Abstract:
  Carbon Nanotubes (CNTs)-polymer composites are promising candidates for a
myriad of applications. Ad-hoc CNTs-polymer composite fabrication techniques
inherently pose roadblock to optimized processing resulting in microstructural
defects i.e., void formation, poor interfacial adhesion, wettability, and
agglomeration of CNTs inside the polymer matrix. Although improvement in the
microstructures can be achieved via additional processing steps such
as-mechanical methods and/or chemical functionalization, the resulting
composites are somewhat limited in structural and functional performances.
Here, we demonstrate that 3D printing technique like-direct ink writing offers
improved processing of CNTs-polymer composites. The shear-induced flow of an
engineered nanocomposite ink through the micronozzle offers some benefits
including reducing the number of voids within the epoxy, improving CNTs
dispersion and adhesion with epoxy, and partially aligns the CNTs. Such
microstructural changes result in superior mechanical performance and heat
transfer in the composites compared to their mold-casted counterparts. This
work demonstrates the advantages of 3D printing over traditional fabrication
methods, beyond the ability to rapidly fabricate complex architectures, to
achieve improved processing dynamics for fabricating CNT-polymer nanocomposites
with better structural and functional properties.


Abstract:
  We construct mixing solutions to the incompressible porous media equation
starting from Muskat type data in the partially unstable regime. In particular,
we consider bubble and turned type interfaces with Sobolev regularity. As a
by-product, we prove the continuation of the evolution of IPM after the
Rayleigh-Taylor and smoothness breakdown exhibited in [18,17]. At each time
slice the space is split into three evolving domains: two non-mixing zones and
a mixing zone which is localized in a neighborhood of the unstable region. In
this way, we show the compatibility between the classical Muskat problem and
the convex integration method.


Abstract:
  Tree-child networks, one of the prominent network classes in phylogenetics,
have been introduced for the purpose of modeling reticulate evolution.
Recently, the first author together with Gittenberger and Mansouri (2019)
showed that the number ${\rm TC}_{\ell,k}$ of tree-child networks with $\ell$
leaves and $k$ reticulation vertices has the first-order asymptotics \[ {\rm
TC}_{\ell,k}\sim c_k\left(\frac{2}{e}\right)^{\ell}\ell^{\ell+2k-1},\qquad
(\ell\rightarrow\infty). \] Moreover, they also computed $c_k$ for $k=1,2,$ and
$3$. In this short note, we give a second approach to the above result which is
based on a recent (algorithmic) approach for the counting of tree-child
networks due to Cardona and Zhang (2020). This second approach is also capable
of giving a simple, closed-form expression for $c_k$, namely,
$c_k=2^{k-1}\sqrt{2}/k!$ for all $k\geq 0$.


Abstract:
  It is well known that value at a non-zero algebraic number of each of the
functions $e^{x}, \ln x, \sin x, \cos x, \tan x, \csc x, \sec x, \cot x, \sinh
x,$ $ \cosh x,$ $ \tanh x,$ and $\coth x$ is transcendental number (see Theorem
9.11 of \cite{N}). In the work, we show that for any one of the above mentioned
functions, $f(x)$, and for a polynomial $g(x)$ with rational coefficients the
zero, if any, of the equation $f(x)=g(x)$ is a transcendental number. We also
show that if $f(x)$ and $g(x)$ are polynomials with rational coefficients, then
a zero of the equation $e^{f(x)}=g(x)$ is a transcendental number. Finally we
show that the existence of an abelian group whose non-zero elements are
transcendental numbers.


Abstract:
  Gravitational Wave interferometers achieve their profound sensitivity by
combining a Michelson interferometer with optical cavities, suspended masses,
and now, squeezed quantum states of light. These states modify the measurement
process of the LIGO, VIRGO and GEO600 interferometers to reduce the quantum
noise that masks astrophysical signals; thus, improvements to squeezing are
essential to further expand our gravitational view of the universe. Further
reducing quantum noise will require both lowering decoherence from losses as
well more sophisticated manipulations to counter the quantum back-action from
radiation pressure. Both tasks require fully understanding the physical
interactions between squeezed light and the many components of km-scale
interferometers. To this end, data from both LIGO observatories in observing
run three are expressed using frequency-dependent metrics to analyze each
detector's quantum response to squeezed states. The response metrics are
derived and used to concisely describe physical mechanisms behind squeezing's
simultaneous interaction with transverse-mode selective optical cavities and
the quantum radiation pressure noise of suspended mirrors. These metrics and
related analysis are broadly applicable for cavity-enhanced optomechanics
experiments that incorporate external squeezing, and -- for the first time --
give physical descriptions of every feature so far observed in the quantum
noise of the LIGO detectors.


Abstract:
  Data augmentation has been widely used to improve deep nerual networks
performance. Numerous approaches are suggested, for example, dropout,
regularization and image augmentation, to avoid over-ftting and enhancing
generalization of neural networks. One of the sub-area within data augmentation
is image mixing and deleting. This specific type of augmentation either mixes
two images or delete image regions to hide or make certain characteristics of
images confusing for the network to force it to emphasize on overall structure
of object in image. The model trained with this approach has shown to perform
and generalize well as compared to one trained without imgage mixing or
deleting. Additional benefit achieved with this method of training is
robustness against image corruptions. Due to its low compute cost and success
in recent past, many techniques of image mixing and deleting are proposed. This
paper provides detailed review on these devised approaches, dividing
augmentation strategies in three main categories cut and delete, cut and mix
and mixup. The second part of paper emprically evaluates these approaches for
image classification, finegrained image recognition and object detection where
it is shown that this category of data augmentation improves the overall
performance for deep neural networks.


Abstract:
  Despite the success of machine learning applications in science, industry,
and society in general, many approaches are known to be non-robust, often
relying on spurious correlations to make predictions. Spuriousness occurs when
some features correlate with labels but are not causal; relying on such
features prevents models from generalizing to unseen environments where such
correlations break. In this work, we focus on image classification and propose
two data generation processes to reduce spuriousness. Given human annotations
of the subset of the features responsible (causal) for the labels (e.g.
bounding boxes), we modify this causal set to generate a surrogate image that
no longer has the same label (i.e. a counterfactual image). We also alter
non-causal features to generate images still recognized as the original labels,
which helps to learn a model invariant to these features. In several
challenging datasets, our data generations outperform state-of-the-art methods
in accuracy when spurious correlations break, and increase the saliency focus
on causal features providing better explanations.


Abstract:
  Rough path analysis is developed in the full Besov scale. This extends, and
essentially concludes, an investigation started by [Pr\"omel--Trabs, Rough
differential equations driven by signals in {B}esov spaces. J. Diff. Equ.
2016], further studied in a series of papers by Liu, Pr\"omel and Teichmann. A
new Besov sewing lemma, a real-analysis result of interest in its own right,
plays a key role, and the flexibility in the choice of Besov parameters allows
for the treatment of equations not available in the H\"older or variation
settings. Important classes of stochastic processes fit in the present
framework.


Abstract:
  With the mass deployment of computing-intensive applications and
delay-sensitive applications on end devices, only adequate computing resources
can meet differentiated services' delay requirements. By offloading tasks to
cloud servers or edge servers, computation offloading can alleviate computing
and storage limitations and reduce delay and energy consumption. However, few
of the existing offloading schemes take into consideration the cloud-edge
collaboration and the constraint of energy consumption and task dependency.
This paper builds a collaborative computation offloading model in cloud and
edge computing and formulates a multi-objective optimization problem.
Constructed by fusing optimal transport and Policy-Based RL, we propose an
Optimal-Transport-Based RL approach to resolve the offloading problem and make
the optimal offloading decision for minimizing the overall cost of delay and
energy consumption. Simulation results show that the proposed approach can
effectively reduce the cost and significantly outperforms existing optimization
solutions.


Abstract:
  We study a model of $N$ fermions in a quantum dot, coupled to $M$ bosons by a
disorder-induced complex Yukawa coupling (Yukawa-SYK model), in order to
explore the interplay between non-Fermi liquid and superconductivity in a
strongly coupled, (quantum-)critical environment. We analyze the phase diagram
of the model for an arbitrary complex interaction and arbitrary ratio of $N/M$,
with special focus on the two regimes of non-Fermi-liquid behavior: an SYK-like
behavior with a power-law frequency dependence of the fermionic self-energy and
an impurity-like behavior with frequency independent self-energy. We show that
the crossover between the two. can be reached by varying either the strength of
the fermion-boson coupling or the ratio $M/N$. We next argue that in both
regimes the system is unstable to superconductivity if the strength of
time-reversal-symmetry-breaking disorder is below a certain threshold. We show
how the corresponding onset temperatures vary between the two regimes. We argue
that the superconducting state is highly unconventional with an infinite set of
minima of the condensation energy at $T=0$, corresponding to topologically
different gap functions. We discuss in detail similarities and differences
between this model and the model of dispersion-full fermions tuned to a
metallic quantum-critical point, with an effective singular dynamical
interaction $V(\Omega) \propto 1/|\Omega|^\gamma$ (the $\gamma-$model).


Abstract:
  We prove that there are at least seeds many exact embedded Lagrangian
fillings for Legendrian links of type $\mathsf{ADE}$. We also provide seeds
many Lagrangian fillings with certain symmetries for type $\mathsf{BCFG}$. Our
main tools are $N$-graphs and the combinatorics of seed patterns of finite
type.


Abstract:
  Credit scoring is a major application of machine learning for financial
institutions to decide whether to approve or reject a credit loan. For sake of
reliability, it is necessary for credit scoring models to be both accurate and
globally interpretable. Simple classifiers, e.g., Logistic Regression (LR), are
white-box models, but not powerful enough to model complex nonlinear
interactions among features. Fortunately, automatic feature crossing is a
promising way to find cross features to make simple classifiers to be more
accurate without heavy handcrafted feature engineering. However, credit scoring
is usually based on different aspects of users, and the data usually contains
hundreds of feature fields. This makes existing automatic feature crossing
methods not efficient for credit scoring. In this work, we find local
piece-wise interpretations in Deep Neural Networks (DNNs) of a specific feature
are usually inconsistent in different samples, which is caused by feature
interactions in the hidden layers. Accordingly, we can design an automatic
feature crossing method to find feature interactions in DNN, and use them as
cross features in LR. We give definition of the interpretation inconsistency in
DNN, based on which a novel feature crossing method for credit scoring
prediction called DNN2LR is proposed. Apparently, the final model, i.e., a LR
model empowered with cross features, generated by DNN2LR is a white-box model.
Extensive experiments have been conducted on both public and business datasets
from real-world credit scoring applications. Experimental shows that, DNN2LR
can outperform the DNN model, as well as several feature crossing methods.
Moreover, comparing with the state-of-the-art feature crossing methods, i.e.,
AutoCross, DNN2LR can accelerate the speed for feature crossing by about 10 to
40 times on datasets with large numbers of feature fields.


Abstract:
  We study the elastic response of rigid, wire frame particles in concentrated,
glassy suspensions to a step strain by applying the simple, geometric methods
developed in part I. The wire frame particles are comprised of thin, rigid rods
of length $L$ and their number density, $\rho$, is such that $\rho L^3 \gg 1$.
We specifically compare rigid rods to L-shapes made of two equal length rods
joined at right angles. The behaviour of wire frames is found to be strikingly
different from that of rods. The linear elasticity scales like $\rho^3 L^6$ for
L-shaped particles, whereas it scales proportional to $\rho$ for rods and the
non-linear response shows a transition from shear hardening to shear softening
at a critical density $\rho_c \sim \sqrt{K / k_B T L^6}$, where $K$ is the
bending modulus of the particles. For realistic particles made of double
stranded DNA, this transition occurs at densities of about $\rho L^3 \sim 10$.
The reason for these differences is that wire frames can be forced to bend by
the entanglements with their surroundings, whereas rods always remain straight.
This is found to be very important even for small strains, with most particles
being bent above a critical strain $\gamma_c \sim (\rho L^3)^{-1}$.


Abstract:
  We generalize the first law of black hole mechanics to the rotating, charged
C-metric and to the Ernst metric, both of which have the charged C-metric as a
special case. All of these metrics are (3+1)-dimensional, have vanishing
cosmological constant, and physically describe a pair of black holes pulled
apart to null infinity by some external force. Our first laws are global in the
sense of applying to an entire patch of spacetime, as opposed to a neighborhood
of the black hole. They are formulated with respect to "boost time", whose
primacy is motivated by the celestial holographic approach to scattering
amplitudes.


Abstract:
  We applied a hybrid-dimensional flow model to pressure transients recorded
during pumping experiments conducted at the Reiche Zeche underground research
laboratory to study the normal opening behavior of fractures due to fluid
injection. Two distinct types of pressure responses to flow-rate steps were
identified and numerically modelled using a radial-symmetric flow formulation
for a fracture that comprises a non-linear constitutive relation for the
contact mechanics governing reversible fracture surface interaction. These two
groups represent radial-symmetric and plane-axisymmetric flow regimes from a
conventional pressure-diffusion perspective. A comprehensive parameter study
into the sensitivity of the applied hydro-mechanical model to changes in
characteristic fracture parameters revealed an interrelation between fracture
length and normal fracture stiffness that yield a match between field
observations and numerical results. Fracture stiffness values increase with
corresponding fracture length. Decomposition of the acting normal stresses into
a stresses associated with the deformation state of the global fracture
geometry and the contact stresses indicates that geometrically induced stresses
contribute the more the lower the total effective normal stress and the shorter
the fracture. Separating the contributions of the local contact mechanics and
the overall fracture geometry to fracture normal stiffness indicates that the
latter, the geometrical stiffness, constitutes a lower bound for total
stiffness; its relevance increases with decreasing fracture length, too. Our
study demonstrates that non-linear hydro-mechanical coupling can lead to vastly
different hydraulic responses and thus provides an alternative to conventional
pressure-diffusion analysis that requires changes in flow regime to cover the
full range of observations.


Abstract:
  As a promising avenue to obtain new extreme ultraviolet light source and
detect electronic properties, high-harmonic generation (HHG) has been actively
developed in both theory and experiment. In solids lacking inversion symmetry,
when electrons undergo a nonadiabatic transition, a directional charge shift
occurs and is characterized by shift vector, which measures the real-space
shift of the photoexcited electron and hole. For the first time, we have
revealed that shift vector plays prominent roles in the real-space tunneling
mechanism of three-step model for electrons under strong laser fields. Since
shift vector is determined by the topological properties of related wave
functions, we expect HHG with its contribution can provide direct knowledge on
the band topology in noncentrosymmetric topological insulators (TIs). In both
Kane-Mele model and realistic material BiTeI, we have found that the shift
vector reverses when band inversion happens during the topological phase
transition between normal and topological insulators. Under oscillating strong
laser fields, the reversal of shift vector leads to completely opposite
radiation time of high-order harmonics. This makes HHG a feasible all-optical
strong-field method to directly identify the band inversion in
non-centrosymmetric TIs.


Abstract:
  The Gierer-Meinhardt system occurs in morphogenesis, where the development of
an organism from a single cell is modelled. One of the steps in the
development, is the formation of spatial patterns of the cell structure,
starting from an almost homogeneous cell distribution. Turing proposed in his
pioneering work different activator-inhibitor systems with different diffusion
rates, which could trigger the emergence of such cell structures.
Mathematically, one describes these activator-inhibitor systems as a coupled
systems of reaction-diffusion equations with hugely different diffusion
coefficients and highly nonlinear interaction. One famous example of these
systems is the Gierer-Meinhardt system. These systems usually are not of
monotone type, such that one has to apply other techniques. The purpose of this
article is to study the stochastic reaction-diffusion Gierer-Meinhardt system
with homogeneous Neumann boundary condition on a one or two-dimensional bounded
spatial domain. To be more precise, we perturb the original Gierer-Meinhardt
system by an infinite-dimensional Wiener process and show under which
conditions on the Wiener process and the system, a solution exists. In
dimension one, we even show the pathwise uniqueness. In dimension two,
uniqueness is still an open question.


Abstract:
  We report the detection of an Al II line at 2669.155 Angstroms in 11
metal-poor stars, using ultraviolet spectra obtained with the Space Telescope
Imaging Spectrograph on board the Hubble Space Telescope. We derive Al
abundances from this line using a standard abundance analysis, assuming local
thermodynamic equilibrium (LTE). The mean [Al/Fe] ratio is -0.06 +/- 0.04
(sigma = 0.22) for these 11 stars spanning -3.9 < [Fe/H] < -1.3, or [Al/Fe] =
-0.10 +/- 0.04 (sigma = 0.18) for 9 stars spanning -3.0 < [Fe/H] < -1.3 if two
carbon-enhanced stars are excluded. We use these abundances to perform an
empirical test of non-LTE (NLTE) abundance corrections predicted for resonance
lines of Al I, including the commonly-used optical Al I line at 3961 Angstroms.
The Al II line is formed in LTE, and the abundance derived from this line
matches that derived from high-excitation Al I lines predicted to have minimal
NLTE corrections. The differences between the abundance derived from the Al II
line and the LTE abundance derived from Al I resonance lines are +0.4 to +0.9
dex, which match the predicted NLTE corrections for the Al I resonance lines.
We conclude that the NLTE abundance calculations are approximately correct and
should be applied to LTE abundances derived from Al I lines.


Abstract:
  This paper introduces a neural network approach for fitting the Lee-Carter
and the Poisson Lee-Carter model on multiple populations. We develop some
neural networks that replicate the structure of the individual LC models and
allow their joint fitting by analysing the mortality data of all the considered
populations simultaneously. The neural network architecture is specifically
designed to calibrate each individual model using all available information
instead of using a population-specific subset of data as in the traditional
estimation schemes. A large set of numerical experiments performed on all the
countries of the Human Mortality Database (HMD) shows the effectiveness of our
approach. In particular, the resulting parameter estimates appear smooth and
less sensitive to the random fluctuations often present in the mortality rates'
data, especially for low-population countries. In addition, the forecasting
performance results significantly improved as well.


Abstract:
  We investigate the dynamics of spheroids immersed in the journal bearing flow
subject to a contractible non-reciprocal loop. We show how geometric phases
appear not only in the position, but also in the orientation of such particles.
We show how control and targeting can be carried out on spheroidal particles in
the flow.


Abstract:
  Most adversarial attack methods on text classification can change the
classifier's prediction by synonym substitution. We propose the adversarial
sentence rewriting sampler (ASRS), which rewrites the whole sentence to
generate more similar and higher-quality adversarial examples. Our method
achieves a better attack success rate on 4 out of 7 datasets, as well as
significantly better sentence quality on all 7 datasets. ASRS is an
indispensable supplement to the existing attack methods, because classifiers
cannot resist the attack from ASRS unless they are trained on adversarial
examples found by ASRS.


Abstract:
  As recent advancements in physics and astronomy rapidly rewrite textbooks,
there is a growing need in keeping abreast of the latest knowledge in these
fields. Reading preprints is one of the effective ways to do this. By having
journal clubs where people can read and discuss journals together, the benefits
of reading journals become more prevalent. We present an investigative study of
understanding the factors that affect the success of preprint journal clubs in
astronomy, more commonly known as Astro-ph/Astro-Coffee (hereafter called AC).
A survey was disseminated to understand how institutions from different
countries implement AC. We interviewed 9 survey respondents and from their
responses we identified four important factors that make AC successful:
commitment (how the organizer and attendees participate in AC), environment
(how conducive and comfortable AC is conducted), content (the discussed topics
in AC and how they are presented), and objective (the main goal/s of conducting
AC). We also present the format of our AC, an elective class which was
evaluated during the Spring Semester 2020 (March 2020 - June 2020). Our
evaluation with the attendees showed that enrollees (those who are enrolled and
are required to present papers regularly) tend to be more committed in
attending compared to audiences (those who are not enrolled and are not
required to present papers regularly). In addition, participants tend to find
papers outside their research field harder to read. Finally, we showed an
improvement in the weekly number of papers read after attending AC of those who
present papers regularly, and a high satisfaction rating of our AC. We
summarize the areas of improvement in our AC implementation, and we encourage
other institutions to evaluate their own AC in accordance with the four
aforementioned factors to assess the effectiveness of their AC in reaching
their goals.


Abstract:
  We develop the Lifshitz theory of van der Waals forces in a wedge of a
dielectric material. The non-planar geometry of the problem requires
determining point-wise distribution of stresses. The findings are relevant to a
wide range of phenomena from crack propagation to contact line motion. First,
the stresses prove to be anisotropic as opposed to the classical fluid
mechanics treatment of the contact line problem. Second, the wedge
configuration is always unstable with its angle tending either to collapse or
unfold. The presented theory unequivocally demonstrates quantum nature of the
forces dictating the wedge behavior, which cannot be accounted for with the
classical methods.


Abstract:
  We determine the dimension of the moduli space of non-Abelian vortices in
Yang-Mills-Chern-Simons-Higgs theory in 2+1 dimensions for gauge groups
$G=({\rm U}(1)\times G')/\mathbb{Z}_{n_0}$ with $G'$ being an arbitrary
semi-simple group and $n_0$ the greatest common divisor of the Abelian charges
of the $G'$ invariants. The calculation is carried out using a Callias-type
index theorem, the moduli matrix approach and a D-brane setup in Type IIB
string theory. We prove that the index theorem gives the number of zeromodes or
moduli of the non-Abelian vortices, extend the moduli matrix approach to the
Yang-Mills-Chern-Simons-Higgs theory and finally derive the effective
Lagrangian of Collie and Tong using string theory.


Abstract:
  Despite rapid progress in the recent past, current speech recognition systems
still require labeled training data which limits this technology to a small
fraction of the languages spoken around the globe. This paper describes
wav2vec-U, short for wav2vec Unsupervised, a method to train speech recognition
models without any labeled data. We leverage self-supervised speech
representations to segment unlabeled audio and learn a mapping from these
representations to phonemes via adversarial training. The right representations
are key to the success of our method. Compared to the best previous
unsupervised work, wav2vec-U reduces the phoneme error rate on the TIMIT
benchmark from 26.1 to 11.3. On the larger English Librispeech benchmark,
wav2vec-U achieves a word error rate of 5.9 on test-other, rivaling some of the
best published systems trained on 960 hours of labeled data from only two years
ago. We also experiment on nine other languages, including low-resource
languages such as Kyrgyz, Swahili and Tatar.


Abstract:
  With near-term quantum devices available and the race for fault-tolerant
quantum computers in full swing, researchers became interested in the question
of what happens if we replace a supervised machine learning model with a
quantum circuit. While such "quantum models" are sometimes called "quantum
neural networks", it has been repeatedly noted that their mathematical
structure is actually much more closely related to kernel methods: they analyse
data in high-dimensional Hilbert spaces to which we only have access through
inner products revealed by measurements. This technical manuscript summarises
and extends the idea of systematically rephrasing supervised quantum models as
a kernel method. With this, a lot of near-term and fault-tolerant quantum
models can be replaced by a general support vector machine whose kernel
computes distances between data-encoding quantum states. Kernel-based training
is then guaranteed to find better or equally good quantum models than
variational circuit training. Overall, the kernel perspective of quantum
machine learning tells us that the way that data is encoded into quantum states
is the main ingredient that can potentially set quantum models apart from
classical machine learning models.


Abstract:
  Software sandboxing or software-based fault isolation (SFI) is a lightweight
approach to building secure systems out of untrusted components. Mozilla, for
example, uses SFI to harden the Firefox browser by sandboxing third-party
libraries, and companies like Fastly and Cloudflare use SFI to safely co-locate
untrusted tenants on their edge clouds. While there have been significant
efforts to optimize and verify SFI enforcement, context switching in SFI
systems remains largely unexplored: almost all SFI systems use
\emph{heavyweight transitions} that are not only error-prone but incur
significant performance overhead from saving, clearing, and restoring registers
when context switching. We identify a set of \emph{zero-cost conditions} that
characterize when sandboxed code has sufficient structured to guarantee
security via lightweight \emph{zero-cost} transitions (simple function calls).
We modify the Lucet Wasm compiler and its runtime to use zero-cost transitions,
eliminating the undue performance tax on systems that rely on Lucet for
sandboxing (e.g., we speed up image and font rendering in Firefox by up to
29.7\% and 10\% respectively). To remove the Lucet compiler and its correct
implementation of the Wasm specification from the trusted computing base, we
(1) develop a \emph{static binary verifier}, VeriZero, which (in seconds)
checks that binaries produced by Lucet satisfy our zero-cost conditions, and
(2) prove the soundness of VeriZero by developing a logical relation that
captures when a compiled Wasm function is semantically well-behaved with
respect to our zero-cost conditions. Finally, we show that our model is useful
beyond Wasm by describing a new, purpose-built SFI system, SegmentZero32, that
uses x86 segmentation and LLVM with mostly off-the-shelf passes to enforce our
zero-cost conditions; our prototype performs on-par with the state-of-the-art
Native Client SFI system.


Abstract:
  We study the properties of eigenvalues and corresponding eigenfunctions
generated by a defect in the gaps of the spectrum of a high-contrast random
operator. We consider a family of elliptic operators $\mathcal{A}^\varepsilon$
in divergence form whose coefficients possess double porosity type scaling and
are perturbed on a fixed-size compact domain. The coefficients of
$\mathcal{A}^\varepsilon$ are random variables generated, in an appropriate
sense, by an ergodic dynamical system. Working in the gaps of the limiting
spectrum of the unperturbed operator $\widehat{\mathcal{A}}^\varepsilon$, we
show that the point spectrum of $\mathcal{A}^\epsilon$ converges in the sense
of Hausdorff to the point spectrum of the homogenised operator
$\mathcal{A}^\mathrm{hom}$ as $\varepsilon \to 0$. Furthermore, we prove that
the eigenfunctions of $\mathcal{A}^\varepsilon$ decay exponentially at infinity
uniformly for sufficiently small $\varepsilon$. This, in turn, yields strong
stochastic two-scale convergence of such eigenfunctions to eigenfunctions of
$\mathcal{A}^\mathrm{hom}$.


Abstract:
  Given a $p$-adic group $G$ equipped with an action of a finite group
$\Gamma\subset\mathrm{Aut}_F(\mathbf{G})$, and a reductive fixed-point subgroup
$G^\Gamma$, we establish a relationship between constructions of types for
these two groups due to Yu, Kim--Yu and Fintzen, and generalizes the
relationship between general linear and classical groups observed by Stevens.
As an application, given a parabolically induced or supercuspidal
representation $\pi$ of $G$, we explicitly identify a number of the inertial
equivalence classes occurring in the representation $\pi|_{G^{[\Gamma]}}$.


Abstract:
  Phytochemical composition of ethanol leaf extract of Diodella sarmentosa was
profiled with GC-MS and the inhibitory property of the extract against total
microbial dehydrogenases were assessed. The major constituents of the extract
were squalene (29.50%), Phytol (24.68%), phenol, 3-pentadecyl- (18.58%),
1-Butanol, 3-methyl- (9.09%) and n-Hexadecanoic acid (7.78%). The minimum
inhibitory concentration (MIC) of the extract against broad spectrum of
microbial population was assessed. Bacillus subtilis, Candidas spp, and
Penicillium spp were more sensitive to the treatment and thus; were further
investigated using Dehydrogenase activity assay method. Total dehydrogenase
activities of Bacillus subtilis, Candidas spp, and Penicillium spp at the
extract concentration range of 0 to 2000mg/ml were progressively inhibited at
increasing extract concentrations. The threshold inhibitory concentrations
(IC50) of the extracts against Candidas spp, Penicillium spp and Bacillus
subtilis were 275micro. g/ml, 322 micro. g/ml and 411 micro. g/ml respectively.
Our findings suggested the extract as a useful source of antimicrobial
phytochemicals for pharmaceutical use.


Abstract:
  A novel first-order moving-average model for analyzing time series observed
at irregularly spaced intervals is introduced. Two definitions are presented,
which are equivalent under Gaussianity. The first one relies on normally
distributed data and the specification of second-order moments. The second
definition provided is more flexible in the sense that it allows for
considering other distributional assumptions. The statistical properties are
investigated along with the one-step linear predictors and their mean squared
errors. It is established that the process is strictly stationary under
normality and weakly stationary in the general case. Maximum likelihood and
bootstrap estimation procedures are discussed and the finite-sample behavior of
these estimates is assessed through Monte Carlo experiments. In these
simulations, both methods perform well in terms of estimation bias and standard
errors, even with relatively small sample sizes. Moreover, we show that for
non-Gaussian data, for t-Student and Generalized errors distributions, the
parameters of the model can be estimated precisely by maximum likelihood. The
proposed IMA model is compared to the continuous autoregressive moving average
(CARMA) models, exhibiting good performance. Finally, the practical application
and usefulness of the proposed model are illustrated with two real-life data
examples.


Abstract:
  The warm absorbers observed in more than half of all nearby active galactic
nuclei (AGN) are tracers of ionized outflows located at parsec scale distances
from the central engine. If the smallest inferred ionization parameters
correspond to plasma at a few $10^4$~K, then the gas undergoes a transition
from being bound to unbound provided it is further heated to $\sim 10^6$~K at
larger radii. Dannen et al. recently discovered that under these circumstances,
thermally driven wind solutions are unsteady and even show very dense clumps
due to thermal instability. To explore the observational consequences of these
new wind solutions, we compute line profiles based on the one-dimensional
simulations of Dannen et al. We show how the line profiles from even a simple
steady state wind solution depend on the ionization energy (IE) of absorbing
ions, which is a reflection of the wind ionization stratification. To organize
the diversity of the line shapes, we group them into four categories: weak
Gaussians, saturated boxy profiles with and without an extended blue wing, and
broad weak profiles. The lines with profiles in the last two categories are
produced by ions with the highest IE that probe the fastest regions. Their
maximum blueshifts agree with the highest flow velocities in thermally unstable
models, both steady state and clumpy versions. In contrast, the maximum
blueshifts of the most high IE lines in thermally stable models can be less
than half of the actual solution velocities. Clumpy solutions can additionally
imprint distinguishable absorption troughs at widely separated velocities.


Abstract:
  Matching module plays a critical role in display advertising systems. Without
query from user, it is challenging for system to match user traffic and ads
suitably. System packs up a group of users with common properties such as the
same gender or similar shopping interests into a crowd. Here term crowd can be
viewed as a tag over users. Then advertisers bid for different crowds and
deliver their ads to those targeted users. Matching module in most industrial
display advertising systems follows a two-stage paradigm. When receiving a user
request, matching system (i) finds the crowds that the user belongs to; (ii)
retrieves all ads that have targeted those crowds. However, in applications
such as display advertising at Alibaba, with very large volumes of crowds and
ads, both stages of matching have to truncate the long-tailed parts for online
serving, under limited latency. That's to say, not all ads have the chance to
participate in online matching. This results in sub-optimal result for both
advertising performance and platform revenue. In this paper, we study the
truncation problem and propose a Truncation Free Matching System (TFMS). The
basic idea is to decouple the matching computation from the online pipeline.
Instead of executing the two-stage matching when user visits, TFMS utilizes a
near-line truncation-free matching to pre-calculate and store those top
valuable ads for each user. Then the online pipeline just needs to fetch the
pre-stored ads as matching results. In this way, we can jump out of online
system's latency and computation cost limitations, and leverage flexible
computation resource to finish the user-ad matching. TFMS has been deployed in
our productive system since 2019, bringing (i) more than 50% improvement of
impressions for advertisers who encountered truncation before, (ii) 9.4%
Revenue Per Mile gain, which is significant enough for the business.


Abstract:
  Systems with absorbing configurations usually lead to a unique stationary
probability measure called quasi stationary state (QSS) defined with respect to
the survived samples. We show that the birth death diffusion (BBD) processes
exhibit universal phases and phase transitions when the birth and death rates
depend on the instantaneous particle density and their time scales are
exponentially separated from the diffusion rates. In absence of birth, these
models exhibit non-unique QSSs and lead to an absorbing phase transition (APT)
at some critical nonzero death rate; the usual notion of universality is broken
as the critical exponents of APT here depend on the initial density
distribution.


Abstract:
  Here we prove that the Hilbert-Kunz mulitiplicity of a quadric hypersurface
of dimension $d$ and odd characteristic $p\geq 2d-4$ is bounded below by
$1+m_d$, where $m_d$ is the $d^{th}$ coefficient in the expansion of
$\mbox{sec}+\mbox{tan}$. This proves a part of the long standing conjecture of
Watanabe-Yoshida. We also give an upper bound on the HK multiplicity of such a
hypersurface.
  We approach the question using the HK density function and the classification
of ACM bundles on the smooth quadrics via matrix factorizations.


Abstract:
  The self-adjoint, positive Markov operator defined by the P\'olya-Gamma Gibbs
sampler (under a proper normal prior) is shown to be trace-class, which implies
that all non-zero elements of its spectrum are eigenvalues. Consequently, the
spectral gap is $1-\lambda_*$, where $\lambda_* \in [0,1)$ is the second
largest eigenvalue. A method of constructing an asymptotically valid confidence
interval for an upper bound on $\lambda_*$ is developed by adapting the
classical Monte Carlo technique of Qin et al. (2019) to the P\'olya-Gamma Gibbs
sampler. The results are illustrated using the German credit data. It is also
shown that, in general, uniform ergodicity does not imply the trace-class
property, nor does the trace-class property imply uniform ergodicity.


Abstract:
  We prove a version of the strong Taylor's conjecture for stable graphs: if
$G$ is a stable graph whose chromatic number is strictly greater than
$\beth_2(\aleph_0)$ then $G$ contains all finite subgraphs of Sh$_n(\omega)$
and thus has elementary extensions of unbounded chromatic number. This
completes the picture from our previous work. The main new model theoretic
ingredient is a generalization of the classical construction of
Ehrenfeucht-Mostowski models to an infinitary setting, giving a new
characterization of stability.


Abstract:
  The excitation of soliton states in optical layers exhibiting Kerr
nonlinearities is theoretically investigated. The optical transmission
coefficient is obtained as a function of the nonlinearity power of an incident
monochromatic electromagnetic wave polarized in the transverse electric
configuration. In materials exhibiting a defocusing nonlinearity, soliton
excitations are not observed if the nonlinearity power associated with the
incident electromagnetic wave is below a well-defined value. At such a limiting
value of the nonlinearity power, a soliton excitation with an electric-field
amplitude independent of the position is found within the nonlinear layer,
regardless of the incident-wave frequency value.


Abstract:
  In order to further overcome the difficulties of the existing models in
dealing with the non-stationary and nonlinear characteristics of high-frequency
financial time series data, especially its weak generalization ability, this
paper proposes an ensemble method based on data denoising methods, including
the wavelet transform (WT) and singular spectrum analysis (SSA), and long-term
short-term memory neural network (LSTM) to build a data prediction model, The
financial time series is decomposed and reconstructed by WT and SSA to denoise.
Under the condition of denoising, the smooth sequence with effective
information is reconstructed. The smoothing sequence is introduced into LSTM
and the predicted value is obtained. With the Dow Jones industrial average
index (DJIA) as the research object, the closing price of the DJIA every five
minutes is divided into short-term (1 hour), medium-term (3 hours) and
long-term (6 hours) respectively. . Based on root mean square error (RMSE),
mean absolute error (MAE), mean absolute percentage error (MAPE) and absolute
percentage error standard deviation (SDAPE), the experimental results show that
in the short-term, medium-term and long-term, data denoising can greatly
improve the accuracy and stability of the prediction, and can effectively
improve the generalization ability of LSTM prediction model. As WT and SSA can
extract useful information from the original sequence and avoid overfitting,
the hybrid model can better grasp the sequence pattern of the closing price of
the DJIA. And the WT-LSTM model is better than the benchmark LSTM model and
SSA-LSTM model.


Abstract:
  This paper describes an application, called Medici, designed to produce
synthetic data for social network graphs, which can be used for analysis,
hypothesis testing and application development by researchers and practitioners
in the field. It builds on previous work by providing an integrated system, and
a user friendly screen interface. It can be run with default values to produce
graph data and statistics, which can then be used for further processing. The
system is made publicly available in a Github Java project. The annex provides
a user manual with a screen by screen guide.


Abstract:
  Leibniz's Monadology mentions perceptional and sentimental variations of the
individual in the city. It is the interaction of people with people and events.
Film festivals are highly sentimental events of multicultural cities. Each
movie has a different sentimental effect and the interactions with the movies
have reflections that can be observed on social media. This analysis aims to
apply distant reading on Berlinale tweets collected during the festival. On
contrary to close reading, distant reading let authors to observe patterns in
large collection of data. The analysis is temporal and sentimental in
multilingual domain and strongly positive and negative time intervals are
analysed. For this purpose, we trained a deep sentiment network with
multilingual embeddings. These multilingual embeddings are aligned in latent
space. We trained the network with a multilingual dataset in three languages
English, German and Spanish. The trained algorithm has a 0.78 test score and
applied on Tweets with Berlinale hashtag during the festival. Although the
sentimental analysis does not reflect the award-winning films, we observe
weekly routine on the relationship between sentimentality, which can mislead a
close reading analysis. We have also remarks on popularity of the director or
actors.


Abstract:
  The field of Artificial Intelligence (AI) has undoubtedly received
significant attention in recent years. AI is being adopted to provide solutions
to problems in fields such as medicine, engineering, education, government and
several other domains. In order to analyze the state of the art of research in
the field of AI, we present a systematic literature review focusing on the
Evolution of AI programming languages. We followed the systematic literature
review method by searching relevant databases like SCOPUS, IEEE Xplore and
Google Scholar. EndNote reference manager was used to catalog the relevant
extracted papers. Our search returned a total of 6565 documents, whereof 69
studies were retained. Of the 69 retained studies, 15 documents discussed LISP
programming language, another 34 discussed PROLOG programming language, the
remaining 20 documents were spread between Logic and Object Oriented
Programming (LOOP), ARCHLOG, Epistemic Ontology Language with Constraints
(EOLC), Python, C++, ADA and JAVA programming languages. This review provides
information on the year of implementation, development team, capabilities,
limitations and applications of each of the AI programming languages discussed.
The information in this review could guide practitioners and researchers in AI
to make the right choice of languages to implement their novel AI methods.


Abstract:
  Explainable AI has attracted much research attention in recent years with
feature attribution algorithms, which compute "feature importance" in
predictions, becoming increasingly popular. However, there is little analysis
of the validity of these algorithms as there is no "ground truth" in the
existing datasets to validate their correctness. In this work, we develop a
method to quantitatively evaluate the correctness of XAI algorithms by creating
datasets with known explanation ground truth. To this end, we focus on the
binary classification problems. String datasets are constructed using formal
language derived from a grammar. A string is positive if and only if a certain
property is fulfilled. Symbols serving as explanation ground truth in a
positive string are part of an explanation if and only if they contributes to
fulfilling the property. Two popular feature attribution explainers, Local
Interpretable Model-agnostic Explanations (LIME) and SHapley Additive
exPlanations (SHAP), are used in our experiments.We show that: (1)
classification accuracy is positively correlated with explanation accuracy; (2)
SHAP provides more accurate explanations than LIME; (3) explanation accuracy is
negatively correlated with dataset complexity.


Abstract:
  We study a sub monolayer He-4 adsorbed on fluorographene (GF) and on
hexagonal boron nitride (hBN) at low coverage. The adsorption potentials have
been computed ab-initio with a suitable density functional theory including
dispersion forces. The properties of the adsorbed He-4 atoms have been computed
at finite temperature with path integral Monte Carlo and at T=0 K with
variational path integral. From both methods we find that the lowest energy
state of He-4 on GF is a superfluid. Due to the very large corrugation of the
adsorption potential this superfluid has a very strong spatial anisotropy, the
ratio between the largest and smallest areal density being about 6, the
superfluid fraction at the lowest T is about 55%, and the temperature of the
transition to the normal state is in the range 0.5-1 K. Thus, GF offers a
platform for studying the properties of a strongly interacting highly
anisotropic bosonic superfluid. At a larger coverage He-4 has a transition to
an ordered commensurate state with occupation of 1/6 of the adsorption sites.
This phase is stable up to a transition temperature located between 0.5 and
1~K. The system has a triangular order similar to that of He-4 on graphite. The
lowest energy state of He-4 on hBN is an ordered commensurate state with
occupation of 1/3 of the adsorption sites and triangular symmetry. A disordered
state is present at lower coverage as a metastable state. In the presence of an
electric field the corrugation of the adsorption potential is slightly
increased but up to a magnitude of 1 V/Ang. the effect is small and does not
change the stability of the phases of He-4 on GF and hBN. We have verified that
also in the case of graphene such electric field does not modify the stability
of the commensurate sqrt{3}*sqrt{3}R30 phase.


Abstract:
  Deceptive attacks exploiting the innate and the acquired vulnerabilities of
human users have posed severe threats to information and infrastructure
security. This work proposes INADVERT, a systematic solution that generates
interactive visual aids in real-time to prevent users from inadvertence and
counter visual-deception attacks. Based on the eye-tracking outcomes and proper
data compression, the INADVERT platform automatically adapts the visual aids to
the user's varying attention status captured by the gaze location and duration.
We extract system-level metrics to evaluate the user's average attention level
and characterize the magnitude and frequency of the user's mind-wandering
behaviors. These metrics contribute to an adaptive enhancement of the user's
attention through reinforcement learning. To determine the optimal
hyper-parameters in the attention enhancement mechanism, we develop an
algorithm based on Bayesian optimization to efficiently update the design of
the INADVERT platform and maximize the accuracy of the users' phishing
recognition.


Abstract:
  Photon is the fundamental quantum of electromagnetic fields, whose mass,
$m_{\gamma}$, should be strictly zero in Maxwell's theory. But not all theories
adopt this hypothesis. If the rest mass of the photon is not zero, there will
be an additional time delay between photons of different frequencies after they
travel through a fixed distance. By analyzing the time delay, we can measure or
constrain the photon mass. Fast radio bursts (FRBs) -- transient radio bursts
characterized by millisecond duration and cosmological propagation -- are
excellent astrophysical laboratories to constrain $m_{\gamma}$. In this work we
use a catalog of 129 FRBs in a Bayesian framework to constrain $m_{\gamma}$. As
a result, we obtain a new bound on the photon mass, $m_{\gamma} \leq 3.1\times
10^{-51}\rm\,kg\simeq 1.7 \times 10^{-15}\,eV/c^2$ ($m_{\gamma} \leq 3.9\times
10^{-51}\rm\,kg \simeq 2.2 \times 10^{-15}\,eV/c^2$) at the $68\%$ $(95\%$)
confidence level. The result represents the best limit purely from kinematic
analysis of light propagation. The bound on the photon mass will be tighter in
the near future with increment in the number of FRBs, more accurate measurement
of the redshift for FRBs, and refinement in the knowledge about the origin of
dispersion measures (DMs).


Abstract:
  Event-driven sensors such as LiDAR and dynamic vision sensor (DVS) have found
increased attention in high-resolution and high-speed applications. A lot of
work has been conducted to enhance recognition accuracy. However, the essential
topic of recognition delay or time efficiency is largely under-explored. In
this paper, we present a spiking learning system that uses the spiking neural
network (SNN) with a novel temporal coding for accurate and fast object
recognition. The proposed temporal coding scheme maps each event's arrival time
and data into SNN spike time so that asynchronously-arrived events are
processed immediately without delay. The scheme is integrated nicely with the
SNN's asynchronous processing capability to enhance time efficiency. A key
advantage over existing systems is that the event accumulation time for each
recognition task is determined automatically by the system rather than pre-set
by the user. The system can finish recognition early without waiting for all
the input events. Extensive experiments were conducted over a list of 7 LiDAR
and DVS datasets. The results demonstrated that the proposed system had
state-of-the-art recognition accuracy while achieving remarkable time
efficiency. Recognition delay was shown to reduce by 56.3% to 91.7% in various
experiment settings over the popular KITTI dataset.


Abstract:
  Real-time, guaranteed safe trajectory planning is vital for navigation in
unknown environments. However, real-time navigation algorithms typically
sacrifice robustness for computation speed. Alternatively, provably safe
trajectory planning tends to be too computationally intensive for real-time
replanning. We propose FaSTrack, Fast and Safe Tracking, a framework that
achieves both real-time replanning and guaranteed safety. In this framework,
real-time computation is achieved by allowing any trajectory planner to use a
simplified \textit{planning model} of the system. The plan is tracked by the
system, represented by a more realistic, higher-dimensional \textit{tracking
model}. We precompute the tracking error bound (TEB) due to mismatch between
the two models and due to external disturbances. We also obtain the
corresponding tracking controller used to stay within the TEB. The
precomputation does not require prior knowledge of the environment. We
demonstrate FaSTrack using Hamilton-Jacobi reachability for precomputation and
three different real-time trajectory planners with three different
tracking-planning model pairs.


Abstract:
  We revisit constraints on annihilating dark matter based on the global 21cm
signature observed by EDGES. For this purpose, we used the numerical data of
the latest N-body simulation performed by state-of-the-art standard in order to
estimate the boost factor at high redshifts ($z$ = 10 - 100), which enhances
the annihilation of dark matter in course of structure formation. By taking
into account to what fraction injected energy from dark matter annihilation
contributes to ionization, excitation and heating of intergalactic medium
during dark ages, we estimated how large the global 21cm absorption can be. By
assuming the thermal freezeout scenario, we find that $m_{\rm DM} < 15$ GeV and
$m_{\rm DM} < 3$ GeV have been excluded at 95$\%$ C.L. for the annihilation
modes into $b\bar{b}$ and $e^+ e^-$, respectively.


Abstract:
  Advances in Reinforcement Learning (RL) span a wide variety of applications
which motivate development in this area. While application tasks serve as
suitable benchmarks for real world problems, RL is seldomly used in practical
scenarios consisting of abrupt dynamics. This allows one to rethink the problem
setup in light of practical challenges. We present Trade Execution using
Reinforcement Learning (TradeR) which aims to address two such practical
challenges of catastrophy and surprise minimization by formulating trading as a
real-world hierarchical RL problem. Through this lens, TradeR makes use of
hierarchical RL to execute trade bids on high frequency real market experiences
comprising of abrupt price variations during the 2019 fiscal year COVID19 stock
market crash. The framework utilizes an energy-based scheme in conjunction with
surprise value function for estimating and minimizing surprise. In a
large-scale study of 35 stock symbols from the S&P500 index, TradeR
demonstrates robustness to abrupt price changes and catastrophic losses while
maintaining profitable outcomes. We hope that our work serves as a motivating
example for application of RL to practical problems.


Abstract:
  A generic qubit unitary operator affected by depolarizing noise is duplicated
and inserted in a quantum switch process realizing a superposition of causal
orders. The characterization of the resulting switched quantum channel is
worked out for its action on the joint state of the probe-control qubit pair.
The switched channel is then specifically investigated for the important
metrological task of phase estimation on the noisy unitary operator, with the
performance assessed by the Fisher information, classical or quantum. A
comparison is made with conventional techniques of estimation where the noisy
unitary is directly probed in a one-stage or two-stage cascade with definite
order, or several uses of them with two or more qubits. In the switched channel
with indefinite order, specific properties are reported, meaningful for
estimation and not present with conventional techniques. It is shown that the
control qubit, although it never directly interacts with the unitary, can
nevertheless be measured alone for effective estimation, while discarding the
probe qubit that interacts with the unitary. Also, measurement of the control
qubit maintains the possibility of efficient estimation in difficult conditions
where conventional estimation becomes less efficient, as with ill-configured
input probes, or in blind situations when the axis of the unitary is unknown.
Effective estimation by measuring the control qubit remains possible even when
the input probe tends to align with the axis of the unitary, or with a fully
depolarized input probe, while in these conditions conventional estimation gets
inoperative. Measurement of the probe qubit of the switched channel is also
shown to add useful capabilities for phase estimation. The results contribute
to the analysis of switched quantum channels with indefinite order for
information processing, and uncover new possibilities for qubit metrology.


Abstract:
  The recent launch of Solar Orbiter and BepiColombo opened a brief window in
which these two spacecraft were positioned in a constellation that allows for
the detailed sampling of any Earth-directed CMEs. Fortunately, two such events
occurred with in situ detections of an ICME by Solar Orbiter on the 19th of
April and the 28th of May 2020. These two events were subsequently also
observed in situ by BepiColombo and Wind around a day later. We attempt to
reconstruct the observed in situ magnetic field measurements for all three
spacecraft simultaneously using an empirical magnetic flux rope model. This
allows us to test the validity of our flux rope model on a larger and more
global scale and allows for cross-validation of the analysis with different
spacecraft combinations. Finally, we can also compare the results from the in
situ modeling to remote observations obtained from the STEREO-A heliospheric
imagers. We make use of the 3D coronal rope ejection model in order to simulate
the ICME evolution. We adapt a previously developed ABC-SMC fitting algorithm
for the application to multi point scenarios. We show that we are able to
generally reconstruct the flux ropes signatures at three different spacecraft
positions simultaneously using our model in combination with the flux rope
fitting algorithm. For the well-behaved 19th of April ICME our approach works
very well. The 28th of May ICME, on the other hand, shows the limitations of
our approach. Unfortunately, the usage of multi-point observations for these
events does not appear to solve inherent issues, such as the estimation of the
magnetic field twist or flux rope aspect-ratios due to the specific
constellation of the spacecraft positions. As our general approach can be used
for any fast forward simulation-based model we give a blueprint for future
studies using more advanced ICME models.


Abstract:
  Disordered hyperuniformity (DHU) is a recently discovered novel state of
many-body systems that is characterized by vanishing normalized
infinite-wavelength density fluctuations similar to a perfect crystal, yet
possesses an amorphous structure like a liquid or glass. Here we investigate
equilibrium DHU states of Brownian particles induced by external potentials. In
particular, we analytically derive sufficient conditions on the external
potentials in order to achieve distinct classes of DHU density distributions of
Brownian particles in thermal equilibrium, based on the stationary-state
solutions of the corresponding Smoluchowski equation. We show for a wide
spectrum of tight-binding potentials, the desirable DHU states of Brownian
particles can be controlled and achieved by imposing proper hyperuniformity
conditions on the potentials. Moreover, we find that thermal motions in these
systems tend to enhance hyperuniformity. We also analyze the evolution dynamics
of an initial density distribution (hyperuniform or non-hyperuniform) to the
desirable equilibrium DHU state determined by the prescribed external
potentials, which is shown to be coupled with the full spectra of the force
fields associated with the imposed potentials. We find that although the
transient density distribution can rapidly develop local patterns reminiscent
of those in the equilibrium distribution, which is governed by the fast
dynamics induced by the external potential, the overall distribution is still
modulated by the initial density fluctuations which are relaxed through slow
diffusive dynamics. Our study has implications for the fabrication of designer
DHU materials.


Abstract:
  While even perfect numbers are completely characterized, the existence or
otherwise of odd perfect numbers is an open problem. We address that problem
and prove that if a natural number is odd, then it's not perfect.


Abstract:
  The increasing availability of structured datasets, from Web tables and
open-data portals to enterprise data, opens up opportunities~to enrich
analytics and improve machine learning models through relational data
augmentation. In this paper, we introduce a new class of data augmentation
queries: join-correlation queries. Given a column $Q$ and a join column $K_Q$
from a query table $\mathcal{T}_Q$, retrieve tables $\mathcal{T}_X$ in a
dataset collection such that $\mathcal{T}_X$ is joinable with $\mathcal{T}_Q$
on $K_Q$ and there is a column $C \in \mathcal{T}_X$ such that $Q$ is
correlated with $C$. A na\"ive approach to evaluate these queries, which first
finds joinable tables and then explicitly joins and computes correlations
between $Q$ and all columns of the discovered tables, is prohibitively
expensive. To efficiently support correlated column discovery, we 1) propose a
sketching method that enables the construction of an index for a large number
of tables and that provides accurate estimates for join-correlation queries,
and 2) explore different scoring strategies that effectively rank the query
results based on how well the columns are correlated with the query. We carry
out a detailed experimental evaluation, using both synthetic and real data,
which shows that our sketches attain high accuracy and the scoring strategies
lead to high-quality rankings.


Abstract:
  User authorization queries in the context of role-based access control have
attracted considerable interest in the last 15 years. Such queries are used to
determine whether it is possible to allocate a set of roles to a user that
enables the user to complete a task, in the sense that all the permissions
required to complete the task are assigned to the roles in that set. Answering
such a query, in general, must take into account a number of factors,
including, but not limited to, the roles to which the user is assigned and
constraints on the sets of roles that can be activated. Answering such a query
is known to be NP-hard. The presence of multiple parameters and the need to
find efficient and exact solutions to the problem suggest that a multi-variate
approach will enable us to better understand the complexity of the user
authorization query problem (UAQ). In this paper, we establish a number of
complexity results for UAQ. Specifically, we show the problem remains hard even
when quite restrictive conditions are imposed on the structure of the problem.
Our FPT results show that we have to use either a parameter with potentially
quite large values or quite a restricted version of UAQ. Moreover, our second
FPT algorithm is complex and requires sophisticated, state-of-the-art
techniques. In short, our results show that it is unlikely that all variants of
UAQ that arise in practice can be solved reasonably quickly in general.


Abstract:
  The Zakharov system in dimension $d=2,3$ is shown to have a local unique
solution for any initial values in the energy space $H^{s} \times H^{l} \times
H^{l-1}$, where the range of regularity $(s, l)$ is extended, especially at
$s=l-1$. The result is obtained mainly by the normal form reduction and the
Strichartz estimates.


Abstract:
  Electron-positron pairs, produced in intense laser-solid interactions, are
diagnosed using magnetic spectrometers with image plates, such as the National
Ignition Facility (NIF) Electron Positron Proton Spectrometers (EPPS). Although
modeling can help infer the quantitative value, the accuracy of the models
needs to be verified to ensure measurement quality. The dispersion of
low-energy electrons and positrons may be affected by fringe magnetic fields
near the entrance of the EPPS. We have calibrated the EPPS with six electron
beams from a Siemens Oncor linear accelerator (linac) ranging in energy from
$2.7$--$15.2$ $\mathrm{MeV}$ as they enter the spectrometer. A Geant4 TOPAS
Monte-Carlo simulation was set up to match depth dose curves and lateral
profiles measured in water at $100$ $\mathrm{cm}$ source-surface distance. An
accurate relationship was established between the bending magnet current
setting and the energy of the electron beam at the exit window. The simulations
and measurements were used to determine the energy distributions of the six
electron beams at the EPPS slit. Analysis of the scanned image plates together
with the determined energy distribution arriving in the spectrometer provide
improved dispersion curves for the EPPS.


Abstract:
  Using cryogenic transmission electron microscopy, we revealed three
dimensional (3D) structural details of the electrochemically plated lithium
(Li) flakes and their solid electrolyte interphase (SEI), including the
composite SEI skin-layer and SEI fossil pieces buried inside the Li matrix. As
the SEI skin-layer is largely comprised of nanocrystalline LiF and Li2O in
amorphous polymeric matrix, when complete Li stripping occurs, the compromised
SEI three-dimensional framework buckles, forming nanoscale bends and wrinkles.
We showed that the flexibility and resilience of the SEI skin-layer plays a
vital role in preserving an intact SEI 3D framework after Li stripping. The
intact SEI network enables the nucleation and growth of the newly plated Li
inside the previously formed SEI network in the subsequent cycles, preventing
additional large amount of SEI formation between newly plated Li metal and the
electrolyte. In addition, cells cycled under the accurately controlled uniaxial
pressure can further enhance the repeated utilization of the SEI framework and
improve the coulombic efficiency (CE) by up to 97%, demonstrating an effective
strategy of reducing the formation of additional SEI and inactive dead Li. The
identification of such flexible and porous 3D SEI framework clarifies the
working mechanism of SEI in lithium metal anode for batteries. The insights
provided in this work will inspire researchers to design more functional
artificial 3D SEI on other metal anodes to improve rechargeable metal battery
with long cycle life.


Abstract:
  In this dissertation, several components of large-scale solar flows are
studied observationally: solar equatorial Rossby waves (waves of radial
vorticity), large-scale convection, and surface flows around active regions.
Maps of horizontal flows are derived from photospheric observations by the
Helioseismic and Magnetic Imager (HMI) aboard the Solar Dynamics Observatory
(SDO) using two different techniques: granulation tracking and local
helioseismology. First, the eigenfunctions of solar Rossby waves are measured
from helioseismic ring-diagram flow maps with a correlation method and a
spectral analysis. Down to $9$ Mm below the surface, the dependence of the
radial vorticity with radius $r$ is consistent with $r^{m-1}$, for a given
longitudinal wavenumber $m$. At the surface, the eigenfunctions are
complex-valued. The real part decreases away from the equator and switches sign
around $\pm 20-30^\circ$. The imaginary part is small, but nonzero, and may be
due to wave attenuation. This may have implications for the transport of
angular momentum in the latitudinal direction. Second, we revisit previous
measurements of power spectra of longitudinal velocities near the solar
surface, obtained from time-distance and ring-diagram helioseismology. Several
issues in these past helioseismic analyses are identified and corrected. The
corrections are not sufficient to remove the discrepancy between the
measurements. I thus present new velocity power spectra from granulation
tracking and ring-diagram helioseismology. The two new measurements are close
to each other near the solar surface, and the corresponding kinetic energy
decreases with increasing spatial scale.


Abstract:
  Iconology is a branch of art history that investigates the meaning of
artworks in relation to their social and cultural background. Nowadays, several
interdisciplinary research fields leverage theoretical frameworks close to
iconology to pursue quantitative Art History with data science methods and
Semantic Web technologies. However, while Iconographic studies have been
recently addressed in ontologies, a complete description of aspects relevant to
iconological studies is still missing. In this article, we present a
preliminary study on eleven case studies selected from the literature and we
envision new terms for extending existing ontologies. We validate new terms
according to a common evaluation method and we discuss our results in the light
of the opportunities that such an extended ontology would arise in the
community of Digital Art History.


Abstract:
  We retrace Davenport's solution to Wahba's classic problem of aligning two
pointclouds using the formalism of Geometric Algebra (GA). GA proves to be a
natural backdrop for this problem involving three-dimensional rotations due to
the isomorphism between unit-length quaternions and rotors. While the solution
to this problem is not a new result, it is hoped that its treatment in GA will
have tutorial value as well as open the door to addressing more complex
problems in a similar way.


Abstract:
  A family of quantum fields is said to be strongly local if it generates a
local net of von Neumann algebras. There are few methods of showing directly
strong locality of a quantum field. Among them, linear energy bounds are the
most widely used, yet a chiral conformal field of conformal weight $d>2$ cannot
admit linear energy bounds. In this paper we give a new direct method to prove
strong locality in two-dimensional conformal field theory. We prove that if a
chiral conformal field satisfies an energy bound of degree $d-1$, then it also
satisfies a certain local version of the energy bound, and this in turn implies
strong locality. A central role in our proof is played by diffeomorphism
symmetry. As a concrete application, we show that the vertex operator algebra
given by a unitary vacuum representation of the $\mathcal{W}_3$-algebra is
strongly local. For central charge $c > 2$, this yields a new conformal net. We
further prove that these nets do not satisfy strong additivity, and hence are
not completely rational.


Abstract:
  In this work, we explore different approaches to combine modalities for the
problem of automated age-suitability rating of movie trailers. First, we
introduce a new dataset containing videos of movie trailers in English
downloaded from IMDB and YouTube, along with their corresponding
age-suitability rating labels. Secondly, we propose a multi-modal deep learning
pipeline addressing the movie trailer age suitability rating problem. This is
the first attempt to combine video, audio, and speech information for this
problem, and our experimental results show that multi-modal approaches
significantly outperform the best mono and bimodal models in this task.


Abstract:
  This paper discusses the results obtained for different techniques applied
for performing the sentiment analysis of social media (Twitter) code-mixed text
written in Hinglish. The various stages involved in performing the sentiment
analysis were data consolidation, data cleaning, data transformation and
modelling. Various data cleaning techniques were applied, data was cleaned in
five iterations and the results of experiments conducted were noted after each
iteration. Data was transformed using count vectorizer, one hot vectorizer,
tf-idf vectorizer, doc2vec, word2vec and fasttext embeddings. The models were
created using various machine learning algorithms such as SVM, KNN, Decision
Trees, Random Forests, Naive Bayes, Logistic Regression, and ensemble voting
classifiers. The data was obtained from a task on Codalab competition website
which was listed as Task:9 on the Semeval-2020 competition website. The models
created were evaluated using the F1-score (macro). The best F1-score of 69.07
was achieved using ensemble voting classifier.


Abstract:
  We observed 51 sources in the Q-U-I JOint TEnerife (QUIJOTE) cosmological
fields which were brighter than 1 Jy at 30 GHz in the Planck Point Source
Catalogue (version 1), with the Very Large Array at 28 -- 40 GHz, in order to
characterise their high-radio-frequency variability and polarization
properties. We find a roughly log-normal distribution of polarization fractions
with a median of 2%, in agreement with previous studies, and a median rotation
measure (RM) of $\approx$ 1110 rad m$^{-2}$ with one outlier up to $\approx$
64000 rad m$^{-2}$ which is among the highest RMs measured in quasar cores. We
find hints of a correlation between the total intensity flux density and median
polarization fraction. We find 59% of sources are variable in total intensity,
and 100% in polarization at $3\sigma$ level, with no apparent correlation
between total intensity variability and polarization variability. This
indicates that it will be difficult to model these sources without simultaneous
polarimetric monitoring observations and they will need to be masked for
cosmological analysis.


Abstract:
  We report about observations of the solar U+III bursts on 5 June of 2020 by
means of a new active antenna designed to receive radiation in 4-70 MHz. This
instrument can serve as a prototype of the ultra-long-wavelength radiotelescope
for observations on the farside of the Moon. Our analysis of experimental data
is based on simultaneous records obtained with the antenna arrays GURT and NDA
in high frequency and time resolution, e-Callisto network as well as by using
the space-based observatories STEREO and WIND. The results from this
observational study confirm the model of Reid and Kontar (2017).


Abstract:
  Continuously Variable Series Reactor (CVSR) can regulate its reactance on the
ac side using the nonlinear ferromagnetic core, shared by an ac and a dc
winding for applications like power flow control, oscillation damping, load
balancing and others. In order to use a CVSR in the power grid, it is essential
to know its operating characteristics. The Gyrator-Capacitor approach has been
applied to model the magnetic circuit coupled with the electrical circuit. In
the paper, we investigate the CVSR behavior in terms of induced voltages across
the dc winding, flux densities (B) through the core legs, and power exchange
between the ac controlled and the dc control circuit.


Abstract:
  As the COVID19 spreads across the world, prevention measures are becoming the
essential weapons to combat the pandemic in the period of crisis. The lockdown
measure is the most controversial one as it imposes an overwhelming impact on
our economy and society. Especially when and how to enforce the lockdown
measures are the most challenging questions considering both economic and
epidemiological costs. In this paper, we extend the classic SIR model to find
optimal decision making to balance between economy and people's health during
the outbreak of COVID-19. In our model, we intend to solve a two phases
optimization problem: policymakers control the lockdown rate to maximize the
overall welfare of the society; people in different health statuses take
different decisions on their working hours and consumption to maximize their
utility. We develop a novel method to estimate parameters for the model through
various additional sources of data. We use the Cournot equilibrium to model
people's behavior and also consider the cost of death in order to leverage
between economic and epidemic costs. The analysis of simulation results
provides scientific suggestions for policymakers to make critical decisions on
when to start the lockdown and how strong it should be during the whole period
of the outbreak. Although the model is originally proposed for the COVID19
pandemic, it can be generalized to address similar problems to control the
outbreak of other infectious diseases with lockdown measures.


Abstract:
  We report an inverse-designed, high numerical aperture (~0.44), extended
depth of focus (EDOF) meta-optic, which exhibit a lens-like point spread
function (PSF). The EDOF meta-optic maintains a focusing efficiency comparable
to that of a hyperboloid metalens throughout its depth of focus. Exploiting the
extended depth of focus and computational post-processing, we demonstrate
broadband imaging across the full visible spectrum using a 1 mm, f/1
meta-optic. Unlike other canonical EDOF meta-optics, characterized by phase
masks such as a log-asphere or cubic function, our design exhibits a highly
invariant PSF across ~290nm optical bandwidth, which leads to significantly
improved image quality, as quantified by structural similarity metrics.


Abstract:
  Over the last several decades, quantum entanglement has been intensively
studied for potential applications in quantum information science. The
Hong-Ou-Mandel (HOM) dip is the most important test tool for direct proof of
entanglement between paired photons, whose coincidence detection results in
anticorrelation due to photon bunching on a beam splitter. Although
anticorrelation is due to destructive quantum interference between paired
photons, a wavelength-sensitive interference fringe has never been observed in
any HOM-type experiments. Here, a typical HOM dip is investigated for entangled
photon pairs generated by parametric down conversion processes (SPDC) to
understand fundamental physics of anticorrelation. In addition, a pure
coherence optics-based Hong-Ou-Mandel scheme is proposed and analyzed for
general understanding of anticorrelation in an interferometric system. This
study sheds light on deterministic quantum correlation control and opens the
door to potential applications of on-demand quantum information science.


Abstract:
  Imitation learning seeks to circumvent the difficulty in designing proper
reward functions for training agents by utilizing expert behavior. With
environments modeled as Markov Decision Processes (MDP), most of the existing
imitation algorithms are contingent on the availability of expert
demonstrations in the same MDP as the one in which a new imitation policy is to
be learned. In this paper, we study the problem of how to imitate tasks when
there exist discrepancies between the expert and agent MDP. These discrepancies
across domains could include differing dynamics, viewpoint, or morphology; we
present a novel framework to learn correspondences across such domains.
Importantly, in contrast to prior works, we use unpaired and unaligned
trajectories containing only states in the expert domain, to learn this
correspondence. We utilize a cycle-consistency constraint on both the state
space and a domain agnostic latent space to do this. In addition, we enforce
consistency on the temporal position of states via a normalized position
estimator function, to align the trajectories across the two domains. Once this
correspondence is found, we can directly transfer the demonstrations on one
domain to the other and use it for imitation. Experiments across a wide variety
of challenging domains demonstrate the efficacy of our approach.


Abstract:
  Context: Pull-based development model is widely used in open source, leading
the trends in distributed software development. One aspect which has garnered
significant attention is studies on pull request decision - identifying factors
for explanation. Objective: This study builds on a decade long research on pull
request decision to explain it. We empirically investigate how factors
influence pull request decision and scenarios that change the influence of
factors. Method: We identify factors influencing pull request decision on
GitHub through a systematic literature review and infer it by mining archival
data. We collect a total of 3,347,937 pull requests with 95 features from
11,230 diverse projects on GitHub. Using this data, we explore the relations of
the factors to each other and build mixed-effect logistic regression models to
empirically explain pull request decision. Results: Our study shows that a
small number of factors explain pull request decision with the integrator same
or different from the submitter as the most important factor. We also noted
that some factors are important only in special cases e.g., the percentage of
failed builds is important for pull request decision when continuous
integration is used.


Abstract:
  High-energy irradiation is a driver for atmospheric evaporation and mass loss
in exoplanets. This work is based on data from eROSITA, the soft X-ray
instrument aboard SRG (Spectrum Roentgen Gamma) mission, as well as archival
data from other missions, we aim to characterise the high-energy environment of
known exoplanets and estimate their mass loss rates. We use X-ray source
catalogues from eROSITA, XMM-Newton, Chandra and ROSAT to derive X-ray
luminosities of exoplanet host stars in the 0.2-2 keV energy band with an
underlying coronal, i.e. optically thin thermal spectrum. We present a
catalogue of stellar X-ray and EUV luminosities, exoplanetary X-ray and EUV
irradiation fluxes and estimated mass loss rates for a total of 287 exoplanets,
96 among them being characterised for the first time from new eROSITA
detections. We identify 14 first time X-ray detections of transiting exoplanets
that are subject to irradiation levels known to cause observable evaporation
signatures in other exoplanets, which makes them suitable targets for follow-up
observations.


Abstract:
  The noteworthy four-planet HR 8799 system teeters on the brink of
gravitational instability and contains an A-type host star which is
characteristic of the progenitors of the majority of known white dwarf
planetary system hosts. Gozdziewski and Migaszewski (2020) have demonstrated
that the system can retain all four planets for at least 1 Gyr along the main
sequence if the planets evolve within an externally unperturbed 8:4:2:1 mean
motion resonance configuration. Here we propagate forward their most stable fit
beyond the main sequence, and incorporate external effects from Galactic tides
and stellar flybys. We find that (i) giant branch mass loss always breaks the
resonance, and usually triggers the ejection of two of the planets, (ii)
stellar flybys and Galactic tides rarely break the resonance during the
main-sequence and giant branch phases, but play a crucial role in determining
the final planetary configurations around the eventual white dwarf host star,
and (iii) the meanderings of the surviving planets vary significantly,
occupying regions from under 1 au to thousands of au. The ubiquitous survival
of at least one planet and the presence of the debris discs in the system
should allow for dynamical pathways for the white dwarf to be metal-polluted.


Abstract:
  We prove that every metric graph which is a tree has an orthonormal sequence
of Laplace-eigenfunctions of full support. This implies that the number of
nodal domains $\nu_n$ of the $n$-th eigenfunction of the Laplacian with
standard conditions satisfies $\nu_n/n \to 1$ along a subsequence and has
previously only been known in special cases such as mutually rationally
dependent or rationally independent side lengths. It shows in particular that
the Pleijel nodal domain asymptotics from two- or higher dimensional domains
cannot occur on these graphs: Despite their more complicated topology, they
still behave as in the one-dimensional case. We prove an analogous result on
general metric graphs under the condition that they have at least one Dirichlet
vertex. Furthermore, we generalize our results to Delta vertex conditions and
to edgewise constant potentials. The main technical contribution is a new
expression for a secular function in which modifications to the graph, to
vertex conditions, and to the potential are particularly easy to understand.


Abstract:
  In this work, we present a family of time and space high order finite volume
schemes for the solution of the full Boltzmann equation. The velocity space is
approximated by using a discrete ordinate approach while the collisional
integral is solved by spectral methods. The space reconstruction is realized by
integrating the distribution function, describing the state of the system, over
arbitrary shaped and closed control volumes using a Central Weighted ENO
(CWENO) technique. Compared to other reconstruction methods, this approach
permits to keep compact stencil sizes which is a remarkable property in the
context of kinetic equations due to the considerable demand of computational
resources. The full discretization is then obtained by combining the previous
phase-space approximation with high order Implicit-Explicit (IMEX) Runge Kutta
schemes. These methods guarantee stability, accuracy and preservation of the
asymptotic state. Comparisons of the Boltzmann model with simpler relaxation
type kinetic models (like BGK) is proposed showing the capability of the
Boltzmann equation to capture different physical solutions. The theoretical
order of convergence is numerically measured in different regimes and the
methods are tested on several standard two-dimensional benchmark problems in
comparison with Direct Simulation Monte Carlo results. The article ends with a
prototype engineering problem consisting of a subsonic and a supersonic flow
around a NACA 0012 airfoil. All test cases are run with MPI parallelization on
several threads, thus making the proposed methods suitable for parallel
distributed memory supercomputers.


Abstract:
  Deep learning is a rapidly developing approach in the field of infrared and
visible image fusion. In this context, the use of dense blocks in deep networks
significantly improves the utilization of shallow information, and the
combination of the Generative Adversarial Network (GAN) also improves the
fusion performance of two source images. We propose a new method based on dense
blocks and GANs , and we directly insert the input image-visible light image in
each layer of the entire network. We use SSIM and gradient loss functions that
are more consistent with perception instead of mean square error loss. After
the adversarial training between the generator and the discriminator, we show
that a trained end-to-end fusion network -- the generator network -- is finally
obtained. Our experiments show that the fused images obtained by our approach
achieve good score based on multiple evaluation indicators. Further, our fused
images have better visual effects in multiple sets of contrasts, which are more
satisfying to human visual perception.


Abstract:
  For a map (function) $F(x):\ftwo^n\rightarrow\ftwo^n$ and a given $y$ in the
image of $F$ the problem of \emph{local inversion} of $F$ is to find all
inverse images $x$ in $\ftwo^n$ such that $y=F(x)$. In Cryptology, such a
problem arises in Cryptanalysis of One way Functions (OWFs). The well known
TMTO attack in Cryptanalysis is a probabilistic algorithm for computing one
solution of local inversion using $O(\sqrt N)$ order computation in offline as
well as online for $N=2^n$. This paper proposes a complete algorithm for
solving the local inversion problem which uses linear complexity for a unique
solution in a periodic orbit. The algorithm is shown to require an offline
computation to solve a hard problem (possibly requiring exponential
computation) and an online computation dependent on $y$ that of repeated
forward evaluation $F(x)$ on points $x$ in $\ff_{2^n}$ which is polynomial time
at each evaluation. However the forward evaluation is repeated at most as many
number of times as the Linear Complexity of the sequence $\{y,F(y),\ldots\}$ to
get one possible solution when this sequence is periodic. All other solutions
are obtained in chains $\{e,F(e),\ldots\}$ for all points $e$ in the Garden of
Eden (GOE) of the map $F$. Hence a solution $x$ exists iff either the former
sequence is periodic or a solution occurs in a chain starting from a point in
GOE. The online computation then turns out to be polynomial time $O(L^k)$ in
the linear complexity $L$ of the sequence to compute one possible solution in a
periodic orbit or $O(l)$ the chain length for a fixed $n$. Hence this is a
complete algorithm for solving the problem of finding all rational solutions
$x$ of the equation $F(x)=y$ for a given $y$ and a map $F$ in $\ff_{2^n}$.


Abstract:
  We give a bijective proof of a result by R.~Mantaci and F.~Rakotondrajao from
2003, regarding even and odd derangement with a fixed number of excedances. We
refine this result by also considering the set of right-to-left minima.


Abstract:
  Multi-modal clustering, which explores complementary information from
multiple modalities or views, has attracted people's increasing attentions.
However, existing works rarely focus on extracting high-level semantic
information of multiple modalities for clustering. In this paper, we propose
Contrastive Multi-Modal Clustering (CMMC) which can mine high-level semantic
information via contrastive learning. Concretely, our framework consists of
three parts. (1) Multiple autoencoders are optimized to maintain each
modality's diversity to learn complementary information. (2) A feature
contrastive module is proposed to learn common high-level semantic features
from different modalities. (3) A label contrastive module aims to learn
consistent cluster assignments for all modalities. By the proposed multi-modal
contrastive learning, the mutual information of high-level features is
maximized, while the diversity of the low-level latent features is maintained.
In addition, to utilize the learned high-level semantic features, we further
generate pseudo labels by solving a maximum matching problem to fine-tune the
cluster assignments. Extensive experiments demonstrate that CMMC has good
scalability and outperforms state-of-the-art multi-modal clustering methods.


Abstract:
  In this paper, incremental decode-and-forward (IDF) and incremental selective
decode-and-forward (ISDF) relaying are proposed to improve the spectral
efficiency of power line communication. Contrary to the traditional
decode-and-forward (DF) relaying, IDF and ISDF strategies utilize the relay
only if the direct link ceases to attain a certain information rate, thereby
improving the spectral efficiency. The path gain through the power line is
assumed to be log-normally distributed with high distance-dependent attenuation
and the additive noise is from a Bernoulli-Gaussian process. Closed-form
expressions for the outage probability, and approximate closed-form expressions
for the end-to-end average channel capacity and the average bit error rate for
binary phase-shift keying are derived. Furthermore, a closed-form expression
for the fraction of times the relay is in use is derived as a measure of the
spectral efficiency. Comparative analysis of IDF and ISDF with traditional DF
relaying is presented. It is shown that IDF is a specific case of ISDF and can
obtain optimal spectral efficiency without compromising the outage performance.
By employing power allocation to minimize the outage probability, it is
realized that the power should be allocated in accordance with the inter-node
distances and channel parameters.


Abstract:
  Location information is proven to benefit the deep learning models on
capturing the manifold structure of target objects, and accordingly boosts the
accuracy of medical image segmentation. However, most existing methods encode
the location information in an implicit way, e.g. the distance transform maps,
which describe the relative distance from each pixel to the contour boundary,
for the network to learn. These implicit approaches do not fully exploit the
position information (i.e. absolute location) of targets. In this paper, we
propose a novel loss function, namely residual moment (RM) loss, to explicitly
embed the location information of segmentation targets during the training of
deep learning networks. Particularly, motivated by image moments, the
segmentation prediction map and ground-truth map are weighted by coordinate
information. Then our RM loss encourages the networks to maintain the
consistency between the two weighted maps, which promotes the segmentation
networks to easily locate the targets and extract manifold-structure-related
features. We validate the proposed RM loss by conducting extensive experiments
on two publicly available datasets, i.e., 2D optic cup and disk segmentation
and 3D left atrial segmentation. The experimental results demonstrate the
effectiveness of our RM loss, which significantly boosts the accuracy of
segmentation networks.


Abstract:
  In monolayer transition metal dichalcogenide semiconductors, valley coherence
degrades rapidly due to a combination of fast scattering and inter-valley
exchange interaction. This leads to a sub-picosecond valley coherence time,
making coherent manipulation of exciton a highly formidable task. Using
monolayer MoS$_2$ sandwiched between top and bottom graphene, here we
demonstrate perfect valley coherence by observing 100% degree of linear
polarization of excitons in steady state photoluminescence. This is achieved in
this unique design through a combined effect of (a) suppression in exchange
interaction due to enhanced dielectric screening, (b) reduction in exciton
lifetime due to a fast inter-layer transfer to graphene, and (c) operating in
the motional narrowing regime. We disentangle the role of the key parameters
affecting valley coherence by using a combination of calculation (solutions of
Bethe-Salpeter and steady-state Maialle-Silva-Sham equations) and choice of
systematic design of experiments using four different stacks with varying
screening and exciton lifetime. To the best of our knowledge, this is the first
report in which the valley coherence timescale has been significantly enhanced
beyond the exciton radiative lifetime in monolayer semiconductors.


Abstract:
  The dominant physical formation mechanism(s) for ultra-diffuse galaxies
(UDGs) is still poorly understood. Here, we combine new, deep imaging from the
Jeanne Rich Telescope with deep integral field spectroscopy from the Keck II
telescope to investigate the formation of UDG1137+16 (dw1137+16). Our new
analyses confirm both its environmental association with the low density UGC
6594 group, along with its large size of 3.3 kpc and status as a UDG. The new
imaging reveals two distinct stellar components for UDG1137+16, indicating that
a central stellar body is surrounded by an outer stellar envelope undergoing
tidal interaction. Both the components have approximately similar stellar
masses. From our integral field spectroscopy we measure a stellar velocity
dispersion within the half-light radius (15 $\pm$ 4 $\mathrm{km\ s^{-1}}$) and
find that UDG1137+16 is similar to some other UDGs in that it is likely dark
matter dominated. Incorporating literature measurements, we also examine the
current state of UDG observational kinematics. Placing these data on the
central stellar velocity dispersion -- stellar mass relation, we suggest there
is little evidence for UDG1137+16 being created through a strong tidal
interaction. Finally, we investigate the constraining power current dynamical
mass estimates (from stellar and globular cluster velocity dispersions) have on
the total halo mass of UDGs. As most are measured within the half-light radius,
they are unable to accurately constrain UDG total halo masses.


Abstract:
  We will use analytic function theory and Fourier analysis to establish a
characterization for some classical umbral calculus, which will focus on the
generalization of the evaluation function. Although we cannot cover all the
umbral calculus people care about, the part about Bernoulli numbers can still
answer an open question about fractional sums raised by M\"uller and Schleicher
in 2005 and synthesize some common analysis results. We will only develop the
results which are sufficient to serve the purpose of this article, but at the
same time, we will briefly mention some possible extensions.


Abstract:
  Single-turn coil (STC) technique is a convenient way to generate ultrahigh
magnetic fields of more than 100 T. During the field generation, the STC
explosively destructs outward due to the Maxwell stress and Joule heating.
Unfortunately, the STC does not work at its full potential because it has
already expanded when the maximum magnetic field is reached. Here, we propose
an easy way to delay the expansion and increase the maximum field by using a
mass-loaded STC. By loading clay on the STC, the field profile drastically
changes, and the maximum field increases by 4 %. This method offers an access
to higher magnetic fields for physical property measurements.


Abstract:
  In Machine Learning, feature selection entails selecting a subset of the
available features in a dataset to use for model development. There are many
motivations for feature selection, it may result in better models, it may
provide insight into the data and it may deliver economies in data gathering or
data processing. For these reasons feature selection has received a lot of
attention in data analytics research. In this paper we provide an overview of
the main methods and present practical examples with Python implementations.
While the main focus is on supervised feature selection techniques, we also
cover some feature transformation methods.