Abstract:
  The transference principle of Green and Tao enabled various authors to
transfer Szemer\'edi's theorem on long arithmetic progressions in dense sets to
various sparse sets of integers, mostly sparse sets of primes. In this paper,
we provide a transference principle which applies to general affine-linear
configurations of finite complexity. We illustrate the broad applicability of
our transference principle with the case of almost twin primes, by which we
mean either Chen primes or ''bounded gap primes'', as well as with the case of
primes of the form $x^2+y^2+1$. Thus, we show that in these sets of primes the
existence of solutions to finite complexity systems of linear equations is
determined by natural local conditions. These applications rely on a recent
work of the last two authors on Bombieri-Vinogradov type estimates for
nilsequences.


Abstract:
  In a context of global economy, addressing SMEs performance within a local
framework appears rather a naive approach. The key drawback of such an approach
stems from its restriction to socio-economic factors that might lead to biased
decisions regarding potential venues for performance improvement. In practice,
the key objective of performance analysis consists in identifying benchmarks
for best managerial practices with respect to resource allocation as well as
production level setting. Conducting the analysis within a specific country,
let it be a developing country, may be misleading. Although, the best of the
class (benchmark) can be a valid reference for its peers within the same class,
its status might not be preserved if the analysis is projected outside the
borders of the class. Indeed, the likelihood for outperformance is high. In
order to set targets for global competition, decision makers ought to look at
the concept of performance from a broader geographical perspective, instead of
confining it to a local scope. Here, we analyze, through a case study, SMEs
performance within local and global production technology frameworks and we
highlight the impact of the economy scope on various decisions. Data
envelopment analysis (DEA) is used as a mathematical tool to support such
decisions.


Abstract:
  The spread of a matrix is defined as the maximum of distances between any two
eigenvalues of that matrix. In this paper we investigate spread maximization as
a function on compact convex subset of the set of real symmetric matrices. We
provide some general results and further, we study spread maximizing problem on
the set of symmetric matrices with entries restricted to the interval. In
particular, we develop some results by X. Zhan, S. M. Fallat and J. J. Xing.


Abstract:
  We discuss a new mass matrix with specific texture zeros for the quarks. The
three flavor mixing angles for the quarks are functions of the quark masses and
can be calculated. The following ratios among CKM matrix elements are given by
ratios of quark masses: |Vtd/Vts| ' q md /ms and |Vub/Vcb| ' p mu/mc . Also we
can calculate two CKM matrix elements: |Vcb| ' |Vts| ' 2 (ms/mb ). This
relation as well as the relation |Vtd/Vts| ' q md /ms are in good agreement
with the experimental data. There is a problem with the relation |Vub/Vcb| ' p
mu/mc , probably due to wrong estimates of the quark masses mu and m


Abstract:
  Context. Solar magnetic pores are, due to their concentrated magnetic fields,
suitable guides for magnetoacoustic waves. Recent observations have shown that
propagating energy flux in pores is subject to strong damping with height;
however, the reason is still unclear. Aims. We investigate possible damping
mechanisms numerically to explain the observations. Methods. We performed 2D
numerical magnetohydrodynamic (MHD) simulations, starting from an equilibrium
model of a single pore inspired by the observed properties. Energy was inserted
into the bottom of the domain via different vertical drivers with a period of
30s. Simulations were performed with both ideal MHD and non-ideal effects.
Results. While the analysis of the energy flux for ideal and non-ideal MHD
simulations with a plane driver cannot reproduce the observed damping, the
numerically predicted damping for a localized driver closely corresponds with
the observations. The strong damping in simulations with localized driver was
caused by two geometric effects, geometric spreading due to diverging field
lines and lateral wave leakage.


Abstract:
  In the conventional robust $T$-colluding private information retrieval (PIR)
system, the user needs to retrieve one of the possible messages while keeping
the identity of the requested message private from any $T$ colluding servers.
Motivated by the possible heterogeneous privacy requirements for different
messages, we consider the $(N, T_1:K_1, T_2:K_2)$ two-level PIR system with a
total of $K_2$ messages in the system, where $T_1\geq T_2$ and $K_1\leq K_2$.
Any one of the $K_1$ messages needs to be retrieved privately against $T_1$
colluding servers, and any one of the full set of $K_2$ messages needs to be
retrieved privately against $T_2$ colluding servers. We obtain a lower bound to
the capacity by proposing two novel coding schemes, namely the non-uniform
successive cancellation scheme and the non-uniform block cancellation scheme. A
capacity upper bound is also derived. The gap between the upper bound and the
lower bounds is analyzed, and shown to vanish when $T_1=T_2$. Lastly, we show
that the upper bound is in general not tight by providing a stronger bound for
a special setting.


Abstract:
  By implicitly recognizing a user based on his/her speech input, speaker
identification enables many downstream applications, such as personalized
system behavior and expedited shopping checkouts. Based on whether the speech
content is constrained or not, both text-dependent (TD) and text-independent
(TI) speaker recognition models may be used. We wish to combine the advantages
of both types of models through an ensemble system to make more reliable
predictions. However, any such combined approach has to be robust to incomplete
inputs, i.e., when either TD or TI input is missing. As a solution we propose a
fusion of embeddings network foenet architecture, combining joint learning with
neural attention. We compare foenet with four competitive baseline methods on a
dataset of voice assistant inputs, and show that it achieves higher accuracy
than the baseline and score fusion methods, especially in the presence of
incomplete inputs.


Abstract:
  Explaining the decisions of models is becoming pervasive in the image
processing domain, whether it is by using post-hoc methods or by creating
inherently interpretable models. While the widespread use of surrogate
explainers is a welcome addition to inspect and understand black-box models,
assessing the robustness and reliability of the explanations is key for their
success. Additionally, whilst existing work in the explainability field
proposes various strategies to address this problem, the challenges of working
with data in the wild is often overlooked. For instance, in image
classification, distortions to images can not only affect the predictions
assigned by the model, but also the explanation. Given a clean and a distorted
version of an image, even if the prediction probabilities are similar, the
explanation may still be different. In this paper we propose a methodology to
evaluate the effect of distortions in explanations by embedding perceptual
distances that tailor the neighbourhoods used to training surrogate explainers.
We also show that by operating in this way, we can make the explanations more
robust to distortions. We generate explanations for images in the Imagenet-C
dataset and demonstrate how using a perceptual distances in the surrogate
explainer creates more coherent explanations for the distorted and reference
images.


Abstract:
  We develop a microscopic and atomistic theory of electron spin-based qubits
in gated quantum dots in a single layer of transition metal dichalcogenides.
The qubits are identified with two degenerate locked spin and valley states in
a gated quantum dot. The two-qubit states are accurately described using a
multi-million atom tight-binding model solved in wavevector space. The
spin-valley locking and strong spin-orbit coupling result in two degenerate
states, one of the qubit states being spin-down located at the $+K$ valley of
the Brillouin zone, and the other state located at the $-K$ valley with spin
up. We describe the qubit operations necessary to rotate the spin-valley qubit
as a combination of the applied vertical electric field, enabling spin-orbit
coupling in a single valley, with a lateral strongly localized valley-mixing
gate.


Abstract:
  Automated systems that negotiate with humans have broad applications in
pedagogy and conversational AI. To advance the development of practical
negotiation systems, we present CaSiNo: a novel corpus of over a thousand
negotiation dialogues in English. Participants take the role of campsite
neighbors and negotiate for food, water, and firewood packages for their
upcoming trip. Our design results in diverse and linguistically rich
negotiations while maintaining a tractable, closed-domain environment. Inspired
by the literature in human-human negotiations, we annotate persuasion
strategies and perform correlation analysis to understand how the dialogue
behaviors are associated with the negotiation performance. We further propose
and evaluate a multi-task framework to recognize these strategies in a given
utterance. We find that multi-task learning substantially improves the
performance for all strategy labels, especially for the ones that are the most
skewed. We release the dataset, annotations, and the code to propel future work
in human-machine negotiations: https://github.com/kushalchawla/CaSiNo


Abstract:
  We introduce codimension three magnetically charged surface operators in
five-dimensional (5d) $\mathcal{N}=1$ supersymmetric gauge on $T^2 \times
\mathbb{R}^3$. We evaluate the vacuum expectation values (vevs) of surface
operators by supersymmetric localization techniques. Contributions of Monopole
bubbling effects to the path integral are given by elliptic genera of world
volume theories on D-branes. Our result gives an elliptic deformation of the
SUSY localization formula \cite{Ito:2011ea} (resp. \cite{Okuda:2019emk,
Assel:2019yzd}) of BPS 't Hooft loops (resp. bare monopole operators) in 4d
$\mathcal{N}=2$ (resp. 3d $\mathcal{N}=4$) gauge theories. We define
deformation quantizations of vevs of surface operators in terms of the
Weyl-Wigner transform, where the $\Omega$-background parameter plays the role
of the Planck constant. For 5d $\mathcal{N}=1^*$ gauge theory, we find that the
deformation quantization of the surface operators in the anti-symmetric
representations agrees with the type A elliptic Ruijsenaars operators. The
mutual commutativity of these difference operators is related to the
commutativity of products of 't Hooft surface operators.


Abstract:
  Squeezed, nonclassical states are an integral tool of quantum metrology due
to their ability to push the sensitivity of a measurement apparatus beyond the
limits of classical states. While their creation in light has become a standard
technique, the production of squeezed states of the collective excitations in
gases of ultracold atoms, the phonons of a Bose-Einstein condensate (BEC), is a
comparably recent problem. This task is continuously gaining relevance with a
growing number of proposals for BEC-based quantum metrological devices and the
possibility to apply them in the detection of gravitational waves. The
objective of this thesis is to find whether the recently described effect of an
oscillating external potential on a uniform BEC can be exploited to generate
two-mode squeezed phonon states, given present day technology. This question
brings together elements of a range of fields beyond cold atoms, such as
general relativity and Efimov physics. To answer it, the full transformation
caused by the oscillating potential on an initially thermal phononic state is
considered, allowing to find an upper bound for the magnitude of this
perturbation as well as to quantify the quality of the final state with respect
to its use in metrology. These findings are then applied to existing
experiments to judge the feasibility of the squeezing scheme and while the
results indicate that they are not well suited for it, a setup is proposed that
allows for its efficient implementation and seems within experimental reach. In
view of the vast parameter space leaving room for optimization, the considered
mechanism could find applications not only in the gravitational wave detector
that originally motivated this work, but more generally in the field of quantum
metrology based on ultracold atoms.


Abstract:
  Artificial intelligence (AI) is supposed to help us make better choices. Some
of these choices are small, like what route to take to work, or what music to
listen to. Others are big, like what treatment to administer for a disease or
how long to sentence someone for a crime. If AI can assist with these big
decisions, we might think it can also help with hard choices, cases where
alternatives are neither better, worse nor equal but on a par. The aim of this
paper, however, is to show that this view is mistaken: the fact of parity shows
that there are hard limits on AI in decision making and choices that AI cannot,
and should not, resolve.


Abstract:
  This paper shows a simple parameter substitution, which makes use of the
reciprocal relation of typical objective functions with typical random
parameters. Thereby, the accuracy of first-order probabilistic analysis
improves significantly at almost no additional computational cost. The
parameter substitution requires a transformation of the stochastic distribution
of the substituted parameter, which is explained for different cases.


Abstract:
  The Covering Salesman Problem (CSP) is a generalization of the Traveling
Salesman Problem in which the tour is not required to visit all vertices, as
long as all vertices are covered by the tour. The objective of CSP is to find a
minimum length Hamiltonian cycle over a subset of vertices that covers an
undirected graph. In this paper, valid inequalities from the generalized
traveling salesman problem are applied to the CSP in addition to new valid
inequalities that explore distinct aspects of the problem. A branch-and-cut
framework assembles exact and heuristic separation routines for integer and
fractional CSP solutions. Computational experiments show that the proposed
framework outperformed methodologies from literature with respect to optimality
gaps. Moreover, optimal solutions were proven for several previously unsolved
instances.


Abstract:
  In this paper, we investigate the complexity of the central path of
semidefinite optimization through the lens of real algebraic geometry. To that
end, we propose an algorithm to compute real univariate representations
describing the central path and its limit point, where the limit point is
described by taking the limit of central solutions, as bounded points in the
field of algebraic Puiseux series. As a result, we derive an upper bound
$2^{O(m+n^2)}$ on the degree of the Zariski closure of the central path, when
$\mu$ is sufficiently small, and for the complexity of describing the limit
point, where $m$ and $n$ denote the number of affine constraints and size of
the symmetric matrix, respectively. Furthermore, by the application of the
quantifier elimination to the real univariate representations, we provide a
lower bound $1/\gamma$, with $\gamma =2^{O(m+n^2)}$, on the convergence rate of
the central path.


Abstract:
  We take a broad look at the problem of identifying the magnetic solar causes
of space weather. With the lackluster performance of extrapolations based upon
magnetic field measurements in the photosphere, we identify a region in the
near UV part of the spectrum as optimal for studying the development of
magnetic free energy over active regions. Using data from SORCE, Hubble Space
Telescope, and SKYLAB, along with 1D computations of the near-UV (NUV) spectrum
and numerical experiments based on the MURaM radiation-MHD and HanleRT
radiative transfer codes, we address multiple challenges. These challenges are
best met through a combination of near UV lines of bright \ion{Mg}{2}, and
lines of \ion{Fe}{2} and \ion{Fe}{1} (mostly within the $4s-4p$ transition
array) which form in the chromosphere up to $2\times10^4$ K. Both Hanle and
Zeeman effects can in principle be used to derive vector magnetic fields.
However, for any given spectral line the $\tau=1$ surfaces are generally
geometrically corrugated owing to fine structure such as fibrils and spicules.
By using multiple spectral lines spanning different optical depths, magnetic
fields across nearly-horizontal surfaces can be inferred in regions of low
plasma $\beta$, from which free energies, magnetic topology and other
quantities can be derived.
  Based upon the recently-reported successful suborbital space measurements of
magnetic fields with the CLASP2 instrument,
  we argue that a modest space-borne telescope will be able to make significant
advances in the attempts to predict solar eruptions. Difficulties associated
with blended lines are shown to be minor in an Appendix.


Abstract:
  To investigate the influence of the orifice geometry on near-field coherent
structures in a jet, Fourier-POD is applied. Velocity and vorticity snapshots
obtained from tomographic particle image velocimetry at the downstream distance
of two equivalent orifice diameters are analysed. Jets issuing from a circular
orifice and from a fractal orifice are examined, where the fractal geometry is
obtained from a repeating fractal pattern applied to a base square shape. While
in the round jet energy is mostly contained at wavenumber m=0, associated to
the characteristic Kelvin-Helmholtz vortex rings, in the fractal jet modal
structures at the fundamental azimuthal wavenumber m=4 capture the largest
amount of energy. The second part of the study focuses on the relationship
between streamwise vorticity and streamwise velocity, to characterise the role
of the orifice geometry on the lift-up mechanism recently found to be active in
turbulent jets. The averaging of the streamwise vorticity conditioned on
intense positive fluctuations of streamwise velocity reveals a pair of
vorticity structures of opposite sign flanking the conditioning point, inducing
a radial flow towards the jet periphery. This pair of structures is observed in
both jets, even if the azimuthal extent of this pattern is 30% larger in the
jet issuing from the circular orifice. This evidences that the orifice geometry
directly influences the interaction between velocity and vorticity.


Abstract:
  Purpose: Radiation therapy treatment planning is a trial-and-error, often
time-consuming process. An optimal dose distribution based on a specific
anatomy can be predicted by pre-trained deep learning (DL) models. However,
dose distributions are often optimized based on not only patient-specific
anatomy but also physician preferred trade-offs between planning target volume
(PTV) coverage and organ at risk (OAR) sparing. Therefore, it is desirable to
allow physicians to fine-tune the dose distribution predicted based on patient
anatomy. In this work, we developed a DL model to predict the individualized 3D
dose distributions by using not only the anatomy but also the desired PTV/OAR
trade-offs, as represented by a dose volume histogram (DVH), as inputs.
Methods: The desired DVH, fine-tuned by physicians from the initially predicted
DVH, is first projected onto the Pareto surface, then converted into a vector,
and then concatenated with mask feature maps. The network output for training
is the dose distribution corresponding to the Pareto optimal DVH. The
training/validation datasets contain 77 prostate cancer patients, and the
testing dataset has 20 patients. Results: The trained model can predict a 3D
dose distribution that is approximately Pareto optimal. We calculated the
difference between the predicted and the optimized dose distribution for the
PTV and all OARs as a quantitative evaluation. The largest average error in
mean dose was about 1.6% of the prescription dose, and the largest average
error in the maximum dose was about 1.8%. Conclusions: In this feasibility
study, we have developed a 3D U-Net model with the anatomy and desired DVH as
inputs to predict an individualized 3D dose distribution. The predicted dose
distributions can be used as references for dosimetrists and physicians to
rapidly develop a clinically acceptable treatment plan.


Abstract:
  Under the environment of big data streams, it is a common situation where the
variable set of a model may change according to the condition of data streams.
In this paper, we propose a homogenization strategy to represent the
heterogenous models that are gradually updated in the process of data streams.
With the homogenized representations, we can easily construct various online
updating statistics such as parameter estimation, residual sum of squares and
$F$-statistic for the heterogenous updating regression models. The main
difference from the classical scenarios is that the artificial covariates in
the homogenized models are not identically distributed as the natural
covariates in the original models, consequently, the related theoretical
properties are distinct from the classical ones. The asymptotical properties of
the online updating statistics are established, which show that the new method
can achieve estimation efficiency and oracle property, without any constraint
on the number of data batches. The behavior of the method is further
illustrated by various numerical examples from simulation experiments.


Abstract:
  Infrared nanospectroscopy based on Fourier transform infrared near-field
spectroscopy (nano-FTIR) is an emerging nanoanalytical tool with large
application potential for label-free mapping and identification of organic and
inorganic materials with nanoscale spatial resolution. However, the detection
of thin molecular layers and nanostructures on standard substrates is still
challenged by weak signals. Here, we demonstrate a significant enhancement of
nano-FTIR signals of a thin organic layer by exploiting polariton-resonant
tip-substrate coupling and surface polariton illumination of the probing tip.
When the molecular vibration matches the tip-substrate resonance, we achieve up
to nearly one order of magnitude signal enhancement on a phonon-polaritonic
quartz (c-SiO2) substrate, as compared to nano-FTIR spectra obtained on metal
(Au) substrates, and up to two orders of magnitude when compared to the
standard infrared spectroscopy substrate CaF2. Our results will be of critical
importance for boosting nano-FTIR spectroscopy towards the routine detection of
monolayers and single molecules.


Abstract:
  We define an attractive gravity probe surface (AGPS) as a compact 2-surface
$S_\alpha$ with positive mean curvature $k$ satisfying $r^a D_a k / k^2 \ge
\alpha$ (for a constant $\alpha>-1/2$) in the local inverse mean curvature
flow, where $r^a D_a k$ is the derivative of $k$ in the outward unit normal
direction. For asymptotically flat spaces, any AGPS is proved to satisfy the
areal inequality $A_\alpha \le 4\pi [ ( 3+4\alpha)/(1+2\alpha) ]^2(Gm)^2$,
where $A_{\alpha}$ is the area of $S_\alpha$ and $m$ is the
Arnowitt-Deser-Misner (ADM) mass. Equality is realized when the space is
isometric to the $t=$constant hypersurface of the Schwarzschild spacetime and
$S_\alpha$ is an $r=\mathrm{constant}$ surface with $r^a D_a k / k^2 = \alpha$.
We adapt the two methods of the inverse mean curvature flow and the conformal
flow. Therefore, our result is applicable to the case where $S_\alpha$ has
multiple components. For anti-de Sitter (AdS) spaces, a similar inequality is
derived, but the proof is performed only by using the inverse mean curvature
flow. We also discuss the cases with asymptotically locally AdS spaces.


Abstract:
  The recent surge of complex attention-based deep learning architectures has
led to extraordinary results in various downstream NLP tasks in the English
language. However, such research for resource-constrained and morphologically
rich Indian vernacular languages has been relatively limited. This paper
proffers team SPPU\_AKAH's solution for the TechDOfication 2020 subtask-1f:
which focuses on the coarse-grained technical domain identification of short
text documents in Marathi, a Devanagari script-based Indian language. Availing
the large dataset at hand, a hybrid CNN-BiLSTM attention ensemble model is
proposed that competently combines the intermediate sentence representations
generated by the convolutional neural network and the bidirectional long
short-term memory, leading to efficient text classification. Experimental
results show that the proposed model outperforms various baseline machine
learning and deep learning models in the given task, giving the best validation
accuracy of 89.57\% and f1-score of 0.8875. Furthermore, the solution resulted
in the best system submission for this subtask, giving a test accuracy of
64.26\% and f1-score of 0.6157, transcending the performances of other teams as
well as the baseline system given by the organizers of the shared task.


Abstract:
  Let $P$ be a bounded convex subset of $\mathbb R^n$ of positive volume.
Denote the smallest degree of a polynomial $p(X_1,\dots,X_n)$ vanishing on
$P\cap\mathbb Z^n$ by $r_P$ and denote the smallest number $u\geq0$ such that
every function on $P\cap\mathbb Z^n$ can be interpolated by a polynomial of
degree at most $u$ by $s_P$. We show that the values $(r_{d\cdot P}-1)/d$ and
$s_{d\cdot P}/d$ for dilates $d\cdot P$ converge from below to some numbers
$v_P,w_P>0$ as $d$ goes to infinity. The limits satisfy $v_P^{n-1}w_P \leq
n!\cdot\operatorname{vol}(P)$. When $P$ is a triangle in the plane, we show
equality: $v_Pw_P = 2\operatorname{vol}(P)$. These results are obtained by
looking at the set of standard monomials of the vanishing ideal of $d\cdot
P\cap\mathbb Z^n$ and by applying the Bernstein--Kushnirenko theorem. Finally,
we study irreducible Laurent polynomials that vanish with large multiplicity at
a point. This work is inspired by questions about Seshadri constants.


Abstract:
  Neural cellular automata (Neural CA) are a recent framework used to model
biological phenomena emerging from multicellular organisms. In these systems,
artificial neural networks are used as update rules for cellular automata.
Neural CA are end-to-end differentiable systems where the parameters of the
neural network can be learned to achieve a particular task. In this work, we
used neural CA to control a cart-pole agent. The observations of the
environment are transmitted in input cells, while the values of output cells
are used as a readout of the system. We trained the model using deep-Q
learning, where the states of the output cells were used as the Q-value
estimates to be optimized. We found that the computing abilities of the
cellular automata were maintained over several hundreds of thousands of
iterations, producing an emergent stable behavior in the environment it
controls for thousands of steps. Moreover, the system demonstrated life-like
phenomena such as a developmental phase, regeneration after damage, stability
despite a noisy environment, and robustness to unseen disruption such as input
deletion.


Abstract:
  Linear regression is a fundamental modeling tool in statistics and related
fields. In this paper, we study an important variant of linear regression in
which the predictor-response pairs are partially mismatched. We use an
optimization formulation to simultaneously learn the underlying regression
coefficients and the permutation corresponding to the mismatches. The
combinatorial structure of the problem leads to computational challenges. We
propose and study a simple greedy local search algorithm for this optimization
problem that enjoys strong theoretical guarantees and appealing computational
performance. We prove that under a suitable scaling of the number of mismatched
pairs compared to the number of samples and features, and certain assumptions
on problem data; our local search algorithm converges to a nearly-optimal
solution at a linear rate. In particular, in the noiseless case, our algorithm
converges to the global optimal solution with a linear convergence rate. We
also propose an approximate local search step that allows us to scale our
approach to much larger instances. We conduct numerical experiments to gather
further insights into our theoretical results and show promising performance
gains compared to existing approaches.


Abstract:
  This is a PhD Thesis on the connection between subfactors (more precisely,
their corresponding fusion categories) and Conformal Field Theory (CFT).
Besides being a mathematically interesting topic on its own, subfactors have
also attracted the attention of physicists, since there is a conjectured
correspondence between these and CFTs. Although there is quite a persuasive
body of evidence for this conjecture, there are some gaps: there exists a set
of exceptional subfactors with no known counterpart CFT. Hence, it is necessary
to develop new techniques for building a CFT from a subfactor. Here, it is
useful to study the underlying mathematical structure in more detail: The even
parts of every subfactor give rise to two Unitary Fusion Categories (UFCs), and
it is a promising direction to study quantum spin systems constructed from
these categories to find a connection to CFTs. The simplest example that
requires new techniques for building a CFT is the Haagerup subfactor, since it
is the smallest subfactor with index larger than 4. In this thesis, we
investigate the question whether there is a CFT corresponding to the Haagerup
subfactor via lattice models in one and two dimensions. The first task here is
to find the F-symbols of the fusion category since these are crucial
ingredients for the construction of a physical model in all of the models we
consider in this thesis. We then investigate microscopic models such as the
golden chain model and the Levin-Wen model in order to find evidence for a
corresponding CFT. We find that there is no evidence for a corresponding CFT
from the investigation of the UFCs directly and it is necessary to expand these
studies to the corresponding unitary modular tensor category, which can, for
instance, be obtained via the excitations of the Levin-Wen model.


Abstract:
  We study a mathematical model capturing the support/resistance line method (a
technique in technical analysis) where the underlying stock price transitions
between two states of nature in a path-dependent manner. For optimal stopping
problems with respect to a general class of reward functions and dynamics,
using probabilistic methods, we show that the value function is $C^1$ and
solves a general free boundary problem. Moreover, for a wide range of
utilities, we prove that the best time to buy and sell the stock is obtained by
solving free boundary problems corresponding to two linked optimal stopping
problems. We use this to numerically compute optimal trading strategies for
several types of dynamics and varying degrees of relative risk aversion. We
then compare the strategies with the standard trading rule to investigate the
viability of this form of technical analysis.


Abstract:
  We give a new criterion guaranteeing existence of model structures
left-induced along a functor admitting both adjoints. This works under the
hypothesis that the functor induces idempotent adjunctions at the homotopy
category level. As an application, we construct new model structures on cubical
sets, prederivators, marked simplicial sets and simplicial spaces modeling
$\infty$-categories and $\infty$-groupoids.


Abstract:
  Active matter comprises individually driven units that convert locally stored
energy into mechanical motion. Interactions between driven units lead to a
variety of non-equilibrium collective phenomena in active matter. One of such
phenomena is anomalously large density fluctuations, which have been observed
in both experiments and theories. Here we show that, on the contrary, density
fluctuations in active matter can also be greatly suppressed. Our experiments
are carried out with marine algae ($\it{Effrenium\ voratum}$) which swim in
circles at the air-liquid interfaces with two different eukaryotic flagella.
Cell swimming generates fluid flow which leads to effective repulsions between
cells in the far field. Long-range nature of such repulsive interactions
suppresses density fluctuations and generates disordered hyperuniform states
under a wide range of density conditions. Emergence of hyperuniformity and
associated scaling exponent are quantitatively reproduced in a numerical model
whose main ingredients are effective hydrodynamic interactions and uncorrelated
random cell motion. Our results demonstrate a new form of collective state in
active matter and suggest the possibility to use hydrodynamic flow for
self-assembly in active matter.


Abstract:
  We consider a fully directed self-avoiding walk model on a cubic lattice to
mimic the conformations of an infinitely long confined flexible polymer chain;
and the confinement condition is achieved by two parallel athermal plates. The
confined polymer system is under good solvent condition and we revisit this
problem to solve the real polymer's model for any length of chain and also for
any separation in between the plates. The equilibrium statistics of the
confined polymer chain is derived using an analytical calculations based on the
generating function technique. The force of the confinement, the surface
tension and the monomer density profile of confined chain is obtained. We
propose that a method of calculations is suitable to understand thermodynamics
of an arbitrary length confined polymer chain.


Abstract:
  With the recent advances of the Internet of Things, and the increasing
accessibility of ubiquitous computing resources and mobile devices, the
prevalence of rich media contents, and the ensuing social, economic, and
cultural changes, computing technology and applications have evolved quickly
over the past decade. They now go beyond personal computing, facilitating
collaboration and social interactions in general, causing a quick proliferation
of social relationships among IoT entities. The increasing number of these
relationships and their heterogeneous social features have led to computing and
communication bottlenecks that prevent the IoT network from taking advantage of
these relationships to improve the offered services and customize the delivered
content, known as relationship explosion. On the other hand, the quick advances
in artificial intelligence applications in social computing have led to the
emerging of a promising research field known as Artificial Social Intelligence
(ASI) that has the potential to tackle the social relationship explosion
problem. This paper discusses the role of IoT in social relationships detection
and management, the problem of social relationships explosion in IoT and
reviews the proposed solutions using ASI, including social-oriented
machine-learning and deep-learning techniques.


Abstract:
  The present paper is a continuation of earlier work by Gunnar Carlsson and
the first author on a motivic variant of the classical Becker-Gottlieb transfer
and an additivity theorem for such a transfer by the present authors. Here, we
establish a motivic variant of the classical Segal-Becker theorem relating the
classifying space of a 1-dimensional torus with the spectrum defining
(algebraic) K-theory.


Abstract:
  Graph Neural Networks have revolutionized many machine learning tasks in
recent years, ranging from drug discovery, recommendation systems, image
classification, social network analysis to natural language understanding. This
paper shows their efficacy in modeling relationships between products and
making predictions for unseen product networks. By representing products as
nodes and their relationships as edges of a graph, we show how an inductive
graph neural network approach, named GraphSAGE, can efficiently learn
continuous representations for nodes and edges. These representations also
capture product feature information such as price, brand, or engineering
attributes. They are combined with a classification model for predicting the
existence of the relationship between products. Using a case study of the
Chinese car market, we find that our method yields double the prediction
performance compared to an Exponential Random Graph Model-based method for
predicting the co-consideration relationship between cars. While a vanilla
GraphSAGE requires a partial network to make predictions, we introduce an
`adjacency prediction model' to circumvent this limitation. This enables us to
predict product relationships when no neighborhood information is known.
Finally, we demonstrate how a permutation-based interpretability analysis can
provide insights on how design attributes impact the predictions of
relationships between products. This work provides a systematic method to
predict the relationships between products in many different markets.


Abstract:
  In Survival Analysis, the observed lifetimes often correspond to individuals
for which the event occurs within a specific calendar time interval. With such
interval sampling, the lifetimes are doubly truncated at values determined by
the birth dates and the sampling interval. This double truncation may induce a
systematic bias in estimation, so specific corrections are needed. A relevant
target in Survival Analysis is the hazard rate function, which represents the
instantaneous probability for the event of interest. In this work we introduce
a flexible estimation approach for the hazard rate under double truncation.
Specifically, a kernel smoother is considered, in both a fully nonparametric
setting and a semiparametric setting in which the incidence process fits a
given parametric model. Properties of the kernel smoothers are investigated
both theoretically and through simulations. In particular, an asymptotic
expression of the mean integrated squared error is derived, leading to a
data-driven bandwidth for the estimators. The relevance of the semiparametric
approach is emphasized, in that it is generally more accurate and, importantly,
it avoids the potential issues of nonexistence or nonuniqueness of the fully
nonparametric estimator. Applications to the age of diagnosis of Acute Coronary
Syndrome (ACS) and AIDS incubation times are included.


Abstract:
  We are interested in dendrites for which all invariant measures of
zero-entropy mappings have discrete spectrum, and we prove that this holds when
the closure of the endpoint set of the dendrite is countable. This solves an
open question which was around for awhile, almost completing the
characterization of dendrites with this property.


Abstract:
  The Radial Basis Function-generated finite differences became a popular
variant of local meshless strong form methods due to its robustness regarding
the position of nodes and its controllable order of accuracy. In this paper, we
present a GPU accelerated numerical solution of Poisson's equation on scattered
nodes in 2D for orders from 2 up to 6. We specifically study the effect of
using different orders on GPU acceleration efficiency.


Abstract:
  This paper considers optimal control of a quadrotor unmanned aerial vehicles
(UAV) using the discrete-time, finite-horizon, linear quadratic regulator
(LQR). The state of a quadrotor UAV is represented as an element of the matrix
Lie group of double direct isometries, $SE_2(3)$. The nonlinear system is
linearized using a left-invariant error about a reference trajectory, leading
to an optimal gain sequence that can be calculated offline. The reference
trajectory is calculated using the differentially flat properties of the
quadrotor. Monte-Carlo simulations demonstrate robustness of the proposed
control scheme to parametric uncertainty, state-estimation error, and initial
error. Additionally, when compared to an LQR controller that uses a
conventional error definition, the proposed controller demonstrates better
performance when initial errors are large.


Abstract:
  We propose an alternative reconstruction for weighted essentially
non-oscillatory schemes with adaptive order (WENO-AO) for solving hyperbolic
conservation laws. The alternative reconstruction has a more concise form than
the original WENO-AO reconstruction. Moreover, it is a strictly convex
combination of polynomials with unequal degrees. Numerical examples show that
the alternative reconstruction maintains the accuracy and robustness of the
WENO-AO schemes.


Abstract:
  This thesis presents three results in geometric analysis. We first analyze
the curve-shortening flow on figure eight curves in the plane. Afterwards, we
examine the point-wise curvature preserving flow on space curves. Lastly, we
present an abridgment of our work on a family of three-dimensional Lie groups,
which, when equipped with canonical left-invariant metrics, interpolate between
Sol and hyperbolic space.


Abstract:
  We learn, in an unsupervised way, an embedding from sequences of radar images
that is suitable for solving place recognition problem using complex radar
data. We experiment on 280 km of data and show performance exceeding
state-of-the-art supervised approaches, localising correctly 98.38% of the time
when using just the nearest database candidate.


Abstract:
  We study the Monge-Ampere equation with some power nonlinear term. A solution
u is called to be Euclidean complete if it is an entire solution defined over
the whole R^n or its graph is a large hypersurface satisfying the large
condition on boundary \partial\Omega in case \Omega\not=R^n. In this paper, we
will give various sharp conditions on p and \Omega classifying the Euclidean
complete solution. Our results clarify and extend largely the existence theorem
of Cirstea-Trombetti (Calc. Var., 31, 2008, 167-186) for bounded convex domain
and p>n.


Abstract:
  Starting from the model in Koch-Vargiolu (2019), we test the real impact of
current renewable installed power in the electricity price in Italy, and assess
how much the renewable installation strategy which was put in place in Italy
deviated from the optimal one obtained from the model in the period 2012--2018.
To do so, we consider the Ornstein-Uhlenbeck (O-U) process, including an
exogenous increasing process influencing the mean reverting term, which is
interpreted as the current renewable installed power. Using real data of
electricity price, photovoltaic and wind energy production from the six main
Italian price zones, we estimate the parameters of the model and obtain
quantitative results, such as the production of photovoltaic energy impacts the
North zone, while wind is significant for Sardinia and the Central North zone
does not present electricity price impact. Then we implement the solution of
the singular optimal control problem of installing renewable power production
devices, in order to maximize the profit of selling the produced energy in the
market net of installation costs. We extend the results of \cite{KV} to the
case when no impact on power price is presented, and to the case when $N$
players can produce electricity by installing renewable power plants. We are
thus able to describe the optimal strategy and compare it with the real
installation strategy that was put in place in Italy.


Abstract:
  On 13 May 1787, a convict fleet of 11 ships left Portsmouth, England, on a
24,000 km, 8-month-long voyage to New South Wales. The voyage would take the
"First Fleet" under Captain Arthur Phillip via Tenerife (Canary Islands), the
port of Rio de Janeiro (Brazil), Table Bay at the southern extremity of the
African continent and the southernmost cape of present-day Tasmania to their
destination of Botany Bay. Given the navigation tools available at the time and
the small size of the convoy's ships, their safe arrival within a few days of
each other was a phenomenal achievement. This was particularly so, because they
had not lost a single ship and only a relatively small number of crew and
convicts. Phillip and his crew had only been able to ensure their success
because of the presence of crew members who were highly proficient in practical
astronomy, most notably Lieutenant William Dawes. We explore in detail his
educational background and the events leading up to Dawes' appointment by the
Board of Longitude as the convoy's dedicated astronomer-cum-Marine. In addition
to Dawes, John Hunter, second captain of the convoy's flagship H.M.S. Sirius,
Lieutenant William Bradley and Lieutenant Philip Gidley King were also experts
in navigation and longitude determination, using both chronometers and "lunar
distance" measurements. The historical record of the First Fleet's voyage is
remarkably accurate, even by today's standards.


Abstract:
  In this paper, we introduce a novel iterative algorithm which carries out
$\alpha$-divergence minimisation by ensuring a systematic decrease in the
$\alpha$-divergence at each step. In its most general form, our framework
allows us to simultaneously optimise the weights and components parameters of a
given mixture model. Notably, our approach permits to build on various methods
previously proposed for $\alpha$-divergence minimisation such as gradient or
power descent schemes. Furthermore, we shed a new light on an integrated
Expectation Maximization algorithm. We provide empirical evidence that our
methodology yields improved results, all the while illustrating the numerical
benefits of having introduced some flexibility through the parameter $\alpha$
of the $\alpha$-divergence.


Abstract:
  The scaling behavior for the rectification of bipolar nanopores is studied
using the Nernst-Planck equation coupled to the Local Equilibrium Monte Carlo
method. The bipolar nanopore's wall carries $\sigma$ and $-\sigma$ surface
charge densities in its two half regions axially. Scaling means that the device
function (rectification) depends on the system parameters (pore length, $H$,
pore radius, $R$, concentration, $c$, voltage, $U$, and surface charge density,
$\sigma$) via a single scaling parameter that is a smooth analytical function
of the system parameters. Here, we suggest using a modified Dukhin number,
$\mathrm{mDu}=|\sigma|l_{\mathrm{B}}^{*}\lambda_{\mathrm{D}}HU/(RU_{0})$, where
$l_{\mathrm{B}}^{*}=8\pi l_{\mathrm{B}}$, $l_{\mathrm{B}}$ is the Bjerrum
length, $\lambda_{\mathrm{D}}$ is the Debye length, and $U_{0}$ is a reference
voltage. We show how scaling depends on $H$, $U$, and $\sigma$ and through what
mechanisms these parameters influence the pore's behavior.


Abstract:
  We present the analytic calculation of two-loop master integrals that are
relevant for $tW$ production at hadron colliders. We focus on the integral
families with only one massive propagator. After choosing a canonical basis,
the differential equations for the master integrals can be transformed into the
$d$ln form. The boundaries are determined by simple direct integrations or
regularity conditions at kinematic points without physical singularities. The
analytical results in this work are expressed in terms of multiple
polylogarithms, and have been checked with numerical computations.


Abstract:
  Deep learning models are vulnerable to adversarial examples. As a more
threatening type for practical deep learning systems, physical adversarial
examples have received extensive research attention in recent years. However,
without exploiting the intrinsic characteristics such as model-agnostic and
human-specific patterns, existing works generate weak adversarial perturbations
in the physical world, which fall short of attacking across different models
and show visually suspicious appearance. Motivated by the viewpoint that
attention reflects the intrinsic characteristics of the recognition process,
this paper proposes the Dual Attention Suppression (DAS) attack to generate
visually-natural physical adversarial camouflages with strong transferability
by suppressing both model and human attention. As for attacking, we generate
transferable adversarial camouflages by distracting the model-shared similar
attention patterns from the target to non-target regions. Meanwhile, based on
the fact that human visual attention always focuses on salient items (e.g.,
suspicious distortions), we evade the human-specific bottom-up attention to
generate visually-natural camouflages which are correlated to the scenario
context. We conduct extensive experiments in both the digital and physical
world for classification and detection tasks on up-to-date models (e.g.,
Yolo-V5) and significantly demonstrate that our method outperforms
state-of-the-art methods.


Abstract:
  In the past few years, researches on advanced driver assistance systems
(ADASs) have been carried out and deployed in intelligent vehicles. Systems
that have been developed can perform different tasks, such as lane keeping
assistance (LKA), lane departure warning (LDW), lane change warning (LCW) and
adaptive cruise control (ACC). Real time lane detection and tracking (LDT) is
one of the most consequential parts to performing the above tasks. Images which
are extracted from the video, contain noise and other unwanted factors such as
variation in lightening, shadow from nearby objects and etc. that requires
robust preprocessing methods for lane marking detection and tracking.
Preprocessing is critical for the subsequent steps and real time performance
because its main function is to remove the irrelevant image parts and enhance
the feature of interest. In this paper, we survey preprocessing methods for
detecting lane marking as well as tracking lane boundaries in real time
focusing on vision-based system.


Abstract:
  Increasing volume of user-generated human-centric video content and their
applications, such as video retrieval and browsing, require compact
representations that are addressed by the video summarization literature.
Current supervised studies formulate video summarization as a
sequence-to-sequence learning problem and the existing solutions often neglect
the surge of human-centric view, which inherently contains affective content.
In this study, we investigate the affective-information enriched supervised
video summarization task for human-centric videos. First, we train a visual
input-driven state-of-the-art continuous emotion recognition model (CER-NET) on
the RECOLA dataset to estimate emotional attributes. Then, we integrate the
estimated emotional attributes and the high-level representations from the
CER-NET with the visual information to define the proposed affective video
summarization architectures (AVSUM). In addition, we investigate the use of
attention to improve the AVSUM architectures and propose two new architectures
based on temporal attention (TA-AVSUM) and spatial attention (SA-AVSUM). We
conduct video summarization experiments on the TvSum database. The proposed
AVSUM-GRU architecture with an early fusion of high level GRU embeddings and
the temporal attention based TA-AVSUM architecture attain competitive video
summarization performances by bringing strong performance improvements for the
human-centric videos compared to the state-of-the-art in terms of F-score and
self-defined face recall metrics.


Abstract:
  Accurate lighting estimation is challenging yet critical to many computer
vision and computer graphics tasks such as high-dynamic-range (HDR) relighting.
Existing approaches model lighting in either frequency domain or spatial domain
which is insufficient to represent the complex lighting conditions in scenes
and tends to produce inaccurate estimation. This paper presents NeedleLight, a
new lighting estimation model that represents illumination with needlets and
allows lighting estimation in both frequency domain and spatial domain jointly.
An optimal thresholding function is designed to achieve sparse needlets which
trims redundant lighting parameters and demonstrates superior localization
properties for illumination representation. In addition, a novel spherical
transport loss is designed based on optimal transport theory which guides to
regress lighting representation parameters with consideration of the spatial
information. Furthermore, we propose a new metric that is concise yet effective
by directly evaluating the estimated illumination maps rather than rendered
images. Extensive experiments show that NeedleLight achieves superior lighting
estimation consistently across multiple evaluation metrics as compared with
state-of-the-art methods.


Abstract:
  We evaluate the rotational velocity of stars observed by the Pristine survey
towards the Galactic anticentre, spanning a wide range of metallicities from
the extremely metal-poor regime ($\mathrm{[Fe/H]}<-3$) to nearly solar
metallicity. In the Galactic anticentre direction, the rotational velocity
($V_{\phi}$) is similar to the tangential velocity in the galactic longitude
direction ($V_{\ell}$). This allows us to estimate $V_{\phi}$ from Gaia early
data-release 3 (Gaia EDR3) proper motions for stars without radial velocity
measurements. This substantially increases the sample of stars in the outer
disc with estimated rotational velocities. Our stellar sample towards the
anticentre is dominated by a kinematical thin disc with a mean rotation of
$\sim -220$ km $\mathrm{s}^{-1}$. However, our analysis reveals the presence of
more stellar substructures. The most intriguing is a well populated extension
of the kinematical thin disc down to $\mathrm{[Fe/H]} \sim -2$. A scarser fast
rotating population reaching the extremely metal-poor regime, down to
$\mathrm{[Fe/H]} \sim -3.5$ is also detected, but without statistical
significance to unambiguously state whether this is the extremely metal-poor
extension of the thin disc or the high rotating tail of hotter structures (like
the thick disc or the halo). In addition, a more slowly rotating kinematical
thick disc component is also required to explain the observed $V_{\ell}$
distribution at $\mathrm{[Fe/H]} > -1.5$. Furthermore, we detect signatures of
a "heated disc", the so-called Splash, at metallicities higher than $\sim-1.5$.
Finally, at $\mathrm{[Fe/H]} < -1.5$ our anticentre sample is dominated by a
kinematical halo with a net prograde motion.


Abstract:
  Van Zuylen et al. introduced the notion of a popular ranking in a voting
context, where each voter submits a strictly-ordered list of all candidates. A
popular ranking $\pi$ of the candidates is at least as good as any other
ranking $\sigma$ in the following sense: if we compare $\pi$ to $\sigma$, at
least half of all voters will always weakly prefer~$\pi$. Whether a voter
prefers one ranking to another is calculated based on the Kendall distance.
  A more traditional definition of popularity -- as applied to popular
matchings, a well-established topic in computational social choice -- is
stricter, because it requires at least half of the voters \emph{who are not
indifferent between $\pi$ and $\sigma$} to prefer~$\pi$. In this paper, we
derive structural and algorithmic results in both settings, also improving upon
the results by van Zuylen et al. We also point out strong connections to the
famous open problem of finding a Kemeny consensus with 3 voters.


Abstract:
  This contribution is a review of the deep and powerful connection between the
large scale properties of critical systems and their description in terms of a
field theory. Although largely applicable to many other models, the details of
this connection are illustrated in the class of two-dimensional Abelian
sandpile models. Bulk and boundary height variables, spanning tree related
observables, boundary conditions and dissipation are all discussed in this
context and found to have a proper match in the field theoretic description.


Abstract:
  Graph convolutional neural networks (GCNNs) are nonlinear processing tools to
learn representations from network data. A key property of GCNNs is their
stability to graph perturbations. Current analysis considers deterministic
perturbations but fails to provide relevant insights when topological changes
are random. This paper investigates the stability of GCNNs to stochastic graph
perturbations induced by link losses. In particular, it proves the expected
output difference between the GCNN over random perturbed graphs and the GCNN
over the nominal graph is upper bounded by a factor that is linear in the link
loss probability. We perform the stability analysis in the graph spectral
domain such that the result holds uniformly for any graph. This result also
shows the role of the nonlinearity and the architecture width and depth, and
allows identifying handle to improve the GCNN robustness. Numerical simulations
on source localization and robot swarm control corroborate our theoretical
findings.


Abstract:
  Potential strategies for the development and large-scale application of
renewable energy sources aimed at reducing the usage of carbon-based fossil
fuels are assessed here, especially in the event of the abandonment of such
fuels. The aim is to aid the initiative to reduce the harmful effects of
carbon-based fossil fuels on the environment and ensure a reduction in
greenhouse gases and sustainability of natural resources. Small-scale renewable
energy application for heating, cooling, and electricity generation in
households and commercial buildings are already underway around the world.
Hydrogen (H2) and ammonia (NH3), which are presently produced using fossil
fuels, already have significant applications in society and industry, and are
therefore good candidates for large-scale production through the use of
renewable energy sources. This will help to reduce the greenhouse gas emissions
appreciably around the world. While the first-generation biofuels production
using food crops may not be suitable for long-range fuel production, due to
competition with the food supply, the 2nd, 3rd and 4th generation biofuels have
the potential to produce large, worldwide supplies of fuels. Production of
advanced biofuels will not increase the emission of greenhouse gases, and the
ammonia produced through the use of renewable energy resources will serve as
fertilizer for biofuels production. The perspective of renewable energy
sources, such as technology status, economics, overall environmental benefits,
obstacles for commercialization, relative competitiveness of various renewable
energy sources, etc., are also discussed whenever applicable.


Abstract:
  Interactions among multiple time series of positive random variables are
crucial in diverse financial applications, from spillover effects to volatility
interdependence. A popular model in this setting is the vector Multiplicative
Error Model (vMEM) which poses a linear iterative structure on the dynamics of
the conditional mean, perturbed by a multiplicative innovation term. A main
limitation of vMEM is however its restrictive assumption on the distribution of
the random innovation term. A Bayesian semiparametric approach that models the
innovation vector as an infinite location-scale mixture of multidimensional
kernels with support on the positive orthant is used to address this major
shortcoming of vMEM. Computational complications arising from the constraints
to the positive orthant are avoided through the formulation of a slice sampler
on the parameter-extended unconstrained version of the model. The method is
applied to simulated and real data and a flexible specification is obtained
that outperforms the classical ones in terms of fitting and predictive power.


Abstract:
  Kagome magnets are believed to have numerous exotic physical properties due
to the possible interplay between lattice geometry, electron correlation and
band topology. Here, we report the large anomalous Hall effect in the kagome
ferromagnet LiMn$_6$Sn$_6$, which has a Curie temperature of 382 K and easy
plane along with the kagome lattice. At low temperatures, unsaturated positive
magnetoresistance and opposite signs of ordinary Hall coefficient for
$\rho_{xz}$ and $\rho_{yx}$ indicate the coexistence of electrons and holes in
the system. A large intrinsic anomalous Hall conductivity of 380 $\Omega^{-1}$
cm$^{-1}$, or 0.44 $e^2/h$ per Mn layer, is observed in $\sigma_{xy}^A$. This
value is significantly larger than those in other $R$Mn$_6$Sn$_6$ ($R$ = rare
earth elements) kagome compounds. Band structure calculations show several band
crossings, including a spin-polarized Dirac point at the K point, close to the
Fermi energy. The calculated intrinsic Hall conductivity agrees well with the
experimental value, and shows a maximum peak near the Fermi energy. We
attribute the large anomalous Hall effect in LiMn$_6$Sn$_6$ to the band
crossings closely located near the Fermi energy.


Abstract:
  Prior work has proved that Translation memory (TM) can boost the performance
of Neural Machine Translation (NMT). In contrast to existing work that uses
bilingual corpus as TM and employs source-side similarity search for memory
retrieval, we propose a new framework that uses monolingual memory and performs
learnable memory retrieval in a cross-lingual manner. Our framework has unique
advantages. First, the cross-lingual memory retriever allows abundant
monolingual data to be TM. Second, the memory retriever and NMT model can be
jointly optimized for the ultimate translation goal. Experiments show that the
proposed method obtains substantial improvements. Remarkably, it even
outperforms strong TM-augmented NMT baselines using bilingual TM. Owning to the
ability to leverage monolingual data, our model also demonstrates effectiveness
in low-resource and domain adaptation scenarios.


Abstract:
  The use of Reinforcement Learning (RL) agents in practical applications
requires the consideration of suboptimal outcomes, depending on the familiarity
of the agent with its environment. This is especially important in
safety-critical environments, where errors can lead to high costs or damage. In
distributional RL, the risk-sensitivity can be controlled via different
distortion measures of the estimated return distribution. However, these
distortion functions require an estimate of the risk level, which is difficult
to obtain and depends on the current state. In this work, we demonstrate the
suboptimality of a static risk level estimation and propose a method to
dynamically select risk levels at each environment step. Our method ARA
(Automatic Risk Adaptation) estimates the appropriate risk level in both known
and unknown environments using a Random Network Distillation error. We show
reduced failure rates by up to a factor of 7 and improved generalization
performance by up to 14% compared to both risk-aware and risk-agnostic agents
in several locomotion environments.


Abstract:
  We extend the theory of Interacting Hopf algebras with an order primitive,
and give a sound and complete axiomatisation of the prop of polyhedral cones.
Next, we axiomatise an affine extension and prove soundness and completeness
for the prop of polyhedra.


Abstract:
  Accurate phase diagrams of multicomponent plasmas are required for the
modeling of dense stellar plasmas, such as those found in the cores of white
dwarf stars and the crusts of neutron stars. Those phase diagrams have been
computed using a variety of standard techniques, which suffer from physical and
computational limitations. Here, we present an efficient and accurate method
that overcomes the drawbacks of previously used approaches. In particular,
finite-size effects are avoided as each phase is calculated separately; the
plasma electrons and volume changes are explicitly taken into account; and
arbitrary analytic fits to simulation data are avoided. Furthermore, no
simulations at uninteresting state conditions, i.e., away from the phase
coexistence curves, are required, which improves the efficiency of the
technique. The method consists of an adaptation of the so-called Gibbs-Duhem
integration approach to electron-ion plasmas, where the coexistence curve is
determined by direct numerical integration of its underlying Clapeyron
equation. The thermodynamics properties of the coexisting phases are evaluated
separately using Monte Carlo simulations in the isobaric semi-grand canonical
ensemble. We describe this Monte Carlo-based Clapeyron integration method,
including its basic principles, our extension to electron-ion plasmas, and our
numerical implementation. We illustrate its applicability and benefits with the
calculation of the melting curve of dense C/O plasmas under conditions relevant
for white dwarf cores and provide analytic fits to implement this new melting
curve in white dwarf models. While this work focuses on the liquid-solid phase
boundary of dense two-component plasmas, a wider range of physical systems and
phase boundaries are within the scope of the Clapeyron integration method,
which had until now only been applied to simple model systems of neutral
particles.


Abstract:
  We prove that if there is an elementary embedding from the universe to
itself, then there is a proper class of measurable successor cardinals.


Abstract:
  In the present paper, we first give a detailed study on the pQCD corrections
to the leading-twist part of BSR. Previous pQCD corrections to the
leading-twist part derived under conventional scale-setting approach up to
${\cal O}(\alpha_s^4)$-level still show strong renormalization scale
dependence. The principle of maximum conformality (PMC) provides a systematic
way to eliminate conventional renormalization scale-setting ambiguity by
determining the accurate $\alpha_s$-running behavior of the process with the
help of renormalization group equation. Our calculation confirms the PMC
prediction satisfies the standard renormalization group invariance, e.g. its
fixed-order prediction does scheme-and-scale independent. In low $Q^2$-region,
the effective momentum of the process is small and to have a reliable
prediction, we adopt four low-energy $\alpha_s$ models to do the analysis. Our
predictions show that even though the high-twist terms are generally power
suppressed in high $Q^2$-region, they shall have sizable contributions in low
and intermediate $Q^2$ domain. By using the more accurate scheme-and-scale
independent pQCD prediction, we present a novel fit of the non-perturbative
high-twist contributions by comparing with the JLab data.


Abstract:
  We investigate a result on convergence of double sequences of numbers and how
it extends to measurable functions.


Abstract:
  The problem of joint design of transmit waveforms and receive filters is
desirable in many application scenarios of multiple-input multiple-output
(MIMO) radar systems. In this paper, the joint design problem is investigated
under the signal-to-interference-plus-noise ratio (SINR) performance metric, in
which case the problem is formulated to maximize the SINR at the receiver side
subject to some practical transmit waveform constraints. A numerical algorithm
is proposed for problem resolution based on the manifold optimization method,
which has been shown to be powerful and flexible to address nonconvex
constrained optimization problems in many engineering applications. The
proposed algorithm is able to efficiently solve the SINR maximization problem
with different waveform constraints under a unified framework. Numerical
experiments show that the proposed algorithm outperforms the existing
benchmarks in terms of computation efficiency and achieves comparable SINR
performance.


Abstract:
  Here, we report successful single crystal growth of SnSb2Te4 using the
self-flux method. Unidirectional crystal growth is confirmed through X Ray
Diffraction (XRD) pattern taken on mechanically cleaved crystal flake while the
rietveld refined Powder XRD (PXRD) pattern confirms the phase purity of the
grown crystal. Scanning Electron Microscopy (SEM) image and Energy Dispersive
X-Ray analysis (EDAX) confirm crystalline morphology and exact stoichiometry of
constituent elements. Vibrational Modes observed in Raman spectra also confirm
the formation of the SnSb2Te4 phase. DC resistivity measurements confirm the
metallic character of the grown crystal. Magneto-transport measurements up to
5T show a nonsaturating low magneto-resistance percentage. V type cusp and
Hikami Larkin Nagaoka (HLN) fitting at lower field confirms the Weak
Anti-localization (WAL) effect in SnSb2Te4. Density Functional Theory (DFT)
calculations were showing topological non-trivial electronic band structure. It
is the first-ever report on MR study and WAL analysis of SnSb2Te4 single
crystal.


Abstract:
  We consider controlling the false discovery rate for testing many time series
with an unknown cross-sectional correlation structure. Given a large number of
hypotheses, false and missing discoveries can plague an analysis. While many
procedures have been proposed to control false discovery, most of them either
assume independent hypotheses or lack statistical power. A problem of
particular interest is in financial asset pricing, where the goal is to
determine which ``factors" lead to excess returns out of a large number of
potential factors. Our contribution is two-fold. First, we show the consistency
of Fama and French's prominent method under multiple testing. Second, we
propose a novel method for false discovery control using double bootstrapping.
We achieve superior statistical power to existing methods and prove that the
false discovery rate is controlled. Simulations and a real data application
illustrate the efficacy of our method over existing methods.


Abstract:
  In this paper, we study how to efficiently and reliably detect active devices
and estimate their channels in a multiple-input multiple-output (MIMO)
orthogonal frequency-division multiplexing (OFDM) based grant-free
non-orthogonal multiple access (NOMA) system to enable massive machine-type
communications (mMTC). First, by exploiting the correlation of the channel
frequency responses in narrow-band mMTC, we propose a block-wise linear channel
model. Specifically, the continuous OFDM subcarriers in the narrow-band are
divided into several sub-blocks and a linear function with only two variables
(mean and slope) is used to approximate the frequency-selective channel in each
sub-block. This significantly reduces the number of variables to be determined
in channel estimation and the sub-block number can be adjusted to reliably
compensate the channel frequency-selectivity. Second, we formulate the joint
active device detection and channel estimation in the block-wise linear system
as a Bayesian inference problem. By exploiting the block-sparsity of the
channel matrix, we develop an efficient turbo message passing (Turbo-MP)
algorithm to resolve the Bayesian inference problem with near-linear
complexity. We further incorporate machine learning approaches into Turbo-MP to
learn unknown prior parameters. Numerical results demonstrate the superior
performance of the proposed algorithm over state-of-the-art algorithms.


Abstract:
  The quest for nonmagnetic Weyl semimetals with high tunability of phase has
remained a demanding challenge. As the symmetry breaking control parameter, the
ferroelectric order can be steered to turn on/off the Weyl semimetals phase,
adjust the band structures around the Fermi level, and enlarge/shrink the
momentum separation of Weyl nodes which generate the Berry curvature as the
emergent magnetic field. Here, we report the realization of a ferroelectric
nonmagnetic Weyl semimetal based on indium doped Pb1 xSnxTe alloy where the
underlying inversion symmetry as well as mirror symmetry is broken with the
strength of ferroelectricity adjustable via tuning indium doping level and
Sn/Pb ratio. The transverse thermoelectric effect, i.e., Nernst effect both for
out of plane and in plane magnetic field geometry, is exploited as a Berry
curvature sensitive experimental probe to manifest the generation of Berry
curvature via the redistribution of Weyl nodes under magnetic fields. The
results demonstrate a clean non-magnetic Weyl semimetal coupled with highly
tunable ferroelectric order, providing an ideal platform for manipulating the
Weyl fermions in nonmagnetic system.


Abstract:
  The processes of the coronal plasma heating and cooling were previously shown
to significantly affect the dynamics of slow magnetoacoustic (MA) waves,
causing amplification or attenuation, and also dispersion. However, the entropy
mode is also excited in such a thermodynamically active plasma and is affected
by the heating/cooling misbalance too. This mode is usually associated with the
phenomenon of coronal rain and formation of prominences. Unlike the adiabatic
plasmas, the properties and evolution of slow MA and entropy waves in
continuously heated and cooling plasmas get mixed. Different regimes of the
misbalance lead to a variety of scenarios for the initial perturbation to
evolve. In order to describe properties and evolution of slow MA and entropy
waves in various regimes of the misbalance, we obtained an exact analytical
solution of the linear evolutionary equation. Using the characteristic
timescales and the obtained exact solution, we identified regimes with
qualitatively different behaviour of slow MA and entropy modes. For some of
those regimes, the spatio-temporal evolution of the initial Gaussian pulse is
shown. In particular, it is shown that slow MA modes may have a range of
non-propagating harmonics. In this regime, perturbations caused by slow MA and
entropy modes in a low-$\beta$ plasma would look identically in observations,
as non-propagating disturbances of the plasma density (and temperature) either
growing or decaying with time. We also showed that the partition of the initial
energy between slow MA and entropy modes depends on the properties of the
heating and cooling processes involved. The obtained exact analytical solution
could be further applied to the interpretation of observations and results of
numerical modelling of slow MA waves in the corona and the formation and
evolution of coronal rain.


Abstract:
  We discuss how colour flows can be used to simplify the computation of matrix
elements, and in the context of parton shower Monte Carlos with accuracy beyond
leading-colour. We show that, by systematically employing them, the results for
tree-level matrix elements and their soft limits can be given in a closed form
that does not require any colour algebra. The colour flows that we define are a
natural generalization of those exploited by existing Monte Carlos; we
construct their representations in terms of different but conceptually
equivalent quantities, namely colour loops and dipole graphs, and examine how
these objects may help to extend the accuracy of Monte Carlos through the
inclusion of subleading-colour effects. We show how the results that we obtain
can be used, with trivial modifications, in the context of QCD+QED simulations,
since we are able to put the gluon and photon soft-radiation patterns on the
same footing. We also comment on some peculiar properties of gluon-only colour
flows, and their relationships with established results in the mathematics of
permutations.


Abstract:
  State-of-the-art music recommender systems are based on collaborative
filtering, which builds upon learning similarities between users and songs from
the available listening data. These approaches inherently face the cold-start
problem, as they cannot recommend novel songs with no listening history.
Content-aware recommendation addresses this issue by incorporating content
information about the songs on top of collaborative filtering. However, methods
falling in this category rely on a shallow user/item interaction that
originates from a matrix factorization framework. In this work, we introduce
neural content-aware collaborative filtering, a unified framework which
alleviates these limits, and extends the recently introduced neural
collaborative filtering to its content-aware counterpart. We propose a
generative model which leverages deep learning for both extracting content
information from low-level acoustic features and for modeling the interaction
between users and songs embeddings. The deep content feature extractor can
either directly predict the item embedding, or serve as a regularization prior,
yielding two variants (strict} and relaxed) of our model. Experimental results
show that the proposed method reaches state-of-the-art results for a cold-start
music recommendation task. We notably observe that exploiting deep neural
networks for learning refined user/item interactions outperforms approaches
using a more simple interaction model in a content-aware framework.


Abstract:
  An automated treatment of iterated integrals based on letters induced by
real-valued quadratic forms and Kummer--Poincar\'e letters is presented. These
quantities emerge in analytic single and multi--scale Feynman diagram
calculations. To compactify representations, one wishes to apply general
properties of these quantities in computer-algebraic implementations. We
provide the reduction to basis representations, expansions, analytic
continuation and numerical evaluation of these quantities.


Abstract:
  As shown in earlier work, skew-adjoint linear differential operators, mapping
efforts into flows, give rise to Dirac structures on a bounded spatial domain
by a proper definition of boundary variables. In the present paper this is
extended to pairs of linear differential operators defining a formally
skew-adjoint relation between flows and efforts. Furthermore it is shown how
the underlying repeated integration by parts operation can be streamlined by
the use of two-variable polynomial calculus. Dirac structures defined by
formally skew adjoint operators and differential operator effort constraints
are treated within the same framework. Finally it is sketched how the approach
can be also used for Lagrangian subspaces on bounded domains.


Abstract:
  Recent observations made with Advanced LIGO and Advanced Virgo have initiated
the era of gravitational-wave astronomy. The number of events detected by these
"2nd Generation" (2G) ground-based observatories is partially limited by noise
arising from temperature-induced position fluctuations of the test mass mirror
surfaces used for probing spacetime dynamics. The design of next-generation
gravitational-wave observatories addresses this limitation by using
cryogenically cooled test masses; current approaches for continuously removing
heat (resulting from absorbed laser light) rely on heat extraction via
black-body radiation or conduction through suspension fibres. As a
complementing approach for extracting heat during observational runs, we
investigate cooling via helium gas impinging on the test mass in free molecular
flow. We establish a relation between cooling power and corresponding
displacement noise, based on analytical models, which we compare to numerical
simulations. Applying this theoretical framework with regard to the conceptual
design of the Einstein Telescope (ET), we find a cooling power of 10 mW at 18 K
for a gas pressure that exceeds the ET design strain noise goal by at most a
factor of $\sim 3$ in the signal frequency band from 3 to 11 Hz. A cooling
power of 100 mW at 18 K corresponds to a gas pressure that exceeds the ET
design strain noise goal by at most a factor of $\sim 11$ in the band from 1 to
28 Hz.


Abstract:
  In 1955, Lehto showed that, for every measurable function $\psi$ on the unit
circle $\mathbb T,$ there is function $f$ holomorphic in the unit disc $\mathbb
D,$ having $\psi$ as radial limit a.e. on $\mathbb T.$ We consider an analogous
boundary value problem, where the unit disc is replaced by a Stein domain on a
complex manifold and radial approach to a boundary point $p$ is replaced by
(asymptotically) total approach to $p.$


Abstract:
  How difficult are interactive theorem provers to use? We respond by reviewing
the formalization of Hilbert's tenth problem in Isabelle/HOL carried out by an
undergraduate research group at Jacobs University Bremen. We argue that, as
demonstrated by our example, proof assistants are feasible for beginners to
formalize mathematics. With the aim to make the field more accessible, we also
survey hurdles that arise when learning an interactive theorem prover. Broadly,
we advocate for an increased adoption of interactive theorem provers in
mathematical research and curricula.


Abstract:
  The ALS-U light source will implement on-axis single-train swap-out injection
employing an accumulator between the booster and storage rings. The accumulator
ring design is a twelve period triple-bend achromat that will be installed
along the inner circumference of the storage-ring tunnel. A non-conventional
injection scheme will be utilized for top-off off-axis injection from the
booster into the accumulator ring meant to accommodate a large $\sim 300$~nm
emittance beam into a vacuum-chamber with a limiting horizontal aperture radius
as small as $8$ mm. The scheme incorporates three dipole kickers distributed
over three sectors, with two kickers perturbing the stored beam and the third
affecting both the stored and the injected beam trajectories. This paper
describes this ``3DK'' injection scheme and how it fits the accumulator ring's
particular requirements. We describe the design and optimization process, and
how we evaluated its fitness as a solution for booster-to-accumulator ring
injection.


Abstract:
  Quantum channels, which break entanglement, incompatibility, or nonlocality,
are not useful for entanglement-based, one-sided device-independent, or
device-independent quantum information processing, respectively. Here, we show
that such breaking channels are related to certain temporal quantum
correlations, i.e., temporal separability, channel unsteerability, temporal
unsteerability, and macrorealism. More specifically, we first define the
steerability-breaking channel, which is conceptually similar to the
entanglement and nonlocality-breaking channels and prove that it is identical
to the incompatibility-breaking channel. Similar to the hierarchy relations of
the temporal and spatial quantum correlations, the hierarchy of non-breaking
channels is discussed. We then introduce the concept of the channels which
break temporal correlations, explain how they are related to the standard
breaking channels, and prove the following results: (1) A certain measure of
temporal nonseparability can be used to quantify a non-entanglement-breaking
channel in the sense that the measure is a memory monotone under the framework
of the resource theory of the quantum memory. (2) A non-steerability-breaking
channel can be certified with channel steering because the
steerability-breaking channel is equivalent to the incompatibility-breaking
channel. (3) The temporal steerability and non-macrorealism can, respectively,
distinguish the steerability-breaking and the nonlocality-breaking unital
channel from their corresponding non-breaking channels. Finally, a
two-dimensional depolarizing channel is experimentally implemented as a
proof-of-principle example to compare the temporal quantum correlations with
non-breaking channels.


Abstract:
  We obtain exact densities of contractible and non-contractible loops in the
O(1) model on a strip of the square lattice rolled into an infinite cylinder of
finite even circumference $L$. They are also equal to the densities of critical
percolation clusters on forty five degree rotated square lattice rolled into a
cylinder, which do not or do wrap around the cylinder respectively. The results
are presented as explicit rational functions of $L$ taking rational values for
any even $L$. Their asymptotic expansions in the large $L$ limit have
irrational coefficients reproducing the earlier results in the leading orders.
The solution is based on a mapping to the six-vertex model and the use of
technique of Baxter's T-Q equation.


Abstract:
  To date, close to fifty presumed black hole binary mergers were observed by
the LIGO and Virgo detectors. The analyses have been done with an assumption
that these objects are black holes by limiting the spin prior to the Kerr
bound. However, the above assumption is not valid for superspinars, which have
the Kerr geometry but rotate beyond the Kerr bound. In this study, we
investigate whether and how the limited spin prior range causes a bias in
parameter estimation for superspinars if they are detected. To this end, we
estimate binary parameters of the simulated inspiral signals of the
gravitational waves of compact binaries by assuming that at least one component
of them is a superspinar. We have found that when the primary is a superspinar,
both mass and spin parameters are biased in parameter estimation due to the
limited spin prior range. In this case, the extended prior range is strongly
favored compared to the limited one. On the other hand, when the primary is a
black hole, we do not see much bias in parameter estimation due to the limited
spin prior range, even though the secondary is a superspinar. We also apply the
analysis to black hole binary merger events GW170608 and GW190814, which have a
long and loud inspiral signal. We do not see any preference of superspinars
from the model selection for both events. We conclude that the extension of the
spin prior range is necessary for accurate parameter estimation if highly
spinning primary objects are found, while it is difficult to identify
superspinars if they are only the secondary objects. Nevertheless, the bias in
parameter estimation of spin for the limited spin prior range can be a clue of
the existence of superspinars.


Abstract:
  Image classification models deployed in the real world may receive inputs
outside the intended data distribution. For critical applications such as
clinical decision making, it is important that a model can detect such
out-of-distribution (OOD) inputs and express its uncertainty. In this work, we
assess the capability of various state-of-the-art approaches for
confidence-based OOD detection through a comparative study and in-depth
analysis. First, we leverage a computer vision benchmark to reproduce and
compare multiple OOD detection methods. We then evaluate their capabilities on
the challenging task of disease classification using chest X-rays. Our study
shows that high performance in a computer vision task does not directly
translate to accuracy in a medical imaging task. We analyse factors that affect
performance of the methods between the two tasks. Our results provide useful
insights for developing the next generation of OOD detection methods.


Abstract:
  Referring to a recent experiment, we theoretically study the process of a
two-channel decay of the diatomic silver anion (Ag$_2^-$), namely the
spontaneous electron ejection giving Ag$_2$ + e$^-$ and the dissociation
leading to Ag$^-$ + Ag. The ground state potential energy curves of the silver
molecules of diatomic neutral and negative ion were calculated using proper
pseudo-potentials and atomic basis sets. We also estimated the non-adiabatic
electronic coupling between the ground state of Ag$_2^-$ and the ground state
of Ag$_2$ + e$^-$, which in turn allowed us to estimate the minimal and mean
values of the electron autodetachment lifetimes. The relative energies of the
rovibrational levels allow the description of the spontaneous electron emission
process, while the description of the rotational dissociation is treated with
the quantum dynamics method as well as time-independent methods. The results of
our calculations are verified by comparison with experimental data.


Abstract:
  To manage the COVID-19 epidemic effectively, decision-makers in public health
need accurate forecasts of case numbers. A potential near real-time predictor
of future case numbers is human mobility; however, research on the predictive
power of mobility is lacking. To fill this gap, we introduce a novel model for
epidemic forecasting based on mobility data, called mobility marked Hawkes
model. The proposed model consists of three components: (1) A Hawkes process
captures the transmission dynamics of infectious diseases. (2) A mark modulates
the rate of infections, thus accounting for how the reproduction number R
varies across space and time. The mark is modeled using a regularized Poisson
regression based on mobility covariates. (3) A correction procedure
incorporates new cases seeded by people traveling between regions. Our model
was evaluated on the COVID-19 epidemic in Switzerland. Specifically, we used
mobility data from February through April 2020, amounting to approximately 1.5
billion trips. Trip counts were derived from large-scale telecommunication
data, i.e., cell phone pings from the Swisscom network, the largest
telecommunication provider in Switzerland. We compared our model against
various state-of-the-art baselines in terms of out-of-sample root mean squared
error. We found that our model outperformed the baselines by 15.52%. The
improvement was consistently achieved across different forecast horizons
between 5 and 21 days. In addition, we assessed the predictive power of
conventional point of interest data, confirming that telecommunication data is
superior. To the best of our knowledge, our work is the first to predict the
spread of COVID-19 from telecommunication data. Altogether, our work
contributes to previous research by developing a scalable early warning system
for decision-makers in public health tasked with controlling the spread of
infectious diseases.


Abstract:
  Dropout has been demonstrated as a simple and effective module to not only
regularize the training process of deep neural networks, but also provide the
uncertainty estimation for prediction. However, the quality of uncertainty
estimation is highly dependent on the dropout probabilities. Most current
models use the same dropout distributions across all data samples due to its
simplicity. Despite the potential gains in the flexibility of modeling
uncertainty, sample-dependent dropout, on the other hand, is less explored as
it often encounters scalability issues or involves non-trivial model changes.
In this paper, we propose contextual dropout with an efficient structural
design as a simple and scalable sample-dependent dropout module, which can be
applied to a wide range of models at the expense of only slightly increased
memory and computational cost. We learn the dropout probabilities with a
variational objective, compatible with both Bernoulli dropout and Gaussian
dropout. We apply the contextual dropout module to various models with
applications to image classification and visual question answering and
demonstrate the scalability of the method with large-scale datasets, such as
ImageNet and VQA 2.0. Our experimental results show that the proposed method
outperforms baseline methods in terms of both accuracy and quality of
uncertainty estimation.


Abstract:
  The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with
currently no effective antiviral drug except treatments for symptomatic
therapy. Flux balance analysis is an efficient method to analyze metabolic
networks. It allows optimizing for a metabolic function and thus e.g.,
predicting the growth rate of a specific cell or the production rate of a
metabolite of interest. Here flux balance analysis was applied on human lung
cells infected with severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) to reposition metabolic drugs and drug combinations against the
replication of the SARS-CoV-2 virus within the host tissue. Making use of
expression data sets of infected lung tissue, genome-scale COVID-19-specific
metabolic models were reconstructed. Then host-specific essential genes and
gene-pairs were determined through in-silico knockouts that permit reducing the
viral biomass production without affecting the host biomass. Key pathways that
are associated with COVID-19 severity in lung tissue are related to oxidative
stress, as well as ferroptosis, sphingolipid metabolism, cysteine metabolism,
and fat digestion. By in-silico screening of FDA approved drugs on the putative
disease-specific essential genes and gene-pairs, 45 drugs and 99 drug
combinations were predicted as promising candidates for COVID-19 focused drug
repositioning (https://github.com/sysbiolux/DCcov). Among the 45 drug
candidates, six antiviral drugs were found and seven drugs that are being
tested in clinical trials against COVID-19. Other drugs like gemcitabine,
rosuvastatin and acetylcysteine, and drug combinations like
azathioprine-pemetrexed might offer new chances for treating COVID-19.


Abstract:
  The ensemble covariance matrix of a wide sense stationary signal spatially
sampled by a full linear array is positive semi-definite and Toeplitz. However,
the direct augmented covariance matrix of an augmentable sparse array is
Toeplitz but not positive semi-definite, resulting in negative eigenvalues that
pose inherent challenges in its applications, including model order estimation
and source localization. The positive eigenvalues-based covariance matrix for
augmentable sparse arrays is robust but the matrix is unobtainable when all
noise eigenvalues of the direct augmented matrix are negative, which is a
possible case. To address this problem, we propose a robust covariance matrix
for augmentable sparse arrays that leverages both positive and negative noise
eigenvalues. The proposed covariance matrix estimate can be used in conjunction
with subspace based algorithms and adaptive beamformers to yield accurate
signal direction estimates.


Abstract:
  Application of Machine Learning algorithms to the medical domain is an
emerging trend that helps to advance medical knowledge. At the same time, there
is a significant a lack of explainable studies that promote informed,
transparent, and interpretable use of Machine Learning algorithms. In this
paper, we present explainable multi-class classification of the Covid-19 mental
health data. In Machine Learning study, we aim to find the potential factors to
influence a personal mental health during the Covid-19 pandemic. We found that
Random Forest (RF) and Gradient Boosting (GB) have scored the highest accuracy
of 68.08% and 68.19% respectively, with LIME prediction accuracy 65.5% for RF
and 61.8% for GB. We then compare a Post-hoc system (Local Interpretable
Model-Agnostic Explanations, or LIME) and an Ante-hoc system (Gini Importance)
in their ability to explain the obtained Machine Learning results. To the best
of these authors knowledge, our study is the first explainable Machine Learning
study of the mental health data collected during Covid-19 pandemics.


Abstract:
  We consider the problem of mapping a logical quantum circuit onto a given
hardware with limited two-qubit connectivity. We model this problem as an
integer linear program, using a network flow formulation with binary variables
that includes the initial allocation of qubits and their routing. We consider
several cost functions: an approximation of the fidelity of the circuit, its
total depth, and a measure of cross-talk, all of which can be incorporated in
the model. Numerical experiments on synthetic data and different hardware
topologies indicate that the error rate and depth can be optimized
simultaneously without significant loss. We test our algorithm on a large
number of quantum volume circuits, optimizing for error rate and depth; our
algorithm significantly reduces the number of CNOTs compared to Qiskit's
default transpiler SABRE, and produces circuits that, when executed on
hardware, exhibit higher fidelity.


Abstract:
  Solar active region 12673 produced two successive X-class flares (X2.2 and
X9.3) approximately 3 hours apart in September 2017. The X9.3 was the recorded
largest solar flare in Solar Cycle 24. In this study we perform a
data-constrained magnetohydrodynamic simulation taking into account the
observed photospheric magnetic field to reveal the initiation and dynamics of
the X2.2 and X9.3 flares. According to our simulation, the X2.2 flare is first
triggered by magnetic reconnection at a local site where at the photosphere the
negative polarity intrudes into the opposite-polarity region. This magnetic
reconnection expels the innermost field lines upward beneath which the magnetic
flux rope is formed through continuous reconnection with external twisted field
lines. Continuous magnetic reconnection after the X2.2 flare enhances the
magnetic flux rope, which is lifted up and eventually erupts via the torus
instability. This gives rise to the X9.3 flare.


Abstract:
  Interconnects are a major discriminator for superconducting digital
technology, enabling energy efficient data transfer and high-bandwidth
heterogeneous integration. We report a method to simulate propagation of
picosecond pulses in superconducting passive transmission lines (PTLs). A
frequency-domain propagator model obtained from the Ansys High Frequency
Structure Simulator (HFSS) field solver is incorporated in a Cadence Spectre
circuit model, so that the particular PTL geometry can be simulated in the
time-domain. The Mattis-Bardeen complex conductivity of the superconductor is
encoded in the HFSS field solver as a complex-conductivity insulator.
Experimental and simulation results show that Nb 20 Ohm microstrip PTLs with
1um width can support propagation of a single-flux-quantum pulse up to 7mm and
a double-flux-quantum pulse up to 28mm.


Abstract:
  We present and analyze optical photometry and high resolution SALT spectra of
the symbiotic recurrent nova V3890 Sgr at quiescence. The orbital period,
P=747.6 days has been derived from both photometric and spectroscopic data. Our
double-line spectroscopic orbits indicate that the mass ratio is
q=M_g/M_WD=0.78+/-0.05, and that the component masses are M_WD=1.35+/-0.13
Msun, and M_g=1.05+/-0.11 Msun. The orbit inclination is approximately 67-69
degr. The red giant is filling (or nearly filling) its Roche lobe, and the
distance set by its Roche lobe radius, d=9 kpc, is consistent with that
resulting from the giant pulsation period. The outburst magnitude of V3890 Sgr
is then very similar to those of RNe in the Large Magellanic Cloud. V3890 Sgr
shows remarkable photometric and spectroscopic activity between the nova
eruptions with timescales similar to those observed in the symbiotic recurrent
novae T CrB and RS Oph and Z And-type symbiotic systems. The active source has
a double-temperature structure which we have associated with the presence of an
accretion disc. The activity would be then caused by changes in the accretion
rate. We also provide evidence that V3890 Sgr contains a CO WD accreting at a
high, a few 1e-8 - 1e-7 Msun/yr, rate. The WD is growing in mass, and should
give rise to a Type Ia supernova within about 1,000,000 yrs - the expected
lifetime of the red giant.