Abstract:
  In this paper we present a hybrid feedback approach to solve the navigation
problem of a point mass in the n-dimensional space containing an arbitrary
number of ellipsoidal shape obstacles. The proposed hybrid control algorithm
guarantees both global asymptotic stabilization to a reference and avoidance of
the obstacles. The intuitive idea of the proposed hybrid feedback is to switch
between two modes of control: stabilization and avoidance. The geometric
construction of the flow and jump sets of the proposed hybrid controller,
exploiting hysteresis regions, guarantees Zeno-free switching between the
stabilization and the avoidance modes. Simulation results illustrate the
performance of the proposed hybrid control approach for 2-dimensional and
3-dimensional scenarios.


Abstract:
  The article is about algorithms for learning Bayesian hierarchical models,
the computational complexity of which scales linearly with the number of
observations and the number of parameters in the model. It focuses on crossed
random effect and nested multilevel models, which are used ubiquitously in
applied sciences, and illustrates the methodology on two challenging real data
analyses on predicting electoral results and real estate prices respectively.
The posterior dependence in both classes is sparse: in crossed random effects
models it resembles a random graph, whereas in nested multilevel models it is
tree-structured. For each class we develop a framework for scalable
computation. We provide a number of negative (for crossed) and positive (for
nested) results for the scalability (or lack thereof) of methods based on
sparse linear algebra, which are relevant also to Laplace approximation methods
for such models. Our numerical experiments compare with off-the-shelf
variational approximations and Hamiltonian Monte Carlo. Our theoretical
results, although partial, are useful in suggesting interesting methodologies
and lead to conclusions that our numerics suggest to hold well beyond the scope
of the underlying assumptions.


Abstract:
  In this paper, we consider the following three dimensional defocusing cubic
nonlinear Schr\"odinger equation (NLS) with partial harmonic potential
  \begin{equation*}\tag{NLS}
  i\partial_t u + \left(\Delta_{\mathbb{R}^3 }-x^2 \right) u = |u|^2 u, \quad
u|_{t=0} = u_0.
  \end{equation*}
  Our main result shows that the solution $u$ scatters for any given initial
data $u_0$ with finite mass and energy.
  The main new ingredient in our approach is to approximate (NLS) in the
large-scale case by a relevant dispersive continuous resonant (DCR) system. The
proof of global well-posedness and scattering of the new (DCR) system is
greatly inspired by the fundamental works of Dodson \cite{D3,D1,D2} in his
study of scattering for the mass-critical nonlinear Schr\"odinger equation. The
analysis of (DCR) system allows us to utilize the additional regularity of the
smooth nonlinear profile so that the celebrated
concentration-compactness/rigidity argument of Kenig and Merle applies.


Abstract:
  Hyperbolic geometry has been successfully applied in modeling brain cortical
and subcortical surfaces with general topological structures. However such
approaches, similar to other surface based brain morphology analysis methods,
usually generate high dimensional features. It limits their statistical power
in cognitive decline prediction research, especially in datasets with limited
subject numbers. To address the above limitation, we propose a novel framework
termed as hyperbolic stochastic coding (HSC). Our preliminary experimental
results show that our algorithm achieves superior results on various
classification tasks. Our work may enrich surface based brain imaging research
tools and potentially result in a diagnostic and prognostic indicator to be
useful in individualized treatment strategies.


Abstract:
  This paper addresses the energy management of a grid-connected photovoltaic
plant coupled with a battery energy storage device, within the capacity firming
specifications of the French Energy Regulatory Commission. The paper
contributions are positioned in the continuity of the studies adopting
stochastic models for optimizing the bids of renewable energy sources in a
day-ahead market by considering a storage device. The proposed deterministic
and stochastic approaches are optimization problems formulated as quadratic
problems with linear constraints. The case study is a real microgrid with PV
production monitored on-site. The results demonstrate the validity of the
stochastic formulation by using an ideal predictor that produces unbiased PV
scenarios.


Abstract:
  Argumentation mining is a field of computational linguistics that is devoted
to extracting from texts and classifying arguments and relations between them,
as well as constructing an argumentative structure. A significant obstacle to
research in this area for the Russian language is the lack of annotated
Russian-language text corpora. This article explores the possibility of
improving the quality of argumentation mining using the extension of the
Russian-language version of the Argumentative Microtext Corpus (ArgMicro) based
on the machine translation of the Persuasive Essays Corpus (PersEssays). To
make it possible to use these two corpora combined, we propose a Joint Argument
Annotation Scheme based on the schemes used in ArgMicro and PersEssays. We
solve the problem of classifying argumentative discourse units (ADUs) into two
classes - "pro" ("for") and "opp" ("against") using traditional machine
learning techniques (SVM, Bagging and XGBoost) and a deep neural network (BERT
model). An ensemble of XGBoost and BERT models was proposed, which showed the
highest performance of ADUs classification for both corpora.


Abstract:
  Empirical, theoretical and historical aspects of Post's "problem of tag" from
1921 are explored. Evidence of strong computational irreducibility is found.
Despite their deterministic origin, the lengths of successive sequences
generated seem to closely approximate random walks. All 10^25 smallest initial
conditions are found to eventually halt, although sometimes in > 6*10^11 steps.
Implications of the Principle of Computational Equivalence are discussed, along
with examples of identifiable computational capabilities of tag systems.
Various minimal examples of complex behavior are found, including a less-biased
analog of the 3n+1 Collatz problem. There is also discussion of the history of
Emil Post and of tag systems in the context of ideas about the foundations of
mathematics and computation.


Abstract:
  In recent years, there has been an ever increasing amount of multivariate
time series (MTS) data in various domains, typically generated by a large
family of sensors such as wearable devices. This has led to the development of
novel learning methods on MTS data, with deep learning models dominating the
most recent advancements. Prior literature has primarily focused on designing
new network architectures for modeling temporal dependencies within MTS.
However, a less studied challenge is associated with high dimensionality of MTS
data. In this paper, we propose a novel neural component, namely Neural Feature
Selector (NFS), as an end-2-end solution for feature selection in MTS data.
Specifically, NFS is based on decomposed convolution design and includes two
modules: firstly each feature stream (a stream corresponds to an univariate
series of MTS) within MTS is processed by a temporal CNN independently; then an
aggregating CNN combines the processed streams to produce input for other
downstream networks. We evaluated the proposed NFS model on four real-world MTS
datasets and found that it achieves comparable results with state-of-the-art
methods while providing the benefit of feature selection. Our paper also
highlights the robustness and effectiveness of feature selection with NFS
compared to using recent autoencoder-based methods.


Abstract:
  The twenty-first century is the century of encounter of the different races,
nations, cultures, religions and customs. In the twenty-first century, man is
more and more exposed to various influences that leave a trace on the entire
sphere of his social life, including education. Given that education systems
play one of the key roles in the formation of both physically and morally
healthy communities, it is of an enormous importance to analyze the phenomenon
of a classroom composed of culturally diverse students.


Abstract:
  These are lecture notes (by the first author) from a course (by the second
author) given over two extended semesters at the University of Sydney. The
first part provides an introduction to the Langlands correspondence from an
arithmetical point of view. The second part gives enough background in
geometric representation theory to understand Bezrukavnikov's equivalence,
which is a categorification of Kazhdan and Lusztig's two realizations of the
affine Hecke algebra.


Abstract:
  The radiative transport equation is considered in the spatial frequency
domain. We extend the analytical discrete-ordinates method to three dimensions
by rotating reference frames.


Abstract:
  In a recent report, Carballo-Rubio [1] utilizes the semi-classical theory of
gravity to obtain a generalized Tolman-Oppenheimer-Volkoff (TOV) equation. This
model has a new coupling constant $l_p$ implying two different modified TOV
equation forms characterized by the sign of $p'$. The negative branch reduces
to the ordinary GR-TOV in the limit of $l_p\to0$, while the positive one does
not. In the positive branch, Carballo-Rubio was able to find the exact
solutions using the constant-$\lambda$ trick. In this work, we investigate
whether this theory's negative branch can also provide a different feature of
the ultra-compact object compared to those obtained from the GR-TOV equation.
We study ultra-compact objects with an isotropically ideal fluid matter where
we use a simple but physically motivated equation of state $\rho=p/w+\rho_0$
with $w$=1 and $w$=1/3. In general, we obtain that the range of $l_p$ is very
restricted and must not be equal to $r_c$. Here $r_c$ is the starting point of
integration located at the center of the star. While $l_p$ should be set to be
much larger than Planck length $L_\text{Pl}$. Consequently, the mass-radius
curves for the various value of $l_p$ for both $w$ cases are still
indistinguishable from the standard GR-TOV results. Hence from the negative
branch of $p'(r)$, the additional free parameter $l_p$ does not provide a
significant effect compared to the standard GR-TOV equation results, even
though $l_p$ is not in the limit of $l_p\to0$ anymore. Therefore, similar to
the conclusion in Ref. [3] with GR theory that the ultra-compact objects from
negative branch of semi-classical gravity with a linear equation of state are
unable to generate demanding gravitational echoes.


Abstract:
  The high-Chern number phases with a Chern number C>1 have been observed in a
recent experiment that performed on the topological insulator (TI) multilayer
structures, consisting of the alternating magnetic-doped and undoped TI layers.
In this paper, we develop an effective method to determine the Chern numbers in
the TI multilayer structures and then make a systematic study on the Chern
number phase diagrams that are modulated by the magnetic doping and the middle
layer thickness. We point out that in the multilayer structure, the high-C
behavior can be attributed to the band inversion mechanisms. Moreover, we find
that the lowest bands may be multifold degenerate around the Gamma point, and
when they are inverted, the Chern number change will be larger than one.
Besides the TI multilayer structures implemented in the experiment, we also
explore the high-C phase realizations in two other kinds of the TI multilayer
structures. The implications of our results for experiments are discussed.


Abstract:
  The Sun, as an active star, is the driver of energetic phenomena that
structure interplanetary space and affect planetary atmospheres. The effects of
Space Weather on Earth and the solar system is of increasing importance as
human spaceflight is preparing for lunar and Mars missions. This review is
focusing on the solar perspective of the Space Weather relevant phenomena,
coronal mass ejections (CMEs), flares, solar energetic particles (SEPs), and
solar wind stream interaction regions (SIR). With the advent of the STEREO
mission (launched in 2006), literally, new perspectives were provided that
enabled for the first time to study coronal structures and the evolution of
activity phenomena in three dimensions. New imaging capabilities, covering the
entire Sun-Earth distance range, allowed to seamlessly connect CMEs and their
interplanetary counterparts measured in-situ (so called ICMEs). This vastly
increased our knowledge and understanding of the dynamics of interplanetary
space due to solar activity and fostered the development of Space Weather
forecasting models. Moreover, we are facing challenging times gathering new
data from two extraordinary missions, NASA's Parker Solar Probe (launched in
2018) and ESA's Solar Orbiter (launched in 2020), that will in the near future
provide more detailed insight into the solar wind evolution and image CMEs from
view points never approached before. The current review builds upon the Living
Reviews paper by Schwenn from 2006, updating on the Space Weather relevant
CME-flare-SEP phenomena from the solar perspective, as observed from multiple
viewpoints and their concomitant solar surface signatures.


Abstract:
  We propose a compact and efficient tetrahedral mesh representation to improve
the ray-tracing performance. We reorder tetrahedral mesh data using a
space-filling curve to improve cache locality. Most importantly, we propose an
efficient ray traversal algorithm. We provide details of common ray tracing
operations on tetrahedral meshes and give the GPU implementation of our
traversal method. We demonstrate our findings through a set of comprehensive
experiments. Our method outperforms existing tetrahedral mesh-based traversal
methods and yields comparable results to the traversal methods based on the
state of the art acceleration structures such as k-dimensional (k-d) trees and
Bounding Volume Hierarchies (BVHs).


Abstract:
  In this paper we pose the question of how one can quantify the amount of
entanglement necessary to create a given steering assemblage, when the original
state and measurements are unknown. To do this, we extend the concepts of
entanglement cost and entanglement of formation to steering assemblages, and
show how to upper bound them with semidefinite programming. We prove that the
entanglement of formation for assemblages is not generally continuous and not a
flat roof extension; and use numerical analysis to illustrate these properties.
Finally, we discuss the consequences of these results for
assemblage-to-assemblage conversion and directions for further research.


Abstract:
  To perform temporal and sequential machine learning tasks, the use of
conventional Recurrent Neural Networks (RNNs) has been dwindling due to the
training complexities of RNNs. To this end, accelerators for delayed feedback
reservoir computing (DFRC) have attracted attention in lieu of RNNs, due to
their simple hardware implementations. A typical implementation of a DFRC
accelerator consists of a delay loop and a single nonlinear neuron, together
acting as multiple virtual nodes for computing. In prior work, photonic DFRC
accelerators have shown an undisputed advantage of fast computation over their
electronic counterparts. In this paper, we propose a more energy-efficient
chip-scale DFRC accelerator that employs a silicon photonic microring (MR)
based nonlinear neuron along with on-chip photonic waveguides-based delayed
feedback loop. Our evaluations show that, compared to a well-known photonic
DFRC accelerator from prior work, our proposed MR-based DFRC accelerator
achieves 35% and 98.7% lower normalized root mean square error (NRMSE),
respectively, for the prediction tasks of NARMA10 and Santa Fe time series. In
addition, our MR-based DFRC accelerator achieves 58.8% lower symbol error rate
(SER) for the Non-Linear Channel Equalization task. Moreover, our MR-based DFRC
accelerator has 98% and 93% faster training time, respectively, compared to an
electronic and a photonic DFRC accelerators from prior work.


Abstract:
  This paper presents a novel neutral-pion reconstruction that takes advantage
of the machine learning technique of semantic segmentation using MINERvA data
collected between 2013-2017, with an average neutrino energy of $6$ GeV.
Semantic segmentation improves the purity of neutral pion reconstruction from
two gammas from 71\% to 89\% and improves the efficiency of the reconstruction
by approximately 40\%. We demonstrate our method in a charged current neutral
pion production analysis where a single neutral pion is reconstructed. This
technique is applicable to modern tracking calorimeters, such as the new
generation of liquid-argon time projection chambers, exposed to neutrino beams
with $\langle E_\nu \rangle$ between 1-10 GeV. In such experiments it can
facilitate the identification of ionization hits which are associated with
electromagnetic showers, thereby enabling improved reconstruction of
charged-current $\nu_e$ events arising from $\nu_{\mu} \rightarrow \nu_{e}$
appearance.


Abstract:
  Pretrained language models demonstrate strong performance in most NLP tasks
when fine-tuned on small task-specific datasets. Hence, these autoregressive
models constitute ideal agents to operate in text-based environments where
language understanding and generative capabilities are essential. Nonetheless,
collecting expert demonstrations in such environments is a time-consuming
endeavour. We introduce a two-stage procedure to learn from a small set of
demonstrations and further improve by interacting with an environment. We show
that language models fine-tuned with only 1.2% of the expert demonstrations and
a simple reinforcement learning algorithm achieve a 51% absolute improvement in
success rate over existing methods in the ALFWorld environment.


Abstract:
  We address the problem of output prediction, ie. designing a model for
autonomous nonlinear systems capable of forecasting their future observations.
We first define a general framework bringing together the necessary properties
for the development of such an output predictor. In particular, we look at this
problem from two different viewpoints, control theory and data-driven
techniques (machine learning), and try to formulate it in a consistent way,
reducing the gap between the two fields. Building on this formulation and
problem definition, we propose a predictor structure based on the
Kazantzis-Kravaris/Luenberger (KKL) observer and we show that KKL fits well
into our general framework. Finally, we propose a constructive solution for
this predictor that solely relies on a small set of trajectories measured from
the system. Our experiments show that our solution allows to obtain an
efficient predictor over a subset of the observation space.


Abstract:
  A concise analytical formula is developed for the inverse of an invertible 3
x 3 matrix using a telescoping method, and is generalized to larger square
matrices. The formula is confirmed using randomly generated matrices in Matlab


Abstract:
  In this paper, kink scattering in the dimensional reduction of the bosonic
sector of a one-parameter family of generalized Wess-Zumino models with three
vacuum points is discussed. The value of the model parameter determines the
specific location of the vacua. The influence of the vacuum arrangements
(evolving from three collinear vacua to three vacua placed at the vertices of
an equilateral triangle) on the kink scattering is investigated. Two different
regimes can be distinguished: in the first one, two symmetric BPS
kinks/antikinks arise whereas in the second one a new different BPS
kink/antikink emerges, with the exception of a three-fold rotational symmetry
case, where the three topological defects are identical. The scattering between
the two symmetric kinks is thoroughly analyzed. Two different scattering
channels have been found: kink-kink reflection and kink-kink hybridization. In
the last case, the collision between the two symmetric kinks gives rise to the
third different kink. Resonance phenomena also appear allowing a vibrating kink
to split into two symmetric kinks moving away.


Abstract:
  Machine learning potential enables molecular dynamics simulations of systems
beyond the capability of classical force fields. The traditional approach to
develop structural sets for training machine learning potential typically
generate a great number of redundant configurations, which will result in
unnecessary computational costs. This work investigates the possibility of
reducing redundancy in an initial data set containing 6183 configurations for a
Li-Si machine learning potential. Starting from the initial data set, we
constructed a series of subsets ranging from 25 to 1500 configurations by
combining a structural similarity analysis algorithm and the farthest point
sampling method. Results show that the machine learning potential trained from
a data set containing 400 configurations can achieve an accuracy comparable to
the one developed from the initial data set of 6183 configurations in
describing potential energies, atomic forces, and structural properties of
Li-Si systems. In addition, the redundancy reducing approach also demonstrates
advantages over the classic stochastic method for constructing a concise
training data set for Li-Si systems.


Abstract:
  Two-dimensional materials (2DMs) are a promising alternative to complement
and upgrade high-frequency electronics. However, in order to boost their
adoption, the availability of numerical tools and physically-based models able
to support the experimental activities and to provide them with useful
guidelines becomes essential. In this context, we propose a theoretical
approach that combines numerical simulations and small-signal modeling to
analyze 2DM-based FETs for radio-frequency applications. This multi-scale
scheme takes into account non-idealities, such as interface traps, carrier
velocity saturation, or short channel effects, by means of self-consistent
physics-based numerical calculations that later feed the circuit level via a
small-signal model based on the dynamic intrinsic capacitances of the device.
At the circuit stage, the possibilities range from the evaluation of the
performance of a single device to the design of complex circuits combining
multiple transistors. In this work, we validate our scheme against experimental
results and exemplify its use and capability assessing the impact of the
channel scaling on the performance of MoS2-based FETs targeting RF
applications.


Abstract:
  Nystr\"om approximation is a fast randomized method that rapidly solves
kernel ridge regression (KRR) problems through sub-sampling the n-by-n
empirical kernel matrix appearing in the objective function. However, the
performance of such a sub-sampling method heavily relies on correctly
estimating the statistical leverage scores for forming the sampling
distribution, which can be as costly as solving the original KRR. In this work,
we propose a linear time (modulo poly-log terms) algorithm to accurately
approximate the statistical leverage scores in the stationary-kernel-based KRR
with theoretical guarantees. Particularly, by analyzing the first-order
condition of the KRR objective, we derive an analytic formula, which depends on
both the input distribution and the spectral density of stationary kernels, for
capturing the non-uniformity of the statistical leverage scores. Numerical
experiments demonstrate that with the same prediction accuracy our method is
orders of magnitude more efficient than existing methods in selecting the
representative sub-samples in the Nystr\"om approximation.


Abstract:
  The goal of this work is the development of a motion model for sequentially
timed movement actions in robotic systems under specific consideration of
temporal stabilization, that is maintaining an approximately constant overall
movement time (isochronous behavior). This is demonstrated both in simulation
and on a physical robotic system for the task of intercepting a moving target
in three-dimensional space. Motivated from humanoid motion, timing plays a
vital role to generate a naturalistic behavior in interaction with the dynamic
environment as well as adaptively planning and executing action sequences
on-line. In biological systems, many of the physiological and anatomical
functions follow a particular level of periodicity and stabilization, which
exhibit a certain extent of resilience against external disturbances. A main
aspect thereof is stabilizing movement timing against limited perturbations.
Especially human arm movement, namely when it is tasked to reach a certain goal
point, pose or configuration, shows a stabilizing behavior. This work
incorporates the utilization of an extended Kalman filter (EKF) which was
implemented to predict the target position while coping with non-linear system
dynamics. The periodicity and temporal stabilization in biological systems was
artificially generated by a Hopf oscillator, yielding a sinusoidal velocity
profile for smooth and repeatable motion.


Abstract:
  Let $R=\oplus_{n\in \N_0}R_n$ be a standard graded ring, $M$ be a finitely
generated graded $R$-module and $R_+:=\oplus_{n\in \N}R_n$ denotes the
irrelevant ideal of $R$. In this paper, considering the new concept of linkage
of ideals over a module, we study the graded components $H^i_{\fa}(M)_n$ when
$\fa$ is an h-linked ideal over $M$. More precisely, we show that
$H^i_{\fa}(M)$ is tame in each of the following cases: \begin{itemize}
  \item [(i)] $i=f_{\fa}^{R_+}(M)$, the first integer $i$ for which
$R_+\nsubseteq \sqrt{0:H^i_{\fa}(M)}$;
  \item [(ii)] $i=\cd(R_+,M)$, the last integer $i$ for which
$H^{i}_{R_+}(M)\neq 0$, and $\fa=\fb+R_+$ where $\fb$ is an h-linked ideal with
$R_+$ over $M$. \end{itemize} Also, among other things, we describe the
components $H^i_{\fa}(M)_n$ where $\fa$ is radically h-$M$-licci with respect
to $R_+$ of length 2.


Abstract:
  We introduce a new quantized enveloping superalgebra
$\mathfrak{U}_q{\mathfrak{p}}_n$ attached to the Lie superalgebra
${\mathfrak{p}}_n$ of type $P$. The superalgebra
$\mathfrak{U}_q{\mathfrak{p}}_n$ is a quantization of a Lie bisuperalgebra
structure on ${\mathfrak{p}}_n$ and we study some of its basic properties. We
also introduce the periplectic $q$-Brauer algebra and prove that it is the
centralizer of the $\mathfrak{U}_q {\mathfrak{p}}_n$-module structure on
${\mathbb C}(n|n)^{\otimes l}$. We end by proposing a definition for a new
periplectic $q$-Schur superalgebra.


Abstract:
  We propose a discontinuous least squares finite element method for solving
the Helmholtz equation. The method is based on the L2 norm least squares
functional with the weak imposition of the continuity across the interior faces
as well as the boundary conditions. We minimize the functional over the
discontinuous polynomial spaces to seek numerical solutions. The wavenumber
explicit error estimates to our method are established. The optimal convergence
rate in the energy norm with respect to a fixed wavenumber is attained. The
least squares functional can naturally serve as a posteriori estimator in the
h-adaptive procedure. It is convenient to implement the code due to the usage
of discontinuous elements. Numerical results in two and three dimensions are
presented to verify the error estimates.


Abstract:
  To generate safe and real-time trajectories for an autonomous vehicle in
dynamic environments, path and speed decoupled planning methods are often
considered. This paper studies speed planning, which mainly deals with dynamic
obstacle avoidance given the planning path. The main challenges lie in the
decisions in non-convex space and the trade-off between safety, comfort and
efficiency performances. This work uses dynamic programming to search heuristic
waypoints on the S-T graph and to construct convex feasible spaces. Further, a
piecewise Bezier polynomials optimization approach with trapezoidal corridors
is presented, which theoretically guarantees the safety and optimality of the
trajectory. The simulations verify the effectiveness of the proposed approach.


Abstract:
  While multiple studies have explored the relation between inter-rater
variability and deep learning model uncertainty in medical segmentation tasks,
little is known about the impact of individual rater style. This study
quantifies rater style in the form of bias and consistency and explores their
impacts when used to train deep learning models. Two multi-rater public
datasets were used, consisting of brain multiple sclerosis lesion and spinal
cord grey matter segmentation. On both datasets, results show a correlation
($R^2 = 0.60$ and $0.93$) between rater bias and deep learning uncertainty. The
impact of label fusion between raters' annotations on this relationship is also
explored, and we show that multi-center consensuses are more effective than
single-center consensuses to reduce uncertainty, since rater style is mostly
center-specific.


Abstract:
  We show how in the standard electroweak model three $SU(2)_L$ Nambu
monopoles, each carrying electromagnetic (EM) and Z- magnetic fluxes, can merge
(through Z-strings) with a single $U(1)_Y$ Dirac monopole to yield a composite
monopole that only carries EM magnetic flux. Compatibility with the Dirac
quantization condition requires this composite monopole to carry six quanta
($12 \pi /e$) of magnetic charge, independent of the electroweak mixing angle
$\theta_w$. The Dirac monopole is not regular at the origin and the energy of
the composite monopole is therefore divergent. We discuss how this problem is
cured by embedding $U(1)_Y$ in a grand unified group such as $SU(5)$. A second
composite configuration with only one Nambu monopole and a colored $U(1)_Y$
Dirac monopole that has minimal EM charge of $4\pi/e$ is also described.
Finally, there exists a configuration with an EM charge of $8\pi/e$ as well as
screened color magnetic charge.


Abstract:
  In this paper, we study the third-order nonlinear optical response due to
transitions between excitonic levels in two-dimensional transition metal
dichalcogeniedes. To accomplish this, we use methods not applied to the
description of excitons in two-dimensional materials so far and combined with a
variational approach to describe the $1s$ excitonic state. The aforementioned
transitions allow to probe dark states which are not revealed in absorption
experiments. We present general formulas capable of describing any third-order
process. The specific case of two-photon absorption in WSe2 is studied. The
case of the circular well is also studied as a benchmark of the theory.


Abstract:
  Recently, sparsification scale-spaces have been obtained as a sequence of
inpainted images by gradually removing known image data. Thus, these
scale-spaces rely on spatial sparsity. In the present paper, we show that
sparsification of the co-domain, the set of admissible grey values, also
constitutes scale-spaces with induced hierarchical quantisation techniques.
These quantisation scale-spaces are closely tied to information theoretical
measures for coding cost, and therefore particularly interesting for
inpainting-based compression. Based on this observation, we propose a
sparsification algorithm for the grey-value domain that outperforms uniform
quantisation as well as classical clustering approaches.


Abstract:
  Let $H$ be a cocommutative weak Hopf algebra and let $(B, \varphi_{B})$ a
weak left $H$-module algebra. In this paper, for a twisted convolution
invertible morphism $\sigma:H\otimes H\rightarrow B$ we define its obstruction
$\theta_{\sigma}$ as a degree three Sweedler 3-cocycle with values in the
center of $B$. We obtain that the class of this obstruction vanish in third
Sweedler cohomology group $\mathcal{H}^3_{\varphi_{Z(B)}}(H, Z(B))$ if, and
only if, there exists a twisted convolution invertible 2-cocycle
$\alpha:H\otimes H\rightarrow B$ such that $H\otimes B$ can be endowed with a
weak crossed product structure with $\alpha$ keeping a cohomological-like
relation with $\sigma$. Then, as a consequence, the class of the obstruction of
$\sigma$ vanish if, and only if, there exists a cleft extension of $B$ by $H$.


Abstract:
  Unsupervised Domain Adaptation (UDA) can tackle the challenge that
convolutional neural network(CNN)-based approaches for semantic segmentation
heavily rely on the pixel-level annotated data, which is labor-intensive.
However, existing UDA approaches in this regard inevitably require the full
access to source datasets to reduce the gap between the source and target
domains during model adaptation, which are impractical in the real scenarios
where the source datasets are private, and thus cannot be released along with
the well-trained source models. To cope with this issue, we propose a
source-free domain adaptation framework for semantic segmentation, namely SFDA,
in which only a well-trained source model and an unlabeled target domain
dataset are available for adaptation. SFDA not only enables to recover and
preserve the source domain knowledge from the source model via knowledge
transfer during model adaptation, but also distills valuable information from
the target domain for self-supervised learning. The pixel- and patch-level
optimization objectives tailored for semantic segmentation are seamlessly
integrated in the framework. The extensive experimental results on numerous
benchmark datasets highlight the effectiveness of our framework against the
existing UDA approaches relying on source data.


Abstract:
  The longstanding goals of federated learning (FL) require rigorous privacy
guarantees and low communication overhead while holding a relatively high model
accuracy. However, simultaneously achieving all the goals is extremely
challenging. In this paper, we propose a novel framework called hierarchical
federated learning (H-FL) to tackle this challenge. Considering the degradation
of the model performance due to the statistic heterogeneity of the training
data, we devise a runtime distribution reconstruction strategy, which
reallocates the clients appropriately and utilizes mediators to rearrange the
local training of the clients. In addition, we design a compression-correction
mechanism incorporated into H-FL to reduce the communication overhead while not
sacrificing the model performance. To further provide privacy guarantees, we
introduce differential privacy while performing local training, which injects
moderate amount of noise into only part of the complete model. Experimental
results show that our H-FL framework achieves the state-of-art performance on
different datasets for the real-world image recognition tasks.


Abstract:
  We present the BrainScaleS-2 mobile system as a compact analog inference
engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at
classifying a medical electrocardiogram dataset. The analog network core of the
ASIC is utilized to perform the multiply-accumulate operations of a
convolutional deep neural network. We measure a total energy consumption of
192uJ for the ASIC and achieve a classification time of 276us per
electrocardiographic patient sample. Patients with atrial fibrillation are
correctly identified with a detection rate of 93.7(7)% at 14.0(10)% false
positives. The system is directly applicable to edge inference applications due
to its small size, power envelope and flexible I/O capabilities. Possible
future applications can furthermore combine conventional machine learning
layers with online-learning in spiking neural networks on a single
BrainScaleS-2 ASIC. The system has successfully participated and proven to
operate reliably in the independently judged competition
"Pilotinnovationswettbewerb 'Energieeffizientes KI-System'" of the German
Federal Ministry of Education and Research (BMBF).


Abstract:
  In runtime verification, a monitor watches a trace of a system and, if
possible, decides after observing each finite prefix whether or not the unknown
infinite trace satisfies a given specification. We generalize the theory of
runtime verification to monitors that attempt to estimate numerical values of
quantitative trace properties (instead of attempting to conclude boolean values
of trace specifications), such as maximal or average response time along a
trace. Quantitative monitors are approximate: with every finite prefix, they
can improve their estimate of the infinite trace's unknown property value.
Consequently, quantitative monitors can be compared with regard to a
precision-cost trade-off: better approximations of the property value require
more monitor resources, such as states (in the case of finite-state monitors)
or registers, and additional resources yield better approximations. We
introduce a formal framework for quantitative and approximate monitoring, show
how it conservatively generalizes the classical boolean setting for monitoring,
and give several precision-cost trade-offs for monitors. For example, we prove
that there are quantitative properties for which every additional register
improves monitoring precision.


Abstract:
  Federated learning is an emerging distributed machine learning framework for
privacy preservation. However, models trained in federated learning usually
have worse performance than those trained in the standard centralized learning
mode, especially when the training data are not independent and identically
distributed (Non-IID) on the local devices. In this survey, we pro-vide a
detailed analysis of the influence of Non-IID data on both parametric and
non-parametric machine learning models in both horizontal and vertical
federated learning. In addition, cur-rent research work on handling challenges
of Non-IID data in federated learning are reviewed, and both advantages and
disadvantages of these approaches are discussed. Finally, we suggest several
future research directions before concluding the paper.


Abstract:
  Deception detection is a task with many applications both in direct physical
and in computer-mediated communication. Our focus is on automatic deception
detection in text across cultures. We view culture through the prism of the
individualism/collectivism dimension and we approximate culture by using
country as a proxy. Having as a starting point recent conclusions drawn from
the social psychology discipline, we explore if differences in the usage of
specific linguistic features of deception across cultures can be confirmed and
attributed to norms in respect to the individualism/collectivism divide. We
also investigate if a universal feature set for cross-cultural text deception
detection tasks exists. We evaluate the predictive power of different feature
sets and approaches. We create culture/language-aware classifiers by
experimenting with a wide range of n-gram features based on phonology,
morphology and syntax, other linguistic cues like word and phoneme counts,
pronouns use, etc., and token embeddings. We conducted our experiments over 11
datasets from 5 languages i.e., English, Dutch, Russian, Spanish and Romanian,
from six countries (US, Belgium, India, Russia, Mexico and Romania), and we
applied two classification methods i.e, logistic regression and fine-tuned BERT
models. The results showed that our task is fairly complex and demanding. There
are indications that some linguistic cues of deception have cultural origins,
and are consistent in the context of diverse domains and dataset settings for
the same language. This is more evident for the usage of pronouns and the
expression of sentiment in deceptive language. The results of this work show
that the automatic deception detection across cultures and languages cannot be
handled in a unified manner, and that such approaches should be augmented with
knowledge about cultural differences and the domains of interest.


Abstract:
  For a semisimple Lie algebra defined over a discrete valuation ring with
field of fractions $K$, we prove that any primitive ideal with rational central
character in the affinoid enveloping algebra, $\widehat{U(\mathfrak{g})_{K}},$
is the annihilator of an affinoid highest weight module.


Abstract:
  30 Doradus C is a superbubble which emits the brightest nonthermal X- and TeV
gamma-rays in the Local Group. In order to explore detailed connection between
the high energy radiation and the interstellar medium, we have carried out new
CO and HI observations using the Atacama Large Millimeter$/$Submillimeter Array
(ALMA), Atacama Submillimeter Telescope Experiment, and the Australia Telescope
Compact Array with resolutions of up to 3 pc. The ALMA data of $^{12}$CO($J$ =
1-0) emission revealed 23 molecular clouds with the typical diameters of
$\sim$6-12 pc and masses of $\sim$600-10000 $M_{\odot}$. The comparison with
the X-rays of $XMM$-$Newton$ at $\sim$3 pc resolution shows that X-rays are
enhanced toward these clouds. The CO data were combined with the HI to estimate
the total interstellar protons. Comparison of the interstellar proton column
density and the X-rays revealed that the X-rays are enhanced with the total
proton. These are most likely due to the shock-cloud interaction modeled by the
magnetohydrodynamical simulations (Inoue et al. 2012, ApJ, 744, 71). Further,
we note a trend that the X-ray photon index varies with distance from the
center of the high-mass star cluster, suggesting that the cosmic-ray electrons
are accelerated by one or multiple supernovae in the cluster. Based on these
results we discuss the role of the interstellar medium in cosmic-ray particle
acceleration.


Abstract:
  Software-Defined Networking (SDN) is an evolutionary networking paradigm
which has been adopted by large network and cloud providers, among which are
Tech Giants. However, embracing a new and futuristic paradigm as an alternative
to well-established and mature legacy networking paradigm requires a lot of
time along with considerable financial resources and technical expertise.
Consequently, many enterprises can not afford it. A compromise solution then is
a hybrid networking environment (a.k.a. Hybrid SDN (hSDN)) in which SDN
functionalities are leveraged while existing traditional network
infrastructures are acknowledged. Recently, hSDN has been seen as a viable
networking solution for a diverse range of businesses and organizations.
Accordingly, the body of literature on hSDN research has improved remarkably.
On this account, we present this paper as a comprehensive state-of-the-art
survey which expands upon hSDN from many different perspectives.


Abstract:
  Liquid phase sensing applications at 1550~nm are highly desirable due to
widely available off-the-shelf components. Generally, liquids at 1550~nm induce
a high absorption loss that limits the overall sensor's sensitivity and
detection limit. One solution is to use an active fiber loop in conjunction
with cavity ring down spectroscopy to overcome these absorption losses.
However, the amplifier inside the fiber loop suffers from inherent gain
fluctuations that limit the sensing system's overall performance. Here, we
provide a novel sensor using the wavelength-scanned phase shift-cavity ring
down spectroscopy (PS-CRDS) in conjunction with a linear active fiber cavity
that potentially offers a more sensitive solution than traditional fiber loop
sensors. We use a tapered fiber as a sensing head inside the active cavity
built from fiber Bragg gratings. We derive a theoretical phase shift expression
for our system and simulate it using the finite element method to determine
optimum tapered fiber diameter for glucose sensing in DI water. Compared to a
non-amplified system, we find that our amplified system can increase the
sensitivity by fourteen times via the amplifier gain tuning. We also conduct
experimental measurements using 0-15.5~mM glucose solutions and find them in
excellent agreement with our theoretical predictions. Experimentally we obtain
the sensor's sensitivity of 0.768~$^o$/mM (1164~$^o$/RIU) and detection limit
of 0.75~mM ( 4.5~$\times$~10$^{-4}$~RIU) without any temperature stabilization
in the system. We anticipate that the present work will find a wide range of
sensing applications in fiber cavities, ring resonators, and other microcavity
structures.


Abstract:
  We introduce a mapping between Maximum Entropy Reinforcement Learning (MaxEnt
RL) and Markovian processes conditioned on rare events. In the long time limit,
this mapping allows us to derive analytical expressions for the optimal policy,
dynamics and initial state distributions for the general case of stochastic
dynamics in MaxEnt RL. We find that soft-$\mathcal{Q}$ functions in MaxEnt RL
can be obtained from the Perron-Frobenius eigenvalue and the corresponding left
eigenvector of a regular, non-negative matrix derived from the underlying
Markov Decision Process (MDP). The results derived lead to novel algorithms for
model-based and model-free MaxEnt RL, which we validate by numerical
simulations. The mapping established in this work opens further avenues for the
application of novel analytical and computational approaches to problems in
MaxEnt RL. We make our code available at:
https://github.com/argearriojas/maxent-rl-mdp-scripts


Abstract:
  The first-principles simulation of the electronic structure of organic
semiconductors in solution poses a number of challenges that are not trivial to
address simultaneously. In this work, we investigate the effects and the mutual
interplay of solvation, alkylization, and doping on the structural, electronic,
and optical properties of sexithiophene, a representative organic semiconductor
molecule. To this end, we employ (time-dependent) density functional theory in
conjunction with the polarizable-continuum model. We find that the torsion
between adjacent monomer units plays a key role, as it strongly influences the
electronic structure of the molecule, including energy gap, ionization
potential, and band widths. Alkylization promotes delocalization of the
molecular orbitals up to the first methyl unit, regardless of the chain length,
leading to an overall shift of the energy levels. The alterations in the
electronic structure are reflected in the optical absorption, which is
additionally affected by dynamical solute-solvent interactions. Taking all
these effects into account, solvents decrease the optical gap by an amount that
depends on its polarity, and concomitantly increase the oscillator strength of
the first excitation. The interaction with a dopant molecule promotes
planarization. In such scenario, solvation and alkylization enhance charge
transfer both in the ground state and in the excited state.


Abstract:
  Our objective is language-based search of large-scale image and video
datasets. For this task, the approach that consists of independently mapping
text and vision to a joint embedding space, a.k.a. dual encoders, is attractive
as retrieval scales and is efficient for billions of images using approximate
nearest neighbour search. An alternative approach of using vision-text
transformers with cross-attention gives considerable improvements in accuracy
over the joint embeddings, but is often inapplicable in practice for
large-scale retrieval given the cost of the cross-attention mechanisms required
for each sample at test time. This work combines the best of both worlds. We
make the following three contributions. First, we equip transformer-based
models with a new fine-grained cross-attention architecture, providing
significant improvements in retrieval accuracy whilst preserving scalability.
Second, we introduce a generic approach for combining a Fast dual encoder model
with our Slow but accurate transformer-based model via distillation and
re-ranking. Finally, we validate our approach on the Flickr30K image dataset
where we show an increase in inference speed by several orders of magnitude
while having results competitive to the state of the art. We also extend our
method to the video domain, improving the state of the art on the VATEX
dataset.


Abstract:
  We demonstrate single crystal growth of wafer-scale hexagonal boron nitride
(hBN), an insulating atomic thin monolayer, on high-symmetry index surface
plane Cu(111). The unidirectional epitaxial growth is guaranteed by large
binding energy difference, ~0.23 eV, between A- and B-steps edges on Cu(111)
docking with B6N7 clusters, confirmed by density functional theory
calculations.


Abstract:
  Bimetallic nanoparticles are often superior candidates for a wide range of
technological and biomedical applications, thanks to their enhanced catalytic,
optical, and magnetic properties, which are often better than their
monometallic counterparts. Most of their properties strongly depend on their
chemical composition, crystallographic structure, and phase distribution.
However, little is known of how their crystal structure, on the nanoscale,
transforms over time at elevated temperatures, even though this knowledge is
highly relevant in case nanoparticles are used in, e.g., high-temperature
catalysis. Au-Fe is a promising bimetallic system where the low-cost and
magnetic Fe is combined with catalytically active and plasmonic Au. Here, we
report on the in situ temporal evolution of the crystalline ordering in Au-Fe
nanoparticles, obtained from a modern laser ablation in liquids synthesis. Our
in-depth analysis, complemented by dedicated atomistic simulations, includes a
detailed structural characterization by X-ray diffraction and transmission
electron microscopy as well as atom probe tomography to reveal elemental
distributions down to a single atom resolution. We show that the Au-Fe
nanoparticles initially exhibit highly complex internal nested nanostructures
with a wide range of compositions, phase distributions, and size-depended
microstrains. The elevated temperature induces a diffusion-controlled
recrystallization and phase merging, resulting in the formation of a single
face-centered-cubic ultrastructure in contact with a body-centered cubic phase,
which demonstrates the metastability of these structures. Uncovering these
unique nanostructures with nested features could be highly attractive from a
fundamental viewpoint as they could give further insights into the nanoparticle
formation mechanism under non-equilibrium conditions.


Abstract:
  Combinatorial Hantzsche-Wendt groups G(n) were defined by W. Craig and P. A.
Linnell. For n = 2 it is a fundamental group of 3-dimensional oriented flat
manifold with no cyclic holonomy group. We calculate the Hilbert-Poincare
series of G(n), n > 0 with Q and F2 coefficients. Moreover, we prove that a
cohomological dimension of G(n) is equal to n + 1. Some other properties of
this group are also considered.


Abstract:
  Transfer learning is a standard technique to improve performance on tasks
with limited data. However, for medical imaging, the value of transfer learning
is less clear. This is likely due to the large domain mismatch between the
usual natural-image pre-training (e.g. ImageNet) and medical images. However,
recent advances in transfer learning have shown substantial improvements from
scale. We investigate whether modern methods can change the fortune of transfer
learning for medical imaging. For this, we study the class of large-scale
pre-trained networks presented by Kolesnikov et al. on three diverse imaging
tasks: chest radiography, mammography, and dermatology. We study both transfer
performance and critical properties for the deployment in the medical domain,
including: out-of-distribution generalization, data-efficiency, sub-group
fairness, and uncertainty estimation. Interestingly, we find that for some of
these properties transfer from natural to medical images is indeed extremely
effective, but only when performed at sufficient scale.


Abstract:
  A quantum Hall (QH) interface is different from an ordinary QH edge, as the
latter has its location determined by the confining potential, while the former
can be unpinned and behave like a free string. In this paper, we demonstrate
this difference by studying three different interfaces formed by (i) the
Laughlin state and the vacuum, (ii) the Pfaffian state and the vacuum, and
(iii) the Pfaffian and the anti-Pfaffian states. We find that string-like
interfaces propagating freely in the QH system lead to very different dynamical
properties from edges. This qualitative difference gives rise to fascinating
new physics and suggests a new direction in future research on QH physics. We
also discuss briefly possible analogies between QH interfaces and concepts in
string theory.


Abstract:
  We introduce the concept of group state transfer on graphs, summarize its
relationship to other concepts in the theory of quantum walks, set up a basic
theory, and discuss examples.
  Let $X$ be a graph with adjacency matrix $A$ and consider quantum walks on
the vertex set $V(X)$ governed by the continuous time-dependent unitary
transition operator $U(t)= \exp(itA)$. For $S,T\subseteq V(X)$, we says $X$
admits "group state transfer" from $S$ to $T$ at time $\tau$ if the submatrix
of $U(\tau)$ obtained by restricting to columns in $S$ and rows not in $T$ is
the all-zero matrix. As a generalization of perfect state transfer, fractional
revival and periodicity, group state transfer satisfies natural monotonicity
and transitivity properties. Yet non-trivial group state transfer is still
rare; using a compactness argument, we prove that bijective group state
transfer (the optimal case where $|S|=|T|$) is absent for almost all $t$.
Focusing on this bijective case, we obtain a structure theorem, prove that
bijective group state transfer is "monogamous", and study the relationship
between the projections of $S$ and $T$ into each eigenspace of the graph.
  Group state transfer is obviously preserved by graph automorphisms and this
gives us information about the relationship between the setwise stabilizer of
$S\subseteq V(X)$ and the stabilizers of naturally defined subsets obtained by
spreading $S$ out over time and crudely reversing this process. These
operations are sufficiently well-behaved to give us a topology on $V(X)$ which
is likely to be simply the topology of subsets for which bijective group state
transfer occurs at that time. We illustrate non-trivial group state transfer in
bipartite graphs with integer eigenvalues, in joins of graphs, and in symmetric
double stars. The Cartesian product allows us to build new examples from old
ones.


Abstract:
  In this paper, we explore multi-task learning (MTL) as a second pretraining
step to learn enhanced universal language representation for transformer
language models. We use the MTL enhanced representation across several natural
language understanding tasks to improve performance and generalization.
Moreover, we incorporate knowledge distillation (KD) in MTL to further boost
performance and devise a KD variant that learns effectively from multiple
teachers. By combining MTL and KD, we propose Robustly Optimized and Distilled
(ROaD) modeling framework. We use ROaD together with the ELECTRA model to
obtain state-of-the-art results for machine reading comprehension and natural
language inference.


Abstract:
  A permutation $\pi$ of a multiset is said to be a {\em quasi-Stirling }
permutation if there does not exist four indices $i<j<k<\ell$ such that
$\pi_i=\pi_k$ and $\pi_j=\pi_{\ell}$. Define $$
\overline{Q}_{\mathcal{M}}(t,u,v)=\sum_{\pi\in
\overline{\mathcal{Q}}_{\mathcal{M}}}t^{des(\pi)}u^{asc(\pi)}v^{plat(\pi)},$$
where $\overline{\mathcal{Q}}_{\mathcal{M}}$ denotes the set of quasi-Stirling
  permutations on the multiset $\mathcal{M}$, and $asc(\pi)$ (resp. $des(\pi)$,
$plat(\pi)$) denotes the number of ascents (resp. descents, plateaux) of $\pi$.
Denote by $\mathcal{M}^{\sigma}$ the multiset $\{1^{\sigma_1}, 2^{\sigma_2},
\ldots, n^{\sigma_n}\}$, where $\sigma=(\sigma_1, \sigma_2, \ldots, \sigma_n)$
is an $n$-composition of $K$ for positive integers $K$ and $n$. In this paper,
we show that
$\overline{Q}_{\mathcal{M}^{\sigma}}(t,u,v)=\overline{Q}_{\mathcal{M}^{\tau}}(t,u,v)$
for any two $n$-compositions $\sigma$ and $\tau$ of $K$. This is accomplished
by establishing an $(asc, des, plat)$-preserving bijection
  between $\overline{\mathcal{Q}}_{\mathcal{M}^{\sigma}}$ and
$\overline{\mathcal{Q}}_{\mathcal{M}^{\tau}}$. As applications, we obtain
generalizations of several results for quasi-Stirling permutations on
$\mathcal{M}=\{1^k,2^k, \ldots, n^k\}$ obtained by Elizalde and solve an open
problem posed by Elizalde.


Abstract:
  In this paper, we explore the collider phenomenology of the charged Higgs
boson in the context of a generic Beyond Standard Model scenario with extended
gauge and scalar sectors. In such scenarios, the charged Higgs boson can decay
via the $W^{'}Z/ WZ^{'}$ channels. We formulate a search strategy for the
$H^{\pm}$ in the channel $\sigma(g b \rightarrow H^{\pm}t)\mathcal{BR}(H^{\pm}
\rightarrow W' Z)$ considering the interesting cascade decay chain $H^{\pm}
\rightarrow W^{'} Z \rightarrow W^{\pm} Z Z$. We find that the charged Higgs
can be discovered in final states with multiple hard leptons and/ or b-quarks
which future LHC experiments with sufficiently large luminosity ($\mathcal{L} =
1000 fb^{-1}$ and above) can probe.


Abstract:
  There has been growing interest on forecasting mortality. In this article, we
propose a novel dynamic Bayesian approach for modeling and forecasting the
age-at-death distribution, focusing on a three-components mixture of a Dirac
mass, a Gaussian distribution and a Skew-Normal distribution. According to the
specified model, the age-at-death distribution is characterized via seven
parameters corresponding to the main aspects of infant, adult and old-age
mortality. The proposed approach focuses on coherent modeling of multiple
countries, and following a Bayesian approach to inference we allow to borrow
information across populations and to shrink parameters towards a common mean
level, implicitly penalizing diverging scenarios. Dynamic modeling across years
is induced trough an hierarchical dynamic prior distribution that allows to
characterize the temporal evolution of each mortality component and to forecast
the age-at-death distribution. Empirical results on multiple countries indicate
that the proposed approach outperforms popular methods for forecasting
mortality, providing interpretable insights on the evolution of mortality.


Abstract:
  Face representation learning solutions have recently achieved great success
for various applications such as verification and identification. However, face
recognition approaches that are based purely on RGB images rely solely on
intensity information, and therefore are more sensitive to facial variations,
notably pose, occlusions, and environmental changes such as illumination and
background. A novel depth-guided attention mechanism is proposed for deep
multi-modal face recognition using low-cost RGB-D sensors. Our novel attention
mechanism directs the deep network "where to look" for visual features in the
RGB image by focusing the attention of the network using depth features
extracted by a Convolution Neural Network (CNN). The depth features help the
network focus on regions of the face in the RGB image that contains more
prominent person-specific information. Our attention mechanism then uses this
correlation to generate an attention map for RGB images from the depth features
extracted by CNN. We test our network on four public datasets, showing that the
features obtained by our proposed solution yield better results on the
Lock3DFace, CurtinFaces, IIIT-D RGB-D, and KaspAROV datasets which include
challenging variations in pose, occlusion, illumination, expression, and
time-lapse. Our solution achieves average (increased) accuracies of 87.3\%
(+5.0\%), 99.1\% (+0.9\%), 99.7\% (+0.6\%) and 95.3\%(+0.5\%) for the four
datasets respectively, thereby improving the state-of-the-art. We also perform
additional experiments with thermal images, instead of depth images, showing
the high generalization ability of our solution when adopting other modalities
for guiding the attention mechanism instead of depth information


Abstract:
  Product safety has been a concern in Europe ever since the early 1960s.
Despite the long and relatively stable historical lineage of product safety
regulations, new technologies, changes in the world economy, and other major
transformations have in recent years brought product safety again to the
forefront of policy debates. As reforms are also underway, there is a
motivation to review the complex safety policy framework in the European Union
(EU). Thus, building on deliberative policy analysis and interpretative
literature review, this paper reviews the safety policy for non-food consumer
products in the EU. The review covers the historical background and the main
laws, administration and enforcement, standardization and harmonization, laws
enacted for specific products, notifications delivered by national safety
authorities, recalls of dangerous products, and the liability of these. Based
on the review and analysis of these themes and the associated literature, some
current policy challenges are further discussed.


Abstract:
  Anime is quite well-received today, especially among the younger generations.
With many genres of available shows, more and more people are increasingly
getting attracted to this niche section of the entertainment industry. As anime
has recently garnered mainstream attention, we have insufficient information
regarding users' penchant and watching habits. Therefore, it is an uphill task
to build a recommendation engine for this relatively obscure entertainment
medium. In this attempt, we have built a novel hybrid recommendation system
that could act both as a recommendation system and as a means of exploring new
anime genres and titles. We have analyzed the general trends in this field and
the users' watching habits for coming up with our efficacious solution. Our
solution employs deep autoencoders for the tasks of predicting ratings and
generating embeddings. Following this, we formed clusters using the embeddings
of the anime titles. These clusters form the search space for anime with
similarities and are used to find anime similar to the ones liked and disliked
by the user. This method, combined with the predicted ratings, forms the novel
hybrid filter. In this article, we have demonstrated this idea and compared the
performance of our implemented model with the existing state-of-the-art
techniques.


Abstract:
  It is shown that every weak Jacobi form of weight zero and index one on a
congruence subgroup of the full Jacobi group can be decomposed into $N=4$
superconformal characters. Additionally, a simple expression for the mock
modular form determining the superconformal character coefficients is obtained,
as well as a universal completion structure. Along the way, a useful
vector-valued mock modular form is also found and studied. These results are
applied to analyze some Jacobi trace functions associated to super vertex
operator algebras and a distinguished sector.


Abstract:
  In this paper, we propose to unify the two aspects of voice synthesis, namely
text-to-speech (TTS) and vocoder, into one framework based on a pair of forward
and reverse-time linear stochastic differential equations (SDE). The solutions
of this SDE pair are two stochastic processes, one of which turns the
distribution of mel spectrogram (or wave), that we want to generate, into a
simple and tractable distribution. The other is the generation procedure that
turns this tractable simple signal into the target mel spectrogram (or wave).
The model that generates mel spectrogram is called It$\hat{\text{o}}$TTS, and
the model that generates wave is called It$\hat{\text{o}}$Wave.
It$\hat{\text{o}}$TTS and It$\hat{\text{o}}$Wave use the Wiener process as a
driver to gradually subtract the excess signal from the noise signal to
generate realistic corresponding meaningful mel spectrogram and audio
respectively, under the conditional inputs of original text or mel spectrogram.
The results of the experiment show that the mean opinion scores (MOS) of
It$\hat{\text{o}}$TTS and It$\hat{\text{o}}$Wave can exceed the current
state-of-the-art methods, and reached 3.925$\pm$0.160 and 4.35$\pm$0.115
respectively. The generated audio samples are available at
https://shiziqiang.github.io/ito_audio/. All authors contribute equally to this
work.


Abstract:
  In the infinite-dimensional separable complex Hilbert space we construct new
abstract examples of unbounded maximal accretive and maximal sectorial
operators $B$ for which ${\rm dom\,}B^{\frac{1}{2}}\ne{\rm
dom\,}B^{*{\frac{1}{2}}}$. New criterions for the equality are established.


Abstract:
  Controlling the properties of organic/inorganic materials requires detailed
knowledge of their molecular adsorption geometries. This is often unattainable,
even with current state-of-the-art tools. Visualizing the structure of complex
non-planar adsorbates with atomic force microscopy (AFM) is challenging, and
identifying it computationally is intractable with conventional structure
search. In this fresh approach, cross-disciplinary tools are integrated for a
robust and automated identification of 3D adsorbate configurations. Bayesian
optimization is employed with first-principles simulations for accurate and
unbiased structure inference of multiple adsorbates. The corresponding AFM
simulations then allow fingerprinting adsorbate structures that appear in AFM
experimental images. In the instance of bulky (1S)-camphor adsorbed on the
Cu(111) surface, three matching AFM image contrasts are found, which allow
correlating experimental image features to distinct cases of molecular
adsorption.


Abstract:
  Millimeter wave (mmWave) communications are vulnerable to blockages and node
mobility due to the highly directional signal beams. The emerging
Reconfigurable Intelligent Surfaces (RISs) technique can effectively mitigate
the blockage problem by exploring the non-line-of-sight (NLOS) path, where the
beam switching is realized by digitally configuring the phases of RIS elements.
To date, most efforts have been made in the stationary scenario. However, when
considering node mobility, beam tracking algorithms designed specifically for
RIS are needed in order to maintain the NLOS link. In this paper, a fast
RIS-based beam tracking algorithm is developed by partly transforming the large
amount of signaling time into the calculation happens at base station in a
mmWave system with mobile users. Specifically, the differential form of optimal
RIS configuration is exploited as the updating beam tracking parameter to avoid
complex channel estimation procedure. The RIS-based beam tracking problem is
then transformed into an optimization problem whose solution is found by a
calculation-based search. Finally, by training on a small set candidate,
RIS-based beam tracking is realized. The effectiveness and efficiency of the
proposed RIS-based beam tracking algorithm is evaluated by simulations. It
shows that the proposed algorithm has near-optimal performance with dramatic
savings in terms of signaling time.


Abstract:
  Under cold conditions in dense cores, gas-phase molecules and atoms are
depleted from the gas-phase to the surface of interstellar grains. Considering
the time scales and physical conditions within these cores, a portion of these
molecules has to be brought back into the gas-phase to explain their
observation by milimeter telescopes. We tested the respective efficiencies of
the different mechanisms commonly included in the models. We also tested the
addition of sputtering of ice grain mantles via a collision with cosmic rays in
the electronic stopping power regime. The ice sputtering induced by cosmic rays
has been added to the Nautilus gas-grain model while the other processes were
already present. Each of these processes were tested on a 1D physical structure
determined by observations in TMC1 cold cores. The resulting 1D chemical
structure was also compared to methanol gas-phase abundances observed in these
cores. We found that all species are not sensitive in the same way to the
non-thermal desorption mechanisms, and the sensitivity also depends on the
physical conditions. Thus, it is mandatory to include all of them. Chemical
desorption seems to be essential in reproducing the observations for H
densities smaller than $4\times 10^4$~cm$^{-3}$, whereas sputtering is
essential above this density. The models are, however, systematically below the
observed methanol abundances. A more efficient chemical desorption and a more
efficient sputtering could better reproduce the observations. In conclusion,
the sputtering of ices by cosmic-rays collisions may be the most efficient
desorption mechanism at high density (a few $10^4$~cm$^{-3}$ under the
conditions studied here) in cold cores, whereas chemical desorption is still
required at smaller densities. Additional works are needed on both mechanisms
to assess their efficiency with respect to the main ice composition.


Abstract:
  Most existing deep neural networks are static, which means they can only do
inference at a fixed complexity. But the resource budget can vary substantially
across different devices. Even on a single device, the affordable budget can
change with different scenarios, and repeatedly training networks for each
required budget would be incredibly expensive. Therefore, in this work, we
propose a general method called MutualNet to train a single network that can
run at a diverse set of resource constraints. Our method trains a cohort of
model configurations with various network widths and input resolutions. This
mutual learning scheme not only allows the model to run at different
width-resolution configurations but also transfers the unique knowledge among
these configurations, helping the model to learn stronger representations
overall. MutualNet is a general training methodology that can be applied to
various network structures (e.g., 2D networks: MobileNets, ResNet, 3D networks:
SlowFast, X3D) and various tasks (e.g., image classification, object detection,
segmentation, and action recognition), and is demonstrated to achieve
consistent improvements on a variety of datasets. Since we only train the model
once, it also greatly reduces the training cost compared to independently
training several models. Surprisingly, MutualNet can also be used to
significantly boost the performance of a single network, if dynamic resource
constraint is not a concern. In summary, MutualNet is a unified method for both
static and adaptive, 2D and 3D networks. Codes and pre-trained models are
available at \url{https://github.com/taoyang1122/MutualNet}.


Abstract:
  We generalize the local-global compatibility result in arXiv:1506.04022 to
higher dimensional cases, by examining the relation between Scholze's functor
and cohomology of Kottwitz-Harris-Taylor type Shimura varieties. Along the way
we prove a cuspidality criterion from type theory. We also deal with
compatibility for torsion classes in the case of semisimple mod $p$ Galois
representations with distinct irreducible components under certain flatness
hypotheses.


Abstract:
  In this work, a first order relativistic dissipative hydrodynamic theory has
been developed that redefines all the concerned hydrodynamic fields in order to
include the out of equilibrium system dissipation from the medium. The
resulting dispersion relation from the theory is observed to produce a
propagating causal mode that was previously absent without the field
redefinition. The condition for causality has been explicitly determined for
linear perturbations around a hydrostatic state. Finally, the causality has
been tested with the field correction coefficients estimated from momentum
dependent relaxation time approximation (MDRTA) in relativistic transport
equation.


Abstract:
  A practical algorithm has been developed for closeness analysis of sequential
data that combines closeness testing with algorithms based on the Markov chain
tester. It was applied to reported sequential data for COVID-19 to analyze the
evolution of COVID-19 during a certain time period (week, month, etc.).


Abstract:
  In this paper, we present a novel unpaired point cloud completion network,
named Cycle4Completion, to infer the complete geometries from a partial 3D
object. Previous unpaired completion methods merely focus on the learning of
geometric correspondence from incomplete shapes to complete shapes, and ignore
the learning in the reverse direction, which makes them suffer from low
completion accuracy due to the limited 3D shape understanding ability. To
address this problem, we propose two simultaneous cycle transformations between
the latent spaces of complete shapes and incomplete ones. The insight of cycle
transformation is to promote networks to understand 3D shapes by learning to
generate complete or incomplete shapes from their complementary ones.
Specifically, the first cycle transforms shapes from incomplete domain to
complete domain, and then projects them back to the incomplete domain. This
process learns the geometric characteristic of complete shapes, and maintains
the shape consistency between the complete prediction and the incomplete input.
Similarly, the inverse cycle transformation starts from complete domain to
incomplete domain, and goes back to complete domain to learn the characteristic
of incomplete shapes. We provide a comprehensive evaluation in experiments,
which shows that our model with the learned bidirectional geometry
correspondence outperforms state-of-the-art unpaired completion methods.


Abstract:
  The infinite number of particles limit in the dual to elliptic Ruijsenaars
model (coordinate trigonometric degeneration of quantum double elliptic model)
is proposed using the Nazarov-Sklyanin approach. For this purpose we describe
double-elliptization of the Cherednik construction. Namely, we derive explicit
expression in terms of the Cherednik operators, which reduces to the generating
function of Dell commuting Hamiltonians on the space of symmetric functions.
Although the double elliptic Cherednik operators do not commute, they can be
used for construction of the $N\rightarrow \infty$ limit.


Abstract:
  Attempt to fully discover the temporal diversity and chronological
characteristics for self-supervised video representation learning, this work
takes advantage of the temporal dependencies within videos and further proposes
a novel self-supervised method named Temporal Contrastive Graph Learning
(TCGL). In contrast to the existing methods that ignore modeling elaborate
temporal dependencies, our TCGL roots in a hybrid graph contrastive learning
strategy to jointly regard the inter-snippet and intra-snippet temporal
dependencies as self-supervision signals for temporal representation learning.
To model multi-scale temporal dependencies, our TCGL integrates the prior
knowledge about the frame and snippet orders into graph structures, i.e., the
intra-/inter- snippet temporal contrastive graphs. By randomly removing edges
and masking nodes of the intra-snippet graphs or inter-snippet graphs, our TCGL
can generate different correlated graph views. Then, specific contrastive
learning modules are designed to maximize the agreement between nodes in
different views. To adaptively learn the global context representation and
recalibrate the channel-wise features, we introduce an adaptive video snippet
order prediction module, which leverages the relational knowledge among video
snippets to predict the actual snippet orders. Experimental results demonstrate
the superiority of our TCGL over the state-of-the-art methods on large-scale
action recognition and video retrieval benchmarks.


Abstract:
  Due to the need to store the intermediate activations for back-propagation,
end-to-end (E2E) training of deep networks usually suffers from high GPUs
memory footprint. This paper aims to address this problem by revisiting the
locally supervised learning, where a network is split into gradient-isolated
modules and trained with local supervision. We experimentally show that simply
training local modules with E2E loss tends to collapse task-relevant
information at early layers, and hence hurts the performance of the full model.
To avoid this issue, we propose an information propagation (InfoPro) loss,
which encourages local modules to preserve as much useful information as
possible, while progressively discard task-irrelevant information. As InfoPro
loss is difficult to compute in its original form, we derive a feasible upper
bound as a surrogate optimization objective, yielding a simple but effective
algorithm. In fact, we show that the proposed method boils down to minimizing
the combination of a reconstruction loss and a normal cross-entropy/contrastive
term. Extensive empirical results on five datasets (i.e., CIFAR, SVHN, STL-10,
ImageNet and Cityscapes) validate that InfoPro is capable of achieving
competitive performance with less than 40% memory footprint compared to E2E
training, while allowing using training data with higher-resolution or larger
batch sizes under the same GPU memory constraint. Our method also enables
training local modules asynchronously for potential training acceleration. Code
is available at: https://github.com/blackfeather-wang/InfoPro-Pytorch.


Abstract:
  Image enhancement aims at processing an input image so that the visual
content of the output image is more pleasing or more useful for certain
applications. Although histogram equalization is widely used in image
enhancement due to its simplicity and effectiveness, it changes the mean
brightness of the enhanced image and introduces a high level of noise and
distortion. To address these problems, this paper proposes image enhancement
using fuzzy intensity measure and adaptive clipping histogram equalization
(FIMHE). FIMHE uses fuzzy intensity measure to first segment the histogram of
the original image, and then clip the histogram adaptively in order to prevent
excessive image enhancement. Experiments on the Berkeley database and
CVF-UGR-Image database show that FIMHE outperforms state-of-the-art histogram
equalization based methods.


Abstract:
  We study the problem of incorporating prior knowledge into a deep
Transformer-based model,i.e.,Bidirectional Encoder Representations from
Transformers (BERT), to enhance its performance on semantic textual matching
tasks. By probing and analyzing what BERT has already known when solving this
task, we obtain better understanding of what task-specific knowledge BERT needs
the most and where it is most needed. The analysis further motivates us to take
a different approach than most existing works. Instead of using prior knowledge
to create a new training task for fine-tuning BERT, we directly inject
knowledge into BERT's multi-head attention mechanism. This leads us to a simple
yet effective approach that enjoys fast training stage as it saves the model
from training on additional data or tasks other than the main task. Extensive
experiments demonstrate that the proposed knowledge-enhanced BERT is able to
consistently improve semantic textual matching performance over the original
BERT model, and the performance benefit is most salient when training data is
scarce.


Abstract:
  We study a model of interacting vector and Kalb-Ramond gauge fields in a
non-trivial Higgs vacuum generated by a charged and a neutral scalar field. The
system admits different vacua for different v.e.v. of the two Higgs fields. Our
primary interest in this paper regards the "mixed phase" where both the photon
and the Kalb-Ramond acquire a mass. In this phase we compute the interaction
potential energy between static test charges. It turns out that the limit in
which the photon becomes massless, while the Kalb-Ramond remains massive, leads
to a Cornell confining potential between test charges.


Abstract:
  Meta learning generalizes the empirical experience with different learning
tasks and holds promise for providing important empirical insight into the
behaviour of machine learning algorithms. In this paper, we present a
comprehensive meta-learning study of data sets and methods for multi-label
classification (MLC). MLC is a practically relevant machine learning task where
each example is labelled with multiple labels simultaneously. Here, we analyze
40 MLC data sets by using 50 meta features describing different properties of
the data. The main findings of this study are as follows. First, the most
prominent meta features that describe the space of MLC data sets are the ones
assessing different aspects of the label space. Second, the meta models show
that the most important meta features describe the label space, and, the meta
features describing the relationships among the labels tend to occur a bit more
often than the meta features describing the distributions between and within
the individual labels. Third, the optimization of the hyperparameters can
improve the predictive performance, however, quite often the extent of the
improvements does not always justify the resource utilization.


Abstract:
  Diversity in personalized news recommender systems is often defined as
dissimilarity, and based on topic diversity (e.g., corona versus farmers
strike). Diversity in news media, however, is understood as multiperspectivity
(e.g., different opinions on corona measures), and arguably a key
responsibility of the press in a democratic society. While viewpoint diversity
is often considered synonymous with source diversity in communication science
domain, in this paper, we take a computational view. We operationalize the
notion of framing, adopted from communication science. We apply this notion to
a re-ranking of topic-relevant recommended lists, to form the basis of a novel
viewpoint diversification method. Our offline evaluation indicates that the
proposed method is capable of enhancing the viewpoint diversity of
recommendation lists according to a diversity metric from literature. In an
online study, on the Blendle platform, a Dutch news aggregator platform, with
more than 2000 users, we found that users are willing to consume viewpoint
diverse news recommendations. We also found that presentation characteristics
significantly influence the reading behaviour of diverse recommendations. These
results suggest that future research on presentation aspects of recommendations
can be just as important as novel viewpoint diversification methods to truly
achieve multiperspectivity in online news environments.


Abstract:
  The 21-cm signal from cosmic dawn and the epoch of reionization (EoR) probes
the characteristics of the high redshift galaxy population. Many of the
astrophysical properties of galaxies at high redshifts are currently
unconstrained due to the lack of observations. This creates a vast space of
possible astrophysical scenarios where the 21-cm signal needs to be modeled in
order to plan for, and eventually fit, future observations. This is done with
fast numerical methods which make simplifying approximations for the underlying
physical processes. In this work we quantify the effect of Poisson fluctuations
and scatter in the star formation efficiency; while Poisson fluctuations are
included in some works and not in others, scatter in the star formation
efficiency is usually neglected, and all galaxies of a given mass are assumed
to have the same properties. We show that both features can have a significant
effect on the 21-cm power spectrum, most importantly in scenarios where the
signal is dominated by massive galaxies. Scatter in the star formation
efficiency does not simply enhance the effect of Poisson fluctuations; for
example we show that the power spectrum shape at cosmic dawn has a feature
corresponding to the width of the galaxy brightness distribution. We also
discuss some of the consequences for 21-cm imaging, and the signature of
reduced correlation between the density and radiation fields.


Abstract:
  Finding and characterizing extrasolar Earth analogs will rely on
interpretation of the planetary system's environmental context. The total
budget and fractionation between C-H-O species sensitively affect the climatic
and geodynamic state of terrestrial worlds, but their main delivery channels
are poorly constrained. We connect numerical models of volatile chemistry and
pebble coagulation in the circumstellar disk with the internal compositional
evolution of planetesimals during the primary accretion phase. Our simulations
demonstrate that disk chemistry and degassing from planetesimals operate on
comparable timescales and can fractionate the relative abundances of major
water and carbon carriers by orders of magnitude. As a result, individual
planetary systems with significant planetesimal processing display increased
correlation in the volatile budget of planetary building blocks relative to no
internal heating. Planetesimal processing in a subset of systems increases the
variance of volatile contents across planetary systems. Our simulations thus
suggest that exoplanetary atmospheric compositions may provide constraints on
$when$ a specific planet formed.


Abstract:
  In the anomalous Hall effect (AHE), the magnetization, electric field and the
Hall current are presumed to be mutually vertical to each other. In this work,
we propose an unconventional AHE where the magnetization, the electric field
and Hall current stay inside the same plane. Such an AHE is odd under
time-reversal and exists even when the magnetization is parallel to the
electric field or Hall current, different from the planar Hall effect which is
even under time-reversal. Here, we term it parallel anomalous Hall effect
(PAHE). We reveal that the PAHE exists when all the point group rotational and
reflection symmetries are broken where the Berry curvature field is not
necessarily parallel to the magnetization axis. We further demonstrate the PAHE
in a ferrimagnetic Weyl semimetal FeCr$_2$Te$_4$.


Abstract:
  As neural networks become able to generate realistic artificial images, they
have the potential to improve movies, music, video games and make the internet
an even more creative and inspiring place. Yet, at the same time, the latest
technology potentially enables new digital ways to lie. In response, the need
for a diverse and reliable method toolbox arises to identify artificial images
and other content. Previous work primarily relies on pixel-space CNN or the
Fourier transform. To the best of our knowledge, synthesized fake image
analysis and detection methods based on a multi-scale wavelet representation,
which is localized in both space and frequency, have been absent thus far. This
paper proposes to learn a model for the detection of synthetic images based on
the wavelet-packet representation of natural and GAN-generated images. We
evaluate our method on FFHQ, CelebA, and LSUN source identification problems
and find improved or competitive performance. Our forensic classifier has a
small network size and can be learned efficiently. Furthermore, a comparison of
the wavelet coefficients from these two sources of images allows an
interpretation and identifies significant differences.


Abstract:
  A great deal of recent research effort on speech spoofing countermeasures has
been invested into back-end neural networks and training criteria. We
contribute to this effort with a comparative perspective in this study. Our
comparison of countermeasure models on the ASVspoof 2019 logical access task
takes into account recently proposed margin-based training criteria, widely
used front ends, and common strategies to deal with varied-length input trials.
We also measured intra-model differences through multiple training-evaluation
rounds with random initialization. Our statistical analysis demonstrates that
the performance of the same model may be significantly different when just
changing the random initial seed. Thus, we recommend similar analysis or
multiple training-evaluation rounds for further research on the database.
Despite the intra-model differences, we observed a few promising techniques
such as the average pooling to process varied-length inputs and a new
hyper-parameter-free loss function. The two techniques led to the best single
model in our experiment, which achieved an equal error rate of 1.92% and was
significantly different in statistical sense from most of the other
experimental models.


Abstract:
  Additional grid points are often introduced for the higher-order polynomial
of a numerical solution with curvilinear elements. However, those points are
likely to be located slightly outside the domain, even when the vertices of the
curvilinear elements lie within the curved domain. This misallocation of grid
points generates a mesh error, called geometric approximation error. This error
is smaller than the discretization error but large enough to significantly
degrade a long-time integration. Moreover, this mesh error is considered to be
the leading cause of conservation error. Two novel schemes are proposed to
improve conservation error and/or discretization error for long-time
integration caused by geometric approximation error: The first scheme retrieves
the original divergence of the original domain; the second scheme reconstructs
the original path of differentiation, called connection, thus retrieving the
original connection. The increased accuracies of the proposed schemes are
demonstrated by the conservation error for various partial differential
equations with moving frames on the sphere.


Abstract:
  This article considers the $\mathcal{H}_\infty$ static output-feedback
control for linear time-invariant uncertain systems with polynomial dependence
on probabilistic time-invariant parametric uncertainties. By applying
polynomial chaos theory, the control synthesis problem is solved using a
high-dimensional expanded system which characterizes stochastic state
uncertainty propagation. A closed-loop polynomial chaos transformation is
proposed to derive the closed-loop expanded system. The approach explicitly
accounts for the closed-loop dynamics and preserves the $\mathcal{L}_2$-induced
gain, which results in smaller transformation errors compared to existing
polynomial chaos transformations. The effect of using finite-degree polynomial
chaos expansions is first captured by a norm-bounded linear differential
inclusion, and then addressed by formulating a robust polynomial chaos based
control synthesis problem. This proposed approach avoids the use of high-degree
polynomial chaos expansions to alleviate the destabilizing effect of truncation
errors, which significantly reduces computational complexity. In addition, some
analysis is given for the condition under which the robustly stabilized
expanded system implies the robust stability of the original system. A
numerical example illustrates the effectiveness of the proposed approach.


Abstract:
  We present high-resolution $^{12}$CO and $^{13}$CO 2-1 ALMA observations, as
well as optical and near-infrared spectroscopy, of the highly-inclined
protoplanetary disk around SSTC2D J163131.2-242627. The spectral type we derive
for the source is consistent with a $\rm 1.2 \, M_{\odot}$ star inferred from
the ALMA observations. Despite its massive circumstellar disk, we find little
to no evidence for ongoing accretion on the star. The CO maps reveal a disk
that is unusually compact along the vertical direction, consistent with its
appearance in scattered light images. The gas disk extends about twice as far
away as both the submillimeter continuum and the optical scattered light. CO is
detected from two surface layers separated by a midplane region in which CO
emission is suppressed, as expected from freeze-out in the cold midplane. We
apply a modified version of the Topographically Reconstructed Distribution
method presented by Dutrey et al. 2017 to derive the temperature structure of
the disk. We find a temperature in the CO-emitting layers and the midplane of
$\sim$33 K and $\sim$20 K at $\rm R<200$ au, respectively. Outside of $\rm
R>200$ au, the disk's midplane temperature increases to $\sim$30 K, with a
nearly vertically isothermal profile. The transition in CO temperature
coincides with a dramatic reduction in the sub-micron and sub-millimeter
emission from the disk. We interpret this as interstellar UV radiation
providing an additional source of heating to the outer part of the disk.


Abstract:
  The spin Hall conductivity is usually calculated for periodic solids, while
the knowledge of local contributions from inhomogeneous regions like interfaces
or surfaces would be desirable. In this work we derive a local expression for
the spin Hall conductivity of two-dimensional systems in analogy to the local
anomalous Hall conductivity discussed recently. Our approach is applicable to
heterogeneous fully bounded systems and nanoribbons with both open and periodic
boundary conditions.


Abstract:
  Shared practices to assess the diversity of retrieval system results are
still debated in the Information Retrieval community, partly because of the
challenges of determining what diversity means in specific scenarios, and of
understanding how diversity is perceived by end-users. The field of Music
Information Retrieval is not exempt from this issue. Even if fields such as
Musicology or Sociology of Music have a long tradition in questioning the
representation and the impact of diversity in cultural environments, such
knowledge has not been yet embedded into the design and development of music
technologies. In this paper, focusing on electronic music, we investigate the
characteristics of listeners, artists, and tracks that are influential in the
perception of diversity. Specifically, we center our attention on 1)
understanding the relationship between perceived diversity and computational
methods to measure diversity, and 2) analyzing how listeners' domain knowledge
and familiarity influence such perceived diversity. To accomplish this, we
design a user-study in which listeners are asked to compare pairs of lists of
tracks and artists, and to select the most diverse list from each pair. We
compare participants' ratings with results obtained through computational
models built using audio tracks' features and artist attributes. We find that
such models are generally aligned with participants' choices when most of them
agree that one list is more diverse than the other, while they present a mixed
behaviour in cases where participants have little agreement. Moreover, we
observe how differences in domain knowledge, familiarity, and demographics can
influence the level of agreement among listeners, and between listeners and
diversity metrics computed automatically.


Abstract:
  The structure tensor of $\mathfrak{sl}_n$, denoted $T_{\mathfrak{sl}_n}$, is
the tensor arising from the Lie bracket bilinear operation on the set of
traceless $n \times n$ matrices over $\mathbb{C}$. This tensor is intimately
related to the well studied matrix multiplication tensor. Studying the
structure tensor of $\mathfrak{sl}_n$ may provide further insight into the
complexity of matrix multiplication and the "hay in a haystack" problem of
finding explicit sequences tensors with high rank or border rank. We aim to
find new bounds on the rank and border rank of this structure tensor in the
case of $\mathfrak{sl}_3$ and $\mathfrak{sl}_4$. We additionally provide bounds
in the case of the lie algebras $\mathfrak{so}_4$ and $\mathfrak{so}_5$. The
lower bounds on the border ranks were obtained via various recent techniques,
namely Koszul flattenings, border substitution, and border apolarity. Upper
bounds on the rank of $T_{\mathfrak{sl}_3}$ are obtained via numerical methods
that allowed us to find an explicit rank decomposition.


Abstract:
  We study the behaviour of Yukawa and Newtonian gravitational forces in a
cubic box with fully periodic boundaries commonly encountered in N-body
simulations of the structure formation. Placing a single gravitating body at
the origin of coordinates, we reveal the scales at which non-negligible
deviation from the Yukawa law occurs when the Newtonian approximation is
employed. We discuss the results in terms of the corresponding physical
distances today as well as earlier, back at the matter-dominated stage.
Revisiting the problem for free boundaries, we also compare the periodic and
plain gravitational forces for Yukawa-type interactions.


Abstract:
  New methodologies are designed to reduce the time complexity of an implicit
discrete-time differentiator and the simulation time to implement it. They rely
on Horner's method and the Shaw-Traub algorithm. The algorithms are compared
for differentiators of orders 3, 7, and 10. The Half-Horner and Full-Horner
methods showed the best performance and time complexity.


Abstract:
  Measuring the obliquity distribution of stars hosting warm Jupiters may help
us to understand the formation of close-orbiting gas giants. Few such
measurements have been performed due to practical difficulties in scheduling
observations of the relatively infrequent and long-duration transits of warm
Jupiters. Here, we report a measurement of the Rossiter-McLaughlin effect for
K2-232b, a warm Jupiter (M_P=0.39 M_Jup) on an 11.17-day orbit with an
eccentricity of 0.26. The data were obtained with the Automated Planet Finder
during two separate transits. The planet's orbit appears to be well-aligned
with the spin axis of the host star, with a projected spin-orbit angle of
lambda = -11.1+/-6.6 deg. Combined with the other available data, we find that
high obliquities are almost exclusively associated with planets that either
have an orbital separation greater than 10 stellar radii or orbit stars with
effective temperatures hotter than 6,000K. This pattern suggests that the
obliquities of the closest-orbiting giant planets around cooler stars have been
damped by tidal effects.


Abstract:
  Thinning antenna arrays through quantum Fourier transform (QFT) is proposed.
Given the lattice of the candidate locations for the array elements, the
problem of selecting which antenna location has to be either occupied or not by
an array element is formulated in the quantum computing (QC) framework and then
addressed with an ad-hoc design method based on a suitable implementation of
the QFT algorithm. Representative numerical results are presented and discussed
to point out the features and the advantages of the proposed QC-based thinning
technique.