Abstract:
  Applying an operator product expansion approach we update the Standard Model
prediction of the $B_c$ lifetime from over 20 years ago. The non-perturbative
velocity expansion is carried out up to third order in the relative velocity of
the heavy quarks. The scheme dependence is studied using three different mass
schemes for the $\bar b$ and $c$ quarks, resulting in three different values
consistent with each other and with experiment. Special focus has been laid on
renormalon cancellation in the computation. Uncertainties resulting from scale
dependence, neglecting the strange quark mass, non-perturbative matrix elements
and parametric uncertainties are discussed in detail. The resulting
uncertainties are still rather large compared to the experimental ones, and
therefore do not allow for clear-cut conclusions concerning New Physics effects
in the $B_c$ decay.


Abstract:
  The Helmholtz equation in one dimension, which describes the propagation of
electromagnetic waves in effectively one-dimensional systems, is equivalent to
the time-independent Schr\"odinger equation. The fact that the potential term
entering the latter is energy-dependent obstructs the application of the
results on low-energy quantum scattering in the study of the low-frequency
waves satisfying the Helmholtz equation. We use a recently developed dynamical
formulation of stationary scattering to offer a comprehensive treatment of the
low-frequency scattering of these waves for a general finite-range scatterer.
In particular, we give explicit formulas for the coefficients of the
low-frequency series expansion of the transfer matrix of the system which in
turn allow for determining the low-frequency expansions of its reflection,
transmission, and absorption coefficients. Our general results reveal a number
of interesting physical aspects of low-frequency scattering particularly in
relation to permittivity profiles having balanced gain and loss.


Abstract:
  We establish the dual equivalence of the category of (potentially nonunital)
operator systems and the category of pointed compact nc (noncommutative) convex
sets, extending a result of Davidson and the first author. We then apply this
dual equivalence to establish a number of results about operator systems, some
of which are new even in the unital setting.
  For example, we show that the maximal and minimal C*-covers of an operator
system can be realized in terms of the C*-algebra of continuous nc functions on
its nc quasistate space, clarifying recent results of Connes and van Suijlekom.
We also characterize "C*-simple" operator systems, i.e. operator systems with
simple minimal C*-cover, in terms of their nc quasistate spaces.
  We develop a theory of quotients of operator systems that extends the theory
of quotients of unital operator algebras. In addition, we extend results of the
first author and Shamovich relating to nc Choquet simplices. We show that an
operator system is a C*-algebra if and only if its nc quasistate space is an nc
Bauer simplex with zero as an extreme point, and we show that a second
countable locally compact group has Kazhdan's property (T) if and only if for
every action of the group on a C*-algebra, the set of invariant quasistates is
the quasistate space of a C*-algebra.


Abstract:
  With the growing use of camera devices, the industry has many image datasets
that provide more opportunities for collaboration between the machine learning
community and industry. However, the sensitive information in the datasets
discourages data owners from releasing these datasets. Despite recent research
devoted to removing sensitive information from images, they provide neither
meaningful privacy-utility trade-off nor provable privacy guarantees. In this
study, with the consideration of the perceptual similarity, we propose
perceptual indistinguishability (PI) as a formal privacy notion particularly
for images. We also propose PI-Net, a privacy-preserving mechanism that
achieves image obfuscation with PI guarantee. Our study shows that PI-Net
achieves significantly better privacy utility trade-off through public image
data.


Abstract:
  The backup control barrier function (CBF) was recently proposed as a
tractable formulation that guarantees the feasibility of the CBF quadratic
programming (QP) via an implicitly defined control invariant set. The control
invariant set is based on a fixed backup policy and evaluated online by forward
integrating the dynamics under the backup policy. This paper is intended as a
tutorial of the backup CBF approach and a comparative study to some benchmarks.
First, the backup CBF approach is presented step by step with the underlying
math explained in detail. Second, we prove that the backup CBF always has a
relative degree 1 under mild assumptions. Third, the backup CBF approach is
compared with benchmarks such as Hamilton Jacobi PDE and Sum-of-Squares on the
computation of control invariant sets, which shows that one can obtain a
control invariant set close to the maximum control invariant set under a good
backup policy for many practical problems.


Abstract:
  Complex fluids flow in complex ways in complex structures. Transport of water
and various organic and inorganic molecules in the central nervous system are
important in a wide range of biological and medical processes [C. Nicholson,
and S. Hrab\v{e}tov\'a, Biophysical Journal, 113(10), 2133(2017)]. However, the
exact driving mechanisms are often not known. In this paper, we investigate
flows induced by action potentials in an optic nerve as a prototype of the
central nervous system (CNS). Different from traditional fluid dynamics
problems, flows in biological tissues such as the CNS are coupled with ion
transport. It is driven by osmosis created by concentration gradient of ionic
solutions, which in term influence the transport of ions. Our mathematical
model is based on the known structural and biophysical properties of the
experimental system used by the Harvard group Orkand et al [R.K. Orkand, J.G.
Nicholls, S.W. Kuffler, Journal of Neurophysiology, 29(4), 788(1966)].
Asymptotic analysis and numerical computation show the significant role of
water in convective ion transport. The full model (including water) and the
electrodiffusion model (excluding water) are compared in detail to reveal an
interesting interplay between water and ion transport. In the full model,
convection due to water flow dominates inside the glial domain. This water flow
in the glia contributes significantly to the spatial buffering of potassium in
the extracellular space. Convection in the extracellular domain does not
contribute significantly to spatial buffering. Electrodiffusion is the dominant
mechanism for flows confined to the extracellular domain.


Abstract:
  A (charged) rotating black hole may be unstable against a (charged) massive
scalar field perturbation due to the existence of superradiance modes. The
stability property depends on the parameters of the system. In this paper, the
superradiant stable parameter space is studied for the four-dimensional
extremal Kerr and Kerr-Newman black holes under massive and charged massive
scalar perturbation. For the extremal Kerr case, it is found that when the
angular frequency and proper mass of the scalar perturbation satisfy the
inequality $\omega<\mu/\sqrt{3}$, the extremal Kerr black hole and scalar
perturbation system is superradiantly stable. For the Kerr-Newman black hole
case, when the angular frequency of the scalar perturbation satisfies
$\omega<qQ/M$ and the product of the mass-to-charge ratios of the black hole
and scalar perturbation satisfies $\frac{\mu}{q}\frac{M}{Q} > \frac{\sqrt{3
k^2+2} }{ \sqrt{k^2+2} },~k=\frac{a}{M}$, the extremal Kerr-Newman black hole
is superradiantly stable under charged massive scalar perturbation.


Abstract:
  Multi-domain image-to-image translation with conditional Generative
Adversarial Networks (GANs) can generate highly photo realistic images with
desired target classes, yet these synthetic images have not always been helpful
to improve downstream supervised tasks such as image classification. Improving
downstream tasks with synthetic examples requires generating images with high
fidelity to the unknown conditional distribution of the target class, which
many labeled conditional GANs attempt to achieve by adding soft-max
cross-entropy loss based auxiliary classifier in the discriminator. As recent
studies suggest that the soft-max loss in Euclidean space of deep feature does
not leverage their intrinsic angular distribution, we propose to replace this
loss in auxiliary classifier with an additive angular margin (AAM) loss that
takes benefit of the intrinsic angular distribution, and promotes intra-class
compactness and inter-class separation to help generator synthesize high
fidelity images.
  We validate our method on RaFD and CIFAR-100, two challenging face expression
and natural image classification data set. Our method outperforms
state-of-the-art methods in several different evaluation criteria including
recently proposed GAN-train and GAN-test metrics designed to assess the impact
of synthetic data on downstream classification task, assessing the usefulness
in data augmentation for supervised tasks with prediction accuracy score and
average confidence score, and the well known FID metric.


Abstract:
  We consider the problem of minimizing the supplied energy of
infinite-dimensional linear port-Hamiltonian systems and prove that optimal
trajectories exhibit the turnpike phenomenon towards certain subspaces induced
by the dissipation of the dynamics.


Abstract:
  We investigate the complexity and performance of recurrent neural network
(RNN) models as post-processing units for the compensation of fibre
nonlinearities in digital coherent systems carrying polarization multiplexed
16-QAM and 32-QAM signals. We evaluate three bi-directional RNN models, namely
the bi-LSTM, bi-GRU and bi-Vanilla-RNN and show that all of them are promising
nonlinearity compensators especially in dispersion unmanaged systems. Our
simulations show that during inference the three models provide similar
compensation performance, therefore in real-life systems the simplest scheme
based on Vanilla-RNN units should be preferred. We compare bi-Vanilla-RNN with
Volterra nonlinear equalizers and exhibit its superiority both in terms of
performance and complexity, thus highlighting that RNN processing is a very
promising pathway for the upgrade of long-haul optical communication systems
utilizing coherent detection.


Abstract:
  The aim of this paper is to show that almost greedy bases induce tighter
embeddings in superreflexive Banach spaces than in general Banach spaces. More
specifically, we show that an almost greedy basis in a superreflexive Banach
space $\mathbb{X}$ induces embeddings that allow squeezing $\mathbb{X}$ between
two superreflexive Lorentz sequence spaces that are close to each other in the
sense that they have the same fundamental function.


Abstract:
  We provide a new degree bound on the weighted sum-of-squares (SOS)
polynomials for Putinar-Vasilescu's Positivstellensatz. This leads to another
Positivstellensatz saying that if $f$ is a polynomial of degree at most $2 d_f$
nonnegative on a semialgebraic set having nonempty interior defined by finitely
many polynomial inequalities $g_j(x)\ge 0$, $j=1,\dots,m$ with
$g_1:=L-\|x\|_2^2$ for some $L>0$, then there exist positive constants $\bar c$
and $c$ depending on $f,g_j$ such that for any $\varepsilon>0$, for all $k\ge
\bar c \varepsilon^{-c}$, $f$ has the decomposition \begin{equation}
\begin{array}{l} (1+\|x\|_2^2)^k(f+\varepsilon)=\sigma_0+\sum_{j=1}^m
\sigma_jg_j \,, \end{array} \end{equation} for some SOS polynomials $\sigma_j$
being such that the degrees of $\sigma_0,\sigma_jg_j$ are at most $2(d_f+k)$.
Here $\|\cdot\|_2$ denotes the $\ell_2$ vector norm. As a consequence, we
obtain a converging hierarchy of semidefinite relaxations for lower bounds in
polynomial optimization on basic compact semialgebraic sets. The complexity of
this hierarchy is $\mathcal{O}(\varepsilon^{-c})$ for prescribed accuracy
$\varepsilon>0$. In particular, if $m=L=1$ then $c=65$, yielding the complexity
$\mathcal{O}(\varepsilon^{-65})$ for the minimization of a polynomial on the
unit ball. Our result improves the complexity bound
$\mathcal{O}(\exp(\varepsilon^{-c}))$ due to Nie and Schweighofer in [Journal
of Complexity 23.1 (2007): 135-150].


Abstract:
  Let $\mathcal{F}\subset 2^{[n]}$ be a set family such that the intersection
of any two members of $\mathcal{F}$ has size divisible by $\ell$. The famous
Eventown theorem states that if $\ell=2$ then $|\mathcal{F}|\leq 2^{\lfloor
n/2\rfloor}$, and this bound can be achieved by, e.g., an `atomic'
construction, i.e. splitting the ground set into disjoint pairs and taking
their arbitrary unions. Similarly, splitting the ground set into disjoint sets
of size $\ell$ gives a family with pairwise intersections divisible by $\ell$
and size $2^{\lfloor n/\ell\rfloor}$. Yet, as was shown by Frankl and Odlyzko,
these families are far from maximal. For infinitely many $\ell$, they
constructed families $\mathcal{F}$ as above of size $2^{\Omega(n\log
\ell/\ell)}$. On the other hand, if the intersection of any number of sets in
$\mathcal{F}\subset 2^{[n]}$ has size divisible by $\ell$, then it is easy to
show that $|\mathcal{F}|\leq 2^{\lfloor n/\ell\rfloor}$. In 1983 Frankl and
Odlyzko conjectured that $|\mathcal{F}|\leq 2^{(1+o(1)) n/\ell}$ holds already
if one only requires that for some $k=k(\ell)$ any $k$ distinct members of
$\mathcal{F}$ have an intersection of size divisible by $\ell$. We completely
resolve this old conjecture in a strong form, showing that $|\mathcal{F}|\leq
2^{\lfloor n/\ell\rfloor}+O(1)$ if $k$ is chosen appropriately, and the $O(1)$
error term is not needed if (and only if) $\ell \, | \, n$, and $n$ is
sufficiently large. Moreover the only extremal configurations have `atomic'
structure as above. Our main tool, which might be of independent interest, is a
structure theorem for set systems with small 'doubling'.


Abstract:
  Visual Object Tracking (VOT) can be seen as an extended task of Few-Shot
Learning (FSL). While the concept of FSL is not new in tracking and has been
previously applied by prior works, most of them are tailored to fit specific
types of FSL algorithms and may sacrifice running speed. In this work, we
propose a generalized two-stage framework that is capable of employing a large
variety of FSL algorithms while presenting faster adaptation speed. The first
stage uses a Siamese Regional Proposal Network to efficiently propose the
potential candidates and the second stage reformulates the task of classifying
these candidates to a few-shot classification problem. Following such a
coarse-to-fine pipeline, the first stage proposes informative sparse samples
for the second stage, where a large variety of FSL algorithms can be conducted
more conveniently and efficiently. As substantiation of the second stage, we
systematically investigate several forms of optimization-based few-shot
learners from previous works with different objective functions, optimization
methods, or solution space. Beyond that, our framework also entails a direct
application of the majority of other FSL algorithms to visual tracking,
enabling mutual communication between researchers on these two topics.
Extensive experiments on the major benchmarks, VOT2018, OTB2015, NFS, UAV123,
TrackingNet, and GOT-10k are conducted, demonstrating a desirable performance
gain and a real-time speed.


Abstract:
  The fundamental processes by which nuclear energy is generated in the Sun
have been known for many years. However, continuous progress in areas such as
neutrino experiments, stellar spectroscopy and helioseismic data and techniques
requires ever more accurate and precise determination of nuclear reaction cross
sections, a fundamental physical input for solar models. In this work, we
review the current status of (standard) solar models and present a detailed
discussion on the relevance of nuclear reactions for detailed predictions of
solar properties. In addition, we also provide an analytical model that helps
understanding the relation between nuclear cross sections, neutrino fluxes and
the possibility they offer for determining physical characteristics of the
solar interior. The latter is of particular relevance in the context of the
conundrum posed by the solar composition, the solar abundance problem, and in
the light of the first ever direct detection of solar CN neutrinos recently
obtained by the Borexino collaboration. Finally, we present a short list of
wishes about the precision with which nuclear reaction rates should be
determined to allow for further progress in our understanding of the Sun.


Abstract:
  We construct non-exact operator spaces satisfying the Weak Expectation
Property (WEP) and the Operator space version of the Local Lifting Property
(OLLP). These examples should be compared with the example we recently gave of
a $C^*$-algebra with WEP and LLP. The construction produces several new
analogues among operator spaces of the Gurarii space, extending Oikhberg's
previous work. Each of our "Gurarii operator spaces" is associated to a class
of finite dimensional operator spaces (with suitable properties). In each case
we show the space exists and is unique up to completely isometric isomorphism.


Abstract:
  In this paper, we propose a novel graph convolutional network architecture,
Graph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The
proposed architecture consists of repeated encoder-decoder, in which
graph-structured features are processed across three different scales of human
skeletal representations. This multi-scale architecture enables the model to
learn both local and global feature representations, which are critical for 3D
human pose estimation. We also introduce a multi-level feature learning
approach using different-depth intermediate features and show the performance
improvements that result from exploiting multi-scale, multi-level feature
representations. Extensive experiments are conducted to validate our approach,
and the results show that our model outperforms the state-of-the-art.


Abstract:
  The electronic bandstructure of a solid is a collection of allowed bands
separated by forbidden bands, revealing the geometric symmetry of the crystal
structures. Comprehensive knowledge of the bandstructure with band parameters
explains intrinsic physical, chemical and mechanical properties of the solid.
Here we report the artificial polaritonic bandstructures of two-dimensional
honeycomb lattices for microcavity exciton-polaritons using GaAs semiconductors
in the wide-range detuning values, from cavity-photon-like (red-detuned) to
exciton-like (blue-detuned) regimes. In order to understand the experimental
bandstructures and their band parameters, such as gap energies, bandwidths,
hopping integrals and density of states, we originally establish a polariton
band theory within an augmented plane wave method with two-kind-bosons, cavity
photons trapped at the lattice sites and freely moving excitons. In particular,
this two-kind-band theory is absolutely essential to elucidate the exciton
effect in the bandstructures of blue-detuned exciton-polaritons, where the
flattened exciton-like dispersion appears at larger in-plane momentum values
captured in our experimental access window. We reach an excellent agreement
between theory and experiments in all detuning values.


Abstract:
  We analyze the observed spatial, chemical and dynamical distributions of
local metal-poor stars, based on photometrically derived metallicity and
distance estimates along with proper motions from the Gaia mission. Along the
Galactic prime meridian, we identify stellar populations with distinct
properties in the metallicity versus rotational velocity space, including Gaia
Sausage/Enceladus (GSE), the metal-weak thick disk (MWTD), and the Splash
(sometimes referred to as the "in situ" halo). We model the observed
phase-space distributions using Gaussian mixtures and refine their positions
and fractional contributions as a function of distances from the Galactic plane
($|Z|$) and the Galactic center ($R_{\rm GC}$), providing a global perspective
of the major stellar populations in the local halo. Within the sample volume
($|Z|<6$ kpc), stars associated with GSE exhibit a larger proportion of
metal-poor stars at greater $R_{\rm GC}$ ($\Delta \langle{\rm[Fe/H]}\rangle
/\Delta R_{\rm GC} =-0.05\pm0.02$ dex kpc$^{-1}$). This observed trend, along
with a mild anticorrelation of the mean rotational velocity with metallicity
($\Delta \langle v_\phi \rangle / \Delta \langle{\rm[Fe/H]} \rangle \sim -10$
km s$^{-1}$ dex$^{-1}$), implies that more metal-rich stars in the inner region
of the GSE progenitor were gradually stripped away, while the prograde orbit of
the merger at infall became radialized by dynamical friction. The metal-rich
GSE stars are causally disconnected from the Splash structure, whose stars are
mostly found on prograde orbits ($>94\%$) and exhibit a more centrally
concentrated distribution than GSE. The MWTD exhibits a similar spatial
distribution to the Splash, suggesting earlier dynamical heating of stars in
the primordial disk of the Milky Way, possibly before the GSE merger.


Abstract:
  Integrating external language models (LMs) into end-to-end (E2E) models
remains a challenging task for domain-adaptive speech recognition. Recently,
internal language model estimation (ILME)-based LM fusion has shown significant
word error rate (WER) reduction from Shallow Fusion by subtracting a weighted
internal LM score from an interpolation of E2E model and external LM scores
during beam search. However, on different test sets, the optimal LM
interpolation weights vary over a wide range and have to be tuned extensively
on well-matched validation sets. In this work, we perform LM fusion in the
minimum WER (MWER) training of an E2E model to obviate the need for LM weights
tuning during inference. Besides MWER training with Shallow Fusion (MWER-SF),
we propose a novel MWER training with ILME (MWER-ILME) where the ILME-based
fusion is conducted to generate N-best hypotheses and their posteriors.
Additional gradient is induced when internal LM is engaged in MWER-ILME loss
computation. During inference, LM weights pre-determined in MWER training
enable robust LM integrations on test sets from different domains. Experimented
with 30K-hour trained transformer transducers, MWER-ILME achieves on average
8.8% and 5.8% relative WER reductions from MWER and MWER-SF training,
respectively, on 6 different test sets


Abstract:
  Aircraft manufacturing relies on pre-order bookings. The configuration of the
to be assembled aircraft is fixed by the design assisted market surveys. The
sensitivity of the supply chain to the market conditions, makes, the
relationship between the product (aircraft) and the associated service
(aviation), precarious. Traditional model to mitigate this risk to
profitability rely on increasing the scales of operations. However, the
emergence of new standards of air quality monitoring and insistence on the
implementation, demands additional corrective measures. In the quest for a
solution, this research commentary establishes a link, between the airport
taxes and the nature of the transporting unit. It warns, that merely,
increasing the number of mid haulage range aircrafts (MHA) in the fleet, may
not be enough, to overcome this challenge. In a two-pronged approach, the
communication proposes, the use of mostly electric assisted air planes, and
small sized airports as the key to solving this complex problem. As a side-note
the appropriateness of South Asian region, as a test-bed for MEAP based
aircrafts is also investigated. The success of this the idea can be potentially
extended, to any other aviation friendly region of the world.


Abstract:
  We present high-angular-resolution radio observations of the Arches cluster
in the Galactic centre, one of the most massive young clusters in the Milky
Way. The data were acquired in two epochs and at 6 and 10 GHz with the Karl G.
Jansky Very Large Array (JVLA). The rms noise reached is three to four times
better than during previous observations and we have almost doubled the number
of known radio stars in the cluster. Nine of them have spectral indices
consistent with thermal emission from ionised stellar winds, one is a confirmed
colliding wind binary (CWB), and two sources are ambiguous cases. Regarding
variability, the radio emission appears to be stable on timescales of a few to
ten years. Finally, we show that the number of radio stars can be used as a
tool for constraining the age and/or mass of a cluster and also its mass
function.


Abstract:
  We consider an elliptic problem with nonlinear boundary condition involving
nonlinearity with superlinear and subcritical growth at infinity and a
bifurcation parameter as a factor. We use re-scaling method, degree theory and
continuation theorem to prove that there exists a connected branch of positive
solutions bifurcating from infinity when the parameter goes to zero. Moreover,
if the nonlinearity satisfies additional conditions near zero, we establish a
global bifurcation result, and discuss the number of positive solution(s) with
respect to the parameter using bifurcation theory and degree theory.


Abstract:
  Real-world machine learning systems need to analyze test data that may differ
from training data. In K-way classification, this is crisply formulated as
open-set recognition, core to which is the ability to discriminate open-set
data outside the K closed-set classes. Two conceptually elegant ideas for
open-set discrimination are: 1) discriminatively learning an open-vs-closed
binary discriminator by exploiting some outlier data as the open-set, and 2)
unsupervised learning the closed-set data distribution with a GAN, using its
discriminator as the open-set likelihood function. However, the former
generalizes poorly to diverse open test data due to overfitting to the training
outliers, which are unlikely to exhaustively span the open-world. The latter
does not work well, presumably due to the instable training of GANs. Motivated
by the above, we propose OpenGAN, which addresses the limitation of each
approach by combining them with several technical insights. First, we show that
a carefully selected GAN-discriminator on some real outlier data already
achieves the state-of-the-art. Second, we augment the available set of real
open training examples with adversarially synthesized "fake" data. Third and
most importantly, we build the discriminator over the features computed by the
closed-world K-way networks. This allows OpenGAN to be implemented via a
lightweight discriminator head built on top of an existing K-way network.
Extensive experiments show that OpenGAN significantly outperforms prior
open-set methods.


Abstract:
  Climate change presents an existential threat to human societies and the
Earth's ecosystems more generally. Mitigation strategies naturally require
solving a wide range of challenging problems in science, engineering, and
economics. In this context, rapidly developing quantum technologies in
computing, sensing, and communication could become useful tools to diagnose and
help mitigate the effects of climate change. However, the intersection between
climate and quantum sciences remains largely unexplored. This preliminary
report aims to identify potential high-impact use-cases of quantum technologies
for climate change with a focus on four main areas: simulating physical
systems, combinatorial optimization, sensing, and energy efficiency. We hope
this report provides a useful resource towards connecting the climate and
quantum science communities, and to this end we identify relevant research
questions and next steps.


Abstract:
  Information theoretic sensor management approaches are an ideal solution to
state estimation problems when considering the optimal control of multi-agent
systems, however they are too computationally intensive for large state spaces,
especially when considering the limited computational resources typical of
large-scale distributed multi-agent systems. Reinforcement learning (RL) is a
promising alternative which can find approximate solutions to distributed
optimal control problems that take into account the resource constraints
inherent in many systems of distributed agents. However, the RL training can be
prohibitively inefficient, especially in low-information environments where
agents receive little to no feedback in large portions of the state space. We
propose a hybrid information-driven multi-agent reinforcement learning (MARL)
approach that utilizes information theoretic models as heuristics to help the
agents navigate large sparse state spaces, coupled with information based
rewards in an RL framework to learn higher-level policies. This paper presents
our ongoing work towards this objective. Our preliminary findings show that
such an approach can result in a system of agents that are approximately three
orders of magnitude more efficient at exploring a sparse state space than naive
baseline metrics. While the work is still in its early stages, it provides a
promising direction for future research.


Abstract:
  Harnessing the quantum computation power of the present
noisy-intermediate-size-quantum devices has received tremendous interest in the
last few years. Here we study the learning power of a one-dimensional
long-range randomly-coupled quantum spin chain, within the framework of
reservoir computing. In time sequence learning tasks, we find the system in the
quantum many-body localized (MBL) phase holds long-term memory, which can be
attributed to the emergent local integrals of motion. On the other hand, MBL
phase does not provide sufficient nonlinearity in learning highly-nonlinear
time sequences, which we show in a parity check task. This is reversed in the
quantum ergodic phase, which provides sufficient nonlinearity but compromises
memory capacity. In a complex learning task of Mackey-Glass prediction that
requires both sufficient memory capacity and nonlinearity, we find optimal
learning performance near the MBL-to-ergodic transition. This leads to a
guiding principle of quantum reservoir engineering at the edge of quantum
ergodicity reaching optimal learning power for generic complex reservoir
learning tasks. Our theoretical finding can be readily tested with present
experiments.


Abstract:
  We present a study of the influence of magnetic field strength and morphology
in Type Ia Supernovae and their late-time light curves and spectra. In order to
both capture self-consistent magnetic field topologies as well evolve our
models to late times, a two stage approach is taken. We study the early
deflagration phase (1s) using a variety of magnetic field strengths, and find
that the topology of the field is set by the burning, independent of the
initial strength. We study late time (~1000 days) light curves and spectra with
a variety of magnetic field topologies, and infer magnetic field strengths from
observed supernovae. Lower limits are found to be 106G. This is determined by
the escape, or lack thereof, of positrons that are tied to the magnetic field.
The first stage employs 3d MHD and a local burning approximation, and uses the
code Enzo. The second stage employs a hybrid approach, with 3D radiation and
positron transport, and spherical hydrodynamics. The second stage uses the code
HYDRA. In our models, magnetic field amplification remains small during the
early deflagration phase. Late-time spectra bear the imprint of both magnetic
field strength and morphology. Implications for alternative explosion scenarios
are discussed.


Abstract:
  Computational Fluid Dynamics (CFD) is a major sub-field of engineering.
Corresponding flow simulations are typically characterized by heavy
computational resource requirements. Often, very fine and complex meshes are
required to resolve physical effects in an appropriate manner. Since all CFD
algorithms scale at least linearly with the size of the underlying mesh
discretization, finding an optimal mesh is key for computational efficiency.
  One methodology used to find optimal meshes is goal-oriented adaptive mesh
refinement. However, this is typically computationally demanding and only
available in a limited number of tools. Within this contribution, we adopt a
machine learning approach to identify optimal mesh densities. We generate
optimized meshes using classical methodologies and propose to train a
convolutional network predicting optimal mesh densities given arbitrary
geometries. The proposed concept is validated along 2d wind tunnel simulations
with more than 60,000 simulations. Using a training set of 20,000 simulations
we achieve accuracies of more than 98.7%.
  Corresponding predictions of optimal meshes can be used as input for any mesh
generation and CFD tool. Thus without complex computations, any CFD engineer
can start his predictions from a high quality mesh.


Abstract:
  Context. The Sun's complex corona is the source of the solar wind and
interplanetary magnetic field. While the large scale morphology is well
understood, the impact of variations in coronal properties on the scale of a
few degrees on properties of the interplanetary medium is not known. Solar
Orbiter, carrying both remote sensing and in situ instruments into the inner
solar system, is intended to make these connections better than ever before.
Aims. We combine remote sensing and in situ measurements from Solar Orbiter's
first perihelion at 0.5 AU to study the fine scale structure of the solar wind
from the equatorward edge of a polar coronal hole with the aim of identifying
characteristics of the corona which can explain the in situ variations.
Methods. We use in situ measurements of the magnetic field, density and solar
wind speed to identify structures on scales of hours at the spacecraft. Using
Potential Field Source Surface mapping we estimate the source locations of the
measured solar wind as a function of time and use EUI images to characterise
these solar sources. Results. We identify small scale stream interactions in
the solar wind with compressed magnetic field and density along with speed
variations which are associated with corrugations in the edge of the coronal
hole on scales of several degrees, demonstrating that fine scale coronal
structure can directly influence solar wind properties and drive variations
within individual streams. Conclusions. This early analysis already
demonstrates the power of Solar Orbiter's combined remote sensing and in situ
payload and shows that with future, closer perihelia it will be possible
dramatically to improve our knowledge of the coronal sources of fine scale
solar wind structure, which is important both for understanding the phenomena
driving the solar wind and predicting its impacts at the Earth and elsewhere.


Abstract:
  It is crucial for policymakers to understand the community prevalence of
COVID-19 so combative resources can be effectively allocated and prioritized
during the COVID-19 pandemic. Traditionally, community prevalence has been
assessed through diagnostic and antibody testing data. However, despite the
increasing availability of COVID-19 testing, the required level has not been
met in most parts of the globe, introducing a need for an alternative method
for communities to determine disease prevalence. This is further complicated by
the observation that COVID-19 prevalence and spread varies across different
spatial, temporal, and demographics. In this study, we understand trends in the
spread of COVID-19 by utilizing the results of self-reported COVID-19 symptoms
surveys as an alternative to COVID-19 testing reports. This allows us to assess
community disease prevalence, even in areas with low COVID-19 testing ability.
Using individually reported symptom data from various populations, our method
predicts the likely percentage of the population that tested positive for
COVID-19. We do so with a Mean Absolute Error (MAE) of 1.14 and Mean Relative
Error (MRE) of 60.40\% with 95\% confidence interval as (60.12, 60.67). This
implies that our model predicts +/- 1140 cases than the original in a
population of 1 million. In addition, we forecast the location-wise percentage
of the population testing positive for the next 30 days using self-reported
symptoms data from previous days. The MAE for this method is as low as 0.15
(MRE of 23.61\% with 95\% confidence interval as (23.6, 13.7)) for New York. We
present an analysis of these results, exposing various clinical attributes of
interest across different demographics. Lastly, we qualitatively analyze how
various policy enactments (testing, curfew) affect the prevalence of COVID-19
in a community.


Abstract:
  Precise quantitative delineation of tumor hypoxia is essential in radiation
therapy treatment planning to improve the treatment efficacy by targeting
hypoxic sub-volumes. We developed a combined imaging system of positron
emission tomography (PET) and electron para-magnetic resonance imaging (EPRI)
of molecular oxygen to investigate the accuracy of PET imaging in assessing
tumor hypoxia. The PET/EPRI combined imaging system aims to use EPRI to
precisely measure the oxygen partial pressure in tissues. This will evaluate
the validity of PET hypoxic tumor imaging by (near) simultaneously acquired
EPRI as ground truth. The combined imaging system was constructed by
integrating a small animal PET scanner (inner ring diameter 62 mm and axial
field of view 25.6 mm) and an EPRI subsystem (field strength 25 mT and resonant
frequency 700 MHz). The compatibility between the PET and EPRI subsystems were
tested with both phantom and animal imaging. Hypoxic imaging on a tumor mouse
model using $^{18}$F-fluoromisonidazole radio-tracer was conducted with the
developed PET/EPRI system. We report the development and initial imaging
results obtained from the PET/EPRI combined imaging system.


Abstract:
  The effects of the evolution force are observable in nature at all structural
levels ranging from small molecular systems to conversely enormous biospheric
systems. However, the evolution force and work associated with formation of
biological structures has yet to be described mathematically or theoretically.
In addressing the conundrum, we consider evolution from a unique perspective
and in doing so introduce the Fundamental Theory of the Evolution Force, FTEF.
Herein, we prove FTEF by proof of concept using a synthetic evolution
artificial intelligence to engineer 14-3-3 {\zeta} docking proteins. Synthetic
genes were engineered by transforming 14-3-3 {\zeta} sequences into time-based
DNA codes that served as templates for random DNA hybridizations and genetic
assembly. Application of time-based DNA codes allowed us to fast forward
evolution, while damping the effect of point mutations. Notably, SYN-AI
engineered a set of three architecturally conserved docking proteins that
retained motion and vibrational dynamics of native Bos taurus 14-3-3 {\zeta}.


Abstract:
  Quantum cascade lasers (QCLs) facilitate compact optical frequency comb
sources that operate in the mid-infrared and terahertz spectral regions, where
many molecules have their fundamental absorption lines. Enhancing the optical
bandwidth of these chip-sized lasers is of paramount importance to address
their application in broadband high-precision spectroscopy. In this work, we
provide a numerical and experimental investigation of the comb spectral width
and show how it can be optimized to obtain its maximum value defined by the
laser gain bandwidth. The interplay of nonoptimal values of the resonant Kerr
nonlinearity and the cavity dispersion can lead to significant narrowing of the
comb spectrum and reveals the best approach for dispersion compensation. The
implementation of high mirror losses is shown to be favourable and results in
proliferation of the comb sidemodes. Ultimately, injection locking of QCLs by
modulating the laser bias around the roundtrip frequency provides a stable
external knob to control the FM comb state and recover the maximum spectral
width of the unlocked laser state.


Abstract:
  The lack of an easily realizable complementary circuit technology offering
low static power consumption has been limiting the utilization of other
semiconductor materials than silicon. In this publication, a novel depletion
mode JFET based complementary circuit technology is presented and herein after
referred to as Complementary Semiconductor (CS) circuit technology. The fact
that JFETs are pure semiconductor devices, i.e. a carefully optimized Metal
Oxide Semiconductor (MOS) gate stack is not required, facilitates the
implementation of CS circuit technology to many semiconductor materials, like
e.g. germanium and silicon carbide. Furthermore, when the CS circuit technology
is idle there are neither conductive paths between nodes that are biased at
different potentials nor forward biased p-n junctions and thus it enables low
static power consumption. Moreover, the fact that the operation of depletion
mode JFETs does not necessitate the incorporation of forward biased p-n
junctions means that CS circuit technology is not limited to wide band-gap
semiconductor materials, low temperatures, and/or low voltage spans. In this
paper the operation of the CS logic is described and proven via simulations.


Abstract:
  Many robot manipulation skills can be represented with deterministic
characteristics and there exist efficient techniques for learning parameterized
motor plans for those skills. However, one of the active research challenge
still remains to sustain manipulation capabilities in situation of a mechanical
failure. Ideally, like biological creatures, a robotic agent should be able to
reconfigure its control policy by adapting to dynamic adversaries. In this
paper, we propose a method that allows an agent to survive in a situation of
mechanical loss, and adaptively learn manipulation with compromised degrees of
freedom -- we call our method Survivable Robotic Learning (SRL). Our key idea
is to leverage Bayesian policy gradient by encoding knowledge bias in posterior
estimation, which in turn alleviates future policy search explorations, in
terms of sample efficiency and when compared to random exploration based policy
search methods. SRL represents policy priors as Gaussian process, which allows
tractable computation of approximate posterior (when true gradient is
intractable), by incorporating guided bias as proxy from prior replays. We
evaluate our proposed method against off-the-shelf model free learning
algorithm (DDPG), testing on a hexapod robot platform which encounters
incremental failure emulation, and our experiments show that our method
improves largely in terms of sample requirement and quantitative success ratio
in all failure modes. A demonstration video of our experiments can be viewed
at: https://sites.google.com/view/survivalrl


Abstract:
  Following ideas introduced by Beardon-Minda and by Baribeau-Rivard-Wegert in
the context of the Schwarz-Pick lemma, we use the iterated hyperbolic
difference quotients to prove a multipoint Julia lemma. As applications, we
give a sharp estimate from below of the angular derivative at a boundary point,
generalizing results due to Osserman, Mercer and others; and we prove a
generalization to multiple fixed points of an interesting estimate due to Cowen
and Pommerenke. These applications show that iterated hyperbolic difference
quotients and multipoint Julia lemmas can be useful tools for exploring in a
systematic way the influence of higher order derivatives on the boundary
behaviour of holomorphic self-maps of the unit disk.


Abstract:
  We develop a mesoscopic lattice model to study the morphology formation in
interacting ternary mixtures with evaporation of one component. As concrete
application of our model, we wish to capture morphologies as they are typically
arising during fabrication of organic solar cells. In this context, we consider
an evaporating solvent into which two other components are dissolved, as a
model for a 2-component coating solution that is drying on a substrate. We
propose a 3-spins dynamics to describe the evolution of the three interacting
species. As main tool, we use a Monte Carlo Metropolis-based algorithm, with
the possibility of varying the system's temperature, mixture composition,
interaction strengths, and evaporation kinetics. The main novelty is the
structure of the mesoscopic model -- a bi-dimensional lattice with periodic
boundary conditions and divided in square cells to encode a mesoscopic range
interaction among the units. We investigate the effect of the model parameters
on the structure of the resulting morphologies. Finally, we compare the results
obtained with the mesoscopic model with corresponding ones based on an
analogous lattice model with a short range interaction among the units, i.e.
when the mesoscopic length scale coincides with the microscopic length scale of
the lattice.


Abstract:
  We consider the direct $s$-channel gravitational production of dark matter
during the reheating process. Independent of the identity of the dark matter
candidate or its non-gravitational interactions, the gravitational process is
always present and provides a minimal production mechanism. During reheating, a
thermal bath is quickly generated with a maximum temperature $T_{\rm max}$, and
the temperature decreases as the inflaton continues to decay until the energy
densities of radiation and inflaton oscillations are equal, at $T_{\rm RH}$.
During these oscillations, $s$-channel gravitational production of dark matter
occurs. We show that the abundance of dark matter (fermionic or scalar) depends
primarily on the combination $T_{\rm max}^4/T_{\rm RH} M_P^3$. We find that a
sufficient density of dark matter can be produced over a wide range of dark
matter masses: from a GeV to a ZeV.


Abstract:
  Magnetic and crystallographic transitions in the Cairo pentagonal magnet
Bi2Fe4O9 are investigated by means of infrared synchrotron-based spectroscopy
as a function of temperature (20 - 300 K) and pressure (0 - 15.5 GPa). One of
the phonon modes is shown to exhibit an anomalous softening as a function of
temperature in the antiferromagnetic phase below 240 K, highlighting
spin-lattice coupling. Moreover, under applied pressure at 40 K, an even larger
softening is observed through the pressure induced structural transition.
Lattice dynamical calculations reveal that this mode is indeed very peculiar as
it involves a minimal bending of the strongest superexchange path in the
pentagonal planes, as well as a decrease of the distances between second
neighbor irons. The latter confirms the hypothesis made by Friedrich et al.,1
about an increase in the oxygen coordination of irons being at the origin of
the pressure-induced structural transition. As a consequence, one expects a new
magnetic superexchange path that may alter the magnetic structure under
pressure.


Abstract:
  Real-time detections of transients and rapid multi-wavelength follow-up are
at the core of modern multi-messenger astrophysics. MeerTRAP is one such
instrument that has been deployed on the MeerKAT radio telescope in South
Africa to search for fast radio transients in real-time. This, coupled with the
ability to rapidly localize the transient in combination with optical
co-pointing by the MeerLICHT telescope gives the instrument the edge in finding
and identifying the nature of the transient on short timescales. The commensal
nature of the project means that MeerTRAP will keep looking for transients even
if the telescope is not being used specifically for that purpose. Here, we
present a brief overview of the MeerTRAP project. We describe the overall
design, specifications and the software stack required to implement such an
undertaking. We conclude with some science highlights that have been enabled by
this venture over the last 10 months of operation.


Abstract:
  Zonotopes are widely used for over-approximating forward reachable sets of
uncertain linear systems for verification purposes. In this paper, we use
zonotopes to achieve more scalable algorithms that under-approximate backward
reachable sets of uncertain linear systems for control design. The main
difference is that the backward reachability analysis is a two-player game and
involves Minkowski difference operations, but zonotopes are not closed under
such operations. We under-approximate this Minkowski difference with a
zonotope, which can be obtained by solving a linear optimization problem. We
further develop an efficient zonotope order reduction technique to bound the
complexity of the obtained zonotopic under-approximations. The proposed
approach is evaluated against existing approaches using randomly generated
instances and illustrated with several examples.


Abstract:
  Identifying a low-dimensional informed parameter subspace offers a viable
path to alleviating the dimensionality challenge in the sampled-based solution
to large-scale Bayesian inverse problems. This paper introduces a novel
gradient-based dimension reduction method in which the informed subspace does
not depend on the data. This permits an online-offline computational strategy
where the expensive low-dimensional structure of the problem is detected in an
offline phase, meaning before observing the data. This strategy is particularly
relevant for multiple inversion problems as the same informed subspace can be
reused. The proposed approach allows controlling the approximation error (in
expectation over the data) of the posterior distribution. We also present
sampling strategies that exploit the informed subspace to draw efficiently
samples from the exact posterior distribution. The method is successfully
illustrated on two numerical examples: a PDE-based inverse problem with a
Gaussian process prior and a tomography problem with Poisson data and a
Besov-$\mathcal{B}^2_{11}$ prior.


Abstract:
  Random Reshuffling (RR), also known as Stochastic Gradient Descent (SGD)
without replacement, is a popular and theoretically grounded method for
finite-sum minimization. We propose two new algorithms: Proximal and Federated
Random Reshuffing (ProxRR and FedRR). The first algorithm, ProxRR, solves
composite convex finite-sum minimization problems in which the objective is the
sum of a (potentially non-smooth) convex regularizer and an average of $n$
smooth objectives. We obtain the second algorithm, FedRR, as a special case of
ProxRR applied to a reformulation of distributed problems with either
homogeneous or heterogeneous data. We study the algorithms' convergence
properties with constant and decreasing stepsizes, and show that they have
considerable advantages over Proximal and Local SGD. In particular, our methods
have superior complexities and ProxRR evaluates the proximal operator once per
epoch only. When the proximal operator is expensive to compute, this small
difference makes ProxRR up to $n$ times faster than algorithms that evaluate
the proximal operator in every iteration. We give examples of practical
optimization tasks where the proximal operator is difficult to compute and
ProxRR has a clear advantage. Finally, we corroborate our results with
experiments on real data sets.


Abstract:
  Conditional on the extended Riemann hypothesis, we show that with high
probability, the characteristic polynomial of a random symmetric $\{\pm
1\}$-matrix is irreducible. This addresses a question raised by Eberhard in
recent work. The main innovation in our work is establishing sharp estimates
regarding the rank distribution of symmetric random $\{\pm 1\}$-matrices over
$\mathbb{F}_p$ for primes $2 < p \leq \exp(O(n^{1/4}))$. Previously, such
estimates were available only for $p = o(n^{1/8})$. At the heart of our proof
is a way to combine multiple inverse Littlewood--Offord-type results to control
the contribution to singularity-type events of vectors in $\mathbb{F}_p^{n}$
with anticoncentration at least $1/p + \Omega(1/p^2)$. Previously, inverse
Littlewood--Offord-type results only allowed control over vectors with
anticoncentration at least $C/p$ for some large constant $C > 1$.


Abstract:
  In recent years, the immiscible polymer blend system has attracted much
attention as the matrix of nanocomposites. Herein, from the perspective of
dynamics, the control of the carbon nanotubes (CNTs) migration aided with the
interface of polystyrene (PS) and poly(methyl methacrylate) (PMMA) blends was
achieved through a facile melt mixing method. Thus, we revealed a comprehensive
relationship between several typical CNTs migrating scenarios and the microwave
dielectric properties of their nanocomposites. Based on the unique morphologies
and phase domain structures of the immiscible matrix, we further investigated
the multiple microwave dielectric relaxation processes and shed new light on
the relation between relaxation peak position and the phase domain size
distribution. Moreover, by integrating the CNTs interface localization control
with the matrix co-continuous structure construction, we found that the
interface promotes double percolation effect to achieve conductive percolation
at low CNTs loading (~1.06 vol%). Overall, the present study provides a unique
nanocomposite material design symphonizing both functional fillers dispersion
and location as well as the matrix architecture optimization for microwave
applications.


Abstract:
  Despite the acclaimed success of the magnetic field (H) formulation for
modeling the electromagnetic behavior of superconductors with the finite
element method, the use of vector-dependent variables in non-conducting domains
leads to unnecessarily long computation times. In order to solve this issue, we
have recently shown how to use a magnetic scalar potential together with the
H-formulation in the COMSOL Multiphysics environment to efficiently and
accurately solve for the magnetic field surrounding superconducting domains.
However, from the definition of the magnetic scalar potential, the
non-conducting domains must be made simply connected in order to obey Ampere's
law. In this work, we use thin cuts to apply a discontinuity in $\phi$ and make
the non-conducting domains simply connected. This approach is shown to be
easily implementable in the COMSOL Multiphysics finite element program, already
widely used by the applied superconductivity community. We simulate three
different models in 2-D and 3-D using superconducting filaments and tapes, and
show that the results are in very good agreement with the H-A and
H-formulations. Finally, we compare the computation times between the
formulations, showing that the H-$\phi$-formulation can be up to seven times
faster than the standard H-formulation in certain applications of interest.


Abstract:
  Calcium scoring, a process in which arterial calcifications are detected and
quantified in CT, is valuable in estimating the risk of cardiovascular disease
events. Especially when used to quantify the extent of calcification in the
coronary arteries, it is a strong and independent predictor of coronary heart
disease events. Advances in artificial intelligence (AI)-based image analysis
have produced a multitude of automatic calcium scoring methods. While most
early methods closely follow standard calcium scoring accepted in clinic,
recent approaches extend this procedure to enable faster or more reproducible
calcium scoring. This chapter provides an introduction to AI for calcium
scoring, and an overview of the developed methods and their applications. We
conclude with a discussion on AI methods in calcium scoring and propose
potential directions for future research.


Abstract:
  Researchers have developed numerous debugging approaches to help programmers
in the debugging process, but these approaches are rarely used in practice. In
this paper, we investigate how programmers debug their code and what
researchers should consider when developing debugging approaches. We conducted
an online questionnaire where 102 programmers provided information about
recently fixed bugs. We found that the majority of bugs (69.6 %) are semantic
bugs. Memory and concurrency bugs do not occur as frequently (6.9 % and 8.8 %),
but they consume more debugging time. Locating a bug is more difficult than
reproducing and fixing it. Programmers often use only IDE build-in tools for
debugging. Furthermore, programmers frequently use a
replication-observation-deduction pattern when debugging. These results suggest
that debugging support is particularly valuable for memory and concurrency
bugs. Furthermore, researchers should focus on the fault localization phase and
integrate their tools into commonly used IDEs.


Abstract:
  Zero-shot learning (ZSL) refers to the problem of learning to classify
instances from the novel classes (unseen) that are absent in the training set
(seen). Most ZSL methods infer the correlation between visual features and
attributes to train the classifier for unseen classes. However, such models may
have a strong bias towards seen classes during training. Meta-learning has been
introduced to mitigate the basis, but meta-ZSL methods are inapplicable when
tasks used for training are sampled from diverse distributions. In this regard,
we propose a novel Task-aligned Generative Meta-learning model for Zero-shot
learning (TGMZ). TGMZ mitigates the potentially biased training and enables
meta-ZSL to accommodate real-world datasets containing diverse distributions.
TGMZ incorporates an attribute-conditioned task-wise distribution alignment
network that projects tasks into a unified distribution to deliver an unbiased
model. Our comparisons with state-of-the-art algorithms show the improvements
of 2.1%, 3.0%, 2.5%, and 7.6% achieved by TGMZ on AWA1, AWA2, CUB, and aPY
datasets, respectively. TGMZ also outperforms competitors by 3.6% in
generalized zero-shot learning (GZSL) setting and 7.9% in our proposed
fusion-ZSL setting.


Abstract:
  We consider the Brenier-Schr{\"o}dinger problem on compact manifolds with
boundary. In the spirit of a work by Arnaudon, Cruzeiro, L{\'e}onard and
Zambrini, we study the kinetic property of regular solutions and obtain a link
to the Navier-Stokes equations with an impermeability condition. We also
enhance the class of models for which the problem admits a unique solution.
This involves a method of taking quotients by reflection groups for which we
give several examples.


Abstract:
  The fast protection of meshed HVDC grids requires the modeling of the
transient phenomena affecting the grid after a fault. In the case of hybrid
lines comprising both overhead and underground parts, the numerous generated
traveling waves may be difficult to describe and evaluate. This paper proposes
a representation of the grid as a graph, allowing to take into account any
waves traveling through the grid. A relatively compact description of the waves
is then derived, based on a combined physical and behavioral modeling approach.
The obtained model depends explicitly on the characteristics of the grid as
well as on the fault parameters. An application of the model to the
identification of the faulty portion of an hybrid line is proposed. The
knowledge of the faulty portion is profitable as faults in overhead lines,
generally temporary, can lead to the reclosing of the line.


Abstract:
  We investigate the feasibility of using deep learning techniques, in the form
of a one-dimensional convolutional neural network (1D-CNN), for the extraction
of signals from the raw waveforms produced by the individual channels of liquid
argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC
detector model is developed to generate realistic noise and signal waveforms
used to train and test the 1D-CNN, and evaluate its performance on low-level
signals. We demonstrate that our approach overcomes the inherent shortcomings
of traditional cut-based methods by extending sensitivity to signals with ADC
values below their imposed thresholds. This approach exhibits great promise in
enhancing the capabilities of future generation neutrino experiments like DUNE
to carry out their low-energy neutrino physics programs.


Abstract:
  Sensing and metrology play an important role in fundamental science and
applications, by fulfilling the ever-present need for more precise data sets,
and by allowing to make more reliable conclusions on the validity of
theoretical models. Sensors are ubiquitous, they are used in applications
across a diverse range of fields including gravity imaging, geology,
navigation, security, timekeeping, spectroscopy, chemistry, magnetometry,
healthcare, and medicine. Current progress in quantum technologies inevitably
triggers the exploration of quantum systems to be used as sensors with new and
improved capabilities. This perspective initially provides a brief review of
existing and tested quantum sensing systems, before discussing future possible
directions of superconducting quantum circuits use for sensing and metrology:
superconducting sensors including many entangled qubits and schemes employing
Quantum Error Correction. The perspective also lists future research directions
that could be of great value beyond quantum sensing, e.g. for applications in
quantum computation and simulation.


Abstract:
  Over the past two decades, open systems that are described by a non-Hermitian
Hamiltonian have become a subject of intense research. These systems encompass
classical wave systems with balanced gain and loss, semiclassical models with
mode selective losses, and minimal quantum systems, and the meteoric research
on them has mainly focused on the wide range of novel functionalities they
demonstrate. Here, we address the following questions: Does anything remain
constant in the dynamics of such open systems? What are the consequences of
such conserved quantities? Through spectral-decomposition method and explicit,
recursive procedure, we obtain all conserved observables for general
$\mathcal{PT}$-symmetric systems. We then generalize the analysis to
Hamiltonians with other antilinear symmetries, and discuss the consequences of
conservation laws for open systems. We illustrate our findings with several
physically motivated examples.


Abstract:
  Study on a rectified current induced by active particles has received a great
attention due to its possible application to a microscopic motor in biological
environments. Insertion of an {\em asymmetric} passive object amid many active
particles has been regarded as an essential ingredient for generating such a
rectified motion. Here, we report that the reverse situation is also possible,
where the motion of an active object can be rectified by its geometric
asymmetry amid many passive particles. This may describe an unidirectional
motion of polar biological agents with asymmetric shape. We also find a weak
but less diffusive rectified motion in a {\em passive} mode without energy
pump-in. This "moving by dissipation" mechanism could be used as a design
principle for developing more reliable microscopic motors.


Abstract:
  It has been argued that supergravity models of inflation with vanishing sound
speeds, $c_s$, lead to an unbounded growth in the production rate of
gravitinos. We consider several models of inflation to delineate the conditions
for which $c_s = 0$. In models with unconstrained superfields, we argue that
the mixing of the goldstino and inflatino in a time-varying background prevents
the uncontrolled production of the longitudinal modes. This conclusion is
unchanged if there is a nilpotent field associated with supersymmetry breaking
with constraint ${\bf S^2} =0$, i.e. sgoldstino-less models. Models with a
second orthogonal constraint, ${\bf S(\Phi-\bar{\Phi})} =0$, where $\bf{\Phi}$
is the inflaton superfield, which eliminates the inflatino, may suffer from the
over-production of gravitinos. However, we point out that these models may be
problematic if this constraint originates from a UV Lagrangian, as this may
require using higher derivative operators. These models may also exhibit other
pathologies such as $c_s > 1$, which are absent in theories with the single
constraint or unconstrained fields.


Abstract:
  Power law size distributions are the hallmarks of nonlinear energy
dissipation processes governed by self-organized criticality. Here we analyze
75 data sets of stellar flare size distributions, mostly obtained from the {\sl
Extreme Ultra-Violet Explorer (EUVE)} and the {\sl Kepler} mission. We aim to
answer the following questions for size distributions of stellar flares: (i)
What are the values and uncertainties of power law slopes? (ii) Do power law
slopes vary with time ? (iii) Do power law slopes depend on the stellar
spectral type? (iv) Are they compatible with solar flares? (v) Are they
consistent with self-organized criticality (SOC) models? We find that the
observed size distributions of stellar flare fluences (or energies) exhibit
power law slopes of $\alpha_E=2.09\pm0.24$ for optical data sets observed with
Kepler. The observed power law slopes do not show much time variability and do
not depend on the stellar spectral type (M, K, G, F, A, Giants). In solar
flares we find that background subtraction lowers the uncorrected value of
$\alpha_E=2.20\pm0.22$ to $\alpha_E=1.57\pm0.19$. Furthermore, most of the
stellar flares are temporally not resolved in low-cadence (30 min) Kepler data,
which causes an additional bias. Taking these two biases into account, the
stellar flare data sets are consistent with the theoretical prediction $N(x)
\propto x^{-\alpha_x}$ of self-organized criticality models, i.e.,
$\alpha_E=1.5$. Thus, accurate power law fits require automated detection of
the inertial range and background subtraction, which can be modeled with the
generalized Pareto distribution, finite-system size effects, and extreme event
outliers.


Abstract:
  If every vertex in a map has one out of two face-cycle types, then the map is
said to be $2$-semiequivelar. A 2-uniform tiling is an edge-to-edge tiling of
regular polygons having $2$ distinct transitivity classes of vertices. Clearly,
a $2$-uniform map is $2$-semiequivelar. The converse of this is not true in
general. There are 20 distinct 2-uniform tilings (these are of $14$ different
types) on the plane. In this article, we prove that a $2$-semiequivelar
toroidal map $K$ has a finite $2$-uniform cover if the universal cover of $K$
is $2$-uniform except of two types.


Abstract:
  To keep up with demand, servers will scale up to handle hundreds of thousands
of clients simultaneously. Much of the focus of the community has been on
scaling servers in terms of aggregate traffic intensity (packets transmitted
per second). However, bottlenecks caused by the increasing number of concurrent
clients, resulting in a large number of concurrent flows, have received little
attention. In this work, we focus on identifying such bottlenecks. In
particular, we define two broad categories of problems; namely, admitting more
packets into the network stack than can be handled efficiently, and increasing
per-packet overhead within the stack. We show that these problems contribute to
high CPU usage and network performance degradation in terms of aggregate
throughput and RTT. Our measurement and analysis are performed in the context
of the Linux networking stack, the the most widely used publicly available
networking stack. Further, we discuss the relevance of our findings to other
network stacks. The goal of our work is to highlight considerations required in
the design of future networking stacks to enable efficient handling of large
numbers of clients and flows.


Abstract:
  This paper presents a new technique for disturbing the algebraic structure of
linear codes in code-based cryptography. Specifically, we introduce the
so-called semilinear transformations in coding theory and then creatively apply
them to the construction of code-based cryptosystems. Note that
$\mathbb{F}_{q^m}$ can be viewed as an $\mathbb{F}_q$-linear space of dimension
$m$, a semilinear transformation $\varphi$ is therefore defined as an
$\mathbb{F}_q$-linear automorphism of $\mathbb{F}_{q^m}$. Then we impose this
transformation to a linear code $\mathcal{C}$ over $\mathbb{F}_{q^m}$. It is
clear that $\varphi(\mathcal{C})$ forms an $\mathbb{F}_q$-linear space, but
generally does not preserve the $\mathbb{F}_{q^m}$-linearity any longer.
Inspired by this observation, a new technique for masking the structure of
linear codes is developed in this paper. Meanwhile, we endow the underlying
Gabidulin code with the so-called partial cyclic structure to reduce the
public-key size. Compared to some other code-based cryptosystems, our proposal
admits a much more compact representation of public keys. For instance, 2592
bytes are enough to achieve the security of 256 bits, almost 403 times smaller
than that of Classic McEliece entering the third round of the NIST PQC project.


Abstract:
  Mel-filterbanks are fixed, engineered audio features which emulate human
perception and have been used through the history of audio understanding up to
today. However, their undeniable qualities are counterbalanced by the
fundamental limitations of handmade representations. In this work we show that
we can train a single learnable frontend that outperforms mel-filterbanks on a
wide range of audio signals, including speech, music, audio events and animal
sounds, providing a general-purpose learned frontend for audio classification.
To do so, we introduce a new principled, lightweight, fully learnable
architecture that can be used as a drop-in replacement of mel-filterbanks. Our
system learns all operations of audio features extraction, from filtering to
pooling, compression and normalization, and can be integrated into any neural
network at a negligible parameter cost. We perform multi-task training on eight
diverse audio classification tasks, and show consistent improvements of our
model over mel-filterbanks and previous learnable alternatives. Moreover, our
system outperforms the current state-of-the-art learnable frontend on Audioset,
with orders of magnitude fewer parameters.


Abstract:
  With the dramatic rise in high-quality galaxy data expected from Euclid and
Vera C. Rubin Observatory, there will be increasing demand for fast
high-precision methods for measuring galaxy fluxes. These will be essential for
inferring the redshifts of the galaxies. In this paper, we introduce Lumos, a
deep learning method to measure photometry from galaxy images. Lumos builds on
BKGnet, an algorithm to predict the background and its associated error, and
predicts the background-subtracted flux probability density function. We have
developed Lumos for data from the Physics of the Accelerating Universe Survey
(PAUS), an imaging survey using 40 narrow-band filter camera (PAUCam). PAUCam
images are affected by scattered light, displaying a background noise pattern
that can be predicted and corrected for. On average, Lumos increases the SNR of
the observations by a factor of 2 compared to an aperture photometry algorithm.
It also incorporates other advantages like robustness towards distorting
artifacts, e.g. cosmic rays or scattered light, the ability of deblending and
less sensitivity to uncertainties in the galaxy profile parameters used to
infer the photometry. Indeed, the number of flagged photometry outlier
observations is reduced from 10% to 2%, comparing to aperture photometry.
Furthermore, with Lumos photometry, the photo-z scatter is reduced by ~10% with
the Deepz machine learning photo-z code and the photo-z outlier rate by 20%.
The photo-z improvement is lower than expected from the SNR increment, however
currently the photometric calibration and outliers in the photometry seem to be
its limiting factor.


Abstract:
  It is well known that quantum effects may lead to remove the intrinsic
singularity point of back holes. Also, the quintessence scalar field is a
candidate model for describing late-time acceleration expansion. Accordingly,
Kazakov and Solodukhin considered the existence of back-reaction of the
spacetime due to the quantum fluctuations of the background metric to deform
Schwarzschild black hole, which led to change the intrinsic singularity of the
black hole to a 2-sphere with a radius of the order of the Planck length. Also,
Kiselev rewrote the Schwarzschild metric by taking into account the
quintessence field in the background. In this study, we consider the
quantum-corrected Schwarzschild black hole inspired by Kazakov-Solodukhin's
work, and Schwarzschild black hole surrounded by quintessence deduced by
Kiselev to study the mutual effects of quantum fluctuations and quintessence on
the accretion onto the black hole. Consequently, the radial component of
4-velocity and the proper energy density of the accreting fluid have a finite
value on the surface of its central 2-sphere due to the presence of quantum
corrections. Also, by comparing the accretion parameters in different kinds of
black holes, we infer that the presence of a point-like electric charge in the
spacetime is somewhat similar to some quantum fluctuations in the background
metric.


Abstract:
  Batched network coding is a variation of random linear network coding which
has low computational and storage costs. In order to adapt to random
fluctuations in the number of erasures in individual batches, it is not optimal
to recode and transmit the same number of packets for all batches. Different
distributed optimization models, which are called adaptive recoding schemes,
were formulated for this purpose. The key component of these optimization
problems is the expected value of the rank distribution of a batch at the next
network node, which is also known as the expected rank. In this paper, we put
forth a unified adaptive recoding framework with an arbitrary recoding field
size. We show that the expected rank functions are concave when the packet loss
pattern is a stationary stochastic process, which covers but not limited to
independent packet loss and Gilbert-Elliott packet loss model. Under this
concavity assumption, we show that there always exists a solution which not
only can minimize the randomness on the number of recoded packets but also can
tolerate rank distribution errors due to inaccurate measurements or limited
precision of the machine. We provide an algorithm to obtain such an optimal
optimal solution, and propose tuning schemes that can turn any feasible
solution into a desired optimal solution.


Abstract:
  Based on direct numerical simulations with point-like inertial particles
transported by homogeneous and isotropic turbulent flows, we present evidence
for the existence of Markov property in Lagrangian turbulence. We show that the
Markov property is valid for a finite step size larger than a Stokes
number-dependent Einstein-Markov memory length. This enables the description of
multi-scale statistics of Lagrangian particles by Fokker-Planck equations,
which can be embedded in an interdisciplinary approach linking the statistical
description of turbulence with fluctuation theorems of non-equilibrium
stochastic thermodynamics and fluctuation theorems, and local flow structures.


Abstract:
  A dedicated in situ heating setup in a scanning electron microscope (SEM)
followed by an ex situ atomic force microscopy (AFM) and electron backscatter
diffraction (EBSD) is used to characterize the nucleation and early growth
stages of Fe-Al intermetallics (IMs) at 596 {\deg}C. A location tracking is
used to interpret further characterization. Ex situ AFM observations reveal a
slight shrinkage and out of plane protrusion of the IM at the onset of IM
nucleation followed by directional growth. The formed interfacial IM compounds
were identified by ex situ EBSD. It is now clearly demonstrated that the
{\theta}-phase nucleates first prior to the diffusion-controlled growth of the
{\eta}-phase. The {\theta}-phase prevails the intermetallic layer.


Abstract:
  We present a conceptual study of a large format imaging spectrograph for the
Large Submillimeter Telescope (LST) and the Atacama Large Aperture
Submillimeter Telescope (AtLAST). Recent observations of high-redshift galaxies
indicate the onset of earliest star formation just a few 100 million years
after the Big Bang (i.e., z = 12--15), and LST/AtLAST will provide a unique
pathway to uncover spectroscopically-identified first forming galaxies in the
pre-reionization era, once it will be equipped with a large format imaging
spectrograph. We propose a 3-band (200, 255, and 350 GHz), medium resolution (R
= 2,000) imaging spectrograph with 1.5 M detectors in total based on the KATANA
concept (Karatsu et al. 2019), which exploits technologies of the integrated
superconducting spectrometer (ISS) and a large-format imaging array. A 1-deg2
drilling survey (3,500 hr) will capture a large number of [O III] 88 um (and [C
II] 158 um) emitters at z = 8--9, and constrain [O III] luminosity functions at
z > 12.


Abstract:
  Score-based diffusion models synthesize samples by reversing a stochastic
process that diffuses data to noise, and are trained by minimizing a weighted
combination of score matching losses. The log-likelihood of score-based
diffusion models can be tractably computed through a connection to continuous
normalizing flows, but log-likelihood is not directly optimized by the weighted
combination of score matching losses. We show that for a specific weighting
scheme, the objective upper bounds the negative log-likelihood, thus enabling
approximate maximum likelihood training of score-based diffusion models. We
empirically observe that maximum likelihood training consistently improves the
likelihood of score-based diffusion models across multiple datasets, stochastic
processes, and model architectures. Our best models achieve negative
log-likelihoods of 2.83 and 3.76 bits/dim on CIFAR-10 and ImageNet 32x32
without any data augmentation, on a par with state-of-the-art autoregressive
models on these tasks.


Abstract:
  We study a model system with nematic and magnetic orders, within a channel
geometry modelled by an interval, $[-D, D]$. The system is characterised by a
tensor-valued nematic order parameter $\mathbf{Q}$ and a vector-valued
magnetisation $\mathbf{M}$, and the observable states are modelled as stable
critical points of an appropriately defined free energy. In particular, the
full energy includes a nemato-magnetic coupling term characterised by a
parameter $c$. We (i) derive $L^\infty$ bounds for $\mathbf{Q}$ and
$\mathbf{M}$; (ii) prove a uniqueness result in parameter regimes defined by
$c$, $D$ and material- and temperature-dependent correlation lengths; (iii)
analyse order reconstruction solutions, possessing domain walls, and their
stabilities as a function of $D$ and $c$ and (iv) perform numerical studies
that elucidate the interplay of $c$ and $D$ for multistability.


Abstract:
  We propose a mechanism to substantially rectify radiative heat flow by
matching thin films of metal-to-insulator transition materials and polar
dielectrics in the electromagnetic near field. By leveraging the distinct
scaling behaviors of the local density of states with film thickness for metals
and insulators, we theoretically achieve rectification ratios over 140-a
10-fold improvement over the state of the art-with nanofilms of vanadium
dioxide and cubic boron nitride in the parallel-plane geometry at
experimentally feasible gap sizes (~100 nm). Our rational design offers
relative ease of fabrication, flexible choice of materials, and robustness
against deviations from optimal film thicknesses. We expect this work to
facilitate the application of thermal diodes in solid-state thermal circuits
and energy conversion devices.


Abstract:
  We propose a predictive model of the turbulent burning velocity over a wide
range of conditions. The model consists of sub models of the stretch factor and
the turbulent flame area. The stretch factor characterizes the flame response
of turbulence stretch and incorporates effects of detailed chemistry and
transport with a lookup table of laminar counterflow flames. The flame area
model captures the area growth based on Lagrangian statistics of propagating
surfaces, and considers effects of turbulence length scales and fuel
characteristics. The present model predicts the turbulent burning velocity via
an algebraic expression without free parameters. It is validated against 285
cases of the direct numerical simulation or experiment reported from various
research groups on planar and Bunsen flames over a wide range of conditions,
covering fuels from hydrogen to n-dodecane, pressures from 1 to 20 atm, lean
and rich mixtures, turbulence intensity ratios from 0.35 to 110, and turbulence
length ratios from 0.5 to 80. The comprehensive comparison shows that the
proposed turbulent burning velocity model has an overall good agreement over
the wide range of conditions, with the averaged modeling error of 25.3%.
Furthermore, the model prediction involves the uncertainty quantification for
model parameters and chemical kinetics to extend the model applicability.


Abstract:
  We propose a distributed approach to train deep convolutional generative
adversarial neural network (DC-CGANs) models. Our method reduces the imbalance
between generator and discriminator by partitioning the training data according
to data labels, and enhances scalability by performing a parallel training
where multiple generators are concurrently trained, each one of them focusing
on a single data label. Performance is assessed in terms of inception score and
image quality on MNIST, CIFAR10, CIFAR100, and ImageNet1k datasets, showing a
significant improvement in comparison to state-of-the-art techniques to
training DC-CGANs. Weak scaling is attained on all the four datasets using up
to 1,000 processes and 2,000 NVIDIA V100 GPUs on the OLCF supercomputer Summit.


Abstract:
  In this paper we show how inter-cellular molecular communication may change
the overall levels of photosynthesis in plants. Individual plant cells respond
to external stimuli, such as illumination levels, to regulate their
photosynthetic output. Here, we present a mathematical model which shows that
by sharing information internally using molecular communication, plants may
increase overall photosynthate production. Numerical results show that higher
mutual information between cells corresponds to an increase in overall
photosynthesis by as much as 25 per cent. This suggests that molecular
communication plays a vital role in maximising the photosynthesis in plants and
therefore suggests new routes to influence plant development in agriculture and
elsewhere.


Abstract:
  In this paper, the split common null point problem in two Banach spaces is
considered. Then, using the generalized resolvents of maximal monotone
operators and the generalized projections and an infinite family of
nonexpansive mappings, a strong convergence theorem for finding a solution of
the split common null point problem in two Banach spaces in the presence of a
sequence of errors will be proved.


Abstract:
  Spectrally-efficient secure non-orthogonal multiple access (NOMA) has
recently attained a substantial research interest for fifth generation
development. This work explores crucial security issue in NOMA which is stemmed
from utilizing the decoding concept of successive interference cancellation.
Considering untrusted users, we design a novel secure NOMA transmission
protocol to maximize secrecy fairness among users. A new decoding order for two
users' NOMA is proposed that provides positive secrecy rate to both users.
Observing the objective of maximizing secrecy fairness between users under
given power budget constraint, the problem is formulated as minimizing the
maximum secrecy outage probability (SOP) between users. In particular,
closed-form expressions of SOP for both users are derived to analyze secrecy
performance. SOP minimization problems are solved using pseudoconvexity
concept, and optimized power allocation (PA) for each user is obtained.
Asymptotic expressions of SOPs, and optimal PAs minimizing these approximations
are obtained to get deeper insights. Further, globally-optimized power control
solution from secrecy fairness perspective is obtained at a low computational
complexity and, asymptotic approximation is obtained to gain analytical
insights. Numerical results validate the correctness of analysis, and present
insights on optimal solutions. Finally, we present insights on global-optimal
PA by which fairness is ensured and gains of about 55.12%, 69.30%, and 19.11%,
respectively are achieved, compared to fixed PA and individual users' optimal
PAs.


Abstract:
  Variational inference enables approximate posterior inference of the highly
over-parameterized neural networks that are popular in modern machine learning.
Unfortunately, such posteriors are known to exhibit various pathological
behaviors. We prove that as the number of hidden units in a single-layer
Bayesian neural network tends to infinity, the function-space posterior mean
under mean-field variational inference actually converges to zero, completely
ignoring the data. This is in contrast to the true posterior, which converges
to a Gaussian process. Our work provides insight into the over-regularization
of the KL divergence in variational inference.


Abstract:
  We present TransitFit, an open-source Python~3 package designed to fit
exoplanetary transit light-curves for transmission spectroscopy studies
(Available at https://github.com/joshjchayes/TransitFit and
https://github.com/spearnet/TransitFit, with documentation at
https://transitfit.readthedocs.io/). TransitFit employs nested sampling to
offer efficient and robust multi-epoch, multi-wavelength fitting of transit
data obtained from one or more telescopes. TransitFit allows per-telescope
detrending to be performed simultaneously with parameter fitting, including the
use of user-supplied detrending alogorithms. Host limb darkening can be fitted
either independently ("uncoupled") for each filter or combined ("coupled")
using prior conditioning from the PHOENIX stellar atmosphere models. For this
TransitFit uses the Limb Darkening Toolkit (LDTk) together with filter
profiles, including user-supplied filter profiles. We demonstrate the
application of TransitFit in three different contexts. First, we model SPEARNET
broadband optical data of the low-density hot-Neptune WASP-127~b. The data were
obtained from a globally-distributed network of 0.5m--2.4m telescopes. We find
clear improvement in our broadband results using the coupled mode over
uncoupled mode, when compared against the higher spectral resolution GTC/OSIRIS
transmission spectrum obtained by Chen et al. (2018). Using TransitFit, we fit
26 transit observations by TESS to recover improved ephemerides of the
hot-Jupiter WASP-91~b and a transit depth determined to a precision of 170~ppm.
Finally, we use TransitFit to conduct an investigation into the contested
presence of TTV signatures in WASP-126~b using 126 transits observed by TESS,
concluding that there is no statistically significant evidence for such
signatures from observations spanning 31 TESS sectors.


Abstract:
  The ongoing Coronavirus disease 2019 (COVID-19) is a major crisis that has
significantly affected the healthcare sector and global economies, which made
it the main subject of various fields in scientific and technical research. To
properly understand and control this new epidemic, mathematical modelling is
presented as a very effective tool that can illustrate the mechanisms of its
propagation. In this regard, the use of compartmental models is the most
prominent approach adopted in the literature to describe the dynamics of
COVID-19. Along the same line, we aim during this study to generalize and
ameliorate many existing works that consecrated to analyse the behaviour of
this epidemic. Precisely, we propose an SQEAIHR epidemic system for
Coronavirus. Our constructed model is enriched by taking into account the media
intervention and vital dynamics. By the use of the next-generation matrix
method, the theoretical basic reproductive number $R_0$ is obtained for
COVID-19. Based on some nonstandard and generalized analytical techniques, the
local and global stability of the disease-free equilibrium are proven when $R_0
< 1$. Moreover, in the case of $R_0 > 1$, the uniform persistence of COVID-19
model is also shown. In order to better adapt our epidemic model to reality,
the randomness factor is taken into account by considering a proportional white
noises, which leads to a well-posed stochastic model. Under appropriate
conditions, interesting asymptotic properties are proved, namely: extinction
and persistence in the mean. The theoretical results show that the dynamics of
the perturbed COVID-19 model are determined by parameters that are closely
related to the magnitude of the stochastic noise. Finally, we present some
numerical illustrations to confirm our theoretical results and to show the
impact of media intervention and quarantine strategies.


Abstract:
  In this work, we generalize the reaction-diffusion equation in statistical
physics, Schr\"odinger equation in quantum mechanics, Helmholtz equation in
paraxial optics into the neural partial differential equations (NPDE), which
can be considered as the fundamental equations in the field of artificial
intelligence research. We take finite difference method to discretize NPDE for
finding numerical solution, and the basic building blocks of deep neural
network architecture, including multi-layer perceptron, convolutional neural
network and recurrent neural networks, are generated. The learning strategies,
such as Adaptive moment estimation, L-BFGS, pseudoinverse learning algorithms
and partial differential equation constrained optimization, are also presented.
We believe it is of significance that presented clear physical image of
interpretable deep neural networks, which makes it be possible for applying to
analog computing device design, and pave the road to physical artificial
intelligence.


Abstract:
  Generative modeling has recently shown great promise in computer vision, but
it has mostly focused on synthesizing visually realistic images. In this paper,
motivated by multi-task learning of shareable feature representations, we
consider a novel problem of learning a shared generative model that is useful
across various visual perception tasks. Correspondingly, we propose a general
multi-task oriented generative modeling (MGM) framework, by coupling a
discriminative multi-task network with a generative network. While it is
challenging to synthesize both RGB images and pixel-level annotations in
multi-task scenarios, our framework enables us to use synthesized images paired
with only weak annotations (i.e., image-level scene labels) to facilitate
multiple visual tasks. Experimental evaluation on challenging multi-task
benchmarks, including NYUv2 and Taskonomy, demonstrates that our MGM framework
improves the performance of all the tasks by large margins, consistently
outperforming state-of-the-art multi-task approaches.


Abstract:
  Understanding the physics of strongly correlated electronic systems has been
a central issue in condensed matter physics for decades. In transition metal
oxides, strong correlations characteristic of narrow $d$ bands is at the origin
of such remarkable properties as the Mott gap opening, enhanced effective mass,
and anomalous vibronic coupling, to mention a few. SrVO$_3$, with V$^{4+}$ in a
$3d^1$ electronic configuration is the simplest example of a 3D correlated
metallic electronic system. Here, we focus on the observation of a (roughly)
quadratic temperature dependence of the inverse electron mobility of this
seemingly simple system, which is an intriguing property shared by other
metallic oxides. The systematic analysis of electronic transport in SrVO$_3$
thin films discloses the limitations of the simplest picture of e-e
correlations in a Fermi liquid; instead, we show that the quasi-2D topology of
the Fermi surface and a strong electron-phonon coupling, contributing to dress
carriers with a phonon cloud, play a pivotal role on the reported electron
spectroscopic, optical, thermodynamic and transport data. The picture that
emerges is not restricted to SrVO$_3$ but can be shared with other $3d$ and
$4d$ metallic oxides.


Abstract:
  Let $ E \subset \mathbb{R}^2 $ be a finite set, and let $ f : E \to
[0,\infty) $. In this paper, we address the algorithmic aspects of nonnegative
$C^2$ interpolation in the plane. Specifically, we provide an efficient
algorithm to compute a nonnegative $C^2(\mathbb{R}^2)$ extension of $ f $ with
norm within a universal constant factor of the least possible. We also provide
an efficient algorithm to approximate the trace norm.


Abstract:
  In the past decades, the revolutionary advances of Machine Learning (ML) have
shown a rapid adoption of ML models into software systems of diverse types.
Such Machine Learning Software Applications (MLSAs) are gaining importance in
our daily lives. As such, the Quality Assurance (QA) of MLSAs is of paramount
importance. Several research efforts are dedicated to determining the specific
challenges we can face while adopting ML models into software systems. However,
we are aware of no research that offered a holistic view of the distribution of
those ML quality assurance challenges across the various phases of software
development life cycles (SDLC). This paper conducts an in-depth literature
review of a large volume of research papers that focused on the quality
assurance of ML models. We developed a taxonomy of MLSA quality assurance
issues by mapping the various ML adoption challenges across different phases of
SDLC. We provide recommendations and research opportunities to improve SDLC
practices based on the taxonomy. This mapping can help prioritize quality
assurance efforts of MLSAs where the adoption of ML models can be considered
crucial.


Abstract:
  Giant Radio Galaxies (GRGs) are the largest single structures in the
Universe. Exhibiting extended radio morphology, their projected sizes range
from 0.7 Mpc up to 4.9 Mpc. LOFAR has opened a new window on the discovery and
investigation of GRGs and, despite the hundreds that are today known, their
main growth catalyst is still debated. One natural explanation for the
exceptional size of GRGs is their old age. In this context, hard X-ray selected
GRGs show evidence of restarting activity, with the giant radio lobes being
mostly disconnected from the nuclear source, if any. In this paper, we present
the serendipitous discovery of a distant ($z=0.629$), medium X-ray selected GRG
in the Bo\"otes field. High-quality, deep Chandra and LOFAR data allow a robust
study of the connection between the nucleus and the lobes, at a larger redshift
so far inaccessible to coded-mask hard X-ray instruments. The radio morphology
of the GRG presented in this work does not show evidence for restarted
activity, and the nuclear radio core spectrum does not appear to be GPS-like.
On the other hand, the X-ray properties of the new GRG are perfectly consistent
with the ones previously studied with Swift/BAT and INTEGRAL at lower redshift.
In particular, the bolometric luminosity measured from the X-ray spectrum is a
factor of six larger than the one derived from the radio lobes, although the
large uncertainties make them formally consistent at $1\sigma$. Finally, the
moderately dense environment around the GRG, traced by the spatial distribution
of galaxies, supports recent findings that the growth of GRGs is not primarily
driven by underdense environments.


Abstract:
  By employing a pseudo-orthonormal coordinate-free approach, the Dirac
equation for particles in the Kerr--Newman spacetime is separated into its
radial and angular parts. In the massless case to which a special attention is
given, the general Heun-type equations turn into their confluent form. We show
how one recovers some results previously obtained in literature, by other
means.


Abstract:
  Although many techniques have been applied to matrix factorization (MF), they
may not fully exploit the feature structure. In this paper, we incorporate the
grouping effect into MF and propose a novel method called Robust Matrix
Factorization with Grouping effect (GRMF). The grouping effect is a
generalization of the sparsity effect, which conducts denoising by clustering
similar values around multiple centers instead of just around 0. Compared with
existing algorithms, the proposed GRMF can automatically learn the grouping
structure and sparsity in MF without prior knowledge, by introducing a
naturally adjustable non-convex regularization to achieve simultaneous sparsity
and grouping effect. Specifically, GRMF uses an efficient alternating
minimization framework to perform MF, in which the original non-convex problem
is first converted into a convex problem through Difference-of-Convex (DC)
programming, and then solved by Alternating Direction Method of Multipliers
(ADMM). In addition, GRMF can be easily extended to the Non-negative Matrix
Factorization (NMF) settings. Extensive experiments have been conducted using
real-world data sets with outliers and contaminated noise, where the
experimental results show that GRMF has promoted performance and robustness,
compared to five benchmark algorithms.


Abstract:
  The increasing market penetration of electric vehicles (EVs) may pose
significant electricity demand on power systems. This electricity demand is
affected by the inherent uncertainties of EVs' travel behavior that makes
forecasting the daily charging demand (CD) very challenging. In this project,
we use the National House Hold Survey (NHTS) data to form sequences of trips,
and develop machine learning models to predict the parameters of the next trip
of the drivers, including trip start time, end time, and distance. These
parameters are later used to model the temporal charging behavior of EVs. The
simulation results show that the proposed modeling can effectively estimate the
daily CD pattern based on travel behavior of EVs, and simple machine learning
techniques can forecast the travel parameters with acceptable accuracy.


Abstract:
  We argue that neutrino oscillations at JUNO offer a unique opportunity to
study Sorkin's triple-path interference, which is predicted to be zero in
canonical quantum mechanics by virtue of the Born rule. In particular, we
compute the expected bounds on triple-path interference at JUNO and demonstrate
that they are comparable to those already available from electromagnetic
probes. Furthermore, the neutrino probe of the Born rule is much more direct
due to an intrinsic independence from any boundary conditions, whereas such
dependence on boundary conditions is always present in the case of
electromagnetic probes. Thus, neutrino oscillations present an ideal probe of
this aspect of the foundations of quantum mechanics.