Abstract:
  Precision medicine involves answering counterfactual questions such as "Would
this patient respond better to treatment A or treatment B?" These types of
questions are causal in nature and require the tools of causal inference to be
answered, e.g., with a structural causal model (SCM). In this work, we develop
an SCM that models the interaction between demographic information, disease
covariates, and magnetic resonance (MR) images of the brain for people with
multiple sclerosis. Inference in the SCM generates counterfactual images that
show what an MR image of the brain would look like if demographic or disease
covariates are changed. These images can be used for modeling disease
progression or used for image processing tasks where controlling for
confounders is necessary.


Abstract:
  Analysis and clustering of multivariate time-series data attract growing
interest in immunological and clinical studies. In such applications,
researchers are interested in clustering subjects based on potentially
high-dimensional longitudinal features, and in investigating how clinical
covariates may affect the clustering results. These studies are often
challenging due to high dimensionality, as well as the sparse and irregular
nature of sample collection along the time dimension. We propose a smoothed
probabilistic PARAFAC model with covariates (SPACO) to tackle these two
problems while utilizing auxiliary covariates of interest. We provide intensive
simulations to test different aspects of SPACO and demonstrate its use on
immunological data sets from two recent cohorts of SARs-CoV-2 patients.


Abstract:
  This report formulates a conjectural combinatorial rule that positively
expands Grothendieck polynomials into Lascoux polynomials. It generalizes one
such formula expanding Schubert polynomials into key polynomials, and refines
another one expanding stable Grothendieck polynomials.


Abstract:
  It is well known that many problems in image recovery, signal processing, and
machine learning can be modeled as finding zeros of the sum of maximal monotone
and Lipschitz continuous monotone operators. Many papers have studied
forward-backward splitting methods for finding zeros of the sum of two monotone
operators in Hilbert spaces. Most of the proposed splitting methods in the
literature have been proposed for the sum of maximal monotone and
inverse-strongly monotone operators in Hilbert spaces. In this paper, we
consider splitting methods for finding zeros of the sum of maximal monotone
operators and Lipschitz continuous monotone operators in Banach spaces. We
obtain weak and strong convergence results for the zeros of the sum of maximal
monotone and Lipschitz continuous monotone operators in Banach spaces. Many
already studied problems in the literature can be considered as special cases
of this paper.


Abstract:
  Unstructured pruning reduces the memory footprint in deep neural networks
(DNNs). Recently, researchers proposed different types of structural pruning
intending to reduce also the computation complexity. In this work, we first
suggest a new measure called mask-diversity which correlates with the expected
accuracy of the different types of structural pruning. We focus on the recently
suggested N:M fine-grained block sparsity mask, in which for each block of M
weights, we have at least N zeros. While N:M fine-grained block sparsity allows
acceleration in actual modern hardware, it can be used only to accelerate the
inference phase. In order to allow for similar accelerations in the training
phase, we suggest a novel transposable fine-grained sparsity mask, where the
same mask can be used for both forward and backward passes. Our transposable
mask guarantees that both the weight matrix and its transpose follow the same
sparsity pattern; thus, the matrix multiplication required for passing the
error backward can also be accelerated. We formulate the problem of finding the
optimal transposable-mask as a minimum-cost flow problem. Additionally, to
speed up the minimum-cost flow computation, we also introduce a fast
linear-time approximation that can be used when the masks dynamically change
during training. Our experiments suggest a 2x speed-up in the matrix
multiplications with no accuracy degradation over vision and language models.
Finally, to solve the problem of switching between different structure
constraints, we suggest a method to convert a pre-trained model with
unstructured sparsity to an N:M fine-grained block sparsity model with little
to no training. A reference implementation can be found at
https://github.com/papers-submission/structured_transposable_masks.


Abstract:
  Skyrmion-containing devices have been proposed as a promising solution for
low energy data storage. These devices include racetrack or logic structures
and require skyrmions to be confined in regions with dimensions comparable to
the size of a single skyrmion. Here we examine Bloch skyrmions in {FeGe} device
shapes using Lorentz transmission electron microscopy (LTEM) to reveal the
consequences of skyrmion confinement in a device structure. Dumbbell-shaped
devices were created by focused ion beam (FIB) milling to provide regions where
single skyrmions are confined adjacent to areas containing a skyrmion lattice.
Simple block shapes of equivalent dimensions were prepared within the specimen
to allow a direct comparison with skyrmion formation in a less complex, yet
still confined, device geometry. The impact of the application of an applied
external field and varying the temperature on skyrmion formation within the
shapes was examined and this revealed that it is not just confinement within a
small device structure that controls the position and number of skyrmions, but
that a complex device geometry changes the skyrmion behaviour, including
allowing formation of skyrmions at lower applied magnetic fields than in simple
shapes. This could allow experimental methods to be developed to control the
positioning and number of skyrmions within device shapes.


Abstract:
  Solar coronal rain is classified generally into two categories: flare-driven
and quiescent coronal rain. The latter is observed to form along both closed
and open magnetic field structures. Recently, we proposed that some of the
quiescent coronal rain events, detected in the transition region and
chromospheric diagnostics, along loop-like paths could be explained by the
formation mechanism for quiescent coronal rain facilitated by interchange
magnetic reconnection between open and closed field lines. In this study, we
revisited 38 coronal rain reports from the literature. From these earlier
works, we picked 15 quiescent coronal rain events out of the solar limb, mostly
suggested to occur in active region closed loops due to thermal nonequilibrium,
to scrutinize their formation mechanism. Employing the extreme ultraviolet
images and line-of-sight magnetograms, the evolution of the quiescent coronal
rain events and their magnetic fields and context coronal structures is
examined. We find that 6, comprising 40%, of the 15 quiescent coronal rain
events could be totally or partially interpreted by the formation mechanism for
quiescent coronal rain along open structures facilitated by interchange
reconnection. The results suggest that the quiescent coronal rain facilitated
by interchange reconnection between open and closed field lines deserves more
attention.


Abstract:
  We characterize the soliton solutions and their interactions for a system of
coupled evolution equations of nonlinear Schr\"odinger (NLS) type that models
the dynamics in one-dimensional repulsive Bose-Einstein condensates with spin
one, taking advantage of the representation of such model as a special
reduction of a 2 x 2 matrix NLS system. Specifically, we study in detail the
case in which solutions tend to a non-zero background at space infinities.
First we derive a compact representation for the multi-soliton solutions in the
system using the Inverse Scattering Transform (IST). We introduce the notion of
canonical form of a solution, corresponding to the case when the background is
proportional to the identity. We show that solutions for which the asymptotic
behavior at infinity is not proportional to the identity, referred to as being
in non-canonical form, can be reduced to canonical form by unitary
transformations that preserve the symmetric nature of the solution (physically
corresponding to complex rotations of the quantization axes). Then we give a
complete characterization of the two families of one-soliton solutions arising
in this problem, corresponding to ferromagnetic and to polar states of the
system, and we discuss how the physical parameters of the solitons for each
family are related to the spectral data in the IST. We also show that any
ferromagnetic one-soliton solution in canonical form can be reduced to a single
dark soliton of the scalar NLS equation, and any polar one-soliton solution in
canonical form is unitarily equivalent to a pair of oppositely polarized
displaced scalar dark solitons up to a rotation of the quantization axes.
Finally, we discuss two-soliton interactions and we present a complete
classification of the possible scenarios that can arise depending on whether
either soliton is of ferromagnetic or polar type.


Abstract:
  The volume of data moving through a network increases with new scientific
experiments and simulations. Network bandwidth requirements also increase
proportionally to deliver data within a certain time frame. We observe that a
significant portion of the popular dataset is transferred multiple times to
different users as well as to the same user for various reasons. In-network
data caching for the shared data has shown to reduce the redundant data
transfers and consequently save network traffic volume. In addition, overall
application performance is expected to improve with in-network caching because
access to the locally cached data results in lower latency. This paper shows
how much data was shared over the study period, how much network traffic volume
was consequently saved, and how much the temporary in-network caching increased
the scientific application performance. It also analyzes data access patterns
in applications and the impacts of caching nodes on the regional data
repository. From the results, we observed that the network bandwidth demand was
reduced by nearly a factor of 3 over the study period.


Abstract:
  Synthetic data generation is an appealing approach to generate novel traffic
scenarios in autonomous driving. However, deep learning perception algorithms
trained solely on synthetic data encounter serious performance drops when they
are tested on real data. Such performance drops are commonly attributed to the
domain gap between real and synthetic data. Domain adaptation methods that have
been applied to mitigate the aforementioned domain gap achieve visually
appealing results, but usually introduce semantic inconsistencies into the
translated samples. In this work, we propose a novel, unsupervised, end-to-end
domain adaptation network architecture that enables semantically consistent
\textit{sim2real} image transfer. Our method performs content disentanglement
by employing shared content encoder and fixed style code.


Abstract:
  By properly considering the propagation dynamics of the dipole field, we
obtain the full magnetic dipolar interaction between two quantum dipoles for
general situations. With the help the Maxwell equation and the corresponding
Green function, this result applies for general boundary conditions, and
naturally unifies all the interaction terms between permanent dipoles, resonant
or non-resonant transition dipoles, and even the counter-rotating interaction
terms altogether. In particular, we study the dipolar interaction in a
rectangular 3D cavity with discrete field modes. When the two dipoles are quite
near to each other and far from the cavity boundary, their interaction simply
returns the freespace result; when the distance between the two dipoles is
comparable to their distance to the cavity boundary and the field mode
wavelength, the dipole images and near-resonant cavity modes bring in
significant changes to the freespace interaction. This approach also provides a
general way to study the interaction mediated by other kinds of fields.


Abstract:
  We present a data-driven approach to construct entropy-based closures for the
moment system from kinetic equations. The proposed closure learns the entropy
function by fitting the map between the moments and the entropy of the moment
system, and thus does not depend on the space-time discretization of the moment
system and specific problem configurations such as initial and boundary
conditions. With convex and $C^2$ approximations, this data-driven closure
inherits several structural properties from entropy-based closures, such as
entropy dissipation, hyperbolicity, and H-Theorem. We construct convex
approximations to the Maxwell-Boltzmann entropy using convex splines and neural
networks, test them on the plane source benchmark problem for linear transport
in slab geometry, and compare the results to the standard, optimization-based
M$_N$ closures. Numerical results indicate that these data-driven closures
provide accurate solutions in much less computation time than the M$_N$
closures.


Abstract:
  Recently the LHAASO Collaboration published the detection of 12
ultra-high-energy gamma-ray sources above 100 TeV, with the highest energy
photon reaching 1.4 PeV. The first detection of PeV gamma rays from
astrophysical sources may provide a very sensitive probe of the effect of the
Lorentz invariance violation (LIV), which results in decay of high-energy gamma
rays in the superluminal scenario and hence a sharp cutoff of the energy
spectrum. Two highest energy sources are studied in this work. No signature of
the existence of LIV is found in their energy spectra, and the lower limits on
the LIV energy scale are derived. Our results show that the first-order LIV
energy scale should be higher than about 10^5 times the Planck scale M_{pl} and
that the second-order LIV scale is >10^{-3}M_{pl}. Both limits improve by at
least one order of magnitude the previous results.


Abstract:
  In 1979 Pisier proved remarkably that a sequence of independent and
identically distributed standard Gaussian random variables determines, via
random Fourier series, a homogeneous Banach algebra $\mathscr{P}$ strictly
contained in $C(\mathbb{T})$, the class of continuous functions on the unit
circle $\mathbb{T}$ and strictly containing the classical Wiener algebra
$\mathbb{A}(\mathbb{T})$, that is, $\mathbb{A}(\mathbb{T}) \subsetneqq
\mathscr{P} \subsetneqq C(\mathbb{T}).$ This improved some previous results
obtained by Zafran in solving a long-standing problem raised by Katznelson. In
this paper we extend Pisier's result by showing that any probability measure on
the unit circle defines a homogeneous Banach algebra contained in
$C(\mathbb{T})$. Thus Pisier algebra is not an isolated object but rather an
element in a large class of Pisier-type algebras. We consider the case of
spectral measures of stationary sequences of Gaussian random variables and
obtain a sufficient condition for the boundedness of the random Fourier series
$\sum_{n\in \mathbb{Z}}\hat f(n) \,\xi_n \exp(2\pi i n t)$ in the general
setting of dependent random variables $(\xi_n)$.


Abstract:
  We present MatchKAT, an algebraic language for modeling match-action packet
processing in network switches. Although the match-action paradigm has remained
a popular low-level programming model for specifying packet forwarding
behavior, little has been done towards giving it formal semantics. With
MatchKAT, we hope to embark on the first steps in exploring how network
programs compiled to match-action rules can be reasoned about formally in a
reliable, algebraic way. In this paper, we give details of MatchKAT and its
metatheory, as well as a formal treatment of match expressions on binary
strings that form the basis of "match" in match-action. Through a
correspondence with NetKAT, we show that MatchKAT's equational theory is sound
and complete with regards to a similar packet filtering semantics. We also
demonstrate the complexity of deciding equivalence in MatchKAT is
PSPACE-complete.


Abstract:
  We develop connections between the qualitative dynamics of Hamiltonian
isotopies on a surface $\Sigma$ and their chain-level Floer theory using ideas
drawn from Hofer-Wysocki-Zehnder's theory of finite energy foliations. We
associate to every collection of capped $1$-periodic orbits which is `maximally
unlinked relative the Morse range' a singular foliation on $S^1 \times \Sigma$
which is positively transverse to the vector field $\partial_t \oplus X^H$ and
which is assembled in a straight-forward way from the relevant Floer moduli
spaces. We derive a purely topological and Turing-computable characterization
of the spectral invariant $c(H;[\Sigma])$ for generic Hamiltonians on arbitrary
closed surfaces. This completes, for generic Hamiltonians, a project initiated
by Humili\`{e}re-Le Roux-Seyfaddini, in addition to fulfilling a desideratum
expressed by Gambaudo-Ghys seeking a topological characterization of the
Entov-Polterovich quasi-morphism on $Ham(S^2)$.


Abstract:
  Placing robots outside controlled conditions requires versatile movement
representations that allow robots to learn new tasks and adapt them to
environmental changes. The introduction of obstacles or the placement of
additional robots in the workspace, the modification of the joint range due to
faults or range-of-motion constraints are typical cases where the adaptation
capabilities play a key role for safely performing the robot's task.
Probabilistic movement primitives (ProMPs) have been proposed for representing
adaptable movement skills, which are modelled as Gaussian distributions over
trajectories. These are analytically tractable and can be learned from a small
number of demonstrations. However, both the original ProMP formulation and the
subsequent approaches only provide solutions to specific movement adaptation
problems, e.g., obstacle avoidance, and a generic, unifying, probabilistic
approach to adaptation is missing. In this paper we develop a generic
probabilistic framework for adapting ProMPs. We unify previous adaptation
techniques, for example, various types of obstacle avoidance, via-points,
mutual avoidance, in one single framework and combine them to solve complex
robotic problems. Additionally, we derive novel adaptation techniques such as
temporally unbound via-points and mutual avoidance. We formulate adaptation as
a constrained optimisation problem where we minimise the Kullback-Leibler
divergence between the adapted distribution and the distribution of the
original primitive while we constrain the probability mass associated with
undesired trajectories to be low. We demonstrate our approach on several
adaptation problems on simulated planar robot arms and 7-DOF Franka-Emika
robots in a dual robot arm setting.


Abstract:
  Many applications require accurate indoor localization. Fingerprint-based
localization methods propose a solution to this problem, but rely on a radio
map that is effort-intensive to acquire. We automate the radio map acquisition
phase using a software-defined radio (SDR) and a wheeled robot. Furthermore, we
open-source a radio map acquired with our automated tool for a 3GPP Long-Term
Evolution (LTE) wireless link. To the best of our knowledge, this is the first
publicly available radio map containing channel state information (CSI).
Finally, we describe first localization experiments on this radio map using a
convolutional neural network to regress for location coordinates.


Abstract:
  This paper addresses the problem of identifying a linear time-varying (LTV)
system characterized by a (possibly infinite) discrete set of delay-Doppler
shifts without a lattice (or other geometry-discretizing) constraint on the
support set. Concretely, we show that a class of such LTV systems is
identifiable whenever the upper uniform Beurling density of the delay-Doppler
support sets, measured uniformly over the class, is strictly less than 1/2. The
proof of this result reveals an interesting relation between LTV system
identification and interpolation in the Bargmann-Fock space. Moreover, we show
that this density condition is also necessary for classes of systems invariant
under time-frequency shifts and closed under a natural topology on the support
sets. We furthermore show that identifiability guarantees robust recovery of
the delay-Doppler support set, as well as the weights of the individual
delay-Doppler shifts, both in the sense of asymptotically vanishing
reconstruction error for vanishing measurement error.


Abstract:
  Topological effects exist from a macroscopic system such as the universe to a
microscopic system described by quantum mechanics. We show here that an
interesting geometric structure can be created by the self replication
procedure of a square with an enclosed circle, in which the sum of the circles
area will remain the same but the sum of the circumference will increase. It is
demonstrated by means of Monte Carlo simulations that these topological
features have great impacts to the vacuum pumping probability and the photon
absorption probability of the active surface. The results show significant
improvement of the system performance and have application potential in vacuum
pumping of large research facilities such as a nuclear fusion reactor,
synchrotron and in photovoltaic industry.


Abstract:
  The use of abundance ratios involving Y, or other slow-neutron capture
elements, are routinely used to infer stellar ages.Aims.We aim to explain the
observed [Y/H] and [Y/Mg] abundance ratios of star clusters located in the
inner disc with a new prescription for mixing in Asymptotic Giant Branch (AGB)
stars. In a Galactic chemical evolution model, we adopt a new set of AGB
stellar yields in which magnetic mixing is included. We compare the results of
the model with a sample of abundances and ages of open clusters located at
different Galactocentric distances. The magnetic mixing causes a less efficient
production of Y at high metallicity. A non-negligible fraction of stars with
super-solar metallicity is produced in the inner disc, and their Y abundances
are affected by the reduced yields. The results of the new AGB model
qualitatively reproduce the observed trends for both [Y/H] and [Y/Mg] vs age at
different Galactocetric distances. Our results confirm from a theoretical point
of view that the relationship between [Y/Mg] and stellar age cannot be
universal, i.e., the same in every part of the Galaxy. It has a strong
dependence on the star formation rate, on the s-process yields and their
relation with metallicity, and thus it varies across the Galactic disc.


Abstract:
  In this paper, a multi-objective approach for the design of composite
data-driven mathematical models is proposed. It allows automating the
identification of graph-based heterogeneous pipelines that consist of different
blocks: machine learning models, data preprocessing blocks, etc. The
implemented approach is based on a parameter-free genetic algorithm (GA) for
model design called GPComp@Free. It is developed to be part of automated
machine learning solutions and to increase the efficiency of the modeling
pipeline automation. A set of experiments was conducted to verify the
correctness and efficiency of the proposed approach and substantiate the
selected solutions. The experimental results confirm that a multi-objective
approach to the model design allows achieving better diversity and quality of
obtained models. The implemented approach is available as a part of the
open-source AutoML framework FEDOT.


Abstract:
  Effective human-vehicle collaboration requires an appropriate un-derstanding
of vehicle behavior for safety and trust. Improvingon our prior work by adding
a future prediction module, we in-troduce our framework, calledAutoPreview, to
enable humans topreview autopilot behaviors prior to direct interaction with
thevehicle. Previewing autopilot behavior can help to ensure
smoothhuman-vehicle collaboration during the initial exploration stagewith the
vehicle. To demonstrate its practicality, we conducted acase study on
human-vehicle collaboration and built a prototypeof our framework with the
CARLA simulator. Additionally, weconducted a between-subject control experiment
(n=10) to studywhether ourAutoPreviewframework can provide a deeper
under-standing of autopilot behavior compared to direct interaction. Ourresults
suggest that theAutoPreviewframework does, in fact, helpusers understand
autopilot behavior and develop appropriate men-tal models


Abstract:
  In this paper, we prove sharp gradient estimates for positive solutions to
the weighted heat equation on smooth metric measure spaces with compact
boundary. As an application, we prove Liouville theorems for ancient solutions
satisfying the Dirichlet boundary condition and some sharp growth restriction
near infinity. Our results can be regarded as a refinement of recent results
due to Kunikawa and Sakurai.


Abstract:
  We extend a semantic verification framework for hybrid systems with the
Isabelle/HOL proof assistant by an algebraic model for hybrid program stores, a
shallow expression model for hybrid programs and their correctness
specifications, and domain-specific deductive and calculational support. The
new store model yields clean separations and dynamic local views of variables,
e.g. discrete/continuous, mutable/immutable, program/logical, and enhanced ways
of manipulating them using combinators, projections and framing. This leads to
more local inference rules, procedures and tactics for reasoning with invariant
sets, certifying solutions of hybrid specifications or calculating derivatives
with increased proof automation and scalability. The new expression model
provides more user-friendly syntax, better control of name spaces and
interfaces connecting the framework with real-world modelling languages.


Abstract:
  Let $(N,\rho)$ be a Riemannian manifold, $S$ a surface of genus at least two
and let $f\colon S \to N$ be a continuous map. We consider the energy spectrum
of $(N,\rho)$ (and $f$) which assigns to each point $[J]\in \mathcal{T}(S)$ in
the Teichm\"uller space of $S$ the infimum of the Dirichlet energies of all
maps $(S,J)\to (N,\rho)$ homotopic to $f$. We study the relation between the
energy spectrum and the simple length spectrum. Our main result is that if
$N=S$, $f=id$ and $\rho$ is a metric of non-positive curvature, then the energy
spectrum determines the simple length spectrum. Furthermore, we prove that the
converse does not hold by exhibiting two metrics on $S$ with equal simple
length spectrum but different energy spectrum. As corollaries to our results we
obtain that the set of hyperbolic metrics and the set of singular flat metrics
induced by quadratic differentials satisfy energy spectrum rigidity, i.e. a
metric in these sets is determined, up to isotopy, by its energy spectrum. We
prove that analogous statements also hold true for Kleinian surface groups.


Abstract:
  We introduce Trankit, a light-weight Transformer-based Toolkit for
multilingual Natural Language Processing (NLP). It provides a trainable
pipeline for fundamental NLP tasks over 100 languages, and 90 pretrained
pipelines for 56 languages. Built on a state-of-the-art pretrained language
model, Trankit significantly outperforms prior multilingual NLP pipelines over
sentence segmentation, part-of-speech tagging, morphological feature tagging,
and dependency parsing while maintaining competitive performance for
tokenization, multi-word token expansion, and lemmatization over 90 Universal
Dependencies treebanks. Despite the use of a large pretrained transformer, our
toolkit is still efficient in memory usage and speed. This is achieved by our
novel plug-and-play mechanism with Adapters where a multilingual pretrained
transformer is shared across pipelines for different languages. Our toolkit
along with pretrained models and code are publicly available at:
https://github.com/nlp-uoregon/trankit. A demo website for our toolkit is also
available at: http://nlp.uoregon.edu/trankit. Finally, we create a demo video
for Trankit at: https://youtu.be/q0KGP3zGjGc.


Abstract:
  I summarize evidence against the hypothesis that `Oumuamua is the artificial
creation of an advanced civilization. An appendix discusses the flaws and
inconsistencies of the "Breakthrough" proposal for laser acceleration of
spacecraft to semi-relativistic speeds. Reality is much more challenging, and
interesting.


Abstract:
  Shared mental models are critical to team success; however, in practice, team
members may have misaligned models due to a variety of factors. In
safety-critical domains (e.g., aviation, healthcare), lack of shared mental
models can lead to preventable errors and harm. Towards the goal of mitigating
such preventable errors, here, we present a Bayesian approach to infer
misalignment in team members' mental models during complex healthcare task
execution. As an exemplary application, we demonstrate our approach using two
simulated team-based scenarios, derived from actual teamwork in cardiac
surgery. In these simulated experiments, our approach inferred model
misalignment with over 75% recall, thereby providing a building block for
enabling computer-assisted interventions to augment human cognition in the
operating room and improve teamwork.


Abstract:
  We study a high throughput satellite system, where the feeder link uses
free-space optical (FSO) and the user link uses radio frequency (RF)
communication. In particular, we first propose a transmit diversity using
Alamouti space time block coding to mitigate the atmospheric turbulence in the
feeder link. Then, based on the concept of average virtual
signal-to-interference-plus-noise ratio and one-bit feedback, we propose a
beamforming algorithm for the user link to maximize the ergodic capacity (EC).
Moreover, by assuming that the FSO links follow the Malaga distribution whereas
RF links undergo the shadowed-Rician fading, we derive a closed-form EC
expression of the considered system. Finally, numerical simulations validate
the accuracy of our theoretical analysis, and show that the proposed schemes
can achieve higher capacity compared with the reference schemes.


Abstract:
  Of the many modern approaches to calculating evolutionary distance via models
of genome rearrangement, most are tied to a particular set of genomic modelling
assumptions and to a restricted class of allowed rearrangements. The "position
paradigm", in which genomes are represented as permutations signifying the
position (and orientation) of each region, enables a refined model-based
approach, where one can select biologically plausible rearrangements and assign
to them relative probabilities/costs. Here, one must further incorporate any
underlying structural symmetry of the genomes into the calculations and ensure
that this symmetry is reflected in the model. In our recently-introduced
framework of {\em genome algebras}, each genome corresponds to an element that
simultaneously incorporates all of its inherent physical symmetries. The
representation theory of these algebras then provides a natural model of
evolution via rearrangement as a Markov chain. Whilst the implementation of
this framework to calculate distances for genomes with `practical' numbers of
regions is currently computationally infeasible, we consider it to be a
significant theoretical advance: one can incorporate different genomic
modelling assumptions, calculate various genomic distances, and compare the
results under different rearrangement models. The aim of this paper is to
demonstrate some of these features.


Abstract:
  The Institute of Materials and Processes, IMP, of the University of Applied
Sciences in Karlsruhe, Germany in cooperation with VDI Verein Deutscher
Ingenieure e.V, AEN Automotive Engineering Network and their cooperation
partners present their competences of AI-based solution approaches in the
production engineering field. The online congress KI 4 Industry on November 12
and 13, 2020, showed what opportunities the use of artificial intelligence
offers for medium-sized manufacturing companies, SMEs, and where potential
fields of application lie. The main purpose of KI 4 Industry is to increase the
transfer of knowledge, research and technology from universities to small and
medium-sized enterprises, to demystify the term AI and to encourage companies
to use AI-based solutions in their own value chain or in their products.


Abstract:
  Given a permutation $\pi:[k] \to [k]$, a function $f:[n] \to \mathbb{R}$
contains a $\pi$-appearance if there exists $1 \leq i_1 < i_2 < \dots < i_k
\leq n$ such that for all $s,t \in [k]$, it holds that $f(i_s) < f(i_t)$ if and
only if $\pi(s) < \pi(t)$. The function is $\pi$-free if it has no
$\pi$-appearances. In this paper, we investigate the problem of testing whether
an input function $f$ is $\pi$-free or whether at least $\varepsilon n$ values
in $f$ need to be changed in order to make it $\pi$-free. This problem is a
generalization of the well-studied monotonicity testing and was first studied
by Newman, Rabinovich, Rajendraprasad and Sohler (Random Structures and
Algorithms 2019). We show that for all constants $k \in \mathbb{N}$,
$\varepsilon \in (0,1)$, and permutation $\pi:[k] \to [k]$, there is a
one-sided error $\varepsilon$-testing algorithm for $\pi$-freeness of functions
$f:[n] \to \mathbb{R}$ that makes $\tilde{O}(n^{o(1)})$ queries. We improve
significantly upon the previous best upper bound $O(n^{1 - 1/(k-1)})$ by
Ben-Eliezer and Canonne (SODA 2018). Our algorithm is adaptive, while the
earlier best upper bound is known to be tight for nonadaptive algorithms.


Abstract:
  In random quantum magnets, like the random transverse Ising chain, the low
energy excitations are localized in rare regions and there are only weak
correlations between them. It is a fascinating question whether these
correlations are completely irrelevant in the sense of the renormalization
group. To answer this question, we calculate the distribution of the excitation
energy of the random transverse Ising chain in the disordered Griffiths phase
with high numerical precision by the strong disorder renormalization group
method and - for shorter chains - by free-fermion techniques. Asymptotically,
the two methods give identical results, which are well fitted by the Fr\'echet
limit law of the extremes of independent and identically distributed random
numbers. Given the finite size corrections, the two numerical methods give very
similar results, but they differ from the correction term for uncorrelated
random variables. This fact shows that the weak correlations between low-energy
excitations in random quantum magnets are not entirely irrelevant.


Abstract:
  New words are regularly introduced to communities, yet not all of these words
persist in a community's lexicon. Among the many factors contributing to
lexical change, we focus on the understudied effect of social networks. We
conduct a large-scale analysis of over 80k neologisms in 4420 online
communities across a decade. Using Poisson regression and survival analysis,
our study demonstrates that the community's network structure plays a
significant role in lexical change. Apart from overall size, properties
including dense connections, the lack of local clusters and more external
contacts promote lexical innovation and retention. Unlike offline communities,
these topic-based communities do not experience strong lexical levelling
despite increased contact but accommodate more niche words. Our work provides
support for the sociolinguistic hypothesis that lexical change is partially
shaped by the structure of the underlying network but also uncovers findings
specific to online communities.


Abstract:
  It is well known that a classical Fubini theorem for Hausdorff dimension
cannot hold; that is, the dimension of the intersections of a fixed set with a
parallel family of planes do not determine the dimension of the set. Here we
prove that a Fubini theorem for Hausdorff dimension does hold modulo sets that
are small on all Lipschitz curves/surfaces.
  We say that $G\subset \mathbb{R}^k\times \mathbb{R}^n$ is $\Gamma_k$-null if
for every Lipschitz function $f:\mathbb{R}^k\to \mathbb{R}^n$ the set $\{t\in
\mathbb{R}^k\,:\,(t,f(t))\in G\}$ has measure zero. We show that for every
compact set $E\subset \mathbb{R}^k\times \mathbb{R}^n$ there is a
$\Gamma_k$-null subset $G\subset E$ such that $$\dim (E\setminus G) =
k+\text{ess-}\sup(\dim E_t)$$ where $\text{ess-}\sup(\dim E_t)$ is the
essential supremum of the Hausdorff dimension of the vertical sections
$\{E_t\}_{t\in \mathbb{R}^k}$ of $E$, assuming that $proj_{\mathbb{R}^k} E$ has
positive measure.
  We also obtain more general results by replacing $\mathbb{R}^k$ by an Ahlfors
regular set. Applications of our results include Fubini-type results for unions
of affine subspaces and related projection theorems.


Abstract:
  Multi-scale, multi-fidelity numerical simulations form the pillar of
scientific applications related to numerically modeling fluids. However,
simulating the fluid behavior characterized by the non-linear Navier Stokes
equations are often times computational expensive. Physics informed machine
learning methods is a viable alternative and as such has seen great interest in
the community [refer to Kutz (2017); Brunton et al. (2020); Duraisamy et al.
(2019) for a detailed review on this topic]. For full physics emulators, the
cost of network inference is often trivial. However, in the current paradigm of
data-driven fluid mechanics models are built as surrogates for complex
sub-processes. These models are then used in conjunction to the Navier Stokes
solvers, which makes ML model inference an important factor in the terms of
algorithmic latency. With the ever growing size of networks, and often times
overparameterization, exploring effective network compression techniques
becomes not only relevant but critical for engineering systems design. In this
study, we explore the applicability of pruning and quantization (FP32 to int8)
methods for one such application relevant to modeling fluid turbulence.
Post-compression, we demonstrate the improvement in the accuracy of network
predictions and build intuition in the process by comparing the compressed to
the original network state.


Abstract:
  We argue that deriving an effective field theory from string theory requires
a Wilsonian perspective with a physical cutoff. Employing proper time
regularization we demonstrate the decoupling of states and contrast this with
what happens in dimensional regularization. In particular we point out that
even if the cosmological constant (CC) calculated from some classical action at
some ultra-violet scale is negative, this does not necessarily imply that the
CC calculated at cosmological scales is also negative, and discuss the possible
criteria for achieving a positive CC starting with a CC at the string/KK scale
which is negative. Obviously this has implications for swampland claims.


Abstract:
  A central problem in Binary Hypothesis Testing (BHT) is to determine the
optimal tradeoff between the Type I error (referred to as false alarm) and Type
II (referred to as miss) error. In this context, the exponential rate of
convergence of the optimal miss error probability -- as the sample size tends
to infinity -- given some (positive) restrictions on the false alarm
probabilities is a fundamental question to address in theory. Considering the
more realistic context of a BHT with a finite number of observations, this
paper presents a new non-asymptotic result for the scenario with monotonic
(sub-exponential decreasing) restriction on the Type I error probability, which
extends the result presented by Strassen in 2009. Building on the use of
concentration inequalities, we offer new upper and lower bounds to the optimal
Type II error probability for the case of finite observations. Finally, the
derived bounds are evaluated and interpreted numerically (as a function of the
number samples) for some vanishing Type I error restrictions.


Abstract:
  Molecular simulations of the forced unfolding and refolding of biomolecules
or molecular complexes allow to gain important kinetic, structural and
thermodynamic information about the folding process and the underlying energy
landscape. In force probe molecular dynamics (FPMD) simulations, one pulls one
end of the molecule with a constant velocity in order to induce the relevant
conformational transitions. Since the extended configuration of the system has
to fit into the simulation box together with the solvent such simulations are
very time consuming. Here, we apply a hybrid scheme in which the solute is
treated with atomistic resolution and the solvent molecules far away from the
solute are described in a coarse-grained manner. We use the adaptive resolution
scheme (AdResS) that has very successfully been applied to various examples of
equilibrium simulations. We perform FPMD simulations using AdResS on a well
studied system, a dimer formed from mechanically interlocked calixarene
capsules. The results of the multiscale simulations are compared to all-atom
simulations of the identical system and we observe that the size of the region
in which atomistic resolution is required depends on the pulling velocity, i.e.
the particular non-equilibrium situation. For large pulling velocities a larger
all atom region is required. Our results show that multiscale simulations can
be applied also in the strong non-equilibrium situations that the system
experiences in FPMD simulations.


Abstract:
  Graph neural networks (GNNs) constitute a class of deep learning methods for
graph data. They have wide applications in chemistry and biology, such as
molecular property prediction, reaction prediction and drug-target interaction
prediction. Despite the interest, GNN-based modeling is challenging as it
requires graph data pre-processing and modeling in addition to programming and
deep learning. Here we present DGL-LifeSci, an open-source package for deep
learning on graphs in life science. DGL-LifeSci is a python toolkit based on
RDKit, PyTorch and Deep Graph Library (DGL). DGL-LifeSci allows GNN-based
modeling on custom datasets for molecular property prediction, reaction
prediction and molecule generation. With its command-line interfaces, users can
perform modeling without any background in programming and deep learning. We
test the command-line interfaces using standard benchmarks MoleculeNet, USPTO,
and ZINC. Compared with previous implementations, DGL-LifeSci achieves a speed
up by up to 6x. For modeling flexibility, DGL-LifeSci provides well-optimized
modules for various stages of the modeling pipeline. In addition, DGL-LifeSci
provides pre-trained models for reproducing the test experiment results and
applying models without training. The code is distributed under an Apache-2.0
License and is freely accessible at https://github.com/awslabs/dgl-lifesci.


Abstract:
  We report the discovery of three new pulsars in the Globular Cluster (GC)
NGC6517, namely NGC 6517 E, F, and G, made with the Five-hundred-meter Aperture
Spherical radio Telescope (FAST). The spin periods of NGC 6517 E, F, and G are
7.60~ms, 24.89~ms, and 51.59~ms, respectively. Their dispersion measures are
183.29, 183.713, and 185.3~pc~cm$^{-3}$, respectively, all slightly larger than
those of the previously known pulsars in this cluster. The spin period
derivatives are at the level of 1$\times$10$^{-18}$~s~s$^{-1}$, which suggests
these are recycled pulsars. In addition to the discovery of these three new
pulsars, we updated the timing solutions of the known isolated pulsars, NGC
6517 A, C, and D. The solutions are consistent with those from Lynch et al.
(2011) and with smaller timing residuals. From the timing solution, NGC 6517 A,
B (position from Lynch et al. 2011), C, E, and F are very close to each other
on the sky and only a few arcseconds from the optical core of NGC 6517. With
currently published and unpublished discoveries, NGC6517 now has 9 pulsars,
ranking 5$^{th}$ of the GCs with the most pulsars. The discoveries take
advantage of the high sensitivity of FAST and a new algorithm used to check and
filter possible candidate signals.


Abstract:
  We report $^{121/123}$Sb nuclear quadrupole resonance (NQR) and $^{51}$V
nuclear magnetic resonance (NMR) measurements on kagome metal CsV$_3$Sb$_5$
with $T_{\rm c}=2.5$ K. Both $^{51}$V NMR spectra and $^{121/123}$Sb NQR
spectra split after a charge density wave (CDW) transition, which demonstrates
a commensurate CDW state. The coexistence of the high temperature phase and the
CDW phase between $91$ K and $94$ K manifests that it is a first order phase
transition. At low temperature, electric-field-gradient fluctuations diminish
and magnetic fluctuations become dominant. Superconductivity emerges in the
charge order state. Knight shift decreases and $1/T_{1}T$ shows a
Hebel--Slichter coherence peak just below $T_{\rm c}$, indicating that
CsV$_3$Sb$_5$ is an s-wave superconductor.


Abstract:
  Vibrational and electronic absorption spectra calculated at the
(time-dependent) density functional theory level for the bismuth carbide
clusters Bi$_{n}$C$_{2n}$$^+$ ($3 \le n \le 9$) indicate significant
differences in types of bonding that depend on cluster geometry. Analysis of
the electronic charge densities of these clusters highlighted bonding trends
consistent with the spectroscopic information. The combined data suggest that
larger clusters ($n > 5$) are likely to be kinetically unstable in agreement
with the cluster mass distribution obtained in gas-aggregation source
experiments. The spectral fingerprints of the different clusters obtained from
our calculations also suggest that identification of specific
Bi$_{n}$C$_{2n}$$^+$ isomers of should be possible based on infra-red and
optical absorption spectroscopy.


Abstract:
  Observations carried out toward starless and pre-stellar cores have revealed
that complex organic molecules are prevalent in these objects, but it is
unclear what chemical processes are involved in their formation. Recently, it
has been shown that complex organics are preferentially produced at an
intermediate-density shell within the L1544 pre-stellar core at radial
distances of ~4000 au with respect to the core center. However, the spatial
distribution of complex organics has only been inferred toward this core and it
remains unknown whether these species present a similar behaviour in other
cores. We report high-sensitivity observations carried out toward two positions
in the L1498 pre-stellar core, the dust peak and a position located at a
distance of ~11000 au from the center of the core where the emission of
CH$_3$OH peaks. Similarly to L1544, our observations reveal that small
O-bearing molecules and N-bearing species are enhanced by factors ~4-14 toward
the outer shell of L1498. However, unlike L1544, large O-bearing organics such
as CH3CHO, CH3OCH3 or CH3OCHO are not detected within our sensitivity limits.
For N-bearing organics, these species are more abundant toward the outer shell
of the L1498 pre-stellar core than toward the one in L1544. We propose that the
differences observed between O-bearing and N-bearing species in L1498 and L1544
are due to the different physical structure of these cores, which in turn is a
consequence of their evolutionary stage, with L1498 being younger than L1544.


Abstract:
  Successful active speaker detection requires a three-stage pipeline: (i)
audio-visual encoding for all speakers in the clip, (ii) inter-speaker relation
modeling between a reference speaker and the background speakers within each
frame, and (iii) temporal modeling for the reference speaker. Each stage of
this pipeline plays an important role for the final performance of the created
architecture. Based on a series of controlled experiments, this work presents
several practical guidelines for audio-visual active speaker detection.
Correspondingly, we present a new architecture called ASDNet, which achieves a
new state-of-the-art on the AVA-ActiveSpeaker dataset with a mAP of 93.5%
outperforming the second best with a large margin of 4.7%. Our code and
pretrained models are publicly available.


Abstract:
  We investigate the optical response of a hybrid electro-optomechanical system
interacting with a qubit. In our experimentally feasible system, tunable
all-optical-switching, double-optomechanically induced transparency (OMIT) and
optomechanically induced absorption (OMIA) can be realized. The proposed system
is also shown to generate anomalous dispersion. Based on our theoretical
results, we provide a tunable switch between OMIT and OMIA of the probe field
by manipulating the relevant system parameters. Also, the normal-mode-splitting
(NMS) effect induced by the interactions between the subsystems are discussed
in detail and the effects of varying the interactions on the NMS are clarified.
These rich optical properties of the probe field may provide a promising
platform for controllable all-optical-switch and various other quantum photonic
devices.


Abstract:
  During the early months of the current COVID-19 pandemic, social-distancing
measures effectively slowed disease transmission in many countries in Europe
and Asia, but the same benefits have not been observed in some developing
countries such as Brazil. In part, this is due to a failure to organise
systematic testing campaigns at nationwide or even regional levels. To gain
effective control of the pandemic, decision-makers in developing countries,
particularly those with large populations, must overcome difficulties posed by
an unequal distribution of wealth combined with low daily testing capacities.
The economic infrastructure of the country, often concentrated in a few cities,
forces workers to travel from commuter cities and rural areas, which induces
strong nonlinear effects on disease transmission. In the present study, we
develop a smart testing strategy to identify geographic regions where COVID-19
testing could most effectively be deployed to limit further disease
transmission. The strategy uses readily available anonymised mobility and
demographic data integrated with intensive care unit (ICU) occupancy data and
city-specific social-distancing measures. Taking into account the heterogeneity
of ICU bed occupancy in differing regions and the stages of disease evolution,
we use a data-driven study of the Brazilian state of Sao Paulo as an example to
show that smart testing strategies can rapidly limit transmission while
reducing the need for social-distancing measures, thus returning life to a
so-called new normal, even when testing capacity is limited.


Abstract:
  Teaching collaborative argumentation is an advanced skill that many K-12
teachers struggle to develop. To address this, we have developed Discussion
Tracker, a classroom discussion analytics system based on novel algorithms for
classifying argument moves, specificity, and collaboration. Results from a
classroom deployment indicate that teachers found the analytics useful, and
that the underlying classifiers perform with moderate to substantial agreement
with humans.


Abstract:
  As is well known, the smallest neutrino mass turns out to be vanishing in the
minimal seesaw model, since the effective neutrino mass matrix $M^{}_\nu$ is of
rank two due to the fact that only two heavy right-handed neutrinos are
introduced. In this paper, we point out that the one-loop matching condition
for the effective dimension-five neutrino mass operator can make an important
contribution to the smallest neutrino mass. By using the available one-loop
matching condition and two-loop renormalization group equations in the
supersymmetric version of the minimal seesaw model, we explicitly calculate the
smallest neutrino mass in the case of normal neutrino mass ordering and find
$m^{}_1 \in [10^{-10}, 10^{-8}]~{\rm eV}$ at the Fermi scale $\Lambda^{}_{\rm
F} = 91.2~{\rm GeV}$, where the range of $m^{}_1$ results from the
uncertainties on the choice of the seesaw scale $\Lambda^{}_{\rm SS}$ and on
the input values of relevant parameters at $\Lambda^{}_{\rm SS}$.


Abstract:
  Hypergraphs are used to model higher-order interactions amongst agents and
there exist many practically relevant instances of hypergraph datasets. To
enable efficient processing of hypergraph-structured data, several hypergraph
neural network platforms have been proposed for learning hypergraph properties
and structure, with a special focus on node classification. However, almost all
existing methods use heuristic propagation rules and offer suboptimal
performance on many datasets. We propose AllSet, a new hypergraph neural
network paradigm that represents a highly general framework for (hyper)graph
neural networks and for the first time implements hypergraph neural network
layers as compositions of two multiset functions that can be efficiently
learned for each task and each dataset. Furthermore, AllSet draws on new
connections between hypergraph neural networks and recent advances in deep
learning of multiset functions. In particular, the proposed architecture
utilizes Deep Sets and Set Transformer architectures that allow for significant
modeling flexibility and offer high expressive power. To evaluate the
performance of AllSet, we conduct the most extensive experiments to date
involving ten known benchmarking datasets and three newly curated datasets that
represent significant challenges for hypergraph node classification. The
results demonstrate that AllSet has the unique ability to consistently either
match or outperform all other hypergraph neural networks across the tested
datasets. Our implementation and dataset will be released upon acceptance.


Abstract:
  The planet-metallicity correlation serves as a potential link between
exoplanet systems as we observe them today and the effects of bulk composition
on the planet formation process. Many observers have noted a tendency for
Jovian planets to form around stars with higher metallicities; however, there
is no consensus on a trend for smaller planets. Here, we investigate the
planet-metallicity correlation for rocky planets in single and multi-planet
systems around Kepler M-dwarf and late K-dwarf stars. Due to molecular
blanketing and the dim nature of these low mass stars, it is difficult to make
direct elemental abundance measurements via spectroscopy. We instead use a
combination of accurate and uniformly measured parallaxes and photometry to
obtain relative metallicities and validate this method with a subsample of
spectroscopically determined metallicities. We use the Kolmogorov-Smirnov (KS)
test, Mann-Whitney U test, and Anderson-Darling test to compare the compact
multiple planetary systems with single transiting planet systems and systems
with no detected transiting planets. We find that the compact multiple
planetary systems are derived from a statistically more metal-poor population,
with a p-value of 0.015 in the KS test, a p-value of 0.005 in the Mann-Whitney
U test, and a value of 2.574 in the Anderson-Darling test statistic, which
exceeds the derived threshold for significance by a factor of 25. We conclude
that metallicity plays a significant role in determining the architecture of
rocky planet systems. Compact multiples either form more readily, or are more
likely to survive on Gyr timescales, around metal-poor stars.


Abstract:
  We construct weight-preserving bijections between column strict shifted plane
partitions with one row and alternating sign trapezoids with exactly one column
in the left half that sums to $1$. Amongst other things, they relate the number
of $-1$s in the alternating sign trapezoids to certain elements in the column
strict shifted plane partitions that generalise the notion of special parts in
descending plane partitions. The advantage of these bijections is that they
include configurations with $-1$s, which is a feature that many of the
bijections in the realm of alternating sign arrays lack.


Abstract:
  The nonlinear response associated with the current dependence of the
superconducting kinetic inductance was studied in capacitively shunted NbTiN
microstrip transmission lines. It was found that the inductance per unit length
of one microstrip line could be changed by up to 20% by applying a DC current,
corresponding to a single pass time delay of 0.7 ns. To investigate nonlinear
dissipation, Bragg reflectors were placed on either end of a section of this
type of transmission line, creating resonances over a range of frequencies.
From the change in the resonance linewidth and amplitude with DC current, the
ratio of the reactive to the dissipative response of the line was found to be
788. The low dissipation makes these transmission lines suitable for a number
of applications that are microwave and millimeter-wave band analogues of
nonlinear optical processes. As an example, by applying a millimeter-wave pump
tone, very wide band parametric amplification was observed between about 3 and
34 GHz. Use as a current variable delay line for an on-chip millimeter-wave
Fourier transform spectrometer is also considered.


Abstract:
  Determining the architecture of multi-planetary systems is one of the
cornerstones of understanding planet formation and evolution. Resonant systems
are especially important as the fragility of their orbital configuration
ensures that no significant scattering or collisional event has taken place
since the earliest formation phase when the parent protoplanetary disc was
still present. In this context, TOI-178 has been the subject of particular
attention since the first TESS observations hinted at a 2:3:3 resonant chain.
Here we report the results of observations from CHEOPS, ESPRESSO, NGTS, and
SPECULOOS with the aim of deciphering the peculiar orbital architecture of the
system. We show that TOI-178 harbours at least six planets in the super-Earth
to mini-Neptune regimes, with radii ranging from 1.152(-0.070/+0.073) to
2.87(-0.13/+0.14) Earth radii and periods of 1.91, 3.24, 6.56, 9.96, 15.23, and
20.71 days. All planets but the innermost one form a 2:4:6:9:12 chain of
Laplace resonances, and the planetary densities show important variations from
planet to planet, jumping from 1.02(+0.28/-0.23) to 0.177(+0.055/-0.061) times
the Earth's density between planets c and d. Using Bayesian interior structure
retrieval models, we show that the amount of gas in the planets does not vary
in a monotonous way, contrary to what one would expect from simple formation
and evolution models and unlike other known systems in a chain of Laplace
resonances. The brightness of TOI-178 allows for a precise characterisation of
its orbital architecture as well as of the physical nature of the six presently
known transiting planets it harbours. The peculiar orbital configuration and
the diversity in average density among the planets in the system will enable
the study of interior planetary structures and atmospheric evolution, providing
important clues on the formation of super-Earths and mini-Neptunes.


Abstract:
  We advance and experimentally implement a protocol to generate perfect
optical coherence lattices (OCL) that are not modulated by an envelope field.
Structuring the amplitude and phase of an input partially coherent beam in a
Fourier plane of an imaging system lies at the heart of our protocol. In the
proposed approach, the OCL node profile depends solely on the degree of
coherence (DOC) of the input beam such that, in principle, any lattice
structure can be attained via proper manipulations in the Fourier plane.
Moreover, any genuine partially coherent source can serve as an input to our
lattice generating imaging system. Our results are anticipated to find
applications to optical field engineering and multi-target probing among
others.


Abstract:
  We carried out a comprehensive study of electronic transport, thermal and
thermodynamic properties in FeCr$_2$Te$_4$ single crystals. It exhibits
bad-metallic behavior and anomalous Hall effect (AHE) below a weak-itinerant
paramagentic-to-ferrimagnetic transition $T_c$ $\sim$ 123 K. The linear scaling
between the anomalous Hall resistivity $\rho_{xy}$ and the longitudinal
resistivity $\rho_{xx}$ implies that the AHE in FeCr$_2$Te$_4$ is most likely
dominated by extrinsic skew-scattering mechanism rather than intrinsic KL or
extrinsic side-jump mechanism, which is supported by our Berry phase
calculations.


Abstract:
  In this article, we study the problem of air-to-ground ultra-reliable and
low-latency communication (URLLC) for a moving ground user. This is done by
controlling multiple unmanned aerial vehicles (UAVs) in real time while
avoiding inter-UAV collisions. To this end, we propose a novel multi-agent deep
reinforcement learning (MADRL) framework, coined a graph attention exchange
network (GAXNet). In GAXNet, each UAV constructs an attention graph locally
measuring the level of attention to its neighboring UAVs, while exchanging the
attention weights with other UAVs so as to reduce the attention mismatch
between them. Simulation results corroborates that GAXNet achieves up to 4.5x
higher rewards during training. At execution, without incurring inter-UAV
collisions, GAXNet achieves 6.5x lower latency with the target 0.0000001 error
rate, compared to a state-of-the-art baseline framework.


Abstract:
  Animals can quickly learn the timing of events with fixed intervals and their
rate of acquisition does not depend on the length of the interval. In contrast,
recurrent neural networks that use gradient based learning have difficulty
predicting the timing of events that depend on stimulus that occurred long ago.
We present the latent time-adaptive drift-diffusion model (LTDDM), an extension
to the time-adaptive drift-diffusion model (TDDM), a model for animal learning
of timing that exhibits behavioural properties consistent with experimental
data from animals. The performance of LTDDM is compared to that of a state of
the art long short-term memory (LSTM) recurrent neural network across three
timing tasks. Differences in the relative performance of these two models is
discussed and it is shown how LTDDM can learn these events time series orders
of magnitude faster than recurrent neural networks.


Abstract:
  In the last three decades, memory safety issues in system programming
languages such as C or C++ have been one of the significant sources of security
vulnerabilities. However, there exist only a few attempts with limited success
to cope with the complexity of C++ program verification. Here we describe and
evaluate a novel verification approach based on bounded model checking (BMC)
and satisfiability modulo theories (SMT) to verify C++ programs formally. Our
verification approach analyzes bounded C++ programs by encoding into SMT
various sophisticated features that the C++ programming language offers, such
as templates, inheritance, polymorphism, exception handling, and the Standard
C++ Libraries. We formalize these features within our formal verification
framework using a decidable fragment of first-order logic and then show how
state-of-the-art SMT solvers can efficiently handle that. We implemented our
verification approach on top of ESBMC. We compare ESBMC to LLBMC and DIVINE,
which are state-of-the-art verifiers to check C++ programs directly from the
LLVM bitcode. Experimental results show that ESBMC can handle a wide range of
C++ programs, presenting a higher number of correct verification results. At
the same time, it reduces the verification time if compared to LLBMC and DIVINE
tools. Additionally, ESBMC has been applied to a commercial C++ application in
the telecommunication domain and successfully detected arithmetic overflow
errors, potentially leading to security vulnerabilities.


Abstract:
  We investigate the asymptotic symmetry group of a scalar field
minimally-coupled to an abelian gauge field using the Hamiltonian formulation.
This extends previous work by Henneaux and Troessaert on the pure
electromagnetic case. We deal with minimally coupled massive and massless
scalar fields and find that they behave differently insofar as the latter do
not allow for canonically implemented asymptotic boost symmetries. We also
consider the abelian Higgs model and show that its asymptotic canonical
symmetries reduce to the Poincar\'e group in an unproblematic fashion.


Abstract:
  Despite the rich literature on scheduling algorithms for wireless networks,
algorithms that can provide deadline guarantees on packet delivery for general
traffic and interference models are very limited. In this paper, we study the
problem of scheduling real-time traffic under a conflict-graph interference
model with unreliable links due to channel fading. Packets that are not
successfully delivered within their deadlines are of no value. We consider
traffic (packet arrival and deadline) and fading (link reliability) processes
that evolve as an unknown finite-state Markov chain. The performance metric is
efficiency ratio which is the fraction of packets of each link which are
delivered within their deadlines compared to that under the optimal (unknown)
policy. We first show a conversion result that shows classical non-real-time
scheduling algorithms can be ported to the real-time setting and yield a
constant efficiency ratio, in particular, Max-Weight Scheduling (MWS) yields an
efficiency ratio of 1/2. We then propose randomized algorithms that achieve
efficiency ratios strictly higher than 1/2, by carefully randomizing over the
maximal schedules. We further propose low-complexity and myopic distributed
randomized algorithms, and characterize their efficiency ratio. Simulation
results are presented that verify that randomized algorithms outperform
classical algorithms such as MWS and GMS.


Abstract:
  As the number of IoT devices has increased rapidly, IoT botnets have
exploited the vulnerabilities of IoT devices. However, it is still challenging
to detect the initial intrusion on IoT devices prior to massive attacks. Recent
studies have utilized power side-channel information to identify this intrusion
behavior on IoT devices but still lack accurate models in real-time for
ubiquitous botnet detection.
  We proposed the first online intrusion detection system called DeepAuditor
for IoT devices via power auditing. To develop the real-time system, we
proposed a lightweight power auditing device called Power Auditor. We also
designed a distributed CNN classifier for online inference in a laboratory
setting. In order to protect data leakage and reduce networking redundancy, we
then proposed a privacy-preserved inference protocol via Packed Homomorphic
Encryption and a sliding window protocol in our system. The classification
accuracy and processing time were measured, and the proposed classifier
outperformed a baseline classifier, especially against unseen patterns. We also
demonstrated that the distributed CNN design is secure against any distributed
components. Overall, the measurements were shown to the feasibility of our
real-time distributed system for intrusion detection on IoT devices.


Abstract:
  Vector control strategies are central to the mitigation and containment of
COVID-19 and have come in the form of municipal ordinances that restrict the
operational status of public and private spaces and associated services. Yet,
little is known about specific population responses in terms of risk behaviors.
To help understand the impact of those vector control variable strategies, a
multi-week, multi-site observational study was undertaken outside of 19 New
York City medical facilities during the peak of the city's initial COVID-19
wave (03/22/20-05/19/20). The aim was to capture perishable data of the touch,
destination choice, and PPE usage behavior of individuals egressing hospitals
and urgent care centers. A major goal was to establish an empirical basis for
future research on the way people interact with three-dimensional vector
environments. Anonymized data were collected via smart phones. Each data record
includes the time, data, and location of an individual leaving a healthcare
facility, their routing, interactions with the build environment, other
individuals, and themselves. Most records also note their PPE usage,
destination, intermediary stops, and transportation choices. The records were
linked with 61 socio-economic factors by the facility zip code and 7
contemporaneous weather factors and the merged in a unified shapefile in an
ARCGIS system. This paper describes the project team and protocols used to
produce over 5,100 publicly accessible observational records and an affiliated
codebook that can be used to study linkages between individual behaviors and
on-the-ground conditions.


Abstract:
  Deep Neural Networks (DNNs), despite their tremendous success in recent
years, could still cast doubts on their predictions due to the intrinsic
uncertainty associated with their learning process. Ensemble techniques and
post-hoc calibrations are two types of approaches that have individually shown
promise in improving the uncertainty calibration of DNNs. However, the
synergistic effect of the two types of methods has not been well explored. In
this paper, we propose a truth discovery framework to integrate ensemble-based
and post-hoc calibration methods. Using the geometric variance of the ensemble
candidates as a good indicator for sample uncertainty, we design an
accuracy-preserving truth estimator with provably no accuracy drop.
Furthermore, we show that post-hoc calibration can also be enhanced by truth
discovery-regularized optimization. On large-scale datasets including CIFAR and
ImageNet, our method shows consistent improvement against state-of-the-art
calibration approaches on both histogram-based and kernel density-based
evaluation metrics. Our codes are available at
https://github.com/horsepurve/truly-uncertain.


Abstract:
  Space-based transit missions such as Kepler and TESS have demonstrated that
planets are ubiquitous. However, the success of these missions heavily depends
on ground-based radial velocity (RV) surveys, which combined with transit
photometry can yield bulk densities and orbital properties. While most Kepler
host stars are too faint for detailed follow-up observations, TESS is detecting
planets orbiting nearby bright stars that are more amenable to RV
characterization. Here we introduce the TESS-Keck Survey (TKS), an RV program
using ~100 nights on Keck/HIRES to study exoplanets identified by TESS. The
primary survey aims are investigating the link between stellar properties and
the compositions of small planets; studying how the diversity of system
architectures depends on dynamical configurations or planet multiplicity;
identifying prime candidates for atmospheric studies with JWST; and
understanding the role of stellar evolution in shaping planetary systems. We
present a fully-automated target selection algorithm, which yielded 103 planets
in 86 systems for the final TKS sample. Most TKS hosts are inactive,
solar-like, main-sequence stars (4500 K < Teff < 6000 K) at a wide range of
metallicities. The selected TKS sample contains 71 small planets (Rp < 4 Re),
11 systems with multiple transiting candidates, 6 sub-day period planets and 3
planets that are in or near the habitable zone of their host star. The target
selection described here will facilitate the comparison of measured planet
masses, densities, and eccentricities to predictions from planet population
models. Our target selection software is publicly available (at
https://github.com/ashleychontos/sort-a-survey) and can be adapted for any
survey which requires a balance of multiple science interests within a given
telescope allocation.


Abstract:
  We present an isomorphism test for graphs of Euler genus $g$ running in time
$2^{O(g^4 \log g)}n^{O(1)}$. Our algorithm provides the first explicit upper
bound on the dependence on $g$ for an fpt isomorphism test parameterized by the
Euler genus of the input graphs. The only previous fpt algorithm runs in time
$f(g)n$ for some function $f$ (Kawarabayashi 2015). Actually, our algorithm
even works when the input graphs only exclude $K_{3,h}$ as a minor. For such
graphs, no fpt isomorphism test was known before.
  The algorithm builds on an elegant combination of simple group-theoretic,
combinatorial, and graph-theoretic approaches. In particular, we introduce
$(t,k)$-WL-bounded graphs which provide a powerful tool to combine
group-theoretic techniques with the standard Weisfeiler-Leman algorithm. This
concept may be of independent interest.


Abstract:
  Consider a branching random walk $(V_u)_{u\in \mathcal T^{IGW}}$ in $\mathbb
Z^d$ with the genealogy tree $\mathcal T^{IGW}$ formed by a sequence of i.i.d.
critical Galton-Watson trees. Let $R_n $ be the set of points in $\mathbb Z^d$
visited by $(V_u)$ when the index $u$ explores the first $n$ subtrees in
$\mathcal T^{IGW}$. Our main result states that for $d\in \{3, 4, 5\}$, the
capacity of $R_n$ is almost surely equal to $n^{\frac{d-2}{2}+o(1)}$ as $n \to
\infty$.


Abstract:
  We investigate magnetic instabilities in charge-neutral twisted bilayer
graphene close to so-called "magic angles" using a combination of real-space
Hartree-Fock and dynamical mean-field theories. In view of the large size of
the unit cell close to magic angles, we examine a previously proposed rescaling
that permits to mimic the same underlying flat minibands at larger twist
angles. We find that localized magnetic states emerge for values of the Coulomb
interaction $U$ that are significantly smaller than what would be required to
render an isolated layer antiferromagnetic. However, this effect is
overestimated in the rescaled system, hinting at a complex interplay of
flatness of the minibands close to the Fermi level and the spatial extent of
the corresponding localized states. Our findings shed new light on perspectives
for experimental realization of magnetic states in charge-neutral twisted
bilayer graphene.


Abstract:
  Locating lesions is important in the computer-aided diagnosis of X-ray
images. However, box-level annotation is time-consuming and laborious. How to
locate lesions accurately with few, or even without careful annotations is an
urgent problem. Although several works have approached this problem with
weakly-supervised methods, the performance needs to be improved. One obstacle
is that general weakly-supervised methods have failed to consider the
characteristics of X-ray images, such as the highly-structural attribute. We
therefore propose the Cross-chest Graph (CCG), which improves the performance
of automatic lesion detection by imitating doctor's training and
decision-making process. CCG models the intra-image relationship between
different anatomical areas by leveraging the structural information to simulate
the doctor's habit of observing different areas. Meanwhile, the relationship
between any pair of images is modeled by a knowledge-reasoning module to
simulate the doctor's habit of comparing multiple images. We integrate
intra-image and inter-image information into a unified end-to-end framework.
Experimental results on the NIH Chest-14 database (112,120 frontal-view X-ray
images with 14 diseases) demonstrate that the proposed method achieves
state-of-the-art performance in weakly-supervised localization of lesions by
absorbing professional knowledge in the medical field.


Abstract:
  We study effects of higher-order antinematic interactions on the critical
behavior of the antiferromagnetic (AFM) $XY$ model on a triangular lattice,
using Monte Carlo simulations. The parameter $q$ of the generalized antinematic
(ANq) interaction is found to have a pronounced effect on the phase diagram
topology by inducing new quasi-long-range ordered phases due to competition
with the conventional AFM interaction as well as geometrical frustration. For
values of $q$ divisible by 3 the conflict between the two interactions results
in a frustrated canted AFM phase appearing at low temperatures wedged between
the AFM and ANq phases. For $q$ nondivisible by 3 with the increase of $q$ one
can observe the evolution of the phase diagram topology featuring two ($q=2$),
three ($q=4,5$) and four ($q \geq 7$) ordered phases. In addition to the two
phases previously found for $q=2$, the first new phase with solely AFM ordering
arises for $q=4$ in the limit of strong AFM coupling and higher temperatures by
separating from the phase with the coexisting AFM and ANq orderings. For $q=7$
another phase with AFM ordering but multimodal spin distribution in each
sublattice appears at intermediate temperatures. All these algebraic phases
also display standard and generalized chiral long-range orderings, which
decouple at higher temperatures in the regime of dominant ANq (AFM) interaction
for $q \geq 4$ ($q \geq 7$) preserving only the generalized (standard) chiral
ordering.


Abstract:
  Deep reinforcement learning (DRL) has great potential for acquiring the
optimal action in complex environments such as games and robot control.
However, it is difficult to analyze the decision-making of the agent, i.e., the
reasons it selects the action acquired by learning. In this work, we propose
Mask-Attention A3C (Mask A3C), which introduces an attention mechanism into
Asynchronous Advantage Actor-Critic (A3C), which is an actor-critic-based DRL
method, and can analyze the decision-making of an agent in DRL. A3C consists of
a feature extractor that extracts features from an image, a policy branch that
outputs the policy, and a value branch that outputs the state value. In this
method, we focus on the policy and value branches and introduce an attention
mechanism into them. The attention mechanism applies a mask processing to the
feature maps of each branch using mask-attention that expresses the judgment
reason for the policy and state value with a heat map. We visualized
mask-attention maps for games on the Atari 2600 and found we could easily
analyze the reasons behind an agent's decision-making in various game tasks.
Furthermore, experimental results showed that the agent could achieve a higher
performance by introducing the attention mechanism.


Abstract:
  This paper presents Contrastive Reconstruction, ConRec - a self-supervised
learning algorithm that obtains image representations by jointly optimizing a
contrastive and a self-reconstruction loss. We showcase that state-of-the-art
contrastive learning methods (e.g. SimCLR) have shortcomings to capture
fine-grained visual features in their representations. ConRec extends the
SimCLR framework by adding (1) a self-reconstruction task and (2) an attention
mechanism within the contrastive learning task. This is accomplished by
applying a simple encoder-decoder architecture with two heads. We show that
both extensions contribute towards an improved vector representation for images
with fine-grained visual features. Combining those concepts, ConRec outperforms
SimCLR and SimCLR with Attention-Pooling on fine-grained classification
datasets.


Abstract:
  Let $\mathfrak{g}=\mathfrak{g}_{\bar{0}}+\mathfrak{g}_{\bar{1}}$ be a basic
Lie superalgebra, $\mathcal{W}_0$ (resp.$\mathcal{W}$) be the finite
W-(resp.super-) algebras constructed from a fixed nilpotent element in
$\mathfrak{g}_{\bar{0}}$. Based on a relation between finite W-algebra
$\mathcal{W}_0$ and W-superalgebra $\mathcal{W}$ found recently by the author
and Shu, we study the finite dimensional representations of finite
W-superalgebras in this paper. We first formulate and prove a version of
Premet's conjecture for the finite W-superalgebras from basic simple Lie
superalgebras. As in the W-algebra case, the Premet's conjecture is very close
to give a classification to the finite dimensional simple
$\mathcal{W}$-modules. In the case of $\ggg$ is Lie superalgebras of basic type
\Rmnum{1}, we prove the set of simple $\mathcal{W}$-supermodules is bijective
with that of simple $\mathcal{W}_0$-modules; presenting a triangular
decomposition to the tensor product of $\mathcal{W}$ with a Clifford algebra,
we also give an algorithm to compute the character of the finite dimensional
simple $\mathcal{W}$-supermodules with integral central character.


Abstract:
  Pulsating ultra-luminous X-ray sources (PULXs) are characterised by an
extremely large luminosity ($ > 10^{40} \text{erg s}^{-1}$). While there is a
general consensus that they host an accreting, magnetized neutron star (NS),
the problem of how to produce luminosities $> 100$ times the Eddington limit,
$L_E$, of a solar mass object is still debated. A promising explanation relies
on the reduction of the opacities in the presence of a strong magnetic field,
which allows for the local flux to be much larger than the Eddington flux.
However, avoiding the onset of the propeller effect may be a serious problem.
Here, we reconsider the problem of column accretion onto a highly magnetized
NS, extending previously published calculations by relaxing the assumption of a
pure dipolar field and allowing for more complex magnetic field topologies. We
find that the maximum luminosity is determined primarily by the magnetic field
strength near the NS surface. We also investigate other factors determining the
accretion column geometry and the emergent luminosity, such as the assumptions
on the parameters governing the accretion flow at the disk-magnetosphere
boundary. We conclude that a strongly magnetized NS with a dipole component of
$\sim 10^{13} \text{G}$, octupole component of $\sim10^{14} \text{G}$ and spin
period $\sim1 \text{s}$ can produce a luminosity of $\sim 10^{41} \text{erg
s}^{-1}$ while avoiding the propeller regime. We apply our model to two PULXs,
NGC 5907 ULX-1 and NGC 7793 P13, and discuss how their luminosity and spin
period rate can be explained in terms of different configurations, either with
or without multipolar magnetic components.


Abstract:
  Key to successfully deal with complex contemporary datasets is the
development of tractable models that account for the irregular structure of the
information at hand. This paper provides a comprehensive and unifying view of
several sampling, reconstruction, and recovery problems for signals defined on
irregular domains that can be accurately represented by a graph. The workhorse
assumption is that the (partially) observed signals can be modeled as the
output of a graph filter to a structured (parsimonious) input graph signal.
When either the input or the filter coefficients are known, this is tantamount
to assuming that the signals of interest live on a subspace defined by the
supporting graph. When neither is known, the model becomes bilinear. Upon
imposing different priors and additional structure on either the input or the
filter coefficients, a broad range of relevant problem formulations arise. The
goal is then to leverage those priors, the shift operator of the supporting
graph, and the samples of the signal of interest to recover: the signal at the
non-sampled nodes (graph-signal interpolation), the input (deconvolution), the
filter coefficients (system identification), or any combination thereof (blind
deconvolution).


Abstract:
  We study the boundary behaviour of solutions to second order parabolic linear
equations in moving domains. Our main result is a higher order boundary Harnack
inequality in $C^1$ and $C^{k,\alpha}$ domains, providing that the quotient of
two solutions vanishing on the boundary of the domain is as smooth as the
boundary.
  As a consequence of our result, we provide a new proof of higher order
regularity of the free boundary in the parabolic obstacle problem.


Abstract:
  In this paper, we analyze the so-called Master Equation of the linear
backreaction of a plasma disk in the central object magnetic field, when small
scale ripples are considered. This study allows to single out two relevant
physical properties of the linear disk backreaction: (i) the appearance of a
vertical growth of the magnetic flux perturbations; (ii) the emergence of
sequence of magnetic field O-points, crucial for the triggering of local plasma
instabilities. We first analyze a general Fourier approach to the solution of
the addressed linear partial differential problem. This technique allows to
show how the vertical gradient of the backreaction is, in general, inverted
with respect to the background one. Instead, the fundamental harmonic solution
constitutes a specific exception for which the background and the perturbed
profiles are both decaying. Then, we study the linear partial differential
system from the point of view of a general variable separation method. The
obtained profile describes the crystalline behavior of the disk. Using a simple
rescaling, the governing equation is reduced to the second order differential
Whittaker equation. The zeros of the radial magnetic field are found by using
the solution written in terms Kummer functions. The possible implications of
the obtained morphology of the disk magnetic profile are then discussed in view
of the jet formation.


Abstract:
  Identifying academic plagiarism is a pressing problem, among others, for
research institutions, publishers, and funding organizations. Detection
approaches proposed so far analyze lexical, syntactical, and semantic text
similarity. These approaches find copied, moderately reworded, and literally
translated text. However, reliably detecting disguised plagiarism, such as
strong paraphrases, sense-for-sense translations, and the reuse of non-textual
content and ideas, is an open research problem.
  The thesis addresses this problem by proposing plagiarism detection
approaches that implement a different concept: analyzing non-textual content in
academic documents, specifically citations, images, and mathematical content.
  To validate the effectiveness of the proposed detection approaches, the
thesis presents five evaluations that use real cases of academic plagiarism and
exploratory searches for unknown cases.
  The evaluation results show that non-textual content elements contain a high
degree of semantic information, are language-independent, and largely immutable
to the alterations that authors typically perform to conceal plagiarism.
Analyzing non-textual content complements text-based detection approaches and
increases the detection effectiveness, particularly for disguised forms of
academic plagiarism.
  To demonstrate the benefit of combining non-textual and text-based detection
methods, the thesis describes the first plagiarism detection system that
integrates the analysis of citation-based, image-based, math-based, and
text-based document similarity. The system's user interface employs
visualizations that significantly reduce the effort and time users must invest
in examining content similarity.


Abstract:
  We introduce "$t$-LC triangulated manifolds" as those triangulations
obtainable from a tree of $d$-simplices by recursively identifying two boundary
$(d-1)$-faces whose intersection has dimension at least $d-t-1$. The $t$-LC
notion interpolates between the class of LC manifolds introduced by
Durhuus--Jonsson (corresponding to the case $t=1$), and the class of all
manifolds (case $t=d$). Benedetti--Ziegler proved that there are at most
$2^{d^2 \, N}$ triangulated $1$-LC $d$-manifolds with $N$ facets. Here we prove
that there are at most $2^{\frac{d^3}{2}N}$ triangulated $2$-LC $d$-manifolds
with $N$ facets. This extends to all dimensions an intuition by Mogami for
$d=3$.
  We also introduce "$t$-constructible complexes", interpolating between
constructible complexes (the case $t=1$) and all complexes (case $t=d$). We
show that all $t$-constructible pseudomanifolds are $t$-LC, and that all
$t$-constructible complexes have (homotopical) depth larger than $d-t$. This
extends the famous result by Hochster that constructible complexes are
(homotopy) Cohen--Macaulay.


Abstract:
  This paper revisits the connection between the girth of a protograph-based
LDPC code given by a parity-check matrix and the properties of powers of the
product between the matrix and its transpose in order to obtain the necessary
and sufficient conditions for a code to have given girth between 6 and 12, and
to show how these conditions can be incorporated into simple algorithms to
construct codes of that girth. To this end, we highlight the role that certain
submatrices that appear in these products have in the construction of codes of
desired girth. In particular, we show that imposing girth conditions on a
parity-check matrix is equivalent to imposing conditions on a square submatrix
obtained from it and we show how this equivalence is particularly strong for a
protograph based parity-check matrix of variable node degree 2, where the
cycles in its Tanner graph correspond one-to-one to the cycles in the Tanner
graph of a square submatrix obtained by adding the permutation matrices (or
products of these) in the composition of the parity-check matrix. We end the
paper with exemplary constructions of codes with various girths and computer
simulations. Although, we mostly assume the case of fully connected protographs
of variable node degree 2 and 3, the results can be used for any parity-check
matrix/protograph-based Tanner graph.


Abstract:
  Two-dimensional electron systems subjected to a perpendicular magnetic field
absorb electromagnetic radiation via the cyclotron resonance (CR). Here we
report a qualitative breach of this well-known behaviour in graphene. Our study
of the terahertz photoresponse reveals a resonant burst at the main overtone of
the CR, drastically exceeding the signal detected at the position of the
ordinary CR. In accordance with the developed theory, the photoresponse
dependencies on the magnetic field, doping level, and sample geometry suggest
that the origin of this anomaly lies in the near-field magnetoabsorption
facilitated by the Bernstein modes, ultra-slow magnetoplasmonic excitations
reshaped by nonlocal electron dynamics. Close to the CR harmonics, these modes
are characterized by a flat dispersion and a diverging plasmonic density of
states that strongly amplifies the radiation absorption. Besides fundamental
interest, our experimental results and developed theory show that the radiation
absorption via nonlocal collective modes can facilitate a strong photoresponse,
a behaviour potentially useful for infrared and terahertz technology.


Abstract:
  We obtain results on the condensation principle called local club
condensation. We prove that in extender models an equivalence between the
failure of local club condensation and subcompact cardinals holds. This gives a
characterization of $\square_{\kappa}$ in terms of local club condensation in
extender models. Assuming $\gch$, given an interval of ordinals $I$ we verify
that iterating the forcing defined by Holy-Welch-Wu, we can preserve $\gch$,
cardinals and cofinalities and obtain a model where local club condensation
holds for every ordinal in $I$ modulo those ordinals which cardinality is a
singular cardinal.
  We prove that if $\kappa$ is a regular cardinal in an interval $I$, the above
iteration provides enough condensation for the combinatorial principle
$\Dl_{S}^{*}(\Pi^{1}_{2})$, and in particular $\diamondsuit(S)$, to hold for
any stationary $S \subseteq \kappa$.


Abstract:
  In this paper we find analytical solutions for the scalar and gauge fields in
the Freedman-Robertson-Walker multiply warped braneworld scenario. With this we
find the precise mass spectra for these fields. We compare these spectra with
that previously found in the literature for the static case.


Abstract:
  The Mn(Bi$_{1-x}$Sb$_x$)$_2$Te$_4$ series is purported to span from
antiferromagnetic (AF) topological insulator at x = 0 to a trivial AF insulator
at x = 1. Here we report on neutron diffraction and inelastic neutron
scattering studies of the magnetic interactions across this series. All
compounds measured possess ferromagnetic (FM) triangular layers and we find a
crossover from AF to FM interlayer coupling near x = 1 for our samples. The
large spin gap at x = 0 closes rapidly and the average FM exchange interactions
within the triangular layer increase with Sb substitution. Similar to a
previous study of MnBi$_2$Te$_4$, we find severe spectral broadening which
increases dramatically across the compositional series. In addition to
broadening, we observe an additional sharp magnetic excitation in
MnSb$_2$Te$_4$ that may indicate the development of local magnetic modes based
on recent reports of antisite disorder between Mn and Sb sublattices. The
results suggest that both substitutional and antisite disorder contribute
substantially to the magnetism in Mn(Bi$_{1-x}$Sb$_x$)$_2$Te$_4$.


Abstract:
  We have built a renormalizable $U(1)_X$ model with a $\Sigma (18)\times Z_4$
symmetry, whose spontaneous breaking yields the observed SM fermion masses and
fermionic mixing parameters. The tiny masses of the light active neutrinos are
produced by the type I seesaw mechanism mediated by very heavy right handed
Majorana neutrinos. To the best of our knowledge, this model is the first
implementation of the $\Sigma (18)$ flavor symmetry in a renormalizable
$U(1)_X$ model. Our model allows a successful fit for the SM fermion masses,
fermionic mixing angles and CP phases for both quark and lepton sectors. The
obtained values for the physical observables of both quark and lepton sectors
are in accordance with the experimental data. We obtain an effective neutrino
mass parameter of $\langle m_{ee}\rangle=1.51\times 10^{-3}\, \mathrm{eV}$ for
normal ordering and $\langle m_{ee}\rangle =4.88\times 10^{-2} \, \mathrm{eV}$
for inverted ordering which are well consistent with the recent experimental
limits on neutrinoless double beta decay.


Abstract:
  Spatial resolution is one of the most important specifications of an imaging
system. Recent results in quantum parameter estimation theory reveal that an
arbitrarily small distance between two incoherent point sources can always be
efficiently determined through the use of a spatial mode sorter. However,
extending this procedure to a general object consisting of many incoherent
point sources remains challenging, due to the intrinsic complexity of
multi-parameter estimation problems. Here, we generalize the Richardson-Lucy
(RL) deconvolution algorithm to address this challenge. We simulate its
application to an incoherent confocal microscope, with a Zernike spatial mode
sorter replacing the pinhole used in a conventional confocal microscope. We
test different spatially incoherent objects of arbitrary geometry, and we find
that the resolution enhancement of sorter-based microscopy is on average over
30% higher than that of a conventional confocal microscope using the standard
RL deconvolution algorithm. Our method could potentially be used in diverse
applications such as fluorescence microscopy and astronomical imaging.


Abstract:
  In this article, we study the phenomenology of a two dimensional dilute
suspension of active amphiphilic Janus particles. We analyze how the morphology
of the aggregates emerging from their self-assembly depends on the strength and
the direction of the active forces. We systematically explore and contrast the
phenomenologies resulting from particles with a range of attractive patch
coverages. Finally, we illustrate how the geometry of the colloids and the
directionality of their interactions can be used to control the physical
properties of the assembled active aggregates and suggest possible strategies
to exploit self-propulsion as a tunable driving force for self-assembly.


Abstract:
  Purpose Infectious agents, such as SARS-CoV-2, can be carried by droplets
expelled during breathing. The spatial dissemination of droplets varies
according to their initial velocity. After a short literature review, our goal
was to determine the velocity of the exhaled air during vocal exercises.
Methods A propylene glycol cloud produced by 2 e-cigarettes' users allowed
visualization of the exhaled air emitted during vocal exercises. Airflow
velocities were measured during the first 200 ms of a long exhalation, a
sustained vowel /a/ and varied vocal exercises. For the long exhalation and the
sustained vowel /a/, the decrease of airflow velocity was measured until 3 s.
Results were compared with a Computational Fluid Dynamics (CFD) study using
boundary conditions consistent with our experimental study. Results Regarding
the production of vowels, higher velocities were found in loud and whispered
voices than in normal voice. Voiced consonants like /3/ or /v/ generated higher
velocities than vowels. Some voiceless consonants, e.g., /t/ generated high
velocities, but long exhalation had the highest velocities. Semi-occluded vocal
tract exercises generated faster airflow velocities than loud speech, with a
decreased velocity during voicing. The initial velocity quickly decreased as
was shown during a long exhalation or a sustained vowel /a/. Velocities were
consistent with the CFD data. Conclusion Initial velocity of the exhaled air is
a key factor influencing droplets trajectory. Our study revealed that vocal
exercises produce a slower airflow than long exhalation. Speech therapy should,
therefore, not be associated with an increased risk of contamination when
implementing standard recommendations.


Abstract:
  Various hardware accelerators have been developed for energy-efficient and
real-time inference of neural networks on edge devices. However, most training
is done on high-performance GPUs or servers, and the huge memory and computing
costs prevent training neural networks on edge devices. This paper proposes a
novel tensor-based training framework, which offers orders-of-magnitude memory
reduction in the training process. We propose a novel rank-adaptive tensorized
neural network model, and design a hardware-friendly low-precision algorithm to
train this model. We present an FPGA accelerator to demonstrate the benefits of
this training method on edge devices. Our preliminary FPGA implementation
achieves $59\times$ speedup and $123\times$ energy reduction compared to
embedded CPU, and $292\times$ memory reduction over a standard full-size
training.