Abstract:
  A variational modeling framework for hydraulically induced fracturing of
elastic-plastic solids is developed in the present work. The developed
variational structure provides a global minimization problem. While fracture
propagation is modeled by means of a phase-field approach to fracture, plastic
effects are taken into account by using a Drucker-Prager-type yield-criterion
function. This yield-criterion function governs the plastic evolution of the
fluid-solid mixture. Fluid storage and transport are described by a
Darcy-Biot-type formulation. Thereby the fluid storage is decomposed into a
contribution due to the elastic deformations and one due to the plastic
deformations. A local return mapping scheme is used for the update of the
plastic quantities. The global minimization structure demands a
$H($div$)$-conforming finite-element formulation. Furthermore this is combined
with an enhanced-assumed-strain formulation in order to overcome locking
phenomena arising from the plastic deformations. The robustness and
capabilities of the presented framework will be shown in a sequence of
numerical examples.


Abstract:
  We initiate a systematic investigation of group actions on compact medain
algebras via the corresponding dynamics on their spaces of measures. We show
that a probability measure which is invariant under a natural push forward
operation must be a uniform measure on a cube and use this to show that every
amenable group action on a locally convex compact median algebra fixed a
sub-cube.


Abstract:
  The transport properties of the strongly-coupled quark-gluon plasma created
in ultra-relativistic heavy-ion collisions are extracted by Bayesian parameter
estimate methods with the latest collision beam energy data from LHC. This
Bayesian analysis includes sophisticated flow harmonic observables for the
first time. We found that the temperature dependence of specific shear
viscosity shows weaker than the previous studies. The results prefer a lower
value of specific bulk viscosity and a higher switching temperature to
reproduce additional observables. However, the improved statistical
uncertainties both on the experimental data and hydrodynamic calculations with
additional observables do not help to reduce the final credibility ranges much,
indicating a need for improving the dynamical collision model before the
hydrodynamic takes place. In addition, the sensitivities of experimental
observables to the parameters in hydrodynamic model calculations are
quantified. It is found that the analysis benefits most from the symmetric
cumulants and non-linear flow modes, which mostly reflect non-linear
hydrodynamic responses, in constraining the temperature dependence of the
specific shear and bulk viscosity in addition to the previously used flow
coefficients.


Abstract:
  We prove bounds for the volume of neighborhoods of algebraic sets, in the
euclidean space or the sphere, in terms of the degree of the defining
polynomials, the number of variables and the dimension of the algebraic set,
without any smoothness assumption. This generalizes previous work of Lotz on
smooth complete intersections in the euclidean space and of B\"urgisser, Cucker
and Lotz on hypersurfaces in the sphere, and gives a complete solution to
Problem 17 in the book titled "Condition" by B\"urgisser and Cucker.


Abstract:
  The discovery of 3C 273 in 1963, and the emergence of the Kerr solution
shortly thereafter, precipitated the current era in astrophysics focused on
using black holes to explain active galactic nuclei (AGN). But while partial
success was achieved in separately explaining the bright nuclei of some AGN via
thin disks, as well as powerful jets with thick disks, the combination of both
powerful jets in an AGN with a bright nucleus, such as in 3C 273, remained
elusive. Although numerical simulations have taken center stage in the last 25
years, they have struggled to produce the conditions that explain them. This is
because radiatively efficient disks have proved a challenge to simulate. Radio
quasars have thus been the least understood objects in high energy
astrophysics. But recent simulations have begun to change this. We explore this
milestone in light of scale-invariance and show that transitory jets, possibly
related to the jets seen in these recent simulations, as some have proposed,
cannot explain radio quasars. We then provide a road map for a resolution.


Abstract:
  We study quasi-convex optimization problems, where only a subset of the
constraints can be sampled, and yet one would like a probabilistic guarantee on
the obtained solution with respect to the initial (unknown) optimization
problem. Even though our results are partly applicable to general quasi-convex
problems, in this work we introduce and study a particular subclass, which we
call "quasi-linear problems". We provide optimality conditions for these
problems. Thriving on this, we extend the approach of chance-constrained convex
optimization to quasi-linear optimization problems. Finally, we show that this
approach is useful for the stability analysis of black-box switched linear
systems, from a finite set of sampled trajectories. It allows us to compute
probabilistic upper bounds on the JSR of a large class of switched linear
systems.


Abstract:
  Content-based music information retrieval has seen rapid progress with the
adoption of deep learning. Current approaches to high-level music description
typically make use of classification models, such as in auto-tagging or genre
and mood classification. In this work, we propose to address music description
via audio captioning, defined as the task of generating a natural language
description of music audio content in a human-like manner. To this end, we
present the first music audio captioning model, MusCaps, consisting of an
encoder-decoder with temporal attention. Our method combines convolutional and
recurrent neural network architectures to jointly process audio-text inputs
through a multimodal encoder and leverages pre-training on audio data to obtain
representations that effectively capture and summarise musical features in the
input. Evaluation of the generated captions through automatic metrics shows
that our method outperforms a baseline designed for non-music audio captioning.
Through an ablation study, we unveil that this performance boost can be mainly
attributed to pre-training of the audio encoder, while other design choices -
modality fusion, decoding strategy and the use of attention - contribute only
marginally. Our model represents a shift away from classification-based music
description and combines tasks requiring both auditory and linguistic
understanding to bridge the semantic gap in music information retrieval.


Abstract:
  The control of Covid 19 epidemics by public health policy in Italy during the
first and the second epidemic waves has been driven by using reproductive
number Rt(t) to identify the supercritical (percolative), the subcritical
(arrested), separated by the critical regime. Here we show that to quantify the
Covid-19 spreading rate with containment measures (CSRwCM) there is a need of a
3D expanded parameter space phase diagram built by the combination of Rt(t) and
doubling time Td(t). In this space we identify the dynamics of the Covid-19
dynamics Italy and its administrative Regions. The supercritical regime is
mathematically characterized by i) the power law of Td vs. [Rt(t)-1] and ii)
the exponential behaviour of Td vs. time, either in the first and in the second
wave. The novel 3D phase diagram shows clearly metastable states appearing
before and after the second wave critical regime. for loosening quarantine and
tracing of actives cases. The metastable states are precursors of the abrupt
onset of a next nascent wave supercritical regime. This dynamic description
allows epidemics predictions needed by policymakers to activate
non-pharmaceutical interventions (NPIs), a key issue for avoiding economical
losses, reduce fatalities and avoid new virus variant during vaccination
campaign


Abstract:
  Sensor placement and feature selection are critical steps in engineering,
modeling, and data science that share a common mathematical theme: the selected
measurements should enable solution of an inverse problem. Most real-world
systems of interest are nonlinear, yet the majority of available techniques for
feature selection and sensor placement rely on assumptions of linearity or
simple statistical models. We show that when these assumptions are violated,
standard techniques can lead to costly over-sensing without guaranteeing that
the desired information can be recovered from the measurements. In order to
remedy these problems, we introduce a novel data-driven approach for sensor
placement and feature selection for a general type of nonlinear inverse problem
based on the information contained in secant vectors between data points. Using
the secant-based approach, we develop three efficient greedy algorithms that
each provide different types of robust, near-minimal reconstruction guarantees.
We demonstrate them on two problems where linear techniques consistently fail:
sensor placement to reconstruct a fluid flow formed by a complicated
shock-mixing layer interaction and selecting fundamental manifold learning
coordinates on a torus.


Abstract:
  Self-assembled topological structures of post-processed two-dimensional
materials exhibit novel physical properties distinct from those of their parent
materials. Herein, the critical role of desulphurization on self-assembled
topological morphologies of molybdenum disulfide ($MoS_2$) monolayer sheets is
explored using molecular dynamics (MD) simulations. MD results show that there
are differences in atomic energetics of $MoS_2$ monolayer sheets with different
desulphurization contents. Both free-standing and substrate-hosted $MoS_2$
monolayer sheets show diversity in topological structures such as flat surface,
wrinkles, folds and scrolls, depending on the desulphurization contents, planar
dimensions and ratios of length-to-width of $MoS_2$ monolayer sheets.
Particularly, at the critical desulphurization contents, they roll up into
nanotube morphology, consistent with previous experimental observations.
Moreover, the observed differences in the molecular morphological diagrams
between free-standing and substrate-hosted $MoS_2$ monolayer sheets can be
attributed to unique interatomic interactions and van der Waals interactions in
them. The study provides important insights into functionalizing structural
morphological properties of two-dimensional materials, e.g., $MoS_2$, via
defect engineering.


Abstract:
  In this paper, we consider the pointwise boundary Lipschitz regularity of
solutions for the semilinear elliptic equations in divergence form mainly under
some weaker assumptions on nonhomogeneous term and the boundary. If the domain
satisfies C^{1,\text{Dini}} condition at a boundary point, and the
nonhomogeneous term satisfies Dini continuous condition and Lipschitz Newtonian
potential condition, then the solution is Lipschitz continuous at this point.
Furthermore, we generalize this result to Reifenberg C^{1,\text{Dini}} domains.


Abstract:
  In this paper we compute the cohomology of moduli space of cubic fourfolds
with ADE type singularities relying on Kirwan's blowup and Laza's GIT
construction. More precisely, we obtain the Betti numbers of Kirwan's
resolution of the moduli space. Furthermore, by applying decomposition theorem
we obtain the Betti numbers of the intersection cohomology of Baily-Borel
compactification of the moduli space.


Abstract:
  Theoretical and experimental studies of the interaction between spins and
temperature are vital for the development of spin caloritronics, as they
dictate the design of future devices. In this work, we propose a two-terminal
cold-atom simulator to study that interaction. The proposed quantum simulator
consists of strongly interacting atoms that occupy two temperature reservoirs
connected by a one-dimensional link. First, we argue that the dynamics in the
link can be described using an inhomogeneous Heisenberg spin chain whose
couplings are defined by the local temperature. Second, we show the existence
of a spin current in a system with a temperature difference by studying the
dynamics that follows the spin-flip of an atom in the link. A temperature
gradient accelerates the impurity in one direction more than in the other,
leading to an overall spin current similar to the spin Seebeck effect.


Abstract:
  Data representation techniques have made a substantial contribution to
advancing data processing and machine learning (ML). Improving predictive power
was the focus of previous representation techniques, which unfortunately
perform rather poorly on the interpretability in terms of extracting underlying
insights of the data. Recently, Kolmogorov model (KM) was studied, which is an
interpretable and predictable representation approach to learning the
underlying probabilistic structure of a set of random variables. The existing
KM learning algorithms using semi-definite relaxation with randomization
(SDRwR) or discrete monotonic optimization (DMO) have, however, limited utility
to big data applications because they do not scale well computationally. In
this paper, we propose a computationally scalable KM learning algorithm, based
on the regularized dual optimization combined with enhanced gradient descent
(GD) method. To make our method more scalable to large-dimensional problems, we
propose two acceleration schemes, namely, eigenvalue decomposition (EVD)
elimination strategy and proximal EVD algorithm. Furthermore, a thresholding
technique by exploiting the approximation error analysis and leveraging the
normalized Minkowski $\ell_1$-norm and its bounds, is provided for the
selection of the number of iterations of the proximal EVD algorithm. When
applied to big data applications, it is demonstrated that the proposed method
can achieve compatible training/prediction performance with significantly
reduced computational complexity; roughly two orders of magnitude improvement
in terms of the time overhead, compared to the existing KM learning algorithms.
Furthermore, it is shown that the accuracy of logical relation mining for
interpretability by using the proposed KM learning algorithm exceeds $80\%$.


Abstract:
  A common classification task situation is where one has a large amount of
data available for training, but only a small portion is annotated with class
labels. The goal of semi-supervised training, in this context, is to improve
classification accuracy by leverage information not only from labeled data but
also from a large amount of unlabeled data. Recent works have developed
significant improvements by exploring the consistency constrain between
differently augmented labeled and unlabeled data. Following this path, we
propose a novel unsupervised objective that focuses on the less studied
relationship between the high confidence unlabeled data that are similar to
each other. The new proposed Pair Loss minimizes the statistical distance
between high confidence pseudo labels with similarity above a certain
threshold. Combining the Pair Loss with the techniques developed by the
MixMatch family, our proposed SimPLE algorithm shows significant performance
gains over previous algorithms on CIFAR-100 and Mini-ImageNet, and is on par
with the state-of-the-art methods on CIFAR-10 and SVHN. Furthermore, SimPLE
also outperforms the state-of-the-art methods in the transfer learning setting,
where models are initialized by the weights pre-trained on ImageNet or
DomainNet-Real. The code is available at github.com/zijian-hu/SimPLE.


Abstract:
  We generically construct a model in which the ${\Pi^1_3}$-uniformization
property is true, thus lowering the best known consistency strength from the
existence of $M_1^{\#}$ to just $\mathsf{ZFC}$. The forcing construction can be
adapted to work over canonical inner models with Woodin cardinals, which
yields, for the first time, universes where the $\Pi^1_{2n}$-uniformization
property holds, thus producing models which contradict the natural
$\mathsf{PD}$-induced pattern. It can also be used to obtain models for the
$\Pi^1_1$-uniformization property in the generalized Baire space.


Abstract:
  Personalized recommender systems are playing an increasingly important role
as more content and services become available and users struggle to identify
what might interest them. Although matrix factorization and deep learning based
methods have proved effective in user preference modeling, they violate the
triangle inequality and fail to capture fine-grained preference information. To
tackle this, we develop a distance-based recommendation model with several
novel aspects: (i) each user and item are parameterized by Gaussian
distributions to capture the learning uncertainties; (ii) an adaptive margin
generation scheme is proposed to generate the margins regarding different
training triplets; (iii) explicit user-user/item-item similarity modeling is
incorporated in the objective function. The Wasserstein distance is employed to
determine preferences because it obeys the triangle inequality and can measure
the distance between probabilistic distributions. Via a comparison using five
real-world datasets with state-of-the-art methods, the proposed model
outperforms the best existing models by 4-22% in terms of recall@K on Top-K
recommendation.


Abstract:
  Density functionals with asymptotic corrections to the long-range potential
are recognized as entry-level methods for electronic structure calculations on
molecules that can sustain charge transfer. Even though charge transfer is also
extremely common in Materials-Science, the application of such methods has been
rare. We implement CAM-B3LYP within the VASP framework. Results obtained for 8
representative materials: aluminum, diamond, graphene, silicon, NaCl, MgO, 2D
h-BN and 3D h-BN, indicate that CAM-B3LYP predictions embody mean-absolute
deviations (MAD) compared to HSE06 that are reduced by a factor of 4 for
lattice parameters, 4 for quasiparticle band gaps, 3 for the lowest optical
excitation energies, and 6 for exciton binding energies; the only property for
which HSE06 performed better was the band-gap of silicon. CAM-B3LYP also
appears to provide similar or improved accuracy compared to ab initio G0W0 and
Bethe-Salpeter equation (BSE) approaches. The CAM-B3LYP implementation in VASP
was verified by comparison of optimized geometries and reaction energies for
isolated molecules taken from the ACCDB database, evaluated in large periodic
unit cells, to analogous results obtained using Gaussian-basis sets by
Gaussian-16. Using standard GW pseudopotentials and energy cutoffs for the
plane-wave calculations and the aug-cc-pV5Z basis set for the atomic-basis
ones, the MAD in energy for 1738 chemical reactions was 0.34 kcal mol-1 (0.015
eV), whilst for 1291 bond lengths this was 0.0036 {\AA}. An approximate scheme
for the speedy implementation of CAM-B3LYP into other electronic-structure
codes for materials-science applications was considered and shown to be less
accurate, but nevertheless useful. Analytical functional derivatives for
CAM-B3LYP were also obtained and implemented, ca. halving computational cost
compared to use of standard numerical derivatives.


Abstract:
  Unforgeable quantum money tokens were the first invention of quantum
information science, but remain technologically challenging as they require
quantum memories and/or long distance quantum communication. More recently,
virtual "S-money" tokens were introduced. These are generated by quantum
cryptography, do not require quantum memories or long distance quantum
communication, and yet in principle guarantee many of the security advantages
of quantum money. Here, we describe implementations of S-money schemes with
off-the-shelf quantum key distribution technology, and analyse security in the
presence of noise, losses, and experimental imperfection. Our schemes satisfy
near instant validation without cross-checking. We show that, given standard
assumptions in mistrustful quantum cryptographic implementations,
unforgeability and user privacy could be guaranteed with attainable refinements
of our off-the-shelf setup. We discuss the possibilities for unconditionally
secure (assumption-free) implementations.


Abstract:
  3D hydrodynamics models of deep stellar convection exhibit turbulent
entrainment at the convective-radiative boundary which follows the entrainment
law, varying with boundary penetrability. We implement the entrainment law in
the 1D Geneva stellar evolution code. We then calculate models between 1.5 and
60 M$_{\odot}$ at solar metallicity ($Z=0.014$) and compare them to previous
generations of models and observations on the main sequence. The boundary
penetrability, quantified by the bulk Richardson number, $Ri_{\mathrm{B}}$,
varies with mass and to a smaller extent with time. The variation of
$Ri_{\mathrm{B}}$ with mass is due to the mass dependence of typical convective
velocities in the core and hence the luminosity of the star. The chemical
gradient above the convective core dominates the variation of $Ri_{\mathrm{B}}$
with time. An entrainment law method can therefore explain the apparent mass
dependence of convective boundary mixing through $Ri_{\mathrm{B}}$. New models
including entrainment can better reproduce the mass dependence of the main
sequence width using entrainment law parameters $A \sim 2 \times 10^{-4}$ and
$n=1$. We compare these empirically constrained values to the results of 3D
hydrodynamics simulations and discuss implications.


Abstract:
  Logarithmic Sobolev inequalities play an important role in understanding the
mixing times of Markov chains on finite state spaces. It is typically not easy
to determine, or indeed approximate, the optimal constant for which such
inequalities hold. In this paper, we describe a semidefinite programming
relaxation for the logarithmic Sobolev constant of a finite Markov chain. This
relaxation gives certified lower bounds on the logarithmic Sobolev constant.
Numerical experiments show that the solution to this relaxation is often very
close to the true constant. Finally, we use this relaxation to obtain a
sum-of-squares proof that the logarithmic Sobolev constant is equal to half the
Poincar\'e constant for the specific case of a simple random walk on the odd
$n$-cycle, with $n\in\{5,7,\dots,21\}$. Previously this was known only for
$n=5$ and even $n$.


Abstract:
  When unexpected incidents occur, new innovative and flexible solutions are
required. If this event is something such radical and dramatic like the
COVID-19 pandemic, these solutions must aim to guarantee as much normality as
possible while protecting lives. After a moment of shock our university decided
that the students have to be able to pursue their studies for guaranteeing a
degree in the expected time since most of them faced immediate financial
problems due to the loss of their student jobs. This implied, for us as
teachers, that we had to reorganise not only the teaching methods from nearly
one day to the next, but we also had to come up with an adjusted way of
examinations which had to take place in person with pen and paper under strict
hygiene rules. On the other hand the correction should avoid personal contacts.
We developed a framework which allowed us to correct the digitalised exams
safely at home while providing the high standards given by the general data
protection regulation of our country. Moreover, the time spent in the offices
could be reduced to a minimum thanks to automatically generated exam sheets,
automatically re-digitalised and sorted worked-on exams.


Abstract:
  Greedy-GQ is a value-based reinforcement learning (RL) algorithm for optimal
control. Recently, the finite-time analysis of Greedy-GQ has been developed
under linear function approximation and Markovian sampling, and the algorithm
is shown to achieve an $\epsilon$-stationary point with a sample complexity in
the order of $\mathcal{O}(\epsilon^{-3})$. Such a high sample complexity is due
to the large variance induced by the Markovian samples. In this paper, we
propose a variance-reduced Greedy-GQ (VR-Greedy-GQ) algorithm for off-policy
optimal control. In particular, the algorithm applies the SVRG-based variance
reduction scheme to reduce the stochastic variance of the two time-scale
updates. We study the finite-time convergence of VR-Greedy-GQ under linear
function approximation and Markovian sampling and show that the algorithm
achieves a much smaller bias and variance error than the original Greedy-GQ. In
particular, we prove that VR-Greedy-GQ achieves an improved sample complexity
that is in the order of $\mathcal{O}(\epsilon^{-2})$. We further compare the
performance of VR-Greedy-GQ with that of Greedy-GQ in various RL experiments to
corroborate our theoretical findings.


Abstract:
  One of the lessons from the COVID-19 pandemic is the importance of social
distancing, even in challenging circumstances such as pre-hurricane evacuation.
To explore the implications of integrating social distancing with evacuation
operations, we describe this evacuation process as a Capacitated Vehicle
Routing Problem (CVRP) and solve it using a DNN (Deep Neural Network)-based
solution (Deep Reinforcement Learning) and a non-DNN solution (Sweep
Algorithm). A central question is whether Deep Reinforcement Learning provides
sufficient extra routing efficiency to accommodate increased social distancing
in a time-constrained evacuation operation. We found that, in comparison to the
Sweep Algorithm, Deep Reinforcement Learning can provide decision-makers with
more efficient routing. However, the evacuation time saved by Deep
Reinforcement Learning does not come close to compensating for the extra time
required for social distancing, and its advantage disappears as the emergency
vehicle capacity approaches the number of people per household.


Abstract:
  We present an analysis of the spatial clustering of a large sample of
high-resolution, interferometically identified, submillimetre galaxies (SMGs).
We measure the projected cross-correlation function of ~350 SMGs in the UKIDSS
Ultra Deep-Survey Field across a redshift range of $z=1.5-3$ utilising a method
that incorporates the uncertainties in the redshift measurements for both the
SMGs and cross-correlated galaxies through sampling their full probability
distribution functions. By measuring the absolute linear bias of the SMGs we
derive halo masses of $\log_{10}(M_{\rm halo}[{h^{-1}\,\rm
M_{\odot}}])\sim12.8$ with no evidence of evolution in the halo masses with
redshift, contrary to some previous work. From considering models of halo mass
growth rates we predict that the SMGs will reside in haloes of mass
$\log_{10}(M_{\rm halo}[{h^{-1}\,\rm M_{\odot}}])\sim13.2$ at $z=0$, consistent
with the expectation that the majority of $z=1.5-3$ SMGs will evolve into
present-day spheroidal galaxies. Finally, comparing to models of
stellar-to-halo mass ratios, we show that SMGs may correspond to systems that
are maximally efficient at converting their gas reservoirs into stars. We
compare them to a simple model for gas cooling in halos that suggests that the
unique properties of the SMG population, including their high levels of
star-formation and their redshift distribution, are a result of the SMGs being
the most massive galaxies that are still able to accrete cool gas from their
surrounding intragalactic medium.


Abstract:
  For a compact $(2n+1)$-dimensional smooth manifold, let $\mu_M : B
Diff_\partial (D^{2n+1}) \to B Diff (M)$ be the map that is defined by
extending diffeomorphisms on an embedded disc by the identity.
  By a classical result of Farrell and Hsiang, the rational homotopy groups and
the rational homology of $ B Diff_\partial (D^{2n+1})$ are known in the
concordance stable range.
  We prove two results on the behaviour of the map $\mu_M$ in the concordance
stable range. Firstly, it is \emph{injective} on rational homotopy groups, and
secondly, it is \emph{trivial} on rational homology, if $M$ contains
sufficiently many embedded copies of $S^n\times S^{n+1} \setminus
int(D^{2n+1})$.
  The homotopical statement is probably not new and follows from the theory of
smooth torsion invariants. The homological statement relies on work by
Botvinnik and Perlmutter on diffeomorphism of odd-dimensional manifolds.


Abstract:
  LIGO and Virgo have initiated the era of gravitational-wave (GW) astronomy;
but in order to fully explore GW frequency spectrum, we must turn our attention
to innovative techniques for GW detection. One such approach is to use binary
systems as dynamical GW detectors by studying the subtle perturbations to their
orbits caused by impinging GWs. We present a powerful new formalism for
calculating the orbital evolution of a generic binary coupled to a stochastic
background of GWs, deriving from first principles a secularly-averaged
Fokker-Planck equation which fully characterises the statistical evolution of
all six of the binary's orbital elements. We also develop practical tools for
numerically integrating this equation, and derive the necessary statistical
formalism to search for GWs in observational data from binary pulsars and
laser-ranging experiments.


Abstract:
  The extreme timing stability of radio millisecond pulsars (MSPs) combined
with their exotic environment and evolutionary history makes them excellent
laboratories to probe matter in extreme condition. Population studies indicate
that we have discovered less than five per cent of the MSPs of our Galaxy,
implying that a huge majority of radio MSPs are waiting to be discovered with
improved search techniques and more sensitive surveys. In this chapter, we
provide an overview of the present status of ongoing and upcoming surveys for
MSPs. Observed spectra, profile and polarisation properties of known radio MSPs
are also summarised. Finally, we describe how the timing studies of radio MSPs
enable a huge science return including attempts to detect gravitational waves
using an array of MSPs, gravity tests using individual interesting MSP systems,
as well as probing the intra-binary material using eclipses observed in MSPs in
compact binary systems.


Abstract:
  Worldwide, several cases go undiagnosed due to poor healthcare support in
remote areas. In this context, a centralized system is needed for effective
monitoring and analysis of the medical records. A web-based patient diagnostic
system is a central platform to store the medical history and predict the
possible disease based on the current symptoms experienced by a patient to
ensure faster and accurate diagnosis. Early disease prediction can help the
users determine the severity of the disease and take quick action. The proposed
web-based disease prediction system utilizes machine learning based
classification techniques on a data set acquired from the National Centre of
Disease Control (NCDC). $K$-nearest neighbor (K-NN), random forest and naive
bayes classification approaches are utilized and an ensemble voting algorithm
is also proposed where each classifier is assigned weights dynamically based on
the prediction confidence. The proposed system is also equipped with a
recommendation scheme to recommend the type of tests based on the existing
symptoms of the patient, so that necessary precautions can be taken. A
centralized database ensures that the medical data is preserved and there is
transparency in the system. The tampering into the system is prevented by
giving the no "updation" rights once the diagnosis is created.


Abstract:
  Replication time is among the most important components of a bacterial cell's
reproductive fitness. Paradoxically, larger cells replicate in less time than
smaller cells despite the fact that building a larger cell requires increased
quantities of raw materials and energy. This feat is primarily accomplished by
the massive over expression of ribosomes, which permits translation of mRNA
into protein, the limiting step in reproduction, to occur at a scale that would
be impossible were it not for the use of parallel processing. In computer
science, spatial parallelism is the distribution of work across the nodes of a
distributed-memory multicomputer system. Despite the fact that a non-negligible
fraction of artificial life research is grounded in formulations based on
spatially parallel substrates, there have been no examples of artificial
organisms that use spatial parallelism to replicate in less time than smaller
organisms. This paper describes artificial cells defined using a
combinator-based artificial chemistry that replicate in less time than smaller
cells. This is achieved by employing extra copies of programs implementing the
limiting steps in the process used by the cells to synthesize their component
parts. Significant speedup is demonstrated, despite the increased complexity of
control and export processes necessitated by the use of a parallel replication
strategy.


Abstract:
  By considering the parity of the degrees and levels of nodes in increasing
trees, a new combinatorial interpretation for the coefficients of the Taylor
expansions of the Jacobi elliptic functions is found. As one application of
this new interpretation, a conjecture of Ma-Mansour-Wang-Yeh is solved.
Unifying the concepts of increasing trees and plane trees, Lin-Ma-Ma-Zhou
introduced weakly increasing trees on a multiset. A symmetry joint distribution
of "even-degree nodes on odd levels" and "odd-degree nodes" on weakly
increasing trees is found, extending the Schett polynomials, a generalization
of the Jacobi elliptic functions introduced by Schett, to multisets. A
combinatorial proof and an algebraic proof of this symmetry are provided, as
well as several relevant interesting consequences. Moreover, via introducing a
group action on trees, we prove the partial $\gamma$-positivity of the multiset
Schett polynomials, a result implies both the symmetry and the unimodality of
these polynomials.


Abstract:
  This article explores the required amount of time series points from a
high-speed traffic network to accurately estimate the Hurst exponent. The
methodology consists in designing an experiment using estimators that are
applied to time series, followed by addressing the minimum amount of points
required to obtain accurate estimates of the Hurst exponent in real-time. The
methodology addresses the exhaustive analysis of the Hurst exponent considering
bias behavior, standard deviation, mean square error, and convergence using
fractional gaussian noise signals with stationary increases. Our results show
that the Whittle estimator successfully estimates the Hurst exponent in series
with few points. Based on the results obtained, a minimum length for the time
series is empirically proposed. Finally, to validate the results, the
methodology is applied to real traffic captures in a high-speed network based
on the IEEE 802.3ab standard.


Abstract:
  Gravitational wave-forms from coalescences of binary black hole systems and
binary neutron star systems with low tidal effects can hardly be distinguished
if the two systems have similar masses. In the absence of discriminating power
based on the gravitational wave-forms, the classification of sources into
binary neutron stars, binary black holes and mixed systems containing a black
hole and a neutron star can only be unambiguous when assuming the standard
model of stellar evolution and using the fact that there exists a mass gap
between neutron stars and black holes. This approach is however limited by its
own assumptions: for instance the 2.6 solar mass object detected in the
GW190814 event remains unclassified, and models of new physics can introduce
new compact objects, like primordial black holes, which may have masses in the
same range as neutron stars. Then, without an electromagnetic counterpart
(kilonova), classifying mergers of compact objects without mass gap criteria
remains a difficult task, unless the source is close enough. In what follows we
investigate a procedure to discriminate a model between binary neutron star
merger and primordial binary black hole merger by using a Bayes factor in
simulated wave-forms that we superimpose to realistic detector noise.


Abstract:
  Sensor technologies are becoming increasingly prevalent in the biomedical
field, with applications ranging from telemonitoring of people at risk, to
using sensor derived information as objective endpoints in clinical trials. To
fully utilize sensor information, signals from distinct sensors often have to
be temporally aligned. However, due to imperfect oscillators and significant
noise, commonly encountered with biomedical signals, temporal alignment of raw
signals is an all but trivial problem, with, to-date, no generally applicable
solution. In this work, we present Deep Canonical Correlation Alignment (DCCA),
a novel, generally applicable solution for the temporal alignment of raw
(biomedical) sensor signals. DCCA allows practitioners to directly align raw
signals, from distinct sensors, without requiring deep domain knowledge. On a
selection of artificial and real datasets, we demonstrate the performance and
utility of DCCA under a variety of conditions. We compare the DCCA algorithm to
other warping based methods, DCCA outperforms dynamic time warping and cross
correlation based methods by an order of magnitude in terms of alignment error.
DCCA performs especially well on almost periodic biomedical signals such as
heart-beats and breathing patterns. In comparison to existing approaches, that
are not tailored towards raw sensor data, DCCA is not only fast enough to work
on signals with billions of data points but also provides automatic filtering
and transformation functionalities, allowing it to deal with very noisy and
even morphologically distinct signals.


Abstract:
  We probe photo-induced loss for chemically stable bosonic $^{23}$Na$^{87}$Rb
and $^{23}$Na$^{39}$K molecules in chopped optical dipole traps where the
molecules spend a significant time in the dark. We expect the effective
two-body decay to be largely suppressed in chopped traps due to the small
expected complex lifetimes of about $13\mu$s and $6\mu$s for $^{23}$Na$^{87}$Rb
and $^{23}$Na$^{39}$K respectively. However, instead we do observe
near-universal loss even at the lowest chopping frequencies we can probe. Our
data thus either suggest a so far unknown loss mechanism or a drastic
underestimation of the complex lifetime by at least one to two orders of
magnitude.


Abstract:
  A regularized version of Mixture Models is proposed to learn a principal
graph from a distribution of $D$-dimensional data points. In the particular
case of manifold learning for ridge detection, we assume that the underlying
manifold can be modeled as a graph structure acting like a topological prior
for the Gaussian clusters turning the problem into a maximum a posteriori
estimation. Parameters of the model are iteratively estimated through an
Expectation-Maximization procedure making the learning of the structure
computationally efficient with guaranteed convergence for any graph prior in a
polynomial time. We also embed in the formalism a natural way to make the
algorithm robust to outliers of the pattern and heteroscedasticity of the
manifold sampling coherently with the graph structure. The method uses a graph
prior given by the minimum spanning tree that we extend using random
sub-samplings of the dataset to take into account cycles that can be observed
in the spatial distribution.


Abstract:
  We investigate the late-time evolution of the Universe within a cosmological
model in which dark matter and dark energy are identified with two interacting
scalar fields. Using methods of qualitative analysis of dynamical systems, we
identify all cosmological solutions of this model. We show that viable
solutions -- in the sense that they correspond to a cosmic evolution in which a
long enough matter-dominated era is followed by a current era of accelerated
expansion -- can be found in several regions of the parameter space. These
solutions can be divided into two categories, namely, solutions that evolve to
a state of everlasting accelerated expansion, in which the energy density of
the dark-matter field rapidly approaches zero and the evolution becomes
entirely dominated by the dark-energy field, and solutions in which the stage
of accelerated expansion is temporary and the ratio between the energy
densities of dark energy and dark matter tends, asymptotically, to a constant
nonzero value.


Abstract:
  This paper focuses on monocular 3D object detection, one of the essential
modules in autonomous driving systems. A key challenge is that the depth
recovery problem is ill-posed in monocular data. In this work, we first conduct
a thorough analysis to reveal how existing methods fail to robustly estimate
depth when different geometry shifts occur. In particular, through a series of
image-based and instance-based manipulations for current detectors, we
illustrate existing detectors are vulnerable in capturing the consistent
relationships between depth and both object apparent sizes and positions. To
alleviate this issue and improve the robustness of detectors, we convert the
aforementioned manipulations into four corresponding 3D-aware data augmentation
techniques. At the image-level, we randomly manipulate the camera system,
including its focal length, receptive field and location, to generate new
training images with geometric shifts. At the instance level, we crop the
foreground objects and randomly paste them to other scenes to generate new
training instances. All the proposed augmentation techniques share the virtue
that geometry relationships in objects are preserved while their geometry is
manipulated. In light of the proposed data augmentation methods, not only the
instability of depth recovery is effectively alleviated, but also the final 3D
detection performance is significantly improved. This leads to superior
improvements on the KITTI and nuScenes monocular 3D detection benchmarks with
state-of-the-art results.


Abstract:
  Extended Hubbard models have proven to bear novel quantum states, but their
experimental realization remains challenging. In this work we propose to use
bosonic quantum gases dressed with molecular bound states in Rydberg
interaction potentials for the observation of these quantum states. We study
the molecular Rabi coupling with respect to principal quantum number and
trapping frequency of the ground state atoms for various molecular potentials
of Rubidium and Potassium, and the hereby resulting dressed interaction
strength. Additionally, we propose a two-color excitation scheme which
significantly increases the dressed interaction and cancels AC Stark shifts
limiting the atomic motion in the itinerant regime. We study the various
equilibrium phases of the corresponding extended Bose-Hubbard model by means of
the Cluster Gutzwiller approach and perform time evolution simulations via the
Lindblad master equation. We find a supersolid phase by slowly ramping the
molecular Rabi coupling of an initially prepared superfluid and discuss the
role of dissipation.


Abstract:
  As the signal-to-noise of Sunyaev-Zeldovich (SZ) cross-correlation
measurements of galaxies improves our ability to infer properties about the
circumgalactic medium (CGM), we will transition from being limited by
statistical uncertainties to systematic uncertainties. Using thermodynamic
profiles of the CGM created from the IllustrisTNG (The Next Generation)
simulations we investigate the importance of specific choices in modeling the
galaxy sample. These choices include different sample selections in the
simulation (stellar versus halo mass, color selections) and different fitting
models (matching by the shape of the mass distribution, inclusion of a two-halo
term). We forward model a mock galaxy sample into projected SZ observable
profiles and fit these profiles to a generalized Navarro-Frenk-White profile
using forecasted errors of the upcoming Simons Observatory experiment. We test
the number of free parameters in the fits and show that this is another
modeling choice that yields different results. Finally, we show how different
fitting models can reproduce parameters of a fiducial profile, and show that
the addition of a two-halo term and matching by the mass distribution of the
sample are extremely important modeling choices to consider.


Abstract:
  The Large High Altitude Air Shower Observatory~(LHAASO) is one of the most
sensitive gamma-ray detector arrays currently operating at TeV and PeV
energies. Recently the LHAASO experiment detected ultra-high-energy~(UHE;
$E_{\gamma}\gtrsim 100~\mathrm{TeV}$) photon emissions up to $1.4~\mathrm{PeV}$
from twelve astrophysical gamma-ray sources. We point out that the detection of
cosmic photons at such energies can constrain the photon self-decay motivated
by superluminal Lorentz symmetry violation~(LV) to a higher level, thus can put
strong constraints to certain LV frameworks. Meanwhile, we suggest that the
current observation of the PeV-scale photon with LHAASO may provide hints to
permit a subluminal type of Lorentz violation in the proximity of the Planckian
regime, and may be compatible with the light speed variation at the scale of
$3.6\times 10^{17}~\mathrm{GeV}$ recently suggested from gamma-ray burst~(GRB)
time delays. We further propose detecting PeV photons coming from extragalactic
sources with future experiments, based on LV-induced threshold anomalies of
$e^{+}e^{-}$ pair-production, as a crucial test of subluminal Lorentz
violation. We comment that these observations are consistent with a
D-brane/string-inspired quantum-gravity framework, the space-time foam model.


Abstract:
  The $\Lambda$CDM cosmology passes demanding tests that establish it as a good
approximation to reality, but it could be improved. I present a list of
possibly interesting and less well explored things that might yield hints to a
better theory.


Abstract:
  Development of control algorithms for enhancing performance in
safety-critical systems such as the Autonomous Emergency Braking system (AEB)
is an important issue in the emerging field of automated electric vehicles. In
this study, we design a safety distance-based hierarchical AEB control system
constituted of a high-level Rule-Based Supervisory control module, an
intermediate-level switching algorithm and a low-level control module. The Rule
Based supervisor determines the required deceleration command that is fed to
the low-level control module via the switching algorithm. In the low-level, two
wheel slip control algorithms were developed, a Robust Sliding Mode controller
and a Gain-Scheduled Linear Quadratic Regulator. For the needs of this control
design, a non-linear dynamic vehicle model was implemented whereas a constant
tire-road friction coefficient was considered. The proposed control system was
validated in Simulink, assuming a straight-line braking maneuver on a flat dry
road. The simulation results demonstrated satisfactory emergency braking
performance with full collision avoidance in both proposed control system
combinations.


Abstract:
  The extensive timespan of modern radial velocity surveys have made the
discovery of long-period substellar companions more common in recent years,
however measuring the true masses of these objects remains challenging.
Astrometry from the Gaia mission is expected to provide mass measurements for
many of these long-period companions, but this data is not yet available.
However, combining proper motion data from Gaia DR2 and the earlier Hipparcos
mission makes it possible to measure true masses of substellar companions in
favourable cases. In this work, we combine radial velocities with
Hipparcos-Gaia astrometry to measure the true masses of two recently discovered
long-period substellar companion candidates, HD 92987 B and HD 221420 b. In
both cases, we find that the true masses are significantly higher than implied
by radial velocities alone. A $2087 \pm 19$ m s$^{-1}$ astrometric signal
reveals that HD 92987 B is not close to its $17$ $M_J$ minimum mass but is
instead a $0.2562 \pm 0.0045$ $M_\odot$ star viewed at a near-polar orbital
inclination, whereas the $22.9 \pm 2.2$ $M_J$ HD 221420 b can be plausibly
interpreted as a high-mass "super-planet" or a low-mass brown dwarf. With
semi-major axes of $\sim$10 AU both companions are interesting targets for
direct imaging, and HD 221420 b in particular would be a benchmark metal-rich
substellar object if it proves possible to directly detect. Our results
demonstrate the power of Hipparcos-Gaia astrometry for studying long-period
planet and brown dwarf candidates discovered from radial velocity surveys.


Abstract:
  The increasing use of cloud computing for latency-sensitive applications has
sparked renewed interest in providing tight bounds on network tail latency.
Achieving this in practice at reasonable network utilization has proved
elusive, due to a combination of highly bursty application demand, faster link
speeds, and heavy-tailed message sizes. While priority scheduling can be used
to reduce tail latency for some traffic, this comes at a cost of much worse
delay behavior for all other traffic on the network. Most operators choose to
run their networks at very low average utilization, despite the added cost, and
yet still suffer poor tail behavior.
  This paper takes a different approach. We build a system, swp, to help
operators (and network designers) to understand and control tail latency
without relying on priority scheduling. As network workload changes, swp is
designed to give real-time advice on the network switch configurations needed
to maintain tail latency objectives for each traffic class. The core of swp is
an efficient model for simulating the combined effect of traffic
characteristics, end-to-end congestion control, and switch scheduling on
service-level objectives (SLOs), along with an optimizer that adjusts
switch-level scheduling weights assigned to each class. Using simulation across
a diverse set of workloads with different SLOs, we show that to meet the same
SLOs as swp provides, FIFO would require 65% greater link capacity, and 79%
more for scenarios with tight SLOs on bursty traffic classes.


Abstract:
  We discuss integrable discretizations of 3-dimensional cyclic systems, that
is, orthogonal coordinate systems with one family of circular coordinate lines.
In particular, the underlying circle congruences are investigated in detail,
and characterized by the existence of a certain flat connection. Within the
developed framework, discrete cyclic systems with a family of discrete flat
fronts in hyperbolic space and discrete cyclic systems, where all coordinate
surfaces are discrete Dupin cyclides, are investigated.


Abstract:
  These are notes on the hyperbolic geometry of surfaces, Teichm{\"u}ller
spaces and Thurston's metric on these spaces. They are associated with lectures
I gave at the Morningside Center of Mathematics of the Chinese Academy of
Sciences in March 2019 and at the Chebyshev Laboratory of the Saint Petersburg
State University in May 2019. In particular, I survey several results on the
behavior of stretch lines, a distinguished class of geodesics for Thurston's
metric and I point out several analogies between this metric and
Teichm{\"u}ller's metric. Several open questions are addressed. The final
version of these notes will appear in the book "Moduli Spaces and Locally
Symmetric Spaces", edited by L. Ji and S.-T. Yau, International Press, 2021.


Abstract:
  Polygenic risk score (PRS) analysis is a powerful method been used to
estimate an individual's genetic risk towards targeted traits. PRS analysis
could be used to obtain evidence of a genetic effect beyond Genome-Wide
Association Studies (GWAS) results i.e. when there are no significant markers.
PRS analysis has been widely applied to investigate the genetic basis of
several traits including rare diseases. However, the accuracy of PRS analysis
depends on the genomic data of the underlying population. For instance, several
studies showed that obtaining higher prediction power of PRS analysis is
challenging for non-Europeans. In this manuscript, we reviewed the conventional
PRS methods and their application to sub-saharan Africa communities. We
concluded that the limiting factor of applying PRS analysis to sub-saharan
populations is the lack of sufficient GWAS data. Also, we recommended
developing African-specific PRS tools


Abstract:
  Well-defined jokes can be divided neatly into a setup and a punchline. While
most works on humor today talk about a joke as a whole, the idea of generating
punchlines to a setup has applications in conversational humor, where funny
remarks usually occur with a non-funny context. Thus, this paper is based
around two core concepts: Classification and the Generation of a punchline from
a particular setup based on the Incongruity Theory. We first implement a
feature-based machine learning model to classify humor. For humor generation,
we use a neural model, and then merge the classical rule-based approaches with
the neural approach to create a hybrid model. The idea behind being: combining
insights gained from other tasks with the setup-punchline model and thus
applying it to existing text generation approaches. We then use and compare our
model with human written jokes with the help of human evaluators in a
double-blind study.


Abstract:
  In the present paper we discuss about a set of geometric and physical
properties of hyper-generalised quasi-Einstein spacetime. At the beginning we
discuss about pseudosymmetry over a hyper-generalised quasi-Einstein spacetime.
Here we discuss about W2-Ricci pseudosymmetry, Z-Ricci pseudosymmetry, Ricci
pseudosymmetry and projective pseudosymmetry over a hyper-generalised
quasi-Einstein spacetime. Later on we take over Ricci symmetric
hyper-generalised quasi-Einstein spacetime and derive a set of important
geometric and physical theorems over it. Moving further we consider some
physical applications of the hyper-generalised quasi-Einstein spacetime. Lastly
we prove the existence of a hyper-generalised quasi-Einstein spacetime by
constructing a non-trivial example.


Abstract:
  Let $G$ be a finite group and let $\{A_1,\ldots,A_k\}$ be a collection of
subsets of $G$ such that $G=A_1\ldots A_k$ is the product of all the $A_i$ and
${\rm card}(G)={\rm card}(A_1)\ldots{\rm card}(A_k)$. We shall write
$G=A_1\cdot\ldots\cdot A_k$, and call this a $k$-fold factorization of the form
$({\rm card}(A_1),\ldots,{\rm card}(A_k))$. We prove that for any integer
$k\geq3$ there exist a finite group $G$ of order $n$ and a factorization of
$n=a_1\ldots a_k$ into $k$ factors, with $a_i>1$, such that $G$ has no $k$-fold
factorization of the form $(a_1,\ldots,a_k)$.


Abstract:
  We investigate the conduction-band structure and electron mobility in
rocksalt ScN based on density functional theory. The first-principles band
structure allows us to obtain band velocities and effective masses as a
function of energy. Electron-phonon scattering is assessed by explicitly
computing the $q$-dependent electron-phonon matrix elements, with the inclusion
of the long-range electrostatic interaction. The influence of free-carrier
screening on the electron transport is assessed using the random phase
approximation. We find a notable enhancement of electron mobility when the
carrier concentration exceeds 10$^{20}$ cm$^{-3}$. We calculate the
room-temperature electron mobility in ScN to be 587 cm$^2$/Vs at low carrier
concentrations. When the carrier concentration is increased, the electron
mobility starts to decrease significantly around $n=10^{19}$ cm$^{-3}$, and
drops to 240 cm$^2$/Vs at $n=10^{21}$ cm$^{-3}$. We also explore the influence
of strain in (111)- and (100)-oriented ScN films. For (111) films, we find that
a 1.0\% compressive epitaxial strain increases the in-plane mobility by 72
cm$^2$/Vs and the out-of-plane mobility by 50 cm$^2$/Vs. For (100) films, a
1.0\% compressive epitaxial strain increases the out-of-plane mobility by as
much as 172 cm$^2$/Vs, but has a weak impact on the in-plane mobility. Our
study sheds light on electron transport in ScN at different electron
concentrations and shows how strain engineering could increase the electron
mobility.


Abstract:
  Generalized Polynomial Chaos (gPC) theory has been widely used for
representing parametric uncertainty in a system, thanks to its ability to
propagate uncertainty evolution. In an optimal control context, gPC can be
combined with several optimization techniques to achieve a control policy that
handles effectively this type of uncertainty. Such a suitable method is
Differential Dynamic Programming (DDP), leading to an algorithm that inherits
the scalability to high-dimensional systems and fast convergence nature of the
latter. In this paper, we expand this combination aiming to acquire
probabilistic guarantees on the satisfaction of nonlinear constraints. In
particular, we exploit the ability of gPC to express higher order moments of
the uncertainty distribution - without any Gaussianity assumption - and we
incorporate chance constraints that lead to expressions involving the state
covariance. Furthermore, we demonstrate that by implementing our algorithm in a
receding horizon fashion, we are able to compute control policies that
effectively reduce the accumulation of uncertainty on the trajectory. The
applicability of our method is verified through simulation results on a
differential wheeled robot and a quadrotor that perform obstacle avoidance
tasks.


Abstract:
  The acoustic radiation force produced by ultrasonic waves is the "workhorse"
of particle manipulation in acoustofluidics. Nonspherical particles are also
subjected to a mean torque known as the acoustic radiation torque. Together
they constitute the mean-acoustic fields exerted on the particle. Analytical
methods alone cannot calculate these fields on arbitrarily shaped particles in
actual fluids and no longer fit for purpose. Here, a semi-analytical approach
is introduced for handling subwavelength axisymmetric particles immersed in an
isotropic Newtonian fluid. The obtained mean-acoustic fields depend on the
scattering coefficients that reflect the monopole and dipole modes. These
coefficients are determined by numerically solving the scattering problem. Our
method is benchmarked by comparison with the exact result for a subwavelength
rigid sphere in water. Besides, a more realistic case of a red blood cell
immersed in blood plasma under a standing ultrasonic wave is investigated with
our methodology.


Abstract:
  Facility Location (FL) problems as one of the most important problems in
operations research aim to determine the location of a set of facilities in a
way that the total costs, including costs of opening facilities and
transportation costs, are minimized. This study addresses an FL problem in
which the capacity of each facility is limited. Because this problem is in the
category of np-hard problems, we use the Benders Decomposition (BD) approach to
efficiently solve the FL problem. In this paper, we implement the classic BD
algorithm and some accelerating BD methods such as Pareto-optimality cut and
L-shaped decomposition methods. Furthermore, we propose and implement the
hybrid Pareto-L-shaped (PL) method, and evaluate the performance of the
implemented algorithms. The results show that the L-shaped decomposition
outperforms the other algorithms in terms of time and the number of iteration,
while the classic BD converges slowly especially on large scales.


Abstract:
  We use the Gemini NIFS instrument to map the H$_2 2.1218\mu$m and Br$\gamma$
flux distributions in the inner 0.04-2 kpc of a sample of 36 nearby active
galaxies ($0.001\lesssim z\lesssim0.056$) at spatial resolutions from 4 to 250
pc. We find extended emission in 34 galaxies. In $\sim$55% of them, the
emission in both lines is most extended along the galaxy major axis, while in
the other 45% the extent follows a distinct orientation. The emission of H$_2$
is less concentrated than that of Br$\gamma$, presenting a radius that contains
half of the flux 60% greater, on average. The H$_2$ emission is driven by
thermal processes - X-ray heating and shocks - at most locations for all
galaxies, where $0.4<H_2/Br\gamma<6$. For regions where H$_2$/Br$\gamma>6$
(seen in 40% of the galaxies), shocks are the main H$_2$ excitation mechanism,
while in regions with H$_2$/Br$\gamma<0.4$ (25% of the sample) the H$_2$
emission is produced by fluorescence. The only difference we found between type
1 and type 2 AGN was in the nuclear emission-line equivalent widths, that are
smaller in type 1 than in type 2 due to a larger contribution to the continuum
from the hot dusty torus in the former. The gas masses in the inner 125 pc
radius are in the range $10^1-10^4$ M$_\odot$ for the hot H$_2$ and $10^3-10^6$
M$_\odot$ for the ionised gas and would be enough to power the AGN in our
sample for $10^5-10^8$ yr at their current accretion rates.


Abstract:
  The cues needed to detect spoofing attacks against automatic speaker
verification are often located in specific spectral sub-bands or temporal
segments. Previous works show the potential to learn these using either
spectral or temporal self-attention mechanisms but not the relationships
between neighbouring sub-bands or segments. This paper reports our use of graph
attention networks (GATs) to model these relationships and to improve spoofing
detection performance. GATs leverage a self-attention mechanism over graph
structured data to model the data manifold and the relationships between nodes.
Our graph is constructed from representations produced by a ResNet. Nodes in
the graph represent information either in specific sub-bands or temporal
segments. Experiments performed on the ASVspoof 2019 logical access database
show that our GAT-based model with temporal attention outperforms all of our
baseline single systems. Furthermore, GAT-based systems are complementary to a
set of existing systems. The fusion of GAT-based models with more conventional
countermeasures delivers a 47% relative improvement in performance compared to
the best performing single GAT system.


Abstract:
  Place Recognition is a crucial capability for mobile robot localization and
navigation. Image-based or Visual Place Recognition (VPR) is a challenging
problem as scene appearance and camera viewpoint can change significantly when
places are revisited. Recent VPR methods based on ``sequential
representations'' have shown promising results as compared to traditional
sequence score aggregation or single image based techniques. In parallel to
these endeavors, 3D point clouds based place recognition is also being explored
following the advances in deep learning based point cloud processing. However,
a key question remains: is an explicit 3D structure based place representation
always superior to an implicit ``spatial'' representation based on sequence of
RGB images which can inherently learn scene structure. In this extended
abstract, we attempt to compare these two types of methods by considering a
similar ``metric span'' to represent places. We compare a 3D point cloud based
method (PointNetVLAD) with image sequence based methods (SeqNet and others) and
showcase that image sequence based techniques approach, and can even surpass,
the performance achieved by point cloud based methods for a given metric span.
These performance variations can be attributed to differences in data richness
of input sensors as well as data accumulation strategies for a mobile robot.
While a perfect apple-to-apple comparison may not be feasible for these two
different modalities, the presented comparison takes a step in the direction of
answering deeper questions regarding spatial representations, relevant to
several applications like Autonomous Driving and Augmented/Virtual Reality.
Source code available publicly https://github.com/oravus/seqNet.


Abstract:
  Protection of gauge invariance in experimental realizations of lattice gauge
theories based on energy-penalty schemes has recently stimulated impressive
efforts both theoretically and in setups of quantum synthetic matter. A major
challenge is the reliability of such schemes in non-Abelian gauge theories
where local conservation laws do not commute. Here, we show through exact
diagonalization that non-Abelian gauge invariance can be reliably controlled
using gauge-protection terms that energetically stabilize the target gauge
sector in Hilbert space, suppressing gauge violations due to unitary
gauge-breaking errors. We present analytic arguments that predict a
volume-independent protection strength $V$, which when sufficiently large leads
to the emergence of an \textit{adjusted} gauge theory with the same local gauge
symmetry up to least a timescale $\propto\sqrt{V/V_0^3}$. Thereafter, a
\textit{renormalized} gauge theory dominates up to a timescale
$\propto\exp(V/V_0)/V_0$ with $V_0$ a volume-independent energy factor, similar
to the case of faulty Abelian gauge theories. Moreover, we show for certain
experimentally relevant errors that single-body protection terms robustly
suppress gauge violations up to all accessible evolution times in exact
diagonalization, and demonstrate that the adjusted gauge theory emerges in this
case as well. These single-body protection terms can be readily implemented
with fewer engineering requirements than the ideal gauge theory itself in
current ultracold-atom setups and NISQ devices.


Abstract:
  Susceptibility of neural networks to adversarial attack prompts serious
safety concerns for lane detection efforts, a domain where such models have
been widely applied. Recent work on adversarial road patches have successfully
induced perception of lane lines with arbitrary form, presenting an avenue for
rogue control of vehicle behavior. In this paper, we propose a modular lane
verification system that can catch such threats before the autonomous driving
system is misled while remaining agnostic to the particular lane detection
model. Our experiments show that implementing the system with a simple
convolutional neural network (CNN) can defend against a wide gamut of attacks
on lane detection models. With a 10% impact to inference time, we can detect
96% of bounded non-adaptive attacks, 90% of bounded adaptive attacks, and 98%
of patch attacks while preserving accurate identification at least 95% of true
lanes, indicating that our proposed verification system is effective at
mitigating lane detection security risks with minimal overhead.


Abstract:
  We apply the finite-temperature variational cluster approach to a strongly
correlated and spin-orbit coupled model for four electrons (i.e. two holes) in
the $t_{2g}$ subshell. We focus on parameters suitable for antiferromagnetic
Mott insulators, in particular Ca$_2$RuO$_4$, and identify a crossover from the
low-temperature regime, where spin-orbit coupling is essential, to the
high-temperature regime where it leaves few signatures. The crossover is seen
in one-particle spectra, where $xz$ and $yz$ spectra are almost one dimensional
(as expected for weak spin-orbit coupling) at high temperature. At lower
temperature, where spin-orbit coupling mixes all three orbitals, they become
more two dimensional. However, stronger effects are seen in two-particle
observables like the weight in states with definite onsite angular momentum. We
thus identify the enigmatic intermediate-temperature 'orbital-order phase
transition', which has been reported in various X-ray diffraction and
absorption experiments at $T\approx 260\;K$, as the signature of the onset of
spin-orbital correlations.


Abstract:
  We present a study of exclusion process on a peculiar topology of network
with two intersected lanes, competing for the particles in a reservoir with
finite capacity. To provide a theoretical ground for our findings, we exploit
mean-field approximation along with domain-wall theory. The stationary
properties of the system including phase transitions, density profiles and
position of the domain-wall are derived analytically. Under the similar
dynamical rules, the particles of both the lanes interact only at the
intersected site. The symmetry of system is maintained till number of particles
do not exceed total number of sites. However, beyond this the symmetry breaking
phenomenon occurs resulting in the appearance of asymmetric phases and
continues to persist even for infnite number of particles. The complexity of
phase diagram shows a non-monotonic behaviour with increasing number of
particles in the system. A bulk induced shock appears in a symmetric phase,
whereas, a boundary induced shock is observed in symmetric as well as
asymmetric phase. Monitoring the location of shock with increasing entry of
particles, we explain the possible phase transitions. The theoretical results
are supported by extensive Monte Carlo simulations and explained using simple
physical arguments.


Abstract:
  On December 22, 2020 at about 23h 23m 33s UTC a very bright fireball was seen
in the sky of south-eastern China. The fireball lasted for several seconds and
ended with an atmospheric explosion that was detected by US surveillance
satellites. According to CNEOS's data, the fireball moved with a mean speed of
13.6 km/s and exploded at an altitude of about 35.5 km (Lat. $31.9^{\circ}$ N;
Long. $96.2^{\circ} $ E). In this paper we estimate the atmospheric trajectory,
the heliocentric orbit and the strewn fields for different mass/section ratio
of the fragments. The trajectory was about from north to south and with low
inclination ($5^{\circ} \pm 2^{\circ}$) with respect to the local surface. The
explosion height appear consistent with a fragmented rocky body and the
heliocentric orbit supports an asteroidal origin. The probable strewn field
extend between two points with coordinates ($+31.3^\circ$ N; $96.3^\circ$ E)
and ($+30.3^\circ$ N; $96.5^\circ$ E), for a total area of about 4000
$\textrm{km}^2$. This large extension is a direct consequence of the low
inclination of the trajectory. Given the unknown uncertainty of the CNEOS's
data, these results should be taken with caution.


Abstract:
  Crimean-Congo haemorrhagic fever (CCHF) is a tick-borne zoonotic disease
caused by the Crimean-Congo hemorrhagic fever virus (CCHFV). Ticks belonging to
the genus \textit{Hyalomma} are the main vectors and reservoir for the virus.
It is maintained in nature in an endemic vertebrate-tick-vertebrate cycle.
CCHFV is prevalent in wide geographical areas including Asia, Africa,
South-Eastern Europe and the Middle East. Over the last decade, several
outbreaks of CCHFV have been observed in Europe, mainly in Mediterranean
countries. Due to the high case/fatality ratio of CCHFV in human sometimes, it
is of great importance for public health. Climate change and the invasion of
CCHFV vectors in Central Europe suggest that the establishment of the
transmission in Central Europe may be possible in future. We developed a
compartment-based nonlinear Ordinary Differential Equation (ODE) system to
model the disease transmission cycle including blood sucking ticks, livestock
and human. Sensitivity analysis of the basic reproduction number $R_0$ shows
that decreasing in the tick survival time is an efficient method to eradicate
the disease. The model supports us in understanding the influence of different
model parameters on the spread of CCHFV. Tick to tick transmission through
co-feeding and the CCHFV circulation through trasstadial and transovarial
stages are important factors to sustain the disease cycle. The proposed model
dynamics are calibrated through an empirical multi-country analysis and
multidimensional scaling reveals the disease-parameter sets of different
countries burdened with CCHF are different. This necessary information may help
us to select most efficient control strategies.


Abstract:
  For the field $\mathbb{K} = \mathbb{R}$ or $\mathbb{C}$, and an integrable
distribution $F \subseteq T_{\mathbb{K}}M = T_M \otimes_{\mathbb{R}}
\mathbb{K}$ on a smooth manifold $M$, we study the Hochschild cohomology of the
dg manifold $(F[1],d_F)$ and establish a canonical isomorphism with the
Hochschild cohomology of the transversal polydifferential operators of $F$. In
particular, for the dg manifold $(T_X^{0,1}[1],\bar{\partial})$ associated with
a complex manifold $X$, we prove that it is canonically isomorphic to the
Hochschild cohomology $HH^\bullet (X)$ of the complex manifold. As an
application, we show that the Kontsevich-Duflo type theorem for the dg manifold
$(T_X^{0,1}[1],\bar{\partial})$ implies the Kontsevich-Duflo theorem for
complex manifolds.


Abstract:
  The 6-element Brandt monoid $B_2^1$ admits a unique addition under which it
becomes an additively idempotent semiring. We show that this addition is a term
operation of $B_2^1$ as an inverse semigroup. As a consequence, we exhibit an
easy proof that the semiring identities of $B_2^1$ are not finitely based.


Abstract:
  We study the behaviour of circular flexible loops sedimenting in a viscous
fluid by numerical simulations and linear stability analysis. The numerical
model involves a local slender-body theory approximation for the flow coupled
to the Euler-Bernoulli elastic forces for an inextensible fibre. Starting from
an inclined circle, we simulate the dynamics using truncated Fourier modes to
observe three distinct regimes of motion: absolute stability, two-, and
three-dimensional dynamics, depending on therelative importance of elastic and
gravitational forces. We identify the governing parameter and develop a simple
semi-analytic stability criterion, which we verify numerically. In all cases,
sedimenting loops converge to stable, planar shape equilibria with one free
parameter related to the initial conditions and material properties of the
fibre.


Abstract:
  In this article we study the numerical solution of the $L^1$-Optimal
Transport Problem on 2D surfaces embedded in $R^3$, via the DMK formulation
introduced in [FaccaCardinPutti:2018]. We extend from the Euclidean into the
Riemannian setting the DMK model and conjecture the equivalence with the
solution Monge-Kantorovich equations, a PDE-based formulation of the
$L^1$-Optimal Transport Problem.
  We generalize the numerical method proposed in
[FaccaCardinPutti:2018,FaccaDaneriCardinPutti:2020] to 2D surfaces embedded in
$\REAL^3$ using the Surface Finite Element Model approach to approximate the
Laplace-Beltrami equation arising from the model. We test the accuracy and
efficiency of the proposed numerical scheme, comparing our approximate solution
with respect to an exact solution on a 2D sphere. The results show that the
numerical scheme is efficient, robust, and more accurate with respect to other
numerical schemes presented in the literature for the solution of
ls$L^1$-Optimal Transport Problem on 2D surfaces.


Abstract:
  A mathematical model of the evolution of plane perturbations in the
cosmological statistical system of completely degenerate scalar-charged
fermions with Higgs scalar interaction for short-wave perturbations is
formulated. These disturbance modes have been found and investigated. It is
shown that in such a model an additional oscillation mode arises, which is
associated directly with degenerate fermions, in which monotonically decreasing
and increasing perturbations of the metric exist. In this case, there is such a
time instant in the system that the potential of the scalar field, together
with the energy density, tends to infinity. In this regard, an assumption is
made about a possible mechanism for the formation of dark matter condensates.


Abstract:
  We use tunable dipolar-interactions between the spins of nitrogen-vacancy
(NV) centers in diamond to rotate a diamond crystal. Specifically, we employ
cross-relaxation between the electronic spin of pairs of NV centers in a
trapped diamond to enhance the anisotropic NV paramagnetism and thus to
increase the associated spin torque. Our observations open a path towards the
use of mechanical oscillators to detect paramagnetic defects that lack optical
transitions, to investigation of angular momentum conservation in spin
relaxation processes and to novel means of cooling the motion of mechanical
oscillators.


Abstract:
  In this paper we provide an abstract characterization of the inverse hulls of
semigroups associated with Markov shifts. As an application of the
characterization we give an example of Markov shifts that are not conjugate,
but have isomorphic inverse hulls.


Abstract:
  Many reinforcement learning algorithms can be seen as versions of approximate
policy iteration (API). While standard API often performs poorly, it has been
shown that learning can be stabilized by regularizing each policy update by the
KL-divergence to the previous policy. Popular practical algorithms such as
TRPO, MPO, and VMPO replace regularization by a constraint on KL-divergence of
consecutive policies, arguing that this is easier to implement and tune. In
this work, we study this implementation choice in more detail. We compare the
use of KL divergence as a constraint vs. as a regularizer, and point out
several optimization issues with the widely-used constrained approach. We show
that the constrained algorithm is not guaranteed to converge even on simple
problem instances where the constrained problem can be solved exactly, and in
fact incurs linear expected regret. With approximate implementation using
softmax policies, we show that regularization can improve the optimization
landscape of the original objective. We demonstrate these issues empirically on
several bandit and RL environments.


Abstract:
  Transformer models are not only successful in natural language processing
(NLP) but also demonstrate high potential in computer vision (CV). Despite
great advance, most of works only focus on improvement of architectures but pay
little attention to the classification head. For years transformer models base
exclusively on classification token to construct the final classifier, without
explicitly harnessing high-level word tokens. In this paper, we propose a novel
transformer model called second-order transformer (SoT), exploiting
simultaneously the classification token and word tokens for the classifier.
Specifically, we empirically disclose that high-level word tokens contain rich
information, which per se are very competent with the classifier and moreover,
are complementary to the classification token. To effectively harness such rich
information, we propose multi-headed global cross-covariance pooling with
singular value power normalization, which shares similar philosophy and thus is
compatible with the transformer block, better than commonly used pooling
methods. Then, we study comprehensively how to explicitly combine word tokens
with classification token for building the final classification head. For CV
tasks, our SoT significantly improves state-of-the-art vision transformers on
challenging benchmarks including ImageNet and ImageNet-A. For NLP tasks,
through fine-tuning based on pretrained language transformers including GPT and
BERT, our SoT greatly boosts the performance on widely used tasks such as CoLA
and RTE. Code will be available at https://peihuali.org/SoT


Abstract:
  The pentagram map, introduced by Schwartz in 1992, is a dynamical system on
the moduli space of polygons in the projective plane. Its real and complex
dynamics have been explored in detail. We study the pentagram map over an
arbitrary algebraically closed field of characteristic not equal to 2. We prove
that the pentagram map on twisted polygons is a discrete integrable system, in
the sense of algebraic complete integrability: the pentagram map is birational
to a self-map of a family of abelian varieties. This generalizes Soloviev's
proof of complex integrability. In the course of the proof, we construct the
moduli space of twisted $n$-gons, derive formulas for the pentagram map, and
calculate the Lax representation by characteristic-independent methods.


Abstract:
  Guided by the intuition of coherent superposition of causal relations, recent
works presented quantum processes without classical common-cause and
direct-cause explanation, that is, processes which cannot be written as
probabilistic mixtures of quantum common-cause and quantum direct-cause
relations (CCDC). In this work, we analyze the minimum requirements for a
quantum process to fail to admit a CCDC explanation and present "simple"
processes, which we prove to be the most robust ones against general noise.
These simple processes can be realized by preparing a maximally entangled state
and applying the identity quantum channel, thus not requiring an explicit
coherent mixture of common-cause and direct-cause, exploiting the possibility
of a process to have both relations simultaneously. We then prove that,
although all bipartite direct-cause processes are bipartite separable
operators, there exist bipartite separable processes which are not
direct-cause. This shows that the problem of deciding weather a process is
direct-cause process is not equivalent to entanglement certification and points
out the limitations of entanglement methods to detect non-classical CCDC
processes. We also present a semi-definite programming hierarchy that can
detect and quantify the non-classical CCDC robustnesses of every non-classical
CCDC process. Among other results, our numerical methods allow us to show that
the simple processes presented here are likely to be also the maximally robust
against white noise. Finally, we explore the equivalence between bipartite
direct-cause processes and bipartite processes without quantum memory, to
present a separable process which cannot be realized as a process without
quantum memory.


Abstract:
  Temporal graph signals are multivariate time series with individual
components associated with nodes of a fixed graph structure. Data of this kind
arises in many domains including activity of social network users, sensor
network readings over time, and time course gene expression within the
interaction network of a model organism. Traditional matrix decomposition
methods applied to such data fall short of exploiting structural regularities
encoded in the underlying graph and also in the temporal patterns of the
signal. How can we take into account such structure to obtain a succinct and
interpretable representation of temporal graph signals?
  We propose a general, dictionary-based framework for temporal graph signal
decomposition (TGSD). The key idea is to learn a low-rank, joint encoding of
the data via a combination of graph and time dictionaries. We propose a highly
scalable decomposition algorithm for both complete and incomplete data, and
demonstrate its advantage for matrix decomposition, imputation of missing
values, temporal interpolation, clustering, period estimation, and rank
estimation in synthetic and real-world data ranging from traffic patterns to
social media activity. Our framework achieves 28% reduction in RMSE compared to
baselines for temporal interpolation when as many as 75% of the observations
are missing. It scales best among baselines taking under 20 seconds on 3.5
million data points and produces the most parsimonious models. To the best of
our knowledge, TGSD is the first framework to jointly model graph signals by
temporal and graph dictionaries.


Abstract:
  We proposed a new modeling method to promote the performance of prohibited
items recognition via X-ray image. We analyzed the characteristics of
prohibited items and X-ray images. We found the fact that the scales of some
items are too small to be recognized which encumber the model performance. Then
we adopted a set of data augmentation and modified the model to adapt the field
of prohibited items recognition. The Convolutional Block Attention Module(CBAM)
and rescoring mechanism has been assembled into the model. By the modification,
our model achieved a mAP of 89.9% on SIXray10, mAP of 74.8%.


Abstract:
  In transfer learning, we wish to make inference about a target population
when we have access to data both from the distribution itself, and from a
different but related source distribution. We introduce a flexible framework
for transfer learning in the context of binary classification, allowing for
covariate-dependent relationships between the source and target distributions
that are not required to preserve the Bayes decision boundary. Our main
contributions are to derive the minimax optimal rates of convergence (up to
poly-logarithmic factors) in this problem, and show that the optimal rate can
be achieved by an algorithm that adapts to key aspects of the unknown transfer
relationship, as well as the smoothness and tail parameters of our
distributional classes. This optimal rate turns out to have several regimes,
depending on the interplay between the relative sample sizes and the strength
of the transfer relationship, and our algorithm achieves optimality by careful,
decision tree-based calibration of local nearest-neighbour procedures.


Abstract:
  In order to increase the bone forming ability of MBG-PCL composite scaffold,
microporosity was created in the struts of 3D-printed MBG-PCL scaffolds for the
manufacturing of a construct with a multiscale porosity consisting of meso-,
micro- and macro-pores. 3D-printing imparted macroporosity while the
microporosity was created by porogen removal from the struts, and the MBG
particles were responsible for the mesoporosity. The scaffolds were 3D-printed
using a mixture of PCL, MBG and phosphate buffered saline (PBS) particles,
subsequently leached out. Microporous-PCL (pPCL) as a negative control,
microporous MBG-PCL (pMBG-PCL) and non-microporous-MBG-PCL (MBG-PCL) were
investigated. Scanning electron microscopy, mercury intrusion porosimetry and
micro-computed tomography demonstrated that the PBS removal resulted in the
formation of micropores inside the struts with porosity of around 30% for both
pPCL and pMBG-PCL, with both constructs displaying an overall porosity of
80-90%. In contrast, the MBG-PCL group had a microporosity of 6% and an overall
porosity of 70%. Early mineralisation was found in the pMBG-PCL post-leaching
out and this resulted in the formation a more homogeneous calcium phosphate
layer when using a biomimetic mineralisation assay. Mechanical properties
ranged from 5 to 25 MPa for microporous and non-microporous specimens, hence
microporosity was the determining factor affecting compressive properties.
MC3T3-E1 metabolic activity was increased in the pMBG-PCL along with an
increased production of RUNX2. Therefore, the microporosity within a 3D-printed
bioceramic composite construct may result in additional physical and biological
benefits.


Abstract:
  In this position paper we approach problems concerning critical digital and
information literacy with ideas to provide more digestible explanations of
abstract concepts through interface design. In particular, we focus on social
media platforms where we see the possibility of counteracting the spread of
misinformation by providing users with more proficiency through our approaches.
We argue that the omnipresent trend to abstract away and hide information from
users via UI/UX design opposes their ability to self-learn. This leads us to
propose a different framework in which we unify elegant and simple interfaces
with nudges that promote a look behind the curtain. Such designs serve to
foster a deeper understanding of employed technologies and aim to increase the
critical assessment of content encountered on social platforms. Furthermore, we
consider users with an intermediary skill level to be largely ignored in
current approaches, as they are given no tools to broaden their knowledge
without consultation of expert material. The resulting stagnation is
exemplified by the tactics of misinformation campaigns, which exploit the
ensuing lack of information literacy and critical thinking. We propose an
approach to design that sufficiently emancipates users in both aspects by
promoting a look behind the abstraction of UI/UX so that an autonomous learning
process is given the chance to occur. Furthermore, we name ideas for future
research within this area that take our considerations into account.


Abstract:
  Topological semimetals are a new class of metallic materials, which exist at
band fillings that ordinarily correspond to insulators or compensated
accidental semimetals with zero Luttinger volume. Their metallicity is a result
of nontrivial topology in momentum space and crystal symmetry, wherein
topological charges may be assigned to point band-touching nodes, preventing
gap opening, unless protecting crystal symmetries are violated. These
topological charges, however, are defined from noninteracting band eigenstates,
which raises the possibility that the physics of topological semimetals may be
modified qualitatively by electron-electron interactions. Here we ask the
following question: what makes the topological semimetals nontrivial beyond
band theory? Alternatively, can strong electron-electron interactions open a
gap in topological semimetals without breaking the protecting symmetries or
introducing topological order? We demonstrate that the answer is generally no,
and what prevents it is their topological response, or quantum anomalies. While
this is familiar in the case of magnetic Weyl semimetals, where the topological
response takes the form of an anomalous Hall effect, analogous responses in
other types of topological semimetals are more subtle and involve crystal
symmetry as well as electromagnetic gauge fields. Physically these responses
are detectable as fractional symmetry charges induced on certain gauge defects.
We discuss the cases of type-I Dirac semimetals and time-reversal invariant
Weyl semimetals in detail. For type-I Dirac semimetals, we also show that the
anomaly vanishes, in a nontrivial manner, if the momenta of the Dirac nodes
satisfy certain exceptional conditions.


Abstract:
  Motivated by the recent experimental realization of twisted trilayer graphene
and the observed superconductivity that is associated with its flat bands at
specific angles, we study trilayer graphene under the influence of different
forms of light in the non-interacting limit. Specifically, we study four
different types of stacking configurations with a single twisted layer. In all
four cases, we study the impact of circularly polarized light and longitudinal
light coming from a waveguide. We derive effective time-independent Floquet
Hamiltonians and review light-induced changes to the band structure. For
circularly polarized light, we find band flattening effects as well as band gap
openings. We emphasize that there is a rich band topology, which we summarize
in Chern number maps that are different for all four studied lattice
configurations. The case of a so-called ABC stacking with top layer twist is
especially rich and shows a different phase diagram depending on the handedness
of the circularly polarized light. Consequently, we propose an experiment where
this difference in typologies could be captured via optical conductivity
measurements. In contrast for the case of longitudinal light that is coming
from a waveguide, we find that the band structure is very closely related to
the equilibrium one but the magic angles can be tuned in-situ by varying the
intensity of the incident beam of light.


Abstract:
  Hexahedral meshes are an ubiquitous domain for the numerical resolution of
partial differential equations. Computing a pure hexahedral mesh from an
adaptively refined grid is a prominent approach to automatic hexmeshing, and
requires the ability to restore the all hex property around the hanging nodes
that arise at the interface between cells having different size. The most
advanced tools to accomplish this task are based on mesh dualization. These
approaches use topological schemes to regularize the valence of inner vertices
and edges, such that dualizing the grid yields a pure hexahedral mesh. In this
paper we study in detail the dual approach, and propose four main contributions
to it: (i) we enumerate all the possible transitions that dual methods must be
able to handle, showing that prior schemes do not natively cover all of them;
(ii) we show that schemes are internally asymmetric, therefore not only their
implementation is ambiguous, but different implementation choices lead to
hexahedral meshes with different singular structure; (iii) we explore the
combinatorial space of dual schemes, selecting the minimum set that covers all
the possible configurations and also yields the simplest singular structure in
the output hexmesh; (iv) we enlarge the class of adaptive grids that can be
transformed into pure hexahedral meshes, relaxing one of the tight requirements
imposed by previous approaches, and ultimately permitting to obtain much
coarser meshes for same geometric accuracy. Last but not least, for the first
time we make grid-based hexmeshing truly reproducible, releasing our code and
also revealing a conspicuous amount of technical details that were always
overlooked in previous literature, creating an entry barrier that was hard to
overcome for practitioners in the field.


Abstract:
  In this paper, we reveal the depreciation mechanism of representative money
(banknotes) from the perspective of logistics warehousing costs. Although it
has long been the dream of economists to stabilize the buying power of the
monetary units, the goal we have honest money always broken since the central
bank depreciate the currency without limit. From the point of view of modern
logistics, the key functions of money are the store of value and low logistics
(circulation and warehouse) cost. Although commodity money (such as gold and
silver) has the advantages of a wealth store, its disadvantage is the high
logistics cost. In comparison to commodity money, credit currency and digital
currency cannot protect wealth from loss over a long period while their
logistics costs are negligible. We proved that there is not such honest money
from the perspective of logistics costs, which is both the store of value like
precious metal and without logistics costs in circulation like digital
currency. The reason hidden in the back of the depreciation of banknotes is the
black hole of storage charge of the anchor overtime after digitizing commodity
money. Accordingly, it is not difficult to infer the inevitable collapse of the
Bretton woods system. Therefore, we introduce a brand-new currency named honest
devalued stable-coin and built a attenuation model of intrinsic value of the
honest money based on the change mechanism of storage cost of anchor assets,
like gold, which will lay the theoretical foundation for a stable monetary
system.


Abstract:
  Understanding common envelope (CE) evolution is an outstanding problem in
binary evolution. Although the CE phase is not driven by gravitational-wave
(GW) emission, the in-spiraling binary emits GWs that passively trace the CE
dynamics. Detecting this GW signal would provide direct insight into the
gas-driven physics. Even a non-detection might offer invaluable constraints. We
investigate the prospects of detection of a Galactic CE by LISA. While the
dynamical phase of the CE is likely sufficiently loud for detection, it is
short and thus rare. We focus instead on the self-regulated phase that proceeds
on a thermal timescale. Based on population synthesis calculations and the
(unknown) signal duration in the LISA band, we expect $\sim 0.1-100$ sources in
the Galaxy during the mission duration. We map the GW observable parameter
space of frequency $f_\mathrm{GW}$ and its derivative $\dot f_\mathrm{GW}$
remaining agnostic on the specifics of the inspiral, and find that signals with
$\mathrm{SNR}>10$ are possible if the CE stalls at separations such that
$f_\mathrm{GW}\gtrsim2\times10^{-3}\,\mathrm{Hz}$. We investigate the
possibility of misidentifying the signal with other known sources. If the
second derivative $\ddot f_\mathrm{GW}$ can also be measured, the signal can be
distinguished from other sources using a GW braking-index. Alternatively,
coupling LISA with electromagnetic observations of peculiar red giant stars
and/or infrared and optical transients might allow for the disentangling of a
Galactic CE from other Galactic and extra-galactic GW sources.


Abstract:
  Modern approaches to sound synthesis using deep neural networks are hard to
control, especially when fine-grained conditioning information is not
available, hindering their adoption by musicians.
  In this paper, we cast the generation of individual instrumental notes as an
inpainting-based task, introducing novel and unique ways to iteratively shape
sounds. To this end, we propose a two-step approach: first, we adapt the
VQ-VAE-2 image generation architecture to spectrograms in order to convert
real-valued spectrograms into compact discrete codemaps, we then implement
token-masked Transformers for the inpainting-based generation of these
codemaps.
  We apply the proposed architecture on the NSynth dataset on masked resampling
tasks. Most crucially, we open-source an interactive web interface to transform
sounds by inpainting, for artists and practitioners alike, opening up to new,
creative uses.


Abstract:
  Kernel embeddings of distributions have recently gained significant attention
in the machine learning community as a data-driven technique for representing
probability distributions. Broadly, these techniques enable efficient
computation of expectations by representing integral operators as elements in a
reproducing kernel Hilbert space. We apply these techniques to the area of
stochastic optimal control theory and present a method to compute approximately
optimal policies for stochastic systems with arbitrary disturbances. Our
approach reduces the optimization problem to a linear program, which can easily
be solved via the Lagrangian dual, without resorting to gradient-based
optimization algorithms. We focus on discrete-time dynamic programming, and
demonstrate our proposed approach on a linear regulation problem, and on a
nonlinear target tracking problem. This approach is broadly applicable to a
wide variety of optimal control problems, and provides a means of working with
stochastic systems in a data-driven setting.


Abstract:
  We introduce a new approach for circuit anonymous communication based on
Lizama's non-invertible Key Exchange Protocol (ni-KEP) which has been conceived
to work in the quantum era. Lizama's protocol has the smallest key size when
compared to main post-quantum schemes thus it becomes a promising alternative
for the quantum era. Circuit-based communication can be scaled to support the
Hidden Service Protocol (HSP) as well as cross-domain digital certificates that
promise greater computing security, speed and efficiency.


Abstract:
  We prove a division algorithm for group rings of high genus surface groups
and use it to show that some $2$-complexes with surface fundamental groups are
standard. We also give an application of division to cohomological dimension of
$2$-relator groups acting on $\mathbb H^n$.