Abstract:
  The lattice Boltzmann method, now widely used for a variety of applications,
has also been extended to model multi-phase flows through different
formulations. While already applied to many different configurations in the low
Weber and Reynolds number regimes, applications to higher Weber/Reynolds
numbers or larger density/viscosity ratios are still the topic of active
research. In this study, through a combination of the decoupled phase-field
formulation -- conservative Allen-Cahn equation -- and a cumulants-based
collision operator for a low-Mach pressure-based flow solver, we present an
algorithm that can be used for higher Reynolds/Weber numbers. The algorithm is
validated through a variety of test-cases, starting with the Rayleigh-Taylor
instability both in 2-D and 3-D, followed by the impact of a droplet on a
liquid sheet. In all simulations, the solver is shown to correctly capture the
dynamics of the flow and match reference results very well. As the final
test-case, the solver is used to model droplet splashing on a thin liquid sheet
in 3-D with a density ratio of 1000 and kinematic viscosity ratio of 15 --
matching the water/air system -- at We=8000 and Re=1000. The results show that
the solver correctly captures the fingering instabilities at the crown rim and
their subsequent breakup, in agreement with experimental and numerical
observations reported in the literature.


Abstract:
  From 22 to 26 June 2020, we hosted ESO's first live e-conference, #H02020,
from within ESO headquarters in Garching, Germany. Every day, between 200 and
320 researchers around the globe tuned in to discuss the nature and
implications of the discord between precise determinations of the Universe's
expansion rate, H0. Originally planned as an in-person meeting, we moved to the
virtual domain to maintain strong scientific discourse despite the SARS-CoV-2
(COVID-19) pandemic. Here, we describe our conference setup, participants
feedback gathered before and after the meeting, and lessons learned from this
unexpected exercise. As e-conferencing will become increasingly common in the
future, we provide our perspective on how e-conferences can make scientific
exchange more effective and inclusive, in addition to climate friendly.


Abstract:
  We show that the Freiman--Ruzsa theorem, characterising finite sets with
bounded doubling, leads to an alternative proof of a characterisation of Meyer
sets, that is, relatively dense subsets of Euclidean spaces whose difference
sets are uniformly discrete.


Abstract:
  We studied the absorption features of CO lines against the continuum
originating from the heated dust in the obscuring tori around active galactic
nuclei (AGNs). We investigated the formation of absorption lines corresponding
to the CO rotational transitions using three-dimensional non-LTE line transfer
simulations considering the dust thermal emission. As in Papers I--III of this
series, we performed post-processed radiative transfer calculations using the
"radiation-driven fountain model" (wada2016}, which yields a geometrically
thick obscuring structure around the nucleus. This model is consistent with the
spectral energy distribution of the nearest type-2 Seyfert galaxy, the Circinus
galaxy. We found that the continuum-subtracted channel maps of $J = 4-3$ and
higher transitions show absorption regions along the disk mid-plane for an
edge-on viewing angle. The spectra consist of multiple absorption and emission
features, reflecting the internal inhomogeneous and turbulent structure of the
torus. The deepest absorption feature is caused by the gas on the near-side of
the torus between $r =10$ and 15 pc, which is located in front of the
AGN-heated dust inside $r \simeq 5$ pc. We also found that a spatial resolution
of 0.5--1.0 pc is necessary to resolve the absorption features. Moreover, the
inclination angle must be close to the edge-on angle (i.e., $\sim 85^\circ$) to
observe the absorption features. The findings of the present study imply that
combining our radiation-hydrodynamic model with high-resolution observations of
CO (7-6) by ALMA can provide new information about the internal structure of
the molecular tori in nearby AGNs.


Abstract:
  We present the results from the multi-wavelength monitoring observations of
the Narrow-Line Seyfert 1 galaxy Mrk 335 with AstroSat. We analysed both the
X-ray (SXT & LAXPC) and UV (UVIT) data of the source at two epochs, separated
by ~18 days. The source was in a low flux state during the observations, and
the X-ray spectra were found to be harder than usual. The presence of soft
X-ray excess was identified in the observations, and the broadband X-ray
continuum was modelled with power-law and blackbody (modified by intrinsic
absorption) and a distant neutral reflection component. We did not find any
variability in the X-ray spectral shape or the flux over this period. However,
the UV flux is found to be variable between the observations. The obtained
results from the X-ray analysis point to a scenario where the primary emission
is suppressed and the component due to distant reflection dominates the
observed spectrum.


Abstract:
  Li and Panigrahi, in recent work, obtained the first deterministic algorithm
for the global minimum cut of a weighted undirected graph that runs in time
$o(mn)$. They introduced an elegant and powerful technique to find isolating
cuts for a terminal set in a graph via a small number of $s$-$t$ minimum cut
computations.
  In this paper we generalize their isolating cut approach to the abstract
setting of symmetric bisubmodular functions (which also capture symmetric
submodular functions). Our generalization to bisubmodularity is motivated by
applications to element connectivity and vertex connectivity. Utilizing the
general framework and other ideas we obtain significantly faster randomized
algorithms for computing global (and subset) connectivity in a number of
settings including hypergraphs, element connectivity and vertex connectivity in
graphs, and for symmetric submodular functions.


Abstract:
  We propose a new approach for defining and searching clusters in graphs that
represent real technological or transaction networks. In contrast to the
standard way of finding dense parts of a graph, we concentrate on the structure
of edges between the clusters, as it is motivated by some earlier observations,
e.g. in the structure of networks in ecology and economics and by applications
of discrete tomography. Mathematically special colorings and chromatic numbers
of graphs are studied.


Abstract:
  Computation of gas dispersal in urban places or hilly grounds requires a
large amount of computer time when addressed with conventional multidimensional
models. Those are usually based on two-phase flow or Navier-Stokes equations.
Different classes of simplified models exist. Among them, two-layer shallow
water models are interesting to address large-scale dispersion. Indeed,
compared to conventional multidimensional approaches, 2D simulations are
carried out to mimic 3D effects. The computational gain in CPU time is
consequently expected to be tremendous. However, such models involve at least
three fundamental difficulties. The first one is related to the lack of
hyperbolicity of most existing formulations, yielding serious consequences
regarding wave propagation. The second is related to the non-conservative terms
in the momentum equations. Those terms account for interactions between fluid
layers. Recently, these two difficulties have been addressed in Chiapolino and
Saurel (2018) and an unconditional hyperbolic model has been proposed along
with a Harten-Lax-van Leer (HLL) type Riemann solver dealing with the
non-conservative terms. In the same reference, numerical experiments showed
robustness and accuracy of the formulation. In the present paper, a third
difficulty is addressed. It consists of the determination of appropriate drag
effect formulation. Such effects also account for interactions between fluid
layers and become of particular importance when dealing with heavy-gas
dispersion. With this aim, the model is compared to laboratory experiments in
the context of heavy gas dispersal in quiescent air. It is shown that the model
accurately reproduces experimental results thanks to an appropriate drag force
correlation. This function expresses drag effects between the heavy and light
gas layers. It is determined thanks to various experimental configurations of
dam-break test problems.


Abstract:
  Discoveries of extended rotation curves have suggested the presence of dark
matter in spiral galaxy haloes. It has led to many studies that estimated the
galaxy total mass, mostly by using the Navarro Frenk and White (NFW) density
profile. We aim at verifying how the choice of the dark-matter profile may
affect the predicted values of extrapolated total masses. We have considered
the recent Milky Way (MW) rotation curve, firstly because of its unprecedented
accuracy, and secondly because the Galactic disk is amongst the least affected
by past major mergers having fully reshaped the initial disk. We find that the
use of NFW profile (or its generalized form, gNFW) for calculating the
dark-matter contribution to the MW rotation curve generates apparently
inconsistent results, e.g., an increase of the baryonic mass leads to increase
of the dark matter mass. Furthermore we find that NFW and gNFW profile narrow
the total mass range, leading to a possible methodological bias particularly
against small MW masses. By using the Einasto profile that is more appropriate
to represent cold dark matter haloes, we finally find that the Milky Way
slightly decreasing rotation curve favors total mass that can be as small as
2.6 $\times 10^{11}$ $M_{\odot}$, disregarding any other dynamical tracers
further out in the MW. It is inconsistent with values larger than 18 $\times
10^{11}$ $M_{\odot}$ for any kind of CDM dark-matter halo profiles, under the
assumption that stars and gas do not influence the predicted dark matter
distribution in the MW. This methodological paper encourages the use of the
Einasto profile for characterizing rotation curves with the aim of evaluating
their total masses.


Abstract:
  We introduced the contemporary Amharic corpus, which is automatically tagged
for morpho-syntactic information. Texts are collected from 25,199 documents
from different domains and about 24 million orthographic words are tokenized.
Since it is partly a web corpus, we made some automatic spelling error
correction. We have also modified the existing morphological analyzer,
HornMorpho, to use it for the automatic tagging.


Abstract:
  Nuclear magnetism in n-doped semiconductors with positive hyperfine constant
is revisited. Two kinds of nuclear magnetic ordering can be induced by resident
electrons in a deeply cooled nuclear spin system. At positive nuclear spin
temperature below a critical value, randomly oriented nuclear spin polarons
similar to that predicted by I. Merkulov [I. Merkulov, Physics of the Solid
State 40, 930 (1998)] should emerge. These polarons are oriented randomly and
within each polaron nuclear and electron spins are aligned
antiferromagnetically. At negative nuclear spin temperature below a critical
value we predict another type of magnetic ordering - dynamically induced
nuclear ferromagnet. This is a long-range ferromagnetically ordered state
involving both electrons and nuclei. It can form if electron spin relaxation is
dominated by the hyperfine coupling, rather than by the spin-orbit interaction.
Application of the theory to the n-doped GaAs suggests that the ferromagnetic
order may be reached at experimentally achievable nuclear spin temperature
$\Theta_N \approx 0.5$ $\mu$K and lattice temperature $T_L \approx 5$ K.


Abstract:
  The electronic properties of bilayer graphene with a magnetic quantum dot and
a magnetic quantum ring are investigated. The eigenenergies and wavefunctions
of quasiparticle states are calculated analytically by solving decoupled
fourth-order differential equations. For the magnetic quantum dot, in the case
of a negative inner magnetic field, two peculiar characteristics of the
eigenenergy evolution are found: (i) the energy eigenstates change in a
stepwise manner owing to energy anticrossing and (ii) the quantum states
approach zero energy. For the magnetic quantum ring, there is an angular
momentum transition of eigenenergy as the inner radius of the ring varies, and
the Aharonov--Bohm effect is observed in the eigenenergy spectra for both
positive and negative magnetic fields inside the inner radius.


Abstract:
  Let (X, d) be a bounded metric space with a base point 0 X , (Y, $\bullet$)
be a Banach space and Lip $\alpha$ 0 (X, Y) be the space of all
$\alpha$-H{\"o}lderfunctions that vanish at 0 X , equipped with its natural
norm (0 < $\alpha$ $\le$ 1). Let 0 < $\alpha$ < $\beta$ $\le$ 1. We prove that
Lip $\beta$ 0 (X, Y) is a $\sigma$-porous subset of Lip $\alpha$ 0 (X, Y), if
(and only if) inf{d(x, x ') : x, x ' $\in$ X; x = x ' } = 0 (i.e. d is
non-uniformly discrete). A more general result will be given.


Abstract:
  Manipulation tasks like loading a dishwasher can be seen as a sequence of
spatial constraints and relationships between different objects. For example, a
plate can be placed in a tray only if the tray is open. We aim to discover such
task-specific rules from demonstrations. We pose manipulation as a
classification problem over a graph, whose nodes represent task relevant
entities like objects and goals, transform the environment scene into a graph
and learn a graph neural network (GNN) policy using imitation learning. In our
experiments, a single learned GNN policy, trained using 20 expert
demonstrations, can solve multiple blockstacking and rearrangement tasks in
both simulation and on hardware, without any task description. The policy
successfully generalizes over the number of objects in the environment, their
positions, and goal configurations (trained on single stacks, generalizes to
pyramids and multiple stacks). We also apply our approach to a complex
simulated dishwasher environment, where a robot learns to load a dishwasher
from only 5 high-level human demonstrations. These experiments show that
imitation learning on a graphical state and policy is a simple, yet powerful
tool for solving complex long-horizon manipulation problems, without requiring
detailed task descriptions. Videos can be found at:
https://youtu.be/x9hcKBh6K0A.


Abstract:
  An aggregated model is proposed, of which the partial-sum process scales to
the Karlin stable processes recently investigated in the literature. The limit
extremes of the proposed model, when having regularly-varying tails, are
characterized by the convergence of the corresponding point processes. The
proposed model is an extension of an aggregated model proposed by Enriquez
(2004) in order to approximate fractional Brownian motions with Hurst index
$H\in(0,1/2)$, and is of a different nature of the other recently investigated
Karlin models which are essentially based on infinite urn schemes.


Abstract:
  We experimentally investigated the magnetic properties of NiCo$_2$O$_4$
epitaxial films known to be conductive oxides with perpendicular magnetic
anisotropy (PMA) at room temperature. Both magneto-torque and magnetization
measurements at various temperatures provide clear experimental evidence of the
spin reorientation transition at which the MA changes from PMA to easy-cone
magnetic anisotropy (ECMA) at a certain temperature ($T_{\rm{SR}}$). ECMA was
commonly observed in films grown by pulsed laser deposition and reactive radio
frequency magnetron sputtering, although $T_{\mathrm{SR}}$ is dependent on the
growth method as well as the conditions. The cone angles measured from the
$c$-axis increased successively at $T_{\mathrm{SR}}$ and approached a maximum
of 45-50 degrees at the lowest measurement temperature of 5 K. Calculation with
the cluster model suggests that the Ni$^{3+}$ ions occupying the $T_d$ site
could be the origin of the ECMA. Both the magnetic properties and the results
of the calculation based on the cluster model indicate that the ECMA is
attributable to the cation anti-site distribution of Ni$^{3+}$, which is
possibly formed during the growth process of the thin films.


Abstract:
  Assessing the complexity of functions computed by a neural network helps us
understand how the network will learn and generalize. One natural measure of
complexity is how the network distorts length - if the network takes a
unit-length curve as input, what is the length of the resulting curve of
outputs? It has been widely believed that this length grows exponentially in
network depth. We prove that in fact this is not the case: the expected length
distortion does not grow with depth, and indeed shrinks slightly, for ReLU
networks with standard random initialization. We also generalize this result by
proving upper bounds both for higher moments of the length distortion and for
the distortion of higher-dimensional volumes. These theoretical results are
corroborated by our experiments.


Abstract:
  The smallest open case for classifying Steiner triple systems is order 21. A
Steiner triple system of order 21, an STS(21), can have subsystems of orders 7
and 9, and it is known that there are 12,661,527,336 isomorphism classes of
STS(21)s with sub-STS(9)s. The classification of STS(21)s with subsystems is
here completed by settling the case of STS(21)s with sub-STS(7)s; there are
116,635,963,205,551 isomorphism classes of such systems. An estimation of the
number of isomorphism classes of STS(21)s is given.


Abstract:
  Single file diffusion (SFD) exhibits anomalously slow collective transport
when particles are able to immobilize by binding and unbinding to the
one-dimensional channel within which the particles diffuse. We have explored
this system for short pore-like channels using a symmetric exclusion process
(SEP) with fully stochastic dynamics. We find that for shorter channels, a
non-Fickian regime emerges for slow binding kinetics. In this regime the
average flux $\langle \Phi \rangle \sim 1/L^3$, where $L$ is the channel length
in units of the particle size. We find that a two-state model describes this
behavior well for sufficiently slow binding rates, where the binding rates
determine the switching time between high-flux bursts of directed transport and
low-flux leaky states. Each high-flux burst is Fickian with $\langle \Phi
\rangle \sim 1/L$. Longer systems are more often in a low flux state, leading
to the non-Fickian behavior.


Abstract:
  In extreme pressures and temperature gradients, heat flow and magnetic fields
are strongly coupled, but although theoretical work shows that strong heat
flows can cause significant changes in the magnetic field, it has long proven
difficult to measure these changes experimentally. Here we describe the first
direct measurement of Nernst-driven magnetic cavitation, in which heat flow
causes expulsion of the magnetic field from the hottest regions of a plasma.
Using laser-driven proton radiography, we demonstrate that Nernst advection
dominates changes to the magnetic field in underdense plasmas on these
nanosecond timescales. Due to the increased magnetic field strength at the edge
of the hot plasma, the observed magnetic cavitation can be accurately described
by fluid models of heat flow, despite mean free paths much longer than the
temperature gradient. Expulsion of the magnetic field from hot regions of the
plasma reduces the effectiveness of magnetised fusion techniques and disrupts
magnetised plasma experiments.


Abstract:
  We examine the role of trustworthiness and trust in statistical inference,
arguing that it is the extent of trustworthiness in inferential statistical
tools which enables trust in the conclusions. Certain tools, such as the
p-value and significance test, have recently come under renewed criticism, with
some arguing that they damage trust in statistics. We argue the contrary,
beginning from the position that the central role of these methods is to form
the basis for trusted conclusions in the face of uncertainty in the data, and
noting that it is the misuse and misunderstanding of these tools which damages
trustworthiness and hence trust. We go on to argue that recent calls to ban
these tools would tackle the symptom, not the cause, and themselves risk
damaging the capability of science to advance, and feeding into public
suspicion of the discipline of statistics. The consequence could be aggravated
mistrust of our discipline and of science more generally. In short, the very
proposals could work in quite the contrary direction from that intended. We
make some alternative proposals for tackling the misuse and misunderstanding of
these methods, and for how trust in our discipline might be promoted.


Abstract:
  Orthogonal Cellular Automata (OCA) have been recently investigated in the
literature as a new approach to construct orthogonal Latin squares for
cryptographic applications such as secret sharing schemes. In this paper, we
consider OCA for a different cryptographic task, namely the generation of
pseudorandom sequences. The idea is to iterate a dynamical system where the
output of an OCA pair is fed back as a new set of coordinates on the superposed
squares. The main advantage is that OCA ensure a certain amount of diffusion in
the generated sequences, a property which is usually missing from traditional
CA-based pseudorandom number generators. We study the problem of finding OCA
pairs with maximal period by first performing an exhaustive search up to local
rules of diameter $d=5$, and then focusing on the subclass of linear
bipermutive rules. In this case, we characterize the periods of the sequences
in terms of the order of the subgroup generated by an invertible Sylvester
matrix. We finally devise an algorithm based on Lagrange's theorem to
efficiently enumerate all linear OCA pairs of maximal period up to diameter
$d=11$.


Abstract:
  In their comment, Angelini et al. object to the conclusion of [J. Phys. A:
Math. Theor., 52:445002, 2019] (1), where we show that in [Phys. Rev. B,
87:134201, 2013] the exponent $\nu$ has been obtained by applying a
mathematical relation in a regime where this relation is not valid. We observe
that the criticism above on the mathematical validity of such relation has not
been addressed in the comment. Our criticism thus remains valid, and disproves
the conclusions of the comment. This constitutes the main point of this reply.
  We also provide a point-by-point response and discussion of Angelini et al.'s
claims. First, Angelini et al. claim that the prediction $2^{1/\nu}=1$ of [1]
is incorrect, because it results from the relation $\lambda_{\rm
max}=2^{1/\nu}$ between the largest eigenvalue of the linearized
renormalization-group (RG) transformation and $\nu$, which cannot be applied to
the ensemble renormalization group (ERG) method, because for the ERG
$\lambda_{\rm max} =1 $. However, the feature $\lambda_{\rm max}=1$ is specific
to the ERG transformation and it does not give any grounds for questioning the
validity of the general relation $\lambda_{\rm max}=2^{1/\nu}$ specifically for
the ERG transformation. Second, Angelini et al. claim that $\nu$ should be
extracted from an early RG regime (A), as opposed to the asymptotic regime (B)
used to estimate $\nu$ in [1] and that (B) is dominated by finite-size effects.
Still, (A) is a small-wavelength, non-critical regime, which cannot
characterize the critical exponent $\nu$ related to the divergence of the
correlation length. Also, the fact that (B) involves finite-size effects is a
feature specific to the ERG, and gives no rationale for extracting $\nu$ from
(A).
  Finally, we refute the remaining claims made by Angelini et al., and thus
stand by our assertion that the ERG method yields a prediction given by
$2^{1/\nu}=1$.


Abstract:
  Hubble tension and the search for preferred direction are two crucial
unresolved issues in modern cosmology. Different measurements of the Hubble
constant provide significantly different values, and this is known as the
Hubble tension. The cosmological principle assumes that the universe is
homogeneous and isotropic; however, deviations from the isotropy have often
been observed.
  We apply the Bayesian tools and the Extreme Value theory dependent statistic
to address the above issues. These techniques have been applied to the
Panstarrs1 type Ia supernovae data. Our analysis for Hubble constant does not
reject the Hubble tension. However, our value is smaller than that of the SHoES
program and agrees with the CCHP value. Extreme value theory-based analysis
indicates that the data does not show direction dependence. As a byproduct of
our technique, we show that the errors in the data are non-Gaussian in nature.


Abstract:
  Background: The $K^\pi=2^-$ excited band emerges systematically in $N=150$
isotones raging from Pu to No with even-$Z$ numbers, and a sharp drop in
energies was observed in Cf. Purpose: I attempt to uncover the microscopic
mechanism for the appearance of such a low-energy $2^-$ state in $^{248}$Cf.
Furthermore, I investigate the possible occurrence of the low-energy
$K^\pi=2^+$ state to elucidate the mechanism that prefers the simultaneous
breaking of the reflection and axial symmetry to the breaking of the axial
symmetry alone in this mass region. Method: I employ a nuclear EDF method: the
Skyrme-Kohn-Sham-Bogoliubov and the quasiparticle random-phase approximation
are used to describe the ground state and the transition to excited states.
Results: The Skyrme-type SkM* and SLy4 functionals reproduce the fall in
energy, but not the absolute value, of the $K^\pi=2^-$ state at $Z=98$, where
the proton 2qp excitation $[633]7/2 \otimes [521]3/2$ plays a decisive role for
the peculiar isotonic dependence. I find interweaving roles by the pairing
correlation of protons and the deformed shell closure at $Z=98$. The SkM* model
predicts the $K^\pi=2^-$ state appears lower in energy in $^{246}$Cf than in
$^{248}$Cf as the Fermi level of neutrons is located in between the $[622]5/2$
and $[734]9/2$ orbitals. Except for $^{250}$Fm in the SkM* calculation, the
$K^\pi=2^+$ state is predicted to appear higher in energy than the $K^\pi=2^-$
state because the quasi-proton $[521]1/2$ orbital is located above the
$[633]7/2$ orbital. Conclusions: A systematic study of low-lying collective
states in heavy actinide nuclei provides a rigorous testing ground for
microscopic nuclear models. The present study shows a need for improvements in
the EDFs to describe pairing correlations and shell structures in heavy nuclei,
that are indispensable in predicting the heaviest nuclei.


Abstract:
  Requiring the effective mass term for a category of fundamental Dirac or
Majorana fermions of the same electric charge to be invariant under the
translational transformations $\psi^{}_{\alpha \rm L (R)} \to \psi^{}_{\alpha
\rm L (R)} + n^{}_{\alpha} z^{}_{\psi \rm L(R)}$ in the flavor space, where
$n^{}_\alpha$ and $z^{}_{\psi \rm L(R)}$ stand respectively for the
flavor-dependent complex numbers and a constant spinor field anticommuting with
the fermion fields, we show that $n^{}_\alpha$ can be identified as the
elements $U^{}_{\alpha i}$ in the $i$-th column of the unitary matrix $U$ used
to diagonalize the corresponding Hermitian or symmetric fermion mass matrix
$M^{}_\psi$, and $m^{}_i = 0$ holds accordingly. We find that the reverse is
also true. Now that the mass spectra of charged leptons, up- and down-type
quarks are all strongly hierarchical and current experimental data allow the
lightest neutrino to be massless, we argue that the zero mass limit for the
first-family fermions and the translational flavor symmetry behind it should be
a natural starting point for building viable fermion mass models.


Abstract:
  The Hamming distance is ubiquitous in computing. Its computation gets
expensive when one needs to compare a string against many strings. Quantum
computers (QCs) may speed up the comparison.
  In this paper, we extend an existing algorithm for computing the Hamming
distance. The extension can compare strings with symbols drawn from an
arbitrary-long alphabet (which the original algorithm could not). We implement
our extended algorithm using the QisKit framework to be executed by a
programmer without the knowledge of a QC (the code is publicly available). We
then provide four pedagogical examples: two from the field of bioinformatics
and two from the field of software engineering. We finish by discussing
resource requirements and the time horizon of the QCs becoming practical for
string comparison.


Abstract:
  All higher-spin s >= 1/2 Ising spin glasses are studied by
renormalization-group theory in spatial dimension d=3. The s-sequence of global
phase diagrams, the chaos Lyapunov exponent, and the spin-glass runaway
exponent are calculated. It is found that, in d=3, a finite-temperature
spin-glass phase occurs for all spin values, including the continuum limit of s
\rightarrow \infty. The phase diagrams, with increasing spin s, saturate to a
limit value. The spin-glass phase, for all s, exhibits chaotic behavior under
rescalings, with the calculated Lyapunov exponent of \lambda = 1.93 and runaway
exponent of y_R=0.24, showing simultaneous strong-chaos and strong-coupling
behaviors. The ferromagnetic-spinglass-antiferromagnetic phase transitions
occurring around p_t = 0.37 and 0.63 are unaffected by s, confirming the
percolative nature of this phase transition.


Abstract:
  Airborne viruses, such as influenza, tuberculosis, and SARS-CoV-2, are
transmitted through virus-laden particles expelled when an infectious person
sneezes, coughs, talks, or breathes. These virus-laden particles are more
highly concentrated in the expiratory jet of an infectious person than in a
well-mixed room, but this near-field enhancement in virion exposure has not
been well quantified. Transmission of airborne viruses depends on factors that
are inherently variable and, in many cases, poorly constrained, and quantifying
this uncertainty requires large ensembles of model simulations that span the
variability in input parameters. However, models that are well-suited to
simulate the near-field evolution of respiratory particles are also
computationally expensive, which limits the exploration of parametric
uncertainty. In order to perform many simulations that span the wide
variability in factors governing transmission, we developed the
Quadrature-based model of Respiratory Aerosol and Droplets (QuaRAD). QuaRAD is
an efficient framework for simulating the evolution of virus-laden particles
after they are expelled from an infectious person, their deposition to the
nasal cavity of a susceptible person, and the subsequent risk of initial
infection. We simulated 10,000 scenarios to quantify the risk of initial
infection by a particular virus, SARS-CoV-2. The predicted risk of infection
was highly variable among scenarios and, in each scenario, was strongly
enhanced near the infectious individual. In more than 50% of scenarios, the
physical distancing needed to avoid near-field enhancements in airborne
transmission was beyond the recommended safe distance of two meters (six feet)
if the infectious person is not wearing a mask, though this distance defining
the near-field extent was also highly variable among scenarios.


Abstract:
  Salicylic acid (SA) is a primary phytohormone released in response to stress,
particularly biotic infections in plants. Monitoring SA levels may provide a
way for early disease detection in crops providing a way for applying effective
measures for reducing agricultural losses while increase our agricultural
efficiency. Additionally, SA is an important chemical used extensively in the
pharmaceutical and healthcare industry due to its analgesic and
anti-inflammatory properties. Developing a fast and accurate way for monitoring
SA levels in human serum can have a life-saving impact for patients suffering
from overdosing and/or mis-dosing. In this work, we present a low-cost,
portable, and field-deployable electrochemical SA sensing system aimed towards
achieving the above-mentioned goals. The developed sensor consists of a
plug-and-play type device equipped with specialized designed high accuracy
sensing electronics and a novel procedure for robust data analysis. The
developed sensor exhibits excellent linearity and sensitivity and selectivity.
The practical applicability of the developed sensor was also demonstrated by
measuring SA levels in real samples with good accuracy.


Abstract:
  First introduced in 1954, linear probing is one of the oldest data structures
in computer science, and due to its unrivaled data locality, it continues to be
one of the fastest hash tables in practice. It is widely believed and taught,
however, that linear probing should never be used at high load factors; this is
because primary-clustering effects cause insertions at load factor $1 - 1 /x$
to take expected time $\Theta(x^2)$ (rather than the ideal $\Theta(x)$). The
dangers of primary clustering, first discovered by Knuth in 1963, have been
taught to generations of computer scientists, and have influenced the design of
some of many widely used hash tables.
  We show that primary clustering is not a foregone conclusion. We demonstrate
that small design decisions in how deletions are implemented have dramatic
effects on the asymptotic performance of insertions, so that, even if a hash
table operates continuously at a load factor $1 - \Theta(1/x)$, the expected
amortized cost per operation is $\tilde{O}(x)$. This is because tombstones
created by deletions actually cause an anti-clustering effect that combats
primary clustering.
  We also present a new variant of linear probing (which we call graveyard
hashing) that completely eliminates primary clustering on \emph{any} sequence
of operations: if, when an operation is performed, the current load factor is
$1 - 1/x$ for some $x$, then the expected cost of the operation is $O(x)$. One
corollary is that, in the external-memory model with a data blocks of size $B$,
graveyard hashing offers the following remarkable guarantee: at any load factor
$1 - 1/x$ satisfying $x = o(B)$, graveyard hashing achieves $1 + o(1)$ expected
block transfers per operation. Past external-memory hash tables have only been
able to offer a $1 + o(1)$ guarantee when the block size $B$ is at least
$\Omega(x^2)$.


Abstract:
  Inhomogeneous phase-type distributions (IPH) are a broad class of laws which
arise from the absorption times of Markov jump processes. In the
time-homogeneous particular case, we recover phase-type (PH) distributions. In
matrix notation, various functionals corresponding to their distributional
properties are explicitly available and succinctly described. As the number of
parameters increases, IPH distributions may converge weakly to any probability
measure on the positive real line, making them particularly attractive
candidates for statistical modelling purposes. Contrary to PH distributions,
the IPH class allows for a wide range of tail behaviours, which often leads to
adequate estimation with a moderate number of parameters. One of the main
difficulties in estimating PH and IPH distributions is their large number of
matrix parameters. This drawback is best handled through the
expectation-maximisation (EM) algorithm, exploiting the underlying and
unobserved Markov structure. The matrixdist package presents tools for IPH
distributions to efficiently evaluate functionals, simulate, and carry out
maximum likelihood estimation through a three-step EM algorithm. Aggregated and
right-censored data are supported by the fitting routines, and in particular,
one may estimate time-to-event data, histograms, or discretised theoretical
distributions.


Abstract:
  Recently, Self-supervised learning methods able to perform image denoising
without ground truth labels have been proposed. These methods create
low-quality images by adding random or Gaussian noise to images and then train
a model for denoising. Ideally, it would be beneficial if one can generate
high-quality CT images with only a few training samples via self-supervision.
However, the performance of CT denoising is generally limited due to the
complexity of CT noise. To address this problem, we propose a novel
self-supervised learning-based CT denoising method. In particular, we train
pre-train CT denoising and noise models that can predict CT noise from Low-dose
CT (LDCT) using available LDCT and Normal-dose CT (NDCT) pairs. For a given
test LDCT, we generate Pseudo-LDCT and NDCT pairs using the pre-trained
denoising and noise models and then update the parameters of the denoising
model using these pairs to remove noise in the test LDCT. To make realistic
Pseudo LDCT, we train multiple noise models from individual images and generate
the noise using the ensemble of noise models. We evaluate our method on the
2016 AAPM Low-Dose CT Grand Challenge dataset. The proposed ensemble noise
model can generate realistic CT noise, and thus our method significantly
improves the denoising performance existing denoising models trained by
supervised- and self-supervised learning.


Abstract:
  In this paper, we study a colorful, impartial combinatorial game played on a
two-dimensional grid, Transverse Wave. We are drawn to this game because of its
apparent simplicity, contrasting intractability, and intrinsic connection to
two other combinatorial games, one inspired by social influence and another
inspired by quantum superpositions.
  More precisely, we show that Transverse Wave is at the intersection of
social-influence-inspired Friend Circle and superposition-based Demi-Quantum
Nim. Transverse Wave is also connected with Schaefer's logic game Avoid True.
In addition to analyzing the mathematical structures and computational
complexity of Transverse Wave, we provide a web-based version of the game,
playable at
https://turing.plymouth.edu/~kgb1013/DB/combGames/transverseWave.html.
Furthermore, we formulate a basic network-influence inspired game, called
Demographic Influence, which simultaneously generalizes Node-Kyles and
Demi-Quantum Nim (which in turn contains as special cases Nim, Avoid True, and
Transverse Wave). These connections illuminate the lattice order, induced by
special-case/generalization relationships over mathematical games, fundamental
to both the design and comparative analyses of combinatorial games.


Abstract:
  Spin and spatial dimensionalities are universal concepts, essential for
describing both phase transitions and dynamics in magnetic materials. Lately,
these ideas have been adopted to describe magnetic properties of metamaterials,
as well as to replicate and explore ensembles of mesospins belonging to
different universality classes. Here, we take the next step by investigating
magnetic metamaterials not conforming to the conventional framework of
continuous phase transitions. Instead of a continuous decrease in the moment
with temperature, discrete steps are possible, resulting in a binary transition
in the interactions of the elements. The transition is enabled by nucleation
and annihilation of vortex cores, shifting topological charges between the
interior and the edges of the elements. Consequently, the mesospins can be
viewed as shifting their spin dimensionality, from 2 (XY-like) to 0, (vortices)
at the transition. The results provide insight into how dynamics at different
length-scales couple, which can lead to thermally driven topological
transitions in magnetic metamaterials.


Abstract:
  Phase transitions in metal halide perovskites triggered by external
provocations produce significantly different material properties, providing a
prodigious opportunity for a comprehensive applications. In the present study,
the first principles calculation has been performed with the help of density
functional theory (DFT) using CASTEP code to investigate the physical
properties of lead-free CsSnBr3 metal halide under various hydrostatic
pressures. The pressure effect is determined in the range of 0-16 GPa.
Subsequently, a significant change is observed in lattice constant and volume
with increasing pressure. The electronic band structure show semiconductor to
metal phase transition under elevated pressure. The investigation of optical
functions displays that the absorption edge of CsSnBr3 perovskite is shifted
remarkably toward the low energy region (red shift) with improved pressure up
to 16 GPa. In addition, the absorptivity and dielectric constant also upsurges
with the applied hydrostatic pressure. Finally, the mechanical properties
reveal that CsSnBr3 perovskite is mechanically stable and highly ductile; the
ductility is increased with raising pressure. This type of semiconductor to
metal phase transition may inspire a wide range of potential applications.


Abstract:
  This paper presents a wearable electrotactile feedback system to enhance
contact information for mid-air interactions with virtual objects. In
particular, we propose the use of electrotactile feedback to render the
interpenetration distance between the user's finger and the virtual content is
touched. Our approach consists of modulating the perceived intensity (frequency
and pulse width modulation) of the electrotactile stimuli according to the
registered interpenetration distance. In a user study (N=21), we assessed the
performance of four different interpenetration feedback approaches:
electrotactile-only, visual-only, electrotactile and visual, and no
interpenetration feedback. First, the results showed that contact precision and
accuracy were significantly improved when using interpenetration feedback.
Second, and more interestingly, there were no significant differences between
visual and electrotactile feedback when the calibration was optimized and the
user was familiarized with electrotactile feedback. Taken together, these
results suggest that electrotactile feedback could be an efficient replacement
of visual feedback for enhancing contact information in virtual reality
avoiding the need of active visual focus and the rendering of additional visual
artefacts.


Abstract:
  Topic modelling has been a successful technique for text analysis for almost
twenty years. When topic modelling met deep neural networks, there emerged a
new and increasingly popular research area, neural topic models, with over a
hundred models developed and a wide range of applications in neural language
understanding such as text generation, summarisation and language models. There
is a need to summarise research developments and discuss open problems and
future directions. In this paper, we provide a focused yet comprehensive
overview of neural topic models for interested researchers in the AI community,
so as to facilitate them to navigate and innovate in this fast-growing research
area. To the best of our knowledge, ours is the first review focusing on this
specific topic.


Abstract:
  We study a single field inflationary model modified by a non-minimal coupling
term between the Ricci scalar $R$ and the scalar field $\varphi$ in the context
of constant-roll inflation. The first-order formalism is used to analyse the
constant-roll inflation instead of the standard methods used in the literature.
In principle, the formalism considers two functions of the scalar field,
$W=W(\varphi)$ and $Z=Z(\varphi)$, which lead to the reduction of the equations
of motion to first-order differential equations. The approach can be applied to
a wide range of cosmological situations since it directly relates the function
$W$ with Hubble parameter $H$. We perform the inflationary analysis for
power-law and exponential couplings, separately. Then, we investigate the
features of constant-roll potentials as inflationary potentials. Finally, we
compare the inflationary parameters of the models with the observations of CMB
anisotropies in view of realize a physically motivated model.


Abstract:
  Quantum field theory of equilibrium and nonequilibrium Bose-Einstein
condensates is formulated so as to satisfy three basic requirements: the
Hugenholtz-Pines relation; conservation laws; identities among vertices
originating from Goldstone's theorem I. The key inputs are irreducible
four-point vertices, in terms of which we derive a closed system of equations
for Green's functions, three- and four-point vertices, and two-particle Green's
functions. It enables us to study correlated Bose-Einstein condensates with a
gapless branch of single-particle excitations without encountering any infrared
divergence. The single- and two-particle Green's functions are found to share
poles, i.e., the structure of the two-particle Green's functions predicted by
Gavoret and Nozi\`eres for a homogeneous condensate at $T=0$ is also shown to
persist at finite temperatures, in the presence of inhomogeneity, and also in
nonequilibrium situations.


Abstract:
  A sizable amount of goodness-of-fit tests involving functional data have
appeared in the last decade. We provide a relatively compact revision of most
of these contributions, within the independent and identically distributed
framework, by reviewing goodness-of-fit tests for distribution and regression
models with functional predictor and either scalar or functional response.


Abstract:
  This paper introduces the notion of exponential arcs in Hilbert space and of
exponential arcs connecting vector states on a sigma-finite von Neumann algebra
in its standard representation. Results from Tomita-Takesaki theory form an
essential ingredient. Starting point is a non-commutative Radon-Nikodym theorem
that involves positive operators affiliated with the commutant algebra. It is
shown that exponential arcs are differentiable and that parts of an exponential
arc are again exponential arcs. Special cases of probability theory and of
quantum probability are used to illustrate the approach.


Abstract:
  Employing very simple electro-mechanical principles known from classical
physics, the Kibble balance establishes a very precise and absolute link
between quantum electrical standards and macroscopic mass or force
measurements. The success of the Kibble balance, in both determining
fundamental constants ($h$, $N_A$, $e$) and realizing a quasi-quantum mass in
the 2019 newly revised International System of Units, relies on the perfection
of Maxwell's equations and the symmetry they describe between Lorentz's force
and Faraday's induction, a principle and a symmetry stunningly demonstrated in
the weighing and velocity modes of Kibble balances to within $1\times10^{-8}$,
with nothing but imperfect wires and magnets. However, recent advances in the
understanding of the current effect in Kibble balances reveal a troubling
paradox. A diamagnetic effect, a force that does not cancel between mass-on and
mass-off measurement, is challenging balance maker's assumptions of symmetry at
levels that are almost two orders of magnitude larger than the reported
uncertainties. The diamagnetic effect, if it exists, shows up in weighing mode
without a readily apparent reciprocal effect in the velocity mode, begging
questions about systematic errors at the very foundation of the new measurement
system. The hypothetical force is caused by the coil current changing the
magnetic field, producing an unaccounted force that is systematically modulated
with the weighing current. Here we show that this diamagnetic force exists, but
the additional force does not change the equivalence between weighing and
velocity measurements. We reveal the unexpected way that symmetry is preserved
and show that for typical materials and geometries the total relative effect on
the measurement is $\approx 1\times10^{-9}$.


Abstract:
  We propose an algorithm to obtain Green's functions as a continued fraction
on quantum computers, which is based on the construction of the Krylov basis
using variational quantum algorithms, and included in a Lanczos iterative
scheme. This allows the integration of quantum algorithms with first principles
material science simulations, as we demonstrate within the dynamical mean-field
theory (DMFT) framework. DMFT enables quantitative predictions for strongly
correlated materials, and relies on the calculation of Green's functions. On
conventional computers the exponential growth of the Hilbert space with the
number of orbitals limits DMFT to small systems. Quantum computers open new
avenues and can lead to a significant speedup in the computation of expectation
values required to obtain the Green's function. We apply our Krylov variational
quantum algorithm combined with DMFT to the charge transfer insulator
La$_{2}$CuO$_4$ using a quantum computing emulator, and show that with 8 qubits
it predicts the correct insulating material properties for the paramagnetic
phase. We therefore expect that the method is ideally suited to perform
simulations for real materials on near term quantum hardware.


Abstract:
  Here, we present a micro-electromechanical system (MEMS) for the
investigation of the electromechanical coupling in graphene and potentially
related 2D materials. Key innovations of our technique include: (1) the
integration of graphene into silicon-MEMS technology; (2) full control over
induced strain fields and doping levels within the graphene membrane and their
characterization via spatially resolved confocal Raman spectroscopy; and (3)
the ability to detect the mechanical coupling of the graphene sheet to the MEMS
device with via their mechanical resonator eigenfrequencies.


Abstract:
  Dynamic jamming is a phenomenon whereby a dense suspension switches from a
fluid-like to a solid-like state when subjected to sufficient stress and
deformation. Large enough systems show that this transition is accompanied by a
distinct jamming front. We present an experimental study where two jamming
fronts are created simultaneously using two cylinders moving in parallel. We
focus our observations on the collision of the jammed regions when the two
fronts meet. Surprisingly, our measurements, combining surface texture
visualization and time-resolved particle image velocimetry, show the formation
of an unjammed region contained within the otherwise jammed suspension.


Abstract:
  In this paper we consider the space-fractional Schr\"odinger equation with a
singular potential for a wide class of fractional hypoelliptic operators. Such
analysis can be conveniently realised in the setting of graded Lie groups. The
paper is a continuation and extension of a previous one where the classical
Schr\"odinger equation on $\mathbb R^n$ with singular potentials was
considered.


Abstract:
  In many multi-agent spatiotemporal systems, agents operate under the
influence of shared, unobserved variables (e.g., the play a team is executing
in a game of basketball). As a result, the trajectories of the agents are often
statistically dependent at any given time step; however, almost universally,
multi-agent models implicitly assume the agents' trajectories are statistically
independent at each time step. In this paper, we introduce baller2vec++, a
multi-entity Transformer that can effectively model coordinated agents.
Specifically, baller2vec++ applies a specially designed self-attention mask to
a mixture of location and "look-ahead" trajectory sequences to learn the
distributions of statistically dependent agent trajectories. We show that,
unlike baller2vec (baller2vec++'s predecessor), baller2vec++ can learn to
emulate the behavior of perfectly coordinated agents in a simulated toy
dataset. Additionally, when modeling the trajectories of professional
basketball players, baller2vec++ outperforms baller2vec by a wide margin.


Abstract:
  This paper describes a quality assessment model for perceptual video
compression applications (PVM), which stimulates visual masking and
distortion-artefact perception using an adaptive combination of noticeable
distortions and blurring artefacts. The method shows significant improvement
over existing quality metrics based on the VQEG database, and provides
compatibility with in-loop rate-quality optimisation for next generation video
codecs due to its latency and complexity attributes. Performance comparison are
validated against a range of different distortion types.


Abstract:
  The study of thermal heat engines was pivotal to establishing the principles
of equilibrium thermodynamics, with implications far wider than only engine
optimization. For nonequilibrium systems, which by definition dissipate energy
even at rest, how to best convert such dissipation into useful work is still
largely an outstanding question, with similar potential to illuminate general
physical principles. We review recent theoretical progress in studying the
performances of engines operating with active matter, where particles are
driven by individual self-propulsion. We distinguish two main classes, either
autonomous engines exploiting a particle current, or cyclic engines applying
periodic transformation to the system, and present the strategies put forward
so far for optimization. We delineate the limitations of previous studies, and
propose some futures perspectives, with a view to building a consistent
thermodynamic framework far from equilibrium.


Abstract:
  Being able to automatically detect the performance issues in apps can
significantly improve apps' quality as well as having a positive influence on
user satisfaction. Application Performance Management (APM) libraries are used
to locate the apps' performance bottleneck, monitor their behaviors at runtime,
and identify potential security risks. Although app developers have been
exploiting application performance management (APM) tools to capture these
potential performance issues, most of them do not fully understand the
internals of these APM tools and the effect on their apps. To fill this gap, in
this paper, we conduct the first systematic study on APMs for apps by
scrutinizing 25 widely-used APMs for Android apps and develop a framework named
APMHunter for exploring the usage of APMs in Android apps. Using APMHunter, we
conduct a large-scale empirical study on 500,000 Android apps to explore the
usage patterns of APMs and discover the potential misuses of APMs. We obtain
two major findings: 1) some APMs still employ deprecated permissions and
approaches, which makes APMs fail to perform as expected; 2) inappropriate use
of APMs can cause privacy leaks. Thus, our study suggests that both APM vendors
and developers should design and use APMs scrupulously.


Abstract:
  The nearby young star beta Pictoris hosts a rich and complex planetary
system, with at least two giant planets and a nearly edge-on debris disk that
contains several dynamical subpopulations of planetesimals. While the inner
ranges of the debris disk have been studied extensively, less information is
known about the outer, fainter parts of the disk. Here we present an analysis
of archival FORS V-band imaging data from 2003-2004, which have previously not
been explored scientifically because the halo substructure of the bright
stellar point spread function is complex. Through a high-contrast scheme based
on angular differential imaging, with a forward-modelling approach to mitigate
self-subtraction, we produced the deepest imaging yet for the outer range of
the beta Pic disk, and extracted its morphological characteristics. A
brightness asymmetry between the two arms of the edge-on disk, which was
previously noted in the inner disk, is even more pronounced at larger angular
separations, reaching a factor ~10 around 1000 AU. Approaching 2000 AU, the
brighter arm is visible at a surface brightness of 27-28 mag/arcsec^2. Much
like for the brightness asymmetry, a tilt angle asymmetry exists between the
two arms that becomes increasingly extreme at large separations. The outer tilt
angle of 7.2 deg can only be explained if the outer disk is farther from an
edge-on inclination than the inner disk, or if its dust has a stronger
scattering anisotropy, or (most likely) both. The strong asymmetries imply the
presence of a highly eccentric kinematic disk component, which may have been
caused by a disruptive event thought to have taken place at a closer-in
location in the disk.


Abstract:
  We achieve two primary goals in this work. First, we propose a flexible
algorithm that can simulate various scenarios of state/government intervention.
Secondly, we analyze the scenario exhibiting the critical behavior of the
market of competing firms, which depends on the degree of government
intervention and the activity level of the firms. Thus, we have analyzed the
second-order phase transition series, finding the levels of critical
intervention and the critical exponent values. As a result, we have observed a
sharp increase of fluctuations at a critical intervention level and the local
breakdown of the average market technology concerning the frontier technology.


Abstract:
  We give a modification of Yu's construction of supercuspidal representations
of a connected reductive group over a non-archimedean local field. This
modification restores the validity of certain key intertwining property claims
made by Yu, which were recently proven to be false for the original
construction.
  This modification is also an essential ingredient in the explicit
construction of supercuspidal L-packets. As further applications, we prove the
stability and many instances of endoscopic character identities of these
supercuspidal L-packets, subject to some conditions on the base field. In
particular, for regular supercuspidal parameters we prove all instances of
standard endoscopy. In addition, we prove that these supercuspidal L-packets
satisfy a certain property, which, together with standard endoscopy, uniquely
characterizes the local Langlands correspondence for supercuspidal L-packets
(again subject to the above mentioned conditions on the base field). These
results are based on a statement of the Harish-Chandra character formula for
the supercuspidal representations arising from the twisted Yu construction.


Abstract:
  Background: Floods are the most common natural disaster in the world,
affecting the lives of hundreds of millions. Flood forecasting is therefore a
vitally important endeavor, typically achieved using physical water flow
simulations, which rely on accurate terrain elevation maps. However, such
simulations, based on solving partial differential equations, are
computationally prohibitive on a large scale. This scalability issue is
commonly alleviated using a coarse grid representation of the elevation map,
though this representation may distort crucial terrain details, leading to
significant inaccuracies in the simulation. Contributions: We train a deep
neural network to perform physics-informed downsampling of the terrain map: we
optimize the coarse grid representation of the terrain maps, so that the flood
prediction will match the fine grid solution. For the learning process to
succeed, we configure a dataset specifically for this task. We demonstrate that
with this method, it is possible to achieve a significant reduction in
computational cost, while maintaining an accurate solution. A reference
implementation accompanies the paper as well as documentation and code for
dataset reproduction.


Abstract:
  Given smooth step-like initial data $V(0,x)$ on the real line, we show that
the Korteweg--de Vries equation is globally well-posed for initial data $u(0,x)
\in V(0,x) + H^{-1}(\mathbb{R})$. The proof uses our general well-posedness
result for exotic spatial asymptotics.
  As a prerequisite, we show that KdV is globally well-posed for
$H^3(\mathbb{R})$ perturbations of step-like initial data. In the case $V
\equiv 0$, we obtain a new proof of the Bona--Smith theorem using the
low-regularity methods that established the sharp well-posedness of KdV in
$H^{-1}$.


Abstract:
  Semantic Scene Completion (SSC) aims to jointly estimate the complete
geometry and semantics of a scene, assuming partial sparse input. In the last
years following the multiplication of large-scale 3D datasets, SSC has gained
significant momentum in the research community because it holds unresolved
challenges. Specifically, SSC lies in the ambiguous completion of large
unobserved areas and the weak supervision signal of the ground truth. This led
to a substantially increasing number of papers on the matter. This survey aims
to identify, compare and analyze the techniques providing a critical analysis
of the SSC literature on both methods and datasets. Throughout the paper, we
provide an in-depth analysis of the existing works covering all choices made by
the authors while highlighting the remaining avenues of research. SSC
performance of the SoA on the most popular datasets is also evaluated and
analyzed.


Abstract:
  We determine the asymptotics for the variance of the number of zeros of
random linear combinations of orthogonal polynomials of degree $\leq n$ in
subintervals $\left [ a,b\right ] $ of the support of the underlying
orthogonality measure $\mu $. We show that, as $n\rightarrow \infty $, this
variance is asymptotic to $cn$, for some explicit constant $c>0$.


Abstract:
  We report numerical simulations of a trapped elastic vortex driven by a
strong ac magnetic field $H(t)=H\sin\omega t$ parallel to the surface of a
superconducting film. The surface resistance and the power dissipated by an
oscillating vortex perpendicular to the film surface were calculated as
functions of $H$ and $\omega$ for different spatial distributions, densities,
and strengths of pinning centers, including bulk pinning, surface pinning, and
cluster pinning. Our simulations were performed for both the Bardeen-Stephen
viscous vortex drag and the Larkin-Ovchinnikov (LO) drag coefficient $\eta(v)$
decreasing with the vortex velocity $v$. The local residual surface resistance
$R_i(H)$ calculated for different statistical realizations of the pinning
potential exhibits strong mesoscopic fluctuations caused by local depinning
jumps of a vortex segment as $H$ increases, but the global surface resistance
$\bar{R}_i(H)$ obtained by averaging $R_i(H)$ over different pin configurations
increases smoothly with the field amplitude at small $H$ and levels off at
higher fields. For strong pinning, the LO decrease of $\eta(v)$ with $v$ can
result in a nonmonotonic field dependence of $R_i(H)$ which decreases with $H$
at higher fields, but cause a runaway instability of the vortex in a thick film
for weak pinning. It is shown that overheating of a single moving vortex can
produce the LO-like velocity dependence of $\eta(v)$, but can mask the decrease
of the surface resistance with $H$ at a higher density of trapped vortices.


Abstract:
  Academic research projects receive hundreds of billions of dollars of
government investment each year. They complement business research projects by
focusing on the generation of new foundational knowledge and addressing
societal challenges. Despite the importance of academic research, the
management of it is often undisciplined and ad hoc. It has been postulated that
the inherent uncertainty and complexity of academic research projects make them
challenging to manage. However, based on this study's analysis of input and
voting from more than 500 academic research team members in facilitated risk
management sessions, the most important perceived risks are general, as opposed
to being research specific. Overall participants' top risks related to funding,
team instability, unreliable partners, study participant recruitment, and data
access. Many of these risks would require system- or organization-level
responses that are beyond the scope of individual academic research teams.


Abstract:
  Electromagnetic waves in a system with a space and time dependent boundary
experience both diffraction and Doppler-like frequency conversion. In order to
analyse such situations, conventional methods call for either the eigenmodes or
the dyadic Green's function in space and time dependent media. Here, we propose
a dynamical differential method which does not require either of them. Our
method utilises a dynamical coordinate transformation in order to simplify the
calculation of the optical response of the space and time dependent system. We
reveal that the diffraction symmetry is broken in the presence of
traveling-wave type spatiotemporal modulation.


Abstract:
  Federated learning (FL) enables multiple clients to collaboratively train a
shared model without disclosing their local datasets. This is achieved by
exchanging local model updates with the help of a parameter server (PS).
However, due to the increasing size of the trained models, the communication
load due to the iterative exchanges between the clients and the PS often
becomes a bottleneck in the performance. Sparse communication is often employed
to reduce the communication load, where only a small subset of the model
updates are communicated from the clients to the PS. In this paper, we
introduce a novel time-correlated sparsification (TCS) scheme, which builds
upon the notion that sparse communication framework can be considered as
identifying the most significant elements of the underlying model. Hence, TCS
seeks a certain correlation between the sparse representations used at
consecutive iterations in FL, so that the overhead due to encoding and
transmission of the sparse representation can be significantly reduced without
compromising the test accuracy. Through extensive simulations on the CIFAR-10
dataset, we show that TCS can achieve centralized training accuracy with 100
times sparsification, and up to 2000 times reduction in the communication load
when employed together with quantization.


Abstract:
  We present results from parallelizing the unpacking and clustering steps of
the raw data from the silicon strip modules for reconstruction of charged
particle tracks. Throughput is further improved by concurrently processing
multiple events using nested OpenMP parallelism on CPU or CUDA streams on GPU.
The new implementation along with earlier work in developing a parallelized and
vectorized implementation of the combinatoric Kalman filter algorithm has
enabled efficient global reconstruction of the entire event on modern computer
architectures. We demonstrate the performance of the new implementation on
Intel Xeon and NVIDIA GPU architectures.


Abstract:
  In this paper, we tackle two important problems in low-rank learning, which
are partial singular value decomposition and numerical rank estimation of huge
matrices. By using the concepts of Krylov subspaces such as Golub-Kahan
bidiagonalization (GK-bidiagonalization) as well as Ritz vectors, we propose
two methods for solving these problems in a fast and accurate way. Our
experiments show the advantages of the proposed methods compared to the
traditional and randomized singular value decomposition methods. The proposed
methods are appropriate for applications involving huge matrices where the
accuracy of the desired singular values and also all of their corresponding
singular vectors are essential. As a real application, we evaluate the
performance of our methods on the problem of Riemannian similarity learning
between two various image datasets of MNIST and USPS.


Abstract:
  Inhibitory neurons play a crucial role in maintaining persistent neuronal
activity. Although connected extensively through electrical synapses
(gap-junctions), these neurons also exhibit interactions through chemical
synapses in certain regions of the brain. When the coupling is sufficiently
strong, the effects of these two synaptic modalities combine in a nonlinear
way. Hence, in this work, we focus on the strong inhibition regime and identify
the parametric conditions that result in the emergence of self-sustained
oscillations in systems of coupled excitable neurons, in the presence of a
brief sub-threshold stimulus. Our investigation on the dynamics in a minimal
network of two neurons reveals a rich set of dynamical behaviors viz., periodic
and various complex oscillations including period-n (n=2,4,8...) dynamics and
chaos. We further extend our study by considering a system of inhibitory
neurons arranged in a one-dimensional ring topology and determine the optimal
conditions for sustained activity. Our work highlights the nonlinear dynamical
behavior arising due to the combined effects of gap-junctions and strong
synaptic inhibition, which can have potential implications in maintaining
robust memory patterns.


Abstract:
  Given a sequence of Borel probability measures on a Hausdorff space which
satisfy a large deviation principle, we consider the corresponding sequence of
measures formed by conditioning on a set $B$. If the large deviation rate
function $I$ is good and effectively continuous and the conditioning set has
the property that (1) $\overline{B^\circ} = \overline{B}$ and (2) $I(x) <
\infty$ for all $x \in \overline{B}$, then the sequence of conditional measures
satisfies a large deviation principle with the good, effectively continuous
rate function $I_B$, where $I_B(x) = I(x)-\inf I(B)$ if $x\in\overline{B}$ and
$I_B(x) = \infty$ otherwise.


Abstract:
  Constructing quantum Hamiltonians for simulating and controlling the exotic
physics of many-body systems belongs to the most important topics of condensed
matter physics and quantum technologies. The main challenge that hinders the
future investigations is the extremely high complexity for either their
numerical simulations or experimental realizations. In this work, we propose
the temperature-adaptive entanglement simulator (TAES) that mimics and tunes
the thermodynamics of the one-dimensional (1D) many-body system by embedding a
small-size model in an entanglement bath. The entanglement bath is described by
the interactions located at the boundaries of the small-size model, whose
coupling constants are optimized by means of differentiable tensor network at
target temperatures. With the benchmark on 1D spin chains, TAES surpasses the
state-of-the-art accuracy compared with the existing finite-temperature
approaches such as linearized and differential tensor renormalization group
algorithms. By tuning the couplings of the entanglement bath with the
temperature fixed, the bulk entropy exhibits similar behavior compared to that
obtained by tuning the temperature. Our work provides novel opportunities of
engineering the distribution of fluctuations and mimicking the non-equilibrium
phenomena in a uniform temperature within the canonical ensemble framework
using the optimized boundary interactions.


Abstract:
  A first principles approach to the theoretical description of the development
of biological forms, from a fertilized egg to a functioning embryo, remains a
central challenge to applied physics and theoretical biology. Rather than refer
to principles of self-organization and non-equilibrium statistical mechanics to
describe a developing embryo from its active cellular constituents, a purely
geometric theory is constructed that references the properties of the ambient
space that the embryo occupies. In 1975 the Fields laureate Ren\'{e} Thom
developed a system of techniques and local dynamical models that are capable of
reconstructing the local dynamic of an embryo at each new growth event of the
system. Each new growth event (the development of a limb, for example) is a
topological change in the dynamic of the system that can be classified only
according to the properties of space. The local models can be non-conservative
flows with robust attractor behavior that serve as organizing centers for
systems development. Hamiltonian flows can also be considered with novel,
self-reproducing vague attractor behavior. The set of growth events become an
unfolding space related to the differentiable manifold of states. The set of
growth events, which Thom refers to as the catastrophe set, has special
algebraic properties which permit these models to be low dimensional--the local
model contains few parameters. We present Thom's work as a research program
outlining a framework for the construction of these local models, and,
ultimately, the synthesis of these models into a full theoretical description
of a developing biological organism. We give examples of the application of
selected models to key growth events in the process of gastrulation.


Abstract:
  The optimal dynamic treatment rule (ODTR) framework offers an approach for
understanding which kinds of patients respond best to specific treatments -- in
other words, treatment effect heterogeneity. Recently, there has been a
proliferation of methods for estimating the ODTR. One such method is an
extension of the SuperLearner algorithm -- an ensemble method to optimally
combine candidate algorithms extensively used in prediction problems -- to
ODTRs. Following the "causal roadmap," we causally and statistically define the
ODTR and provide an introduction to estimating it using the ODTR SuperLearner.
Additionally, we highlight practical choices when implementing the algorithm,
including choice of candidate algorithms, metalearners to combine the
candidates, and risk functions to select the best combination of algorithms.
Using simulations, we illustrate how estimating the ODTR using this
SuperLearner approach can uncover treatment effect heterogeneity more
effectively than traditional approaches based on fitting a parametric
regression of the outcome on the treatment, covariates and treatment-covariate
interactions. We investigate the implications of choices in implementing an
ODTR SuperLearner at various sample sizes. Our results show the advantages of:
(1) including a combination of both flexible machine learning algorithms and
simple parametric estimators in the library of candidate algorithms; (2) using
an ensemble metalearner to combine candidates rather than selecting only the
best-performing candidate; (3) using the mean outcome under the rule as a risk
function. Finally, we apply the ODTR SuperLearner to the "Interventions" study,
an ongoing randomized controlled trial, to identify which justice-involved
adults with mental illness benefit most from cognitive behavioral therapy (CBT)
to reduce criminal re-offending.


Abstract:
  This paper describes the automatic measurement of the spatial organization of
the visual axes of insect compound eyes, which consist of several thousands of
visual units called ommatidia. Each ommatidium samples the optical information
from a small solid angle, with an approximately Gaussian-distributed
sensitivity (half-width of the order of one degree) centered around a visual
axis. Together, the ommatidia gather the optical information from virtually the
full surroundings. The spatial distribution of the visual axes thus determines
the eye's spatial resolution. Knowledge of the optical organization of a
compound eye and its visual acuity is crucial for quantitative studies of the
neural processing of visual information. Here we present an automated procedure
for mapping a compound eye's visual axes, using an intrinsic optical
phenomenon, the pseudopupil. We outline the optomechanical setup for scanning
insect eyes, and use experimental results obtained from a housefly, Musca
domestica, to illustrate the steps in the measurement procedure.


Abstract:
  The purpose of this article is to show a close relationship between the
generalized central series of Leibniz algebras. Some analogues of the classical
group-theoretical theorems of Schur and Baer for Leibniz algebras are proved.


Abstract:
  Quantum channel discrimination is a fundamental problem in quantum
information science. In this study, we consider general quantum channel
discrimination problems, and derive the lower bounds of the error probability.
Our lower bounds are based on the triangle inequalities of the Bures angle and
the trace distance. As a consequence of the lower bound based on the Bures
angle, we prove the optimality of Grover's search if the number of marked
elements is fixed to some integer $k$. This result generalizes Zalka's result
for $k=1$. We also present several numerical results in which our lower bounds
based on the trace distance outperform recently obtained lower bounds.


Abstract:
  Affective Analysis is not a single task, and the valence-arousal value,
expression class, and action unit can be predicted at the same time. Previous
researches did not pay enough attention to the entanglement and hierarchical
relation of these three facial attributes. We propose a novel model named
feature pyramid networks for multi-task affect analysis. The hierarchical
features are extracted to predict three labels and we apply a teacher-student
training strategy to learn from pretrained single-task models. Extensive
experiment results demonstrate the proposed model outperforms other models.
This is a submission to The 2nd Workshop and Competition on Affective Behavior
Analysis in the wild (ABAW). The code and model are available for research
purposes at https://github.com/ryanhe312/ABAW2-FPNMAA.


Abstract:
  Fractional equations have become the model of choice in several applications
where heterogeneities at the microstructure result in anomalous diffusive
behavior at the macroscale. In this work we introduce a new fractional operator
characterized by a doubly-variable fractional order and possibly truncated
interactions. Under certain conditions on the model parameters and on the
regularity of the fractional order we show that the corresponding Poisson
problem is well-posed. We also introduce a finite element discretization and
describe an efficient implementation of the finite-element matrix assembly in
the case of piecewise constant fractional order. Through several numerical
tests, we illustrate the improved descriptive power of this new operator across
media interfaces. Furthermore, we present one-dimensional and two-dimensional
$h$-convergence results that show that the variable-order model has the same
convergence behavior as the constant-order model.


Abstract:
  Secure Device-to-Device (D2D) communication is becoming increasingly
important with the ever-growing number of Internet-of-Things (IoT) devices in
our daily life. To achieve secure D2D communication, the key agreement between
different IoT devices without any prior knowledge is becoming desirable.
Although various approaches have been proposed in the literature, they suffer
from a number of limitations, such as low key generation rate and short pairing
distance. In this paper, we present InaudibleKey, an inaudible acoustic
signal-based key generation protocol for mobile devices. Based on acoustic
channel reciprocity, InaudibleKey exploits the acoustic channel frequency
response of two legitimate devices as a common secret to generating keys.
InaudibleKey employs several novel technologies to significantly improve its
performance. We conduct extensive experiments to evaluate the proposed system
in different real environments. Compared to state-of-the-art works,
InaudibleKey improves key generation rate by 3-145 times, extends pairing
distance by 3.2-44 times, and reduces information reconciliation counts by
2.5-16 times. Security analysis demonstrates that InaudibleKey is resilient to
a number of malicious attacks. We also implement InaudibleKey on modern
smartphones and resource-limited IoT devices. Results show that it is
energy-efficient and can run on both powerful and resource-limited IoT devices
without incurring excessive resource consumption.


Abstract:
  Modern and emerging architectures demand increasingly complex compiler
analyses and transformations. As the emphasis on compiler infrastructure moves
beyond support for peephole optimizations and the extraction of
instruction-level parallelism, they should support custom tools designed to
meet these demands with higher-level analysis-powered abstractions of wider
program scope. This paper introduces NOELLE, a robust open-source
domain-independent compilation layer built upon LLVM providing this support.
NOELLE is modular and demand-driven, making it easy-to-extend and adaptable to
custom-tool-specific needs without unduly wasting compile time and memory. This
paper shows the power of NOELLE by presenting a diverse set of ten custom tools
built upon it, with a 33.2% to 99.2% reduction in code size (LoC) compared to
their counterparts without NOELLE.


Abstract:
  Cardiac segmentation of atriums, ventricles, and myocardium in computed
tomography (CT) images is an important first-line task for presymptomatic
cardiovascular disease diagnosis. In several recent studies, deep learning
models have shown significant breakthroughs in medical image segmentation
tasks. Unlike other organs such as the lungs and liver, the cardiac organ
consists of multiple substructures, i.e., ventricles, atriums, aortas,
arteries, veins, and myocardium. These cardiac substructures are proximate to
each other and have indiscernible boundaries (i.e., homogeneous intensity
values), making it difficult for the segmentation network focus on the
boundaries between the substructures. In this paper, to improve the
segmentation accuracy between proximate organs, we introduce a novel model to
exploit shape and boundary-aware features. We primarily propose a shape-aware
attention module, that exploits distance regression, which can guide the model
to focus on the edges between substructures so that it can outperform the
conventional contour-based attention method. In the experiments, we used the
Multi-Modality Whole Heart Segmentation dataset that has 20 CT cardiac images
for training and validation, and 40 CT cardiac images for testing. The
experimental results show that the proposed network produces more accurate
results than state-of-the-art networks by improving the Dice similarity
coefficient score by 4.97%. Our proposed shape-aware contour attention
mechanism demonstrates that distance transformation and boundary features
improve the actual attention map to strengthen the responses in the boundary
area. Moreover, our proposed method significantly reduces the false-positive
responses of the final output, resulting in accurate segmentation.


Abstract:
  We consider $W^+W^-$ production in hadronic collisions and present the
computation of next-to-next-to-leading order accurate predictions consistently
matched to parton showers (NNLO+PS) using the MiNNLO$_{\rm PS}$ method. Spin
correlations, interferences and off-shell effects are included by calculating
the full process $pp \to e^+\nu_e \mu^-\bar{\nu}_\mu$. This is the first
NNLO+PS calculation for $W^+W^-$ production that does not require an
a-posteriori multi-differential reweighting. The evaluation time of the
two-loop contribution has been reduced by more than one order of magnitude
through a four-dimensional cubic spline interpolation. We find good agreement
with the inclusive and fiducial cross sections measured by ATLAS and CMS. Both
NNLO corrections and matching to parton showers are important for an accurate
simulation of the $W^+W^-$ signal, and their matching provides the best
description of fully exclusive $W^+W^-$ events to date.


Abstract:
  Motivated by [G. Cannizzaro, M. Hairer, arXiv:2010.02766], we provide an
alternative characterisation of the Brownian Web (see [T\'oth B., Werner W.,
Probab. Theory Related Fields, '98] and [L. R. G. Fontes, M. Isopi, C. M.
Newman, and K. Ravishankar, Ann. Probab., '04]), i.e. a family of coalescing
Brownian motions starting from every point in $\mathbb R^2$ simultaneously, and
fit it into the wider framework of random (spatial) $\mathbb R$-trees. We
determine some of its properties (e.g. its box-counting dimension) and recover
some which were determined in earlier works, such as duality, special points
and convergence of the graphical representation of coalescing random walks.
Along the way, we introduce a modification of the topology of spatial $\mathbb
R$-trees in [T. Duquesne, J.-F. Le Gall, Probab. Theory Related Fields, '05]
and [M. T. Barlow, D. A. Croydon, T. Kumagai, Ann. Probab. '17] which makes it
Polish and could be of independent interest.


Abstract:
  An accurate and reliable technique for predicting Remaining Useful Life (RUL)
for battery cells proves helpful in battery-operated IoT devices, especially in
remotely operated sensor nodes. Data-driven methods have proved to be the most
effective methods until now. These IoT devices have low computational
capabilities to save costs, but Data-Driven battery health techniques often
require a comparatively large amount of computational power to predict SOH and
RUL due to most methods being feature-heavy. This issue calls for ways to
predict RUL with the least amount of calculations and memory. This paper
proposes an effective and novel peak extraction method to reduce computation
and memory needs and provide accurate prediction methods using the least number
of features while performing all calculations on-board. The model can
self-sustain, requires minimal external interference, and hence operate
remotely much longer. Experimental results prove the accuracy and reliability
of this method. The Absolute Error (AE), Relative error (RE), and Root Mean
Square Error (RMSE) are calculated to compare effectiveness. The training of
the GPR model takes less than 2 seconds, and the correlation between SOH from
peak extraction and RUL is 0.97.


Abstract:
  In this paper we show that the closure of the numerical range of an
$n+1$-periodic tridiagonal operator is equal to the numerical range of a
$2(n+1)\times 2(n+1)$ complex matrix.


Abstract:
  To highlight the challenges of achieving representation disentanglement for
text domain in an unsupervised setting, in this paper we select a
representative set of successfully applied models from the image domain. We
evaluate these models on 6 disentanglement metrics, as well as on downstream
classification tasks and homotopy. To facilitate the evaluation, we propose two
synthetic datasets with known generative factors. Our experiments highlight the
existing gap in the text domain and illustrate that certain elements such as
representation sparsity (as an inductive bias), or representation coupling with
the decoder could impact disentanglement. To the best of our knowledge, our
work is the first attempt on the intersection of unsupervised representation
disentanglement and text, and provides the experimental framework and datasets
for examining future developments in this direction.


Abstract:
  In recent work, the first two authors constructed a generalized continued
fraction called the $p$-continued fraction, characterized by the property that
its convergents (a subsequence of the regular convergents) are best
approximations with respect to the $L^p$ norm, where $p\geq 1$. We extend this
construction to the region $0<p<1$, where now the $L^p$ quasinorm is
non-convex. We prove that the approximation coefficients of the $p$-continued
fraction are bounded above by $1/\sqrt{5}+\varepsilon_p$, where
$\varepsilon_p\to 0$ as $p\to 0$. In light of Hurwitz's theorem, this upper
bound is sharp, in the limit. We also measure the maximum number of consecutive
regular convergents that are skipped by the $p$-continued fraction.


Abstract:
  Hate Speech has become a major content moderation issue for online social
media platforms. Given the volume and velocity of online content production, it
is impossible to manually moderate hate speech related content on any platform.
In this paper we utilize a multi-task and multi-lingual approach based on
recently proposed Transformer Neural Networks to solve three sub-tasks for hate
speech. These sub-tasks were part of the 2019 shared task on hate speech and
offensive content (HASOC) identification in Indo-European languages. We expand
on our submission to that competition by utilizing multi-task models which are
trained using three approaches, a) multi-task learning with separate task
heads, b) back-translation, and c) multi-lingual training. Finally, we
investigate the performance of various models and identify instances where the
Transformer based models perform differently and better. We show that it is
possible to to utilize different combined approaches to obtain models that can
generalize easily on different languages and tasks, while trading off slight
accuracy (in some cases) for a much reduced inference time compute cost. We
open source an updated version of our HASOC 2019 code with the new improvements
at https://github.com/socialmediaie/MTML_HateSpeech.


Abstract:
  A smart meta-superconductor Bi(Pb)SrCaCuO (B(P)SCCO) may increase the
critical transition temperature (TC) of B(P)SCCO by electroluminescence (EL)
energy injection of inhomogeneous phases. However, the increase amplitude
{\Delta}TC ({\Delta}TC=TC-T(C,pure)) of TC is relatively small. In this study,
a smart meta-superconductor B(P)SCCO with different matrix sizes was designed.
Three kinds of raw materials with different particle sizes were used, and
different series of Y2O3:Sm3+, Y2O3, Y2O3:Eu3+, and Y2O3:Eu3++Ag doped samples
and pure B(P)SCCO were prepared. Results indicated that the TC of the Y2O3 or
Y2O3:Sm3+ non-luminescent dopant doping sample is lower than that of pure
B(P)SCCO. However, the TC of the Y2O3:Eu3++Ag or Y2O3:Eu3+ luminescent
inhomogeneous phase doping sample is higher than that of pure B(P)SCCO. With
the decrease of the raw material particle size from 30 to 5 {\mu}m, the
particle size of the B(P)SCCO superconducting matrix in the prepared samples
decreases, and the doping content of the Y2O3:Eu3++Ag or Y2O3:Eu3+ increases
from 0.2% to 0.4%. Meanwhile, the increase of the inhomogeneous phase content
enhances the {\Delta}TC. When the particle size of raw material is 5 {\mu}m,
the doping concentration of the luminescent inhomogeneous phase can be
increased to 0.4%. At this time, the zero-resistance temperature and onset
transition temperature of the Y2O3:Eu3++Ag doped sample are 4 and 6.3 K higher
than those of pure B(P)SCCO, respectively.


Abstract:
  Let $M$ be the maximal operator associated to a smooth curve in $\mathbb R^3$
which has nonvanishing curvature and torsion. We prove that $M$ is bounded on
$L^p$ if and only if $p>3$.


Abstract:
  Consider an irreducible Markov chain which satisfies a ratio limit theorem,
and let $\rho$ be the spectral radius of the chain. We investigate the relation
of the the $\rho\,$-Martin boundary with the boundary induced by the
$\rho\,$-harmonic kernel which appears in the ratio limit. Special emphasis is
on random walks on non-amenable groups, specifically, free groups and
hyperbolic groups.


Abstract:
  Constrained reinforcement learning involves multiple rewards that must
individually accumulate to given thresholds. In this class of problems, we show
a simple example in which the desired optimal policy cannot be induced by any
linear combination of rewards. Hence, there exist constrained reinforcement
learning problems for which neither regularized nor classical primal-dual
methods yield optimal policies. This work addresses this shortcoming by
augmenting the state with Lagrange multipliers and reinterpreting primal-dual
methods as the portion of the dynamics that drives the multipliers evolution.
This approach provides a systematic state augmentation procedure that is
guaranteed to solve reinforcement learning problems with constraints. Thus,
while primal-dual methods can fail at finding optimal policies, running the
dual dynamics while executing the augmented policy yields an algorithm that
provably samples actions from the optimal policy.


Abstract:
  A sweep of a point configuration is any ordered partition induced by a linear
functional. Posets of sweeps of planar point configurations were formalized and
abstracted by Goodman and Pollack under the theory of allowable sequences of
permutations. We introduce two generalizations that model posets of sweeps of
higher dimensional configurations.
  Mimicking the fact that sweep polytopes of point configurations (the monotone
path polytopes of the associated zonotopes) are projections of permutahedra, we
define sweep oriented matroids as strong maps of the braid oriented matroid.
Allowable sequences are then the sweep oriented matroids of rank 2, and many of
their properties extend to higher rank. We show strong ties between sweep
oriented matroids and both modular hyperplanes and Dilworth truncations from
(unoriented) matroid theory. We also explore their connection with the
generalized Baues problem for cellular strings, where sweep oriented matroids
can play the role of monotone path polytopes, even for non-realizable oriented
matroids. In particular, we show that for oriented matroids that admit a sweep
oriented matroid, their poset of pseudo-sweeps deformation retracts to a sphere
of the appropriate dimension.
  A second generalization are allowable graphs of permutations: symmetric sets
of permutations pairwise connected by allowable sequences. They have the
structure of acycloids and include sweep oriented matroids.


Abstract:
  We propose a worldsheet description for the ${\rm AdS}_5\times {\rm S}^5$
string theory dual to large $N$, free ${\cal N}=4$ super Yang-Mills in four
dimensions. The worldsheet theory is a natural generalisation of the recently
investigated tensionless string on ${\rm AdS}_3\times {\rm S}^3\times
\mathbb{T}^4$. As in the case of ${\rm AdS}_3$ it has a free field description,
with spectrally flowed sectors, and is closely related to an (ambi-)twistor
string theory. Here, however, we view it as a critical $N=4$ (closed) string
background. We argue that the corresponding worldsheet gauge constraints reduce
the degrees of freedom to a finite number of oscillators (string bits) in each
spectrally flowed sector. Imposing a set of residual gauge constraints on this
reduced oscillator Fock space then determines the physical spectrum of the
string theory. Quite remarkably, we find compelling evidence that this
prescription reproduces precisely the entire planar spectrum - of single trace
operators - of the free super Yang-Mills theory.


Abstract:
  We investigate an interpolation/extrapolation method that, given scattered
observations of the Fourier transform, approximates its inverse. The
interpolation algorithm takes advantage of modelling the available data via a
shape-driven interpolation based on Variably Scaled Kernels (VSKs), whose
implementation is here tailored for inverse problems. The so-constructed
interpolants are used as inputs for a standard iterative inversion scheme.
After providing theoretical results concerning the spectrum of the VSK
collocation matrix, we test the method on astrophysical imaging benchmarks.


Abstract:
  Intelligent diagnosis method based on data-driven and deep learning is an
attractive and meaningful field in recent years. However, in practical
application scenarios, the imbalance of time-series fault is an urgent problem
to be solved. This paper proposes a novel deep metric learning model, where
imbalanced fault data and a quadruplet data pair design manner are considered.
Based on such data pair, a quadruplet loss function which takes into account
the inter-class distance and the intra-class data distribution are proposed.
This quadruplet loss pays special attention to imbalanced sample pair. The
reasonable combination of quadruplet loss and softmax loss function can reduce
the impact of imbalance. Experiment results on two open-source datasets show
that the proposed method can effectively and robustly improve the performance
of imbalanced fault diagnosis.


Abstract:
  We study the Lipschitz stability in time for $\alpha$-dissipative solutions
to the Hunter-Saxton equation, where $\alpha \in [0,1]$ is a constant. We
define metrics in both Lagrangian and Eulerian coordinates, and establish
Lipschitz stability for those metrics.


Abstract:
  Edge intelligence, which is a new paradigm to accelerate artificial
intelligence (AI) applications by leveraging computing resources on the network
edge, can be used to improve intelligent transportation systems (ITS). However,
due to physical limitations and energy-supply constraints, the computing powers
of edge equipment are usually limited. High altitude platform station (HAPS)
computing can be considered as a promising extension of edge computing. HAPS is
deployed in the stratosphere to provide wide coverage and strong computational
capabilities. It is suitable to coordinate terrestrial resources and store the
fundamental data associated with ITS-based applications. In this work, three
computing layers,i.e., vehicles, terrestrial network edges, and HAPS, are
integrated to build a computation framework for ITS, where the HAPS data
library stores the fundamental data needed for the applications. In addition,
the caching technique is introduced for network edges to store some of the
fundamental data from the HAPS so that large propagation delays can be reduced.
We aim to minimize the delay of the system by optimizing computation offloading
and caching decisions as well as bandwidth and computing resource allocations.
The simulation results highlight the benefits of HAPS computing for mitigating
delays and the significance of caching at network edges.


Abstract:
  We develop a general theory of categories that admit a functorial invariant
(the triangulation functor) which generalizes the tree-width of graphs. Our
triangulation functor provides a uniform construction for various
tree-width-like invariants including hypergraph tree-width, and the tree-width
of the modular quotient in the category of modular partition functions.


Abstract:
  Epigenetic alterations have an important role in the development of several
types of cancer. Epigenetic studies generate a large amount of data, which
makes it essential to develop novel models capable of dealing with large-scale
data. In this work, we propose a deep embedded refined clustering method for
breast cancer differentiation based on DNA methylation. In concrete, the deep
learning system presented here uses the levels of CpG island methylation
between 0 and 1. The proposed approach is composed of two main stages. The
first stage consists in the dimensionality reduction of the methylation data
based on an autoencoder. The second stage is a clustering algorithm based on
the soft-assignment of the latent space provided by the autoencoder. The whole
method is optimized through a weighted loss function composed of two terms:
reconstruction and classification terms. To the best of the authors' knowledge,
no previous studies have focused on the dimensionality reduction algorithms
linked to classification trained end-to-end for DNA methylation analysis. The
proposed method achieves an unsupervised clustering accuracy of 0.9927 and an
error rate (%) of 0.73 on 137 breast tissue samples. After a second test of the
deep-learning-based method using a different methylation database, an accuracy
of 0.9343 and an error rate (%) of 6.57 on 45 breast tissue samples is
obtained. Based on these results, the proposed algorithm outperforms other
state-of-the-art methods evaluated under the same conditions for breast cancer
classification based on DNA methylation data.