Abstract:
  The origin of the gamma-ray emission of the blazar Mrk 421 is still a matter
of debate. We used 5.5 years of unbiased observing campaign data, obtained
using the FACT telescope and the Fermi LAT detector at TeV and GeV energies,
the longest and densest so far, together with contemporaneous multi-wavelength
observations, to characterise the variability of Mrk 421 and to constrain the
underlying physical mechanisms. We studied and correlated light curves obtained
by ten different instruments and found two significant results. The TeV and
X-ray light curves are very well correlated with a lag of <0.6 days. The GeV
and radio (15 Ghz band) light curves are widely and strongly correlated.
Variations of the GeV light curve lead those in the radio. Lepto-hadronic and
purely hadronic models in the frame of shock acceleration predict proton
acceleration or cooling timescales that are ruled out by the short variability
timescales and delays observed in Mrk 421. Instead the observations match the
predictions of leptonic models.


Abstract:
  A pair-density wave state has been suggested to exist in underdoped cuprate
superconductors, with some supporting experimental evidence emerging over the
past few years from scanning tunneling spectroscopy. Several studies have also
linked the observed quantum oscillations in these systems to a reconstruction
of the Fermi surface by a pair-density wave. Here, we show, using semiclassical
analysis and numerical calculations, that a Fermi pocket created by first-order
scattering from a pair-density wave cannot induce such oscillations. In
contrast, pockets resulting from second-order scattering can cause
oscillations. We consider the effects of a finite pair-density wave correlation
length on the signal, and demonstrate that it is only weakly sensitive to
disorder in the form of $\pi$-phase slips. Finally, we discuss our results in
the context of the cuprates and show that a bidirectional pair-density wave may
produce observed oscillation frequencies.


Abstract:
  Federated Learning (FL) is emerging as a promising paradigm of
privacy-preserving machine learning, which trains an algorithm across multiple
clients without exchanging their data samples. Recent works highlighted several
privacy and robustness weaknesses in FL and addressed these concerns using
local differential privacy (LDP) and some well-studied methods used in
conventional ML, separately. However, it is still not clear how LDP affects
adversarial robustness in FL. To fill this gap, this work attempts to develop a
comprehensive understanding of the effects of LDP on adversarial robustness in
FL. Clarifying the interplay is significant since this is the first step
towards a principled design of private and robust FL systems. We certify that
local differential privacy has both positive and negative effects on
adversarial robustness using theoretical analysis and empirical verification.


Abstract:
  We define the flow group of any component of any stratum of rooted abelian or
quadratic differentials (those marked with a horizontal separatrix) to be the
group generated by almost-flow loops. We prove that the flow group is equal to
the fundamental group of the component. As a corollary, we show that the plus
and minus modular Rauzy--Veech groups are finite-index subgroups of their
ambient modular monodromy groups. This partially answers a question of Yoccoz.
  Using this, and recent advances on algebraic hulls and Zariski closures of
monodromy groups, we prove that the Rauzy--Veech groups are Zariski dense in
their ambient symplectic groups. Density, in turn, implies the simplicity of
the plus and minus Lyapunov spectra of any component of any stratum of
quadratic differentials. Thus, we establish the Kontsevich--Zorich conjecture.


Abstract:
  By using the Boole summation formula, we obtain asymptotic expansions for the
first and higher order derivatives of the alternating Hurwitz zeta function
$$\zeta_{E}(z,q)=\sum_{n=0}^\infty\frac{(-1)^{n}}{(n+q)^z}$$
  with respect to its first argument
$$\zeta_{E}^{(m)}(z,q)\equiv\frac{\partial^m}{\partial z^m}\zeta_E(z,q).$$


Abstract:
  We show that every finite semilattice can be represented as an atomized
semilattice, an algebraic structure with additional elements (atoms) that
extend the semilattice's partial order. Each atom maps to one subdirectly
irreducible component, and the set of atoms forms a hypergraph that fully
defines the semilattice. An atomization always exists and is unique up to
"redundant atoms". Atomized semilattices are representations that can be used
as computational tools for building semilattice models from sentences, as well
as building its subalgebras and products. Atomized semilattices can be applied
to machine learning and to the study of semantic embeddings into algebras with
idempotent operators.


Abstract:
  The recent emergence of machine-learning based generative models for speech
suggests a significant reduction in bit rate for speech codecs is possible.
However, the performance of generative models deteriorates significantly with
the distortions present in real-world input signals. We argue that this
deterioration is due to the sensitivity of the maximum likelihood criterion to
outliers and the ineffectiveness of modeling a sum of independent signals with
a single autoregressive model. We introduce predictive-variance regularization
to reduce the sensitivity to outliers, resulting in a significant increase in
performance. We show that noise reduction to remove unwanted signals can
significantly increase performance. We provide extensive subjective performance
evaluations that show that our system based on generative modeling provides
state-of-the-art coding performance at 3 kb/s for real-world speech signals at
reasonable computational complexity.


Abstract:
  We consider the problem of estimating an object's physical properties such as
mass, friction, and elasticity directly from video sequences. Such a system
identification problem is fundamentally ill-posed due to the loss of
information during image formation. Current solutions require precise 3D labels
which are labor-intensive to gather, and infeasible to create for many systems
such as deformable solids or cloth. We present gradSim, a framework that
overcomes the dependence on 3D supervision by leveraging differentiable
multiphysics simulation and differentiable rendering to jointly model the
evolution of scene dynamics and image formation. This novel combination enables
backpropagation from pixels in a video sequence through to the underlying
physical attributes that generated them. Moreover, our unified computation
graph -- spanning from the dynamics and through the rendering process --
enables learning in challenging visuomotor control tasks, without relying on
state-based (3D) supervision, while obtaining performance competitive to or
better than techniques that rely on precise 3D labels.


Abstract:
  In this paper, we study quasi post-critically finite degenerations for
rational maps. We construct limits for such degenerations as geometrically
finite rational maps on a finite tree of Riemann spheres. We prove the
boundedness for such degenerations of hyperbolic rational maps with Sierpinski
carpet Julia set and give criteria for the convergence for quasi-Blaschke
products $\mathcal{QB}_d$, making progress towards the analogues of Thurston's
compactness theorem for acylindrical $3$-manifold and the double limit theorem
for quasi-Fuchsian groups in complex dynamics. In the appendix, we apply such
convergence results to show the existence of certain polynomial matings.


Abstract:
  Optical wireless communications (OWCs) have been recognized as a candidate
enabler of next generation in-body nano-scale networks and implants. The
development of an accurate channel model capable of accommodating the
particularities of different type of tissues is expected to boost the design of
optimized communication protocols for such applications. Motivated by this,
this paper focuses on presenting a general pathloss model for in-body OWCs. In
particular, we use experimental measurements in order to extract analytical
expressions for the absorption coefficients of the five main tissues'
constitutions, namely oxygenated and de-oxygenated blood, water, fat, and
melanin. Building upon these expressions, we derive a general formula for the
absorption coefficient evaluation of any biological tissue. To verify the
validity of this formula, we compute the absorption coefficient of complex
tissues and compare them against respective experimental results reported by
independent research works. Interestingly, we observe that the analytical
formula has high accuracy and is capable of modeling the pathloss and,
therefore, the penetration depth in complex tissues.


Abstract:
  We theoretically analyze a possibility of electromagnetic wave emission due
to electron transitions between spin subbands in a ferromagnet. Different
mechanisms of such spin-flip transitions are cousidered. One mechanism is the
electron transitions caused by magnetic field of the wave. Another mechanism is
due to Rashba spin-orbit interaction. While two mentioned mechanisms exist in a
homogeneously magnetized ferromagnet, there are two other mechanisms that exist
in non-collinearly magnetized medium. First mechanism is known and is due to
the dependence of exchange interaction constant on the quasimomentum of
conduction electrons. Second one exists in any non-collinearly magnetized
medium. We study these mechanisms in a non-collinear ferromagnet with
helicoidal magnetization distribution. The estimations of probabilities of
electron transitions due to different mechanisms are made for realistic
parameters, and we compare the mechanisms. We also estimate the radiation power
and threshold current in a simple model in which spin is injected into the
ferromagnet by a spin-polarized electric current through a tunnel barrier.


Abstract:
  We consider the sharp interface limit for the scalar-valued and vector-valued
Allen-Cahn equation with homogeneous Neumann boundary condition in a bounded
smooth domain $\Omega$ of arbitrary dimension $N\geq 2$ in the situation when a
two-phase diffuse interface has developed and intersects the boundary
$\partial\Omega$. The limit problem is mean curvature flow with
$90${\deg}-contact angle and we show convergence in strong norms for
well-prepared initial data as long as a smooth solution to the limit problem
exists. To this end we assume that the limit problem has a smooth solution on
$[0,T]$ for some time $T>0$. Based on the latter we construct suitable
curvilinear coordinates and set up an asymptotic expansion for the
scalar-valued and the vector-valued Allen-Cahn equation. Finally, we prove a
spectral estimate for the linearized Allen-Cahn operator in both cases in order
to estimate the difference of the exact and approximate solutions with a
Gronwall-type argument.


Abstract:
  We prove that the unique possible flow in an Alexandroff $T_{0}$-space is the
trivial one. On the way of motivation, we relate Alexandroff spaces with
topological hyperspaces.


Abstract:
  Transformer models have demonstrated superior performance in natural language
processing. The dot product self-attention in Transformer allows us to model
interactions between words. However, this modeling comes with significant
computational overhead. In this work, we revisit the memory-compute trade-off
associated with Transformer, particularly multi-head attention, and show a
memory-heavy but significantly more compute-efficient alternative to
Transformer. Our proposal, denoted as PairConnect, a multilayer perceptron
(MLP), models the pairwise interaction between words by explicit pairwise word
embeddings. As a result, PairConnect substitutes self dot product with a simple
embedding lookup. We show mathematically that despite being an MLP, our
compute-efficient PairConnect is strictly more expressive than Transformer. Our
experiment on language modeling tasks suggests that PairConnect could achieve
comparable results with Transformer while reducing the computational cost
associated with inference significantly.


Abstract:
  Multi-agent collision-free trajectory planning and control subject to
different goal requirements and system dynamics has been extensively studied,
and is gaining recent attention in the realm of machine and reinforcement
learning. However, in particular when using a large number of agents,
constructing a least-restrictive collision avoidance policy is of utmost
importance for both classical and learning-based methods. In this paper, we
propose a Least-Restrictive Collision Avoidance Module (LR-CAM) that evaluates
the safety of multi-agent systems and takes over control only when needed to
prevent collisions. The LR-CAM is a single policy that can be wrapped around
policies of all agents in a multi-agent system. It allows each agent to pursue
any objective as long as it is safe to do so. The benefit of the proposed
least-restrictive policy is to only interrupt and overrule the default
controller in case of an upcoming inevitable danger. We use a Long Short-Term
Memory (LSTM) based Variational Auto-Encoder (VAE) to enable the LR-CAM to
account for a varying number of agents in the environment. Moreover, we propose
an off-policy meta-reinforcement learning framework with a novel reward
function based on a Hamilton-Jacobi value function to train the LR-CAM. The
proposed method is fully meta-trained through a ROS based simulation and tested
on real multi-agent system. Our results show that LR-CAM outperforms the
classical least-restrictive baseline by 30 percent. In addition, we show that
even if a subset of agents in a multi-agent system use LR-CAM, the success rate
of all agents will increase significantly.


Abstract:
  In this article we will show $2$ different proofs for the fact that there
exist relatively prime positive integers $a,b$ such that: $a^2+ab+b^2=7^n$.


Abstract:
  We theoretically analyze the typical learning performance of
$\ell_{1}$-regularized linear regression ($\ell_1$-LinR) for Ising model
selection using the replica method from statistical mechanics. For typical
random regular graphs in the paramagnetic phase, an accurate estimate of the
typical sample complexity of $\ell_1$-LinR is obtained. Remarkably, despite the
model misspecification, $\ell_1$-LinR is model selection consistent with the
same order of sample complexity as $\ell_{1}$-regularized logistic regression
($\ell_1$-LogR), i.e., $M=\mathcal{O}\left(\log N\right)$, where $N$ is the
number of variables of the Ising model. Moreover, we provide an efficient
method to accurately predict the non-asymptotic behavior of $\ell_1$-LinR for
moderate $M, N$, such as precision and recall. Simulations show a fairly good
agreement between theoretical predictions and experimental results, even for
graphs with many loops, which supports our findings. Although this paper mainly
focuses on $\ell_1$-LinR, our method is readily applicable for precisely
characterizing the typical learning performances of a wide class of
$\ell_{1}$-regularized $M$-estimators including $\ell_1$-LogR and interaction
screening.


Abstract:
  We consider the recent surge of information on the potential benefits of
acid-suppression drugs in the context of COVID-19, with an eye on the
variability (and confusion) across the reported findings--at least as regards
the popular antacid famotidine. The inconsistencies reflect contradictory
conclusions from independent clinical-based studies that took roughly similar
approaches, in terms of experimental design (retrospective, cohort-based, etc.)
and statistical analyses (propensity-score matching and stratification, etc.).
The confusion has significant ramifications in choosing therapeutic
interventions: e.g., do potential benefits of famotidine indicate its use in a
particular COVID-19 case? Beyond this pressing therapeutic issue, conflicting
information on famotidine must be resolved before its integration in
ontological and knowledge graph-based frameworks, which in turn are useful in
drug repurposing efforts. To begin systematically structuring the rapidly
accumulating information, in the hopes of clarifying and reconciling the
discrepancies, we consider the contradictory information along three proposed
'axes': (1) a context-of-disease axis, (2) a degree-of-[therapeutic]-benefit
axis, and (3) a mechanism-of-action axis. We suspect that incongruencies in how
these axes have been (implicitly) treated in past studies has led to the
contradictory indications for famotidine and COVID-19. We also trace the
evolution of information on acid-suppression agents as regards the
transmission, severity, and mortality of COVID-19, given the many literature
reports that have accumulated. By grouping the studies conceptually and
thematically, we identify three eras in the progression of our understanding of
famotidine and COVID-19. Harmonizing these findings is a key goal for both
clinical standards-of-care (COVID and beyond) as well as ontological and
knowledge graph-based approaches.


Abstract:
  We consider the asymmetric simple exclusion process (ASEP) with forward
hopping rate 1, backward hopping rate q and periodic boundary conditions. We
show that the Bethe equations of ASEP can be decoupled, at all order in
perturbation in the variable q, by introducing a formal Laurent series mapping
the Bethe roots of the totally asymmetric case q=0 (TASEP) to the Bethe roots
of ASEP. The probability of the height for ASEP is then written as a single
contour integral on the Riemann surface on which symmetric functions of TASEP
Bethe roots live.


Abstract:
  In this study, we investigated a method allowing the determination of the
femur bone surface as well as its mechanical axis from some easy-to-identify
bony landmarks. The reconstruction of the whole femur is therefore performed
from these landmarks using a Statistical Shape Model (SSM). The aim of this
research is therefore to assess the impact of the number, the position, and the
accuracy of the landmarks for the reconstruction of the femur and the
determination of its related mechanical axis, an important clinical parameter
to consider for the lower limb analysis. Two statistical femur models were
created from our in-house dataset and a publicly available dataset. Both were
evaluated in terms of average point-to-point surface distance error and through
the mechanical axis of the femur. Furthermore, the clinical impact of using
landmarks on the skin in replacement of bony landmarks is investigated. The
predicted proximal femurs from bony landmarks were more accurate compared to
on-skin landmarks while both had less than 3.5 degrees mechanical axis angle
deviation error. The results regarding the non-invasive determination of the
mechanical axis are very encouraging and could open very interesting clinical
perspectives for the analysis of the lower limb either for orthopedics or
functional rehabilitation.


Abstract:
  Several novel statistical methods have been developed to estimate large
integrated volatility matrices based on high-frequency financial data. To
investigate their asymptotic behaviors, they require a sub-Gaussian or finite
high-order moment assumption for observed log-returns, which cannot account for
the heavy tail phenomenon of stock returns. Recently, a robust estimator was
developed to handle heavy-tailed distributions with some bounded fourth-moment
assumption. However, we often observe that log-returns have heavier tail
distribution than the finite fourth-moment and that the degrees of heaviness of
tails are heterogeneous over the asset and time period. In this paper, to deal
with the heterogeneous heavy-tailed distributions, we develop an adaptive
robust integrated volatility estimator that employs pre-averaging and
truncation schemes based on jump-diffusion processes. We call this an adaptive
robust pre-averaging realized volatility (ARP) estimator. We show that the ARP
estimator has a sub-Weibull tail concentration with only finite 2$\alpha$-th
moments for any $\alpha>1$. In addition, we establish matching upper and lower
bounds to show that the ARP estimation procedure is optimal. To estimate large
integrated volatility matrices using the approximate factor model, the ARP
estimator is further regularized using the principal orthogonal complement
thresholding (POET) method. The numerical study is conducted to check the
finite sample performance of the ARP estimator.


Abstract:
  Context. Turbulent transport in stellar radiative zones is a key ingredient
of stellar evolution theory, but the anisotropy of the transport due to the
stable stratification and the rotation of these regions is poorly understood.
The assumption of shellular rotation, which is a cornerstone of the so-called
rotational mixing, relies on an efficient horizontal transport. However, this
transport is included in many stellar evolution codes through phenomenological
models that have never been tested.
  Aims. We investigate the impact of horizontal shear on the anisotropy of
turbulent transport.
  Methods. We used a relaxation approximation (also known as {\tau}
approximation) to describe the anisotropising effect of stratification,
rotation, and shear on a background turbulent flow by computing velocity
correlations.
  Results. We obtain new theoretical scalings for velocity correlations that
include the effect of horizontal shear. These scalings show an enhancement of
turbulent motions, which would lead to a more efficient transport of chemicals
and angular momentum, in better agreement with helio- and asteroseismic
observations of rotation in the whole Hertzsprung-Russell diagram. Moreover, we
propose a new choice for the non-linear time used in the relaxation
approximation, which characterises the source of the turbulence.
  Conclusions. For the first time, we describe the effect of stratification,
rotation, and vertical and horizontal shear on the anisotropy of turbulent
transport in stellar radiative zones. The new prescriptions need to be
implemented in stellar evolution calculations. To do so, it may be necessary to
implement non-diffusive transport.


Abstract:
  Industrial robots can solve very complex tasks in controlled environments,
but modern applications require robots able to operate in unpredictable
surroundings as well. An increasingly popular reactive policy architecture in
robotics is Behavior Trees but as with other architectures, programming time
still drives cost and limits flexibility. There are two main branches of
algorithms to generate policies automatically, automated planning and machine
learning, both with their own drawbacks. We propose a method for generating
Behavior Trees using a Genetic Programming algorithm and combining the two
branches by taking the result of an automated planner and inserting it into the
population. Experimental results confirm that the proposed method of combining
planning and learning performs well on a variety of robotic assembly problems
and outperforms both of the base methods used separately. We also show that
this type of high level learning of Behavior Trees can be transferred to a real
system without further training.


Abstract:
  Kernel segmentation aims at partitioning a data sequence into several
non-overlapping segments that may have nonlinear and complex structures. In
general, it is formulated as a discrete optimization problem with combinatorial
constraints. A popular algorithm for optimally solving this problem is dynamic
programming (DP), which has quadratic computation and memory requirements.
Given that sequences in practice are too long, this algorithm is not a
practical approach. Although many heuristic algorithms have been proposed to
approximate the optimal segmentation, they have no guarantee on the quality of
their solutions. In this paper, we take a differentiable approach to alleviate
the aforementioned issues. First, we introduce a novel sigmoid-based
regularization to smoothly approximate the combinatorial constraints. Combining
it with objective of the balanced kernel clustering, we formulate a
differentiable model termed Kernel clustering with sigmoid-based regularization
(KCSR), where the gradient-based algorithm can be exploited to obtain the
optimal segmentation. Second, we develop a stochastic variant of the proposed
model. By using the stochastic gradient descent algorithm, which has much lower
time and space complexities, for optimization, the second model can perform
segmentation on overlong data sequences. Finally, for simultaneously segmenting
multiple data sequences, we slightly modify the sigmoid-based regularization to
further introduce an extended variant of the proposed model. Through extensive
experiments on various types of data sequences performances of our models are
evaluated and compared with those of the existing methods. The experimental
results validate advantages of the proposed models. Our Matlab source code is
available on github.


Abstract:
  Phishing is the number one threat in the world of internet. Phishing attacks
are from decades and with each passing year it is becoming a major problem for
internet users as attackers are coming with unique and creative ideas to breach
the security. In this paper, different types of phishing and anti-phishing
techniques are presented. For this purpose, the Systematic Literature
Review(SLR) approach is followed to critically define the proposed research
questions. At first 80 articles were extracted from different repositories.
These articles were then filtered out using Tollgate Approach to find out
different types of phishing and anti-phishing techniques. Research study
evaluated that spear phishing, Email Spoofing, Email Manipulation and phone
phishing are the most commonly used phishing techniques. On the other hand,
according to the SLR, machine learning approaches have the highest accuracy of
preventing and detecting phishing attacks among all other anti-phishing
approaches.


Abstract:
  This paper proposes a model-free nonparametric estimator of conditional
quantile of a time series regression model where the covariate vector is
repeated many times for different values of the response. This type of data is
abound in climate studies. To tackle such problems, our proposed method
exploits the replicated nature of the data and improves on restrictive linear
model structure of conventional quantile regression. Relevant asymptotic theory
for the nonparametric estimators of the mean and variance function of the model
are derived under a very general framework. We provide a detailed simulation
study which clearly demonstrates the gain in efficiency of the proposed method
over other benchmark models, especially when the true data generating process
entails nonlinear mean function and heteroskedastic pattern with time dependent
covariates. The predictive accuracy of the non-parametric method is remarkably
high compared to other methods when attention is on the higher quantiles of the
variable of interest. Usefulness of the proposed method is then illustrated
with two climatological applications, one with a well-known tropical cyclone
wind-speed data and the other with an air pollution data.


Abstract:
  Polarimetric imaging is one of the most effective techniques for
high-contrast imaging and characterization of circumstellar environments. These
environments can be characterized through direct-imaging polarimetry at
near-infrared wavelengths. The SPHERE/IRDIS instrument installed on the Very
Large Telescope in its dual-beam polarimetric imaging (DPI) mode, offers the
capability to acquire polarimetric images at high contrast and high angular
resolution. However dedicated image processing is needed to get rid of the
contamination by the stellar light, of instrumental polarization effects, and
of the blurring by the instrumental point spread function. We aim to
reconstruct and deconvolve the near-infrared polarization signal from
circumstellar environments. We use observations of these environments obtained
with the high-contrast imaging infrared polarimeter SPHERE-IRDIS at the VLT. We
developed a new method to extract the polarimetric signal using an inverse
approach method that benefits from the added knowledge of the detected signal
formation process. The method includes weighted data fidelity term, smooth
penalization, and takes into account instrumental polarization. The method
enables to accurately measure the polarized intensity and angle of linear
polarization of circumstellar disks by taking into account the noise statistics
and the convolution of the observed objects by the instrumental point spread
function. It has the capability to use incomplete polarimetry cycles which
enhance the sensitivity of the observations. The method improves the overall
performances in particular for low SNR/small polarized flux compared to
standard methods.


Abstract:
  Traditional and deep learning-based fusion methods generated the intermediate
decision map to obtain the fusion image through a series of post-processing
procedures. However, the fusion results generated by these methods are easy to
lose some source image details or results in artifacts. Inspired by the image
reconstruction techniques based on deep learning, we propose a multi-focus
image fusion network framework without any post-processing to solve these
problems in the end-to-end and supervised learning way. To sufficiently train
the fusion model, we have generated a large-scale multi-focus image dataset
with ground-truth fusion images. What's more, to obtain a more informative
fusion image, we further designed a novel fusion strategy based on unity fusion
attention, which is composed of a channel attention module and a spatial
attention module. Specifically, the proposed fusion approach mainly comprises
three key components: feature extraction, feature fusion and image
reconstruction. We firstly utilize seven convolutional blocks to extract the
image features from source images. Then, the extracted convolutional features
are fused by the proposed fusion strategy in the feature fusion layer. Finally,
the fused image features are reconstructed by four convolutional blocks.
Experimental results demonstrate that the proposed approach for multi-focus
image fusion achieves remarkable fusion performance compared to 19
state-of-the-art fusion methods.


Abstract:
  In this work, the aim is to study the spread of a contagious disease and
information on a multilayer social system. The main idea is to find a criterion
under which the adoption of the spreading information blocks or suppresses the
epidemic spread. A two-layer network is the base of the model. The first layer
describes the direct contact interactions, while the second layer is the
information propagation layer. Both layers consist of the same nodes. The
society consists of five different categories of individuals: susceptibles,
infective, recovered, vaccinated and precautioned. Initially, only one infected
individual starts transmitting the infection. Direct contact interactions
spread the infection to the susceptibles. The information spreads through the
second layer. The SIR model is employed for the infection spread, while the
Bass equation models the adoption of information. The control parameters of the
competition between the spread of information and spread of disease are the
topology and the density of connectivity. The topology of the information layer
is a scale-free network with increasing density of edges. In the contact layer,
regular and scale-free networks with the same average degree per node are used
interchangeably. The observation is that increasing complexity of the contact
network reduces the role of individual awareness. If the contact layer consists
of networks with limited range connections, or the edges sparser than the
information network, spread of information plays a significant role in
controlling the epidemics.


Abstract:
  This paper first formalises a new observability concept, called weak regular
observability, that is adapted to Fast Moving Horizon Estimation where one aims
to estimate the state of a nonlinear system efficiently on rolling time windows
in the case of small initial error. Additionally, sufficient conditions of weak
regular observability are provided in a problem of Simultaneous Localisation
and Mapping (SLAM) for different measurement models. In particular it is shown
that following circular trajectories leads to weak regular observability in a
second order 2D SLAM problem with several possible types of sensors.


Abstract:
  The dark matter halo surface density, given by the product of the dark matter
core radius ($r_c$) and core density ($\rho_c$) has been shown to be a constant
for a wide range of isolated galaxy systems. Here, we carry out a test of this
{\em ansatz} using a sample of 17 relaxed galaxy groups observed using Chandra
and XMM-Newton, as an extension of our previous analysis with galaxy clusters.
We find that $\rho_c \propto r_c^{-1.35^{+0.16}_{-0.17}}$, with an intrinsic
scatter of about 27.3%, which is about 1.5 times larger than that seen for
galaxy clusters. Our results thereby indicate that the surface density is
discrepant with respect to scale invariance by about 2$\sigma$, and its value
is about four times greater than that for galaxies. Therefore, the elevated
values of the halo surface density for groups and clusters indicate that the
surface density cannot be a universal constant for all dark matter dominated
systems. Furthermore, we also implement a test of the radial acceleration
relation for this group sample. We find that the residual scatter in the radial
acceleration relation is about 0.32 dex and a factor of three larger than that
obtained using galaxy clusters. The acceleration scale which we obtain is
in-between that seen for galaxies and clusters.


Abstract:
  The nature of unconventional superconductivity is intimately linked to the
microscopic nature of the pairing interactions. In this work, motivated by
cubic heavy fermion compounds with embedded multipolar moments, we
theoretically investigate superconducting instabilities instigated by
multipolar Kondo interactions. Employing multipolar fluctuations (mediated by
RKKY interaction) coupled to conduction electrons via two-channel Kondo and
novel multipolar Kondo interactions, we uncover a variety of superconducting
states characterized by higher-angular momentum Cooper pairs, $J=0,1,2,3$. We
demonstrate that both odd and even parity pairing functions are possible,
regardless of the total angular momentum of the Cooper pairs, which can be
traced back to the atypical nature of the multipolar Kondo interaction that
intertwines conduction electron spin and orbital degrees of freedom. We
determine that different (point-group) irrep classified pairing functions may
coexist with each other, with some of them characterized by gapped and point
node structures in their corresponding quasiparticle spectra. This work lays
the foundation for discovery and classification of superconducting states in
rare-earth metallic compounds with multipolar local moments.


Abstract:
  Satellites, are both crucial and, despite common misbelieve, very fragile
parts our civilian and military critical infrastructure. While, many efforts
are focused on securing ground and space segments, especially when national
security or large businesses interests are affected, the small-sat, newspace
revolution democratizes access to, and exploitation of the near earth orbits.
This brings new players to the market, typically in the form of small to medium
sized companies, offering new or more affordable services. Despite the
necessity and inevitability of this process, it also opens potential new venues
for targeted attacks against space-related infrastructure. Since sources of
satellite ephemerides are very often centralized, they are subject to classical
Man-in-the-Middle attacks which open venues for TLE spoofing attack, which may
result in unnecessary collision avoidance maneuvers, in best case and
orchestrated crashes, in worst case. In this work, we propose a countermeasure
to the presented problem that include distributed solution, which will have no
central authority responsible for storing and disseminating TLE information.
Instead, each of the peers participating to the system, have full access to all
of the records stored in the system, and distribute the data in a consensual
manner,ensuring information replication at each peer node. This way, single
point of failure syndromes of classic systems, which currently exist due to the
direct ephemerids distribution mechanism, are removed. Our proposed solution is
to build data dissemination systems using permissioned, private ledgers where
peers have strong and verifiable identities, which allow also for redundancy in
SST data sourcing.


Abstract:
  Opioid Use Disorder (OUD) is a public health crisis costing the US billions
of dollars annually in healthcare, lost workplace productivity, and crime.
Analyzing longitudinal healthcare data is critical in addressing many
real-world problems in healthcare. Leveraging the real-world longitudinal
healthcare data, we propose a novel multi-stream transformer model called MUPOD
for OUD identification. MUPOD is designed to simultaneously analyze multiple
types of healthcare data streams, such as medications and diagnoses, by
attending to segments within and across these data streams. Our model tested on
the data from 392,492 patients with long-term back pain problems showed
significantly better performance than the traditional models and recently
developed deep learning models.


Abstract:
  This paper presents an analytical study for the metric properties of the
paraboloidal double projection, i.e. central and orthogonal projections used in
the catadioptric camera system. Metric properties have not sufficiently studied
in previous treatments of such system. These properties incorporate the
determination of the true lengths of projected lines and areas bounded by
projected lines. The advantageous main gain of determining metric elements of
the paraboloidal double projection is studying distortion analysis and camera
calibration, which is considered an essential tool in testing camera accuracy.
Also, this may be considered as a significant utility in studying comparison
analysis between different cameras projection systems.


Abstract:
  Anti-ferromagnetic materials have the possibility to offer ultra fast, high
data density spintronic devices. A significant challenge is the reliable
detection of the state of the antiferromagnet, which can be achieved using
exchange bias. Here we develop an atomistic spin model of the athermal training
effect, a well known phenomenon in exchange biased systems where the bias is
significantly reduced after the first hysteresis cycle. We find that the
setting process in granular thin films relies on the presence of interfacial
mixing between the ferromagnetic and antiferromagnetic layers. We
systematically investigate the effect of the intermixing and find that the
exchange bias, switching field and coercivity all increase with increased
intermixing. The interfacial spin state is highly frustrated leading to a
systematic decrease in interfacial ordering of the ferromagnet. This metastable
spin structure of initially irreversible spins leads to a large effective
exchange coupling and thus large increase in the switching field. After the
first hysteresis cycle these metastable spins drop into a reversible ground
state that is repeatable for all subsequent hysteresis cycles, demonstrating
that the effect is truly athermal. Our simulations provide new insights into
the role of interface mixing and the importance of metastable spin structures
in exchange biased systems which could help with the design an optimisation of
antiferromagnetic spintronic devices.


Abstract:
  The optical matrix formalism is applied to find parameters such as focal
distance, back and front focal points, principal planes, and the equation
relating object and image distances for a thick spherical lens immerse in air.
Then, the formalism is applied to systems compound of two, three and N thick
lenses in cascade. It is found that a simple Gaussian equation is enough to
relate object and image distances no matter the number of lenses.


Abstract:
  Recent works have shown that learned models can achieve significant
performance gains, especially in terms of perceptual quality measures, over
traditional methods. Hence, the state of the art in image restoration and
compression is getting redefined. This special issue covers the state of the
art in learned image/video restoration and compression to promote further
progress in innovative architectures and training methods for effective and
efficient networks for image/video restoration and compression.


Abstract:
  The Laser Interferometer Space Antenna, LISA, will detect gravitational wave
signals from Extreme Mass Ratio Inspirals, where a stellar mass compact object
orbits a supermassive black hole and eventually plunges into it. Here we report
on LISA's capability to detect whether the smaller compact object in an Extreme
Mass Ratio Inspiral is endowed with a scalar field, and to measure its scalar
charge -- a dimensionless quantity that acts as a measure of how much scalar
field the object carries. By direct comparison of signals, we show that LISA
will be able to detect and measure the scalar charge with an accuracy of the
order of percent, which is an unprecedented level of precision. This result is
independent of the origin of the scalar field and of the structure and other
properties of the small compact object, so it can be seen as a generic
assessment of LISA's capabilities to detect new fundamental fields.


Abstract:
  Communication between workers and the master node to collect local stochastic
gradients is a key bottleneck in a large-scale federated learning system.
Various recent works have proposed to compress the local stochastic gradients
to mitigate the communication overhead. However, robustness to malicious
attacks is rarely considered in such a setting. In this work, we investigate
the problem of Byzantine-robust federated learning with compression, where the
attacks from Byzantine workers can be arbitrarily malicious. We point out that
a vanilla combination of compressed stochastic gradient descent (SGD) and
geometric median-based robust aggregation suffers from both stochastic and
compression noise in the presence of Byzantine attacks. In light of this
observation, we propose to jointly reduce the stochastic and compression noise
so as to improve the Byzantine-robustness. For the stochastic noise, we adopt
the stochastic average gradient algorithm (SAGA) to gradually eliminate the
inner variations of regular workers. For the compression noise, we apply the
gradient difference compression and achieve compression for free. We
theoretically prove that the proposed algorithm reaches a neighborhood of the
optimal solution at a linear convergence rate, and the asymptotic learning
error is in the same order as that of the state-of-the-art uncompressed method.
Finally, numerical experiments demonstrate effectiveness of the proposed
method.


Abstract:
  Many emerging cyber-physical systems, such as autonomous vehicles and robots,
rely heavily on artificial intelligence and machine learning algorithms to
perform important system operations. Since these highly parallel applications
are computationally intensive, they need to be accelerated by graphics
processing units (GPUs) to meet stringent timing constraints. However, despite
the wide adoption of GPUs, efficiently scheduling multiple GPU applications
while providing rigorous real-time guarantees remains a challenge. In this
paper, we propose RTGPU, which can schedule the execution of multiple GPU
applications in real-time to meet hard deadlines. Each GPU application can have
multiple CPU execution and memory copy segments, as well as GPU kernels. We
start with a model to explicitly account for the CPU and memory copy segments
of these applications. We then consider the GPU architecture in the development
of a precise timing model for the GPU kernels and leverage a technique known as
persistent threads to implement fine-grained kernel scheduling with improved
performance through interleaved execution. Next, we propose a general method
for scheduling parallel GPU applications in real time. Finally, to schedule
multiple parallel GPU applications, we propose a practical real-time scheduling
algorithm based on federated scheduling and grid search (for GPU kernel
segments) with uniprocessor fixed priority scheduling (for multiple CPU and
memory copy segments). Our approach provides superior schedulability compared
with previous work, and gives real-time guarantees to meet hard deadlines for
multiple GPU applications according to comprehensive validation and evaluation
on a real NVIDIA GTX1080Ti GPU system.


Abstract:
  The Supervisory control and data acquisition (SCADA) systems have been
continuously leveraging the evolution of network architecture, communication
protocols, next-generation communication techniques (5G, 6G, Wi-Fi 6), and the
internet of things (IoT). However, SCADA system has become the most profitable
and alluring target for ransomware attackers. This paper proposes the deep
learning-based novel ransomware detection framework in the SCADA controlled
electric vehicle charging station (EVCS) with the performance analysis of three
deep learning algorithms, namely deep neural network (DNN), 1D convolution
neural network (CNN), and long short-term memory (LSTM) recurrent neural
network. All three-deep learning-based simulated frameworks achieve around 97%
average accuracy (ACC), more than 98% of the average area under the curve
(AUC), and an average F1-score under 10-fold stratified cross-validation with
an average false alarm rate (FAR) less than 1.88%. Ransomware driven
distributed denial of service (DDoS) attack tends to shift the SOC profile by
exceeding the SOC control thresholds. The severity has been found to increase
as the attack progress and penetration increases. Also, ransomware driven false
data injection (FDI) attack has the potential to damage the entire BES or
physical system by manipulating the SOC control thresholds. It's a design
choice and optimization issue that a deep learning algorithm can deploy based
on the tradeoffs between performance metrics.


Abstract:
  Spectral lines from formaldehyde (H2CO) molecules at cm wavelengths are
typically detected in absorption and trace a broad range of environments, from
diffuse gas to giant molecular clouds. In contrast, thermal emission of
formaldehyde lines at cm wavelengths is rare. In previous observations with the
100m Robert C. Byrd Green Bank Telescope (GBT), we detected 2 cm formaldehyde
emission toward NGC7538 IRS1 - a high-mass protostellar object in a prominent
star-forming region of our Galaxy. We present further GBT observations of the 2
cm and 1 cm H2CO lines to investigate the nature of the 2 cm H2CO emission. We
conducted observations to constrain the angular size of the 2 cm emission
region based on a East-West and North-South cross-scan map. Gaussian fits of
the spatial distribution in the East-West direction show a deconvolved size (at
half maximum) of the 2 cm emission of 50" +/- 8". The 1 cm H2CO observations
revealed emission superimposed on a weak absorption feature. A non-LTE
radiative transfer analysis shows that the H2CO emission is consistent with
quasi-thermal radiation from dense gas (~10^5 to 10^6 cm^-3). We also report
detection of 4 transitions of CH3OH (12.2, 26.8, 28.3, 28.9 GHz), the (8,8)
transition of NH3 (26.5 GHz), and a cross-scan map of the 13 GHz SO line that
shows extended emission (> 50").


Abstract:
  Dynamically switchable half-/quarter-wave plates have recently been the focus
in the terahertz regime. Conventional design philosophy leads to multilayer
metamaterials or narrowband metasurfaces. Here we propose a novel design
philosophy and a VO2-metal hybrid metasurface for achieving broadband
dynamically switchable half-/quarter-wave plate (HWP/QWP) based on the
transition from the overdamped to the underdamped resonance. Results show that,
by varying the VO2 conductivity by three orders of magnitude, the proposed
metasurface's function can be switched between an HWP with polarization
conversion ratio larger than 96% and a QWP with ellipticity close to -1 over
the broad working band of 0.8-1.2 THz. We expect that the proposed design
philosophy will advance the engineering of metasurfaces for dynamically
switchable functionalities beyond the terahertz regime.


Abstract:
  We develop a new type of orthogonal polynomial, the modified discrete
Laguerre (MDL) polynomials, designed to accelerate the computation of bosonic
Matsubara sums in statistical physics. The MDL polynomials lead to a rapidly
convergent Gaussian "quadrature" scheme for Matsubara sums, and more generally
for any sum $F(0)/2 + F(h) + F(2h) + \cdots$ of exponentially decaying summands
$F(nh) = f(nh)e^{-nhs}$ where $hs>0$. We demonstrate this technique for
computation of finite-temperature Casimir forces arising from quantum field
theory, where evaluation of the summand $F$ requires expensive electromagnetic
simulations. A key advantage of our scheme, compared to previous methods, is
that the convergence rate is nearly independent of the spacing $h$
(proportional to the thermodynamic temperature). We also prove convergence for
any polynomially decaying $F$.


Abstract:
  The growth rate of the number of scientific publications is constantly
increasing, creating important challenges in the identification of valuable
research and in various scholarly data management applications, in general. In
this context, measures which can effectively quantify the scientific impact
could be invaluable. In this work, we present BIP! DB, an open dataset that
contains a variety of impact measures calculated for a large collection of more
than 100 million scientific publications from various disciplines.


Abstract:
  Context{The high energy emission regions of rotation powered pulsars are
studied using folded light curve (FLCs) and phase resolved spectra (PRS).}
aims{This work uses the NICER observatory to obtain the highest resolution FLC
and PRS of the Crab pulsar at soft X-ray energies.} methods{NICER has
accumulated about 347 ksec of data on the Crab pulsar. The data are processed
using the standard analysis pipeline. Stringent filtering is done for spectral
analysis. The individual detectors are calibrated in terms of long time light
curve (LTLC), raw spectrum and deadtime. The arrival times of the photons are
referred to the solar system's barycenter and the rotation frequency $\nu$ and
its time derivative $\dot \nu$ are used to derive the rotation phase of each
photon.} results{The LTLCs, raw spectra and deadtimes of the individual
detectors are statistically similar; the latter two show no evolution with
epoch; detector deadtime is independent of photon energy. The deadtime for the
Crab pulsar, taking into account the two types of deadtime, is only approx 7%
to 8% larger than that obtained using the cleaned events. Detector 00 behaves
slightly differently from the rest, but can be used for spectral work. The PRS
of the two peaks of the Crab pulsar are obtained at a resolution of better than
1/512 in rotation phase. The FLC very close to the first peak rises slowly and
falls faster. The spectral index of the PRS is almost constant very close to
the first peak.} conclusions{The high resolution FLC and PRS of the {{peaks}}
of the Crab pulsar provide important constraints for the formation of caustics
in the emission zone.}


Abstract:
  The many-body-theory approach to positronium-atom interactions developed in
[Phys. Rev. Lett. \textbf{120}, 183402 (2018)] is applied to the sequence of
noble-gas atoms He-Xe. The Dyson equation is solved separately for an electron
and positron moving in the field of the atom, with the entire system enclosed
in a hard-wall spherical cavity. The two-particle Dyson equation is solved to
give the energies and wave functions of the Ps eigenstates in the cavity. From
these, we determine the scattering phase shifts and cross sections, and values
of the pickoff annihilation parameter $^1Z_\text{eff}$ including short-range
electron-positron correlations via vertex enhancement factors. Comparisons are
made with available experimental data for elastic and momentum-transfer cross
sections and $^1Z_\text{eff}$. Values of $^1Z_\text{eff}$ for He and Ne,
previously reported in [Phys. Rev. Lett. \textbf{120}, 183402 (2018)], are
found to be in near-perfect agreement with experiment, and for Ar, Kr, and Xe
within a factor of 1.2.


Abstract:
  Search-based test generation is guided by feedback from one or more fitness
functions - scoring functions that judge solution optimality. Choosing
informative fitness functions is crucial to meeting the goals of a tester.
Unfortunately, many goals - such as forcing the class-under-test to throw
exceptions, increasing test suite diversity, and attaining Strong Mutation
Coverage - do not have effective fitness function formulations. We propose that
meeting such goals requires treating fitness function identification as a
secondary optimization step. An adaptive algorithm that can vary the selection
of fitness functions could adjust its selection throughout the generation
process to maximize goal attainment, based on the current population of test
suites. To test this hypothesis, we have implemented two reinforcement learning
algorithms in the EvoSuite unit test generation framework, and used these
algorithms to dynamically set the fitness functions used during generation for
the three goals identified above.
  We have evaluated our framework, EvoSuiteFIT, on a set of Java case examples.
EvoSuiteFIT techniques attain significant improvements for two of the three
goals, and show limited improvements on the third when the number of
generations of evolution is fixed. Additionally, for two of the three goals,
EvoSuiteFIT detects faults missed by the other techniques. The ability to
adjust fitness functions allows strategic choices that efficiently produce more
effective test suites, and examining these choices offers insight into how to
attain our testing goals. We find that adaptive fitness function selection is a
powerful technique to apply when an effective fitness function does not already
exist for achieving a testing goal.


Abstract:
  While object semantic understanding is essential for most service robotic
tasks, 3D object classification is still an open problem. Learning from
artificial 3D models alleviates the cost of annotation necessary to approach
this problem, but most methods still struggle with the differences existing
between artificial and real 3D data. We conjecture that the cause of those
issue is the fact that many methods learn directly from point coordinates,
instead of the shape, as the former is hard to center and to scale under
variable occlusions reliably. We introduce spherical kernel point convolutions
that directly exploit the object surface, represented as a graph, and a voting
scheme to limit the impact of poor segmentation on the classification results.
Our proposed approach improves upon state-of-the-art methods by up to 36% when
transferring from artificial objects to real objects.


Abstract:
  After observing the Higgs boson by the ATLAS and CMS experiments at the LHC,
accurate measurements of its properties, which allow us to study the
electroweak symmetry breaking mechanism, become a high priority for particle
physics. The most promising of extracting the Higgs self-coupling at hadron
colliders is by examining the double Higgs production, especially in the $b
\bar{b} \gamma \gamma$ channel. In this work, we presented full loop
calculation for both SM and New Physics effects of the Higgs pair production to
next-to-leading-order (NLO), including loop-induced processes $gg\to HH$,
$gg\to HHg$, and $qg \to qHH$. We also included the calculation of the
corrections from diagrams with only one QCD coupling in $qg \to qHH$, which was
neglected in the previous studies. With the latest observed limit on the HH
production cross-section, we studied the constraints on the effective Higgs
couplings for the LHC at center-of-mass energies of 14 TeV and a provisional
100 TeV proton collider within the Future-Circular-Collider (FCC) project. To
obtain results better than using total cross-section alone, we focused on the
$b \bar{b} \gamma \gamma$ channel and divided the differential cross-section
into low and high bins based on the total invariant mass and $p_{T}$ spectra.
The new physics effects are further constrained by including extra kinematic
information. However, some degeneracy persists, as shown in previous studies,
especially in determining the Higgs trilinear coupling. Our analysis shows that
the degeneracy is reduced by including the full NLO corrections.


Abstract:
  In this paper, we propose an energy-efficient optimal altitude for an aerial
access point (AAP), which acts as a flying base station to serve a set of
ground user equipment (UE). Since the ratio of total energy consumed by the
aerial vehicle to the communication energy is very large, we include the aerial
vehicle's energy consumption in the problem formulation. After considering the
energy consumption model of the aerial vehicle, our objective is translated
into a non-convex optimization problem of maximizing the global energy
efficiency (GEE) of the aerial communication system, subject to altitude and
minimum individual data rate constraints. At first, the non-convex fractional
objective function is solved by using sequential convex programming (SCP)
optimization technique. To compare the result of SCP with the global optimum of
the problem, we reformulate the initial problem as a monotonic fractional
optimization problem (MFP) and solve it using the polyblock outer approximation
(PA) algorithm. Numerical results show that the candidate solution obtained
from SCP is the same as the global optimum found using the monotonic fractional
programming technique. Furthermore, the impact of the aerial vehicle's energy
consumption on the optimal altitude determination is also studied.


Abstract:
  Generative Adversarial Networks (GANs) have demonstrated unprecedented
success in various image generation tasks. The encouraging results, however,
come at the price of a cumbersome training process, during which the generator
and discriminator are alternately updated in two stages. In this paper, we
investigate a general training scheme that enables training GANs efficiently in
only one stage. Based on the adversarial losses of the generator and
discriminator, we categorize GANs into two classes, Symmetric GANs and
Asymmetric GANs, and introduce a novel gradient decomposition method to unify
the two, allowing us to train both classes in one stage and hence alleviate the
training effort. We also computationally analyze the efficiency of the proposed
method, and empirically demonstrate that, the proposed method yields a solid
$1.5\times$ acceleration across various datasets and network architectures.
Furthermore, we show that the proposed method is readily applicable to other
adversarial-training scenarios, such as data-free knowledge distillation. The
code is available at https://github.com/zju-vipa/OSGAN.


Abstract:
  In order to satisfy timing constraints, modern real-time applications require
massively parallel accelerators such as General Purpose Graphic Processing
Units (GPGPUs). Generation after generation, the number of computing clusters
made available in novel GPU architectures is steadily increasing, hence,
investigating suitable scheduling approaches is now mandatory. Such scheduling
approaches are related to mapping different and concurrent compute kernels
within the GPU computing clusters, hence grouping GPU computing clusters into
schedulable partitions. In this paper we propose novel techniques to define GPU
partitions; this allows us to define suitable task-to-partition allocation
mechanisms in which tasks are GPU compute kernels featuring different timing
requirements. Such mechanisms will take into account the interference that GPU
kernels experience when running in overlapping time windows. Hence, an
effective and simple way to quantify the magnitude of such interference is also
presented. We demonstrate the efficiency of the proposed approaches against the
classical techniques that considered the GPU as a single, non-partitionable
resource.


Abstract:
  Electrons in low-temperature solids are governed by the non-relativistic
Schr$\ddot{o}$dinger equation, since the electron velocities are much slower
than the speed of light. Remarkably, the low-energy quasi-particles given by
electrons in various materials can behave as relativistic Dirac/Weyl fermions
that obey the relativistic Dirac/Weyl equation. We refer to these materials as
"Dirac/Weyl materials", which provide a tunable platform to test relativistic
quantum phenomena in table-top experiments. More interestingly, different types
of physical fields in these Weyl/Dirac materials, such as magnetic
fluctuations, lattice vibration, strain, and material inhomogeneity, can couple
to the "relativistic" quasi-particles in a similar way as the $U(1)$ gauge
coupling. As these fields do not have gauge-invariant dynamics in general, we
refer to them as "pseudo-gauge fields". In this chapter, we overview the
concept and physical consequences of pseudo-gauge fields in Weyl/Dirac
materials. In particular, we will demonstrate that pseudo-gauge fields can
provide a unified understanding of a variety of physical phenomena, including
chiral zero modes inside a magnetic vortex core of magnetic Weyl semimetals, a
giant current response at magnetic resonance in magnetic topological
insulators, and piezo-electromagnetic response in time-reversal invariant
systems. These phenomena are deeply related to various concepts in high-energy
physics, such as chiral anomaly and axion electrodynamics.


Abstract:
  The bug triaging process, an essential process of assigning bug reports to
the most appropriate developers, is related closely to the quality and costs of
software development. As manual bug assignment is a labor-intensive task,
especially for large-scale software projects, many machine-learning-based
approaches have been proposed to automatically triage bug reports. Although
developer collaboration networks (DCNs) are dynamic and evolving in the
real-world, most automated bug triaging approaches focus on static tossing
graphs at a single time slice. Also, none of the previous studies consider
periodic interactions among developers. To address the problems mentioned
above, in this article, we propose a novel spatial-temporal dynamic graph
neural network (ST-DGNN) framework, including a joint random walk (JRWalk)
mechanism and a graph recurrent convolutional neural network (GRCNN) model. In
particular, JRWalk aims to sample local topological structures in a graph with
two sampling strategies by considering both node importance and edge
importance. GRCNN has three components with the same structure, i.e.,
hourly-periodic, daily-periodic, and weekly-periodic components, to learn the
spatial-temporal features of dynamic DCNs. We evaluated our approach's
effectiveness by comparing it with several state-of-the-art graph
representation learning methods in two domain-specific tasks that belong to
node classification. In the two tasks, experiments on two real-world,
large-scale developer collaboration networks collected from the Eclipse and
Mozilla projects indicate that the proposed approach outperforms all the
baseline methods.


Abstract:
  We propose an algorithm that uses linear function approximation (LFA) for
stochastic shortest path (SSP). Under minimal assumptions, it obtains sublinear
regret, is computationally efficient, and uses stationary policies. To our
knowledge, this is the first such algorithm in the LFA literature (for SSP or
other formulations). Our algorithm is a special case of a more general one,
which achieves regret square root in the number of episodes given access to a
certain computation oracle.


Abstract:
  This paper develops a new empirical Bayesian inference algorithm for solving
a linear inverse problem given multiple measurement vectors (MMV) of
under-sampled and noisy observable data. Specifically, by exploiting the joint
sparsity across the multiple measurements in the sparse domain of the
underlying signal or image, we construct a new support informed sparsity
promoting prior. Several applications can be modeled using this framework, and
as a prototypical example we consider reconstructing an image from synthetic
aperture radar (SAR) observations using nearby azimuth angles. Our numerical
experiments demonstrate that using this new prior not only improves accuracy of
the recovery, but also reduces the uncertainty in the posterior when compared
to standard sparsity producing priors.


Abstract:
  The current interests in the universe motivate us to go beyond Einstein's
General theory of relativity. One of the interesting proposals comes from a new
class of teleparallel gravity named symmetric teleparallel gravity, i.e.,
$f(Q)$ gravity, where the non-metricity term $Q$ is accountable for fundamental
interaction. These alternative modified theories of gravity's vital role are to
deal with the recent interests and to present a realistic cosmological model.
This manuscript's main objective is to study the traversable wormhole
geometries in $f(Q)$ gravity. We construct the wormhole geometries for three
cases: (i) by assuming a relation between the radial and lateral pressure, (ii)
considering phantom energy equation of state (EoS), and (iii) for a specific
shape function in the fundamental interaction of gravity (i.e. for linear form
of $f(Q)$). Besides, we discuss two wormhole geometries for a general case of
$f(Q)$ with two specific shape functions. Then, we discuss the viability of
shape functions and the stability analysis of the wormhole solutions for each
case. We have found that the null energy condition (NEC) violates each wormhole
model which concluded that our outcomes are realistic and stable. Finally, we
discuss the embedding diagrams and volume integral quantifier to have a
complete view of wormhole geometries.


Abstract:
  We study temporally localized structures in doubly resonant degenerate
optical parametric oscillators in the absence of temporal walk-off. We focus on
states formed through the locking of domain walls between the zero and a
non-zero continuous wave solution. We show that these states undergo collapsed
snaking and we characterize their dynamics in the parameter space.


Abstract:
  Experience replay enables off-policy reinforcement learning (RL) agents to
utilize past experiences to maximize the cumulative reward. Prioritized
experience replay that weighs experiences by the magnitude of their
temporal-difference error ($|\text{TD}|$) significantly improves the learning
efficiency. But how $|\text{TD}|$ is related to the importance of experience is
not well understood. We address this problem from an economic perspective, by
linking $|\text{TD}|$ to value of experience, which is defined as the value
added to the cumulative reward by accessing the experience. We theoretically
show the value metrics of experience are upper-bounded by $|\text{TD}|$ for
Q-learning. Furthermore, we successfully extend our theoretical framework to
maximum-entropy RL by deriving the lower and upper bounds of these value
metrics for soft Q-learning, which turn out to be the product of $|\text{TD}|$
and "on-policyness" of the experiences. Our framework links two important
quantities in RL: $|\text{TD}|$ and value of experience. We empirically show
that the bounds hold in practice, and experience replay using the upper bound
as priority improves maximum-entropy RL in Atari games.


Abstract:
  We tackle the problem of unsupervised synthetic-to-real domain adaptation for
single image depth estimation. An essential building block of single image
depth estimation is an encoder-decoder task network that takes RGB images as
input and produces depth maps as output. In this paper, we propose a novel
training strategy to force the task network to learn domain invariant
representations in a selfsupervised manner. Specifically, we extend
self-supervised learning from traditional representation learning, which works
on images from a single domain, to domain invariant representation learning,
which works on images from two different domains by utilizing an image-to-image
translation network. Firstly, we use an image-to-image translation network to
transfer domain-specific styles between synthetic and real domains. This style
transfer operation allows us to obtain similar images from the different
domains. Secondly, we jointly train our task network and Siamese network with
the same images from the different domains to obtain domain invariance for the
task network. Finally, we fine-tune the task network using labeled synthetic
and unlabeled realworld data. Our training strategy yields improved
generalization capability in the real-world domain. We carry out an extensive
evaluation on two popular datasets for depth estimation, KITTI and Make3D. The
results demonstrate that our proposed method outperforms the state-of-the-art
on all metrics, e.g. by 14.7% on Sq Rel on KITTI. The source code and model
weights will be made available.


Abstract:
  We propose an algorithm for automatic, targetless, extrinsic calibration of a
LiDAR and camera system using semantic information. We achieve this goal by
maximizing mutual information (MI) of semantic information between sensors,
leveraging a neural network to estimate semantic mutual information, and matrix
exponential for calibration computation. Using kernel-based sampling to sample
data from camera measurement based on LiDAR projected points, we formulate the
problem as a novel differentiable objective function which supports the use of
gradient-based optimization methods. We also introduce an initial calibration
method using 2D MI-based image registration. Finally, we demonstrate the
robustness of our method and quantitatively analyze the accuracy on a synthetic
dataset and also evaluate our algorithm qualitatively on KITTI360 and RELLIS-3D
benchmark datasets, showing improvement over recent comparable approaches.


Abstract:
  Providing multi-connectivity services is an important goal for next
generation wireless networks, where multiple access networks are available and
need to be integrated into a coherent solution that efficiently supports both
reliable and non reliable traffic. Based on virtual network interfaces and per
path congestion controlled tunnels, the MP-DCCP based multiaccess aggregation
framework presents as a novel solution that flexibly supports different path
schedulers and congestion control algorithms as well as reordering modules. The
framework has been implemented within the Linux kernel space and has been
tested over different prototypes. Experimental results have shown that the
overall performance strongly depends upon the congestion control algorithm used
on the individual DCCP tunnels, denoted as CCID. In this paper, we present an
implementation of the BBR (Bottleneck Bandwidth Round Trip propagation time)
congestion control algorithm for DCCP in the Linux kernel. We show how BBR is
integrated into the MP-DCCP multi-access framework and evaluate its performance
over both single and multi-path environments. Our evaluation results show that
BBR improves the performance compared to CCID2 for multi-path scenarios due to
the faster response to changes in the available bandwidth, which reduces
latency and increases performance, especially for unreliable traffic. the
MP-DCCP framework code, including the new CCID5 is available as OpenSource.


Abstract:
  Recent developments in representational learning for information retrieval
can be organized in a conceptual framework that establishes two pairs of
contrasts: sparse vs. dense representations and unsupervised vs. learned
representations. Sparse learned representations can further be decomposed into
expansion and term weighting components. This framework allows us to understand
the relationship between recently proposed techniques such as DPR, ANCE,
DeepCT, DeepImpact, and COIL, and furthermore, gaps revealed by our analysis
point to "low hanging fruit" in terms of techniques that have yet to be
explored. We present a novel technique dubbed "uniCOIL", a simple extension of
COIL that achieves to our knowledge the current state-of-the-art in sparse
retrieval on the popular MS MARCO passage ranking dataset. Our implementation
using the Anserini IR toolkit is built on the Lucene search library and thus
fully compatible with standard inverted indexes.


Abstract:
  We prove a Gannon-Lee theorem for non-globally hyperbolic Lo\-rentzian
metrics of regularity $C^1$, the most general regularity class currently
available in the context of the classical singularity theorems. Along the way
we also prove that any maximizing causal curve in a $C^1$-spacetime is a
geodesic and hence of $C^2$-regularity.


Abstract:
  Alphas are stock prediction models capturing trading signals in a stock
market. A set of effective alphas can generate weakly correlated high returns
to diversify the risk. Existing alphas can be categorized into two classes:
Formulaic alphas are simple algebraic expressions of scalar features, and thus
can generalize well and be mined into a weakly correlated set. Machine learning
alphas are data-driven models over vector and matrix features. They are more
predictive than formulaic alphas, but are too complex to mine into a weakly
correlated set. In this paper, we introduce a new class of alphas to model
scalar, vector, and matrix features which possess the strengths of these two
existing classes. The new alphas predict returns with high accuracy and can be
mined into a weakly correlated set. In addition, we propose a novel alpha
mining framework based on AutoML, called AlphaEvolve, to generate the new
alphas. To this end, we first propose operators for generating the new alphas
and selectively injecting relational domain knowledge to model the relations
between stocks. We then accelerate the alpha mining by proposing a pruning
technique for redundant alphas. Experiments show that AlphaEvolve can evolve
initial alphas into the new alphas with high returns and weak correlations.


Abstract:
  In this article we generalize the concepts that were used in the PhD thesis
of Drudge to classify Cameron-Liebler line classes in PG$(n,q), n\geq 3$, to
Cameron-Liebler sets of $k$-spaces in PG$(n,q)$ and AG$(n,q)$. In his PhD
thesis, Drudge proved that every Cameron-Liebler line class in PG$(n,q)$
intersects every $3$-dimensional subspace in a Cameron-Liebler line class in
that subspace. We are using the generalization of this result for sets of
$k$-spaces in PG$(n,q)$ and AG$(n,q)$. Together with a basic counting argument
this gives a very strong non-existence condition, $n\geq 3k+3$. This condition
can also be improved for $k$-sets in AG$(n,q)$, with $n\geq 2k+2$.


Abstract:
  We consider the problem of communicating a general bivariate function of two
classical sources observed at the encoders of a classical-quantum multiple
access channel. Building on the techniques developed for the case of a
classical channel, we propose and analyze a coding scheme based on coset codes.
The proposed technique enables the decoder recover the desired function without
recovering the sources themselves. We derive a new set of sufficient conditions
that are weaker than the current known for identified examples. This work is
based on a new ensemble of coset codes that are proven to achieve the capacity
of a classical-quantum point-to-point channel.


Abstract:
  Motivated by recent observations of ergodicity breaking due to Hilbert space
fragmentation in 1D Fermi-Hubbard chains with a tilted potential [Scherg et
al., arXiv:2010.12965], we show that the same system also hosts quantum
many-body scars in a regime $U\approx \Delta \gg J$ at electronic filling
factor $\nu=1$. We numerically demonstrate that the scarring phenomenology in
this model is similar to other known realisations such as Rydberg atom chains,
including persistent dynamical revivals and ergodicity-breaking many-body
eigenstates. At the same time, we show that the mechanism of scarring in the
Fermi-Hubbard model is different from other examples in the literature: the
scars originate from a subgraph, representing a free spin-1 paramagnet, which
is weakly connected to the rest of the Hamiltonian's adjacency graph. Our work
demonstrates that correlated fermions in tilted optical lattices provide a
platform for understanding the interplay of many-body scarring and other forms
of ergodicity breaking, such as localisation and Hilbert space fragmentation.


Abstract:
  We map the likelihood of GW190521, the heaviest detected binary black hole
(BBH) merger, by sampling under different mass and spin priors designed to be
uninformative. We find that a source-frame total mass of $\sim$$150 M_{\odot}$
is consistently supported, but posteriors in mass ratio and spin depend
critically on the choice of priors. We confirm that the likelihood has a
multi-modal structure with peaks in regions of mass ratio representing very
different astrophysical scenarios. The unequal-mass region ($m_2 / m_1 < 0.3$)
has an average likelihood $\sim$$e^6$ times larger than the equal-mass region
($m_2 / m_1 > 0.3$) and a maximum likelihood $\sim$$e^2$ larger. Using
ensembles of samples across priors, we examine the implications of
qualitatively different BBH sources that fit the data. We find that the
equal-mass solution has poorly constrained spins and at least one black hole
mass that is difficult to form via stellar collapse due to pair instability.
The unequal-mass solution can avoid this mass gap entirely but requires a
negative effective spin and a precessing primary. Either of these scenarios is
more easily produced by dynamical formation channels than field binary
co-evolution. The sensitive comoving volume-time of the mass gap solution is
$\mathcal{O}(10)$ times larger than the gap-avoiding solution. After accounting
for this distance effect, the likelihood still reverses the advantage to favor
the gap-avoiding scenario by a factor of $\mathcal{O}(100)$ before considering
mass and spin priors. Posteriors are easily driven away from this
high-likelihood region by common prior choices meant to be uninformative,
making GW190521 parameter inference sensitive to the assumed mass and spin
distributions of mergers in the source's astrophysical channel. This may be a
generic issue for similarly heavy events given current detector sensitivity and
waveform degeneracies.


Abstract:
  The ESO workshop "Ground-based thermal infrared astronomy" was held on-line
October 12-16, 2020. Originally planned as a traditional in-person meeting at
ESO in Garching in April 2020, it was rescheduled and transformed into a fully
on-line event due to the COVID-19 pandemic. With 337 participants from 36
countries the workshop was a resounding success, demonstrating the wide
interest of the astronomical community in the science goals and the toolkit of
ground-based thermal infrared astronomy.


Abstract:
  Federated Learning (FL) is an emerging decentralized learning framework
through which multiple clients can collaboratively train a learning model.
However, a major obstacle that impedes the wide deployment of FL lies in
massive communication traffic. To train high dimensional machine learning
models (such as CNN models), heavy communication traffic can be incurred by
exchanging model updates via the Internet between clients and the parameter
server (PS), implying that the network resource can be easily exhausted.
Compressing model updates is an effective way to reduce the traffic amount.
However, a flexible unbiased compression algorithm applicable for both uplink
and downlink compression in FL is still absent from existing works. In this
work, we devise the Model Update Compression by Soft Clustering (MUCSC)
algorithm to compress model updates transmitted between clients and the PS. In
MUCSC, it is only necessary to transmit cluster centroids and the cluster ID of
each model update. Moreover, we prove that: 1) The compressed model updates are
unbiased estimation of their original values so that the convergence rate by
transmitting compressed model updates is unchanged; 2) MUCSC can guarantee that
the influence of the compression error on the model accuracy is minimized.
Then, we further propose the boosted MUCSC (B-MUCSC) algorithm, a biased
compression algorithm that can achieve an extremely high compression rate by
grouping insignificant model updates into a super cluster. B-MUCSC is suitable
for scenarios with very scarce network resource. Ultimately, we conduct
extensive experiments with the CIFAR-10 and FEMNIST datasets to demonstrate
that our algorithms can not only substantially reduce the volume of
communication traffic in FL, but also improve the training efficiency in
practical networks.


Abstract:
  We consider string theory vacua with tadpoles for dynamical fields and
uncover universal features of the resulting spacetime-dependent solutions. We
argue that the solutions can extend only a finite distance $\Delta$ away in the
spacetime dimensions over which the fields vary, scaling as $\Delta^n\sim {\cal
T}$ with the strength of the tadpole ${\cal T}$. We show that naive
singularities arising at this distance scale are physically replaced by ends of
spacetime, related to the cobordism defects of the swampland cobordism
conjecture and involving stringy ingredients like orientifold planes and
branes, or exotic variants thereof. We illustrate these phenomena in large
classes of examples, including AdS$_5\times T^{1,1}$ with 3-form fluxes, 10d
massive IIA, M-theory on K3, the 10d non-supersymmetric $USp(32)$ strings, and
type IIB compactifications with 3-form fluxes and/or magnetized D-branes. We
also describe a 6d string model whose tadpole triggers spontaneous
compactification to a semirealistic 3-family MSSM-like particle physics model.


Abstract:
  For a bounded domain $D \subset \mathbb{C}^n$, let $K_D = K_D(z) > 0$ denote
the Bergman kernel on the diagonal and consider the reproducing kernel Hilbert
space of holomorphic functions on $D$ that are square integrable with respect
to the weight $K_D^{-d}$, where $d \geq 0$ is an integer. The corresponding
weighted kernel $K_{D, d}$ transforms appropriately under biholomorphisms and
hence produces an invariant K\"{a}hler metric on $D$. Thus, there is a
hierarchy of such metrics starting with the classical Bergman metric that
corresponds to the case $d=0$. This note is an attempt to study this class of
metrics in much the same way as the Bergman metric has been with a view towards
identifying properties that are common to this family. When $D$ is strongly
pseudoconvex, the scaling principle is used to obtain the boundary asymptotics
of these metrics and several invariants associated to them. It turns out that
all these metrics are complete on strongly pseudoconvex domains.


Abstract:
  We present evolutionary models for solar-like stars with an improved
treatment of convection that results in a more accurate estimate of the radius
and effective temperature. This is achieved by improving the calibration of the
mixing-length parameter, which sets the length scale in the 1D convection model
implemented in the stellar evolution code. Our calibration relies on the
results of 2D and 3D radiation hydrodynamics simulations of convection to
specify the value of the adiabatic specific entropy at the bottom of the
convective envelope in stars as a function of their effective temperature,
surface gravity and metallicity. For the first time, this calibration is fully
integrated within the flow of a stellar evolution code, with the mixing-length
parameter being continuously updated at run-time. This approach replaces the
more common, but questionable, procedure of calibrating the length scale
parameter on the Sun, and then applying the solar-calibrated value in modeling
other stars, regardless of their mass, composition and evolutionary status. The
internal consistency of our current implementation makes it suitable for
application to evolved stars, in particular to red giants. We show that the
entropy calibrated models yield a revised position of the red giant branch that
is in better agreement with observational constraints than that of standard
models.


Abstract:
  In this chapter we describe the history and evolution of the iCub humanoid
platform. We start by describing the first version as it was designed during
the RobotCub EU project and illustrate how it evolved to become the platform
that is adopted by more than 30 laboratories world wide. We complete the
chapter by illustrating some of the research activities that are currently
carried out on the iCub robot, i.e. visual perception, event driven sensing and
dynamic control. We conclude the Chapter with a discussion of the lessons we
learned and a preview of the upcoming next release of the robot, iCub 3.0.


Abstract:
  Unfitted finite element methods have emerged as a popular alternative to
classical finite element methods for the solution of partial differential
equations and allow modeling arbitrary geometries without the need for a
boundary-conforming mesh. On the other hand, the efficient solution of the
resultant system is a challenging task because of the numerical
ill-conditioning that typically entails from the formulation of such methods.
We use an adaptive geometric multigrid solver for the solution of the mixed
finite cell formulation of saddle-point problems and investigate its
convergence in the context of the Stokes and Navier-Stokes equations. We
present two smoothers for the treatment of cutcells in the finite cell method
and analyze their effectiveness for the model problems using a numerical
benchmark. Results indicate that the presented multigrid method is capable of
solving the model problems independently of the problem size and is robust with
respect to the depth of the grid hierarchy.


Abstract:
  For finite samples with binary outcomes penalized logistic regression such as
ridge logistic regression (RR) has the potential of achieving smaller mean
squared errors (MSE) of coefficients and predictions than maximum likelihood
estimation. There is evidence, however, that RR is sensitive to small or sparse
data situations, yielding poor performance in individual datasets. In this
paper, we elaborate this issue further by performing a comprehensive simulation
study, investigating the performance of RR in comparison to Firth's correction
that has been shown to perform well in low-dimensional settings. Performance of
RR strongly depends on the choice of complexity parameter that is usually tuned
by minimizing some measure of the out-of-sample prediction error or information
criterion. Alternatively, it may be determined according to prior assumptions
about true effects. As shown in our simulation and illustrated by a data
example, values optimized in small or sparse datasets are negatively correlated
with optimal values and suffer from substantial variability which translates
into large MSE of coefficients and large variability of calibration slopes. In
contrast, if the degree of shrinkage is pre-specified, accurate coefficients
and predictions can be obtained even in non-ideal settings such as encountered
in the context of rare outcomes or sparse predictors.


Abstract:
  In this paper, for a given compact 3-manifold with an initial Riemannian
metric and a symmetric tensor, we establish the short-time existence and
uniqueness theorem for extension of cross curvature flow. We give an example of
this flow on manifolds.


Abstract:
  We describe Substitutional Neural Image Compression (SNIC), a general
approach for enhancing any neural image compression model, that requires no
data or additional tuning of the trained model. It boosts compression
performance toward a flexible distortion metric and enables bit-rate control
using a single model instance. The key idea is to replace the image to be
compressed with a substitutional one that outperforms the original one in a
desired way. Finding such a substitute is inherently difficult for conventional
codecs, yet surprisingly favorable for neural compression models thanks to
their fully differentiable structures. With gradients of a particular loss
backpropogated to the input, a desired substitute can be efficiently crafted
iteratively. We demonstrate the effectiveness of SNIC, when combined with
various neural compression models and target metrics, in improving compression
quality and performing bit-rate control measured by rate-distortion curves.
Empirical results of control precision and generation speed are also discussed.


Abstract:
  When two solids at different temperatures are separated by a vacuum gap they
relax toward their equilibrium state by exchanging heat either by radiation,
phonon or electron tunneling, depending on their separation distance and on the
nature of materials. The interplay between this exchange of energy and its
spreading through each solid entirely drives the relaxation dynamics. Here we
highlight a significant slowing down of this process in the extreme near-field
regime at distances where the heat flux exchanged between the two solids is
comparable or even dominates over the flux carried by conduction inside each
solid. This mechanism, leading to a strong effective increase of the system
thermal inertia, should play an important role in the temporal evolution of
thermal state of interacting solids systems at nanometric and subnanometric
scales.


Abstract:
  We investigate the modification of gravitational fields generated by
topological defects on a generalized Duffin-Kemmer-Petiau (DKP) oscillator for
spin-0 particle under spinning cosmic string background. The generalized DKP
oscillator equation under spinning cosmic string background is established, and
the impact of the Cornell potential on the generalized DKP oscillator is
presented. We give the influence of space-time and potential parameters on
energy levels.


Abstract:
  Two non-intrusive uncertainty propagation approaches are proposed for the
performance analysis of engineering systems described by expensive-to-evaluate
deterministic computer models with parameters defined as interval variables.
These approaches employ a machine learning based optimization strategy, the
so-called Bayesian optimization, for evaluating the upper and lower bounds of a
generic response variable over the set of possible responses obtained when each
interval variable varies independently over its range. The lack of knowledge
caused by not evaluating the response function for all the possible
combinations of the interval variables is accounted for by developing a
probabilistic description of the response variable itself by using a Gaussian
Process regression model. An iterative procedure is developed for selecting a
small number of simulations to be evaluated for updating this statistical model
by using well-established acquisition functions and to assess the response
bounds. In both approaches, an initial training dataset is defined. While one
approach builds iteratively two distinct training datasets for evaluating
separately the upper and lower bounds of the response variable, the other
builds iteratively a single training dataset. Consequently, the two approaches
will produce different bound estimates at each iteration. The upper and lower
bound responses are expressed as point estimates obtained from the mean
function of the posterior distribution. Moreover, a confidence interval on each
estimate is provided for effectively communicating to engineers when these
estimates are obtained for a combination of the interval variables for which no
deterministic simulation has been run. Finally, two metrics are proposed to
define conditions for assessing if the predicted bound estimates can be
considered satisfactory.


Abstract:
  We report constraints on light dark matter through its interactions with
shell electrons in the PandaX-II liquid xenon detector with a total 46.9
tonne$\cdot$day exposure. To effectively search for these very low energy
electron recoils, ionization-only signals are selected from the data. 1821
candidates are identified within ionization signal range between 50 to 75
photoelectrons, corresponding to a mean electronic recoil energy from 0.08 to
0.15 keV. The 90% C.L. exclusion limit on the scattering cross section between
the dark matter and electron is calculated based on Poisson statistics. Under
the assumption of point interaction, we provide the world's most stringent
limit within the dark matter mass range from 15 to 30 $\rm MeV/c^2$, with the
corresponding cross section from $2.5\times10^{-37}$ to $3.1\times10^{-38}$
cm$^2$.


Abstract:
  As of 2020, the international workshop on Procedural Content Generation
enters its second decade. The annual workshop, hosted by the international
conference on the Foundations of Digital Games, has collected a corpus of 95
papers published in its first 10 years. This paper provides an overview of the
workshop's activities and surveys the prevalent research topics emerging over
the years.


Abstract:
  Ma-Ma-Yeh made a beautiful observation that a transformation of the grammar
of Dumont instantly leads to the $\gamma$-positivity of the Eulerian
polynomials. We notice that the transformed grammar bears a striking
resemblance to the grammar for 0-1-2 increasing trees also due to Dumont. The
appearance of the factor of two fits perfectly in a grammatical labeling of
0-1-2 increasing plane trees. Furthermore, the grammatical calculus is
instrumental to the computation of the generating functions. This approach can
be adapted to study the $e$-positivity of the trivariate second-order Eulerian
polynomials first introduced by Dumont in the contexts of ternary trees and
Stirling permutations, and independently defined by Janson, in connection with
the joint distribution of the numbers of ascents, descents and plateaux over
Stirling permutations.


Abstract:
  High-Performance Big Data Analytics (HPDA) applications are characterized by
huge volumes of distributed and heterogeneous data that require efficient
computation for knowledge extraction and decision making. Designers are moving
towards a tight integration of computing systems combining HPC, Cloud, and IoT
solutions with artificial intelligence (AI). Matching the application and data
requirements with the characteristics of the underlying hardware is a key
element to improve the predictions thanks to high performance and better use of
resources.
  We present EVEREST, a novel H2020 project started on October 1st, 2020 that
aims at developing a holistic environment for the co-design of HPDA
applications on heterogeneous, distributed, and secure platforms. EVEREST
focuses on programmability issues through a data-driven design approach, the
use of hardware-accelerated AI, and an efficient runtime monitoring with
virtualization support. In the different stages, EVEREST combines
state-of-the-art programming models, emerging communication standards, and
novel domain-specific extensions. We describe the EVEREST approach and the use
cases that drive our research.


Abstract:
  This article summarises the current status of classical communication
networks and identifies some critical open research challenges that can only be
solved by leveraging quantum technologies. By now, the main goal of quantum
communication networks has been security. However, quantum networks can do more
than just exchange secure keys or serve the needs of quantum computers. In
fact, the scientific community is still investigating on the possible use
cases/benefits that quantum communication networks can bring. Thus, this
article aims at pointing out and clearly describing how quantum communication
networks can enhance in-network distributed computing and reduce the overall
end-to-end latency, beyond the intrinsic limits of classical technologies.
Furthermore, we also explain how entanglement can reduce the communication
complexity (overhead) that future classical virtualised networks will
experience.


Abstract:
  We present a new multiscale method to study the N-Methyl-D-Aspartate (NMDA)
neuroreceptor starting from the reconstruction of its crystallographic
structure. Thanks to the combination of homology modelling, Molecular Dynamics
and Lattice Boltzmann simulations, we analyse the allosteric transition of NDMA
upon ligand binding and compute the receptor response to ionic passage across
the membrane.


Abstract:
  This paper presents an analytical model to quantify noise in a bolometer
readout circuit. A frequency domain analysis of the noise model is presented
which includes the effect of noise from the bias resistor, sensor resistor,
voltage and current noise of amplifier and cable capacitance. The analytical
model is initially verified by using several standard SMD resistors as a sensor
in the range of 0.1 - 100 Mohm and measuring the RMS noise of the bolometer
readout circuit. Noise measurement on several indigenously developed neutron
transmutation doped Ge temperature sensor has been carried out over a
temperature range of 20 - 70 mK and the measured data is compared with the
noise calculated using analytical model. The effect of different sensor
resistances on the noise of bolometer readout circuit, in line with the
analytical model and measured data, is presented in this paper.


Abstract:
  This paper presents an AI system applied to location and robotic grasping.
Experimental setup is based on a parameter study to train a deep-learning
network based on Mask-RCNN to perform waste location in indoor and outdoor
environment, using five different classes and generating a new waste dataset.
Initially the AI system obtain the RGBD data of the environment, followed by
the detection of objects using the neural network. Later, the 3D object shape
is computed using the network result and the depth channel. Finally, the shape
is used to compute grasping for a robot arm with a two-finger gripper. The
objective is to classify the waste in groups to improve a recycling strategy.