Abstract:
  In this paper, we analyze the interplay between the use of offensive language
and mental health. We acquired publicly available datasets created for
offensive language identification and depression detection and we train
computational models to compare the use of offensive language in social media
posts written by groups of individuals with and without self-reported
depression diagnosis. We also look at samples written by groups of individuals
whose posts show signs of depression according to recent related studies. Our
analysis indicates that offensive language is more frequently used in the
samples written by individuals with self-reported depression as well as
individuals showing signs of depression. The results discussed here open new
avenues in research in politeness/offensiveness and mental health.


Abstract:
  Letter to the Editor about how the diet soda and Mentos reaction can be
produced by the direct immersion of a plastic soda bottle in liquid nitrogen.


Abstract:
  We introduce a framework to justify hydrodynamic limits of the
Vlasov-Navier-Stokes system. We specifically study high friction regimes, which
take into account the fact that particles of the dispersed phase are light
(resp. small) compared to the fluid part, and lead to the derivation of
Transport-Navier-Stokes (resp. Inhomogeneous Navier-Stokes) systems.


Abstract:
  We present a procedure for averaging one-parameter random unitary groups and
random self-adjoint groups. Central to this is a generalization of the notion
of weak convergence of a sequence of measures and the corresponding
generalization of the concept of convergence in distribution. The convergence
is established in determination of the sequence of compositions of independent
random transformations. When sequences of compositions of independent random
transformations of the shift by the Euclidean vector in space, the results
obtained coincide with the central limit theorem for the sums independent
random vectors. The results are applied to the dynamics of quantum systems
arising random quantization of the classical Hamiltonian system.


Abstract:
  Image quality is the basis of image communication and understanding tasks.
Due to the blur and noise effects caused by imaging, transmission and other
processes, the image quality is degraded. Blind image restoration is widely
used to improve image quality, where the main goal is to faithfully estimate
the blur kernel and the latent sharp image. In this study, based on
experimental observation and research, an adaptively sparse regularized
minimization method is originally proposed. The high-order gradients combine
with low-order ones to form a hybrid regularization term, and an adaptive
operator derived from the image entropy is introduced to maintain a good
convergence. Extensive experiments were conducted on different blur kernels and
images. Compared with existing state-of-the-art blind deblurring methods, our
method demonstrates superiority on the recovery accuracy.


Abstract:
  Machine learning is a powerful method for modeling in different fields such
as education. Its capability to accurately predict students' success makes it
an ideal tool for decision-making tasks related to higher education. The
accuracy of machine learning models depends on selecting the proper
hyper-parameters. However, it is not an easy task because it requires time and
expertise to tune the hyper-parameters to fit the machine learning model. In
this paper, we examine the effectiveness of automated hyper-parameter tuning
techniques to the realm of students' success. Therefore, we develop two
automated Hyper-Parameter Optimization methods, namely grid search and random
search, to assess and improve a previous study's performance. The experiment
results show that applying random search and grid search on machine learning
algorithms improves accuracy. We empirically show automated methods'
superiority on real-world educational data (MIDFIELD) for tuning HPs of
conventional machine learning classifiers. This work emphasizes the
effectiveness of automated hyper-parameter optimization while applying machine
learning in the education field to aid faculties, directors', or non-expert
users' decisions to improve students' success.


Abstract:
  We consider the inhomogeneous biharmonic nonlinear Schr\"odinger equation $$
i u_t +\Delta^2 u+\lambda|x|^{-b}|u|^\alpha u = 0, $$ where $\lambda=\pm 1$ and
$\alpha$, $b>0$. In the subctritical case, we improve the global well-posedness
result obtained in \cite{GUZPAS} for dimensions $N=5,6,7$ in the Sobolev space
$H^2(\mathbb{R}^N)$. The fundamental tools to establish our results are the
standard Strichartz estimates related to the linear problem and the
Hardy-Littlewood inequality. Results concerning the energy-critical case, that
is, $\alpha=\frac{8-2b}{N-4}$ are also reported. More precisely, we show
well-posedness and a stability result with initial data in the critical space
$\dot{H}^2$.


Abstract:
  As a generalization of the traditional connectivity, the g-component edge
connectivity c{\lambda}g(G) of a non-complete graph G is the minimum number of
edges to be deleted from the graph G such that the resulting graph has at least
g components. Hypercube-like networks (HL-networks for short) are obtained by
manipulating some pairs of edges in hypercubes, which contain several famous
interconnection networks such as twisted cubes, Mobius cubes, crossed cubes,
locally twisted cubes. In this paper, we determine the (g + 1)-component edge
connectivity of the n-dimensional HL-networks.


Abstract:
  Denoising diffusion probabilistic models (DDPMs) (Ho et al. 2020) have shown
impressive results on image and waveform generation in continuous state spaces.
Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs),
diffusion-like generative models for discrete data that generalize the
multinomial diffusion model of Hoogeboom et al. 2021, by going beyond
corruption processes with uniform transition probabilities. This includes
corruption with transition matrices that mimic Gaussian kernels in continuous
space, matrices based on nearest neighbors in embedding space, and matrices
that introduce absorbing states. The third allows us to draw a connection
between diffusion models and autoregressive and mask-based generative models.
We show that the choice of transition matrix is an important design decision
that leads to improved results in image and text domains. We also introduce a
new loss function that combines the variational lower bound with an auxiliary
cross entropy loss. For text, this model class achieves strong results on
character-level text generation while scaling to large vocabularies on LM1B. On
the image dataset CIFAR-10, our models approach the sample quality and exceed
the log-likelihood of the continuous-space DDPM model.


Abstract:
  Magnetoelastic measurements in the piezomagnetic antiferromagnet UO$_{2}$
were performed via the fiber Bragg grating method in magnetic fields up to
$150\,\mathrm{T}$ generated by a single-turn coil setup. We show that in short
timescales, order of a few micro seconds, pulsed-magnetic fields excite
mechanical resonances at temperatures ranging from $10\,\mathrm{K}$ to
$300\,\mathrm{K}$, in the paramagnetic as well as within the robust
antiferromagnetic state of the material. These resonances, which are barely
attenuated within the 100 ms observations, are attributed to the strong
magnetoelastic coupling in UO$_{2}$ combined with the high crystallographic
quality of the single crystal samples. They compare well with mechanical
resonances obtained by a resonant ultrasound technique and superimpose on the
known non-monotonic magnetostriction background. A clear phase-shift of $\pi$
in the lattice oscillations is, unexpectedly, observed in the antiferromagnetic
state when the magnetic field overcomes the piezomagnetic switch-field $H_c
\simeq -18\,\mathrm{T}$. We further present simulations and a theoretical
argument to explain the observed phenomena.


Abstract:
  Difference schemes are considered for dynamical systems $ \dot x = f (x) $
with a quadratic right-hand side, which have $t$-symmetry and are reversible.
Reversibility is interpreted in the sense that the Cremona transformation is
performed at each step in the calculations using a difference scheme. The
inheritance of periodicity and the Painlev\'e property by the approximate
solution is investigated. In the computer algebra system Sage, such values are
found for the step $ \Delta t $, for which the approximate solution is a
sequence of points with the period $ n \in \mathbb N $. Examples are given and
hypotheses about the structure of the sets of initial data generating sequences
with the period $ n $ are formulated.


Abstract:
  We apply generative adversarial convolutional neural networks to the problem
of style transfer to underdrawings and ghost-images in x-rays of fine art
paintings with a special focus on enhancing their spatial resolution. We build
upon a neural architecture developed for the related problem of synthesizing
high-resolution photo-realistic image from semantic label maps. Our neural
architecture achieves high resolution through a hierarchy of generators and
discriminator sub-networks, working throughout a range of spatial resolutions.
This coarse-to-fine generator architecture can increase the effective
resolution by a factor of eight in each spatial direction, or an overall
increase in number of pixels by a factor of 64. We also show that even just a
few examples of human-generated image segmentations can greatly improve --
qualitatively and quantitatively -- the generated images. We demonstrate our
method on works such as Leonardo's Madonna of the carnation and the
underdrawing in his Virgin of the rocks, which pose several special problems in
style transfer, including the paucity of representative works from which to
learn and transfer style information.


Abstract:
  In this work, we develop a pair of rate-diverse encoder and decoder for a
two-user Gaussian multiple access channel (GMAC). The proposed scheme enables
the users to transmit with the same codeword length but different coding rates
under diverse user channel conditions. First, we propose the row-combining (RC)
method and row-extending (RE) method to design practical low-density
parity-check (LDPC) channel codes for rate-diverse GMAC. Second, we develop an
iterative rate-diverse joint user messages decoding (RDJD) algorithm for GMAC,
where all user messages are decoded with a single parity-check matrix. In
contrast to the conventional network-coded multiple access (NCMA) and
compute-forward multiple access (CFMA) schemes that first recover a linear
combination of the transmitted codewords and then decode both user messages,
this work can decode both the user messages simultaneously. Extrinsic
information transfer (EXIT) chart analysis and simulation results indicate that
RDJD can achieve gains up to 1.0 dB over NCMA and CFMA in the two-user GMAC. In
particular, we show that there exists an optimal rate allocation for the two
users to achieve the best decoding performance given the channel conditions and
sum rate.


Abstract:
  Finding the shortest path distance between an arbitrary pair of vertices is a
fundamental problem in graph theory. A tremendous amount of research has been
successfully attempted on this problem, most of which is limited to static
graphs. Due to the dynamic nature of real-world networks, there is a pressing
need to address this problem for dynamic networks undergoing changes. In this
paper, we propose an \emph{online incremental} method to efficiently answer
distance queries over very large dynamic graphs. Our proposed method
incorporates incremental update operations, i.e. edge and vertex additions,
into a highly scalable framework of answering distance queries. We
theoretically prove the correctness of our method and the preservation of
labelling minimality. We have also conducted extensive experiments on 12 large
real-world networks to empirically verify the efficiency, scalability, and
robustness of our method.


Abstract:
  The sense of touch is fundamental in several manipulation tasks, but rarely
used in robot manipulation. In this work we tackle the problem of learning rich
touch features from cross-modal self-supervision. We evaluate them identifying
objects and their properties in a few-shot classification setting. Two new
datasets are introduced using a simulated anthropomorphic robotic hand equipped
with tactile sensors on both synthetic and daily life objects. Several
self-supervised learning methods are benchmarked on these datasets, by
evaluating few-shot classification on unseen objects and poses. Our experiments
indicate that cross-modal self-supervision effectively improves touch
representation, and in turn has great potential to enhance robot manipulation
skills.


Abstract:
  This paper evaluates and assesses the risk associated with capital allocation
in cryptocurrencies (CCs). In this regard, we take a basket of 27 CCs and the
CC index EWCI$^-$ into account. After considering a series of statistical tests
we find the stable distribution (SDI) to be the most appropriate to model the
body of CCs returns. However, as we find the SDI to possess less favorable
properties in the tail area for high quantiles, the generalized Pareto
distribution is adapted for a more precise risk assessment. We use a
combination of both distributions to calculate the Value at Risk and the
Conditional Value at Risk, indicating two subgroups of CCs with differing risk
characteristics.


Abstract:
  In statistical network analysis it is common to observe so called interaction
data. Such data is characterized by the actors who form the vertices of a
network. These are able to interact with each other along the edges of the
network. One usually assumes that the edges in the network are randomly formed
and dissolved over the observation horizon. In addition covariates are observed
and the interest is to model the impact of the covariates on the interactions.
In this paper we develop a framework to test if a non-parametric form of the
baseline intensity allows for more flexibility than a baseline which is
parametrically dependent on system-wide covariates (i.e. covariates which take
the same value for all individuals, e.g. time). This allows to test if certain
seasonality effects can be explained by simple covariates like the time. The
procedure is applied to modeling the baseline intensity in a bike-sharing
network by using weather and time information.


Abstract:
  We have performed a quantum mechanical analysis of travelling-wave parametric
amplifiers (TWPAs) in order to investigate five experimental phenomena related
to their operations, namely the effect of impedance mismatch, the presence of
upper idler modes, the presence of quantum and thermal noise, the generation of
squeezed states, and the preservation of pre-squeezed states during
amplification. Our analysis uses momentum operators to describe the spatial
evolution of quantised modes along a TWPA. We calculate the restriction placed
on pump amplitude as well as amplifier gain as a result of impedance mismatch
between a TWPA and its external system. We apply our analysis to upper idler
modes and demonstrate that they will result in suppressed gain. We show that an
ideal TWPA is indeed quantum-limited - i.e. it introduces a half-quantum of
zero-point fluctuation which is the minimum possible noise contribution for a
phase-preserving linear amplifier. We analyse the thermal noise associated with
a TWPA by considering the effect of distributed sources along an amplifier
transmission line. Our analysis predicts a doubling of thermal noise in the
high gain limit as a result of wave-mixing between signal and idler modes. We
study the operation of a TWPA in the presence of a DC bias current, and have
shown that highly squeezed states can in principle be generated. However,
amplifying a pre-squeezed state using a non-degenerate TWPA generally reduces
the squeezing advantage.


Abstract:
  The plug-and-play priors (PnP) and regularization by denoising (RED) methods
have become widely used for solving inverse problems by leveraging pre-trained
deep denoisers as image priors. While the empirical imaging performance and the
theoretical convergence properties of these algorithms have been widely
investigated, their recovery properties have not previously been theoretically
analyzed. We address this gap by showing how to establish theoretical recovery
guarantees for PnP/RED by assuming that the solution of these methods lies near
the fixed-points of a deep neural network. We also present numerical results
comparing the recovery performance of PnP/RED in compressive sensing against
that of recent compressive sensing algorithms based on generative models. Our
numerical results suggest that PnP with a pre-trained artifact removal network
provides significantly better results compared to the existing state-of-the-art
methods.


Abstract:
  We consider inference from non-random samples in data-rich settings where
high-dimensional auxiliary information is available both in the sample and the
target population, with survey inference being a special case. We propose a
regularized prediction approach that predicts the outcomes in the population
using a large number of auxiliary variables such that the ignorability
assumption is reasonable while the Bayesian framework is straightforward for
quantification of uncertainty. Besides the auxiliary variables, inspired by
Little & An (2004), we also extend the approach by estimating the propensity
score for a unit to be included in the sample and also including it as a
predictor in the machine learning models. We show through simulation studies
that the regularized predictions using soft Bayesian additive regression trees
yield valid inference for the population means and coverage rates close to the
nominal levels. We demonstrate the application of the proposed methods using
two different real data applications, one in a survey and one in an
epidemiology study.


Abstract:
  An approach to adjusting the conduction band population for tuning the
magnetic and magnetocaloric response of EuO1-{\delta} thin films through
control of oxygen vacancies ({\delta} = 0, 0.025, and 0.09) is presented. The
films each showed a paramagnetic to ferromagnetic transition around 65 K, with
an additional magnetic ordering transition at higher temperatures in the oxygen
deficient samples. All transitions are observed to be of second order. A
maximum magnetic entropy change of 6.4 J/kg K over a field change of 2 T with a
refrigerant capacity of 223 J/kg was found in the sample with {\delta} = 0, and
in all cases the refrigerant capacities of the thin films under study were
found to exceed that reported for bulk EuO. Adjusting the oxygen content was
shown to produce table-like magnetocaloric effects, desirable for ideal
Ericsson-cycle magnetic refrigeration. These films are thus excellent
candidates for small-scale magnetic cooling technology in the liquid nitrogen
temperature range.


Abstract:
  Adversarial training (AT) in order to achieve adversarial robustness wrt
single $l_p$-threat models has been discussed extensively. However, for
safety-critical systems adversarial robustness should be achieved wrt all
$l_p$-threat models simultaneously. In this paper we develop a simple and
efficient training scheme to achieve adversarial robustness against the union
of $l_p$-threat models. Our novel $l_1+l_\infty$-AT scheme is based on
geometric considerations of the different $l_p$-balls and costs as much as
normal adversarial training against a single $l_p$-threat model. Moreover, we
show that using our $l_1+l_\infty$-AT scheme one can fine-tune with just 3
epochs any $l_p$-robust model (for $p \in \{1,2,\infty\}$) and achieve multiple
norm adversarial robustness. In this way we boost the previous state-of-the-art
reported for multiple-norm robustness by more than $6\%$ on CIFAR-10 and report
up to our knowledge the first ImageNet models with multiple norm robustness.
Moreover, we study the general transfer of adversarial robustness between
different threat models and in this way boost the previous SOTA
$l_1$-robustness on CIFAR-10 by almost $10\%$.


Abstract:
  We prove existence and uniqueness of global in time weak solutions for the
Stokes system for compressible fluids with a general, non-monotone pressure. We
construct the solution at the level of Lagrangian formulation and then define
the transformation to the original Eulerian coordinates. For nonnegative and
bounded initial density the solution is also nonnegative for all $t$ and
belongs to $L^\infty([0,\infty)\times\mathbb{T}^d)$. A key point of our
considerations is the uniqueness of such transformation. Since the velocity
might not be Lipschitz continuous, we develop a method which relies on the
results of Crippa \& De Lellis, concerning regular Lagriangian flows. The
uniqueness is obtained thanks to the application of a certain weighted flow and
detail analysis based on the properties of the $BMO$ space.


Abstract:
  The possibility of using mixed crystals highly doped with rare earth ions
(REIs) as physical systems for creating fast quantum computers with a sampling
time of nanoseconds is discussed. The electronic 4f states of rare earth ions
with small values of the diagonal elements of the Judd-Ofelt matrix U(2) are
proposed as optical frequency qubit levels. CNOT and other conditional gate
operations are performed by exciting the rare earth ion into the 4f state with
a large diagonal element of U(2), causing a Stark blockade. It is found that
the main interaction responsible for this blockade is the quadrupole-quadrupole
interaction. The large inhomogeneous broadening of the frequencies of the
electronic transitions in mixed crystals and the weak interaction of 4f
electrons with phonons make it possible to achieve a high computation rate and
a long decoherence time of the qubits. An ensemble of closest REIs is described
that can act as an OQC instance; the frequencies of the corresponding qubits
can be found using the spectral hole burning method.


Abstract:
  A growing number of empirical software engineering researchers suggest that a
complementary focus on theory is required if the discipline is to mature. A
first step in theory-building involves the establishment of suitable
theoretical constructs. For researchers studying software projects, the lack of
a theoretical construct for context is problematic for both experimentation and
effort estimation. For experiments, insufficiently understood contextual
factors confound results, and for estimation, unstated contextual factors
affect estimation reliability. We have earlier proposed a framework that we
suggest may be suitable as a construct for context i.e. represents a minimal,
spanning set for the space of software contexts. The framework has six
dimensions, described as Who, Where, What, When, How and Why. In this paper, we
report the outcomes of a pilot study to test its suitability by categorising
contextual factors from the software engineering literature into the framework.
We found that one of the dimensions, Why, does not represent context, but
rather is associated with objectives. We also identified some factors that do
not clearly fit into the framework and require further investigation. Our
contributions are the pursuing of a theoretical approach to understanding
software context, the initial establishment and evaluation of a construct for
context and the exposure of a lack of clarity of meaning in many 'contexts'
currently applied as factors for estimating project outcomes.


Abstract:
  Recent years have witnessed tremendous interest in deep learning on
graph-structured data. Due to the high cost of collecting labeled
graph-structured data, domain adaptation is important to supervised graph
learning tasks with limited samples. However, current graph domain adaptation
methods are generally adopted from traditional domain adaptation tasks, and the
properties of graph-structured data are not well utilized. For example, the
observed social networks on different platforms are controlled not only by the
different crowd or communities but also by the domain-specific policies and the
background noise. Based on these properties in graph-structured data, we first
assume that the graph-structured data generation process is controlled by three
independent types of latent variables, i.e., the semantic latent variables, the
domain latent variables, and the random latent variables. Based on this
assumption, we propose a disentanglement-based unsupervised domain adaptation
method for the graph-structured data, which applies variational graph
auto-encoders to recover these latent variables and disentangles them via three
supervised learning modules. Extensive experimental results on two real-world
datasets in the graph classification task reveal that our method not only
significantly outperforms the traditional domain adaptation methods and the
disentangled-based domain adaptation methods but also outperforms the
state-of-the-art graph domain adaptation algorithms.


Abstract:
  The first binary neutron star merger has already been detected in
gravitational waves. The signal was accompanied by an electromagnetic
counterpart including a kilonova component powered by the decay of radioactive
nuclei, as well as a short $\gamma$-ray burst. In order to understand the
radioactively-powered signal, it is necessary to simulate the outflows and
their nucleosynthesis from the post-merger disk. Simulating the disk and
predicting the composition of the outflows requires general relativistic
magnetohydrodynamical (GRMHD) simulations that include a realistic,
finite-temperature equation of state (EOS) and self-consistently calculating
the impact of neutrinos. In this work, we detail the implementation of a
finite-temperature EOS and the treatment of neutrinos in the GRMHD code
HARM3D+NUC, based on HARM3D. We include formal tests of both the
finite-temperature EOS and the neutrino leakage scheme. We further test the
code by showing that, given conditions similar to those of published remnant
disks following neutron star mergers, it reproduces both recombination of free
nucleons to a neutron-rich composition and excitation of a thermal wind.


Abstract:
  With the growing capabilities of intelligent systems, the integration of
robots in our everyday life is increasing. However, when interacting in such
complex human environments, the occasional failure of robotic systems is
inevitable. The field of explainable AI has sought to make complex-decision
making systems more interpretable but most existing techniques target domain
experts. On the contrary, in many failure cases, robots will require recovery
assistance from non-expert users. In this work, we introduce a new type of
explanation, that explains the cause of an unexpected failure during an agent's
plan execution to non-experts. In order for error explanations to be
meaningful, we investigate what types of information within a set of
hand-scripted explanations are most helpful to non-experts for failure and
solution identification. Additionally, we investigate how such explanations can
be autonomously generated, extending an existing encoder-decoder model, and
generalized across environments. We investigate such questions in the context
of a robot performing a pick-and-place manipulation task in the home
environment. Our results show that explanations capturing the context of a
failure and history of past actions, are the most effective for failure and
solution identification among non-experts. Furthermore, through a second user
evaluation, we verify that our model-generated explanations can generalize to
an unseen office environment, and are just as effective as the hand-scripted
explanations.


Abstract:
  This demo presents the implementation and usage details of GASSERT, the first
tool to automatically improve assertion oracles. Assertion oracles are
executable boolean expressions placed inside the program that should pass
(return true) for all correct executions and fail (return false) for all
incorrect executions. Because designing perfect assertion oracles is difficult,
assertions are prone to both false positives (the assertion fails but should
pass) and false negatives (the assertion passes but should fail). Given a Java
method containing an assertion oracle to improve, GASSERT returns an improved
assertion with fewer false positives and false negatives than the initial
assertion. Internally, GASSERT implements a novel co-evolutionary algorithm
that explores the space of possible assertions guided by two fitness functions
that reward assertions with fewer false positives, fewer false negatives, and
smaller size.


Abstract:
  We consider the zero-average Gaussian free field on a certain class of finite
$d$-regular graphs for fixed $d\ge 3$. This class includes $d$-regular
expanders of large girth and typical realisations of random $d$-regular graphs.
We show that the level set of the zero-average Gaussian free field above level
$h$ has a giant component in the whole supercritical phase, that is for all
$h<h_\star$, with probability tending to one as the size of the graphs tends to
infinity. In addition, we show that this component is unique. This
significantly improves the result of [AC20b], where it was shown that a linear
fraction of vertices is in mesoscopic components if $h<h_\star$.


Abstract:
  We present a new version of the Ogre open source Python package with the
capability to perform structure prediction of epitaxial inorganic interfaces by
lattice and surface matching. In the lattice matching step a scan over
combinations of substrate and film Miller indices is performed to identify the
domain-matched interfaces with the lowest mismatch. Subsequently, surface
matching is conducted by Bayesian optimization to find the optimal interfacial
distance and in-plane registry between the substrate and film. For the
objective function, a geometric score function is proposed, based on the
overlap and empty space between atomic spheres at the interface. The score
function reproduces the results of density functional theory (DFT) at a
fraction of the computational cost. The optimized interfaces are pre-ranked
using a score function based on the similarity of the atomic environment at the
interface to the bulk environment. Final ranking of the top candidate
structures is performed with DFT. Ogre streamlines DFT calculations of
interface energies and electronic properties by automating the construction of
interface models. The application of Ogre is demonstrated for two interfaces of
interest for quantum computing and spintronics, Al/InAs and Fe/InSb.


Abstract:
  In this note we estimate herd immunity levels for the Covid-19 epidemic using
a standard SEIR system that models the disease dynamics in homogeneous
populations. The results obtained are indicative of values between 80% and 90%
for unprotected, fully susceptible populations. Basic protective measures such
as hand hygiene and mask wearing may be effective to lower herd immunity levels
down to values between 50% and 60% of the total population.


Abstract:
  Gauge field theory with rank-one field $T_{\mu}$ is a quantum field theory
that describes the interaction of elementary spin-1 particles, of which being
massless to preserve gauge symmetry. In this paper, we give a generalized,
extended study of abelian gauge field theory under successive rotor model in
general $D$-dimensional flat spacetime for spin-1 particles in the context of
higher order derivatives. We establish a theorem that $n$ rotor contributes to
the $\Box^n T^{\mu}$ fields in the integration-by-parts formalism of the
action. This corresponds to the transformation of gauge field $T^{\mu}
\rightarrow \Box^n T^{\mu}$ and gauge field strength $G_{\mu\nu}\rightarrow
\Box^n G_{\mu\nu} $ in the action. The $n=0$ case restores back to the standard
abelian gauge field theory. The equation of motion and Noether's conserved
current of the theory are also studied.


Abstract:
  We study an abelian $U(1)_{X}$ extension to the Standard Model group
consisting of an extended scalar sector of two doublets and one singlet plus
three additional exotic quarks and two exotic leptons through non-universal
interaction of the new $X$ quantum number and a $\mathbb{Z}_{2}$ parity. In
this model, the lightest fermions are massless at tree-level so effective
operators up to dimension seven are considered to fill all zeros in mass
matrices, providing an upper bound for the $\Lambda$ energy scale from the
electron, up, down and strange quark masses. We obtain that the model can
explain the $B$ meson anomaly while it provides an upper bound for $v_{\chi}$
of $8.4$ TeV which translates in an upper bound for the new $Z_{2}^{\mu}$ gauge
boson of $9.9$ TeV. Finally, muon $g-2$ contributions are also studied, where
we found that positive contributions coming from charged $W^{+}$ bosons and
exotic Majorana neutrinos while contributions coming from heavy charged and
neutral scalar bosons, are negative. In particular, the allowed regions
according to the experimental muon $g-2$ parameter, shows that as the exotic
neutrino mass increases, heavy scalars and the exotic lepton masses increase as
well.


Abstract:
  The forthcoming space-based gravitational wave observatory LISA will open a
new window for the measurement of galactic binaries, which will deliver
unprecedented information about these systems. However, the detection of
galactic binary gravitational wave signals is challenged by the presence of
gaps in the data. Whether being planned or not, gapped data dramatically reduce
our ability to detect faint signals and the risk of misdetection soars.
Inspired by advances in signal processing, we introduce a \nonparametric
inpainting algorithm based on the sparse representation of the galactic binary
signal in the Fourier domain. In contrast to traditional inpainting approaches,
noise statistics are known theoretically on ungapped measurements only. This
calls for the joint recovery of both the ungapped noise and the galactic binary
signal. We thoroughly show that sparse inpainting yields an accurate estimation
of the gravitational imprint of the galactic binaries. Additionally, we
highlight that the proposed algorithm produces a statistically consistent
ungapped noise estimate. We further evaluate the performances of the proposed
inpainting methods to recover the gravitational wave signal on a simple example
involving verification galactic binaries recently proposed in LISA data
challenges.


Abstract:
  Impact induced attrition processes are, beyond being essential models of
industrial ore processing, broadly regarded as the key to decipher the
provenance of sedimentary particles. A detailed understanding of single impact
phenomena of solid bodies has been obtained in physics and engineering,
however, the description of gradual mass reduction and shape evolution in
impact sequences relies on approximate mathematical models of mean field type,
formulated as curvature-driven partial differential equations. Here we
establish the first link between microscopic, particle-based material models
and the mean field theory for these processes. Based on realistic computer
simulations of particle-wall collision sequences, we first identify the
well-known damage and fragmentation energy phases, then we show that the former
is split into the abrasion phase with infinite sample lifetime, analogous to
Sternberg's Law, at finite asymptotic mass and the cleavage phase with finite
sample lifetime, decreasing as a power law of the impact velocity, analogous to
Basquin's Law. We demonstrate that only in the abrasion phase does shape
evolution emerging in microscopic material models reproduce with startling
accuracy the spatio-temporal patterns predicted by macroscopic mean field
approaches. Our results substantially extend the phase diagram of impact
phenomena and set the boundaries of the applicability of geometric mean field
theories for geological shape evolution. Additionally, the scaling laws
obtained can be exploited for quantitative predictions of evolution histories.


Abstract:
  This paper presents a novel approach of representing dynamic visual scenes
with static maps generated from video/image streams. Such representation allows
easy visual assessment of motion in dynamic environments. These maps are 2D
matrices calculated recursively, in a pixel-wise manner, that is based on the
recently introduced concept of Eccentricity data analysis. Eccentricity works
as a metric of a discrepancy between a particular pixel of an image and its
normality model, calculated in terms of mean and variance of past readings of
the same spatial region of the image. While Eccentricity maps carry temporal
information about the scene, actual images do not need to be stored nor
processed in batches. Rather, all the calculations are done recursively, based
on a small amount of statistical information stored in memory, thus resulting
in a very computationally efficient (processor- and memory-wise) method. The
list of potential applications includes video-based activity recognition,
intent recognition, object tracking, video description, and so on.


Abstract:
  Experimental studies of cell motility in culture have shown that under
adequate conditions these living organisms possess the ability to organize
themselves into complex structures. Such structures may exhibit a synergy that
greatly increases their survival rate and facilitate growth or spreading to
different tissues. These properties are even more significant for cancer cells
and related pathologies. Theoretical studies supported by experimental evidence
have also shown that adhesion plays a significant role in cellular
organization. Here we show that the directional persistence observed in
polarized displacements permits the formation of stable cell aggregates in the
absence of adhesion, even in low-density regimes. We introduce a discrete
stochastic model for the dispersal of polarized cells with exclusion and derive
the hydrodynamic limit. We demonstrate that the persistence coupled with the
cell-cell exclusion hinders the cellular motility around other cells, leading
to a non-linear diffusion which facilitates their capture into larger
aggregates.


Abstract:
  We establish existence and uniqueness results for initial-boundary value
problems for transport equations in one space dimension with nearly
incompressible velocity fields, under the sole assumption that the fields are
bounded. In the case where the velocity field is either nonnegative or
nonpositive, one can rely on similar techniques as in the case of the Cauchy
problem. Conversely, in the general case we introduce a new and more
technically demanding construction, which heuristically speaking relies on a
"lagrangian formulation" of the problem, albeit in a highly irregular setting.
We also establish stability of the solution in weak and strong topologies, and
propagation of the $BV$ regularity. In the case of either nonnegative or
nonpositive velocity fields we also establish a $BV$-in-time regularity result,
and we exhibit a counterexample showing that the result is false in the case of
sign-changing vector fields. To conclude, we establish a trace renormalization
property.


Abstract:
  Deep Neural Networks (DNNs) are known to be strong predictors, but their
prediction strategies can rarely be understood. With recent advances in
Explainable Artificial Intelligence, approaches are available to explore the
reasoning behind those complex models' predictions. One class of approaches are
post-hoc attribution methods, among which Layer-wise Relevance Propagation
(LRP) shows high performance. However, the attempt at understanding a DNN's
reasoning often stops at the attributions obtained for individual samples in
input space, leaving the potential for deeper quantitative analyses untouched.
As a manual analysis without the right tools is often unnecessarily labor
intensive, we introduce three software packages targeted at scientists to
explore model reasoning using attribution approaches and beyond: (1) Zennit - a
highly customizable and intuitive attribution framework implementing LRP and
related approaches in PyTorch, (2) CoRelAy - a framework to easily and quickly
construct quantitative analysis pipelines for dataset-wide analyses of
explanations, and (3) ViRelAy - a web-application to interactively explore
data, attributions, and analysis results.


Abstract:
  In robot navigation, generalizing quickly to unseen environments is
essential. Hierarchical methods inspired by human navigation have been
proposed, typically consisting of a high-level landmark proposer and a
low-level controller. However, these methods either require precise high-level
information to be given in advance or need to construct such guidance from
extensive interaction with the environment. In this work, we propose an
approach that leverages a rough 2-D map of the environment to navigate in novel
environments without requiring further learning. In particular, we introduce a
dynamic topological map that can be initialized from the rough 2-D map along
with a high-level planning approach for proposing reachable 2-D map patches of
the intermediate landmarks between the start and goal locations. To use
proposed 2-D patches, we train a deep generative model to generate intermediate
landmarks in observation space which are used as subgoals by low-level
goal-conditioned reinforcement learning. Importantly, because the low-level
controller is only trained with local behaviors (e.g. go across the
intersection, turn left at a corner) on existing environments, this framework
allows us to generalize to novel environments given only a rough 2-D map,
without requiring further learning. Experimental results demonstrate the
effectiveness of the proposed framework in both seen and novel environments.


Abstract:
  We derive an approximate but explicit formula for the Mean First Passage Time
of a random walker between a source and a target node of a directed and
weighted network. The formula does not require any matrix inversion, and it
takes as only input the transition probabilities into the target node. It is
derived from the calculation of the average resolvent of a deformed ensemble of
random sub-stochastic matrices $H=\langle H\rangle +\delta H$, with $\langle
H\rangle$ rank-$1$ and non-negative. The accuracy of the formula depends on the
spectral gap of the reduced transition matrix, and it is tested numerically on
several instances of (weighted) networks away from the high sparsity regime,
with an excellent agreement.


Abstract:
  It is important to build a rigorous verification and validation (V&V) process
to evaluate the safety of highly automated vehicles (HAVs) before their wide
deployment on public roads. In this paper, we propose an interaction-aware
framework for HAV safety evaluation which is suitable for some
highly-interactive driving scenarios including highway merging, roundabout
entering, etc. Contrary to existing approaches where the primary other vehicle
(POV) takes predetermined maneuvers, we model the POV as a game-theoretic
agent. To capture a wide variety of interactions between the POV and the
vehicle under test (VUT), we characterize the interactive behavior using
level-k game theory and social value orientation and train a diverse set of
POVs using reinforcement learning. Moreover, we propose an adaptive test case
sampling scheme based on the Gaussian process regression technique to generate
customized and diverse challenging cases. The highway merging is used as the
example scenario. We found the proposed method is able to capture a wide range
of POV behaviors and achieve better coverage of the failure modes of the VUT
compared with other evaluation approaches.


Abstract:
  In this paper, we study the nonlinear Schr\"odinger equation with focusing
point nonlinearity in dimension one. First, we establish a scattering criterion
for the equation based on Kenig-Merle's compactness-rigidity argument. Then we
prove the energy scattering below and above the mass-energy threshold. We also
describe the dynamics of solutions with data at the ground state threshold.
Finally, we prove a blow-up criteria for the equation with initial data with
arbitrarily large energy.


Abstract:
  Several active organisms in nature tend to reside as a community in a viscous
fluid medium. We analyze the variation of swimming characteristics of an active
swimmer present in a dilute and disperse suspension, modeled as an effective
Brinkman medium. This idealized representation of a collection of active
swimmers allows one to distinguish the impact of the interior domain available
to an individual swimmer as well as the contribution of its neighbors. Darcy's
law along with the analytical solution enables the effective resistivity to be
predicted as a function of the volume fraction which is in close agreement with
the well-known Carman-Kozeny equation. This facilitates the successive analysis
of the propulsion speed, power dissipation, and swimming efficiency of the
targeted swimmer as a function of the volume fraction which is decisive in
nutrient transport and uptake or reproduction in a collective environment. A
stress-jump condition is also imposed across a cell to indicate a mean
effective force due to the nearby swimmers. At suitable values of this
stress-jump coefficient, the relative increase of the migration velocity and
swimming efficiency is noticeably higher at an optimum occupancy.


Abstract:
  A full-fledged data exploration system must combine different access
modalities with a powerful concept of guiding the user in the exploration
process, by being reactive and anticipative both for data discovery and for
data linking. Such systems are a real opportunity for our community to cater to
users with different domain and data science expertise. We introduce INODE --
an end-to-end data exploration system -- that leverages, on the one hand,
Machine Learning and, on the other hand, semantics for the purpose of Data
Management (DM). Our vision is to develop a classic unified, comprehensive
platform that provides extensive access to open datasets, and we demonstrate it
in three significant use cases in the fields of Cancer Biomarker Reearch,
Research and Innovation Policy Making, and Astrophysics. INODE offers
sustainable services in (a) data modeling and linking, (b) integrated query
processing using natural language, (c) guidance, and (d) data exploration
through visualization, thus facilitating the user in discovering new insights.
We demonstrate that our system is uniquely accessible to a wide range of users
from larger scientific communities to the public. Finally, we briefly
illustrate how this work paves the way for new research opportunities in DM.


Abstract:
  Deep neural network (DNN) usually learns the target function from low to high
frequency, which is called frequency principle or spectral bias. This frequency
principle sheds light on a high-frequency curse of DNNs -- difficult to learn
high-frequency information. Inspired by the frequency principle, a series of
works are devoted to develop algorithms for overcoming the high-frequency
curse. A natural question arises: what is the upper limit of the decaying rate
w.r.t. frequency when one trains a DNN? In this work, our theory, confirmed by
numerical experiments, suggests that there is a critical decaying rate w.r.t.
frequency in DNN training. Below the upper limit of the decaying rate, the DNN
interpolates the training data by a function with a certain regularity.
However, above the upper limit, the DNN interpolates the training data by a
trivial function, i.e., a function is only non-zero at training data points.
Our results indicate a better way to overcome the high-frequency curse is to
design a proper pre-condition approach to shift high-frequency information to
low-frequency one, which coincides with several previous developed algorithms
for fast learning high-frequency information. More importantly, this work
rigorously proves that the high-frequency curse is an intrinsic difficulty of
DNNs.


Abstract:
  We report observations of the flickering variability of the dwarf nova RX And
in five bands (UBVRI) on two nights. On 25 October 2019 the brightness of the
star was $B\approx 13.9$ mag, the amplitude of the flickering was 0.47 mag, and
we estimate for the flickering source temperature $T_{fl} = 10700 \pm 400$ K,
and radius $R_{fl} =0.046 \pm 0.004$ $R_\odot$. On 2 January 2020, the star was
about 3 magnitudes brighter ($B \approx 10.7$), the amplitude of the flickering
was significantly lower (0.07 mag) and we derive for the flickering source
$T_{fl} = 9600 \pm 700$ K, and radius $R_{fl} = 0.098 \pm 0.009$ $R_\odot$. The
results indicate that 3 magnitudes brightening of the star doubled the radius
of the flickering source.
  The data are available upon request from the authors.


Abstract:
  In this paper, we present the results of a numerical study of air-water
turbulent bubbly flow in a periodic vertical square duct. The study is
conducted using a novel numerical technique which leverages Volume of Fluid
method for interface capturing and Sharp Surface Force method for accurate
representation of the surface tension forces. A three-dimensional geometry
construction method is employed during solution of interface equation which
gives absolute conservation of mass and sharp interface between gas and liquid
phases. The entire algorithm has been implemented on a data parallel mode on
multiple graphics processing units (GPU) taking advantage of the large number
of available cores.
  We have studied the dynamics of a swarm of spherical bubbles co-flowing with
the upward turbulent flow and compared results with an unladen turbulent flow.
The frictional Reynolds number of the unladen $Re_{\tau}$ is 360, which is
sufficient to sustain a turbulent flow. We observe the turbulence-driven
secondary flows in the mean flow, with complex instantaneous turbulent vortical
structures. The interaction of these secondary flows with the upwards rising
bubbles is very complex and leads to significant changes in the instantaneous
and time-averaged flow field.
  We present the results of mean void fraction distribution, mean velocities,
longitudinal and transverse turbulence intensities along the wall, corner
bisector, and wall bisector. A peak in the void fraction distribution near the
walls is observed representing the migration of bubbles to a preferred section
of the duct. The effects of turbulence-driven secondary flows and instantaneous
large eddies on preferential concentration of the bubbles are discussed. The
dispersed bubbles are seen to break the long elongated turbulent structures
commonly observed in the unladen turbulent flow.


Abstract:
  Multiple instance learning (MIL) is the preferred approach for whole slide
image classification. However, most MIL approaches do not exploit the
interdependencies of tiles extracted from a whole slide image, which could
provide valuable cues for classification. This paper presents a novel MIL
approach that exploits the spatial relationship of tiles for classifying whole
slide images. To do so, a sparse map is built from tiles embeddings, and is
then classified by a sparse-input CNN. It obtained state-of-the-art performance
over popular MIL approaches on the classification of cancer subtype involving
10000 whole slide images. Our results suggest that the proposed approach might
(i) improve the representation learning of instances and (ii) exploit the
context of instance embeddings to enhance the classification performance. The
code of this work is open-source at {github censored for review}.


Abstract:
  The degree of porosity in interstellar dust-grain material is poorly defined,
although recent work has suggested that the grains could be highly porous.
Aside from influencing the optical properties of the dust, porosity has the
potential to affect the chemistry occurring on dust-grain surfaces, via
increased surface area, enhanced local binding energies, and the possibility of
trapping of molecules within the pores as ice mantles build up on the grains.
Through computational kinetics simulations, we investigate how interstellar
grain-surface chemistry and ice composition are affected by the porosity of the
underlying dust-grain material. Using a simple routine, idealized
three-dimensional dust-grains are constructed, atom by atom, with varying
degrees of porosity. Diffusive chemistry is then simulated on these surfaces
using the off-lattice microscopic Monte Carlo chemical kinetics model, MIMICK,
assuming physical conditions appropriate to dark interstellar clouds. On the
porous grain surface, the build-up of ice mantles, mostly composed of water,
leads to the covering over of the pores, leaving empty pockets. Once the pores
are completely covered, the chemical and structural behavior is similar to
non-porous grains of the same size. The most prominent chemical effect of the
presence of grain porosity is the trapping of molecular hydrogen, formed on the
grain surfaces, within the ices and voids inside the grain pores. Trapping of
H2 in this way may indicate that other volatiles, such as inert gases not
included in these models, could be trapped within dust-grain porous structures
when ices begin to form.


Abstract:
  Differential evolution (DE) is an effective global evolutionary optimization
algorithm using to solve global optimization problems mainly in a continuous
domain. In this field, researchers pay more attention to improving the
capability of DE to find better global solutions, however, the computational
performance of DE is also a very interesting aspect especially when the problem
scale is quite large. Firstly, this paper analyzes the design of parallel
computation of DE which can easily be executed in Math Kernel Library (MKL) and
Compute Unified Device Architecture (CUDA). Then the essence of the exponential
crossover operator is described and we point out that it cannot be used for
better parallel computation. Later, we propose a new exponential crossover
operator (NEC) that can be executed parallelly with MKL/CUDA. Next, the
extended experiments show that the new crossover operator can speed up DE
greatly. In the end, we test the new parallel DE structure, illustrating that
the former is much faster.


Abstract:
  We study the Ehresmann--Schauenburg bialgebroid of a noncommutative principal
bundle as a quantization of the gauge groupoid of a classical principal bundle.
We show that the gauge group of the noncommutative bundle is isomorphic to the
group of bisections of the bialgebroid, and we give a crossed module structure
for the bisections and the automorphisms of the bialgebroid. Examples
illustrating these constructions include: Galois objects of Taft algebras, a
monopole bundle over a quantum spheres and a not faithfully flat Hopf--Galois
extension of commutative algebras. The latter two examples have in fact a
structure of Hopf algebroid for a suitable invertible antipode.


Abstract:
  In arXiv1312.7267, the first non-trivial example of a Poisson manifold of
strong compact type is given. The construction uses the theory of K3 surfaces
and results in a Poisson manifold with leaf space $S^1$. We modify the
construction to obtain a new class of examples. Specifically, we obtain for
each strongly integral affine 2-torus a Poisson manifold of strong compact type
with said torus as leaf space.


Abstract:
  Identifying phase transitions is one of the key challenges in quantum
many-body physics. Recently, machine learning methods have been shown to be an
alternative way of localising phase boundaries also from noisy and imperfect
data and without the knowledge of the order parameter. Here we apply different
unsupervised machine learning techniques including anomaly detection and
influence functions to experimental data from ultracold atoms. In this way we
obtain the topological phase diagram of the Haldane model in a completely
unbiased fashion. We show that the methods can successfully be applied to
experimental data at finite temperature and to data of Floquet systems, when
postprocessing the data to a single micromotion phase. Our work provides a
benchmark for unsupervised detection of new exotic phases in complex many-body
systems.


Abstract:
  Defining an efficient training set is one of the most delicate phases for the
success of remote sensing image classification routines. The complexity of the
problem, the limited temporal and financial resources, as well as the high
intraclass variance can make an algorithm fail if it is trained with a
suboptimal dataset. Active learning aims at building efficient training sets by
iteratively improving the model performance through sampling. A user-defined
heuristic ranks the unlabeled pixels according to a function of the uncertainty
of their class membership and then the user is asked to provide labels for the
most uncertain pixels. This paper reviews and tests the main families of active
learning algorithms: committee, large margin and posterior probability-based.
For each of them, the most recent advances in the remote sensing community are
discussed and some heuristics are detailed and tested. Several challenging
remote sensing scenarios are considered, including very high spatial resolution
and hyperspectral image classification. Finally, guidelines for choosing the
good architecture are provided for new and/or unexperienced user.


Abstract:
  We study the modular symmetry on magnetized toroidal orbifolds with
Scherk-Schwarz phases. In particular, we investigate finite modular flavor
groups for three-generation modes on magnetized orbifolds. The three-generation
modes can be the three-dimensional irreducible representations of covering
groups and central extended groups of $\Gamma_N$ for $N=3,4,5,7,8,16$, that is,
covering groups of $\Delta(6(N/2)^2)$ for $N=$ even and central extensions of
$PSL(2,\mathbb{Z}_{N})$ for $N=$odd with Scherk-Schwarz phases. We also study
anomaly behaviors.


Abstract:
  In this paper, we describe a new algorithm to compute the extreme
eigenvalue/eigenvector pairs of a symmetric matrix. The proposed algorithm can
be viewed as an extension of the Jacobi transformation method for symmetric
matrix diagonalization to the case where we want to compute just a few
eigenvalues/eigenvectors. The method is also particularly well suited for the
computation of sparse eigenspaces. We show the effectiveness of the method for
sparse low-rank approximations and show applications to random symmetric
matrices, graph Fourier transforms, and with the sparse principal component
analysis in image classification experiments.


Abstract:
  Space weather is a collective term for different solar or space phenomena
that can detrimentally affect technology. However, current understanding of
space weather hazards is still relatively embryonic in comparison to
terrestrial natural hazards such as hurricanes or earthquakes. Indeed, certain
types of space weather such as large Coronal Mass Ejections (CMEs) are an
archetypal example of a low probability, high severity hazard. Few major
events, short time-series data and a lack of consensus regarding the potential
impacts on critical infrastructure have hampered the economic impact assessment
of space weather. Yet, space weather has the potential to disrupt a wide range
of Critical National Infrastructure (CNI) systems including electricity
transmission, satellite communications and positioning, aviation and rail
transportation. Recently there has been growing interest in these potential
economic and societal impacts. Estimates range from millions of dollars of
equipment damage from the Quebec 1989 event, to some analysts reporting
billions of lost dollars in the wider economy from potential future disaster
scenarios. Hence, this provides motivation for this article which tracks the
origin and development of the socio-economic evaluation of space weather, from
1989 to 2017, and articulates future research directions for the field.


Abstract:
  Humans approach driving in a holistic fashion which entails, in particular,
understanding road events and their evolution. Injecting these capabilities in
an autonomous vehicle has thus the potential to take situational awareness and
decision making closer to human-level performance. To this purpose, we
introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to
our knowledge the first of its kind. ROAD is designed to test an autonomous
vehicle's ability to detect road events, defined as triplets composed by a
moving agent, the action(s) it performs and the corresponding scene locations.
ROAD comprises 22 videos, originally from the Oxford RobotCar Dataset,
annotated with bounding boxes showing the location in the image plane of each
road event. We also provide as baseline a new incremental algorithm for online
road event awareness, based on inflating RetinaNet along time, which achieves a
mean average precision of 16.8% and 6.1% for frame-level and video-level event
detection, respectively, at 50% overlap. Though promising, these figures
highlight the challenges faced by situation awareness in autonomous driving.
Finally, ROAD allows scholars to investigate exciting tasks such as complex
(road) activity detection, future road event anticipation and the modelling of
sentient road agents in terms of mental states. Dataset can be obtained from
https://github.com/gurkirt/road-dataset and baseline code from
https://github.com/gurkirt/3D-RetinaNet.


Abstract:
  In this paper we study numerically solving optimal control problems with
bang-bang control functions. We present a formal Lagrangian approach for
solving the optimal control problem, and address difficulties encountered when
numerically solving the state and adjoint equations by using the immersed
interface method. We note that our numerical approach does not approximate the
discontinuous control function with smooth functions, instead we solve the true
bang-bang optimal control problem. Our approach for solving the optimal control
problem uses an adjoint-based gradient. We use the gradient in our first-order
trust-region method to generate a local minimizing control. We present detailed
numerical results to demonstrate the effectiveness of our method.


Abstract:
  A synergistic approach for optimizing devices, circuits, and neural network
architectures was used to abate junction-temperature-change-induced performance
degradation of a Fe-FinFET-based artificial neural network. We demonstrated
that the digital nature of the binarized neural network, with the "0" state
programmed deep in the subthreshold and the "1" state in strong inversion, is
crucial for robust DNN inference. The performance of a purely software-based
binary neural network (BNN), with 96.1% accuracy for Modified National
Institute of Standards and Technology (MNIST) handwritten digit recognition,
was used as a baseline. The Fe-FinFET-based BNN (including device-to-device
variation at 300 K) achieved 95.7% inference accuracy on the MNIST dataset.
Although substantial inference accuracy degradation with temperature change was
observed in a nonbinary neural network, the BNN with optimized Fe-FinFETs as
synaptic devices had excellent resistance to temperature change effects and
maintained a minimum inference accuracy of 95.2% within a temperature range of
-233K to 398K after gate stack and bias optimization. However, reprogramming to
adjust device conductance was necessary for temperatures higher than 398K.


Abstract:
  Electroanatomical maps are a key tool in the diagnosis and treatment of
atrial fibrillation. Current approaches focus on the activation times recorded.
However, more information can be extracted from the available data. The fibers
in cardiac tissue conduct the electrical wave faster, and their direction could
be inferred from activation times. In this work, we employ a recently developed
approach, called physics informed neural networks, to learn the fiber
orientations from electroanatomical maps, taking into account the physics of
the electrical wave propagation. In particular, we train the neural network to
weakly satisfy the anisotropic eikonal equation and to predict the measured
activation times. We use a local basis for the anisotropic conductivity tensor,
which encodes the fiber orientation. The methodology is tested both in a
synthetic example and for patient data. Our approach shows good agreement in
both cases, with an RMSE of 2.2ms on the in-silico data and outperforming a
state of the art method on the patient data. The results show a first step
towards learning the fiber orientations from electroanatomical maps with
physics-informed neural networks.


Abstract:
  We propose an approach to the semantics of package management which relates
it to general event structures, well-known mathematical objects used in the
semantics of concurrent, nondeterministic systems. In this approach, the data
of a package repository is treated as a declarative specification of a
nondeterministic, concurrent program. We introduce a process calculus
corresponding to this data, and investigate its operational and categorical
semantics. Our hope is this lays the basis for further formal study of package
management in which the weight of existing tools can be brought to bear.


Abstract:
  The quantum states of the Kapitza pendulum are described within the effective
potential obtained by the method of averaging over the fast oscillations. An
analytical estimate of the energy spectrum of stabilized states is given using
approximate model potential. For the lowest states of an inverted pendulum, the
spectrum is repeduced by the energies of a harmonic oscillator with
perturbation theory corrections. Tunneling effect contribution to the energies
of resonance states in the double-well effective potential is estimated. The
results of numerical simulations of vibrational and rotational spectra of the
Kapitza pendulum by the semiclassical method and by the Numerov algorithm are
compared.


Abstract:
  We consider a harmonic oscillator under periodic driving and coupled to two
harmonic-oscillator heat baths at different temperatures. We use the
thermofield transformation with chain mapping for this setup, which allows us
to study the unitary evolution of the system and the baths up to a time when
the periodic steady state emerges in the system. We characterize this periodic
steady state, and we show that, by tuning the system and the bath parameters,
one can turn this system from an engine to an accelerator or even to a heater.
The possibility to study the unitary evolution of the system and baths also
allows us to evaluate the steady correlations that build between the system and
the baths, and correlations that grow between the baths.


Abstract:
  In quantum mechanics, each particle is described by a complex valued wave
function characterized by amplitude and phase. When many particles interact
each other, cooperative phenomena give rise to a quantum many-body state with a
specific quantum coherence. What is the interplay between particle's phase
coherence and many-body quantum coherence? Over the years, such question has
been object of profound analysis in quantum physics. Here, we demonstrate how
state of the art quantum technology allows to explore the question operatively.
To this end, we study the time-dependent interference formed by releasing an
interacting degenerate Fermi-gas from a specific matter-wave circuit in an
effective magnetic field. Particle phase coherence, indicated by the
first-order correlator, and many-body quantum coherence, indicated by the noise
correlator, are displayed as distinct features of the interferogram. Particle
phase coherence produces spiral interference of the Fermi orbitals at
intermediate times. Many-body quantum coherence emerges at long times. The
coupling between particles coherence and many-body coherence is reflected in a
specific dependence of the interference pattern on the effective magnetic
field.


Abstract:
  Probabilistic time series forecasting involves estimating the distribution of
future based on its history, which is essential for risk management in
downstream decision-making. We propose a deep state space model for
probabilistic time series forecasting whereby the non-linear emission model and
transition model are parameterized by networks and the dependency is modeled by
recurrent neural nets. We take the automatic relevance determination (ARD) view
and devise a network to exploit the exogenous variables in addition to time
series. In particular, our ARD network can incorporate the uncertainty of the
exogenous variables and eventually helps identify useful exogenous variables
and suppress those irrelevant for forecasting. The distribution of multi-step
ahead forecasts are approximated by Monte Carlo simulation. We show in
experiments that our model produces accurate and sharp probabilistic forecasts.
The estimated uncertainty of our forecasting also realistically increases over
time, in a spontaneous manner.


Abstract:
  Let $\{c_k\}$ be a nonincreasing sequence of positive numbers (more general
classes of sequences are also considered), and $\alpha>0$ be not an integer. We
find necessary and sufficient conditions for the uniform convergence of the
series $\sum_k c_k\sin k^{\alpha}x$ and $\sum_k c_k\cos k^{\alpha}x$ on the
real line and its bounded subsets.


Abstract:
  In this paper we deal with positive singular solutions to semilinear elliptic
problems involving a first order term and a singular nonlinearity. Exploiting a
fine adaptation of the well-known moving plane method of Alexandrov-Serrin and
a careful choice of the cutoff functions, we deduce symmetry and monotonicity
properties of the solutions.


Abstract:
  This paper aims to address few-shot semantic segmentation. While existing
prototype-based methods have achieved considerable success, they suffer from
uncertainty and ambiguity caused by limited labelled examples. In this work, we
propose attentional prototype inference (API), a probabilistic latent variable
framework for few-shot semantic segmentation. We define a global latent
variable to represent the prototype of each object category, which we model as
a probabilistic distribution. The probabilistic modeling of the prototype
enhances the model's generalization ability by handling the inherent
uncertainty caused by limited data and intra-class variations of objects. To
further enhance the model, we introduce a local latent variable to represent
the attention map of each query image, which enables the model to attend to
foreground objects while suppressing background. The optimization of the
proposed model is formulated as a variational Bayesian inference problem, which
is established by amortized inference networks.We conduct extensive experiments
on three benchmarks, where our proposal obtains at least competitive and often
better performance than state-of-the-art methods. We also provide comprehensive
analyses and ablation studies to gain insight into the effectiveness of our
method for few-shot semantic segmentation.


Abstract:
  Rare information on photodisintegration reactions of nuclei with mass numbers
$A \approx 160$ at astrophysical conditions impedes our understanding of the
origin of $p$-nuclei. Experimental determination of the key ($p,\gamma$) cross
sections has been playing an important role to verify nuclear reaction models
and to provide rates of relevant ($\gamma,p$) reactions in $\gamma$-process. In
this paper we report the first cross section measurements of
$^{160}$Dy($p,\gamma$)$^{161}$Ho and $^{161}$Dy($p,n$)$^{161}$Ho in the beam
energy range of 3.4 - 7.0 MeV, partially covering the Gamow window. Such
determinations are possible by using two targets with various isotopic
fractions. The cross section data can put a strong constraint on the nuclear
level densities and gamma strength functions for $A \approx$ 160 in the
Hauser-Feshbach statistical model. Furthermore, we find the best parameters for
TALYS that reproduce the A $\thicksim$ 160 data available,
$^{160}$Dy($p,\gamma$)$^{161}$Ho and $^{162}$Er($p,\gamma$)$^{163}$Tm, and
recommend the constrained $^{161}$Ho($\gamma,p$)$^{160}$Dy reaction rates over
a wide temperature range for $\gamma$-process network calculations. Although
the determined $^{161}$Ho($\gamma$, p) stellar reaction rates at the
temperature of 1 to 2 GK can differ by up to one order of magnitude from the
NON-SMOKER predictions, it has a minor effect on the yields of $^{160}$Dy and
accordingly the $p$-nuclei, $^{156,158}$Dy. A sensitivity study confirms that
the cross section of $^{160}$Dy($p$, $\gamma$)$^{161}$Ho is measured precisely
enough to predict yields of $p$-nuclei in the $\gamma$-process.


Abstract:
  We consider the adjacency matrix $A$ of the Erd{\H o}s--R\'enyi graph on $N$
vertices with edge probability $d/N$. For $(\log \log N)^4 \ll d \lesssim \log
N$, we prove that the eigenvalues near the spectral edge form asymptotically a
Poisson process and the associated eigenvectors are exponentially localized. As
a corollary, at the critical scale $d \asymp \log N$, the limiting distribution
of the largest nontrivial eigenvalue does not match with any previously known
distribution. Together with [arXiv:2005.14180], our result establishes the
coexistence of a fully delocalized phase and a fully localized phase in the
spectrum of $A$. The proof relies on a three-scale rigidity argument, which
characterizes the fluctuations of the eigenvalues in terms of the fluctuations
of sizes of spheres of radius 1 and 2 around vertices of large degree.


Abstract:
  Carrinheiros are collectors of recyclable materials that use human-powered
vehicles. Carrinheiro's collection routes can be tiring depending on the paths
chosen. Therefore, this work proposes an algorithm for suggesting customizable
routes based on three edge costing policies: Less Work Policy, Less Impedance
Policy, and Short Distance Policy. This work used the tools osmnx and networkx
to construct graphs, geographic data from Open Street Map, and elevations from
Topodata. The simulations performed in Simulation of Urban MObility (SUMO)
demonstrated that the proposed algorithm could minimize the power applied to
push the vehicle, the distance, and the travel time, according to the policy
used.


Abstract:
  The paper is concerned with the finite-time stabilization of a hybrid PDE-ODE
system describing the motion of an overhead crane with a flexible cable. The
dynamics of the flexible cable is described by the wave equation with a
variable coefficient which is an affine function of the curvilinear abscissa
along the cable. Using several changes of variables, a backstepping
transformation, and a finite-time stable second-order ODE for the dynamics of a
conveniently chosen variable, we prove that a global finite-time stabilization
occurs for the full system constituted of the platform and the cable. The
kernel equations and the finite-time stable ODE are numerically solved in order
to compute the nonlinear feedback law, and numerical simulations validating our
finite-time stabilization approach are presented.


Abstract:
  A recent line of research focuses on the study of the stochastic multi-armed
bandits problem (MAB), in the case where temporal correlations of specific
structure are imposed between the player's actions and the reward distributions
of the arms (Kleinberg and Immorlica [FOCS18], Basu et al. [NeurIPS19]). As
opposed to the standard MAB setting, where the optimal solution in hindsight
can be trivially characterized, these correlations lead to (sub-)optimal
solutions that exhibit interesting dynamical patterns -- a phenomenon that
yields new challenges both from an algorithmic as well as a learning
perspective. In this work, we extend the above direction to a combinatorial
bandit setting and study a variant of stochastic MAB, where arms are subject to
matroid constraints and each arm becomes unavailable (blocked) for a fixed
number of rounds after each play. A natural common generalization of the
state-of-the-art for blocking bandits, and that for matroid bandits, yields a
$(1-\frac{1}{e})$-approximation for partition matroids, yet it only guarantees
a $\frac{1}{2}$-approximation for general matroids. In this paper we develop
new algorithmic ideas that allow us to obtain a polynomial-time $(1 -
\frac{1}{e})$-approximation algorithm (asymptotically and in expectation) for
any matroid, and thus to control the $(1-\frac{1}{e})$-approximate regret. A
key ingredient is the technique of correlated (interleaved) scheduling. Along
the way, we discover an interesting connection to a variant of Submodular
Welfare Maximization, for which we provide (asymptotically) matching upper and
lower approximability bounds.


Abstract:
  Tomographic reconstruction recovers an unknown image given its projections
from different angles. State-of-the-art methods addressing this problem assume
the angles associated with the projections are known a-priori. Given this
knowledge, the reconstruction process is straightforward as it can be
formulated as a convex problem. Here, we tackle a more challenging setting: 1)
the projection angles are unknown, 2) they are drawn from an unknown
probability distribution. In this set-up our goal is to recover the image and
the projection angle distribution using an unsupervised adversarial learning
approach. For this purpose, we formulate the problem as a distribution matching
between the real projection lines and the generated ones from the estimated
image and projection distribution. This is then solved by reaching the
equilibrium in a min-max game between a generator and a discriminator. Our
novel contribution is to recover the unknown projection distribution and the
image simultaneously using adversarial learning. To accommodate this, we use
Gumbel-softmax approximation of samples from categorical distribution to
approximate the generator's loss as a function of the unknown image and the
projection distribution. Our approach can be generalized to different inverse
problems. Our simulation results reveal the ability of our method in
successfully recovering the image and the projection distribution in various
settings.


Abstract:
  Floquet theory has spawned many exciting possibilities for electronic
structure control with light with enormous potential for future applications.
The experimental realization in solids, however, largely remains pending. In
particular, the influence of scattering on the formation of Floquet-Bloch
states remains poorly understood. Here we combine time- and angle-resolved
photoemission spectroscopy with time-dependent density functional theory and a
two-level model with relaxation to investigate the survival of Floquet-Bloch
states in the presence of scattering. We find that Floquet-Bloch states will be
destroyed if scattering -- activated by electronic excitations -- prevents the
Bloch electrons from following the driving field coherently. The two-level
model also shows that Floquet-Bloch states reappear at high field intensities
where energy exchange with the driving field dominates over energy dissipation
to the bath. Our results clearly indicate the importance of long scattering
times combined with strong driving fields for the successful realization of
various Floquet phenomena.


Abstract:
  Deep kernel learning (DKL) and related techniques aim to combine the
representational power of neural networks with the reliable uncertainty
estimates of Gaussian processes. One crucial aspect of these models is an
expectation that, because they are treated as Gaussian process models optimized
using the marginal likelihood, they are protected from overfitting. However, we
identify situations where this is not the case. We explore this behavior,
explain its origins and consider how it applies to real datasets. Through
careful experimentation on the UCI, CIFAR-10, and the UTKFace datasets, we find
that the overfitting from overparameterized maximum marginal likelihood, in
which the model is "somewhat Bayesian", can in certain scenarios be worse than
that from not being Bayesian at all. We explain how and when DKL can still be
successful by investigating optimization dynamics. We also find that failures
of DKL can be rectified by a fully Bayesian treatment, which leads to the
desired performance improvements over standard neural networks and Gaussian
processes.


Abstract:
  Quantum mass functions (QMFs), which are tightly related to decoherence
functionals, were introduced by Loeliger and Vontobel [IEEE Trans. Inf. Theory,
2017, 2020] as a generalization of probability mass functions toward modeling
quantum information processing setups in terms of factor graphs.
  Simple quantum mass functions (SQMFs) are a special class of QMFs that do not
explicitly model classical random variables. Nevertheless, classical random
variables appear implicitly in an SQMF if some marginals of the SQMF satisfy
some conditions; variables of the SQMF corresponding to these "emerging" random
variables are called classicable variables. Of particular interest are jointly
classicable variables.
  In this paper we initiate the characterization of the set of marginals given
by the collection of jointly classicable variables of a graphical model and
compare them with other concepts associated with graphical models like the sets
of realizable marginals and the local marginal polytope.
  In order to further characterize this set of marginals given by the
collection of jointly classicable variables, we generalize the CHSH inequality
based on the Pearson correlation coefficients, and thereby prove a conjecture
proposed by Pozsgay et al. A crucial feature of this inequality is its
nonlinearity, which poses difficulties in the proof.


Abstract:
  With the increase of research in self-adaptive systems, there is a need to
better understand the way research contributions are evaluated. Such insights
will support researchers to better compare new findings when developing new
knowledge for the community. However, so far there is no clear overview of how
evaluations are performed in self-adaptive systems. To address this gap, we
conduct a mapping study. The study focuses on experimental evaluations
published in the last decade at the prime venue of research in software
engineering for self-adaptive systems -- the International Symposium on
Software Engineering for Adaptive and Self-Managing Systems (SEAMS). Results
point out that specifics of self-adaptive systems require special attention in
the experimental process, including the distinction of the managing system
(i.e., the target of evaluation) and the managed system, the presence of
uncertainties that affect the system behavior and hence need to be taken into
account in data analysis, and the potential of managed systems to be reused
across experiments, beyond replications. To conclude, we offer a set of
suggestions derived from our study that can be used as input to enhance future
experiments in self-adaptive systems.


Abstract:
  When managing wide-area networks, network architects must decide how to
balance multiple conflicting metrics, and ensure fair allocations to competing
traffic while prioritizing critical traffic. The state of practice poses
challenges since architects must precisely encode their (somewhat fuzzy) intent
into formal optimization models using abstract notions such as utility
functions, and ad-hoc manually tuned knobs. In this paper, we present the first
effort to synthesize network designs with indeterminate objectives using an
interactive program-synthesis-based approach. We make three contributions.
First, we present a novel framework in which a user's design objective, and the
synthesis of a program (network design) that optimizes that objective are done
in tandem. Second, we develop a novel algorithm for our framework in which a
voting-guided learner makes two kinds of queries (Propose and Compare) to the
user, with the aim of minimizing the number of queries. We present theoretical
analysis of the convergence rate of the algorithm. Third, we implemented
Net10Q, a system based on our approach, and demonstrate its effectiveness on
four real-world network case studies using black-box oracles and simulation
experiments, as well as a pilot user study comprising network researchers and
practitioners. Both theoretical and experimental results show the promise of
our approach.


Abstract:
  Uniform asymptotic expansions are derived for Whittaker's confluent
hypergeometric functions $M_{\kappa,\mu}(z)$ and $W_{\kappa,\mu}(z)$, as well
as the numerically satisfactory companion function $W_{-\kappa,\mu}(ze^{-\pi
i})$. The expansions are uniformly valid for $\mu \rightarrow \infty$, $0 \leq
\kappa/\mu \leq 1-\delta <1$, and for $0 \leq \arg(z) \leq \pi$. By using
appropriate connection and analytic continuation formulas these expansions can
be extended to all unbounded nonzero complex $z$. The approximations come from
recent asymptotic expansions involving elementary functions and Airy functions,
and explicit error bounds are either provided or available.


Abstract:
  Carbon nanotubes tend to collapse when their diameters exceed a certain
threshold, or when a sufficiently large external pressure is applied on their
walls. Their radial stability of tubes has been studied in each of these cases,
however a general theory able to predict collapse is still lacking. Here, we
propose a simple model predicting stability limits as a function of the tube
diameter, the number of walls and the pressure. The model is supported by
atomistic simulations, experiments, and is used to plot collapse phase
diagrams. We have identified the most stable carbon nanotube, which can support
a maximum pressure of 18 GPa before collapsing. The latter was identified as a
multiwall tube with an internal tube diameter of 12nm and 30 walls. This
maximum pressure is lowered depending on the internal tube diameter and the
number of walls. We then identify a tube diameter domain in which the radial
mechanical stability can be treated as equivalent to macroscopic tubes, known
to be described by the canonical L\'evy-Carrier law. This multiscale behavior
is shown to be in good agreement with experiments based on O-ring gaskets
collapse, proposed as a simple macroscopic parallel to nanotubes in this
domain.


Abstract:
  The formation and evolution of light elements in the Universe act as
important cosmological constraints. It has long been assumed that the oldest
stars of the Galaxy display the primordial Li abundance in their outer layers,
although studies of stellar physics have proven that this abundance must have
decreased with time. The primordial Li abundance deduced from the observations
of the cosmic background is larger than the maximum observed in these stars.
Recent observations have given evidence of a large Li abundance dispersion in
very metal-poor stars. Many of these stars are carbon-rich, the carbon-enhanced
metal-poor (CEMP) stars. We address the general question of the observed
abundances in metal-poor stars and we focus our study on the case of CEMP-s
stars. We study how the accretion of the wind of stellar companions, especially
asymptotic giant branch stars, modifies the element abundances of metal-poor
stars and, in particular Li, taking into account the stellar structure and the
hydrodynamic processes that take place after accretion. We compare the results
with the observations of Li and heavier elements in these old stars on the main
sequence. We use the Montr\'eal/Montpellier stellar evolution code, which
includes atomic diffusion and thermohaline convection, to compute the internal
structure of the proto-CEMP-s stars and their evolution, from [Fe/H]=-2.31 down
to [Fe/H]=-5.45. We study a number of cases that vary according to the masses
of the stars, their ages, metallicities, and the distances to their respective
companions. We show that the observations of Li dispersion that is associated
(or not) with carbon enrichment are well accounted for in terms of accretion on
to the metal-poor stars of the winds of stellar companions, with accreted
masses smaller than those considered in previous studies. The derived
primordial value is in accordance with the cosmological results.


Abstract:
  Promising access to high-speed quantum networks relies on the creation of
high-dimensional entangled memories that provide quantum communication with
higher capacity of noisy quantum channels, thereby reducing the transmission
time of information. Yet, the distribution of multidimensional entanglement
between remote memory nodes is still faintly investigated. We propose an
experimentally feasible protocol of deterministic generation of
high-dimensional entanglement between distant multi-level atoms confined in
high-finesse optical cavities and driven by laser pulses. Three-dimensional
entanglement is generated deterministically between remote atoms by triggering
a two-photon wavepacket, which mediates a superposition of states chosen by the
laser pulse parameters. The efficient transfer of atomic states between remote
nodes allows the construction of a three-dimensional quantum repeater, where
the successful creation of entanglement can be verified by a simple method for
reliable measurement of atomic ground Zeeman states.


Abstract:
  The rapid spreading of SARS-CoV-2 and its dramatic consequences, are forcing
policymakers to take strict measures in order to keep the population safe. At
the same time, societal and economical interactions are to be safeguarded. A
wide spectrum of containment measures have been hence devised and implemented,
in different countries and at different stages of the pandemic evolution.
Mobility towards workplace or retails, public transit usage and permanence in
residential areas constitute reliable tools to indirectly photograph the actual
grade of the imposed containment protocols. In this paper, taking Italy as an
example, we will develop and test a deep learning model which can forecast
various spreading scenarios based on different mobility indices, at a regional
level. We will show that containment measures contribute to "flatten the curve"
and quantify the minimum time frame necessary for the imposed restrictions to
result in a perceptible impact, depending on their associated grade.


Abstract:
  Graph Neural Networks (GNNs) are a wide class of connectionist models for
graph processing. They perform an iterative message passing operation on each
node and its neighbors, to solve classification/ clustering tasks -- on some
nodes or on the whole graph -- collecting all such messages, regardless of
their order. Despite the differences among the various models belonging to this
class, most of them adopt the same computation scheme, based on a local
aggregation mechanism and, intuitively, the local computation framework is
mainly responsible for the expressive power of GNNs. In this paper, we prove
that the Weisfeiler--Lehman test induces an equivalence relationship on the
graph nodes that exactly corresponds to the unfolding equivalence, defined on
the original GNN model. Therefore, the results on the expressive power of the
original GNNs can be extended to general GNNs which, under mild conditions, can
be proved capable of approximating, in probability and up to any precision, any
function on graphs that respects the unfolding equivalence.


Abstract:
  We study embeddings of groups of Lie type $H$ in characteristic $p$ into
exceptional algebraic groups $\mathbf G$ of the same characteristic. We exclude
the case where $H$ is of type $\mathrm{PSL}_2$. A subgroup of $\mathbf G$ is
\emph{Lie primitive} if it is not contained in any proper, positive-dimensional
subgroup of $\mathbf G$.
  With a few possible exceptions, we prove that there are no Lie primitive
subgroups $H$ in $\mathbf G$, with the conditions on $H$ and $\mathbf G$ given
above. The exceptions are for $H$ one of $\mathrm{PSL}_3(3)$,
$\mathrm{PSU}_3(3)$, $\mathrm{PSL}_3(4)$, $\mathrm{PSU}_3(4)$,
$\mathrm{PSU}_3(8)$, $\mathrm{PSU}_4(2)$, $\mathrm{PSp}_4(2)'$ and
${}^2\!B_2(8)$, and $\mathbf G$ of type $E_8$. No examples are known of such
Lie primitive embeddings.
  We prove a slightly stronger result, including stability under automorphisms
of $\mathbf G$. This has the consequence that, with the same exceptions, any
almost simple group with socle $H$, that is maximal inside an almost simple
exceptional group of Lie type $F_4$, $E_6$, ${}^2\!E_6$, $E_7$ and $E_8$, is
the fixed points under the Frobenius map of a corresponding maximal closed
subgroup inside the algebraic group.
  The proof uses a combination of representation-theoretic, algebraic
group-theoretic, and computational means.


Abstract:
  It is crucial to distinguish mislabeled samples for dealing with noisy
labels. Previous methods such as Coteaching and JoCoR introduce two different
networks to select clean samples out of the noisy ones and only use these clean
ones to train the deep models. Different from these methods which require to
train two networks simultaneously, we propose a simple and effective method to
identify clean samples only using one single network. We discover that the
clean samples prefer to reach consistent predictions for the original images
and the transformed images while noisy samples usually suffer from inconsistent
predictions. Motivated by this observation, we introduce to constrain the
transform consistency between the original images and the transformed images
for network training, and then select small-loss samples to update the
parameters of the network. Furthermore, in order to mitigate the negative
influence of noisy labels, we design a classification loss by using the
off-line hard labels and on-line soft labels to provide more reliable
supervisions for training a robust model. We conduct comprehensive experiments
on CIFAR-10, CIFAR-100 and Clothing1M datasets. Compared with the baselines, we
achieve the state-of-the-art performance. Especially, in most cases, our
proposed method outperforms the baselines by a large margin.


Abstract:
  Set theoretical paradoxes have a common root -- lack of understanding of why
some multitudes are not sets. Why some multitudes of objects of thought cannot
themselves be objects of thought? Moreover, it is a logical truth that such
multitudes do exist. However we do not understand this logical truth so well as
we understand, for example, the logical truth $\forall x \ x=x$. In this paper,
we formulate a logical truth which we call the productivity principle. Bertrand
Rusell was the first one to formulate this principle, but in a restricted form
and with a different purpose. The principle explicates a logical mechanism that
lies behind paradoxical multitudes and is understandable as well as any simple
logical truth. However, it does not explain the concept of set. It only sets
logical bounds of the concept within the framework of the classical two-valued
$\in$ - language. The principle behaves as a logical regulator of any theory we
formulate to explain and describe sets. It provides tools to identify
paradoxical classes inside the theory. We show how the known paradoxical
classes follow from the productivity principle and how the principle gives us a
uniform way to generate new paradoxical classes. In the case of $ZFC$ set
theory, the productivity principle shows that the limitation of size principles
are of a restrictive nature and that they do not explain which classes are
sets. The productivity principle, as a logical regulator, can have a definite
heuristic role in the development of a consistent set theory. We sketch such a
theory -- the cumulative cardinal theory of sets.


Abstract:
  In this work, we present a per-instant pose optimization method that can
generate configurations that achieve specified pose or motion objectives as
best as possible over a sequence of solutions, while also simultaneously
avoiding collisions with static or dynamic obstacles in the environment. We
cast our method as a multi-objective, non-linear constrained optimization-based
IK problem where each term in the objective function encodes a particular pose
objective. We demonstrate how to effectively incorporate environment collision
avoidance as a single term in this multi-objective, optimization-based IK
structure, and provide solutions for how to spatially represent and organize
external environments such that data can be efficiently passed to a real-time,
performance-critical optimization loop. We demonstrate the effectiveness of our
method by comparing it to various state-of-the-art methods in a testbed of
simulation experiments and discuss the implications of our work based on our
results.


Abstract:
  This paper presents maplet, an open-source R package for the creation of
highly customizable, fully reproducible statistical pipelines for omics data
analysis, with a special focus on metabolomics-based methods. It builds on the
SummarizedExperiment data structure to create a centralized pipeline framework
for storing data, analysis steps, results, and visualizations. maplet's key
design feature is its modularity, which offers several advantages, such as
ensuring code quality through the individual maintenance of functions and
promoting collaborative development by removing technical barriers to code
contribution. With over 90 functions, the package includes a wide range of
functionalities, covering many widely used statistical approaches and data
visualization techniques.


Abstract:
  Singularity of the potential function makes quantum tunneling problem
mathematically underdetermined. To circumvent the difficulties it introduced in
physics, a potential singularity cutoff is often used, followed by a reverse
limit transition, or is a suitable self-adjoint extension of the Hamiltonian
along the entire coordinate axis made. However, both of them somehow affect the
singular nature of the problem, and so I discuss here how quantum tunneling
will behave if the original singular nature of the Schrodinger equation left
untuched. To do this, I use the property of the probability density current
that the singularities are mutually destroyed in it. It is found that the
mildly singular potential has a finite, but unusual tunneling transparency, in
particular, a non-zero value at zero energy of incident particle. The tunneling
of one dimensional Coulomb potential exhibits infinitely fast and complete
oscillation at the zero energy boundary and a suppresion to zero in the
high-energy limit. In the more singular region, the tunneling becomes
forbidden, theby repeating the well-known result of the regularized
counterparts.