Abstract:
  We propose an asymptotic-preserving (AP), uniformly convergent numerical
scheme for the relativistic collisional Drift-Kinetic Equation (rDKE) to
simulate runaway electrons in axisymmetric toroidal magnetic field geometries
typical of tokamak devices. The approach is derived from an exact Green's
function solution with numerical approximations of quantifiable impact, and
results in a simple, two-step operator-split algorithm, consisting of a
collisional Eulerian step, and a Lagrangian orbit-integration step with
analytically prescribed kernels. The AP character of the approach is
demonstrated by analysis of the dominant numerical errors, as well as by
numerical experiments. We demonstrate the ability of the algorithm to provide
accurate answers regardless of plasma collisionality on a circular axisymmetric
tokamak geometry.


Abstract:
  We study the fundamental problem of counting the number of nodes in a sparse
network (of unknown size) under the presence of a large number of Byzantine
nodes. We assume the full information model where the Byzantine nodes have
complete knowledge about the entire state of the network at every round
(including random choices made by all the nodes), have unbounded computational
power, and can deviate arbitrarily from the protocol.
  Our main contribution is a randomized distributed algorithm that estimates
the size of a network under the presence of a large number of Byzantine nodes.
In particular, our algorithm estimates the size of a sparse, "small-world",
expander network with up to $O(n^{1 - \delta})$ Byzantine nodes, where $n$ is
the (unknown) network size and $\delta$ can be be any arbitrarily small (but
fixed) positive constant. Our algorithm outputs a (fixed) constant factor
estimate of $\log(n)$ with high probability; the correct estimate of the
network size will be known to a large fraction ($(1 - \epsilon)$-fraction, for
any fixed positive constant $\epsilon$) of the honest nodes. Our algorithm is
fully distributed, lightweight, and simple to implement, runs in $O(\log^3{n})$
rounds, and requires nodes to send and receive messages of only small-sized
messages per round; any node's local computation cost per round is also small.


Abstract:
  Low dose computed tomography is a mainstream for clinical applications.
How-ever, compared to normal dose CT, in the low dose CT (LDCT) images, there
are stronger noise and more artifacts which are obstacles for practical
applications. In the last few years, convolution-based end-to-end deep learning
methods have been widely used for LDCT image denoising. Recently, transformer
has shown superior performance over convolution with more feature interactions.
Yet its ap-plications in LDCT denoising have not been fully cultivated. Here,
we propose a convolution-free T2T vision transformer-based Encoder-decoder
Dilation net-work (TED-net) to enrich the family of LDCT denoising algorithms.
The model is free of convolution blocks and consists of a symmetric
encoder-decoder block with sole transformer. Our model is evaluated on the
AAPM-Mayo clinic LDCT Grand Challenge dataset, and results show outperformance
over the state-of-the-art denoising methods.


Abstract:
  A wave view of the universe is proposed in which each natural phenomenon is
equipped with its own unique natural viewing lens. A self-sameness modeling
principle and its systematic application in Fourier-Laplace transform space is
proposed as a novel, universal discrete modeling paradigm for
advection-diffusion-reaction equations (ADREs) across non-integer derivatives,
time scales, and wave spectral signatures. Its implementation is a novel exact
spectral derivative discretization finite difference method (ESDDFD), a way for
crafting wave viewing lenses by obtaining discrete wave models from ADRE
models. The template for building these lenses come in the form of natural
derivative representations obtained from the wave signature probability
distribution function and its harmonic oscillation in FL transform space; use
of the ESDDFD method in the discrete numerical modeling of wave equations
requires no a-priori theory of any mathematical derivative. A major
mathematical consequence of this viewpoint is that all notions of the
mathematical integer or non-integer derivatives have representation as limits
of such natural derivative representations; this and other consequences are
discussed and a discretization of a simple integer derivative
diffusion-reaction equation is presented to illustrate the method. The
resulting view lenses, in the form of ESDDFD models, work well in detecting
both local and non-local Debye or Kohlrausch-Williams-Watts exponential
patterns; only Brownian motion and sub-diffusion are discussed in the present
article.


Abstract:
  Based on a nonlocal Laplacian operator, a novel edge detection method of the
grayscale image is proposed in this paper. This operator utilizes the
information of neighbor pixels for a given pixel to obtain effective and
delicate edge detection. The nonlocal edge detection method is used as an
initialization for solving the Allen-Cahn equation to achieve two-phase
segmentation of the grayscale image. Efficient exponential time differencing
(ETD) solvers are employed in the time integration, and finite difference
method is adopted in space discretization. The maximum bound principle and
energy stability of the proposed numerical schemes are proved. The capability
of our segmentation method has been verified in numerical experiments for
different types of grayscale images.


Abstract:
  Steam power turbine-based power plant approximately contributes 90% of the
total electricity produced in the United States. Mainly steam turbine consists
of multiple types of turbine, boiler, attemperator, reheater, etc. Power is
produced through the steam with high pressure and temperature that is conducted
by the turbines. The total power generation of the power plant is highly
nonlinear considering all these elements in the model. We perform a predictive
modeling approach to detect the power generation from these turbines by the
Gaussian process (GP) model. As there are multiple interconnected turbines, we
consider a multivariate Gaussian process (MGP) modeling to predict the power
generation from these turbines which can capture the cross-correlations between
the turbines. Also, the sensitivity analysis of the input parameters is
constructed for each turbine to find out the most important parameters.


Abstract:
  LB-1 has variously been proposed as either an X-ray dim B-type star plus
black hole (B+BH) binary, or a Be star plus an inflated stripped star (Be+Bstr)
binary. The Space Telescope Imaging Spectrograph (STIS) on board HST was used
to obtain a flux-calibrated spectrum that is compared with non-LTE spectral
energy distributions (SED) and line profiles for the proposed models. The
Hubble data, together with the Gaia EDR3 parallax, provide tight constraints on
the properties and stellar luminosities of the system. In the case of the
Be+Bstr model we adopt the published flux ratio for the Be and Bstr stars,
re-determine the T$_{eff}$ of the Bstr using the silicon ionization balance,
and infer Teff for the Be star from the fit to the SED. We derive stellar
parameters consistent with previous results, but with greater precision enabled
by the Hubble SED. While the Be+Bstr model is a better fit to the HeI lines and
cores of the Balmer lines in the optical, the B+BH model provides a better fit
to the Si iv resonance lines in the UV. The analysis also implies that the Bstr
star has roughly twice solar silicon abundance, difficult to reconcile with a
stripped star origin. The Be star on the other hand has a rather low
luminosity, and a spectroscopic mass inconsistent with its possible dynamical
mass. The fit to the UV can be significantly improved by reducing the T$_{eff}$
and radius of the Be star, though at the expense of leading to a different mass
ratio. In the B+BH model, the single B-type spectrum is a good match to the UV
spectrum. Adopting a mass ratio of 5.1$\pm$0.1 (Liu et al. 2020) implies a BH
mass of $\sim$21$^{+9}_{-8}M_{\odot}$.


Abstract:
  When a rising bubble in a Newtonian liquid reaches the liquid-air interface,
it can burst, leading to the formation of capillary waves and a jet on the
surface. Here, we numerically study this phenomenon in a yield stress fluid. We
show how viscoplasticity controls the fate of these capillary waves and their
interaction at the bottom of the cavity. Unlike Newtonian liquids, the free
surface converges to a non-flat final equilibrium shape once the driving
stresses inside the pool fall below the yield stress. Details of the dynamics,
including the flow's energy budgets, are discussed. The work culminates in a
regime map with four main regimes with different characteristic behaviours.


Abstract:
  Telecom industry is significantly evolving all over the globe than ever.
Mobile users number is increasing remarkably. Telecom operators are investing
to get more users connected and to improve user experience, however, they are
facing various challenges. Decrease of main revenue streams of voice calls, SMS
(Short Message Service) and LDC (Long distance calls) with a significant
increase in data traffic. In contrary, with free cost, OTT (Over the top)
providers such as WhatsApp and Facebook communication services rendered over
networks that built and owned by MNOs. Recently, OTT services gradually
substituting the traditional MNOs` services and became ubiquitous with the help
of the underlying data services provided by MNOs. The OTTs` services massive
penetration into telecom industry is driving the MNOs to reconsider their
strategies and revenue sources.


Abstract:
  The Boolean matrix factorization problem consists in approximating a matrix
by the Boolean product of two smaller Boolean matrices. To obtain optimal
solutions when the matrices to be factorized are small, we propose SAT and
MaxSAT encoding; however, when the matrices to be factorized are large, we
propose a heuristic based on the search for maximal biclique edge cover. We
experimentally demonstrate that our approaches allow a better factorization
than existing approaches while keeping reasonable computation times. Our
methods also allow the handling of incomplete matrices with missing entries.


Abstract:
  Automatic differentiation is a set of techniques to efficiently and
accurately compute the derivative of a function represented by a computer
program. Existing C++ libraries for automatic differentiation (e.g. Adept, Stan
Math Library), however, exhibit large memory consumptions and runtime
performance issues. This paper introduces FastAD, a new C++ template library
for automatic differentiation, that overcomes all of these challenges in
existing libraries by using vectorization, simpler memory management using a
fully expression-template-based design, and other compile-time optimizations to
remove some run-time overhead. Benchmarks show that FastAD performs 2-10 times
faster than Adept and 2-19 times faster than Stan across various test cases
including a few real-world examples.


Abstract:
  High degree of valley polarization of optically active excitons in
transition-metal dichalcogenide monolayers (TMD-MLs) is vital in valley-based
photonic applications but known to be likely spoiled by the intrinsic
electron-hole exchange interactions. In this study, we present a theoretical
investigation of the valley and optical properties of finite-momentum dark
excitons in WSe$_2$-MLs by solving the density-functional-theory(DFT)-based
Bethe-Salpeter equation (BSE) under the guidance of symmetry analysis. %We
reveal that, in general, finite-momentum excitons are actually well immune from
the exchange-induced valley depolarization, except for those with specific
exciton momenta coincident with the $3\sigma_v$ and $3C_2'$ symmetries in the
$D_{3h}$ point group of TMD-MLs. We reveal that, unlike the bright exciton
inevitably subjected to electron-hole exchange interaction, inter-valley
finite-momentum dark excitons in WSe$_2$-MLs are well immune from the
exchange-induced valley depolarization and inherently highly valley-polarized
under the enforcement of the crystal symmetries. More importantly, the superior
valley polarizations of the inter-valley dark excitons in WSe$_2$-MLs are shown
almost fully transferable to the optical polarization in the phonon-assisted
photo-luminescences because of the native suppression of exchange-induced
depolarization in the second-order optical processes. The analysis of
phonon-assisted photo-luminescences accounts for the recently observed
brightness, high degree of optical polarization and long lifetime of the
inter-valley dark exciton states in tungsten-based TMD-MLs.


Abstract:
  Scaled physical modeling is an important means to understand the behavior of
fluids in nature. However, a common source of errors is conflicting similarity
criteria. Here, we present using hypergravity to improve the scaling similarity
of gravity-dominated fluid convection, e.g. natural convection and multi-phase
flow. We demonstrate the validity of the approach by investigating water-brine
buoyant jet experiments conducted under hypergravity created by a centrifuge.
Results show that the scaling similarity increases with the gravitational
acceleration. In particular, the model best represents the prototype under N3g
with a spatial scale of 1/N and a time scale of 1/N2 by simultaneously
satisfying the Froude and Reynolds criteria. The significance of centrifuge
radius and fluid velocity in determining the accuracy of the scaled model is
discussed in the light of Coriolis force and turbulence. This study
demonstrates a new direction for the physical modeling of fluids subject to
gravity with broad application prospects.


Abstract:
  We study a class of polaron-type Hamiltonians with sufficiently regular form
factor in the interaction term. We investigate the strong-coupling limit of the
model, and prove suitable bounds on the ground state energy as a function of
the total momentum of the system. These bounds agree with the semiclassical
approximation to leading order. The latter corresponds here to the situation
when the particle undergoes harmonic motion in a potential well whose frequency
is determined by the corresponding Pekar functional. We show that for all such
models the effective mass diverges in the strong coupling limit, in all spatial
dimensions. Moreover, for the case when the phonon dispersion relation grows at
least linearly with momentum, the bounds result in an asymptotic formula for
the effective mass quotient, a quantity generalizing the usual notion of the
effective mass. This asymptotic form agrees with the semiclassical
Landau--Pekar formula and can be regarded as the first rigorous confirmation,
in a slightly weaker sense than usually considered, of the validity of the
semiclassical formula for the effective mass.


Abstract:
  As current- and next-generation astronomical instruments come online, they
will generate an unprecedented deluge of data. Analyzing these data in real
time presents unique conceptual and computational challenges, and their
long-term storage and archiving is scientifically essential for generating
reliable, reproducible results. We present here the real-time processing (RTP)
system for the Hydrogen Epoch of Reionization Array (HERA), a radio
interferometer endeavoring to provide the first detection of the highly
redshifted 21 cm signal from Cosmic Dawn and the Epoch of Reionization by an
interferometer. The RTP system consists of analysis routines run on raw data
shortly after they are acquired, such as calibration and detection of
radio-frequency interference (RFI) events. RTP works closely with the
Librarian, the HERA data storage and transfer manager which automatically
ingests data and transfers copies to other clusters for post-processing
analysis. Both the RTP system and the Librarian are public and open source
software, which allows for them to be modified for use in other scientific
collaborations. When fully constructed, HERA is projected to generate over 50
terabytes (TB) of data each night, and the RTP system enables the successful
scientific analysis of these data.


Abstract:
  We estimate the effect of Medicaid expansion on the adult uninsurance rate in
states that did not expand Medicaid in 2014. Using data from the American
Community Survey (ACS), we estimate this effect - the treatment effect on the
controls (ETC) - by re-weighting expansion regions to approximately balance the
covariates from non-expansion regions using an extension of the stable
balancing weights objective function (Zubizaretta (2015)). We contribute to the
balancing weights literature by accounting for hierarchical data structure and
covariate measurement error when calculating our weights, and to the synthetic
controls literature (see, e.g. Abadie et al 2010) by outlining a set of
assumptions that identifies the ETC using time-series cross-sectional data. We
estimate that Medicaid expansion would have changed the uninsurance rate by
-2.33 (-3.49, -1.16) percentage points. These results are smaller in absolute
magnitude than existing estimates of the treatment effect on the treated (ETT),
though may not be directly comparable due to the study design, target
population, and level of analysis. Regardless, we caution against making
inferences about the ETC using estimates of the ETT, and emphasize the need to
directly estimate the appropriate counterfactual when they are the quantity of
interest.


Abstract:
  We study {\em sign-restricted matrices} (SRMs), a class of rectangular $(0,
\pm 1)$-matrices generalizing the alternating sign matrices (ASMs). In an SRM
each partial column sum, starting from row 1, equals 0 or 1, and each partial
row sum, starting from column 1, is nonnegative. We determine the maximum
number of nonzeros in SRMs and characterize the possible row and column sum
vectors. Moreover, a number of results on interchange operations are shown,
both for SRMs and, more generally, for $(0, \pm 1)$-matrices. The Bruhat order
on ASMs can be extended to SRMs with the result a distributive lattice. Also,
we study polytopes associated with SRMs and some relates decompositions.


Abstract:
  We compute the flux-induced F-term potential in 4d F-theory compactifications
at large complex structure. In this regime, each complex structure field splits
as an axionic field plus its saxionic partner, and the classical F-term
potential takes the form $V = Z^{AB} \rho_A\rho_B$ up to
exponentially-suppressed terms, with $\rho$ depending on the fluxes and axions
and $Z$ on the saxions. We provide explicit, general expressions for $Z$ and
$\rho$, and from there analyse the set of flux vacua, for an arbitrary number
of fields. We identify two families of vacua with all complex structure fields
fixed and a flux contribution to the tadpole $N_{\rm flux}$ which is bounded.
In the first and most generic one, the saxion vevs are bounded from above by a
power of $N_{\rm flux}$. In the second their vevs may be unbounded and $N_{\rm
flux}$ is a product of two arbitrary integers, unlike what is claimed by the
Tadpole Conjecture. We specialise to type IIB orientifolds, where both families
of vacua are present, and link our analysis with several results in the
literature. We finally illustrate our findings with several examples.


Abstract:
  This is a tutorial and survey paper on unification of spectral dimensionality
reduction methods, kernel learning by Semidefinite Programming (SDP), Maximum
Variance Unfolding (MVU) or Semidefinite Embedding (SDE), and its variants. We
first explain how the spectral dimensionality reduction methods can be unified
as kernel Principal Component Analysis (PCA) with different kernels. This
unification can be interpreted as eigenfunction learning or representation of
kernel in terms of distance matrix. Then, since the spectral methods are
unified as kernel PCA, we say let us learn the best kernel for unfolding the
manifold of data to its maximum variance. We first briefly introduce kernel
learning by SDP for the transduction task. Then, we explain MVU in detail.
Various versions of supervised MVU using nearest neighbors graph, by class-wise
unfolding, by Fisher criterion, and by colored MVU are explained. We also
explain out-of-sample extension of MVU using eigenfunctions and kernel mapping.
Finally, we introduce other variants of MVU including action respecting
embedding, relaxed MVU, and landmark MVU for big data.


Abstract:
  Placeholder translation systems enable the users to specify how a specific
phrase is translated in the output sentence. The system is trained to output
special placeholder tokens, and the user-specified term is injected into the
output through the context-free replacement of the placeholder token. However,
this approach could result in ungrammatical sentences because it is often the
case that the specified term needs to be inflected according to the context of
the output, which is unknown before the translation. To address this problem,
we propose a novel method of placeholder translation that can inflect specified
terms according to the grammatical construction of the output sentence. We
extend the sequence-to-sequence architecture with a character-level decoder
that takes the lemma of a user-specified term and the words generated from the
word-level decoder to output the correct inflected form of the lemma. We
evaluate our approach with a Japanese-to-English translation task in the
scientific writing domain, and show that our model can incorporate specified
terms in the correct form more successfully than other comparable models.


Abstract:
  CUDA (formerly an abbreviation of Compute Unified Device Architecture) is a
parallel computing platform and API model created by Nvidia allowing software
developers to use a CUDA-enabled graphics processing unit (GPU) for general
purpose processing. This 90-pages tutorial introduces the CUDA concepts in an
easy-to-grasp and interactive way with ready-to-run code samples tested on
Windows and Linux. Starting from scratch, a complete stand-alone GPU tool is
implemented which automatically performs a ciphertext-only attack on
ciphertexts encrypted by monoalphabetic substitution and columnar
transposition. Throughout this process, you will learn how to architect the
tool, what optimizations could significantly accelerate the routines, why the
choice of an adequate metaheuristic is critical, and how to draw sketches to
enlighten the design process. This tutorial will be incorporated in the
CrypTool book as chapter 13.


Abstract:
  We demonstrate phase locking of two ultraviolet laser sources by modulating a
fundamental infrared laser with 4th-order sidebands using an electro-optic
modulator and phase locking of one sideband to a second fundamental infrared
laser. Subsequent sum frequency generation and second harmonic generation
successfully translates the frequency offset to the ultraviolet domain. The
phase lock at 139 GHz is confirmed through stimulated Raman transitions for
thermometry of $^9$Be$^+$ ions confined in a cryogenic Penning trap. This
technique might be used for sideband cooling of single $^9$Be$^+$ ions as well
as sympathetic cooling schemes and quantum logic based measurements in Penning
traps in the future.


Abstract:
  Motivated by applications in machine learning and archival data storage, we
introduce function-correcting codes, a new class of codes designed to protect a
function evaluation of the data against errors. We show that
function-correcting codes are equivalent to irregular-distance codes, i.e.,
codes that obey some given distance requirement between each pair of codewords.
Using these connections, we study irregular-distance codes and derive general
upper and lower bounds on their optimal redundancy. Since these bounds heavily
depend on the specific function, we provide simplified, suboptimal bounds that
are easier to evaluate. We further employ our general results to specific
functions of interest and compare our results to standard error-correcting
codes which protect the whole data.


Abstract:
  In this work we demonstrate the use of neural networks for rapid extraction
of signal parameters of discretely sampled signals. In particular, we use dense
autoencoder networks to extract the parameters of interest from exponentially
decaying signals and decaying oscillations. By using a three-stage training
method and careful choice of the neural network size, we are able to retrieve
the relevant signal parameters directly from the latent space of the
autoencoder network at significantly improved rates compared to traditional
algorithmic signal-analysis approaches. We show that the achievable precision
and accuracy of this method of analysis is similar to conventional
algorithm-based signal analysis methods, by demonstrating that the extracted
signal parameters are approaching their fundamental parameter estimation limit
as provided by the Cram\'er-Rao bound. Furthermore, we demonstrate that
autoencoder networks are able to achieve signal analysis, and, hence, parameter
extraction, at rates of 75 kHz, orders-of-magnitude faster than conventional
techniques with similar precision. Finally, we explore the limitations of our
approach, demonstrating that analysis rates of $>$200 kHz are feasible with
further optimization of the transfer rate between the data-acquisition system
and data-analysis system.


Abstract:
  When a liquid film lies on a non-wettable substrate, the configuration is
unstable and the film then retracts from a solid substrate to form droplets.
This phenomenon, known as dewetting, commonly leads to undesirable
morphological changes. Nevertheless, recent works have demonstrated the
possibility to harness dewetting by employing templated substrates with a
degree of precision on par with advanced lithographic processes for
high-performance nanophotonic applications. Since resonant behavior is highly
sensitive to geometrical changes, predicting quantitatively dewetting dynamics
is of high interest. In this work, we develop a continuum model that predicts
the evolution of a thin film on a patterned substrate, from the initial reflow
to the nucleation and growth of holes. We provide an operative framework based
on macroscopic measurements to model the intermolecular interactions at the
origin of the dewetting process, involving length scales that span from
sub-nanometer to micron range. A comparison of experimental and simulated
results shows that the model can accurately predict the final distributions,
thereby offering novel predictive tools to tailor the optical response of
dewetted nanostructures.


Abstract:
  Modeling and simulating a power distribution network (PDN) for printed
circuit boards (PCBs) with irregular board shapes and multi-layer stackup is
computationally inefficient using full-wave simulations. This paper presents a
new concept of using deep learning for PDN impedance prediction. A boundary
element method (BEM) is applied to efficiently calculate the impedance for
arbitrary board shape and stackup. Then over one million boards with different
shapes, stackup, IC location, and decap placement are randomly generated to
train a deep neural network (DNN). The trained DNN can predict the impedance
accurately for new board configurations that have not been used for training.
The consumed time using the trained DNN is only 0.1 seconds, which is over 100
times faster than the BEM method and 5000 times faster than full-wave
simulations.


Abstract:
  This paper presents a end-to-end framework for the F0 transformation in the
context of expressive voice conversion. A single neural network is proposed, in
which a first module is used to learn F0 representation over different temporal
scales and a second adversarial module is used to learn the transformation from
one emotion to another. The first module is composed of a convolution layer
with wavelet kernels so that the various temporal scales of F0 variations can
be efficiently encoded. The single decomposition/transformation network allows
to learn in a end-to-end manner the F0 decomposition that are optimal with
respect to the transformation, directly from the raw F0 signal.


Abstract:
  One common approach to computing the magnetic field produced by a filamentary
current-carrying coil is to approximate the coil as a series of straight
segments. The Biot-Savart field from each straight segment is known
analytically. However, if the endpoints of the straight segments are chosen to
lie on the coil, then the accuracy of the Biot-Savart computation is generally
only second-order in the number of endpoints. We propose a simple modification:
shift each endpoint off the coil in the outwards normal direction by an amount
proportional to the local curvature. With this modification, the Biot-Savart
accuracy increases to fourth order and the numerical error is dramatically
reduced for a given number of discretization points.


Abstract:
  Stochastic PDE eigenvalue problems often arise in the field of uncertainty
quantification, whereby one seeks to quantify the uncertainty in an eigenvalue,
or its eigenfunction. In this paper we present an efficient multilevel
quasi-Monte Carlo (MLQMC) algorithm for computing the expectation of the
smallest eigenvalue of an elliptic eigenvalue problem with stochastic
coefficients. Each sample evaluation requires the solution of a PDE eigenvalue
problem, and so tackling this problem in practice is notoriously
computationally difficult. We speed up the approximation of this expectation in
four ways: 1) we use a multilevel variance reduction scheme to spread the work
over a hierarchy of FE meshes and truncation dimensions; 2) we use QMC methods
to efficiently compute the expectations on each level; 3) we exploit the
smoothness in parameter space and reuse the eigenvector from a nearby QMC point
to reduce the number of iterations of the eigensolver; and 4) we utilise a
two-grid discretisation scheme to obtain the eigenvalue on the fine mesh with a
single linear solve. The full error analysis of a basic MLQMC algorithm is
given in the companion paper [Gilbert and Scheichl, 2021], and so in this paper
we focus on how to further improve the efficiency and provide theoretical
justification of the enhancement strategies 3) and 4). Numerical results are
presented that show the efficiency of our algorithm, and also show that the
four strategies we employ are complementary.


Abstract:
  Neuromorphic computing systems are embracing memristors to implement high
density and low power synaptic storage as crossbar arrays in hardware. These
systems are energy efficient in executing Spiking Neural Networks (SNNs). We
observe that long bitlines and wordlines in a memristive crossbar are a major
source of parasitic voltage drops, which create current asymmetry. Through
circuit simulations, we show the significant endurance variation that results
from this asymmetry. Therefore, if the critical memristors (ones with lower
endurance) are overutilized, they may lead to a reduction of the crossbar's
lifetime. We propose eSpine, a novel technique to improve lifetime by
incorporating the endurance variation within each crossbar in mapping machine
learning workloads, ensuring that synapses with higher activation are always
implemented on memristors with higher endurance, and vice versa. eSpine works
in two steps. First, it uses the Kernighan-Lin Graph Partitioning algorithm to
partition a workload into clusters of neurons and synapses, where each cluster
can fit in a crossbar. Second, it uses an instance of Particle Swarm
Optimization (PSO) to map clusters to tiles, where the placement of synapses of
a cluster to memristors of a crossbar is performed by analyzing their
activation within the workload. We evaluate eSpine for a state-of-the-art
neuromorphic hardware model with phase-change memory (PCM)-based memristors.
Using 10 SNN workloads, we demonstrate a significant improvement in the
effective lifetime.


Abstract:
  Within the past decade, the rise of applications based on artificial
intelligence (AI) in general and machine learning (ML) in specific has led to
many significant contributions within different domains. The applications range
from robotics over medical diagnoses up to autonomous driving. However, nearly
all applications rely on trained data. In case this data consists of 3D images,
it is of utmost importance that the labeling is as accurate as possible to
ensure high-quality outcomes of the ML models. Labeling in the 3D space is
mostly manual work performed by expert workers, where they draw 3D bounding
boxes around target objects the ML model should later automatically identify,
e.g., pedestrians for autonomous driving or cancer cells within radiography.
  While a small range of recent 3D labeling tools exist, they all share three
major shortcomings: (i) they are specified for autonomous driving applications,
(ii) they lack convenience and comfort functions, and (iii) they have high
dependencies and little flexibility in data format. Therefore, we propose a
novel labeling tool for 3D object detection in point clouds to address these
shortcomings.


Abstract:
  Probabilistic message-passing algorithms are developed for routing
transmissions in multi-wavelength optical communication networks, under node
and edge-disjoint routing constraints and for various objective functions.
Global routing optimization is a hard computational task on its own but is made
much more difficult under the node/edge-disjoint constraints and in the
presence of multiple wavelengths, a problem which dominates routing efficiency
in real optical communication networks that carry most of the world's Internet
traffic. The scalable principled method we have developed is exact on trees but
provides good approximate solutions on locally tree-like graphs. It
accommodates a variety of objective functions that correspond to low latency,
load balancing and consolidation of routes, and can be easily extended to
include heterogeneous signal-to-noise values on edges and a restriction on the
available wavelengths per edge. It can be used for routing and managing
transmissions on existing topologies as well as for designing and modifying
optical communication networks. Additionally, it provides the tool for settling
an open and much debated question on the merit of wavelength-switching nodes
and the added capabilities they provide. The methods have been tested on
generated networks such as random-regular, Erd\H{o}s R\'{e}nyi and power-law
graphs, as well as on the UK and US optical communication networks. They show
excellent performance with respect to existing methodology on small networks
and have been scaled up to network sizes that are beyond the reach of most
existing algorithms.


Abstract:
  We present a method of training character manipulation of amorphous materials
such as those often used in cooking. Common examples of amorphous materials
include granular materials (salt, uncooked rice), fluids (honey), and
visco-plastic materials (sticky rice, softened butter). A typical task is to
spread a given material out across a flat surface using a tool such as a
scraper or knife. We use reinforcement learning to train our controllers to
manipulate materials in various ways. The training is performed in a physics
simulator that uses position-based dynamics of particles to simulate the
materials to be manipulated. The neural network control policy is given
observations of the material (e.g. a low-resolution density map), and the
policy outputs actions such as rotating and translating the knife. We
demonstrate policies that have been successfully trained to carry out the
following tasks: spreading, gathering, and flipping. We produce a final
animation by using inverse kinematics to guide a character's arm and hand to
match the motion of the manipulation tool such as a knife or a frying pan.


Abstract:
  Worker monitoring and protection in collaborative robot (cobots) industrial
environments requires advanced sensing capabilities and flexible solutions to
monitor the movements of the operator in close proximity of moving robots.
Collaborative robotics is an active research area where Internet of Things
(IoT) and novel sensing technologies are expected to play a critical role.
Considering that no single technology can currently solve the problem of
continuous worker monitoring, the paper targets the development of an IoT
multisensor data fusion (MDF) platform. It is based on an edge-cloud
architecture that supports the combination and transformation of multiple
sensing technologies to enable the passive and anonymous detection of workers.
Multidimensional data acquisition from different IoT sources, signal
pre-processing, feature extraction, data distribution and fusion, along with
machine learning (ML) and computing methods are described. The proposed IoT
platform also comprises a practical solution for data fusion and analytics. It
is able to perform opportunistic and real-time perception of workers by fusing
and analyzing radio signals obtained from several interconnected IoT
components, namely a multi-antenna WiFi installation (2.4-5 GHz), a sub-THz
imaging camera (100 GHz), a network of radars (122 GHz) and infrared sensors
(8-13 {\mu}m). The performance of the proposed IoT platform is validated
through real use case scenarios inside a pilot industrial plant in which
protective human--robot distance must be guaranteed considering latency and
detection uncertainties.


Abstract:
  A ring is said to satisfy the $2$-nil-sum property if every non central-unit
is the sum of two nilpotents. We prove that a ring satisfies the $2$-nil-sum
property iff it is either a simple ring with the $2$-nil-sum property or a
commutative local ring with nil Jacobson radical, and we provide an example of
a simple ring with the $2$-nil-sum property that is not commutative. Moreover,
a simple right Goldie ring has the $2$-nil-sum property iff it is a field.


Abstract:
  One of the main challenges of reactive synthesis, an automated procedure to
obtain a correct-by-construction reactive system, is to deal with unrealizable
specifications. One means to deal with unrealizability, in the context of
GR(1), an expressive assume-guarantee fragment of LTL that enables efficient
synthesis, is the computation of an unrealizable core, which can be viewed as a
fault-localization approach. Existing solutions, however, are computationally
costly, are limited to computing a single core, and do not correctly support
specifications with constructs beyond pure GR(1) elements.
  In this work we address these limitations. First, we present QuickCore, a
novel algorithm that accelerates unrealizable core computations by relying on
the monotonicity of unrealizability, on an incremental computation, and on
additional properties of GR(1) specifications. Second, we present Punch, a
novel algorithm to efficiently compute all unrealizable cores of a
specification. Finally, we present means to correctly handle specifications
that include higher-level constructs beyond pure GR(1) elements.
  We implemented our ideas on top of Spectra, an open-source language and
synthesis environment. Our evaluation over benchmarks from the literature shows
that QuickCore is in most cases faster than previous algorithms, and that its
relative advantage grows with scale. Moreover, we found that most
specifications include more than one core, and that Punch finds all the cores
significantly faster than a competing naive algorithm.


Abstract:
  We present a method to linearize, without approximation, a specific class of
eigenvalue problems with eigenvector nonlinearities (NEPv), where the
nonlinearities are expressed by scalar functions that are defined by a quotient
of linear functions of the eigenvector. The exact linearization relies on an
equivalent multiparameter problem (MEP) that contains the exact solutions of
the NEPv. Due to the characterization of MEPs in terms of a generalized
eigenvalue problem this provides a direct way to compute all NEPv solutions for
small problems, and it opens up the possibility to develop locally convergent
iterative methods for larger problems. Moreover, the linear formulation allows
us to easily determine the number of solutions of the NEPv. We propose two
numerical schemes that exploit the structure of the linearization: inverse
iteration and residual inverse iteration. We show how symmetry in the MEP can
be used to improve reliability and reduce computational cost of both methods.
Two numerical examples verify the theoretical results and a third example shows
the potential of a hybrid scheme that is based on a combination of the two
proposed methods.


Abstract:
  In this paper, we derive the action for N = 3 conformal supergravity in four
space-time dimensions. We construct a density formula for N = 3 conformal
supergravity based on the superform action principle. Finally, we embed the N =
3 Weyl multiplet in the density formula to obtain the invariant action for N =
3 conformal supergravity. We will show that there are two inequivalent
embeddings by changing a particular coefficient from real to imaginary. They
lead to invariant actions, which will either be the supersymmetrization of the
Weyl square term or the Pontryagin density in the eventuality of gauge fixing
to Poincar\'e supergravity. We also discuss consistent truncation of N = 4 Weyl
multiplet to N = 3 Weyl multiplet and use it for a robust check of our results
using the earlier known results in N = 4 conformal supergravity.


Abstract:
  Power grid primary frequency response will be significantly impaired by
Photovoltaic (PV) penetration increase because of the decrease in inertia and
governor response. PV inertia and governor emulation requires reserving PV
output and leads to solar energy waste. This paper exploits current grid
resources and explores energy storage for primary frequency response under high
PV penetration at the interconnection level. Based on the actual models of the
U.S. Eastern Interconnection grid and the Texas grid, effects of multiple
factors associated with primary frequency response, including the governor
ratio, governor deadband, droop rate, and fast load response, are assessed
under high PV penetration scenarios. In addition, performance of batteries and
supercapacitors using different control strategies is studied in the two
interconnections. The paper quantifies the potential of various resources to
improve interconnection-level primary frequency response under high PV
penetration without curtailing solar output.


Abstract:
  Model inversion, whose goal is to recover training data from a pre-trained
model, has been recently proved feasible. However, existing inversion methods
usually suffer from the mode collapse problem, where the synthesized instances
are highly similar to each other and thus show limited effectiveness for
downstream tasks, such as knowledge distillation. In this paper, we propose
Contrastive Model Inversion~(CMI), where the data diversity is explicitly
modeled as an optimizable objective, to alleviate the mode collapse issue. Our
main observation is that, under the constraint of the same amount of data,
higher data diversity usually indicates stronger instance discrimination. To
this end, we introduce in CMI a contrastive learning objective that encourages
the synthesizing instances to be distinguishable from the already synthesized
ones in previous batches. Experiments of pre-trained models on CIFAR-10,
CIFAR-100, and Tiny-ImageNet demonstrate that CMI not only generates more
visually plausible instances than the state of the arts, but also achieves
significantly superior performance when the generated data are used for
knowledge distillation. Code is available at
\url{https://github.com/zju-vipa/DataFree}.


Abstract:
  Given that there are 94 clinics and more than 200,000 patients treated
worldwide, proton and carbon are the most used heavily charged particles in
heavy ion therapy. However, there is a recent increasing trend in using new ion
beams. Each heavy ion has a different effect on the target. As each heavy ion
moves through the tissue, they lose enormous energy in collisions, so their
range is not long. Ionization accounts for the majority of this loss in energy.
During this interaction of the heavily charged particles with the target, the
particles do not only ionize, but also lose energy with the recoil. Recoil
occurs by atom-to-atom collisions. With these collisions, crystalline atoms
react with different combinations and form cascades in accordance with their
energies. Thus, secondary particles create ionization and recoil. In this
study, recoil values of boron, carbon, nitrogen and oxygen beams in the water
phantom were computed in the energy range of 2.0-2.5 GeV using Monte Carlo
simulation and the results were compared with carbon. It was observed that
there is a regular increase in the recoil peak amplitude for carbon and boron
ions, unlike oxygen and nitrogen where such a regularity could not be seen.
Moreover, the gaps in the crystal structure increased as the energy increases.


Abstract:
  Bias stress degradation in conjugated polymer field-effect transistors is a
fundamental problem in these disordered materials and can be traced back to
interactions of the material with environmental species,1,2,3 as well as
fabrication-induced defects.4,5 However, the effect of the end groups of the
polymer gate dielectric and the associated dipole-induced disorder on bias
stress stability has not been studied so far in high-performing n-type
materials, such as N2200.6,7 In this work, the performance metrics of N2200
transistors are examined with respect to dielectrics with different end groups
(Cytop-M and Cytop-S8). We hypothesize that the polar end groups would lead to
increased dipole-induced disorder, and worse performance.1,9,10 The long-time
annealing scheme at lower temperatures used in the paper is assumed to lead to
better crystallization by allowing the crystalline domains to reorganize in the
presence of the solvent.11 It is hypothesized that the higher crystallinity
could narrow down the range at which energy carriers are induced and thus
decrease the gate dependence of the mobility. The results show that the
dielectric end groups do not influence the bias stress stability of N2200
transistors. However, long annealing times result in a dramatic improvement in
bias stress stability, with the most stable devices having a mobility that is
only weakly dependent on or independent of gate voltage.


Abstract:
  In the companion paper [Phys. Rev. D 103 (2021) 10, [2101.02951]] we have
derived the short-ranged potentials for the Teukolsky equations for massless
spins $(0,1/2,1,2)$ in general spherically-symmetric and static metrics. Here
we apply these results to numerically compute the Hawking radiation spectra of
such particles emitted by black holes (BHs) in three different ansatz: charged
BHs, higher-dimensional BHs, and polymerized BHs arising from models of quantum
gravity. In order to ensure the robustness of our numerical procedure, we show
that it agrees with newly derived analytic formulas for the cross-sections in
the high and low energy limits. We show how the short-ranged potentials and
precise Hawking radiation rates can be used inside the code
$\texttt{BlackHawk}$ to predict future primordial BH evaporation signals for a
very wide class of BH solutions, including the promising regular BH solutions
derived from loop quantum gravity. In particular, we derive the first Hawking
radiation constraints on polymerized BHs from AMEGO. We prove that the mass
window $10^{16}-10^{18}\,$g for all dark matter into primordial BHs can be
reopened with high values of the polymerization parameter, which encodes the
typical scale and strength of quantum gravity corrections.


Abstract:
  The secure domination numbers of the Cartesian products of two small graphs
with paths or cycles is determined, as well as for Mobius ladder graphs. Prior
to this work, in all cases where the secure domination number has been
determined, the proof has either been trivial, or has been derived from lower
bounds established by considering different forms of domination. However, the
latter mode of proof is not applicable for most graphs, including those
considered here. Hence, this work represents the first attempt to determine
secure domination numbers via the properties of secure domination itself, and
it is expected that these methods may be used to determine further results in
the future.


Abstract:
  As the major factors affecting the safety of deep learning models, corner
cases and related detection are crucial in AI quality assurance for
constructing safety- and security-critical systems. The generic corner case
researches involve two interesting topics. One is to enhance DL models
robustness to corner case data via the adjustment on parameters/structure. The
other is to generate new corner cases for model retraining and improvement.
However, the complex architecture and the huge amount of parameters make the
robust adjustment of DL models not easy, meanwhile it is not possible to
generate all real-world corner cases for DL training. Therefore, this paper
proposes to a simple and novel study aiming at corner case data detection via a
specific metric. This metric is developed on surprise adequacy (SA) which has
advantages on capture data behaviors. Furthermore, targeting at characteristics
of corner case data, three modifications on distanced-based SA are developed
for classification applications in this paper. Consequently, through the
experiment analysis on MNIST data and industrial data, the feasibility and
usefulness of the proposed method on corner case data detection are verified.


Abstract:
  An analysis of sub-MeV dark photon as dark matter is given which is achieved
with two hidden sectors, one of which interacts directly with the visible
sector while the second has only indirect coupling with the visible sector. The
formalism for the evolution of three bath temperatures for the visible sector
and the two hidden sectors is developed and utilized in solution of Boltzmann
equations coupling the three sectors. We present exclusion plots where the
sub-MeV dark photon can be dark matter. The analysis can be extended to a
multi-temperature universe with multiple hidden sectors and multiple heat
baths.


Abstract:
  Power control in decentralized wireless networks poses a complex stochastic
optimization problem when formulated as the maximization of the average sum
rate for arbitrary interference graphs. Recent work has introduced data-driven
design methods that leverage graph neural network (GNN) to efficiently
parametrize the power control policy mapping channel state information (CSI) to
the power vector. The specific GNN architecture, known as random edge GNN
(REGNN), defines a non-linear graph convolutional architecture whose spatial
weights are tied to the channel coefficients, enabling a direct adaption to
channel conditions. This paper studies the higher-level problem of enabling
fast adaption of the power control policy to time-varying topologies. To this
end, we apply first-order meta-learning on data from multiple topologies with
the aim of optimizing for a few-shot adaptation to new network configurations.


Abstract:
  Concepts similar to emergence have been used since antiquity, but we lack an
agreed definition of emergence. Still, emergence has been identified as one of
the features of complex systems. Most would agree on the statement "life is
complex". Thus, understanding emergence and complexity should benefit the study
of living systems. It can be said that life emerges from the interactions of
complex molecules. But how useful is this to understand living systems?
Artificial life (ALife) has been developed in recent decades to study life
using a synthetic approach: build it to understand it. ALife systems are not so
complex, be them soft (simulations), hard (robots), or wet (protocells). Then,
we can aim at first understanding emergence in ALife, for then using this
knowledge in biology. I argue that to understand emergence and life, it becomes
useful to use information as a framework. In a general sense, emergence can be
defined as information that is not present at one scale but is present at
another scale. This perspective avoids problems of studying emergence from a
materialistic framework, and can be useful to study self-organization and
complexity.


Abstract:
  Primordial Black Holes (PBHs) are remnants of objects formed in the early
Universe. Their lifetime is an increasing function of their mass, so PBHs in
the right mass range can end their lives in an evaporation event that is
potentially detectable by our instruments now. This evaporation may result in a
$\gamma$-ray flash that can be detected by the current generation of
Very-High-Energy $\gamma$-ray detectors. The Southern Wide field of view
Gamma-ray Observatory (SWGO) will be part of the next generation of these
instruments. It will be able to establish limits on PBH evaporations for
integration windows between 0.5 and 5 s, in a radius of 0.25 pc around the
Earth, being sensitive to a rate of the order of $\sim$50 pc$^{-3}$ yr$^{-1}$,
more than one order of magnitude more constraining than the currently
established best limits.


Abstract:
  Training with Reinforcement Learning requires a reward function that is used
to guide the agent towards achieving its objective. However, designing smooth
and well-behaved rewards is in general not trivial and requires significant
human engineering efforts. Generating rewards in self-supervised way, by
inspiring the agent with an intrinsic desire to learn and explore the
environment, might induce more general behaviours. In this work, we propose a
curiosity-based bonus as intrinsic reward for Reinforcement Learning, computed
as the Bayesian surprise with respect to a latent state variable, learnt by
reconstructing fixed random features. We extensively evaluate our model by
measuring the agent's performance in terms of environment exploration, for
continuous tasks, and looking at the game scores achieved, for video games. Our
model is computationally cheap and empirically shows state-of-the-art
performance on several problems. Furthermore, experimenting on an environment
with stochastic actions, our approach emerged to be the most resilient to
simple stochasticity. Further visualization is available on the project
webpage.(https://lbsexploration.github.io/)


Abstract:
  Let $\mathcal{P}_s(n)$ denote the $n$th $s$-gonal number. We consider the
equation \[\mathcal{P}_s(n) = y^m, \] for integers $n,s,y,$ and $m$. All
solutions to this equation are known for $m>2$ and $s \in \{3,5,6,8,20 \}$. We
consider the case $s=10$, that of decagonal numbers. Using a descent argument
and the modular method, we prove that the only decagonal number $>1$
expressible as a perfect $m$th power with $m>1$ is $\mathcal{P}_{10}(3) = 3^3$.


Abstract:
  We investigate the solar wind energy flux in the inner heliosphere using
12-day observations around each perihelion of Encounter One (E01), Two (E02),
Four (E04), and Five (E05) of Parker Solar Probe (PSP), respectively, with a
minimum heliocentric distance of 27.8 solar radii ($R_\odot{}$). Energy flux
was calculated based on electron parameters (density $n_e$, core electron
temperature $T_{c}$, and suprathermal electron temperature $T_{h}$) obtained
from the simplified analysis of the plasma quasi-thermal noise (QTN) spectrum
measured by RFS/FIELDS and the bulk proton parameters (bulk speed $V_p$ and
temperature $T_p$) measured by the Faraday Cup onboard PSP, SPC/SWEAP.
Combining observations from E01, E02, E04, and E05, the averaged energy flux
value normalized to 1 $R_\odot{}$ plus the energy necessary to overcome the
solar gravitation ($W_{R_\odot{}}$) is about 70$\pm$14 $W m^{-2}$, which is
similar to the average value (79$\pm$18 $W m^{-2}$) derived by Le Chat et al
from 24-year observations by Helios, Ulysses, and Wind at various distances and
heliolatitudes. It is remarkable that the distributions of $W_{R_\odot{}}$ are
nearly symmetrical and well fitted by Gaussians, much more so than at 1 AU,
which may imply that the small heliocentric distance limits the interactions
with transient plasma structures.


Abstract:
  Competing interactions produce finite-size textures in myriad condensed
matter systems, typically forming elongated stripe or round bubble domains.
Transitions between stripe and bubble phases, driven by field or temperature,
are expected to be reversible in nature. Here we report on the distinct
character of the analogous transition for nanoscale spin textures in chiral
Co/Pt-based multilayer films, known to host N\'{e}el skyrmions, using
microscopy, magnetometry, and micromagnetic simulations. Upon increasing field,
individual stripes fission into multiple skyrmions, and this transition
exhibits a macroscopic signature of irreversibility. Crucially, upon field
reversal, the skyrmions do not fuse back into stripes, with many skyrmions
retaining their morphology down to zero field. Both the macroscopic
irreversibility and the microscopic zero-field skyrmion density are governed by
the thermodynamic material parameter determining chiral domain stability. These
results establish the thermodynamic and microscopic framework underlying
ambient skyrmion generation and stability in chiral multilayer films and
provide immediate directions for their functionalization in devices.


Abstract:
  Reconciling the geology of Mars with models of atmospheric evolution remains
a major challenge. Martian geology is characterized by past evidence for
episodic surface liquid water, and geochemistry indicating a slow and
intermittent transition from wetter to drier and more oxidizing surface
conditions. Here we present a new model incorporating randomized injection of
reducing greenhouse gases and oxidation due to hydrogen escape, to investigate
the conditions responsible for these diverse observations. We find that Mars
could have transitioned repeatedly from reducing (H2-rich) to oxidizing
(O2-rich) atmospheric conditions in its early history. Our model predicts a
generally cold early Mars, with mean annual temperatures below 240 K. If peak
reducing gas release rates and background CO2 levels are high enough, it
nonetheless exhibits episodic warm intervals sufficient to degrade crater
walls, form valley networks and create other fluvial/lacustrine features. Our
model also predicts transient buildup of atmospheric O2, which can help explain
the occurrence of oxidized mineral species such as manganese oxides at Gale
Crater. We suggest that the apparent Noachian--Hesperian transition from
phyllosilicate deposition to sulfate deposition around 3.5 billion years ago
can be explained as a combined outcome of increasing planetary oxidation,
decreasing groundwater availability and a waning bolide impactor flux, which
dramatically slowed the remobilization and thermochemical destruction of
surface sulfates. Ultimately, rapid and repeated variations in Mars' early
climate and surface chemistry would have presented both challenges and
opportunities for any emergent microbial life.


Abstract:
  Samarium substituted bismuth ferrite (BiFeO3) ceramics prepared by sol-gel
synthesis method were studied using both local scale and microscopic
measurement techniques in order to clarify an evolution of the crystal
structure of the compounds across the morphotropic phase boundary region. X-ray
diffraction analysis, transmission and scanning electron microscopies, XPS,
EDS/EDX experiments and piezoresponse force microscopy were used to study the
structural transitions from the polar active rhombohedral phase to the
anti-polar orthorhombic phase and then to the non-polar orthorhombic phase,
observed in the Bi1-xSmxFeO3 compounds within the concentration range of 0.08 <
x < 0.2. The results obtained by microscopic techniques testify that the
compounds in the range of 0.12 < x < 0.15 are characterized by two phase
structural state formed by a coexistence of the rhombohedral and the anti-polar
orthorhombic phases; two phase structural state observed in the compounds with
0.15 < x < 0.18 is associated with a coexistence of the anti-polar orthorhombic
and the non-polar orthorhombic phases. Local scale measurements have revealed a
notable difference in the concentration range ascribed to the morphotropic
phase boundary estimated by microscopic measurements, the obtained results
testify a wider concentration range ascribed to a coexistence of different
structural phases, the background of the mentioned difference is discussed.


Abstract:
  In this paper, we address the speech denoising problem, where Gaussian, pink
and blue additive noises are to be removed from a given speech signal. Our
approach is based on a redundant, analysis-sparse representation of the
original speech signal. We pick an eigenvector of the Zauner unitary matrix and
-- under certain assumptions on the ambient dimension -- we use it as window
vector to generate a spark deficient Gabor frame. The analysis operator
associated with such a frame, is a (highly) redundant Gabor transform, which we
use as a sparsifying transform in denoising procedure. We conduct computational
experiments on real-world speech data, using as baseline three Gabor transforms
generated by state-of-the-art window vectors in time-frequency analysis and
compare their performance to the proposed Gabor transform. The results show
that our proposed redundant Gabor transform outperforms all others,
consistently for all signals.


Abstract:
  We propose a novel class of responsive polymer brushes, where the effective
grafting density can be controlled by external stimuli. This is achieved by
using end-grafted polymer chains that have an affinity to the substrate. For
sufficiently strong surface interactions, a fraction of chains condenses into a
near-surface layer, while the remaining ones form the outer brush. The dense
layer and the more tenuous outer brush can be seen as coexisting microphases.
The effective grafting density of the outer brush is controlled by the
adsorption strength and can be changed reversibly and in a controlled way as a
response to changes in environmental parameters. The effect is demonstrated by
numerical SCF calculations and analyzed by scaling arguments. Since the
thickness of the denser layer is about a few monomer sizes, its capacity to
form a microphase is limited by the product of the brush chain length and the
grafting density. We explore the range of chain lengths and grafting densities
where the effect is most pronounced. In this range, the SCF studies suggest
that individual chains inside the brush show large rapid fluctuations between
two states that are separated by only a small free energy barrier. The behavior
of the brush as a whole, however, does not reflect these large fluctuations,
and the effective grafting density varies smoothly as a function of the control
parameters.


Abstract:
  Post-training quantization is a representative technique for compressing
neural networks, making them smaller and more efficient for deployment on edge
devices. However, an inaccessible user dataset often makes it difficult to
ensure the quality of the quantized neural network in practice. In addition,
existing approaches may use a single uniform bit-width across the network,
resulting in significant accuracy degradation at extremely low bit-widths. To
utilize multiple bit-width, sensitivity metric plays a key role in balancing
accuracy and compression. In this paper, we propose a novel sensitivity metric
that considers the effect of quantization error on task loss and interaction
with other layers. Moreover, we develop labeled data generation methods that
are not dependent on a specific operation of the neural network. Our
experiments show that the proposed metric better represents quantization
sensitivity, and generated data are more feasible to be applied to
mixed-precision quantization.


Abstract:
  We study acceleration phenomena in monostable integro-differential equations
with weak Allee effect. Previous works have shown its occurrence and obtained
correct upper bounds on the rate of expansion, but precise lower bounds were
left open. In this paper, we provide a sharp lower bound of acceleration for a
large class of dispersion operators. Indeed, our results cover fractional
Laplace operators and standard convolutions in a unified way, which is also a
contribution of this paper. To achieve this, we construct a refined
sub-solution that captures the expected dynamics of the accelerating solution,
and this is here the main difficulty.


Abstract:
  Based on a recent result by de Klerk, Glineur, and Taylor (SIAM J. Optim.,
30(3):2053--2082, 2020) on the attainable convergence rate of gradient descent
for smooth and strongly convex functions in terms of function values, a
convergence analysis for general descent methods with fixed step sizes is
presented. It covers variable metric methods as well as gradient related search
directions under angle and scaling conditions. An application to inexact
gradient methods is also presented.


Abstract:
  When the charge densities on the inner and outer surface of the vesicle
membrane are similar one may assume that the potential at a point closer to the
outer/inner surface is more affected by the charges located at the outer/inner
surface of the vesicle membrane. However, because of the curvature of the
vesicle and the screening effect of the electrolyte ions the situation is more
complicated. When the charge densities at the inner and outer surface of the
vesicle membrane are the same then at any finite distance Z from the center of
the vesicle the contribution of the charges, located at the outer membrane
surface, to the vesicle potential Vex is different from the contribution of the
charges, located at the inner membrane surface, to the vesicle potential Vin.
At low electrolyte ion concentrations (from C=0 to 0.00001 [Mol]) Vex/Vin>1 and
increases with increasing distance from the center of the vesicle until the
external surface of the vesicle membrane. With further increasing distance
Vex/Vin remains constant at C=0 [Mol], while at C>0 [Mol] it starts decreasing.
However, at high electrolyte ion concentrations (C>0.01 [Mol]) with increasing
distance from the center of the vesicle Vex/Vin reaches its maximum at the
internal surface of the vesicle membrane. With further increasing distance,
close to the outer surface of the vesicle Vex/Vin becomes less than 1 (i.e. the
charges at the inner surface of the membrane contribute to the potential more
than the charges at the outer surface). Further increasing the distance Vex/Vin
reaches its minimum and then increases to 1 (i.e. the charges at the inner
surface of the membrane contribute to the potential like the charges at the
outer surface of the membrane). With increasing electrolyte ion concentration
the minimum of Vex/Vin is getting deeper (approaching zero) and its location is
getting closer to the outer surface of the vesicle.


Abstract:
  Recently, passive unmanned aerial vehicle (UAV) localization has become
popular due to mobility and convenience. In this paper, we consider a scenario
of using distributed drone cluster to estimate the position of a passive
emitter via received signal strength (RSS). First, a distributed
majorizeminimization (DMM) RSS-based localization method is proposed. To
accelerate its convergence, a tight upper bound of the objective function from
the primary one is derived. Furthermore, to reduce communication overhead, a
distributed estimation scheme using the Fisher information matrix (DEF) is
presented, with only requiring one-round communication between edge UAVs and
center UAV. Additionally, a local search solution is used as the initial value
of DEF. Simulation results show that the proposed DMM performs better than the
existing distributed Gauss-Newton method (DGN) in terms of root of mean square
error (RMSE) under a limited low communication overhead constraint. Moreover,
the proposed DEF performs much better than MM in terms of RMSE, but has a
higher computational complexity than the latter.


Abstract:
  We study a version of the fractal uncertainty principle in the joint
time-frequency representation. Namely, we consider Daubechies' localization
operator projecting onto spherically symmetric $n$-iterate Cantor sets with an
arbitrary base $M>1$ and alphabet $\mathscr{A}$. We derive an upper bound
asymptote up to a multiplicative constant for the operator norm in terms of the
base $M$ and alphabet size $|\mathscr{A}|$ of the Cantor set. For any fixed
base and alphabet size, we show that there are Cantor sets such that the
asymptote is optimal. In particular, the asymptote is precise for the mid-third
Cantor set, which was studied in part I. Nonetheless, this does not extend to
every Cantor set as we provide examples where the optimal asymptote is not
achieved.


Abstract:
  Most non-professional photo manipulations are not made using propriety
software like Adobe Photoshop, which is expensive and complicated to use for
the average consumer selfie-taker or meme-maker. Instead, these individuals opt
for user friendly mobile applications like FaceTune and Pixlr to make human
face edits and alterations. Unfortunately, there is no existing dataset to
train a model to classify these type of manipulations. In this paper, we
present a generative model that approximates the distribution of human face
edits and a method for detecting Facetune and Pixlr manipulations to human
faces.


Abstract:
  Let $p$ be a prime number and $K$ a finite extension of $\mathbb{Q}_p$. We
state conjectures on the smooth representations of $\mathrm{GL}_n(K)$ that
occur in spaces of mod $p$ automorphic forms (for compact unitary groups). In
particular, when $K$ is unramified, we conjecture that they are of finite
length and predict their internal structure (extensions, form of subquotients)
from the structure of a certain algebraic representation of $\mathrm{GL}_n$.
When $n=2$ and $K$ is unramified, we prove several cases of our conjectures,
including new finite length results.


Abstract:
  A methodology is proposed for formulating dynamic equations in
thermo-piezoelectric and dissipative media from the first principle of energy
conservation. The results are in agreement with those from Hamiltonian
principle. Our formulations based on energy conservation are much easier to
understand. What is more is that, the energy conservation based framework is
able to handle dissipation problems, which is usually behind the scope of
Hamiltonian principle. In our case, the mechanical, electric and thermal
phenomena firstly are taken into account, and then the medium with dissipation
is included. The formulations on acoustic dynamic equation and the associated
constitutive relations of the medium will also pave an alternative way in
computational dynamic modelling based on weak formulations. In addition, our
methodology may be extended to other dynamic equation formulations, such as in
electrodynamics, fluid mechanics and quantum mechanics. This is especially true
for tackling the problems with multi-physical field interactions and coupling.


Abstract:
  In this paper we investigate two-point algebraic-geometry codes (AG codes)
coming from the Beelen-Montanucci (BM) maximal curve. We study properties of
certain two-point Weierstrass semigroups of the curve and use them for
determining a lower bound on the minimum distance of such codes. AG codes with
better parameters with respect to comparable two-point codes from the
Garcia-G\"uneri-Stichtenoth (GGS) curve are discovered.


Abstract:
  Let $(K,\Delta)$ be a Hermitian form field and $n\geq 3$. We prove that if
$\sigma\in U_{2n+1}(K,\Delta)$ is a unitary matrix of level $(K,\Delta)$, then
any short root transvection $T_{ij}(x)$ is a product of $4$ elementary unitary
conjugates of $\sigma$ and $\sigma^{-1}$. Moreover, the bound $4$ is sharp. We
also show that any extra short root transvection $T_i(x,y)$ is a product of
$12$ elementary unitary conjugates of $\sigma$ and $\sigma^{-1}$. If the level
of $\sigma$ is $(0,K\times 0)$, then any $(0,K\times 0)$-elementary extra short
root transvection $T_i(x,0)$ is a product of $2$ elementary unitary conjugates
of $\sigma$ and $\sigma^{-1}$.


Abstract:
  Medication errors continue to be the leading cause of avoidable patient harm
in hospitals. This paper sets out a framework to assure medication safety that
combines machine learning and safety engineering methods. It uses safety
analysis to proactively identify potential causes of medication error, based on
expert opinion. As healthcare is now data rich, it is possible to augment
safety analysis with machine learning to discover actual causes of medication
error from the data, and to identify where they deviate from what was predicted
in the safety analysis. Combining these two views has the potential to enable
the risk of medication errors to be managed proactively and dynamically. We
apply the framework to a case study involving thoracic surgery, e.g.
oesophagectomy, where errors in giving beta-blockers can be critical to control
atrial fibrillation. This case study combines a HAZOP-based safety analysis
method known as SHARD with Bayesian network structure learning and process
mining to produce the analysis results, showing the potential of the framework
for ensuring patient safety, and for transforming the way that safety is
managed in complex healthcare environments.


Abstract:
  This paper mainly focuses on solving the dynamic optimization of the planar
controlled crowd motion models with obstacles which is an application of a
class of optimal control problems governed by a general perturbed nonconvex
sweeping process. This can be considered as a significant extension of the
previous work regarding the controlled crowd motion models, where the obstacles
have not been considered. The necessary optimality conditions for the problem
under consideration are established and illustrated by a nontrivial example of
practical importance.


Abstract:
  With almost 50% of annual commercial drug approvals being Personalised
Medicine (PM) and its huge potential to improve quality of life, this emerging
medical sector has received increased attention from the industry and medical
research, driven by health and care services, and us, the patients.
Notwithstanding the power of Advanced Therapy Medicinal Products (ATMPs) to
treat progressive illnesses and rare genetic conditions, their delivery on
large scale is still problematic. The biopharmaceutical companies are currently
struggling to meet timely delivery and, given high prices of up to $2 million
per patient, prove the cost-effectiveness of their ATMP. The fragility of ATMPs
combined with the impossibility for replacements due to the nature of the
treatment and the advanced stages of the patient's condition are some of the
bottlenecks added to a generally critical supply chain. As a consequence, ATMPs
are currently used in most cases only as a last resort. ATMPs are at the
intersection of multiple healthcare logistic networks and, due to their
novelty, research around their commercialisation is still in its infancy from
an operations research perspective. To accelerate technology adoption in this
domain, we characterize pertinent practical challenges in a PM supply chain and
then capture them in a holistic mathematical model ready for optimisation. The
identified challenges and derived model will be contrasted with literature of
related supply chains in terms of model formulations and suitable optimisation
methods. Finally, needed technological advancements are discussed to pave the
way to affordable commercialisation of PM.


Abstract:
  Deterministic photon-photon gates enable the controlled generation of
entanglement between mobile carriers of quantum information. Such gates have
thus far been exclusively realized in the optical domain and by relying on
post-selection. Here, we present a non-post-selected, deterministic,
photon-photon gate in the microwave frequency range realized using
superconducting circuits. We emit photonic qubits from a source chip and route
those qubits to a gate chip with which we realize a universal gate set by
combining controlled absorption and re-emission with single-qubit gates and
qubit-photon controlled-phase gates. We measure quantum process fidelities of
$75\,\%$ for single- and of $57\,\%$ for two-qubit gates, limited mainly by
radiation loss and decoherence. This universal gate set has a wide range of
potential applications in superconducting quantum networks.


Abstract:
  Presented is a data-driven Machine Learning (ML) framework for the
identification and modeling of traveling wave spatiotemporal dynamics. The
presented framework is based on the steadily-propagating traveling wave ansatz,
$u(x,t) = U(\xi=x - ct + a)$. For known evolution equations, this coordinate
transformation reduces governing partial differential equations (PDEs) to a set
of coupled ordinary differential equations (ODEs) in the traveling wave
coordinate $\xi$. Although traveling waves are readily observed in many
physical systems, the underlying governing equations may be unknown. For these
instances, the traveling wave ODEs can be (i) identified in an interpretable
manner through an implementation of sparse regression techniques or (ii)
modeled empirically with neural ODEs. Presented are these methods applied to
several physical systems that admit traveling waves. Examples include traveling
wave fronts, pulses, and wavetrains restricted to one-wave wave propagation in
a single spatial dimension.


Abstract:
  The early Cosmology driven by a single scalar field, both massless and
massive, in the context of Eddington-inspired Born-Infeld gravity, is explored.
We show the existence of nonsingular solutions of bouncing and loitering type
(depending on the sign of the gravitational theory's parameter, $\epsilon$)
replacing the Big Bang singularity, and discuss their properties. In addition,
in the massive case we find some new features of the cosmological evolution
depending on the value of the mass parameter, including asymmetries in the
expansion/contraction phases, or a continuous transition between a contracting
phase to an expanding one via an intermediate loitering phase. We also provide
a combined analysis of cosmic chronometers, standard candles, BAO, and CMB data
to constrain the model, finding that for roughly $\vert \epsilon \vert \lesssim
5\cdot 10^{-8} \text{ m}^2$ the model is compatible with the latest
observations while successfully removing the Big Bang singularity. This bound
is several orders of magnitude stronger than the most stringent constraints
currently available in the literature


Abstract:
  Photonic crystals (PCs) offer unique ways to control light-matter
interactions. The measurement of dispersion relations is a fundamental
prerequisite if we are to create novel functionalities in PC devices.
Angle-resolved spectroscopic techniques are commonly used for characterizing
PCs that work in the visible and near-infrared regions. However, the techniques
cannot be applied to the mid- and long-wavelength infrared regions due to the
limited sensitivity of infrared detectors. Here, we propose an alternative
approach to measuring infrared dispersion relations. We construct a
high-precision angle-resolved setup compatible with a Fourier-transform
spectrometer with an angle resolution as high as 0.3 degrees. Hence, the
reflection spectra are mapped to the 2D photonic band structures of
In(Ga,Al)As/InP based PC slabs, which are designed as mid-infrared PC
surface-emitting lasers. We identify complex PC modes with the aid of
polarization selection rules derived by the group theory. Spectral analysis
makes it possible to evaluate the mode quality (Q) factors. Therefore,
angle-resolved reflection is a useful way of optimizing 2D PC parameters for
mid-infrared devices.


Abstract:
  Given a family $\mathcal F$ of $k$-element sets, $S_1,\ldots,S_r\in\mathcal
F$ form an {\em $r$-sunflower} if $S_i \cap S_j =S_{i'} \cap S_{j'}$ for all $i
\neq j$ and $i' \neq j'$. According to a famous conjecture of Erd\H os and Rado
(1960), there is a constant $c=c(r)$ such that if $|\mathcal F|\ge c^k$, then
$\mathcal F$ contains an $r$-sunflower.
  We come close to proving this conjecture for families of bounded {\em
Vapnik-Chervonenkis dimension}, VC-dim$(\mathcal F)\le d$. In this case, we
show that $r$-sunflowers exist under the slightly stronger assumption
$|\mathcal F|\ge2^{10k(dr)^{2\log^{*} k}}$. Here, $\log^*$ denotes the iterated
logarithm function.
  We also verify the Erd\H os-Rado conjecture for families $\mathcal F$ of
bounded {\em Littlestone dimension} and for some geometrically defined set
systems.


Abstract:
  Neural image compression leverages deep neural networks to outperform
traditional image codecs in rate-distortion performance. However, the resulting
models are also heavy, computationally demanding and generally optimized for a
single rate, limiting their practical use. Focusing on practical image
compression, we propose slimmable compressive autoencoders (SlimCAEs), where
rate (R) and distortion (D) are jointly optimized for different capacities.
Once trained, encoders and decoders can be executed at different capacities,
leading to different rates and complexities. We show that a successful
implementation of SlimCAEs requires suitable capacity-specific RD tradeoffs.
Our experiments show that SlimCAEs are highly flexible models that provide
excellent rate-distortion performance, variable rate, and dynamic adjustment of
memory, computational cost and latency, thus addressing the main requirements
of practical image compression.


Abstract:
  Einstein's theory of General Relativity is one of the pillars of modern
physics. For decades, the theory has been mainly tested in the weak field
regime with experiments in the Solar System and observations of binary pulsars.
Thanks to a new generation of observational facilities, the past 5 years have
seen remarkable changes in this field and there are now numerous efforts for
testing General Relativity in the strong field regime with black holes and
neutron stars using different techniques. Here I will review the work of my
group at Fudan University devoted to test General Relativity with black hole
X-ray data.


Abstract:
  A commonly used technique for the higher-order PageRank problem is the power
method that is computationally intractable for large-scale problems. The
truncated power method proposed recently provides us with another idea to solve
this problem, however, its accuracy and efficiency can be poor in practical
computations. In this work, we revisit the higher-order PageRank problem and
consider how to solve it efficiently. The contribution of this work is as
follows. First, we accelerate the truncated power method for high-order
PageRank. In the improved version, it is neither to form and store the vectors
arising from the dangling states, nor to store an auxiliary matrix. Second, we
propose a truncated power method with partial updating to further release the
overhead, in which one only needs to update some important columns of the
approximation in each iteration. On the other hand, the truncated power method
solves a modified high-order PageRank model for convenience, which is not
mathematically equivalent to the original one. Thus, the third contribution of
this work is to propose a sparse power method with partial updating for the
original higher-order PageRank problem. The convergence of all the proposed
methods are discussed. Numerical experiments on large and sparse real-world and
synthetic data sets are performed. The numerical results show the superiority
of our new algorithms over some state-of-the-art ones for large and sparse
higher-order PageRank problems.


Abstract:
  We theoretically investigate the one-color injection currents and shift
currents in zigzag graphene nanoribbons with applying a static electric field
across the ribbon, which breaks the inversion symmetry to generate nonzero
second order optical responses by dipole interaction. These two types of
currents can be separately excited by specific light polarization, circularly
polarized lights for injection currents and linearly polarized lights for shift
currents. Based on a tight binding model formed by carbon 2p$_z$ orbitals, we
numerically calculate the spectra of injection coefficients and shift
conductivities, as well as their dependence on the static field strength and
ribbon width. The spectra show many peaks associated with the optical
transition between different subbands, and the positions and amplitudes of
these peaks can be effectively controlled by the static electric field. By
constructing a simple two band model, the static electric fields are found to
modify the edge states in a nonperturbative way, and their associated optical
transitions dominate the current generation at low photon energies. For typical
parameters, such as a static field 10$^6$ V/m and light intensity 0.1
GW/cm$^2$, the magnitude of the injection and shift currents for a ribbon with
width 5 nm can be as large as the order of 1 $\mu$A. Our results provide a
physical basis for realizing passive optoelectronic devices based on graphene
nanoribbons.


Abstract:
  Graph neural networks (GNNs) have achieved tremendous success in graph
mining. However, the inability of GNNs to model substructures in graphs remains
a significant drawback. Specifically, message-passing GNNs (MPGNNs), as the
prevailing type of GNNs, have been theoretically shown unable to distinguish,
detect or count many graph substructures. While efforts have been paid to
complement the inability, existing works either rely on pre-defined
substructure sets, thus being less flexible, or are lacking in theoretical
insights. In this paper, we propose GSKN, a GNN model with a theoretically
stronger ability to distinguish graph structures. Specifically, we design GSKN
based on anonymous walks (AWs), flexible substructure units, and derive it upon
feature mappings of graph kernels (GKs). We theoretically show that GSKN
provably extends the 1-WL test, and hence the maximally powerful MPGNNs from
both graph-level and node-level viewpoints. Correspondingly, various
experiments are leveraged to evaluate GSKN, where GSKN outperforms a wide range
of baselines, endorsing the analysis.


Abstract:
  Increasingly common open data and open application programming interfaces
(APIs) together with the progress of data science -- such as artificial
intelligence (AI) and especially machine learning (ML) -- create opportunities
to build novel services by combining data from different sources. In this
experience report, we describe our firsthand experiences on open data and in
the domain of marine traffic in Finland and Sweden and identified technological
opportunities for novel services. We enumerate five challenges that we have
encountered with the application of open data: relevant data, historical data,
licensing, runtime quality, and API evolution. These challenges affect both
business model and technical implementation. We discuss how these challenges
could be alleviated by better governance practices for provided open APIs and
data.


Abstract:
  The chapter presents the problem of the complexity of plastic flow in alloys,
which is manifested by serrated deformation curves and transient plastic strain
localizations. This phenomenon uncovers an inherently collective nature of the
dynamics of crystal defects. The chapter discusses the state-of-the art of this
research under the angle of distinct regimes of collective dislocation dynamics
found in conventional, basically binary alloys, e.g., self-organized
criticality, deterministic chaos, or synchronization. It concludes with the
formulation of diverse open questions, notably concerning manifestations of the
collective dislocation dynamics pertaining to finer scales of plastic
deformation processes, as compared to the macroscopic plastic instability.


Abstract:
  KPD 0005+5106, with an effective temperature of $\simeq$200,000 K, is one of
the hottest white dwarfs (WDs). ROSAT unexpectedly detected "hard" ($\sim$1
keV) X-rays from this apparently single WD. We have obtained Chandra
observations that confirm the spatial coincidence of this hard X-ray source
with KPD 0005+5106. We have also obtained XMM-Newton observations of KPD
0005+5106, as well as PG 1159$-$035 and WD 0121$-$756, which are also
apparently single and whose hard X-rays were detected by ROSAT at
3$\sigma$-4$\sigma$ levels. The XMM-Newton spectra of the three WDs show
remarkably similar shapes that can be fitted by models including a blackbody
component for the stellar photospheric emission, a thermal plasma emission
component, and a power-law component. Their X-ray luminosities in the $0.6-3.0$
keV band range from $4\times10^{29}$ to $4\times10^{30}$ erg~s$^{-1}$. The
XMM-Newton EPIC-pn soft-band ($0.3-0.5$ keV) lightcurve of KPD 0005+5106 is
essentially constant, but the hard-band ($0.6-3.0$ keV) lightcurve shows
periodic variations. An analysis of the generalized Lomb-Scargle periodograms
for the XMM-Newton and Chandra hard-band lightcurves finds a convincing
modulation (false alarm probability of 0.41%) with a period of 4.7$\pm$0.3 hr.
Assuming that this period corresponds to a binary orbital period, the Roche
radii of three viable types of companion have been calculated: M9V star, T
brown dwarf, and Jupiter-like planet. Only the planet has a size larger than
its Roche radius, although the M9V star and T brown dwarf may be heated by the
WD and inflate past the Roche radius. Thus, all three types of companion may be
donors to fuel accretion-powered hard X-ray emission.


Abstract:
  As an important perceptual characteristic of the Human Visual System (HVS),
the Just Noticeable Difference (JND) has been studied for decades with
image/video processing (e.g., perceptual image/video coding). However, there is
little exploration on the existence of JND for AI, like Deep Machine Vision
(DMV), although the DMV has made great strides in many machine vision tasks. In
this paper, we take an initial attempt, and demonstrate that DMV does have the
JND, termed as DMVJND. Besides, we propose a JND model for the classification
task in DMV. It has been discovered that DMV can tolerate distorted images with
average PSNR of only 9.56dB (the lower the better), by generating JND via
unsupervised learning with our DMVJND-NET. In particular, a semantic-guided
redundancy assessment strategy is designed to constrain the magnitude and
spatial distribution of the JND. Experimental results on classification tasks
demonstrate that we successfully find and model the JND for deep machine
vision. Meanwhile, our DMV-JND paves a possible direction for DMV oriented
image/video compression, watermarking, quality assessment, deep neural network
security, and so on.


Abstract:
  We use density-functional theory calculations to explore the magnetic
properties of perovskite rare-earth nickelates, $\mathcal{R}$NiO$_3$, by
constructing microscopic magnetic models containing all relevant exchange
interactions via Wannierization and Green's function techniques. These models
elucidate the mechanism behind the formation of antiferromagnetic order with
the experimentally observed propagation vector, and explain the reason previous
DFT plus Hubbard $U$ calculations favored ferromagnetic order. We perform
calculations of magnetic moments and exchange-coupling parameters for different
amplitudes of the $R_1^+$ breathing mode distortion, which results in expanded
and compressed NiO$_6$ octahedra. We find that the magnetic moment vanishes for
the "short bond" nickels, i.e., the ones in the compressed octahedra. The
inclusion of spin-orbit coupling demonstrates that the magnetic anisotropy is
very small, while the magnetic moment of the short bond nickel atoms tend to
zero even for the noncollinear case. Our results provide a clear picture of the
trends of the magnetic order across the nickelate series and give insights into
the coupling between magnetic order and structural distortions.


Abstract:
  Demonstrating acrobatic behavior of a humanoid robot such as flips and
spinning jumps requires systematic approaches across hardware design, motion
planning, and control. In this paper, we present a new humanoid robot design,
an actuator-aware kino-dynamic motion planner, and a landing controller as part
of a practical system design for highly dynamic motion control of the humanoid
robot. To achieve the impulsive motions, we develop two new proprioceptive
actuators and experimentally evaluate their performance using our
custom-designed dynamometer. The actuator's torque, velocity, and power limits
are reflected in our kino-dynamic motion planner by approximating the
configuration-dependent reaction force limits and in our dynamics simulator by
including actuator dynamics along with the robot's full-body dynamics. For the
landing control, we effectively integrate model-predictive control and
whole-body impulse control by connecting them in a dynamically consistent way
to accomplish both the long-time horizon optimal control and high-bandwidth
full-body dynamics-based feedback. Actuators' torque output over the entire
motion are validated based on the velocity-torque model including battery
voltage droop and back-EMF voltage. With the carefully designed hardware and
control framework, we successfully demonstrate dynamic behaviors such as back
flips, front flips, and spinning jumps in our realistic dynamics simulation.


Abstract:
  The most simple and famous divergent power series coming from ODE may be the
so-called Euler series $\sum_{n\ge 0}(-1)^n\,n!\,x^{n+1}$, that, as well as all
its positive powers, is Borel-summable in any direction excepted the negative
real half-axis. By considering a family of linear $q$-difference operators
associated with a given first order non-homogenous $q$-difference equation, it
will be shown that the summability order of $q$-analoguous counterparties of
Euler series depends upon of the degree of power under consideration.


Abstract:
  Certification of nonlocality of quantum mechanics is an important fundamental
test that typically requires prolonged data collection and is only revealed in
an in-depth analysis. These features are often particularly exposed in hybrid
systems, such as interfaces between light and atomic ensembles. Certification
of entanglement from images acquired with single-photon camera can mitigate
this issue by exploiting multiplexed photon generation. Here we demonstrate
this feature in a quantum memory (QM) operating in a real-time feedback mode.
Through spatially-multimode spin-wave storage the QM enables operation of the
real-time ghost imaging (GI) protocol. By properly preparing the spatial phase
of light emitted by the atoms we enable observation of Bell-type nonlocality
from a single image acquired in the far field as witnessed by the Bell
parameter of $S=2.227\pm0.007>2$. Our results are an important step towards
fast and efficient utilization of multimode quantum memories both in protocols
and in fundamental tests.


Abstract:
  Given a finite tensor category $\mathcal{C}$, we prove that a modified trace
on the tensor ideal of projective objects can be obtained from a suitable
trivialization of the Nakayama functor as right $\mathcal{C}$-module functor.
Using a result of Costello, this allows us to associate to any finite tensor
category equipped with such a trivialization of the Nakayama functor a chain
complex valued topological conformal field theory, the trace field theory. The
trace field theory topologically describes the modified trace, the
Hattori-Stallings trace, and also the structures induced by them on the
Hochschild complex of $\mathcal{C}$. In this article, we focus on implications
in the linear (as opposed to differential graded) setting: We use the trace
field theory to define a non-unital homotopy commutative product on the
Hochschild chains in degree zero. This product is block diagonal and can be
described through the handle elements of the trace field theory. Taking the
modified trace of the handle elements recovers the Cartan matrix of
$\mathcal{C}$.


Abstract:
  The Cattle Tracing System database is an online recording system for cattle
births, deaths and between--herd movements in the United Kingdom. Although it
has been thoroughly examined, the most recently reported movement analysis is
from 2009. This article uses the database to construct weighted directed
monthly movement networks for two distinct periods of time, 2004--2006 and
2015--2017, to quantify by how much the underlying structure of the network has
changed. Substantial changes in network structure may influence policy--makers
directly or may influence models built upon the network data, and these in turn
could impact policy--makers and their assessment of risk. Four general network
measures are used (total number of nodes with movements, movements, births and
deaths), in conjunction with network metrics to describe each monthly network.
Two updates of the database were examined to determine by how much the movement
data stored for a particular time period had been cleansed between updates.
Statistical models show that there is a statistically significant effect of the
time period (2004--2006 vs 2015--2017) in the values of all network measures
and six of nine network metrics. Changes in the sizes of both the Giant and
Weakly Strongly Connected components predict reductions in the upper and lower
bounds of the maximum epidemic size. Examination of the updates of the database
show that there are differences in records between updates and therefore
evidence of historical data changing between updates. Accurate modelling of
disease spread through a network requires representative descriptions of the
network. The authors recommend that where possible the most recent available
data always be used for network modelling and that methods of network
prediction be examined to mitigate for the time required for data to become
available.


Abstract:
  In this paper we study relations between various natural structures on
F-manifolds. In particular, given an arbitrary Riemannian F-manifold we present
a construction of a canonical flat F-manifold associated to it. We also
describe a construction of a canonical homogeneous Riemannian F-manifold
associated to an arbitrary exact homogeneous flat pencil of metrics satisfying
a certain non-degeneracy assumption. In the last part of the paper we construct
Legendre transformations for Riemannian F-manifolds.