Abstract:
  A zero-dimensional (volume-averaged) and a pseudo-one-dimensional (plug-flow)
model are developed to investigate atmospheric-pressure plasma jet devices
operated with He, He/O$_2$, He/N$_2$ and He/N$_2$/O$_2$ mixtures. The models
are coupled with the Boltzmann equation under the two-term approximation to
self-consistently calculate the electron energy distribution function (EEDF).
The simulation results are verified against spatially resolved model
calculations and validated against a wide variety of measurement data. The
nitric oxide (NO) concentration is thoroughly characterized for a variation of
the gas mixture ratio, helium flow rate and absorbed power. The concentration
measurements at low power are better captured by the simulation with a larger
hypothetical "effective" rate coefficient value for the reactive quenching
N$_2$(A$^3\Sigma$,B$^3\Pi$) + O($^3$P) $\to$ NO + N($^2$D). This suggests that
the NO production at low power is also covered by the species
N$_2$(A$^3\Sigma$,B$^3\Pi$;v>0) and multiple higher N$_2$ electronically
excited states instead of only N$_2$(A$^3{\Sigma}$,B$^3{\Pi}$;v=0) in this
quenching. Furthermore, the O($^3$P) density measurements under the same
operation conditions are also better predicted by the simulations with a
consideration of the aforementioned hypothetical rate coefficient value. It is
found that the contribution of the vibrationally excited nitrogen molecules
N$_2$(v$\geqslant$13) to the net NO formation rate gains more significance at
higher power. The vibrational distribution functions (VDFs) of O$_2$(v<41) and
N$_2$(v<58) are investigated. The sensitivity of the zero-dimensional model
with respect to a variation of the VDF resolutions, wall reaction probabilities
and synthetic air impurity levels is presented. The simulated plasma properties
are sensitive to the variation especially for a feeding gas mixture containing
nitrogen.


Abstract:
  In this paper, we examine the Nash equilibrium convergence properties of
no-regret learning in general N-player games. For concreteness, we focus on the
archetypal follow the regularized leader (FTRL) family of algorithms, and we
consider the full spectrum of uncertainty that the players may encounter - from
noisy, oracle-based feedback, to bandit, payoff-based information. In this
general context, we establish a comprehensive equivalence between the stability
of a Nash equilibrium and its support: a Nash equilibrium is stable and
attracting with arbitrarily high probability if and only if it is strict (i.e.,
each equilibrium strategy has a unique best response). This equivalence extends
existing continuous-time versions of the folk theorem of evolutionary game
theory to a bona fide algorithmic learning setting, and it provides a clear
refinement criterion for the prediction of the day-to-day behavior of no-regret
learning in games


Abstract:
  Within the model of self-consistent connection of quasi-Rayleigh wave with
adsorbed atoms, a method of constructing a new class of radiometric sensors of
the temperature and concentration of adsorbed atoms on the surface acoustic
waves is proposed. Based on the developed theory of the dispersion and acoustic
mode width of a quasi-Rayleigh wave on the adsorbed surface of monocrystals
with a Zinc blende structure, the temperature coefficient of the resonant
frequency of the surface acoustic wave is calculated depending on the
temperature and on the concentration of adsorbed atoms.


Abstract:
  Recent studies have shown that neural network (NN) based image classifiers
are highly vulnerable to adversarial examples, which poses a threat to
security-sensitive image recognition task. Prior work has shown that JPEG
compression can combat the drop in classification accuracy on adversarial
examples to some extent. But, as the compression ratio increases, traditional
JPEG compression is insufficient to defend those attacks but can cause an
abrupt accuracy decline to the benign images. In this paper, with the aim of
fully filtering the adversarial perturbations, we firstly make modifications to
traditional JPEG compression algorithm which becomes more favorable for NN.
Specifically, based on an analysis of the frequency coefficient, we design a
NN-favored quantization table for compression. Considering compression as a
data augmentation strategy, we then combine our model-agnostic preprocess with
noisy training. We fine-tune the pre-trained model by training with images
encoded at different compression levels, thus generating multiple classifiers.
Finally, since lower (higher) compression ratio can remove both perturbations
and original features slightly (aggressively), we use these trained multiple
models for model ensemble. The majority vote of the ensemble of models is
adopted as final predictions. Experiments results show our method can improve
defense efficiency while maintaining original accuracy.


Abstract:
  In this note we report some advances in the study of thermodynamic formalism
for a class of partially hyperbolic system -- center isometries, that includes
regular elements in Anosov actions. The techniques are of geometric flavor (in
particular, not relying in symbolic dynamics) and even provide new information
in the classical case.
  For such systems, we give in particular a constructive proof of the existence
of the SRB measure and of the entropy maximizing measure. It is also
established very fine statistical properties (Bernoulliness), and it is given a
characterization of equilibrium states in terms of their conditional measures
in the stable/unstable lamination, similar to the SRB case. The construction is
applied to obtain the uniqueness of quasi-invariant measures associated to
H\"older Jacobian for the horocyclic flow.


Abstract:
  Internet survey experiment is conducted to examine how providing peer
information of evaluation about progressive firms changed individual's
evaluations. Using large sample including over 13,000 observations collected by
two-step experimental surveys, I found; (1) provision of the information leads
individuals to expect higher probability of rising of stocks and be more
willing to buy it. (2) the effect on willingness to buy is larger than the
expected probability of stock price rising, (3) The effect for woman is larger
than for man. (4) individuals who prefer environment (woman's empowerment)
become more willing to buy stock of pro-environment (gender-balanced) firms
than others if they have the information. (5) The effect of the peer
information is larger for individuals with "warm -glow" motivation.


Abstract:
  We study zero-sum repeated games where the minimizing player has to pay a
certain cost each time he changes his action. Our contribution is twofold.
First, we show that the value of the game exists in stationary strategies,
depending solely on the previous action of the minimizing player, not the
entire history. We provide a full characterization of the value and the optimal
strategies. The strategies exhibit a robustness property and typically do not
change with a small perturbation of the switching costs. Second, we consider a
case where the minimizing player is limited to playing simpler strategies that
are completely history-independent. Here too, we provide a full
characterization of the (minimax) value and the strategies for obtaining it.
Moreover, we present several bounds on the loss due to this limitation.


Abstract:
  This paper is devoted to the maximal $L^1$ regularity and asymptotic behavior
for solutions to the inhomogeneous incompressible Navier-Stokes equations under
a scaling-invariant smallness assumption on the initial velocity. We obtain a
new global $L^1$-in-time estimate for the Lipschitz seminorm of the velocity
field without any smallness assumption on the initial density fluctuation. In
the derivation of this estimate, we study the maximal $L^1$ regularity for a
linear Stokes system with variable coefficients. The analysis tools are a use
of the semigroup generated by a generalized Stokes operator to characterize
some Besov norms and a new gradient estimate for a class of second-order
elliptic equations of divergence form. Our method might be used to study some
other issues arising from incompressible or compressible viscous fluids.


Abstract:
  The behavior of self driving cars may differ from people expectations, (e.g.
an autopilot may unexpectedly relinquish control). This expectation mismatch
can cause potential and existing users to distrust self driving technology and
can increase the likelihood of accidents. We propose a simple but effective
framework, AutoPreview, to enable consumers to preview a target autopilot
potential actions in the real world driving context before deployment. For a
given target autopilot, we design a delegate policy that replicates the target
autopilot behavior with explainable action representations, which can then be
queried online for comparison and to build an accurate mental model. To
demonstrate its practicality, we present a prototype of AutoPreview integrated
with the CARLA simulator along with two potential use cases of the framework.
We conduct a pilot study to investigate whether or not AutoPreview provides
deeper understanding about autopilot behavior when experiencing a new autopilot
policy for the first time. Our results suggest that the AutoPreview method
helps users understand autopilot behavior in terms of driving style
comprehension, deployment preference, and exact action timing prediction.


Abstract:
  We consider amplituhedron-like geometries which are defined in a similar way
to the intrinsic definition of the amplituhedron but with non-maximal winding
number. We propose that for the cases with minimal number of points the
canonical form of these geometries corresponds to the product of parity
conjugate amplitudes at tree as well as loop level. The product of amplitudes
in superspace lifts to a star product in bosonised superspace which we give a
precise definition of. We give an alternative definition of amplituhedron-like
geometries, analogous to the original amplituhedron definition, and also a
characterisation as a sum over pairs of on-shell diagrams that we use to prove
the conjecture at tree level. The union of all amplituhedron-like geometries
has a very simple definition given by only physical inequalities. Although such
a union does not give a positive geometry, a natural extension of the standard
definition of canonical form, the globally oriented canonical form, acts on
this union and gives the square of the amplitude.


Abstract:
  Motivated by recent experiments with Rydberg atoms in an optical tweezer
array, we accurately map out the ground-state phase diagram of the
antiferromagnetic Ising model on a square lattice with longitudinal and
transverse magnetic fields using the quantum Monte Carlo method. For a small
but nonzero transverse field, the transition longitudinal field is found to
remain nearly constant. By scrutinizing the phase diagram, we uncover a narrow
region where the system exhibits reentrant transitions between the disordered
and antiferromagnetic phases with increasing transverse field. Our phase
diagram provides a useful benchmark for quantum simulation of a Rydberg atom
system.


Abstract:
  This paper addresses the ability to enable machines to automatically detect
failures on machine tool components as well as estimating the severity of the
failures, which is a critical step towards autonomous production machines.
Extracting information about the severity of failures has been a substantial
part of classical, as well as Machine Learning based machine vision systems.
Efforts have been undertaken to automatically predict the severity of failures
on machine tool components for predictive maintenance purposes. Though, most
approaches only partly cover a completely automatic system from detecting
failures to the prognosis of their future severity. To the best of the authors
knowledge, this is the first time a vision-based system for defect detection
and prognosis of failures on metallic surfaces in general and on Ball Screw
Drives in specific has been proposed. The authors show that they can do both,
detect and prognose the evolution of a failure on the surface of a Ball Screw
Drive.


Abstract:
  A serious and ubiquitous issue in existing mapped WENO schemes is that most
of them can hardly preserve high resolutions and in the meantime prevent
spurious oscillations on solving hyperbolic conservation laws with long output
times. Our goal in this article is to address this widely concerned problem [3,
4, 15, 29, 16, 18]. In our previous work [18], the order-preserving (OP)
criterion was originally introduced and carefully used to devise a new mapped
WENO scheme that performs satisfactorily in long-run simulations, and hence it
was indicated that the OP criterion plays a critical role in the maintenance of
low-dissipation and robustness for the mapped WENO schemes. Thus, in our
present work, we firstly define the family of the mapped WENO schemes, whose
mappings meet the OP criterion, as OP-Mapped WENO. Next, we attentively take a
closer look at the mappings of various existing mapped WENO schemes and devise
a general formula for them. It helps us to extend the OP criterion into the
design of the improved mappings. Then, we propose the generalized
implementation of obtaining a group of OP-Mapped WENO schemes, named MOP-WENO-X
as they are developed from the existing mapped WENO-X schemes, where the
notation "X" is used to identify the version of the existing mapped WENO
scheme. Finally, extensive numerical experiments and comparisons with competing
schemes are conducted to demonstrate the enhanced performances of the
MOP-WENO-X schemes.


Abstract:
  The significance of heating load (HL) accurate approximation is the primary
motivation of this research to distinguish the most efficient predictive model
among several neural-metaheuristic models. The proposed models are through
synthesizing multi-layer perceptron network (MLP) with ant lion optimization
(ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA),
evolutionary strategy (ES), invasive weed optimization (IWO), and league
champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in
terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP,
ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population
sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was
carried out by implementing a ranking system. Based on the obtained overall
scores (OSs), the BBO (OS = 36) featured as the most capable optimization
technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient
performance of these algorithms, the corresponding MLPs can be promising
substitutes for traditional methods used for HL analysis.


Abstract:
  In this paper, we study the existence of extremal functions of the discrete
Sobolev inequality and Hardy-Littlewood-Sobolev inequality on lattice graphs.
  We introduce the discrete Concentration-Compactness principle, and prove the
existence of extremal functions for the best constants in the supercritical
cases.


Abstract:
  In this paper we define and explore the analytic spread $\ell(\mathcal I)$ of
a filtration in a local ring. We show that, especially for divisorial and
symbolic filtrations, some basic properties of the analytic spread of an ideal
extend to filtrations, even when the filtration is non Noetherian. We also
illustrate some significant differences between the analytic spread of a
filtration and the analytic spread of an ideal with examples.
  In the case of an ideal $I$, we have the classical bounds
$\mbox{ht}(I)\le\ell(I)\le \dim R$. The upper bound $\ell(\mathcal I)\le \dim
R$ is true for filtrations $\mathcal I$, but the lower bound is not true for
all filtrations. We show that for the filtration $\mathcal I$ of symbolic
powers of a height two prime ideal $\mathfrak p$ in a regular local ring of
dimension three (a space curve singularity), so that $\mbox{ht}(\mathcal I) =2$
and $\dim R=3$, we have that $0\le \ell(\mathcal I)\le 2$ and all values of 0,1
and 2 can occur. In the cases of analytic spread 0 and 1 the symbolic algebra
is necessarily non-Noetherian. The symbolic algebra is non-Noetherian if and
only if $\ell(\mathfrak p^{(n)})=3$ for all symbolic powers of $\mathfrak p$
and if and only if $\ell(\mathcal I_a)=3$ for all truncations $\mathcal I_a$ of
$\mathcal I$.


Abstract:
  We analyze the propagation of high-frequency gravitational waves (GW) in
scalar-tensor theories of gravity, with the aim of examining properties of
cosmological distances as inferred from GW measurements. By using symmetry
principles, we first determine the most general structure of the GW linearized
equations and of the GW energy momentum tensor, assuming that GW move with the
speed of light. Modified gravity effects are encoded in a small number of
parameters, and we study the conditions for ensuring graviton number
conservation in our covariant set-up. We then apply our general findings to the
case of GW propagating through a perturbed cosmological space-time, deriving
the expressions for the GW luminosity distance $d_L^{({\rm GW})}$ and the GW
angular distance $d_A^{({\rm GW})}$. We prove for the first time the validity
of Etherington reciprocity law $d_L^{({\rm GW})}\,=\,(1+z)^2\,d_A^{({\rm GW})}$
for a perturbed universe within a scalar-tensor framework. We find that besides
the GW luminosity distance, also the GW angular distance can be modified with
respect to General Relativity. We discuss implications of this result for
gravitational lensing, focussing on time-delays of lensed GW and lensed photons
emitted simultaneously during a multimessenger event. We explicitly show how
modified gravity effects compensate between different coefficients in the GW
time-delay formula: lensed GW arrive at the same time as their lensed
electromagnetic counterparts, in agreement with causality constraints.


Abstract:
  This paper develops a novel method to detect cold pools from atmospheric
soundings over tropical oceans and applies it to sounding data from the EUREC4A
field campaign, which took place south and east of Barbados in January-February
2020. The proposed method exploits the fact that the air in a cold pool is
denser than the air above it. It leads us to define cold pool soundings as
those for which the mixed-layer height is smaller than 400 m. We first test
this criterion by verifying its consistency with surface temperature and
precipitation in a realistic high-resolution simulation over the western
tropical Atlantic. Applying it to EUREC4A data, we then identify 7% of EUREC4A
dropsondes and radiosondes as cold pool soundings. In two selected case
studies, we find that cold pools soundings coincide with mesoscale cloud arcs
and temperature drops in the surface time series. Statistics for the entire
campaign further characterize the signature of cold pools in temperature,
humidity and wind profiles. In the presence of wind shear, we show in
particular that the spreading of cold pools is favored downshear, suggesting
downward momentum transport by unsaturated downdrafts. These results support
the robustness of our simple method in different environmental conditions and
illustrate the new insights it offers for the characterization of cold pools
and their environment.


Abstract:
  In recent work, Gourv\`es, Lesca, and Wilczynski propose a variant of the
classic housing markets model where the matching between agents and objects
evolves through Pareto-improving swaps between pairs of adjacent agents in a
social network. To explore the swap dynamics of their model, they pose several
basic questions concerning the set of reachable matchings. In their work and
other follow-up works, these questions have been studied for various classes of
graphs: stars, paths, generalized stars (i.e., trees where at most one vertex
has degree greater than two), trees, and cliques. For generalized stars and
trees, it remains open whether a Pareto-efficient reachable matching can be
found in polynomial time.
  In this paper, we pursue the same set of questions under a natural variant of
their model. In our model, the social network is replaced by a network of
objects, and a swap is allowed to take place between two agents if it is
Pareto-improving and the associated objects are adjacent in the network. In
those cases where the question of polynomial-time solvability versus
NP-hardness has been resolved for the social network model, we are able to show
that the same result holds for the network-of-objects model. In addition, for
our model, we present a polynomial-time algorithm for computing a
Pareto-efficient reachable matching in generalized star networks. Moreover, the
object reachability algorithm that we present for path networks is
significantly faster than the known polynomial-time algorithms for the same
question in the social network model.


Abstract:
  Real-time identification of electrical equivalent circuit models is a
critical requirement in many practical systems, such as batteries and electric
motors. Significant work has been done in the past developing different types
of algorithms for system identification using reduced equivalent circuit
models. However, little work was done in analyzing the theoretical performance
bounds of these approaches. Proper understanding of theoretical bounds will
help in designing a system that is economical in cost and robust in
performance. In this paper, we analyze the performance of a linear recursive
least squares approach to equivalent circuit model identification and show that
the least squares approach is both unbiased and efficient when the
signal-to-noise ratio is high enough. However, we show that, when the
signal-to-noise ratio is low - resembling the case in many practical
applications - the least squares estimator becomes significantly biased.
Consequently, we develop a parameter estimation approach based on total least
squares method and show it to be asymptotically unbiased and efficient at
practically low signal-to-noise ratio regions. Further, we develop a recursive
implementation of the total least square algorithm and find it to be slow to
converge; for this, we employ a Kalman filter to improve the convergence speed
of the total least squares method. The resulting total Kalman filter is shown
to be both unbiased and efficient in equivalent circuit model parameter
identification. The performance of this filter is analyzed using real-world
current profile under fluctuating signal-to-noise ratios. Finally, the
applicability of the algorithms and analysis in this paper in identifying
higher order electrical equivalent circuit models is explained.


Abstract:
  The composition of dark matter is one of the puzzling topics in astrophysics.
To address this issue, several experiments searching for the existence of
axions have been designed, built and realized in the last twenty years. Among
all the others, light shining through walls experiments promise to push the
exclusion limits to lower energies. For this reason, effort is put for the
development of single-photon detectors operating at frequencies $<100$ GHz.
Here, we review recent advancements in superconducting single-photon detection.
In particular, we present two sensors based on one-dimensional Josephson
junctions with the capability to be in situ tuned by simple current bias: the
nanoscale transition edge sensor (nano-TES) and the Josephson escape sensor
(JES). These two sensors are the ideal candidates for the realization of
microwave light shining through walls (LSW) experiments, since they show
unprecedented frequency resolutions of about 100 GHz and 2 GHz for the nano-TES
and JES, respectively.


Abstract:
  We study the propagation of 0.05-1 TW power, ultrafast laser pulses in a 10
meter long rubidium vapor cell. The central wavelength of the laser is resonant
with the $D_2$ line of rubidium and the peak intensity in the $10^{12}-10^{14}
~W/cm^2$ range, enough to create a plasma channel with single electron
ionization. We observe the absorption of the laser pulse for low energy, a
regime of transverse confinement of the laser beam by the strong resonant
nonlinearity for higher energies and the transverse broadening of the output
beam when the nonlinearity is saturated due to full medium ionization. We
compare experimental observations of transmitted pulse energy and transverse
fluence profile with the results of computer simulations modeling pulse
propagation. We find a qualitative agreement between theory and experiment that
corroborates the validity of our propagation model. While the quantitative
differences are substantial, the results show that the model can be used to
interpret the observed phenomena in terms of self-focusing and channeling of
the laser pulses by the saturable nonlinearity and the transparency of the
fully ionized medium along the propagation axis.


Abstract:
  Deep ensembles perform better than a single network thanks to the diversity
among their members. Recent approaches regularize predictions to increase
diversity; however, they also drastically decrease individual members'
performances. In this paper, we argue that learning strategies for deep
ensembles need to tackle the trade-off between ensemble diversity and
individual accuracies. Motivated by arguments from information theory and
leveraging recent advances in neural estimation of conditional mutual
information, we introduce a novel training criterion called DICE: it increases
diversity by reducing spurious correlations among features. The main idea is
that features extracted from pairs of members should only share information
useful for target class prediction without being conditionally redundant.
Therefore, besides the classification loss with information bottleneck, we
adversarially prevent features from being conditionally predictable from each
other. We manage to reduce simultaneous errors while protecting class
information. We obtain state-of-the-art accuracy results on CIFAR-10/100: for
example, an ensemble of 5 networks trained with DICE matches an ensemble of 7
networks trained independently. We further analyze the consequences on
calibration, uncertainty estimation, out-of-distribution detection and online
co-distillation.


Abstract:
  The gap between a recently developed dynamical version of relaxed
magnetohydrodynamics (RxMHD) and ideal MHD (IMHD) is bridged by approximating
the zero-resistivity "Ideal" Ohm's Law (IOL) constraint using an augmented
Lagrangian method borrowed from optimization theory. The augmentation combines
a pointwise vector Lagrange multiplier method and global penalty function
method and can be used either for iterative enforcement of the IOL to arbitrary
accuracy, or for constructing a continuous sequence of magnetofluid dynamics
models running between RxMHD (no IOL) and weak IMHD (IOL almost everywhere).
This is illustrated by deriving dispersion relations for linear waves on an MHD
equilibrium.


Abstract:
  We define graded, quasi-coherent $\mathcal{O}_S$-algebras over a given base
derived scheme $S$, and show that these are equivalent to derived
$\mathbb{G}_{m,S}$-schemes which are affine over $S$. We then use this
$\mathbb{G}_{m,S}$-action to define the projective spectrum $\mathrm{Proj}
(\mathcal{B})$ of a graded algebra $\mathcal{B}$ as a quotient stack, show that
$\mathrm{Proj} (\mathcal{B})$ is representable by a derived scheme over $S$,
and describe the functor of points of $\mathrm{Proj} (\mathcal{B})$ in terms of
line bundles. The theory of graded algebras and projective spectra is then used
to define the blow-up of a closed immersion of derived schemes. Our
construction will coincide with the existing one for the quasi-smooth case. The
construction is done by generalizing the extended Rees algebra to the derived
setting, using Weil restrictions. We close by also generalizing the deformation
to the normal cone to the derived setting.


Abstract:
  In this note we consider asymptotics of the multipoint Pad\'e approximants to
Cauchy integrals of analytic non-vanishing densities defined on a Jordan arc
connecting $ -1 $ and $ 1 $. We allow for the situation where the (symmetric)
contour attracting the poles of the approximants does separate the plane.


Abstract:
  Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are
increasingly being deployed across multiple functionalities, ranging from
healthcare devices and wearables to critical infrastructures, e.g., nuclear
power plants, autonomous vehicles, smart cities, and smart homes. These devices
are inherently not secure across their comprehensive software, hardware, and
network stacks, thus presenting a large attack surface that can be exploited by
hackers. In this article, we present an innovative technique for detecting
unknown system vulnerabilities, managing these vulnerabilities, and improving
incident response when such vulnerabilities are exploited. The novelty of this
approach lies in extracting intelligence from known real-world CPS/IoT attacks,
representing them in the form of regular expressions, and employing machine
learning (ML) techniques on this ensemble of regular expressions to generate
new attack vectors and security vulnerabilities. Our results show that 10 new
attack vectors and 122 new vulnerability exploits can be successfully generated
that have the potential to exploit a CPS or an IoT ecosystem. The ML
methodology achieves an accuracy of 97.4% and enables us to predict these
attacks efficiently with an 87.2% reduction in the search space. We demonstrate
the application of our method to the hacking of the in-vehicle network of a
connected car. To defend against the known attacks and possible novel exploits,
we discuss a defense-in-depth mechanism for various classes of attacks and the
classification of data targeted by such attacks. This defense mechanism
optimizes the cost of security measures based on the sensitivity of the
protected resource, thus incentivizing its adoption in real-world CPS/IoT by
cybersecurity practitioners.


Abstract:
  Prolonged focused work periods decrease efficiency with related decline of
attention and performance. Therefore, emergency fleet break scheduling should
consider both area coverage by idle vehicles (related to the fleet's target
arrival time to incidents) and vehicle crews' service requirements for breaks
to avoid fatigue. In this paper, we propose a break assignment problem
considering area coverage (BAPCAC) addressing this issue. Moreover, we
formulate a mathematical model for the BAPCAC problem. Based on available
historical spatio-temporal incident data and service requirements, the BAPCAC
model can be used not only to dimension the size of the fleet at the tactical
level, but also to decide upon the strategies related with break scheduling.
Moreover, the model could be used to compute (suboptimal) locations for idle
vehicles in each time period and arrange vehicles' crews' work breaks
considering given break and coverage constraints.


Abstract:
  In this work, we investigate crystallization from droplets formed on
micro-patterned surfaces. By solvent exchange in a micro-chamber, a ternary
solution consisting of a model compound beta-alanine, water, and isopropanol,
was displaced by a flow of isopropanol. In the process, oiling-out droplets
formed and crystallized. Our results showed that the shape and size of the
crystals on micro-patterned surfaces could be simply mediated by the flow
conditions of solvent exchange. Varying flow rate, concentration, or channel
height led to the formation of a thin film with micro-holes, connected network
of crystals, or small diamond-shaped crystals. Rough micro-structures on the
surface allowed the easy detachment of crystals from the surface. Beyond
oiling-out crystallization, we demonstrated that the crystal formation from
another solute dissolved in the droplets could be triggered by solvent
exchange. The length of crystal fibers after the solvent exchange process was
shorter at a faster flow rate. This study may provide further understanding to
effectively obtain crystallization from surface droplets through the solvent
exchange approach.


Abstract:
  We study shadowing and chain recurrence in the context of linear operators
acting on Banach spaces or even on normed vector spaces. We show that for
linear operators there is only one chain recurrent set, and this set is
actually a closed invariant subspace. We prove that every chain transitive
linear dynamical system with the shadowing property is frequently hypercyclic,
and as a corollary obtain that every shadowing hypercyclic linear dynamical
system is frequently hypercyclic.


Abstract:
  The Hong-Ou-Mandel effect lies at the heart of quantum interferometry, having
multiple applications in the field of quantum information processing and no
classical counterpart. Despite its popularity, only a few works have considered
polarization-frequency interaction within the interferometer. In this paper, we
fill this gap. Our system of interest is a general biphoton polarization state
that experiences effective dephasing noise by becoming entangled with the same
photons' frequency state, as the photons propagate through birefringent media.
The photons then meet at a beam splitter, where either coincidence or bunching
occurs, after which the polarization-frequency interaction continues on the
output paths. Alongside performing extensive theoretical analysis on the
coincidence probability and different polarization states, we outline multiple
interesting applications that range from constructing Bell states to an
alternative delayed choice quantum eraser.


Abstract:
  Convolutional neural networks (CNNs) have been the de facto standard for
nowadays 3D medical image segmentation. The convolutional operations used in
these networks, however, inevitably have limitations in modeling the long-range
dependency due to their inductive bias of locality and weight sharing. Although
Transformer was born to address this issue, it suffers from extreme
computational and spatial complexities in processing high-resolution 3D feature
maps. In this paper, we propose a novel framework that efficiently bridges a
{\bf Co}nvolutional neural network and a {\bf Tr}ansformer {\bf (CoTr)} for
accurate 3D medical image segmentation. Under this framework, the CNN is
constructed to extract feature representations and an efficient deformable
Transformer (DeTrans) is built to model the long-range dependency on the
extracted feature maps. Different from the vanilla Transformer which treats all
image positions equally, our DeTrans pays attention only to a small set of key
positions by introducing the deformable self-attention mechanism. Thus, the
computational and spatial complexities of DeTrans have been greatly reduced,
making it possible to process the multi-scale and high-resolution feature maps,
which are usually of paramount importance for image segmentation. We conduct an
extensive evaluation on the Multi-Atlas Labeling Beyond the Cranial Vault (BCV)
dataset that covers 11 major human organs. The results indicate that our CoTr
leads to a substantial performance improvement over other CNN-based,
transformer-based, and hybrid methods on the 3D multi-organ segmentation task.
Code is available at \def\UrlFont{\rm\small\ttfamily}
\url{https://github.com/YtongXie/CoTr}


Abstract:
  Nieh-Yan anomaly describes the non-conservation of chiral charges induced by
the coupling between Dirac fermions and torsion fields. Since the torsion field
is beyond general relativity, this effect remains hypothetical and its
relevance to our universe is unclear in the context of high-energy physics. In
this work, we propose that the phonons can induce a torsion field for the
Kramers-Weyl fermions through electron-phonon interaction in a non-magnetic
chiral crystal, thus leading to the occurrence of the Nieh-Yan anomaly in this
condensed matter system. As a consequence, the Nieh-Yan term can strongly
influence the phonon dynamics and lead to the helicity of acoustic phonons,
namely, two transverse phonon modes mix with each other to form a circular
polarization with a non-zero angular momentum at a finite phonon momentum and
the phonon angular momentum reverses its sign for opposite momenta due to time
reversal symmetry. The phonon helicity can be probed through measuring the
total phonon angular momentum driven by a temperature gradient.


Abstract:
  Knowledge of how science is consumed in public domains is essential for a
deeper understanding of the role of science in human society. While science is
heavily supported by public funding, common depictions suggest that scientific
research remains an isolated or 'ivory tower' activity, with weak connectivity
to public use, little relationship between the quality of research and its
public use, and little correspondence between the funding of science and its
public use. This paper introduces a measurement framework to examine public
good features of science, allowing us to study public uses of science, the
public funding of science, and how use and funding relate. Specifically, we
integrate five large-scale datasets that link scientific publications from all
scientific fields to their upstream funding support and downstream public uses
across three public domains - government documents, the news media, and
marketplace invention. We find that the public uses of science are extremely
diverse, with different public domains drawing distinctively across scientific
fields. Yet amidst these differences, we find key forms of alignment in the
interface between science and society. First, despite concerns that the public
does not engage high-quality science, we find universal alignment, in each
scientific field and public domain, between what the public consumes and what
is highly impactful within science. Second, despite myriad factors underpinning
the public funding of science, the resulting allocation across fields presents
a striking alignment with the field's collective public use. Overall, public
uses of science present a rich landscape of specialized consumption, yet
collectively science and society interface with remarkable, quantifiable
alignment between scientific use, public use, and funding.


Abstract:
  Over the last decade, artificial intelligence has found many applications
areas in the society. As AI solutions have become more sophistication and the
use cases grew, they highlighted the need to address performance and energy
efficiency challenges faced during the implementation process. To address these
challenges, there has been growing interest in neuromorphic chips. Neuromorphic
computing relies on non von Neumann architectures as well as novel devices,
circuits and manufacturing technologies to mimic the human brain. Among such
technologies, 3D integration is an important enabler for AI hardware and the
continuation of the scaling laws. In this paper, we overview the unique
opportunities 3D integration provides in neuromorphic chip design, discuss the
emerging opportunities in next generation neuromorphic architectures and review
the obstacles. Neuromorphic architectures, which relied on the brain for
inspiration and emulation purposes, face grand challenges due to the limited
understanding of the functionality and the architecture of the human brain.
Yet, high-levels of investments are dedicated to develop neuromorphic chips. We
argue that 3D integration not only provides strategic advantages to the
cost-effective and flexible design of neuromorphic chips, it may provide design
flexibility in incorporating advanced capabilities to further benefits the
designs in the future.


Abstract:
  Intersectional bias is a bias caused by an overlap of multiple social factors
like gender, sexuality, race, disability, religion, etc. A recent study has
shown that word embedding models can be laden with biases against
intersectional groups like African American females, etc. The first step
towards tackling such intersectional biases is to identify them. However,
discovering biases against different intersectional groups remains a
challenging task. In this work, we present WordBias, an interactive visual tool
designed to explore biases against intersectional groups encoded in static word
embeddings. Given a pretrained static word embedding, WordBias computes the
association of each word along different groups based on race, age, etc. and
then visualizes them using a novel interactive interface. Using a case study,
we demonstrate how WordBias can help uncover biases against intersectional
groups like Black Muslim Males, Poor Females, etc. encoded in word embedding.
In addition, we also evaluate our tool using qualitative feedback from expert
interviews. The source code for this tool can be publicly accessed for
reproducibility at github.com/bhavyaghai/WordBias.


Abstract:
  Massive star evolution is still poorly understood, and observational tests
are required to discriminate between different implementations of physical
phenomenon in stellar evolution codes. By confronting stellar evolution models
with observed properties of blue supergiants, such as pulsations, chemical
composition and position in the Hertzsprung-Russell diagram, we aim at
determining which of the criterion used for convection (Schwarzschild or
Ledoux) is best able to explain the observations. We compute state-of-the-art
stellar evolution models with either the Schwarzschild or the Ledoux criterion
for convection. Models are for $14$ to $35\,M_\odot$ at solar or Large
Magellanic Cloud metallicity. For each model, we compute the pulsation
properties to know when radial modes are excited. We then compare our results
with the position of blue supergiants in the Hertzsprung-Russell diagram, with
their surface chemical composition, and with their variability. Our results at
Large Magellanic Cloud metallicity shows only a slight preference for the
Ledoux criterion over the Schwarzschild one in reproducing at the same time the
observed properties of blue supergiants, even if the Schwarzschild criterion
cannot be excluded at this metallicity. We check that changing the overshoot
parameter at solar metallicity does not improve the situation. We also check
that our models are able to reproduce the position of Galactic blue supergiant
in the flux-weighted-gravity -- luminosity relation. We confirm that overall,
models computed with the Ledoux criterion are slightly better in matching
observations. Our results also support the idea that most Galactic $\alpha$ Cyg
variables are blue supergiants of the group 2, i.e. stars that have been
through a previous red supergiant phase where they have lost large amount of
mass.


Abstract:
  Accurate segmentation of the pelvic CTs is crucial for the clinical diagnosis
of pelvic bone diseases and for planning patient-specific hip surgeries. With
the emergence and advancements of deep learning for digital healthcare, several
methodologies have been proposed for such segmentation tasks. But in a low data
scenario, the lack of abundant data needed to train a Deep Neural Network is a
significant bottle-neck. In this work, we propose a methodology based on
modulation of image tonal distributions and deep learning to boost the
performance of networks trained on limited data. The strategy involves
pre-processing of test data through histogram specification. This simple yet
effective approach can be viewed as a style transfer methodology. The
segmentation task uses a U-Net configuration with an EfficientNet-B0 backbone,
optimized using an augmented BCE-IoU loss function. This configuration is
validated on a total of 284 images taken from two publicly available CT
datasets, TCIA (a cancer imaging archive) and the Visible Human Project. The
average performance measures for the Dice coefficient and Intersection over
Union are 95.7% and 91.9%, respectively, give strong evidence for the
effectiveness of the approach, which is highly competitive with
state-of-the-art methodologies.


Abstract:
  We find an analytic solution of the backreacted coupled fermion-baby-Skyrmion
system valid at all values of the coupling parameter. The solution, built on a
finite cylinder, is generally given in terms of the Heun functions and
satisfies the physical requirements of finite energy. For a special value of
the coupling parameter, the solution becomes a periodic crystal of
baby-Skyrmions and fermions defined on the plane $\mathbb{R}^2$. These
solutions are trivially extended to multi-solitonic branches of higher Baryon
number.


Abstract:
  We investigate, using numerical simulations, the conformations of isolated
active ring polymers. We find that the their behaviour depends crucially on
their size: short rings ($N \lesssim$ 100) are swelled whereas longer rings ($N
\gtrsim$ 200) collapse, at sufficiently high activity. By investigating the
non-equilibrium process leading to the steady state, we find a universal route
driving both outcomes; we highlight the central role of steric interactions, at
variance with linear chains, and of topology conservation. We further show that
the collapsed rings are arrested by looking at different observables, all
underlining the presence of an extremely long time scales at the steady state,
associated with the internal dynamics of the collapsed section. Finally, we
found that is some circumstances the collapsed state spins about its axis.


Abstract:
  In the present paper, we consider multidimensional nonlinear backward
stochastic differential equations (BSDEs) with a driver depending on the
martingale part $M$ of a solution. We assume that the nonlinear term is merely
monotone continuous with respect to the state variable. As to the regularity of
the driver with respect to the martingale variable, we consider a very general
condition which permits path-dependence on "the future" of the process $M$ as
well as a dependence of its law (McKean-Vlasov-type equations). For such
driver, we prove the existence and uniqueness of a global solution (i.e. for
any maturity $T>0$) to BSDE with data satisfying natural integrability
conditions.


Abstract:
  We find the relation between reliability and entropy production in a
realistic model of electronic memory (low-power MOS-based SRAM) where logical
values are encoded as metastable non-equilibrium states. We employ large
deviations techniques to obtain an analytical expression for the bistable
quasipotential describing the non-equilibrium steady state and use it to derive
an explicit expression bounding the error rate of the memory. Our results go
beyond the dominant contribution given by classical instanton theory and
provide accurate estimates of the error rate as confirmed by comparison with
stochastic simulations. The methods developed can be adapted to study the
reliability of broad classes of nonlinear devices subjected to thermal noise.


Abstract:
  Creating a good drum track to imitate a skilled performer in digital audio
workstations (DAWs) can be a time-consuming process, especially for those
unfamiliar with drums. In this work, we introduce PocketVAE, a groove
generation system that applies grooves to users' rudimentary MIDI tracks, i.e,
templates. Grooves can be either transferred from a reference track, generated
randomly or with conditions, such as genres. Our system, consisting of
different modules for each groove component, takes a two-step approach that is
analogous to a music creation process. First, the note module updates the user
template through addition and deletion of notes; Second, the velocity and
microtiming modules add details to this generated note score. In order to model
the drum notes, we apply a discrete latent representation method via Vector
Quantized Variational Autoencoder (VQ-VAE), as drum notes have a discrete
property, unlike velocity and microtiming values. We show that our two-step
approach and the usage of a discrete encoding space improves the learning of
the original data distribution. Additionally, we discuss the benefit of
incorporating control elements - genre, velocity and microtiming patterns -
into the model.


Abstract:
  This short note regards an observation about the recent theory of prismatic
cohomology developed by Bhatt and Scholze. In particular, by applying a functor
of Mandell, we see that the \'etale comparison theorem in the prismatic theory
reproduces the $p$-adic homotopy type for a smooth proper complex variety with
good reduction mod $p$.


Abstract:
  How do healthcare organizations (from small Practices to large HDOs) evaluate
adherence to the cybersecurity and privacy protection of Medical Internet of
Things (MIoT) used in clinical settings? This paper suggests an approach for
such evaluation using National Institute of Standards and Technology (NIST)
guidance. Through application of NISTIR 8228 Expectations it is possible to
quantitatively assess cybersecurity and privacy protection, and determine
relative compliance with recommended standards. This approach allows
organizations to evaluate the level of risk a MiOT device poses to IT systems
and to determine whether or not to permit its use in healthcare/IT
environments.
  This paper reviews the current state of IoT/MiOT cybersecurity and privacy
protection using historical and current industry guidance & best-practices;
recommendations by federal agencies; NIST publications; and federal law. It
then presents similarities and differences between IOT/MiOT devices and
"traditional" (or classic) Information Technology (IT) hardware, and cites
several challenges IoT/MiOT pose to cybersecurity and privacy protection.
  Finally, a practical approach to evaluating cybersecurity and privacy
protection is offered along with enhancements for validating assessment
results. In so doing it will demonstrate general compliance with both NIST
guidance and HIPAA/HITECH requirements.


Abstract:
  Naviers Stokes and energy equations of hydrocarbon liquid droplet in
evaporation in natural convection are solved numerically using PLIC VOF method.
Our new technique allows us to observe clearly the droplet radius regression in
each cell of the mesh. This regression is not affected by the changing of the
droplet shape and temperature.


Abstract:
  In this article we study modules over wild canonical algebras which
correspond to extension bundles [9] over weighted projective lines. We prove
that all modules attached to extension bundles can be established by matrices
with coefficients related to the relations of the considered algebra. Moreover,
we expand the concept of extension bundles over weighted projective lines with
three weights to general weight type and establish similar results in this
situation. Finally, we present a method to compute matrices for all modules
attached to extension bundles using cokernels of maps between direct sums of
line bundles.


Abstract:
  We address the questions of identifying pairs of interacting neurons from the
observation of their spiking activity. The neuronal network is modeled by a
system of interacting point processes with memory of variable length. The
influence of a neuron on another can be either excitatory or inhibitory. To
identify the existence and the nature of an interaction we propose an algorithm
based only on the observation of joint activity of the two neurons in
successive time slots. This reduces the amount of computation and storage
required to run the algorithm, thereby making the algorithm suitable for the
analysis of real neuronal data sets. We obtain computable upper bounds for the
probabilities of false positive and false negative detection. As a corollary we
prove the consistency of the identification algorithm.


Abstract:
  When transporting an object, we unconsciously adapt our movement to its
properties, for instance by slowing down when the item is fragile. The most
relevant features of an object are immediately revealed to a human observer by
the way the handling occurs, without any need for verbal description. It would
greatly facilitate collaboration to enable humanoid robots to perform movements
that convey similar intuitive cues to the observers. In this work, we focus on
how to generate robot motion adapted to the hidden properties of the
manipulated objects, such as their weight and fragility. We explore the
possibility of leveraging Generative Adversarial Networks to synthesize new
actions coherent with the properties of the object. The use of a generative
approach allows us to create new and consistent motion patterns, without the
need of collecting a large number of recorded human-led demonstrations.
Besides, the informative content of the actions is preserved. Our results show
that Generative Adversarial Nets can be a powerful tool for the generation of
novel and meaningful transportation actions, which result effectively modulated
as a function of the object weight and the carefulness required in its
handling.


Abstract:
  Locally recoverable codes (LRCs) were proposed for the recovery of data in
distributed and cloud storage systems about nine years ago. A lot of progress
on the study of LRCs has been made by now. However, there is a lack of general
theory on the minimum linear locality of linear codes. In addition, the minimum
linear locality of many known families of linear codes is not studied in the
literature. Motivated by these two facts, this paper develops some general
theory about the minimum linear locality of linear codes, and investigates the
minimum linear locality of a number of families of linear codes, such as
$q$-ary Hamming codes, $q$-ary Simplex codes, generalized Reed-Muller codes,
ovoid codes, maximum arc codes, the extended hyperoval codes, and near MDS
codes. Many classes of both distance-optimal and dimension-optimal LRCs are
presented in this paper. The minimum linear locality of many families of linear
codes are settled with the general theory developed in this paper.


Abstract:
  First-order primal-dual methods are appealing for their low memory overhead,
fast iterations, and effective parallelization. However, they are often slow at
finding high accuracy solutions, which creates a barrier to their use in
traditional linear programming (LP) applications. This paper exploits the
sharpness of primal-dual formulations of LP instances to achieve linear
convergence using restarts in a general setting that applies to ADMM
(alternating direction method of multipliers), PDHG (primal-dual hybrid
gradient method) and EGM (extragradient method). In the special case of PDHG,
without restarts we show a lower bound of $\Omega(\kappa^2 \log(1/\epsilon))$,
while with restarts we show an upper bound of $O(\kappa \log(1/\epsilon))$,
where $\kappa$ is a condition number and $\epsilon$ is the desired accuracy.
Moreover, the upper bound is optimal for a wide class of primal-dual methods,
and applies to the strictly more general class of sharp primal-dual problems.
We develop an adaptive restart scheme and verify that restarts significantly
improve the ability of PDHG to find high accuracy solutions to LP problems.


Abstract:
  The Horseshoe is a widely used and popular continuous shrinkage prior for
high-dimensional Bayesian linear regression. Recently, regularized versions of
the Horseshoe prior have also been introduced in the literature. Various Gibbs
sampling Markov chains have been developed in the literature to generate
approximate samples from the corresponding intractable posterior densities.
Establishing geometric ergodicity of these Markov chains provides crucial
technical justification for the accuracy of asymptotic standard errors for
Markov chain based estimates of posterior quantities. In this paper, we
establish geometric ergodicity for various Gibbs samplers corresponding to the
Horseshoe prior and its regularized variants in the context of linear
regression. First, we establish geometric ergodicity of a Gibbs sampler for the
original Horseshoe posterior under strictly weaker conditions than existing
analyses in the literature. Second, we consider the regularized Horseshoe prior
introduced in Piironen and Vehtari (2017), and prove geometric ergodicity for a
Gibbs sampling Markov chain to sample from the corresponding posterior without
any truncation constraint on the global and local shrinkage parameters.
Finally, we consider a variant of this regularized Horseshoe prior introduced
in Nishimura and Suchard (2020), and again establish geometric ergodicity for a
Gibbs sampling Markov chain to sample from the corresponding posterior.


Abstract:
  We observe and comprehend the dynamical Coulomb blockade suppression of the
electrical conductance across an electronic quantum channel submitted to a
temperature difference. A broadly tunable, spin-polarized Ga(Al)As quantum
channel is connected on-chip, through a micron-scale metallic node, to a linear
$RC$ circuit. The latter is made up of the node's geometrical capacitance $C$
in parallel with an adjustable resistance $R\in \{1/2,1/3,1/4\}\times h/e^2$
formed by 2--4 quantum Hall channels. The system is characterized by three
temperatures: a temperature of the electrons in the large electrodes ($T$) and
in the node ($T_\mathrm{node}$), and a temperature of the electromagnetic modes
of the $RC$ circuit ($T_\mathrm{env}$). The temperature in the node is
selectively increased by local Joule dissipation, and characterized from
current fluctuations. For a quantum channel in the tunnel regime, a close match
is found between conductance measurements and tunnel dynamical Coulomb blockade
theory. In the opposite near ballistic regime, we develop a theory that
accounts for different electronic and electromagnetic bath temperatures, again
in very good agreement with experimental data. Beyond these regimes, for an
arbitrary quantum channel set in the far out-of-equilibrium situation where the
temperature in the node significantly exceeds the one in the large electrodes,
the equilibrium (uniform temperature) prediction for the conductance is
recovered, albeit at a rescaled temperature $\alpha T_\mathrm{node}$.


Abstract:
  Constrained Horn Clauses (CHCs) are an intermediate program representation
that can be generated by several verification tools, and that can be processed
and solved by a number of Horn solvers. One of the main challenges when using
CHCs in verification is the encoding of heap-allocated data-structures: such
data-structures are today either represented explicitly using the theory of
arrays, or transformed away with the help of invariants or refinement types,
defeating the purpose of CHCs as a representation that is language-independent
as well as agnostic of the algorithm implemented by the Horn solver. This paper
presents an SMT-LIB theory of heap tailored to CHCs, with the goal of enabling
a standard interchange format for programs with heap data-structures. We
introduce the syntax of the theory of heap, define its semantics in terms of
axioms and using a reduction to SMT-LIB arrays and data-types, and discuss its
properties and outline possible extensions and future work.


Abstract:
  Quantum secure direct communication (QSDC) based on entanglement can directly
transmit confidential information. However, the inability to simultaneously
distinguish the four sets of encoded entangled states limits its practical
application. Here, we explore a deterministic QSDC network based on time-energy
entanglement and sum-frequency generation. 15 users are in a fully connected
QSDC network, and the fidelity of the entangled state shared by any two users
is greater than 97%. The results show that when any two users are performing
QSDC over 40 kilometers of optical fiber, the fidelity of the entangled state
shared by them is still greater than 95%, and the rate of information
transmission can be maintained above 1Kbp/s. Our Letter demonstrates the
feasibility of a proposed QSDC network, and hence lays the foundation for the
realization of satellite-based long-distance and global QSDC in the future.


Abstract:
  In this work we introduce, test and discuss three quantum methods for
computing hub and authority centrality scores in directed networks. The methods
are based on unitary, continuous-time quantum walks; the construction of a
suitable Hermitian Hamiltonian is achieved by performing a quantum walk on the
associated bipartite graph.
  Two methods, called CQAu and CQAw, use the same evolution operator, inspired
by the classical HITS algorithm, but with different initial states; the
computation of hub and authority scores is performed simultaneously. The third
method, called CQG and inspired by classical PageRank, requires instead two
separate runs with different evolution operators, one for hub and one for
authority scores.
  The methods are tested on several directed graphs with different sizes and
properties; a comparison with other well-established ranking algorithms is
provided. CQAw emerges as the most reliable of the three methods and yields
rankings that are largely compatible with results from HITS, although CQAu and
CQG also present interesting features and potential for applications.


Abstract:
  We study the coherent transport of one or two photons in a 1D waveguide
chirally coupled to a nonlinear resonator. Analytic solutions of the one-photon
and two-photon scattering is derived. Although the resonator acts as a
non-reciprocal phase shifter, light transmission is reciprocal at one-photon
level. However, the forward and reverse transmitted probabilities for two
photons incident from either the left side or the right side of the nonlinear
resonator are nonreciprocal due to the energy redistribution of the two-photon
bound state. Hence, the nonlinear resonator acts as an optical diode at
two-photon level.


Abstract:
  We investigate the non-perturbative renormalization group flow of the scalar
Galileon model in flat space. We discuss different expansion schemes of the
Galileon truncation, including a heat-kernel based derivative expansion, a
vertex expansion in momentum space and a curvature expansion in terms of a
covariant geometric formulation. We find that the Galileon symmetry prevents a
quantum induced renormalization group running of the Galileon couplings.
Consequently, the Galileon truncation only features a trivial Gaussian fixed
point.


Abstract:
  In this work, we are interested in the determination of the shape of the
scatterer for the two dimensional time harmonic inverse medium scattering
problems in acoustics. The scatterer is assumed to be a piecewise constant
function with a known value inside inhomogeneities, and its shape is
represented by the level set functions for which we investigate the information
using the Bayesian method. In the Bayesian framework, the solution of the
geometric inverse problem is defined as a posterior probability distribution.
The well-posedness of the posterior distribution would be discussed, and the
Markov chain Monte Carlo (MCMC) methods will be applied to generate samples
from the arising posterior distribution. Numerical experiments will be
presented to demonstrate the effectiveness of the proposed method.


Abstract:
  In this paper, we present a deep reinforcement learning platform named FIXAR
which employs fixed-point data types and arithmetic units for the first time
using a SW/HW co-design approach. Starting from 32-bit fixed-point data,
Quantization-Aware Training (QAT) reduces its data precision based on the range
of activations and performs retraining to minimize the reward degradation.
FIXAR proposes the adaptive array processing core composed of configurable
processing elements to support both intra-layer parallelism and intra-batch
parallelism for high-throughput inference and training. Finally, FIXAR was
implemented on Xilinx U50 and achieves 25293.3 inferences per second (IPS)
training throughput and 2638.0 IPS/W accelerator efficiency, which is 2.7 times
faster and 15.4 times more energy efficient than those of the CPU-GPU platform
without any accuracy degradation.


Abstract:
  Quiescent black hole X-ray binaries (X-ray luminosities <1e34 erg/s) are
believed to be fed by hot accretion flows that launch compact, relativistic
jets. However, due to their low luminosities, quiescent jets have been detected
in the radio waveband from only five systems so far. Here, we present radio
observations of two quiescent black hole X-ray binaries with the Australia
Telescope Compact Array. One system, GS 1124-684, was not detected. The other
system, BW Cir, was detected over two different epochs in 2018 and 2020, for
which we also obtained quasi-simultaneous X-ray detections with Chandra and
Swift. BW Cir is now the sixth quiescent X-ray binary with a confirmed radio
jet. However, the distance to BW Cir is uncertain, and we find that BW Cir
shows different behaviour in the radio/X-ray luminosity plane depending on the
correct distance. Estimates based on its G-type subgiant donor star place BW
Cir at >25 kpc, while initial optical astrometric measurements from Gaia Data
Release 2 suggested likely distances of just a few kpc. Here, we use the most
recent measurements from Gaia Early Data Release 3 and find a distance
d=7.1(+4.8/-3.9) kpc and a potential kick velocity PKV=165(+81/-17) km/s, with
distances up to ~20 kpc possible based on its parallax and proper motion. Even
though there is now less tension between the parallax and donor-star based
distance measurements, it remains an unresolved matter, and we conclude with
suggestions on how to reconcile the two measurements.


Abstract:
  To develop a machine sound monitoring system, a method for detecting
anomalous sound is proposed. Exact likelihood estimation using Normalizing
Flows is a promising technique for unsupervised anomaly detection, but it can
fail at out-of-distribution detection since the likelihood is affected by the
smoothness of the data. To improve the detection performance, we train the
model to assign higher likelihood to target machine sounds and lower likelihood
to sounds from other machines of the same machine type. We demonstrate that
this enables the model to incorporate a self-supervised classification-based
approach. Experiments conducted using the DCASE 2020 Challenge Task2 dataset
showed that the proposed method improves the AUC by 4.6% on average when using
Masked Autoregressive Flow (MAF) and by 5.8% when using Glow, which is a
significant improvement over the previous method.


Abstract:
  We show that the exceptional surfaces of linear three-dimensional
non-Hermitian parity-time-symmetric two-band models attain the form of
topologically stable tilted exceptional cones. By relating the exceptional
cones to the eigenvalues of Dirac operators, we find a connection between the
exceptional cone and the light cone of an observer in the vicinity of a
Schwarzschild black hole. When the cone overtilts, light-like
particle-antiparticle pairs are created resembling Hawking radiation. We also
investigate dissipative features of the non-Hermitian Hamiltonian related to
the latter and comment on potential realizations in laboratory setups.


Abstract:
  Measuring the star-forming properties of AGN hosts is key to our
understanding of galaxy formation and evolution. However, this topic remains
debated, partly due to the difficulties in separating the infrared (i.e.
1--1000 $\mu$m) emission into AGN and star-forming components. Taking advantage
of archival far-infrared data from Herschel, we present a new set of AGN and
galaxy infrared templates, and introduce the spectral energy distribution
fitting code IRAGNSEP. Both can be used to measure infrared host galaxy
properties, free of AGN contamination. To build these, we used a sample of 100
local ($z$ < 0.3), low-to-high luminosity AGNs (i.e. $L_{\rm
bol}~\sim~10^{42--46}~\rm erg~s^{-1}$), selected from the 105-month Swift - BAT
X-ray survey, which have archival Spitzer - IRS spectra and Herschel
photometry. We first built a set of seven galaxy templates using a sample of 55
star-forming galaxies selected via infrared diagnostics. Using these templates,
combined with a flexible model for the AGN contribution, we extracted the
intrinsic infrared emission of our AGN sample. We further demonstrate that we
can reduce the diversity in the intrinsic shapes of AGN spectral energy
distributions down to a set of three AGN templates, of which two represent AGN
continuum, and one represents silicate emission. Our results indicate that, on
average, the contribution of AGNs to the far-infrared ($\lambda~\gtrsim$ 50
$\mu$m) is not as high as suggested by some recent work. We further show that
the need for two infrared AGN continuum templates could be related to nuclear
obscuration, where one of our templates appears dominated by the emission of
the extended polar dust.


Abstract:
  We introduce two simple online activities to explore the physics of neutron
stars. These provide an introduction to the basic properties of compact
objects, like their masses and radii, for secondary school students. The first
activity explores the idea of the minimum mass of a neutron star. It is
directly linked to the concept of binding energy and follows on from our
previous activities. The second activity focuses on the maximum mass of neutron
stars using a solvable model of the neutron star interior. The activities are
based on spreadsheets, provided as Supplementary Material, and can be easily
adapted to different levels, age groups and discussion topics. In particular,
these activities can naturally lead towards discussions on extrapolations and
limits of theoretical models.


Abstract:
  Edge computing has been an efficient way to provide prompt and near-data
computing services for resource-and-delay sensitive IoT applications via
computation offloading. Effective computation offloading strategies need to
comprehensively cope with several major issues, including the allocation of
dynamic communication and computational resources, the deadline constraints of
heterogeneous tasks, and the requirements for computationally inexpensive and
distributed algorithms. However, most of the existing works mainly focus on
part of these issues, which would not suffice to achieve expected performance
in complex and practical scenarios. To tackle this challenge, in this paper, we
systematically study a distributed computation offloading problem with hard
delay constraints, where heterogeneous computational tasks require continually
offloading to a set of edge servers via a limiting number of stochastic
communication channels. The task offloading problem is then cast as a
delay-constrained long-term stochastic optimization problem under unknown
priori statistical knowledge. To resolve this problem, we first provide a
technical path to transform and decompose it into several slot-level
subproblems, then we develop a distributed online algorithm, namely TODG, to
efficiently allocate the resources and schedule the offloading tasks with delay
guarantees. Further, we present a comprehensive analysis for TODG, in terms of
the optimality gap, the delay guarantees, and the impact of system parameters.
Extensive simulation results demonstrate the effectiveness and efficiency of
TODG.


Abstract:
  We study the problem of bounding path-dependent expectations (within any
finite time horizon $d$) over the class of discrete-time martingales whose
marginal distributions lie within a prescribed tolerance of a given collection
of benchmark marginal distributions. This problem is a relaxation of the
martingale optimal transport (MOT) problem and is motivated by applications to
super-hedging in financial markets. We show that the empirical version of our
relaxed MOT problem can be approximated within $O\left( n^{-1/2}\right)$ error
where $n$ is the number of samples of each of the individual marginal
distributions (generated independently) and using a suitably constructed
finite-dimensional linear programming problem.


Abstract:
  We consider positive critical points of Caffarelli-Kohn-Nirenberg
inequalities and prove a Liouville type result which allows us to give a
complete classification of the solutions in a certain range of parameters,
providing a symmetry result for positive solutions. The governing operator is a
weighted $p$-Laplace operator, which we consider for a general $p \in (1,d)$.
For $p=2$, the symmetry breaking region for extremals of
Caffarelli-Kohn-Nirenberg inequalities was completely characterized in [J.
Dolbeault, M. Esteban, M. Loss, Invent. Math. 44 (2016)]. Our results extend
this result to a general $p$ and are optimal in some cases.


Abstract:
  The aim of this work is to give a pointfree description of the Cantor set. It
can be shown that the Cantor set is homeomorphic to the $p$-adic integers
$\mathbb{Z}_{p}:=\{x\in\mathbb{Q}_{p}: |x|_p\leq 1\}$ for every prime number
$p$. To give a pointfree description of the Cantor set, we specify the frame of
$\mathbb{Z}_{p}$ by generators and relations. We use the fact that the open
balls centered at integers generate the open subsets of $\mathbb{Z}_{p}$ and
thus we think of them as the basic generators; on this poset we impose some
relations and then the resulting quotient is the frame of the Cantor set
$\mathcal{L}(\mathbb{Z}_{p})$. We prove that $\mathcal{L}(\mathbb{Z}_{p})$ is a
spatial frame whose space of points is homeomorphic to $\mathbb{Z}_{p}$. In
particular, we show with pointfree arguments that $\mathcal{L}(\mathbb{Z}_{p})$
is $0$-dimensional, (completely) regular, compact, and metrizable (it admits a
countably generated uniformity). Finally, we give a point-free counterpart of
the Hausdorff-Alexandroff Theorem which states that \emph{every compact metric
space is a continuous image of the Cantor space} (see, e.g. \cite{Alexandroff}
and \cite{Hausdorff}). We prove the point-free analog: if $L$ is a compact
metrizable frame, then there is an injective frame homomorphism from $L$ into
$\mathcal{L}(\mathbb{Z}_{2})$.


Abstract:
  The theoretical approach to a sequential heavy ion double charge exchange
reaction is presented. A brief introduction into the formal theory of
second-order nuclear reactions and their application to Double Single Charge
Exchange (DSCE) reactions by distorted wave theory is given, thereby completing
the theoretical background to our recent work [1]. Formally, the DSCE reaction
amplitudes are shown to be separable into superpositions of distortion factors,
accounting for initial and final state ion--ion interactions, and nuclear
matrix elements. A broad space is given to the construction of nuclear DSCE
response functions on the basis of polarization propagator theory. The nuclear
response tensors resemble the nuclear matrix elements of $2\nu\beta\beta$ decay
in structure but contain in general a considerable more complex multipole and
spin structure. The QRPA theory is used to derive explicit expressions for
nuclear matrix elements (NMEs). The differences between the NME of the first
and the second interaction vertexes in a DSCE reaction is elucidated. Reduction
schemes for the transition form factors are discussed by investigating the
closure approximation and the momentum structure of form factors. DSCE unit
strength cross sections are derived.


Abstract:
  Let $f$ be a martingale with values in a uniformly $p$-smooth Banach space
and $w$ any positive weight. We show that $\mathbb{E} (f^* \cdot w) \lesssim
\mathbb{E}(S_p f \cdot w^*)$, where $\cdot^*$ is the martingale maximal
operator and $S_p$ is the $\ell^p$ sum of martingale increments.


Abstract:
  Given the semantic descriptions of classes, Zero-Shot Learning (ZSL) aims to
recognize unseen classes without labeled training data by exploiting semantic
information, which contains knowledge between seen and unseen classes. Existing
ZSL methods mainly use vectors to represent the embeddings to the semantic
space. Despite the popularity, such vector representation limits the
expressivity in terms of modeling the intra-class variability for each class.
We address this issue by leveraging the use of distribution embeddings. More
specifically, both image embeddings and class embeddings are modeled as
Gaussian distributions, where their similarity relationships are preserved
through the use of triplet constraints. The key intuition which guides our
approach is that for each image, the embedding of the correct class label
should be closer than that of any other class label. Extensive experiments on
multiple benchmark data sets show that the proposed method achieves highly
competitive results for both traditional ZSL and more challenging Generalized
Zero-Shot Learning (GZSL) settings.


Abstract:
  Knowledge distillation constitutes a simple yet effective way to improve the
performance of a compact student network by exploiting the knowledge of a more
powerful teacher. Nevertheless, the knowledge distillation literature remains
limited to the scenario where the student and the teacher tackle the same task.
Here, we investigate the problem of transferring knowledge not only across
architectures but also across tasks. To this end, we study the case of object
detection and, instead of following the standard detector-to-detector
distillation approach, introduce a classifier-to-detector knowledge transfer
framework. In particular, we propose strategies to exploit the classification
teacher to improve both the detector's recognition accuracy and localization
performance. Our experiments on several detectors with different backbones
demonstrate the effectiveness of our approach, allowing us to outperform the
state-of-the-art detector-to-detector distillation methods.


Abstract:
  The lowest critical point of one unitary matrix model with cosine plus
logarithmic potential is known to correspond with the $(A_1, A_3)$
Argyres-Douglas (AD) theory and its double scaling limit derives the
Painlev\'{e} II equation with parameter. Here, we consider the critical points
associated with all cosine potentials and determine the scaling operators,
their vevs and their scaling dimensions from perturbed string equations at
planar level. These dimensions agree with those of $(A_1,A_{4k-1})$ AD theory.


Abstract:
  We consider the effects of electron-hole interaction, 2D confinement and
applied electric field on direct allowed transitions in III-V semiconductors,
with InGaAs as a study case. Instead of Coulomb interaction, we use Gaussian
potential. It is finite at the origin and has a finite effective range, which
allows for a more efficient numerical solution of Schr\"{o}dinger equation.
Yet, we can expect electroabsorption phenomena to remain qualitatively similar
to the ones observed for Coulomb excitons. Moreover, we use variation of
parameters to fit both position and magnitude of the first absorption peak to
the Coulomb case. We combine and compare several numerical and approximate
methods, including spectral expansion, finite differences, separation of
variables and variational approximation. We find that separation of variables
approach works only for quantum well widths smaller than the exciton radius.
After separation of variables, finite difference solution of the resulting
interaction equation gives a much better agreement with the full spectral
solution than naive variational approximation. We observe that electric field
has a critical effect on the magnitudes of exciton absorption peaks,
suppressing previously allowed transitions and enhancing forbidden ones.
Moreover, for excited states, initially suppressed transitions are enhanced
again at higher field strengths.


Abstract:
  We investigate the gravitational settling of a long, model elastic filament
in homogeneous isotropic turbulence. We show that the flow produces a strongly
fluctuating settling velocity, whose mean is moderately enhanced over the
still-fluid terminal velocity, and whose variance has a power-law dependence on
the filament's weight but is surprisingly unaffected by its elasticity. In
contrast, the tumbling of the filament is shown to be closely coupled to its
stretching, and manifests as a Poisson process with a tumbling time that
increases with the elastic relaxation time of the filament.


Abstract:
  Meta-learning models, or models that learn to learn, have been a long-desired
target for their ability to quickly solve new tasks. Traditional meta-learning
methods can require expensive inner and outer loops, thus there is demand for
algorithms that discover strong learners without explicitly searching for them.
We draw parallels to the study of evolvable genomes in evolutionary systems --
genomes with a strong capacity to adapt -- and propose that meta-learning and
adaptive evolvability optimize for the same objective: high performance after a
set of learning iterations. We argue that population-based evolutionary systems
with non-static fitness landscapes naturally bias towards high-evolvability
genomes, and therefore optimize for populations with strong learning ability.
We demonstrate this claim with a simple evolutionary algorithm,
Population-Based Meta Learning (PBML), that consistently discovers genomes
which display higher rates of improvement over generations, and can rapidly
adapt to solve sparse fitness and robotic control tasks.


Abstract:
  In this paper, we introduce the class of $(A,(m,n))$-isosymmetric operators
and we study some of their properties, for a positive semi-definite operator
$A$ and $ m,n\in\mathbb{ N}$, which extend, by changing the initial inner
product with the semi-inner product induced by $A$, the well-known class of
$(m,n)$-isosymmetric operators introduced by Mark Stankus (\cite{mark1},
\cite{mark}). In particular, we characterize a family of $A$-isosymmetric
$(2\times2)$ upper triangular operator matrices. Moreover, we show that that if
$T$ is $(A,(m,n))$-isosymmetric and if $Q$ is a nilpotent operator of order $r$
doubly commuting with $T$, then $T^p$ is $(A,(m,n))$-isosymmetric symmetric for
any $p\in \mathbb{N}$ and $(T +Q)$ is $\big(A,(m+2r -2, n+2r
-1)\big)$-isosymmetric. Some properties of the spectrum are also investigated.


Abstract:
  The dynamical properties of free and bound domain-wall excitations in
Ising-chain materials have recently become the focus of intense research
interest. New materials and spectrometers have made it possible to control the
environment of coupled Ising chains by both effective internal and applied
external fields, which can be both longitudinal and transverse, and thus to
demonstrate how the resulting magnetic phase transitions and the nature of the
associated excited states obey fundamental symmetry properties. In RbCoCl$_3$,
the weakly coupled Ising chains form a triangular lattice whose frustrated
geometry and magnetic ordering transitions at low temperature open new
possibilities for the Ising-chain environment. We have investigated the
structure and magnetism in RbCoCl$_3$ by high-resolution x-ray diffraction and
neutron scattering measurements on powder and single crystal samples between
1.5 K and 300 K. Upon cooling, the Co$^{2+}$ spins develop one-dimensional
antiferromagnetic correlations along the chain axis ($c$-axis) below 90 K.
Below the first N\'eel temperature, $T_{N1}$ = 28 K, a partial 3D magnetic
order sets in, with propagation vector ${\vec k}_1$ = (1/3,1/3,1), the moments
aligned along the $c$-axis and every third chain uncorrelated from its
neighbours. Only below a second magnetic phase transition at $T_{N2}$ = 13 K
does the system achieve a fully ordered state, with two additional propagation
vectors: ${\vec k}_2$ = (0,0,1) establishes a "honeycomb" $c$-axis order, in
which 1/3 of the chains are subject to a strong effective mean field due to
their neighbours whereas 2/3 experience no net field, while ${\vec k}_3$ =
(1/2,0,1) governs a small, staggered in-plane ordered moment. We conclude that
RbCoCl$_3$ is an excellent material to study the physics of Ising chains in a
wide variety of temperature-controlled environments.


Abstract:
  Out-of-domain (OOD) input detection is vital in a task-oriented dialogue
system since the acceptance of unsupported inputs could lead to an incorrect
response of the system. This paper proposes OutFlip, a method to generate
out-of-domain samples using only in-domain training dataset automatically. A
white-box natural language attack method HotFlip is revised to generate
out-of-domain samples instead of adversarial examples. Our evaluation results
showed that integrating OutFlip-generated out-of-domain samples into the
training dataset could significantly improve an intent classification model's
out-of-domain detection performance.


Abstract:
  Shubnikov-de Haas oscillation measurements were performed on CaFeAsF up to a
high temperature of $T$ = 9 K. The oscillation frequency of the $\alpha$ Dirac
electron cylinder exhibits a negative shift as the temperature is raised, while
that of the $\beta$ Schr\"odinger hole cylinder shows a positive shift. At high
temperatures ($T \geqslant 5$ K) where the $\beta$ oscillation is negligible,
the shift of the $\alpha$ frequency is proportional to $T^2$ and its magnitude
agrees within experimental accuracy with the topological frequency shift
proposed by Guo, Alexandradinata, \textit{et al.} (arXiv:1910.07608), who
argues that the energy dependence of the effective mass peculiar to a linear
band dispersion of Dirac/Weyl fermions gives rise to a frequency shift
proportional to $T^2$. At low temperatures where the $\beta$ oscillation is not
negligible, the two frequencies ($\alpha$ and $\beta$) could not be determined
so accurately as to allow quantitative comparison to the theory.


Abstract:
  Image segmentation is a common and challenging task in autonomous driving.
Availability of sufficient pixel-level annotations for the training data is a
hurdle. Active learning helps learning from small amounts of data by suggesting
the most promising samples for labeling. In this work, we propose a new
pool-based method for active learning, which proposes promising patches
extracted from full image, in each acquisition step. The problem is framed in
an exploration-exploitation framework by combining an embedding based on
Uniform Manifold Approximation to model representativeness with entropy as
uncertainty measure to model informativeness. We applied our proposed method to
the autonomous driving datasets CamVid and Cityscapes and performed a
quantitative comparison with state-of-the-art baselines. We find that our
active learning method achieves better performance compared to previous
methods.


Abstract:
  Graphical User Interface (GUI) provides visual bridges between software apps
and end users. However, due to the compatibility of software or hardware, UI
display issues such as text overlap, blurred screen, image missing always occur
during GUI rendering on different devices. Because these UI display issues can
be found directly by human eyes, in this paper, we implement an online UI
display issue detection tool OwlEyes-Online, which provides a simple and
easy-to-use platform for users to realize the automatic detection and
localization of UI display issues. The OwlEyes-Online can automatically run the
app and get its screenshots and XML files, and then detect the existence of
issues by analyzing the screenshots. In addition, OwlEyes-Online can also find
the detailed area of the issue in the given screenshots to further remind
developers. Finally, OwlEyes-Online will automatically generate test reports
with UI display issues detected in app screenshots and send them to users. The
OwlEyes-Online was evaluated and proved to be able to accurately detect UI
display issues. Tool Link: http://www.owleyes.online:7476 Github Link:
https://github.com/franklinbill/owleyes Demo Video Link:
https://youtu.be/002nHZBxtCY


Abstract:
  In twisted homobilayer transition metal dichalcogenides, intra- and
inter-layer valley excitons hybridize with the layer configurations spatially
varying in the moir\'e. The ground state valley excitons are trapped at two
high-symmetry points with opposite electric dipoles in a moir\'e supercell,
forming a honeycomb superlattice of nearest-neighbor dipolar attraction. We
find that the spatial texture of layer configuration results in a luminescence
anomaly of the moir\'e trapped excitons, where a tiny displacement by
interactions dramatically increases the brightness and changes polarization
from circular to linear. At full filling, radiative recombination predominantly
occurs at edges and vacancies of the exciton superlattice. The anomaly also
manifests in the cascaded emission of small clusters, producing chains of
polarization entangled photons. An interlayer bias can switch the superlattice
into a single-orbital triangular lattice with repulsive interactions only,
where the luminescence anomaly can be exploited to distinguish ordered states
and domain boundaries at fractional filling.


Abstract:
  In this work we propose and analyze a new framework to learn feedback control
policies that exhibit provable guarantees on the closed-loop performance and
robustness to bounded (adversarial) perturbations. These policies are learned
from expert demonstrations without any prior knowledge of the task, its cost
function, and system dynamics. In contrast to the existing algorithms in
imitation learning and inverse reinforcement learning, we use a
Lipschitz-constrained loss minimization scheme to learn control policies with
certified robustness. We establish robust stability of the closed-loop system
under the learned control policy and derive an upper bound on its regret, which
bounds the sub-optimality of the closed-loop performance with respect to the
expert policy. We also derive a robustness bound for the deterioration of the
closed-loop performance under bounded (adversarial) perturbations on the state
measurements. Ultimately, our results suggest the existence of an underlying
tradeoff between nominal closed-loop performance and adversarial robustness,
and that improvements in nominal closed-loop performance can only be made at
the expense of robustness to adversarial perturbations. Numerical results
validate our analysis and demonstrate the effectiveness of our robust feedback
policy learning framework.


Abstract:
  Two-dimensional (2D) MoS$_2$ has been intensively investigated for its use in
the fields of microelectronics, nanoelectronics, and optoelectronics. However,
intrinsic 2D MoS$_2$ is usually used as the n-type semiconductor due to the
unintentional sulphur vacancies and surface gas adsorption.The synthesis and
characterization of 2D MoS$_2$ semiconductor of p-type are crucial for the
development of relevant p-n junction devices, as well as the practical
applications of 2D MoS$_2$ in the next-generation CMOS integrated circuit.
Here, we synthesize high-quality, wafer-scale, 2D p-type MoS$_2$
(Mo$_{1-x}$Nb$_x$S$_2$) with various niobium (Nb) mole fractions from 0 to 7.6%
by a creative two-step method. The dielectric functions of 2D Mo1-xNbxS2 are
accurately determined by spectroscopic ellipsometry. We find that the
increasing fraction of Nb dopant in 2D MoS$_2$ can modulate and promote the
combination of A and B exciton peaks of 2D MoS$_2$. The direct causes of this
impurity-tunable combination are interpreted as the joint influence of
decreasing peak A and broadening peak B. We explain the broadening peak B as
the multiple transitions from the impurity-induced valance bands to the
conductive band minimum at K point of Brillouin zone by comparing and analyzing
the simulated electronic structure of intrinsic and 2D Nb-doped MoS$_2$. A
p-type FET based on the 2D Nb-doped MoS$_2$ was fabricated for
characterization, and its working performance is expected to be adjustable as a
function of concentration of Nb dopant according to our theoretical research.
Our study is informative for comprehending optical and electronic properties of
extrinsic 2D transitional metal dichalcogenides, which is important and
imperative for the development and optimization of corresponding photonics and
optoelectronics devices.


Abstract:
  We discuss the realization of a universal set of ultrafast single- and
two-qubit operations with superconducting quantum circuits and investigate the
most relevant physical and technical limitations that arise when pushing for
faster and faster gates. With the help of numerical optimization techniques, we
establish a fundamental bound on the minimal gate time, which is determined
independently of the qubit design solely by its nonlinearity. In addition,
important practical restrictions arise from the finite qubit transition
frequency and the limited bandwidth of the control pulses. We show that for
highly anharmonic flux qubits and commercially available control electronics,
elementary single- and two-qubit operations can be implemented in about 100
picoseconds with residual gate errors below $10^{-4}$. Under the same
conditions, we simulate the complete execution of a compressed version of
Shor's algorithm for factoring the number 15 in about one nanosecond. These
results demonstrate that compared to state-of-the-art implementations with
transmon qubits, a hundredfold increase in the speed of gate operations with
superconducting circuits is still feasible.


Abstract:
  A measure-preserving formalism (MPF) is constructed and applied to spin/band
models, which yield observations about pumping. It occurs at topological phase
transition (TPT), i.e., a $Z_2$-flip, suggesting that $Z_2$ can imply bulk
effects. The model's asymptotic behavior is analytically solved via MPF. The
pumping probability is geometric, fractional, and has a ceiling of
$\frac{1}{2}$. Intriguingly, theorems are proved about occurrence conditions,
which are linked to the system's dimension and the distinction between rational
and irrational numbers. Experimental detection is discussed.


Abstract:
  Word embeddings are a basic building block of modern NLP pipelines. Efforts
have been made to learn rich, efficient, and interpretable embeddings for large
generic datasets available in the public domain. However, these embeddings have
limited applicability for small corpora from specific domains such as
automotive, manufacturing, maintenance and support, etc. In this work, we
present a comprehensive notion of interpretability for word embeddings and
propose a novel method to generate highly interpretable and efficient
embeddings for a domain-specific small corpus. We report the evaluation results
of our resulting word embeddings and demonstrate their novel features for
enhanced interpretability.


Abstract:
  Optical bottle beams can be used to trap atoms and small low-index particles.
We introduce a figure of merit for optical bottle beams, specifically in the
context of optical traps, and use it to compare optical bottle-beam traps
obtained by three different methods. Using this figure of merit and an
optimization algorithm, we identified optical bottle-beam traps based on a
Gaussian beam illuminating a metasurface that are superior in terms of power
efficiency than existing approaches. We numerically demonstrate a silicon
metasurface for creating an optical bottle-beam trap.


Abstract:
  An effective and efficient method that solves the high-order and the
non-linear ordinary differential equations is provided. The method is based on
the ratio net. By comparing the method with existing methods such as the
polynomial based method and the multilayer perceptron network based method, we
show that the ratio net gives good results and has higher efficiency.


Abstract:
  Automatic speech recognition (ASR) is a relevant area in multiple settings
because it provides a natural communication mechanism between applications and
users. ASRs often fail in environments that use language specific to particular
application domains. Some strategies have been explored to reduce errors in
closed ASRs through post-processing, particularly automatic spell checking, and
deep learning approaches. In this article, we explore using a deep neural
network to refine the results of a phonetic correction algorithm applied to a
telesales audio database. The results exhibit a reduction in the word error
rate (WER), both in the original transcription and in the phonetic correction,
which shows the viability of deep learning models together with post-processing
correction strategies to reduce errors made by closed ASRs in specific language
domains.


Abstract:
  Over the last few decades, many architectures have been developed that
harness the power of neural networks to detect objects in near real-time.
Training such systems requires substantial time across multiple GPUs and
massive labeled training datasets. Although the goal of these systems is
generalizability, they are often impractical in real-life applications due to
flexibility, robustness, or speed issues. This paper proposes RMOPP: A robust
multi-objective post-processing algorithm to boost the performance of fast
pre-trained object detectors with a negligible impact on their speed.
Specifically, RMOPP is a statistically driven, post-processing algorithm that
allows for simultaneous optimization of precision and recall. A unique feature
of RMOPP is the Pareto frontier that identifies dominant possible
post-processed detectors to optimize for both precision and recall. RMOPP
explores the full potential of a pre-trained object detector and is deployable
for near real-time predictions. We also provide a compelling test case on
YOLOv2 using the MS-COCO dataset.


Abstract:
  Surface stresses, in nano-sized anode particles undergoing chemomechanical
interactions, have a relaxing effect on the diffusion-induced stresses thus
improving the mechanical endurance of the particles, whereas, the compressive
effect of surface stresses degrades the electrochemical performance. However,
this straightforward prediction of an improved mechanical performance is
challenged in this work. Silicon nanowires undergoing huge volumetric changes
during lithiation, may undergo significant axial length-increase, which serves
as an important criterion in determining the mechanical performance of SiNWs.
Interestingly, surface stresses tend to reduce the length-increase under
potentiostatic charging condition, but under galvanostatic charging, the
length-increase gets enhanced, thus degrading the mechanical performance of the
SiNWs. To further make the study more inclusive, the nanowire is modelled with
a constraining material at its core, and a competitive analysis is presented
for the overall performance of the anode particles under the combined effects
of surface stresses and constraining material. The mathematical model is based
on large deformation theory, considering two-way coupling of diffusion-induced
stresses and stress-enhanced diffusion. It is hoped that this study will
provide a fresh perspective in designing next-generation lithium-ion battery
particles.