC. Unambiguous
identification of jet quenching due to final-state interactions is key to
confirming QGP formation in these reactions. We compute the nuclear
modification factors $R_{AA}$ and $R_{p(d)A}$ of charged hadrons and heavy
flavor mesons in large (Au-Au, Xe-Xe, Pb-Pb) and small ($d$-Au, $p$-Pb, O-O)
colliding systems, respectively. Our results include the Cronin effect and
initial-state parton energy loss in cold nuclear matter. In the final state,
hard partons undergo collisional energy loss and branching that was recently
derived using Soft-Collinear-Effective-Theory with Glauber Gluon (SCET$_{\rm
G}$). In large colliding systems, medium-modified QCD evolution of the
fragmentation functions dominates the nuclear correction. As the system size
decreases, we find that cold nuclear matter effects, collisional energy loss,
and QGP-induced radiations can become equally important. A systematic scan over
the medium size and mass/flavor dependence of $R_{AA}$ provides the opportunity
to separate these individual contributions and identify QGP signatures in small
systems. Predictions for $R_{AA}^{h}$, $R_{AA}^{D}$, $R_{AA}^{B}$ in O-O
collisions at $\sqrt{s}=7$ TeV are presented with and without the formation of
a QGP and contrasted with the corresponding $R_{p(d)A}$ calculations. Upcoming
single-hadron measurements at the LHC will not only test the O-O predictions
for both light and heavy flavor production, but will shed light on the possibly
very different dynamics of $p$-A and A-A reactions at similar soft particle
production multiplicities.


title:
Nearby galaxies in LoTSS-DR2: insights into the non-linearity of the
  radio-SFR relation

abstract:
  Context. Cosmic rays and magnetic fields are key ingredients in galaxy
evolution, regulating both stellar feedback and star formation. Their
properties can be studied with low-frequency radio continuum observations, free
from thermal contamination. Aims. We define a sample of 76 nearby (< 30 Mpc)
galaxies, with rich ancillary data in the radio continuum and infrared from the
CHANG-ES and KINGFISH surveys, which will be observed with the LOFAR Two-metre
Sky Survey (LoTSS) at 144 MHz. Methods. We present maps for 45 of them as part
of the LoTSS data release 2 (LoTSS-DR2), where we measure integrated flux
densities and study integrated and spatially resolved radio spectral indices.
We investigate the radio-SFR relation, using star-formation rates (SFR) from
total infrared and H $\alpha$ + 24-$\mu$m emission. Results. The radio-SFR
relation at 144 MHz is clearly super-linear with $L_{144} \propto
SFR^{1.4-1.5}$. The mean integrated radio spectral index between 144 and
$\approx$1400 MHz is $\langle \alpha\rangle = -0.56 \pm 0.14$, in agreement
with the injection spectral index for cosmic ray electrons (CRE). However, the
radio spectral index maps show a variation of spectral indices with flatter
spectra associated with star-forming regions and steeper spectra in galaxy
outskirts and, in particular, in extra-planar regions. We found that galaxies
with high star-formation rates (SFR) have steeper radio spectra; we find
similar correlations with galaxy size, mass, and rotation speed. Conclusions.
Galaxies that are larger and more massive are better electron calorimeters,
meaning that the CRE lose a higher fraction of their energy within the
galaxies. This explains the super-linear radio-SFR relation, with more massive,
star-forming galaxies being radio bright. We propose a semi-calorimetric
radio-SFR relation, which employs the galaxy mass as a proxy for the
calorimetric efficiency.


title:
The Andromeda Gamma-Ray Excess: Background Systematics of the
  Millisecond Pulsars and Dark Matter Interpretations

abstract:
  Since the discovery of an excess in gamma rays in the direction of M31, its
cause has been unclear. Published interpretations focus on a dark matter or
stellar related origin. Studies of a similar excess in the Milky Way center
motivate a correlation of the spatial morphology of the signal with the
distribution of stellar mass in M31. However, a robust determination of the
best theory for the observed excess emission is very challenging due to large
uncertainties in the astrophysical gamma-ray foreground model. Here we perform
a spectro-morphological analysis of the M31 gamma-ray excess using
state-of-the-art templates for the distribution of stellar mass in M31 and
novel astrophysical foreground models for its sky region. We construct maps for
the old stellar populations of M31 based on observational data from the PAndAS
survey and carefully remove the foreground stars. We also produce improved
astrophysical foreground models by using novel image inpainting techniques
based on machine learning methods. We find that our stellar maps, taken as a
proxy for the location of a putative population of millisecond pulsars in the
bulge of M31, reach a statistical significance of $5.4\sigma$, making them as
strongly favoured as the simple phenomenological models usually considered in
the literature, e.g., a disk-like template with uniform brightness. Our
detection of the stellar templates is robust to generous variations of the
astrophysical foreground model. Once the stellar templates are included in the
astrophysical model, we show that the dark matter annihilation interpretation
of the signal is unwarranted. Using the results of a binary population
synthesis model we demonstrate that a population of about one million
unresolved MSPs could naturally explain the observed gamma-ray luminosity per
stellar mass, energy spectrum, and stellar bulge-to-disk flux ratio.


title: The extended HI halo of NGC 4945 as seen by MeerKAT

abstract:
  Observations of the neutral atomic hydrogen (HI) in the nuclear starburst
galaxy NGC 4945 with MeerKAT are presented. We find a large amount of halo gas,
previously missed by HI observations, accounting for 6.8% of the total HI mass.
This is most likely gas blown into the halo by star formation. Our maps go down
to a $3\sigma$ column density level of $5\times10^{18} cm^{-2}$ . We model the
HI distribution using tilted-ring fitting techniques and find a warp on the
galaxy's approaching and receding sides. The HI in the northern side of the
galaxy appears to be suppressed. This may be the result of ionisation by the
starburst activity in the galaxy, as suggested by a previous study. The origin
of the warp is unclear but could be due to past interactions or ram pressure
stripping. Broad, asymmetric HI absorption lines extending beyond the HI
emission velocity channels are present towards the nuclear region of NGC 4945.
Such broad lines suggest the existence of a nuclear ring moving at a high
circular velocity. This is supported by the clear rotation patterns in the HI
absorption velocity field. The asymmetry of the absorption spectra can be
caused by outflows or inflows of gas in the nuclear region of NGC 4945. The
continuum map shows small extensions on both sides of the galaxy's major axis
that might be signs of outflows resulting from the starburst activity.