Abstract:
  Given a graph $G$, a function $c:V(G)\longrightarrow \{1,\ldots,k\}$ with the
property that $c(u)=c(v)=i$ implies that the distance between $u$ and $v$ is
greater than $i$, is called a $k$-packing coloring of $G$. The smallest integer
$k$ for which there exists a $k$-packing coloring of $G$ is called the packing
chromatic number of $G$, and is denoted by $\chi_\rho$. Packing chromatic
vertex-critical graphs are the graphs $G$ for which $\chi_\rho(G-x) <
\chi_\rho(G)$ holds for every vertex $x$ of $G$. A graph $G$ is called a
packing chromatic critical graph if for every proper subgraph $H$ of $G$,
$\chi_\rho(H) < \chi_\rho(G)$. Both of the mentioned variations of critical
graphs with respect to the packing chromatic number have already been studied.
All packing chromatic (vertex-)critical graphs $G$ with $\chi_\rho(G)=3$ were
characterized, while there were known only partial results for graphs $G$ with
$\chi_\rho(G)=4$. In this paper, we provide characterizations of all packing
chromatic vertex-critical graphs $G$ with $\chi_\rho(G)=4$ and all packing
chromatic critical graphs $G$ with $\chi_\rho(G)=4$.


Abstract:
  We present polarimetric observations of the Keyhole Nebula in the Carina
Nebula Complex carried out using the Stratospheric Observatory for Infrared
Astronomy. The Keyhole Nebula located to the west of $\eta$ Carinae is believed
to be disturbed by the stellar winds from the star. We observed the Keyhole
Nebula at 89 $\mu$m wavelength with the HAWC+ instrument. The observations
cover the entire Keyhole Nebula spanning 8$'$ by 5$'$ with central position RA
= 10:44:43 and Dec = -59:38:04. The typical uncertainty of polarization
measurement is less than 0.5\% in the region with intensity above 5,500 MJy
sr$^{-1}$. The polarization has a mean of 2.4\% with a standard deviation of
1.6\% in the region above this intensity, similar to values in other high--mass
star--forming regions. The magnetic field orientation in the bar--shaped
structure is similar to the large--scale magnetic field orientation. On the
other hand, the magnetic field direction in the loop is not aligned with the
large--scale magnetic fields but has tight alignment with the loop itself.
Analysis of the magnetic field angles and the gas turbulence suggests that the
field strength is $\sim$70 $\mu$G in the loop. A simple comparison of the
magnetic field tension to the ram pressure of $\eta$ Carinae's stellar wind
suggests that the magnetic fields in the Keyhole Nebula are not strong enough
to maintain the current structure against the impact of the stellar wind, and
that the role of the magnetic field in resisting stellar feedback in the
Keyhole Nebula is limited.


Abstract:
  In this work, we study the relation of the eikonal quasinormal modes (EQNMs)
and the unstable fundamental photon orbits (UFPOs) in the Kerr-Newman
spacetime. We find that in the eikonal limit the gravitational and
electromagnetic perturbations of the Kerr-Newman black hole are naturally
decoupled, and a single one-dimensional Schr\"odinger-like equation encoding
the QNM spectrum can be derived. We then show that the decoupled Teukolsky
master equation and the Klein-Gordon equation for the massless scalar field in
the Kerr-Newman spacetime are of the same form in the eikonal limit. As a
direct consequence, taking into account of the boundary conditions for EQNMs we
show an exact correspondence between EQNMs and UFPOs, that is, EQNM/UFPO
correspondence. More precisely, similar to the Kerr case, the real part of
EQNM's frequency is a linear combination of the precessional and (polar)
orbital frequencies, while the imaginary part of the frequency is proportional
to the Lyapunov exponent of the UFPO.


Abstract:
  We report on a windowless, high-density, gas flow target at Jefferson Lab
that was used to measure $r_p$, the root-mean-square charge radius of the
proton. To our knowledge, this is the first such system used in a fixed-target
experiment at a (non-storage ring) electron accelerator. The target achieved
its design goal of an areal density of 2$\times$10$^{18}$ atoms/cm$^2$, with
the gas uniformly distributed over the 4 cm length of the cell and less than 1%
residual gas outside the cell. This design eliminated scattering from the end
caps of the target cell, a problem endemic to previous measurements of the
proton charge radius in electron scattering experiments, and permitted a
precise, model-independent extraction of $r_p$ by reaching unprecedentedly low
values of $Q^2$, the square of the electron's transfer of four-momentum to the
proton.


Abstract:
  The undecidability of basic decision problems for general FIFO machines such
as reachability and unboundedness is well-known. In this paper, we provide an
underapproximation for the general model by considering only runs that are
input-bounded (i.e.\ the sequence of messages sent through a particular channel
belongs to a given bounded language). We prove, by reducing this model to a
counter machine with restricted zero tests, that the rational-reachability
problem (and by extension, control-state reachability, unboundedness, deadlock,
etc.) is decidable. This class of machines subsumes input-letter-bounded
machines, flat machines, linear FIFO nets, and monogeneous machines, for which
some of these problems were already shown to be decidable. These theoretical
results can form the foundations to build a tool to verify general FIFO
machines based on the analysis of input-bounded machines.


Abstract:
  Let $S$ be an unramified regular local ring of mixed characteristic two and
$R$ the integral closure of $S$ in a biquadratic extension of its quotient
field obtained by adjoining roots of sufficiently general square free elements
$f,g\in S$. Let $S^2$ denote the subring of $S$ obtained by lifting to $S$ the
image of the Frobenius map on $S/2S$. When at least one of $f,g\in S^2$, we
characterize the Cohen-Macaulayness of $R$ and show that $R$ admits a
birational small Cohen-Macaulay module. It is noted that $R$ is not
automatically Cohen-Macaulay in case $f,g\in S^2$ or if $f,g\notin S^2$.


Abstract:
  Given a nonnegative, smooth potential $V: \mathbb{R}^k \to \mathbb{R}$ ($k
\geq 2$) with multiple zeros, we say that a curve $\mathfrak{q}: \mathbb{R} \to
\mathbb{R}^k$ is a connecting orbit if it solves the autonomous system of
ordinary differential equations \begin{equation} \mathfrak{q}''=
\nabla_{\mathbf{u}} V(\mathfrak{q}) , \hspace{2mm} \mbox{ in } \mathbb{R}
\end{equation} and tends to a zero of $V$ at $\pm \infty$. Broadly, our goal is
to study the existence of connecting orbits for the problem above using
variational methods. Despite the rich previous literature concerning the
existence of connecting orbits for other types of second order systems, to our
knowledge only connecting orbits which minimize the associated energy
functional in a suitable function space were proven to exist for autonomous
multi-well potentials. The contribution of this paper is to provide, for a
class of such potentials, some existence results regarding non-minimizing
connecting orbits. Our results are closely related to the ones in the same
spirit obtained by J. Bisgard in his PhD thesis (University of
Wisconsin-Madison, 2005), where non-autonomous periodic multi-well potentials
(ultimately excluding autonomous potentials) are considered. Our approach is
based on several refined versions of the classical Mountain Pass Lemma.


Abstract:
  We investigate the problem of determining the shape of a rotating celestial
object - e.g., a comet or an asteroid - under its own gravitational field. More
specifically, we consider an object symmetric with respect to one axis - such
as a dumbbell - that rotates around a second axis perpendicular to the symmetry
axis. We assume that the object can be modeled as an incompressible fluid of
constant mass density, which is regarded as a first approximation of an
aggregate of particles.
  In the literature, the gravitational field of a body is often described as a
multipolar expansion involving spherical coordinates (Kaula, 1966). In this
work we describe the shape in terms of cylindrical coordinates, which are most
naturally adapted to the symmetry of the body, and we express the gravitational
potential generated by the rotating body as a simple formula in terms of
elliptic integrals. An equilibrium shape occurs when the gravitational
potential energy and the rotational kinetic energy at the surface of the body
balance each other out. Such an equilibrium shape can be derived as a solution
of an optimization problem, which can be found via the variational method. We
give an example where we apply this method to a two-parameter family of
dumbbell shapes, and find approximate numerical solutions to the corresponding
optimization problem.


Abstract:
  Recent experiments have elucidated that novel nonequilibrium states inherent
in the so-called hydrodynamic regime are realized in ultrapure metals with
sufficiently strong momentum-conserving scattering. In this letter, we
formulate a theory of electron hydrodynamics with broken inversion symmetry
under magnetic fields and find that novel terms emerge in hydrodynamic
equations which play a crucial role for the realization of the nonreciprocal
responses. Specifically, we clarify that there exist a novel type of
nonreciprocal collective modes dubbed nonreciprocal surface magnetoplasmons
arising from an interplay between magnetic fields and the orbital magnetic
moment. We reveal that these nonreciprocal collective modes indeed give rise to
the nonreciprocity in magneto-optical responses such as the reflectivity. The
physics discussed here will bridge the two important notions of
magnetoplasmonics and nonreciprocity in electron hydrodynamic materials with
inversion symmetry breaking and magnetic fields.


Abstract:
  A remarkable approach for grasping the relevant statistical features of real
networks with the help of random graphs is offered by hyperbolic models,
centred around the idea of placing nodes in a low-dimensional hyperbolic space,
and connecting node pairs with a probability depending on the hyperbolic
distance. It is widely appreciated that these models can generate random graphs
that are small-world, highly clustered and scale-free at the same time; thus,
reproducing the most fundamental common features of real networks. In the
present work, we focus on a less well-known property of the
popularity-similarity optimisation (PSO) model and the
$\mathbb{S}^1/\mathbb{H}^2$ model from this model family, namely that the
networks generated by these approaches also contain communities for a wide
range of the parameters, which was certainly not an intention at the design of
the models. We extracted the communities from the studied networks using
well-established community finding methods such as Louvain, Infomap and label
propagation. The observed high modularity values indicate that the community
structure can become very pronounced under certain conditions. In addition, the
modules found by the different algorithms show good consistency, implying that
these are indeed relevant and apparent structural units. Since the appearance
of communities is rather common in networks representing real systems as well,
this feature of hyperbolic models makes them even more suitable for describing
real networks than thought before.


Abstract:
  In this paper, we investigate the state estimation problem over multiple
Markovian packet drop channels. In this problem setup, a remote estimator
receives measurement data transmitted from multiple sensors over individual
channels. By the method of Markovian jump linear systems, an optimal stationary
estimator that minimizes the error variance in the steady state is obtained,
based on the mean-square (MS) stabilizing solution to the coupled algebraic
Riccati equations. An explicit necessary and sufficient condition is derived
for the existence of the MS stabilizing solution, which coincides with that of
the standard Kalman filter. More importantly, we provide a sufficient condition
under which the MS detectability with multiple Markovian packet drop channels
can be decoupled, and propose a locally optimal stationary estimator but
computationally more tractable. Analytic sufficient and necessary MS
detectability conditions are presented for the decoupled subsystems
subsequently. Finally, numerical simulations are conducted to illustrate the
results on the MS stabilizing solution, the MS detectability, and the
performance of the optimal and locally optimal stationary estimators.


Abstract:
  While Shapley Values (SV) are one of the gold standard for interpreting
machine learning models, we show that they are still poorly understood, in
particular in the presence of categorical variables or of variables of low
importance. For instance, we show that the popular practice that consists in
summing the SV of dummy variables is false as it provides wrong estimates of
all the SV in the model and implies spurious interpretations. Based on the
identification of null and active coalitions, and a coalitional version of the
SV, we provide a correct computation and inference of important variables.
Moreover, a Python library (All the experiments and simulations can be
reproduced with the publicly available library Active Coalition of Variables,
https://www.github.com/salimamoukou/acv00) that computes reliably conditional
expectations and SV for tree-based models, is implemented and compared with
state-of-the-art algorithms on toy models and real data sets.


Abstract:
  In this paper, it is shown that for a minimal system $(X,T)$ and $d,k\in
\mathbb{N}$, if $(x,x_i)$ is regionally proximal of order $d$ for $1\leq i\leq
k$, then $(x,x_1,\ldots,x_k)$ is $(k+1)$-regionally proximal of order $d$.
  Meanwhile, we introduce the notion of $\mathrm{IN}^{[d]}$-pair: for a
dynamical system $(X,T)$ and $d\in \mathbb{N}$, a pair $(x_0,x_1)\in X\times X$
is called an $\mathrm{IN}^{[d]}$-pair if for any $k\in \mathbb{N}$ and any
neighborhoods $U_0 ,U_1 $ of $x_0$ and $x_1$ respectively, there exist integers
$p_j^{(i)},1\leq i\leq k,$ $1\leq j\leq d$ such that $$ \bigcup_{i=1}^k\{
p_1^{(i)}\epsilon(1)+\ldots+p_d^{(i)} \epsilon(d):\epsilon(j)\in \{0,1\},1\leq
j\leq d\}\backslash \{0\}\subset \mathrm{Ind}(U_0,U_1), $$ where
$\mathrm{Ind}(U_0,U_1)$ denotes the collection of all independence sets for
$(U_0,U_1)$. It turns out that for a minimal system, if it dose not contain any
nontrivial $\mathrm{IN}^{[d]}$-pair, then it is an almost one-to-one extension
of its maximal factor of order $d$.


Abstract:
  Mortality forecasting plays a pivotal role in insurance and financial risk
management of life insurers, pension funds, and social securities. Mortality
data is usually high-dimensional in nature and favors factor model approaches
to modelling and forecasting. This paper introduces a new forecast-driven
hierarchical factor model (FHFM) customized for mortality forecasting. Compared
to existing models, which only capture the cross-sectional variation or
time-serial dependence in the dimension reduction step, the new model captures
both features efficiently under a hierarchical structure, and provides insights
into the understanding of dynamic variation of mortality patterns over time. By
comparing with static PCA utilized in Lee and Carter 1992, dynamic PCA
introduced in Lam et al. 2011, as well as other existing mortality modelling
methods, we find that this approach provides both better estimation results and
superior out-of-sample forecasting performance. Simulation studies further
illustrate the advantages of the proposed model based on different data
structures. Finally, empirical studies using the US mortality data demonstrate
the implications and significance of this new model in life expectancy
forecasting and life annuities pricing.


Abstract:
  We present a novel unsupervised framework for instance-level image-to-image
translation. Although recent advances have been made by incorporating
additional object annotations, existing methods often fail to handle images
with multiple disparate objects. The main cause is that, during inference, they
apply a global style to the whole image and do not consider the large style
discrepancy between instance and background, or within instances. To address
this problem, we propose a class-aware memory network that explicitly reasons
about local style variations. A key-values memory structure, with a set of
read/update operations, is introduced to record class-wise style variations and
access them without requiring an object detector at the test time. The key
stores a domain-agnostic content representation for allocating memory items,
while the values encode domain-specific style representations. We also present
a feature contrastive loss to boost the discriminative power of memory items.
We show that by incorporating our memory, we can transfer class-aware and
accurate style representations across domains. Experimental results demonstrate
that our model outperforms recent instance-level methods and achieves
state-of-the-art performance.


Abstract:
  This paper proposes a novel ternary hash encoding for learning to hash
methods, which provides a principled more efficient coding scheme with
performances better than those of the state-of-the-art binary hashing
counterparts. Two kinds of axiomatic ternary logic, Kleene logic and
{\L}ukasiewicz logic are adopted to calculate the Ternary Hamming Distance
(THD) for both the learning/encoding and testing/querying phases. Our work
demonstrates that, with an efficient implementation of ternary logic on
standard binary machines, the proposed ternary hashing is compared favorably to
the binary hashing methods with consistent improvements of retrieval mean
average precision (mAP) ranging from 1\% to 5.9\% as shown in CIFAR10, NUS-WIDE
and ImageNet100 datasets.


Abstract:
  We demonstrate an approach to obtaining near quantum limited far-field
imaging resolution of thermal, incoherent sources with arbitrary distributions.
Our method assumes no prior knowledge of the source distribution, but rather
uses an adaptive approach to imaging via spatial-mode demultiplexing that
iteratively updates both the form of the spatial imaging modes and the estimate
of the source distribution. The optimal imaging modes are determined by
minimizing the estimated Cram\'er-Rao bound over the manifold of all possible
sets of orthogonal modes. We have observed through Monte Carlo simulations that
the manifold-optimized spatial mode demultiplexing measurement consistently
outperforms standard imaging techniques in the resolution and precision of
source reconstructions, and approaches the quantum limits as set by the quantum
Cram\'er-Rao bound. The adaptive framework presented here allows for a
consistent approach to achieving near quantum-limited imaging resolution of
arbitrarily distributed thermal sources.


Abstract:
  In the last decade, the growing influence of open source software has
necessitated the need to reduce the abstraction levels in hardware design. Open
source hardware significantly reduces the development time, increasing the
probability of first-pass success and enable developers to optimize software
solutions based on hardware features, thereby reducing the design costs. The
recent introduction of open source Process Development Kit (OpenPDK) by
Skywater technologies in June 2020 has eliminated the barriers to
Application-Specific Integrated Circuit (ASIC) design, which is otherwise
considered expensive and not easily accessible. The OpenPDK is the first
concrete step towards achieving the goal of open source circuit blocks that can
be imported to reuse and modify in ASIC design. With process technologies
scaling down for better performance, the need for entirely digital designs,
which can be synthesized in any standard Automatic Place-and-Route (APR) tool,
has increased considerably, for mapping physical design to the new process
technology. This work presents the first open source all-digital
Serializer/Deserializer (SerDes) for multi-GHz serial links designed using
Skywater OpenPDK 130nm process node. To ensure that the design is fully
synthesizable, the SerDes uses CMOS inverter-based drivers at the Tx, while the
Rx front end comprises a resistive feedback inverter as a sensing element,
followed by sampling elements. A fully digital oversampling CDR at the Rx
recovers the Tx clock for proper decoding of data bits. The physical design
flow utilizes OpenLANE, which is an end-to-end tool for generating GDS from
RTL. Virtuoso has been used for extracting parasitics for post-layout
simulations, which exhibit the SerDes functionality at 2 Gbps for 34 dB channel
loss while consuming 438 mW power. The GDS and netlist files of the SerDes are
uploaded in a GitHub repository for public access.


Abstract:
  We prove Lojasiewicz inequalities for the harmonic map energy for maps from
surfaces of positive genus into general target manifolds which are close to
simple bubble trees. Our results are not restricted to particular targets but
apply whenever the underlying bubble is not branched, or more generally
attached at a point which is not a branch point, and has integrable Jacobi
fields.


Abstract:
  Vision-and-language navigation (VLN) is a multimodal task where an agent
follows natural language instructions and navigates in visual environments.
Multiple setups have been proposed, and researchers apply new model
architectures or training techniques to boost navigation performance. However,
recent studies witness a slow-down in the performance improvements in both
indoor and outdoor VLN tasks, and the agents' inner mechanisms for making
navigation decisions remain unclear. To the best of our knowledge, the way the
agents perceive the multimodal input is under-studied and clearly needs
investigations. In this work, we conduct a series of diagnostic experiments to
unveil agents' focus during navigation. Results show that indoor navigation
agents refer to both object tokens and direction tokens in the instruction when
making decisions. In contrast, outdoor navigation agents heavily rely on
direction tokens and have a poor understanding of the object tokens.
Furthermore, instead of merely staring at surrounding objects, indoor
navigation agents can set their sights on objects further from the current
viewpoint. When it comes to vision-and-language alignments, many models claim
that they are able to align object tokens with certain visual targets, but we
cast doubt on the reliability of such alignments.


Abstract:
  In this paper, we consider a wireless network consisting of a base station
(BS) that is serving multiple real-time traffic streams forwarding information
updates to their destinations in order to sustain the freshness of information
for time-critical applications. Since the wireless channels may be unreliable
due to the impurities of the propagation environments, such as deep fading,
blockages, etc., we integrate a reconfigurable intelligent surface (RIS) to the
wireless system in order to mitigate the propagation-induced impairments,
enhance the quality of the wireless links, and ensure that the required
freshness of information is achieved for these real-time applications. For this
network set-up, we investigate the joint optimization of the traffic streams
scheduling and the RIS phase-shift matrix with the goal of minimizing the
long-term average Age of Information (AoI). The formulated optimization problem
is a mixed-integer non-convex optimization problem, which is difficult to
solve. To circumvent the high-coupled optimization variables, we decompose the
original problem into two sub-problems, namely, a traffic stream scheduling
sub-problem and a RIS phase-shift matrix sub-problem. Then, we resort to deep
reinforcement learning to obtain the traffic stream scheduling solution and
semi-definite relaxation (SDR) to determine the optimal RIS configuration.
Finally, we show that our approach evaluates the combined impact of scheduling
policy and RIS configuration on the long-term average AoI through extensive
simulations, where we demonstrate its superiority against other baseline
schemes.


Abstract:
  Quantum magnonics is an emerging research field, with great potential for
applications in magnon based hybrid systems and quantum information processing.
Quantum correlation, such as entanglement, is a central resource in many
quantum information protocols that naturally comes about in any study toward
quantum technologies. This applies also to quantum magnonics. Here, we
investigate antiferromagnets in which sublattices with ferromagnetic
interactions can have two different magnon modes, and we show how this may lead
to experimentally detectable bipartite continuous variable magnon-magnon
entanglement. The entanglement can be fully characterized via a single
squeezing parameter, or, equivalently, entanglement parameter. The clear
relation between the entanglement parameter and the Einstein, Podolsky, and
Rosen (EPR) function of the ground state opens up for experimental observation
of magnon-magnon continuous variable entanglement and EPR non-locality. We
propose a practical experimental realization to detect the EPR function of the
ground state, in a setting that relies on magnon-photon interaction in a
microwave cavity.


Abstract:
  Domain adaptation (DA) aims at transferring knowledge from a labeled source
domain to an unlabeled target domain. Though many DA theories and algorithms
have been proposed, most of them are tailored into classification settings and
may fail in regression tasks, especially in the practical keypoint detection
task. To tackle this difficult but significant task, we present a method of
regressive domain adaptation (RegDA) for unsupervised keypoint detection.
Inspired by the latest theoretical work, we first utilize an adversarial
regressor to maximize the disparity on the target domain and train a feature
generator to minimize this disparity. However, due to the high dimension of the
output space, this regressor fails to detect samples that deviate from the
support of the source. To overcome this problem, we propose two important
ideas. First, based on our observation that the probability density of the
output space is sparse, we introduce a spatial probability distribution to
describe this sparsity and then use it to guide the learning of the adversarial
regressor. Second, to alleviate the optimization difficulty in the
high-dimensional space, we innovatively convert the minimax game in the
adversarial training to the minimization of two opposite goals. Extensive
experiments show that our method brings large improvement by 8% to 11% in terms
of PCK on different datasets.


Abstract:
  Computing devices are vital to all areas of modern life and permeate every
aspect of our society. The ubiquity of computing and our reliance on it has
been accelerated and amplified by the COVID-19 pandemic. From education to work
environments to healthcare to defense to entertainment - it is hard to imagine
a segment of modern life that is not touched by computing. The security of
computers, systems, and applications has been an active area of research in
computer science for decades. However, with the confluence of both the scale of
interconnected systems and increased adoption of artificial intelligence, there
are many research challenges the community must face so that our society can
continue to benefit and risks are minimized, not multiplied. Those challenges
range from security and trust of the information ecosystem to adversarial
artificial intelligence and machine learning.
  Along with basic research challenges, more often than not, securing a system
happens after the design or even deployment, meaning the security community is
routinely playing catch-up and attempting to patch vulnerabilities that could
be exploited any minute. While security measures such as encryption and
authentication have been widely adopted, questions of security tend to be
secondary to application capability. There needs to be a sea-change in the way
we approach this critically important aspect of the problem: new incentives and
education are at the core of this change. Now is the time to refocus research
community efforts on developing interconnected technologies with security
"baked in by design" and creating an ecosystem that ensures adoption of
promising research developments. To realize this vision, two additional
elements of the ecosystem are necessary - proper incentive structures for
adoption and an educated citizenry that is well versed in vulnerabilities and
risks.


Abstract:
  The Perseus-Pisces supercluster is known as one of the largest structures in
the nearby Universe that has been charted by the galaxy and galaxy cluster
distributions. For the latter mostly clusters from the Abell catalogue have
been used. Here we take a new approach to a quantitative characterisation of
the Perseus-Pisces supercluster using a statistically complete sample of X-ray
luminous galaxy groups and clusters from our CLASSIX galaxy cluster redshift
survey. We used a friends-of-friends technique to construct the supercluster
membership. We also studied the structure of the Southern Great Wall, which
merges with the Perseus-Pisces supercluster with a slightly increased
friends-of-friends linking length. In this work we discuss the geometric
structure of the superclusters, compare the X-ray luminosity distribution of
the members with that of the surroundings, and provide an estimate of the
supercluster mass. These results establish Perseus-Pisces as the largest
superstructure in the Universe at redshifts z <= 0.03. With the new data this
supercluster extends through the zone of avoidance, which has also been
indicated by some studies of the galaxy distribution by means of HI
observations. We investigated whether the shapes of the member groups and
clusters in X-rays are aligned with the major axis of the supercluster. We find
no evidence for a pronounced alignment, except for the ellipticities of Perseus
and AWM7, which are aligned with the separation vector of the two systems and
weakly with the supercluster.


Abstract:
  In this work, we investigate the diffusive optical tomography (DOT) problem
in the case that limited boundary measurements are available. Motivated by the
direct sampling method (DSM), we develop a deep direct sampling method (DDSM)
to recover the inhomogeneous inclusions buried in a homogeneous background. In
this method, we design a convolutional neural network (CNN) to approximate the
index functional that mimics the underling mathematical structure. The benefits
of the proposed DDSM include fast and easy implementation, capability of
incorporating multiple measurements to attain high-quality reconstruction, and
advanced robustness against the noise. Numerical experiments show that the
reconstruction accuracy is improved without degrading the efficiency,
demonstrating its potential for solving the real-world DOT problems.


Abstract:
  The impact of machine learning models on healthcare will depend on the degree
of trust that healthcare professionals place in the predictions made by these
models. In this paper, we present a method to provide people with clinical
expertise with domain-relevant evidence about why a prediction should be
trusted. We first design a probabilistic model that relates meaningful latent
concepts to prediction targets and observed data. Inference of latent variables
in this model corresponds to both making a prediction and providing supporting
evidence for that prediction. We present a two-step process to efficiently
approximate inference: (i) estimating model parameters using variational
learning, and (ii) approximating maximum a posteriori estimation of latent
variables in the model using a neural network, trained with an objective
derived from the probabilistic model. We demonstrate the method on the task of
predicting mortality risk for patients with cardiovascular disease.
Specifically, using electrocardiogram and tabular data as input, we show that
our approach provides appropriate domain-relevant supporting evidence for
accurate predictions.


Abstract:
  This paper is preoccupied with the following question: given a (possibly
opaque) learning system, how can we understand whether its behaviour adheres to
governance constraints? The answer can be quite simple: we just need to "ask"
the system about it. We propose to construct an investigator agent to query a
learning agent -- the suspect agent -- to investigate its adherence to a given
ethical policy in the context of an information-seeking dialogue, modeled in
formal argumentation settings. This formal dialogue framework is the main
contribution of this paper. Through it, we break down compliance checking
mechanisms into three modular components, each of which can be tailored to
various needs in a vast amount of ways: an investigator agent, a suspect agent,
and an acceptance protocol determining whether the responses of the suspect
agent comply with the policy. This acceptance protocol presents a fundamentally
different approach to aggregation: rather than using quantitative methods to
deal with the non-determinism of a learning system, we leverage the use of
argumentation semantics to investigate the notion of properties holding
consistently. Overall, we argue that the introduced formal dialogue framework
opens many avenues both in the area of compliance checking and in the analysis
of properties of opaque systems.


Abstract:
  Non-Gaussian quantum states have been deterministically prepared and
autonomously stabilized in single- and two-mode circuit quantum electrodynamics
architectures via engineered dissipation. However, it is currently unknown how
to scale up this technique to multi-mode non-Gaussian systems. Here, we upgrade
dissipation engineering to collective (normal) modes of nonlinear resonator
arrays and show how to stabilize multi-mode Schrodinger cat states. These
states are multi-photon and multi-mode quantum superpositions of coherent
states in a single normal mode delocalized over an arbitrary number of
cavities. We consider tailored dissipative coupling between resonators that are
parametrically driven and feature an on-site nonlinearity, which is either a
Kerr-type nonlinearity or an engineered two-photon loss. For both types of
nonlinearity, we find the same exact closed-form solutions for the
two-dimensional steady-state manifold spanned by superpositions of multi-mode
Schrodinger cat states. We further show that, in the Zeno limit of strong
dissipative coupling, the even parity multi-mode cat state can be
deterministically prepared from the vacuum. Remarkably, engineered two-photon
loss gives rise to a fast relaxation towards the steady state, protecting the
state preparation against decoherence due to intrinsic single-photon losses and
imperfections in tailored dissipative coupling, which sets in at longer times.
The relaxation time is independent of system size making the state preparation
scalable. Multi-mode cat states are naturally endowed with a noise bias that
increases exponentially with system size and can thus be exploited for enhanced
robust encoding of quantum information.


Abstract:
  Using a sample of 96,201 primary red clump (RC) stars selected from the
LAMOST and Gaia surveys, we investigate the stellar structure of the Galactic
disk. The sample stars show two separated sequences of high-[{\alpha}/Fe] and
low-[{\alpha}/Fe] in the [{\alpha}/Fe]-[Fe/H] plane. We divide the sample stars
into five mono-abundance populations (MAPs) with different ranges of
[{\alpha}/Fe] and [Fe/H], named as the high-[{\alpha}/Fe], high-[{\alpha}/Fe] &
high-[Fe/H], low-[Fe/H], solar, high-[Fe/H] MAPs respectively. We present the
stellar number density distributions in the R R Z plane, and the scale heights
and scale lengths of the individual MAPs by fitting their vertical and radial
density profiles. The vertical profiles, the variation trend of scale height
with the Galactocentric radius, indicate that there is a clear disk flare in
the outer disk both for the low-[{\alpha}/Fe] and the high-[{\alpha}/Fe] MAPs.
While the radial surface density profiles show a peak radius of 7 kpc and 8 kpc
for the high-[{\alpha}/Fe] and low-[{\alpha}/Fe] MAPs, respectively. We also
investigate the correlation between the mean rotation velocity and metallicity
of the individual MAPs, and find that the mean rotation velocities are well
separated and show different trends between the high-[{\alpha}/Fe] and the
low-[{\alpha}/Fe] MAPs. At last, we discuss the character of the
high-[{\alpha}/Fe] & high-[Fe/H] MAP and find that it is more similar to the
high-[{\alpha}/Fe] MAP either in the radial and vertical density profiles or in
the rotation velocity.


Abstract:
  We studied $\alpha$ cluster states in $^{26}$Mg via the
$^{22}$Ne($^{6}$Li,$d\gamma$)$^{26}$Mg reaction in inverse kinematics at an
energy of $7$ MeV/nucleon. States between $E_x$ = 4 - 12 MeV in $^{26}$Mg were
populated and relative $\alpha$ spectroscopic factors were determined. Some of
these states correspond to resonances in the Gamow window of the
$^{22}$Ne($\alpha$,n)$^{25}$Mg reaction, which is one of the main neutron
sources in the astrophysical $s$-process. We show that $\alpha$-cluster
strength of the states analyzed in this work have critical impact on s-process
abundances. Using our new $^{22}$Ne($\alpha$,n)$^{25}$Mg and
$^{22}$Ne($\alpha$,$\gamma$)$^{26}$Mg reaction rates, we performed new
s-process calculations for massive stars and Asymptotic Giant Branch stars and
compared the resulting yields with the yields obtained using other
$^{22}$Ne+$\alpha$ rates from the literature. We observe an impact on the
s-process abundances up to a factor of three for intermediate-mass AGB stars
and up to a factor of ten for massive stars. Additionally, states in $^{25}$Mg
at $E_x$ $<$ 5 MeV are identified via the $^{22}$Ne($^{6}$Li,$t$)$^{25}$Mg
reaction for the first time. We present the ($^6$Li, $t$) spectroscopic factors
of these states and note similarities to the $(d,p$) reaction in terms of
reaction selectivity.


Abstract:
  Climate models encapsulate our best understanding of the Earth system,
allowing research to be conducted on its future under alternative assumptions
of how human-driven climate forces are going to evolve. An important
application of climate models is to provide metrics of mean and extreme climate
changes, particularly under these alternative future scenarios, as these
quantities drive the impacts of climate on society and natural systems. Because
of the need to explore a wide range of alternative scenarios and other sources
of uncertainties in a computationally efficient manner, climate models can only
take us so far, as they require significant computational resources, especially
when attempting to characterize extreme events, which are rare and thus demand
long and numerous simulations in order to accurately represent their changing
statistics. Here we use deep learning in a proof of concept that lays the
foundation for emulating global climate model output for different scenarios.
We train two "loosely conditioned" Generative Adversarial Networks (GANs) that
emulate daily precipitation output from a fully coupled Earth system model: one
GAN modeling Fall-Winter behavior and the other Spring-Summer. Our GANs are
trained to produce spatiotemporal samples: 32 days of precipitation over a
64x128 regular grid discretizing the globe. We evaluate the generator with a
set of related performance metrics based upon KL divergence, and find the
generated samples to be nearly as well matched to the test data as the
validation data is to test. We also find the generated samples to accurately
estimate the mean number of dry days and mean longest dry spell in the 32 day
samples. Our trained GANs can rapidly generate numerous realizations at a
vastly reduced computational expense, compared to large ensembles of climate
models, which greatly aids in estimating the statistics of extreme events.


Abstract:
  The notion of long-memory is considered in the case of multivariate time
series, not necessarily Gaussian nor stationary. The long-memory
characteristics are defined by the long-memory parameters describing the
autocorrelation structure of each process and the long-run covariance measuring
the coupling between time series. A phase term is present in the model to widen
the classes of models. We introduce a representation of the time series by
quasi-analytic wavelets for inference in this setting. We first show that the
covariance of the wavelet coefficients provides an adequate estimator of the
covariance structure of the processes, including the phase term. Consistent
estimators are then proposed which is based on a Whittle approximation.
Simulations highlight a satisfactory behavior of the estimation on finite
samples on some linear time series and on multivariate fractional Brownian
motions. An application on a real dataset in neuroscience is displayed, where
long-memory and brain connectivity are inferred.


Abstract:
  Gamma-ray bursts (GRBs) are known to be highly collimated events, and are
mostly detectable when they are seen on-axis or very nearly on-axis. However,
GRBs can be seen from off-axis angles, and the recent detection of a short GRB
associated to a gravitational wave event has conclusively shown such a
scenario. The observer viewing angle plays an important role in the observable
spectral shape and the energetic of such events. We present a numerical model
which is based on the single-pulse approximation with emission from a top-hat
jet and has been developed to investigate the effects of the observer viewing
angle. We assume a conical jet parametrized by a radius $R_{\rm jet}$,
half-opening angle $\theta_{\rm jet}$, a comoving-frame emissivity law and an
observer viewing angle $\theta_{\rm obs}$, and then study the effects for the
conditions $\theta_{\rm obs} < \theta_{\rm jet}$ and $\theta_{\rm obs}
>\theta_{\rm jet}$. We present results considering a smoothly broken power-law
emissivity law in jet comoving frame, albeit the model implementation easily
allows to consider other emissivity laws. We find that the relation $E^{\rm
i}_{\rm p} \propto E_{\rm iso}^{0.5}$ (Amati relation) is naturally obtained
from pure relativistic kinematic when $\Gamma \gtrsim 10$ and $\theta_{\rm obs}
< \theta_{\rm jet}$; on the contrary, when $\theta_{\rm obs} > \theta_{\rm
jet}$ it results $E^{\rm i}_{\rm p} \propto E_{\rm iso}^{0.25}$. Using data
from literature for a class of well-know sub-energetic GRBs, we show that their
position in the $E^{\rm i}_{\rm p}-E_{\rm iso}$ plane is consistent with event
observed off-axis. The presented model is developed as a module to be
integrated in spectral fitting software package XSPEC and can be used by the
scientific community.


Abstract:
  Gas flow and heat transfer in confined geometries at micro and nano scales
differ considerably from those at macro-scales, mainly due to nonequilibrium
effects such as velocity slip and temperature jump. The nonequilibrium effects
enhance with a decrease in the characteristic length-scale of the fluid flow or
the gas density, leading to the failure of the standard Navier-Stokes-Fourier
(NSF) equations in predicting thermal and fluid flow fields. The direct
simulation Monte-Carlo (DSMC) method is employed in the present work to
investigate pressure-driven nitrogen flow in divergent microchannels with
various divergence angles and isothermal walls. The thermal fields obtained
from numerical simulations are analysed for different inlet-to-outlet pressure
ratios (1.5 $\leq \Pi \leq$ 2.5), tangential momentum accommodation
coefficients and Knudsen numbers (0.05 $\leq \mathrm{Kn} \leq$ 12.5), covering
slip to free-molecular rarefaction regimes. The thermal field in the
microchannel is predicted, heat-lines are visualised, and the physics of heat
transfer in the microchannel is discussed. Due to the rarefaction effects, the
direction of heat flow is largely opposite to that of the mass flow. However,
the interplay between thermal and pressure gradients, which are affected by
geometrical configurations of the microchannel and applied boundary conditions,
determines the net heat flow direction. Additionally, the occurrence of thermal
separation and cold-to-hot heat transfer (also known as anti-Fourier heat
transfer) in divergent microchannels is explained.


Abstract:
  By adopting a divide-and-conquer strategy, subsystem-DFT (sDFT) can
dramatically reduce the computational cost of large-scale electronic structure
calculations. The key ingredients of sDFT are the nonadditive kinetic energy
and exchange-correlation functionals which dominate it's accuracy. Even though,
semilocal nonadditive functionals find a broad range of applications, their
accuracy is somewhat limited especially for those systems where achieving
balance between exchange-correlation interactions on one side and nonadditive
kinetic energy on the other is crucial. In eQE 2.0, we improve dramatically the
accuracy of sDFT simulations by (1) implementing nonlocal nonadditive kinetic
energy functionals based on the LMGP family of functionals; (2) adapting
Quantum ESPRESSO's implementation of rVV10 and vdW-DF nonlocal
exchange-correlation functionals to be employed in sDFT simulations; (3)
implementing "deorbitalized" meta GGA functionals (e.g., SCAN-L). We carefully
assess the performance of the newly implemented tools on the S22-5 test set.
eQE 2.0 delivers excellent interaction energies compared to conventional
Kohn-Sham DFT and CCSD(T). The improved performance does not come at a loss of
computational efficiency. We show that eQE 2.0 with nonlocal nonadditive
functionals retains the same linear scaling behavior achieved in eQE 1.0 with
semilocal nonadditive functionals.


Abstract:
  Typical ASR systems segment the input audio into utterances using purely
acoustic information, which may not resemble the sentence-like units that are
expected by conventional machine translation (MT) systems for Spoken Language
Translation. In this work, we propose a model for correcting the acoustic
segmentation of ASR models for low-resource languages to improve performance on
downstream tasks. We propose the use of subtitles as a proxy dataset for
correcting ASR acoustic segmentation, creating synthetic acoustic utterances by
modeling common error modes. We train a neural tagging model for correcting ASR
acoustic segmentation and show that it improves downstream performance on MT
and audio-document cross-language information retrieval (CLIR).


Abstract:
  W prove a dimension-free estimate for the $L^2(\mathbb{R}^d)$ norm of the
maximal truncated Riesz transform in terms of the $L^2(\mathbb{R}^d)$ norm of
the Riesz transform. Consequently, the vector of maximal truncated Riesz
transforms has a dimension-free estimate on $L^2(\mathbb{R}^d)$. We also show
that the maximal function of the vector of truncated Riesz transforms has a
dimension-free estimate on all $L^p(\mathbb{R}^d)$ spaces, $1<p<\infty.$


Abstract:
  Main visual techniques used to obtain information from speckle patterns are
Fujii method, generalized difference, weighted generalized difference, mean
windowed difference, structural function (SF), modified SF, etc. In this work,
a comparative analysis of major visual techniques for natural gum sample is
carried out. Obtained results conclusively establish SF based method as an
optimum tool for visual inspection of dynamic speckle data.


Abstract:
  Pixelizations of Platonic solids such as the cube and icosahedron have been
widely used to represent spherical data, from climate records to Cosmic
Microwave Background maps. Platonic solids have well-known global symmetries.
Once we pixelize each face of the solid, each face also possesses its own local
symmetries in the form of Euclidean isometries. One way to combine these
symmetries is through a hierarchy. However, this approach does not adequately
model the interplay between the two levels of symmetry transformations. We show
how to model this interplay using ideas from group theory, identify the
equivariant linear maps, and introduce equivariant padding that respects these
symmetries. Deep networks that use these maps as their building blocks
generalize gauge equivariant CNNs on pixelized spheres. These deep networks
achieve state-of-the-art results on semantic segmentation for climate data and
omnidirectional image processing. Code is available at https://git.io/JGiZA.


Abstract:
  GRETA, the Gamma-Ray Energy Tracking Array, is an array of highly-segmented
HPGe detectors designed to track gamma-rays emitted in beam-physics
experiments. Its high detection efficiency and state-of-the-art position
resolution make it well-suited for imaging applications. In this paper, we use
simulated imaging data to illustrate how imaging can be applied to nuclear
lifetime measurments. This approach can offer multiple benefits over
traditional lifetime techniques such as RDM.


Abstract:
  One of the most striking and curious features of the small Kuiper Belt Object
(KB), Arrokoth, explored by New Horizons, is the bright, annular neck it
exhibits at the junction between its two lobes. Here we summarize past reported
findings regarding the properties of this feature and report new findings
regarding its dimensions, reflectivity and color, shape profile, and its lack
of identifiable craters. We conclude by enumerating possible origin scenarios
for this unusual feature. New results include a new measurement of the observed
neck area of 8+/-1.5 km2, a total neck surface area of 32 km2, a 12.5:1 ratio
of neck circumference to height, a normal reflectance histogram of the observed
neck, and the fact that no significant (i.e., >2 sigma) color units were
identified, meaning the neck's color is generally spatially uniform at the 1.5
km/pixel scale of the best color images. Although several origin hypotheses for
the bright material in the neck are briefly discussed, none can be conclusively
demonstrated to be the actual origin mechanism at this time; some future tests
are identified.


Abstract:
  While deep learning has obtained state-of-the-art results in many
applications, the adaptation of neural network architectures to incorporate new
output features remains a challenge, as neural networks are commonly trained to
produce a fixed output dimension. This issue is particularly severe in online
learning settings, where new output features, such as items in a recommender
system, are added continually with few or no associated observations. As such,
methods for adapting neural networks to novel features which are both time and
data-efficient are desired. To address this, we propose the Contextual
HyperNetwork (CHN), an auxiliary model which generates parameters for extending
the base model to a new feature, by utilizing both existing data as well as any
observations and/or metadata associated with the new feature. At prediction
time, the CHN requires only a single forward pass through a neural network,
yielding a significant speed-up when compared to re-training and fine-tuning
approaches.
  To assess the performance of CHNs, we use a CHN to augment a partial
variational autoencoder (P-VAE), a deep generative model which can impute the
values of missing features in sparsely-observed data. We show that this system
obtains improved few-shot learning performance for novel features over existing
imputation and meta-learning baselines across recommender systems, e-learning,
and healthcare tasks.


Abstract:
  The standard problem setting in Dec-POMDPs is self-play, where the goal is to
find a set of policies that play optimally together. Policies learned through
self-play may adopt arbitrary conventions and implicitly rely on multi-step
reasoning based on fragile assumptions about other agents' actions and thus
fail when paired with humans or independently trained agents at test time. To
address this, we present off-belief learning (OBL). At each timestep OBL agents
follow a policy $\pi_1$ that is optimized assuming past actions were taken by a
given, fixed policy ($\pi_0$), but assuming that future actions will be taken
by $\pi_1$. When $\pi_0$ is uniform random, OBL converges to an optimal policy
that does not rely on inferences based on other agents' behavior (an optimal
grounded policy). OBL can be iterated in a hierarchy, where the optimal policy
from one level becomes the input to the next, thereby introducing multi-level
cognitive reasoning in a controlled manner. Unlike existing approaches, which
may converge to any equilibrium policy, OBL converges to a unique policy,
making it suitable for zero-shot coordination (ZSC). OBL can be scaled to
high-dimensional settings with a fictitious transition mechanism and shows
strong performance in both a toy-setting and the benchmark human-AI & ZSC
problem Hanabi.


Abstract:
  High precision studies of Beyond-Standard-Model physics through
accelerator-based neutrino oscillation experiments require a very accurate
description of neutrino-nucleus cross sections in a broad energy region, going
from quasielastic scattering up to deep inelastic scattering. In this work we
focus on the following processes: quasielastic scattering,
two-particle-two-hole excitations, and the excitation of the first (Delta) and
second (Roper) resonances of the nucleon. The nuclear model is fully
relativistic and includes both one- and two-body currents. We compare our
results with recent T2K and MicroBooNE data on carbon and argon targets, and
present predictions for DUNE kinematics.


Abstract:
  Chemical bonding in 2D layered materials and van der Waals solids is central
to understanding and harnessing their unique electronic, magnetic, optical,
thermal and superconducting properties. Here we report the discovery of
spontaneous, bidirectional, bilayer twisting (twist angle ~ 4.5{\deg}) in the
metallic kagom\'e MgCo6Ge6 at T = 100(2) K via X-ray diffraction measure-ments,
enabled by the preparation of single crystals by the Laser Bridgman method.
Despite the appearance of static twisting on cooling from T ~ 300 K to 100 K,
no evidence for a phase transition was found in physical properties
measurements. Combined with the presence of an Einstein phonon mode
contribution in the specific heat, this implies that the twisting exists at all
temperatures but is thermally fluctuating at room temperature. Crystal Orbital
Hamilton Population analysis demonstrates that the cooperative twisting between
layers stabilizes the Co-kagom\'e network when coupled to strongly bonded and
rigid (Ge2) dimers that connect adjacent layers. Further modelling of the
displacive disorder in the crystal structure shows the presence of second,
Mg-deficient, stacking sequence. This alternative stacking sequence also
exhibits inter-layer twisting, but with a different pattern, consistent with
the change in electron count due to removal of Mg. Magnetization, resistivity,
and low-temperature specific heat measurements are all consistent with a Pauli
paramagnetic, strongly correlated metal. Our results provide crucial insight
into how chemical concepts lead to interesting electronic structures and
behaviors in layered materials.


Abstract:
  We examine the impact of a faster expanding Universe on the phenomenology of
scalar dark matter (DM) associated with $SU(2)_L$ multiplets. Earlier works
with radiation dominated Universe have reported the presence of desert region
for both inert $SU(2)_L$ doublet and triplet DM candidates where the DM is
under abundant. We find that the existence of a faster expanding component
before BBN can revive a substantial part of the desert parameter space
consistent with relic density requirements and other direct and indirect search
bounds. We also review the possible collider search prospects of the newly
obtained parameter space and predict that such region might be probed at the
future colliders with improved sensitivity via a disappearing/stable charged
track.


Abstract:
  Object orientation has become the predominant paradigm for conceptual
modeling (e.g., UML), where the notions of class and object form the primitive
building blocks of thought. Classes act as templates for objects that have
attributes and methods (actions). The modeled systems are not even necessarily
software systems: They can be human and artificial systems of many different
kinds (e.g., teaching and learning systems). The UML class diagram is described
as a central component of model-driven software development. It is the most
common diagram in object-oriented models and used to model the static design
view of a system. Objects both carry data and execute actions. According to
some authorities in modeling, a certain degree of difficulty exists in
understanding the semantics of these notions in UML class diagrams. Some
researchers claim class diagrams have limited use for conceptual analysis and
that they are best used for logical design. Performing conceptual analysis
should not concern the ways facts are grouped into structures. Whether a fact
will end up in the design as an attribute is not a conceptual issue. UML leads
to drilling down into physical design details (e.g., private/public attributes,
encapsulated operations, and navigating direction of an association). This
paper is a venture to further the understanding of object-orientated concepts
as exemplified in UML with the aim of developing a broad comprehension of
conceptual modeling fundamentals. Thinging machine (TM) modeling is a new
modeling language employed in such an undertaking. TM modeling interlaces
structure (components) and actionality where actions infiltrate the attributes
as much as the classes. Although space limitations affect some aspects of the
class diagram, the concluding assessment of this study reveals the class
description is a kind of shorthand for a richer sematic TM construct.


Abstract:
  The present study reports on flow past airfoils (stationary and moving) using
sharp interface immersed-boundary approach. Non-boundary conforming approach
like immersed-boundary method offers a viable alternative over traditional
boundary conforming approach by allowing us to model flow past arbitrarily
complex shapes, by eliminating the need to re-grid the flow domain as the body
exhibits motion. We present flow past a NACA 0012 airfoil at stationary
conditions as well as exhibiting pitching motion. Evolution of vortex dynamics
and wake structures are presented to show that the developed sharp interface
immersed-boundary approach captures the flow physics of dynamic stall
accurately. Moving body problems involving immersed-boundary approach usually
encounter the issues of spurious oscillations and mass conservation. This is
handled through a field extension strategy based on ghost cell approach, which
allows for extrapolating the flow field value onto the ghost nodes, ensuring
smooth temporal transition as the immersed surface moves through time. The
results presented here show excellent agreement with the experimental results
found in the literature.


Abstract:
  The temperatures of red supergiants (RSGs) are expected to depend on
metallicity (Z) in such a way that lower-Z RSGs are warmer. In this work, we
investigate the Z-dependence of the Hayashi limit by analysing RSGs in the
low-Z galaxy Wolf-Lundmark-Mellote (WLM), and compare with the RSGs in the
higher-Z environments of the Small Magellanic Cloud (SMC) and Large Magellanic
Cloud (LMC). We determine the effective temperature ($T_{\textrm{eff}}$) of
each star by fitting their spectral energy distributions, as observed by
VLT+SHOOTER, with MARCS model atmospheres. We find average temperatures of
$T_{\textrm{eff}_{\textrm{WLM}}}=4400\pm202$ K,
$T_{\textrm{eff}_{\textrm{SMC}}}=4130\pm103$ K, and
$T_{\textrm{eff}_{\textrm{LMC}}}=4140\pm148$ K. From population synthesis
analysis, we find that although the Geneva evolutionary models reproduce this
trend qualitatively, the RSGs in these models are systematically too cool. We
speculate that our results can be explained by the inapplicability of the
standard solar mixing length to RSGs.


Abstract:
  Networks with a scale-free degree distribution are widely thought to promote
cooperation in various games. Herein, by studying the well-known prisoner's
dilemma game, we demonstrate that this need not necessarily be true. For the
very same degree sequence and degree distribution, we present a variety of
possible behaviour. We reassess the perceived importance of hubs in a network
towards the maintenance of cooperation. We also reevaluate the dependence of
cooperation on network clustering and assortativity.


Abstract:
  Aims: The giant HII region 30 Doradus (30 Dor) located in the eastern part of
the Large Magellanic Cloud is one of the most active star-forming regions in
the Local Group. Studies of HI data have revealed two large gas structures
which must have collided with each other in the region around 30 Dor. In X-rays
there is extended emission ($\sim 1$ kpc) south of 30 Dor called the X-ray
spur, which appears to be anticorrelated with the HI gas. We study the
properties of the hot interstellar medium (ISM) in the X-ray spur and
investigate its origin including related interactions in the ISM. Methods: We
analyzed new and archival XMM-Newton data of the X-ray spur and its
surroundings to determine the properties of the hot diffuse plasma. We created
detailed plasma property maps by utilizing the Voronoi tessellation algorithm.
We also studied HI and CO data, as well as optical line emission data of
H$\alpha$ and [SII], and compared them to the results of the X-ray spectral
analysis. Results: We find evidence of two hot plasma components with
temperatures of $kT_1 \sim 0.2$ keV and $kT_2 \sim 0.5-0.9$ keV, with the
hotter component being much more pronounced near 30 Dor and the X-ray spur. In
30 Dor, the plasma has most likely been heated by massive stellar winds and
supernova remnants. In the X-ray spur, we find no evidence of heating by stars.
Instead, the X-ray spur must have been compressed and heated by the collision
of the HI gas.


Abstract:
  We show the equivalences of several notions of entropy, like a version of the
topological entropy of the geodesic flow and the Minkowski dimension of the
boundary, in metric spaces with convex geodesic bicombings satisfying a uniform
packing condition. Similar estimates will be given in case of closed subsets of
the boundary of Gromov-hyperbolic metric spaces with convex geodesic
bicombings.


Abstract:
  Currently, deep learning methods have been widely applied in and thus
promoted the development of different fields. In the financial accounting
field, the rapid increase in the number of financial tickets dramatically
increases labor costs; hence, using a deep learning method to relieve the
pressure on accounting is necessary. At present, a few works have applied deep
learning methods to financial ticket recognition. However, first, their
approaches only cover a few types of tickets. In addition, the precision and
speed of their recognition models cannot meet the requirements of practical
financial accounting systems. Moreover, none of the methods provides a detailed
analysis of both the types and content of tickets. Therefore, this paper first
analyzes the different features of 482 kinds of financial tickets, divides all
kinds of financial tickets into three categories and proposes different
recognition patterns for each category. These recognition patterns can meet
almost all types of financial ticket recognition needs. Second, regarding the
fixed format types of financial tickets (accounting for 68.27\% of the total
types of tickets), we propose a simple yet efficient network named the
Financial Ticket Faster Detection network (FTFDNet) based on a Faster RCNN.
Furthermore, according to the characteristics of the financial ticket text, in
order to obtain higher recognition accuracy, the loss function, Region Proposal
Network (RPN), and Non-Maximum Suppression (NMS) are improved to make FTFDNet
focus more on text. Finally, we perform a comparison with the best ticket
recognition model from the ICDAR2019 invoice competition. The experimental
results illustrate that FTFDNet increases the processing speed by 50\% while
maintaining similar precision.


Abstract:
  There have been recent calls for research on the human side of software
engineering and its impact on various factors such as productivity, developer
happiness and project success. An analysis of which challenges in software
engineering teams are most frequent is still missing.
  We aim to provide a starting point for a theory about relevant human
challenges and their causes in software engineering. We establish a reusable
set of challenges and start out by investigating the effect of team
virtualization. Virtual teams often use digital communication and consist of
members with different nationalities.
  We designed a survey instrument and asked respondents to assess the frequency
and criticality of a set of challenges, separated in context "within teams" as
well as "between teams and clients", compiled from previous empiric work, blog
posts and pilot survey feedback. For the team challenges, we asked if
mitigation measures were already in place. Respondents were also asked to
provide information about their team setup. The survey also measured Schwartz
human values. Finally, respondents were asked if there were additional
challenges at their workplace.
  We report on the results obtained from 192 respondents. We present a set of
challenges that takes the survey feedback into account and introduce two
categories of challenges; "interpersonal" and "intrapersonal". We found no
evidence for links between human values and challenges. We found some
significant links between the number of distinct nationalities in a team and
certain challenges, with less frequent and critical challenges occurring if 2-3
different nationalities were present compared to a team having members of just
one nationality or more than three. A higher degree of virtualization seems to
increase the frequency of some human challenges.


Abstract:
  The present paper is devoted to the investigation of the long time dynamics
for a double free boundary system with nonlocal diffusions, which models the
infectious diseases transmitted via digestive system such as fecal-oral
diseases, cholera, hand-foot and mouth, etc...We start by proving the existence
and uniqueness of the Cauchy problem, which is not a trivial step due to
presence of couple dispersals and new types of nonlinear reaction terms. Next,
we provide simple conditions on comparing the basic reproduction numbers
$\CR_0$ and $\CR^*$ with some certain numbers to characterize the global
dynamics, as $t\to\infty$. We further obtain the sharp criteria for the
spreading and vanishing in term of the initial data. This is also called the
vanishing-spreading phenomena. The couple dispersals yield significant obstacle
that we cannot employ the approach of Zhao, Zhang, Li, Du \cite{WWZ1} and Du-Ni
\cite{DN}. To overcome this, we must prove the existence and the variational
characterization for the principal eigenvalue of a linear system with nonlocal
dispersals, then use it to obtain the right limits as the dispersal rates and
domain tend to zero or infinity. The maximum principle and sliding method for
the nonlocal operator are ingeniously employed to achieve the desired results.


Abstract:
  In the moduli space $\mathcal{R}_g$ of double \'etale covers of curves of a
fixed genus $g$, the locus formed by covers of curves with a semicanonical
pencil consists of two irreducible divisors $\mathcal T^e_g$ and $\mathcal
T^o_g$. We study the Prym map on these divisors, which shows significant
differences between them and has a rich geometry in the cases of low genus. In
particular, the analysis of $\mathcal T^o_5$ has enumerative consequences for
lines on cubic threefolds.


Abstract:
  This paper presents a distributed multi-robot printing method which utilizes
an optimization approach to decompose and allocate a printing task to a group
of mobile robots. The motivation for this problem is to minimize the printing
time of the robots by using an appropriate task decomposition algorithm. We
present one such algorithm which decomposes an image into rasterized geodesic
cells before allocating them to the robots for printing. In addition to this,
we also present the design of a numerically controlled holonomic robot capable
of spraying ink on smooth surfaces. Further, we use this robot to
experimentally verify the results of this paper.


Abstract:
  A formal approach to rephrase nonlinear filtering of stochastic differential
equations is the Kushner setting in applied mathematics and dynamical systems.
Thanks to the ability of the Carleman linearization, the nonlinear stochastic
differential equation can be equivalently expressed as a finite system of
bilinear stochastic differential equations with the augmented state under the
finite closure. Interestingly, the novelty of this paper is to embed the
Carleman linearization into a stochastic evolution of the Markov process. To
illustrate the Carleman linearization of the Markov process, this paper embeds
the Carleman linearization into a nonlinear swing stochastic differential
equation. Furthermore, we achieve the nonlinear swing equation filtering in the
Carleman setting. Filtering in the Carleman setting has simplified algorithmic
procedure. The concerning augmented state accounts for the nonlinearity as well
as stochasticity. We show that filtering of the nonlinear stochastic swing
equation in the Carleman framework is more refined as well as sharper in
contrast to benchmark nonlinear EKF. This paper suggests the usefulness of the
Carleman embedding into the stochastic differential equation to filter the
concerning nonlinear stochastic differential system. This paper will be of
interest to nonlinear stochastic dynamists exploring and unfolding
linearization embedding techniques to their research.


Abstract:
  A hallmark of fluid turbulence theory is the universal power law scaling of
the velocity difference statistics between two points in space in the inertial
range between the large energy injection scale and the small energy dissipation
scale. Even at the highest Reynolds numbers available, laboratory and natural
flows such universal power laws have not been convincingly demonstrated. Here
we show for the decaying active grid turbulence of the Max Planck Variable
Density Turbulence Tunnel that the velocity difference statistics at high
Reynolds numbers do not exhibit a power law, but have a universal functional
form independent of the Reynolds number. We separate this functional form from
the power law exponent and discuss potential consequences for turbulence
modelling.


Abstract:
  Density-functional theory within a Berry-phase formulation of the dynamical
polarization is used to determine the second-order susceptibility $\chi^{(2)}$
of lithium niobate (LiNbO$_3$). Defect trapped polarons and bipolarons are
found to strongly enhance the nonlinear susceptibility of the material, in
particular if localized at Nb$_\mathrm{V}$-V$_{\mathrm{Li}}$ defect pairs. This
is essentially a consequence of the polaronic excitation resulting in
relaxation-induced gap states. The occupation of these levels leads to strongly
enhanced $\chi^{(2)}$ coefficients and allows for the spatial and transient
modification of the second-harmonic generation of macroscopic samples.


Abstract:
  We study the problem of identifying the best arm in a stochastic multi-armed
bandit game. Given a set of $n$ arms indexed from $1$ to $n$, each arm $i$ is
associated with an unknown reward distribution supported on $[0,1]$ with mean
$\theta_i$ and variance $\sigma_i^2$. Assume $\theta_1 > \theta_2 \geq \cdots
\geq\theta_n$. We propose an adaptive algorithm which explores the gaps and
variances of the rewards of the arms and makes future decisions based on the
gathered information using a novel approach called \textit{grouped median
elimination}. The proposed algorithm guarantees to output the best arm with
probability $(1-\delta)$ and uses at most $O \left(\sum_{i = 1}^n
\left(\frac{\sigma_i^2}{\Delta_i^2} + \frac{1}{\Delta_i}\right)(\ln \delta^{-1}
+ \ln \ln \Delta_i^{-1})\right)$ samples, where $\Delta_i$ ($i \geq 2$) denotes
the reward gap between arm $i$ and the best arm and we define $\Delta_1 =
\Delta_2$. This achieves a significant advantage over the variance-independent
algorithms in some favorable scenarios and is the first result that removes the
extra $\ln n$ factor on the best arm compared with the state-of-the-art. We
further show that $\Omega \left( \sum_{i = 1}^n \left(
\frac{\sigma_i^2}{\Delta_i^2} + \frac{1}{\Delta_i} \right) \ln \delta^{-1}
\right)$ samples are necessary for an algorithm to achieve the same goal,
thereby illustrating that our algorithm is optimal up to doubly logarithmic
terms.


Abstract:
  Multi-messenger observations of binary neutron star mergers offer a promising
path towards resolution of the Hubble constant ($H_0$) tension, provided their
constraints are shown to be free from systematics such as the Malmquist bias.
In the traditional Bayesian framework, accounting for selection effects in the
likelihood requires calculation of the expected number (or fraction) of
detections as a function of the parameters describing the population and
cosmology; a potentially costly and/or inaccurate process. This calculation
can, however, be bypassed completely by performing the inference in a framework
in which the likelihood is never explicitly calculated, but instead fit using
forward simulations of the data, which naturally include the selection. This is
Likelihood-Free Inference (LFI). Here, we use density-estimation LFI, coupled
to neural-network-based data compression, to infer $H_0$ from mock catalogues
of binary neutron star mergers, given noisy redshift, distance and peculiar
velocity estimates for each object. We demonstrate that LFI yields
statistically unbiased estimates of $H_0$ in the presence of selection effects,
with precision matching that of sampling the full Bayesian hierarchical model.
Marginalizing over the bias increases the $H_0$ uncertainty by only $6\%$ for
training sets consisting of $O(10^4)$ populations. The resulting LFI framework
is applicable to population-level inference problems with selection effects
across astrophysics.


Abstract:
  Polymeric materials are prized for their formability, low density, and
functional versatility. However, the refractive indices of common polymers fall
in a relatively narrow range between 1.4 and 1.6. Here, we demonstrate that
loading commercially-available polymers with large concentrations of a
plant-based pigment can effectively enhance their refractive index.For
polystyrene loaded with 67w/w\% $\beta$-carotene, we achieve a peak value of
2.2 near the absorption edge at $531~\mathrm{nm}$, while maintaining values
above 1.75 across longer wavelengths of the visible spectrum. Despite high
pigment loadings, this blend maintains the thermoforming ability of
polystyrene, and $\beta$-carotene remains molecularly dispersed. Similar
results are demonstrated for the plant-derived polymer ethyl cellulose. Since
the refractive index enhancement is intimately connected to the introduction of
strong absorption, it is best suited to applications where light travels short
distances through the material, such as reflectors and nanophotonic systems.We
experimentally demonstrate enhanced reflectance from films, as large as
seven-fold for ethyl cellulose at selected wavelengths. Theoretical
calculations that highlight that this simple strategy can significantly
increase light scattering by nanoparticles and enhance the performance of Bragg
reflectors.


Abstract:
  We improve and refine a method for certifying that the values' sizes computed
by an imperative program will be bounded by polynomials in the program's
inputs' sizes. Our work ''tames'' the non-determinism of the original analysis,
and offers an innovative way of completing the analysis when a non-polynomial
growth is found. We furthermore enrich the analyzed language by adding function
definitions and calls, allowing to compose the analysis of different libraries
and offering generally more modularity. The implementation of our improved
method, discussed in a tool paper
(https://hal.archives-ouvertes.fr/hal-03269121), also required to reason about
the efficiency of some of the needed operations on the matrices produced by the
analysis. It is our hope that this work will enable and facilitate static
analysis of source code to guarantee its correctness with respect to resource
usages.


Abstract:
  Multi-vehicle coordinated decision making and control can improve traffic
efficiency while guaranteeing driving safety. Formation control is a typical
multi-vehicle coordination method in the multi-lane scenario. Among the
existing formation control methods, the formation switching process is
predefined and the collision-free behavior of vehicles has not been considered
and explained in detail. This paper proposes a formation control method for
multiple Connected and Automated Vehicles (CAVs) on multi-lane roads. Firstly,
a bi-level planning framework is proposed to switch the structure of the
formation in different scenarios smoothly and effectively. Secondly, the
relative coordinate system is established and the conflict-free relative paths
are planned in the upper level. Then, multi-stage trajectory planning and
tracking are modeled and solved as an optimal control problem with path
constraints in the lower level. Next, case study is conducted to verify the
function of the proposed method in different scenarios. Finally, simulation in
the lane-drop bottleneck scenario is carried out under different traffic volume
and numerical results indicate that the proposed method can improve both
traffic efficiency and fuel economy at high traffic volume.


Abstract:
  Fully Bayesian approaches to sequential decision-making assume that problem
parameters are generated from a known prior, while in practice, such
information is often lacking, and needs to be estimated through learning. This
problem is exacerbated in decision-making setups with partial information,
where using a misspecified prior may lead to poor exploration and inferior
performance. In this work we prove, in the context of stochastic linear bandits
and Gaussian priors, that as long as the prior estimate is sufficiently close
to the true prior, the performance of an algorithm that uses the misspecified
prior is close to that of the algorithm that uses the true prior. Next, we
address the task of learning the prior through metalearning, where a learner
updates its estimate of the prior across multiple task instances in order to
improve performance on future tasks. The estimated prior is then updated within
each task based on incoming observations, while actions are selected in order
to maximize expected reward. In this work we apply this scheme within a linear
bandit setting, and provide algorithms and regret bounds, demonstrating its
effectiveness, as compared to an algorithm that knows the correct prior. Our
results hold for a broad class of algorithms, including, for example, Thompson
Sampling and Information Directed Sampling.


Abstract:
  Oscillons are long-lived, slowly radiating solutions of nonlinear classical
relativistic field theories. Recently it was discovered that in one spatial
dimension their decay may proceed in "staccato" bursts. Here we perform a
systematic numerical study to demonstrate that although this behaviour is not
confined to one spatial dimension, it quickly becomes unobservable when the
dimension of space is increased, at least for the class of potentials
considered here. To complete the picture we also present explicit results on
the dimension dependence of the collapse instability observed for
three-dimensional oscillons.


Abstract:
  In this work we describe semiclassical states in graphene under a constant
perpendicular magnetic field by constructing coherent states in the
Barut-Girardello sense. Since we want to keep track of the angular momentum,
the use of the symmetric gauge and polar coordinates seemed the most logical
choice. Different classes of coherent states are obtained by means of the
underlying algebra system, which consists of the direct sum of two
Heisenberg-Weyl algebras. The most interesting cases are a kind of partial
coherent states and the coherent states with a well-defined total angular
momentum.


Abstract:
  Click logs are valuable resources for a variety of information retrieval (IR)
tasks. This includes query understanding/analysis, as well as learning
effective IR models particularly when the models require large amounts of
training data. We release a large-scale domain-specific dataset of click logs,
obtained from user interactions of the Trip Database health web search engine.
Our click log dataset comprises approximately 5.2 million user interactions
collected between 2013 and 2020. We use this dataset to create a standard IR
evaluation benchmark -- TripClick -- with around 700,000 unique free-text
queries and 1.3 million pairs of query-document relevance signals, whose
relevance is estimated by two click-through models. As such, the collection is
one of the few datasets offering the necessary data richness and scale to train
neural IR models with a large amount of parameters, and notably the first in
the health domain. Using TripClick, we conduct experiments to evaluate a
variety of IR models, showing the benefits of exploiting this data to train
neural architectures. In particular, the evaluation results show that the best
performing neural IR model significantly improves the performance by a large
margin relative to classical IR models, especially for more frequent queries.


Abstract:
  Artificial Intelligence has been a growth catalyst to our society and is
cosidered across all idustries as a fundamental technology. However, its
development has been limited to the signal processing domain that relies on the
generated and collected data from other sensors. In recent research, concepts
of Digital Artificial Intelligence and Physicial Artifical Intelligence have
emerged and this can be considered a big step in the theoretical development of
Artifical Intelligence. In this paper we explore the concept of Physicial
Artifical Intelligence and propose two subdomains: Integrated Physicial
Artifical Intelligence and Distributed Physicial Artifical Intelligence. The
paper will also examine the trend and governance of Physicial Artifical
Intelligence.


Abstract:
  We report detection of PSR B0656$+$14 with the Gran Telescopio Canarias in
narrow optical $F657$, $F754$, $F802$, and $F902$ and near-infrared $JHK_s$
bands. The pulsar detection in the $K_s$ band extends its spectrum to 2.2
$\mu$m and confirms its flux increase towards the infrared. We also present a
thorough analysis of the optical spectrum obtained by us with the VLT. For a
consistency check, we revised the pulsar near-infrared and narrow-band
photometry obtained with the \textit{HST}. We find no narrow spectral lines in
the optical spectrum. We compile available near-infrared-optical-UV and
archival 0.3-20keV X-ray data and perform a self-consistent analysis of the
rotation phase-integrated spectrum of the pulsar using unified spectral models.
The spectrum is best fitted by the four-component model including two
blackbodies, describing the thermal emission from the neutron star surface and
its hot polar cap, the broken power-law, originating from the pulsar
magnetosphere, and an absorption line near $\sim$0.5 keV detected previously.
The fit provides better constraints on the model parameters than using only a
single spectral domain. The derived surface temperature is
$T_{NS}^{\infty}=7.9(3)\times10^5$K. The intrinsic radius (7.8-9.9 km) of the
emitting region is smaller than a typical neutron star radius (13km) and
suggests a nonuniform temperature distribution over the star surface. In
contrast, the derived radius of the hot polar cap is about twice as large as
the `canonical' one. The spectrum of the nonthermal emission steepens from the
optical to X-rays and has a break near 0.1 keV. The X-ray data suggest the
presence of another absorption line near 0.3keV.


Abstract:
  One of the primary reasons behind the difficulty in observing the Unruh
effect is that for achievable acceleration scales the finite temperature
effects are significant only for the low frequency modes of the field. Since
the density of field modes falls for small frequencies in free space, the field
modes which are relevant for the thermal effects would be less in number to
make an observably significant effect. In this work, we investigate the
response of a Unruh-DeWitt detector coupled to a massless scalar field which is
confined in a long cylindrical cavity. The density of field modes inside such a
cavity shows a {\it resonance structure} i.e. it rises abruptly for some
specific cavity configurations. We show that an accelerating detector inside
the cavity exhibits a non-trivial excitation and de-excitation rates for {\it
small} accelerations around such resonance points. If the cavity parameters are
adjusted to lie in a neighborhood of such resonance points, the (small)
acceleration-induced emission rate can be made much larger than the already
observable inertial emission rate. We comment on the possibilities of employing
this detector-field-cavity system in the experimental realization of Unruh
effect, and argue that the necessity of extremely high acceleration can be
traded off in favor of precision in cavity manufacturing for realizing
non-inertial field theoretic effects in laboratory settings.


Abstract:
  Obtaining high resolution images from low resolution data with clipped noise
is algorithmically challenging due to the ill-posed nature of the problem. So
far such problems have hardly been tackled, and the few existing approaches use
simplistic regularisers. We show the usefulness of two adaptive regularisers
based on anisotropic diffusion ideas: Apart from evaluating the classical
edge-enhancing anisotropic diffusion regulariser, we introduce a novel
non-local one with one-sided differences and superior performance. It is termed
sector diffusion. We combine it with all six variants of the classical
super-resolution observational model that arise from permutations of its three
operators for warping, blurring, and downsampling. Surprisingly, the evaluation
in a practically relevant noisy scenario produces a different ranking than the
one in the noise-free setting in our previous work (SSVM 2017).


Abstract:
  Data analysis in modern science using extensive experimental and
observational facilities, such as a gravitational wave detector, is essential
in the search for novel scientific discoveries. Accordingly, various techniques
and mathematical principles have been designed and developed to date. A
recently proposed approximate correlation method based on the information
theory is widely adopted in science and engineering. Although the maximal
information coefficient (MIC) method remains in the phase of improving its
algorithm, it is particularly beneficial in identifying the correlations of
multiple noise sources in gravitational-wave detectors including non-linear
effects. This study investigates various prospects for determining MIC
parameters to improve the reliability of handling multi-channel time-series
data, reduce high computing costs, and propose a novel method of determining
optimized parameter sets for identifying noise correlations in gravitational
wave data.


Abstract:
  The nearby young open cluster eta Chamaeleontis has been observed by
eROSITA/SRG during its CalPV phase for 150 ks. The eROSITA data were taken in
the field-scan mode, an observing mode of Spectrum-Roentgen-Gamma (SRG) that
follows a rectangular grid-like pattern, here covering a 5x5 deg field with an
exposure depth of about 5 ks. We study the known members in X-rays and search
for potential new members of the anticipated dispersed low-mass cluster
population. Detected sources were identified by cross-matching X-ray sources
with Gaia and 2MASS, and young stars were identified by their X-ray activity,
the position in the color-magnitude diagram, and by their astrometric and
kinematic properties. X-ray-luminosities, light curves, and spectra of cluster
members were obtained and compared with previous X-ray data. Literature results
of other member searches were used to verify our new member candidates in the
observed field. We determine X-ray properties of virtually all known eta Cha
members and identify five additional stellar systems that show basically
identical characteristics, but are more dispersed. Four of them were previously
proposed as potential members; this status is supported by our X-ray study.
Based on their spatial distribution, further members are expected beyond the
sky region we surveyed. The identified stellar systems very likely belong to
the ejected halo population, which brings the total number of eta Cha cluster
members to at least 23.


Abstract:
  This paper surveys the field of multiagent deep reinforcement learning. The
combination of deep neural networks with reinforcement learning has gained
increased traction in recent years and is slowly shifting the focus from
single-agent to multiagent environments. Dealing with multiple agents is
inherently more complex as (a) the future rewards depend on the joint actions
of multiple players and (b) the computational complexity of functions
increases. We present the most common multiagent problem representations and
their main challenges, and identify five research areas that address one or
more of these challenges: centralised training and decentralised execution,
opponent modelling, communication, efficient coordination, and reward shaping.
We find that many computational studies rely on unrealistic assumptions or are
not generalisable to other settings; they struggle to overcome the curse of
dimensionality or nonstationarity. Approaches from psychology and sociology
capture promising relevant behaviours such as communication and coordination.
We suggest that, for multiagent reinforcement learning to be successful, future
research addresses these challenges with an interdisciplinary approach to open
up new possibilities for more human-oriented solutions in multiagent
reinforcement learning.


Abstract:
  We show that the resistivity plateau of SmB$_6$ at low temperature, typically
taken as a hallmark of its conducting surface state, can systematically be
influenced by different surface treatments. We investigate the effect of
inflicting an increasing number of hand-made scratches and microscopically
defined focused ion beam-cut trenches on the surfaces of flux-grown
Sm$_{1-x}$Gd$_x$B$_6$ with $x =$ 0, 0.0002. Both treatments increase the
resistance of the low-temperature plateau, whereas the bulk resistance at
higher temperature largely remains unaffected. Notably, the temperature at
which the resistance deviates from the thermally activated behavior decreases
with cumulative surface damage. These features are more pronounced for the
focused ion beam treated samples, with the difference likely being related to
the absence of microscopic defects like subsurface cracks. Therefore, our
method presents a systematic way of controlling the surface conductance.


Abstract:
  We consider optimal intervention in the Elliott-Golub-Jackson network model
and show that it can be transformed into an influence maximization problem,
interpreted as the reverse of a default cascade. Our analysis of the optimal
intervention problem extends well-established targeting results to the economic
network setting, which requires additional theoretical steps. We prove several
results about optimal intervention: it is NP-hard and additionally hard to
approximate to a constant factor in polynomial time. In turn, we show that
randomizing failure thresholds leads to a version of the problem which is
monotone submodular, for which existing powerful approximations in polynomial
time can be applied. In addition to optimal intervention, we also show
practical consequences of our analysis to other economic network problems: (1)
it is computationally hard to calculate expected values in the economic
network, and (2) influence maximization algorithms can enable efficient
importance sampling and stress testing of large failure scenarios. We
illustrate our results on a network of firms connected through input-output
linkages inferred from the World Input Output Database.


Abstract:
  Feedback design is an important aspect of person-following robots for older
adults. This paper presents a user-centred design approach to ensure the design
is focused on the needs and preferences of the users. A sequence of user
studies with a total of 35 older adults (aged 62 years and older) was conducted
to explore their preferences regarding feedback parameters for a socially
assistive person-following robot. The preferred level of robot transparency and
the desired content for the feedback was first explored. This was followed by
an assessment of the preferred mode and timing of feedback. The chosen feedback
parameters were then implemented and evaluated in a final experiment to
evaluate the effectiveness of the design. Results revealed that older adults
preferred to receive only basic status information. They preferred voice
feedback overtone, and at a continuous rate to keep them constantly aware of
the state and actions of the robot. The outcome of the study is a further step
towards feedback design guidelines that could improve interaction quality for
person-following robots for older adults.


Abstract:
  It is well-known fact that there exists 1-1 correspondence between so-called
double (or flou) sets and intuitionistic sets (also known as orthopairs). At
first glance, these two concepts seem to be irreconcilable. However, one must
remember that algebraic operations in these two classes are also defined
differently. Hence, the expected compatibility is possible. Contrary to this
approach, we combine standard definition of double set with operations which
are typical for intuitionistic sets. We show certain advantages and limitations
of this viewpoint. Moreover, we suggest an interpretation of our sets and
operations in terms of logic, data clustering and multi-criteria decision
making. As a result, we obtain a structure of discussion between several
participants who propose their "necessary" and "allowable" requirements or
propositions.


Abstract:
  Recently, Intelligent Transportation Systems are leveraging the power of
increased sensory coverage and computing power to deliver data-intensive
solutions achieving higher levels of performance than traditional systems.
Within Traffic Signal Control (TSC), this has allowed the emergence of Machine
Learning (ML) based systems. Among this group, Reinforcement Learning (RL)
approaches have performed particularly well. Given the lack of industry
standards in ML for TSC, literature exploring RL often lacks comparison against
commercially available systems and straightforward formulations of how the
agents operate. Here we attempt to bridge that gap. We propose three different
architectures for TSC RL agents and compare them against the currently used
commercial systems MOVA, SurTrac and Cyclic controllers and provide pseudo-code
for them. The agents use variations of Deep Q-Learning and Actor Critic, using
states and rewards based on queue lengths. Their performance is compared in
across different map scenarios with variable demand, assessing them in terms of
the global delay and average queue length. We find that the RL-based systems
can significantly and consistently achieve lower delays when compared with
existing commercial systems.


Abstract:
  An initial-boundary value problem for the $n$-dimensional wave equation is
considered. A three-level explicit in time and conditionally stable 4th-order
compact scheme constructed recently for $n=2$ and the square mesh is
generalized to the case of any $n\geq 1$ and the rectangular uniform mesh.
Another approach to approximate the solution at the first time level (not
exploiting high-order derivatives of the initial functions) is suggested. New
stability bounds in the mesh energy norms and the discrete energy conservation
laws are given, and the 4th order error bound is rigorously proved.
Generalizations to the cases of the non-uniform meshes in space and time as
well as of the wave equation with variable coefficients are suggested.


Abstract:
  We study the evolution of cosmological perturbations in dark-matter models
with elastic and velocity-independent self interactions. Such interactions are
imprinted in the matter-power spectrum as dark acoustic oscillations, which can
be experimentally explored to determine the strength of the self scatterings.
Models with self interactions have similarities to warm dark matter, as they
lead to suppression of power on small scales when the dark-matter velocity
dispersion is sizable. Nonetheless, both the physical origin and the extent of
the suppression differ for self-interacting dark matter from conventional warm
dark matter, with a dark sound horizon controlling the reduction of power in
the former case, and a free-streaming length in the latter. We thoroughly
analyze these differences by performing computations of the linear power
spectrum using a newly developed Boltzmann code. We find that while current
Lyman-$\alpha$ data disfavor conventional warm dark matter with a mass less
than 5.3 keV, when self interactions are included at their maximal value
consistent with bounds from the Bullet Cluster, the limits are relaxed to 4.4
keV. Finally, we make use of our analysis to set novel bounds on light scalar
singlet dark matter.


Abstract:
  This paper introduces ROmodel, an open source Python package extending the
modeling capabilities of the algebraic modeling language Pyomo to robust
optimization problems. ROmodel helps practitioners transition from
deterministic to robust optimization through modeling objects which allow
formulating robust models in close analogy to their mathematical formulation.
ROmodel contains a library of commonly used uncertainty sets which can be
generated using their matrix representations, but it also allows users to
define custom uncertainty sets using Pyomo constraints. ROmodel supports
adjustable variables via linear decision rules. The resulting models can be
solved using ROmodels solvers which implement both the robust reformulation and
cutting plane approach. ROmodel is a platform to implement and compare custom
uncertainty sets and reformulations. We demonstrate ROmodel's capabilities by
applying it to six case studies. We implement custom uncertainty sets based on
(warped) Gaussian processes to show how ROmodel can integrate data-driven
models with optimization.