Abstract:
  The detection of spatial or temporal variations in very thin samples has
important applications in the biological sciences. For example, cellular
membranes exhibit changes in lipid composition and order, which in turn
modulate their function in space and time. Simultaneous measurement of
thickness and refractive index would be one way to observe these variations,
yet doing it noninvasively remains an elusive goal. Here we present a
microscopic-imaging technique to simultaneously measure the thickness and
refractive index of thin layers in a spatially resolved manner using
reflectometry. The heterodyne-detected interference between a light field
reflected by the sample and a reference field allows measurement of the
amplitude and phase of the reflected field and thus determination of the
complex reflection coefficient. Comparing the results with the simulated
reflection of a thin layer under coherent illumination of high numerical
aperture by the microscope objective, the refractive index and thickness of the
layer can be determined. We present results on a layer of polyvinylacetate
(PVA) with a thickness of approximately 80~nm. These results have a precision
better than 10\% in the thickness and better than 1\% in the refractive index
and are consistent within error with measurements by quantitative differential
interference contrast (qDIC) and literature values. We discuss the significance
of these results, and the possibility of performing accurate measurements on
nanometric layers. Notably, the shot-noise limit of the technique is below
0.5~nm in thickness and 0.0005 in refractive index for millisecond measurement
times.


Abstract:
  We consider the scenario where human-driven/autonomous vehicles with low/high
occupancy are sharing a segment of highway and autonomous vehicles are capable
of increasing the traffic throughput by preserving a shorter headway than
human-driven vehicles. We propose a toll lane framework where a lane on the
highway is reserved freely for autonomous vehicles with high occupancy, which
have the greatest capability to increase social mobility, and the other three
classes of vehicles can choose to use the toll lane with a toll or use the
other regular lanes freely. All vehicles are assumed to be only interested in
minimizing their own travel costs. We explore the resulting lane choice
equilibria under the framework and establish desirable properties of the
equilibria, which implicitly compare high-occupancy vehicles with autonomous
vehicles in terms of their capabilities to increase social mobility. We further
use numerical examples in the optimal toll design, the occupancy threshold
design, and the policy design problems to clarify the various potential
applications of this toll lane framework that unites high-occupancy vehicles
and autonomous vehicles. To our best knowledge, this is the first work that
systematically studies a toll lane framework that unites autonomous vehicles
and high-occupancy vehicles on the roads.


Abstract:
  We explore explicit virtual resolutions, as introduced by Berkesch, Erman,
and Smith, for ideals of sets of points in $\mathbb{P}^1 \times \mathbb{P}^1$.
Specifically, we describe a virtual resolution for a sufficiently general set
of points $X$ in $\mathbb{P}^1 \times \mathbb{P}^1$ that only depends on $|X|$.
We also improve an existence result of Berkesch, Erman, and Smith in the
special case of points in $\mathbb{P}^1 \times \mathbb{P}^1$; more precisely,
we give an effective bound for their construction that gives a virtual
resolution of length two for any set of points in $\mathbb{P}^1 \times
\mathbb{P}^1$.


Abstract:
  Many defenses have emerged with the development of adversarial attacks.
Models must be objectively evaluated accordingly. This paper systematically
tackles this concern by proposing a new parameter-free benchmark we coin RoBIC.
RoBIC fairly evaluates the robustness of image classifiers using a new
half-distortion measure. It gauges the robustness of the network against white
and black box attacks, independently of its accuracy. RoBIC is faster than the
other available benchmarks. We present the significant differences in the
robustness of 16 recent models as assessed by RoBIC.


Abstract:
  The automorphism groups of the Fano-Mukai fourfold of genus 10 were studied
in our previous paper [arXiv:1706.04926]. In particular, we found in
[arXiv:1706.04926] the neutral components of these groups. In the present paper
we finish the description of the discrete parts.
  Up to isomorphism, there are two special Fano--Mukai fourfold of genus 10
with the automorphism groups $GL_2(k)\rtimes\mathbb{Z}/2\mathbb{Z}$ and
$(\mathbb{G}_a\times\mathbb{G}m)\rtimes\mathbb{Z}/2\mathbb{Z}$, respectively.
For any other Fano-Mukai fourfold $V$ of genus 10 one has
$\mathrm{Aut}(V)=\mathbb{G}_m^2\rtimes \mathbb{Z}/2\mathbb{Z}$, except for
exactly one of them with $\mathrm{Aut}(V)=\mathbb{G}_m^2\rtimes \mathbb{Z}/6
\mathbb{Z}$.


Abstract:
  Piezo ion channels underlie many forms of mechanosensation in vertebrates,
and have been found to bend the membrane into strongly curved dome shapes. We
develop here a methodology describing the self-assembly of lipids and Piezo
into polyhedral bilayer vesicles. We validate this methodology for bilayer
vesicles formed from bacterial mechanosensitive channels of small conductance,
for which experiments found a polyhedral arrangement of proteins with snub cube
symmetry and a well-defined characteristic vesicle size. On this basis, we
calculate the self-assembly diagram for polyhedral bilayer vesicles formed from
Piezo. We find that the radius of curvature of the Piezo dome provides a
critical control parameter for the self-assembly of Piezo vesicles, with high
abundances of Piezo vesicles with octahedral, icosahedral, and snub cube
symmetry with increasing Piezo dome radius of curvature.


Abstract:
  Heart rate variability (HRV), defined as the variability between consecutive
heartbeats, is a surrogate measure of cardiac vagal tone. It is widely accepted
that a decreased HRV is associated to several risk factors and cardiovascular
diseases. However, a possible association between HRV and altered cerebral
hemodynamics is still debated, suffering from HRV short-term measures and the
paucity of high-resolution deep cerebral data. We propose a computational
approach to evaluate the deep cerebral and central hemodynamics subject to
physiological alterations of HRV in an ideal young healthy patient at rest. The
cardiovascular-cerebral model was validated and recently exploited to
understand the hemodynamic mechanisms between cardiac arrythmia and cognitive
deficit. Three configurations (baseline, increased HRV, and decreased HRV) are
built based on the standard deviation (SDNN) of RR beats. In the cerebral
circulation, our results show that HRV has overall a stronger impact on
pressure than flow rate mean values but similarly alters pressure and flow rate
in terms of extreme events. By comparing reduced and increased HRV, this latter
induces a higher probability of altered mean and extreme values, and is
therefore more detrimental at distal cerebral level. On the contrary, at
central level a decreased HRV induces a higher cardiac effort without improving
the mechano-contractile performance, thus overall reducing the heart
efficiency. Present results suggest that: (i) the increase of HRV per se does
not seem to be sufficient to trigger a better cerebral hemodynamic response;
(ii) by accounting for both central and cerebral circulations, the optimal HRV
configuration is found at baseline. Given the relation inversely linking HRV
and HR, the presence of this optimal condition can contribute to explain why
the mean HR of the general population settles around the baseline value (70
bpm).


Abstract:
  This paper is concerned with the localized behaviors of the solution $u$ to
the Navier-Stokes equations near the potential singular points. We establish
the concentration rate for the $L^{p,\infty}$ norm of $u$ with $3\leq
p\leq\infty$. Namely, we show that if $z_0=(t_0,x_0)$ is a singular point, then
for any $r>0$, it holds \begin{align} \limsup_{t\to
t_0^-}||u(t,x)-u(t)_{x_0,r}||_{L^{3,\infty}(B_r(x_0))}>\delta^*,\notag
\end{align} and \begin{align} \limsup_{t\to
t_0^-}(t_0-t)^{\frac{1}{\mu}}r^{\frac{2}{\nu}-\frac{3}{p}}||u(t)||_{L^{p,\infty}(B_r(x_0))}>\delta^*\notag
for~3<p\leq\infty,
~\frac{1}{\mu}+\frac{1}{\nu}=\frac{1}{2}~and~2\leq\nu\leq\frac{2}{3}p,\notag
\end{align}where $\delta^*$ is a positive constant independent of $p$ and
$\nu$. Our main tools are some $\varepsilon$-regularity criteria in
$L^{p,\infty}$ spaces and an embedding theorem from $L^{p,\infty}$ space into a
Morrey type space. These are of independent interests.


Abstract:
  Multiple Kernel Learning is a conventional way to learn the kernel function
in kernel-based methods. MKL algorithms enhance the performance of kernel
methods. However, these methods have a lower complexity compared to deep
learning models and are inferior to these models in terms of recognition
accuracy. Deep learning models can learn complex functions by applying
nonlinear transformations to data through several layers. In this paper, we
show that a typical MKL algorithm can be interpreted as a one-layer neural
network with linear activation functions. By this interpretation, we propose a
Neural Generalization of Multiple Kernel Learning (NGMKL), which extends the
conventional multiple kernel learning framework to a multi-layer neural network
with nonlinear activation functions. Our experiments on several benchmarks show
that the proposed method improves the complexity of MKL algorithms and leads to
higher recognition accuracy.


Abstract:
  The density power divergence (DPD) and related measures have produced many
useful statistical procedures which provide a good balance between model
efficiency on one hand, and outlier stability or robustness on the other. The
large number of citations received by the original DPD paper (Basu et al.,
1998) and its many demonstrated applications indicate the popularity of these
divergences and the related methods of inference. The estimators that are
derived from this family of divergences are all M-estimators where the defining
$\psi$ function is based explicitly on the form of the model density. The
success of the minimum divergence estimators based on the density power
divergence makes it imperative and meaningful to look for other, similar
divergences in the same spirit. The logarithmic density power divergence (Jones
et al., 2001), a logarithmic transform of the density power divergence, has
also been very successful in producing inference procedures with a high degree
of efficiency simultaneously with a high degree of robustness. This further
strengthens the motivation to look for statistical divergences that are
transforms of the density power divergence, or, alternatively, members of the
functional density power divergence class. This note characterizes the
functional density power divergence class, and thus identifies the available
divergence measures within this construct that may possibly be explored for
robust and efficient statistical inference.


Abstract:
  Experiments observe an enhanced superconducting gap over impurities as
compared to the clean-bulk value. In order to shed more light on this
phenomenon, we perform simulations within the framework of Bogoliubov-deGennes
theory applied to the attractive Hubbard model. The simulations qualitatively
reproduce the experimentally observed enhancement effect; it can be traced back
to an increased particle density in the metal close to the impurity site. In
addition, the simulations display significant differences between a thin (2D)
and a very thick (3D) film. In 2D pronounced Friedel oscillations can be
observed, which decay much faster in (3D) and therefore are more difficult to
resolve. Also this feature is in qualitative agreement with the experiment.


Abstract:
  Supermassive black hole (SMBH) binaries represent the main target for
missions such as the Laser Interferometer Space Antenna and Pulsar Timing
Arrays. The understanding of their dynamical evolution prior to coalescence is
therefore crucial to improving detection strategies and for the astrophysical
interpretation of the gravitational wave data. In this paper, we use
high-resolution $N$-body simulations to model the merger of two equal-mass
galaxies hosting a central SMBH. In our models, all binaries are initially
prograde with respect to the galaxy sense of rotation. But, binaries that form
with a high eccentricity, $e\gtrsim 0.7$, quickly reverse their sense of
rotation and become almost perfectly retrograde at the moment of binary
formation. The evolution of these binaries proceeds towards larger
eccentricities, as expected for a binary hardening in a counter-rotating
stellar distribution. Binaries that form with lower eccentricities remain
prograde and at comparatively low eccentricities. We study the origin of the
orbital flip by using an analytical model that describes the early stages of
binary evolution. This model indicates that the orbital plane flip is due to
the torque from the triaxial background mass distribution that naturally arises
from the galactic merger process. Our results imply the existence of a
population of SMBH binaries with a high eccentricity and could have significant
implications for the detection of the gravitational wave signal emitted by
these systems.


Abstract:
  In this work, we deal with the problem of rating in sports, where the skills
of the players/teams are inferred from the observed outcomes of the games. Our
focus is on the online rating algorithms which estimate the skills after each
new game by exploiting the probabilistic models of the relationship between the
skills and the game outcome. We propose a Bayesian approach which may be seen
as an approximate Kalman filter and which is generic in the sense that it can
be used with any skills-outcome model and can be applied in the individual --
as well as in the group-sports. We show how the well-know algorithms (such as
the Elo, the Glicko, and the TrueSkill algorithms) may be seen as instances of
the one-fits-all approach we propose. In order to clarify the conditions under
which the gains of the Bayesian approach over the simpler solutions can
actually materialize, we critically compare the known and the new algorithms by
means of numerical examples using the synthetic as well as the empirical data.


Abstract:
  Our experiments demonstrate that alloying the cubic--phase YbN into the
wurtzite--phase AlN results in clear mechanical softening and enhanced
electromechanical coupling of AlN. The first principle calculations reproduce
experimental results well, and predict a maximum of 270% increase in
electromechanical coupling coefficient caused by 1) enhanced piezoelectric
response induced by the local strain of Yb ions and 2) structural flexibility
of the YbAlN alloy. Extensive calculations suggest that the substitutional
neighbor Yb--Yb pairs in wurtzite AlN are energetically stable along c axis,
and avoid to form on the basal plane of wurtzite structure due to the repulsion
between them, which explains that YbAlN films with high Yb concentrations are
difficult to fabricate in our sputtering experiments. Moreover, the neighbor
Yb--Yb pair interactions also promote structural flexibility of YbAlN, and are
considered a cause for mechanical softening of YbAlN.


Abstract:
  In the simplest game-theoretic formulation of Schelling's model of
segregation on graphs, agents of two different types each select their own
vertex in a given graph such as to maximize the fraction of agents of their
type in their occupied neighborhood. Two ways of modeling agent movement here
are either to allow two agents to swap their vertices or to allow an agent to
jump to a free vertex. The contributions of this paper are twofold. First, we
prove that deciding the existence of a swap-equilibrium and a jump-equilibrium
in this simplest model of Schelling games is NP-hard, thereby answering
questions left open by Agarwal et al. [AAAI '20] and Elkind et al. [IJCAI '19].
Second, we introduce a measure for the robustness of equilibria in Schelling
games in terms of the minimum number of edges that need to be deleted to make
an equilibrium unstable. We prove tight lower and upper bounds on the
robustness of swap-equilibria in Schelling games on different graph classes.


Abstract:
  In this work, we study the problem of co-optimize communication,
pre-computing, and computation cost in one-round multi-way join evaluation. We
propose a multi-way join approach ADJ (Adaptive Distributed Join) for complex
join which finds one optimal query plan to process by exploring cost-effective
partial results in terms of the trade-off between pre-computing, communication,
and computation.We analyze the input relations for a given join query and find
one optimal over a set of query plans in some specific form, with high-quality
cost estimation by sampling. Our extensive experiments confirm that ADJ
outperforms the existing multi-way join methods by up to orders of magnitude.


Abstract:
  This study delves into semi-supervised object detection (SSOD) to improve
detector performance with additional unlabeled data. State-of-the-art SSOD
performance has been achieved recently by self-training, in which training
supervision consists of ground truths and pseudo-labels. In current studies, we
observe that class imbalance in SSOD severely impedes the effectiveness of
self-training. To address the class imbalance, we propose adaptive
class-rebalancing self-training (ACRST) with a novel memory module called
CropBank. ACRST adaptively rebalances the training data with foreground
instances extracted from the CropBank, thereby alleviating the class imbalance.
Owing to the high complexity of detection tasks, we observe that both
self-training and data-rebalancing suffer from noisy pseudo-labels in SSOD.
Therefore, we propose a novel two-stage filtering algorithm to generate
accurate pseudo-labels. Our method achieves satisfactory improvements on
MS-COCO and VOC benchmarks. When using only 1\% labeled data in MS-COCO, our
method achieves 17.02 mAP improvement over supervised baselines, and 5.32 mAP
improvement compared with state-of-the-art methods.


Abstract:
  Cyclotron line scattering features are detected in a few tens of X-ray
pulsars (XRPs) and used as direct indicators of a strong magnetic field at the
surface of accreting neutron stars (NSs). In a few cases, cyclotron lines are
known to be variable with accretion luminosity of XRPs. It is accepted that the
observed variations of cyclotron line scattering features are related to
variations of geometry and dynamics of accretion flow above the magnetic poles
of a NS. A positive correlation between the line centroid energy and luminosity
is typical for sub-critical XRPs, where the accretion results in hot spots at
the magnetic poles. The negative correlation was proposed to be a specific
feature of bright super-critical XRPs, where radiation pressure supports
accretion columns above the stellar surface. Cyclotron line in spectra of
Be-transient X-ray pulsar GRO J1008-57 is detected at energies from $\sim 75
-90$ keV, the highest observed energy of cyclotron line feature in XRPs. We
report the peculiar relation of cyclotron line centroid energies with
luminosity in GRO J1008-57 during the Type II outburst in August 2017 observed
by Insight-HXMT. The cyclotron line energy was detected to be negatively
correlated with the luminosity at $3.2\times 10^{37}\,\ergs<L<4.2\times
10^{37}\,\ergs$, and positively correlated at $L\gtrsim 5\times
10^{37}\,\ergs$. We speculate that the observed peculiar behavior of a
cyclotron line would be due to variations of accretion channel geometry.


Abstract:
  Reconfiguration aims at recovering a system from a fault by automatically
adapting the system configuration, such that the system goal can be reached
again. Classical approaches typically use a set of pre-defined faults for which
corresponding recovery actions are defined manually. This is not possible for
modern hybrid systems which are characterized by frequent changes. Instead,
AI-based approaches are needed which leverage on a model of the non-faulty
system and which search for a set of reconfiguration operations which will
establish a valid behavior again.
  This work presents a novel algorithm which solves three main challenges: (i)
Only a model of the non-faulty system is needed, i.e. the faulty behavior does
not need to be modeled. (ii) It discretizes and reduces the search space which
originally is too large -- mainly due to the high number of continuous system
variables and control signals. (iii) It uses a SAT solver for propositional
logic for two purposes: First, it defines the binary concept of validity.
Second, it implements the search itself -- sacrificing the optimal solution for
a quick identification of an arbitrary solution. It is shown that the approach
is able to reconfigure faults on simulated process engineering systems.


Abstract:
  We study the $T\bar T$ deformation on a multi-quantum mechanical systems. By
introducing the dynamical coordinate transformation, we obtain the deformed
theory as well as the solution. We further study the thermo-field-double state
under the $T\bar T$ deformation on these systems, including conformal quantum
mechanical system, the Sachdev-Ye-Kitaev model, and the model satisfying
Eigenstate Thermalization Hypothesis. We find common regenesis phenomena where
the signal injected into one local system can regenerate from the other local
system. From the bulk picture, we study the deformation on Jackiw-Teitelboim
gravity governed by Schwarzian action and find that the regenesis phenomena
here are not related to the causal structure of semi-classical wormhole.


Abstract:
  Labelled networks are an important class of data, naturally appearing in
numerous applications in science and engineering. A typical inference goal is
to determine how the vertex labels (or features) affect the network's
structure. In this work, we introduce a new generative model, the feature-first
block model (FFBM), that facilitates the use of rich queries on labelled
networks. We develop a Bayesian framework and devise a two-level Markov chain
Monte Carlo approach to efficiently sample from the relevant posterior
distribution of the FFBM parameters. This allows us to infer if and how the
observed vertex-features affect macro-structure. We apply the proposed methods
to a variety of network data to extract the most important features along which
the vertices are partitioned. The main advantages of the proposed approach are
that the whole feature-space is used automatically and that features can be
rank-ordered implicitly according to impact.


Abstract:
  This paper concerns a new optimization problem arising in the management of a
multi-object spectrometer with a configurable slit unit. The field of view of
the spectrograph is divided into contiguous and parallel spatial bands, each
one associated with two opposite sliding metal bars that can be positioned to
observe one astronomical object. Thus several objects can be analyzed
simultaneously within a configuration of the bars called a mask. Due to the
high demand from astronomers, pointing the spectrograph's field of view to the
sky, rotating it, and selecting the objects to conform a mask is a crucial
optimization problem for the efficient use of the spectrometer. The paper
describes this problem, presents a Mixed Integer Linear Programming formulation
for the case where the rotation angle is fixed, presents a non-convex
formulation for the case where the rotation angle is unfixed, describes a
heuristic approach for the general problem, and discusses computational results
on real-world and randomly-generated instances.


Abstract:
  In this work, the electrical and spin properties of monolayer MoSi2X4 (X= N,
P, As, and Sb) under vertical strain are investigated. The band structures
state that MoSi2N4 is an indirect semiconductor, whereas other compounds are
direct semiconductors. The vertical strain has been selected to modify the
electrical properties. The bandgap shows a maximum and decreases for both
tensile and compressive strains. The valence band at K-point displays a large
spin-splitting, whereas the conduction band has a negligible splitting. On the
other hand, the second conduction band has a large spin-splitting and moves
down under vertical strain which leads to a large spin-splitting in both
conduction and valence bands edges. The projected density of states along with
the projected band structure clarifies the origin of these large
spin-splittings. These three spin-splittings can be controlled by vertical
strain.


Abstract:
  Let $\Gamma$ be a graph with vertex set $V$, and let $a$ and $b$ be
nonnegative integers. A subset $C$ of $V$ is called an $(a,b)$-regular set in
$\Gamma$ if every vertex in $C$ has exactly $a$ neighbors in $C$ and every
vertex in $V\setminus C$ has exactly $b$ neighbors in $C$. In particular, $(0,
1)$-regular sets and $(1, 1)$-regular sets in $\Ga$ are called perfect codes
and total perfect codes in $\Ga$, respectively. A subset $C$ of a group $G$ is
said to be an $(a,b)$-regular set of $G$ if there exists a Cayley graph of $G$
which admits $C$ as an $(a,b)$-regular set. In this paper we prove that, for
any generalized dihedral group $G$ or any group $G$ of order $4p$ or $pq$ for
some primes $p$ and $q$, if a nontrivial subgroup $H$ of $G$ is a $(0,
1)$-regular set of $G$, then it must also be an $(a,b)$-regular set of $G$ for
any $0\leqslant a\leqslant|H|-1$ and $0\leqslant b\leqslant |H|$ such that $a$
is even when $|H|$ is odd. A similar result involving $(1, 1)$-regular sets of
such groups is also obtained in the paper.


Abstract:
  We consider the convex geometry of the cone of nonnegative quadratics over
Stanley-Reisner varieties. Stanley-Reisner varieties (which are unions of
coordinate planes) are amongst the simplest real projective varieties, so this
is potentially a starting point that can generalize to more complicated real
projective varieties. This subject has some suprising connections to algebraic
topology and category theory, which we exploit heavily in our work.
  These questions are also valuable in applied math, because they directly
translate to questions about positive semidefinite (PSD) matrices. In
particular, this relates to a long line of work concerning the extent to which
it is possible to approximately check that a matrix is PSD by checking that
some principle submatrices are PSD, or to check if a partial matrix can be
approximately completed to full PSD matrix.
  We systematize both these practical and theoretical questions using a
framework based on algebraic topology, category theory, and convex geometry. As
applications of this framework we are able to classify the extreme nonnegative
quadratics over many Stanley-Reisner varieties. We plan to follow these
structural results with a paper that is more focused on quantitative questions
about PSD matrix completion, which have applications in sparse semidefinite
programming.


Abstract:
  Chiral antiferromagnets are currently considered for broad range of
applications in spintronics, spin-orbitronics and magnonics. In contrast to the
established approach relying on materials screening, the anisotropic and chiral
responses of low-dimensional antifferromagnets can be tailored relying on the
geometrical curvature. Here, we consider an achiral, anisotropic
antiferromagnetic spin chain and demonstrate that these systems possess
geometry-driven effects stemming not only from the exchange interaction but
also from the anisotropy. Peculiarly, the anisotropy-driven effects are
complementary to the curvature effects stemming from the exchange interaction
and rather strong as they are linear in curvature. These effects are
responsible for the tilt of the equilibrium direction of vector order
parameters and the appearance of the homogeneous Dzyaloshinskii-Moriya
interaction. The latter is a source of the geometry-driven weak ferromagnetism
emerging in curvilinear antiferromagnetic spin chains. Our findings provide a
deeper fundamental insight into the physics of curvilinear antiferromagnets
beyond the $\sigma$-model and offer an additional degree of freedom in the
design of spintronic and magnonic devices.


Abstract:
  In a recent paper we showed that the collapse to a black hole in
one-parameter families of initial data for massless, minimally coupled scalar
fields in spherically symmetric semi-classical loop quantum gravity exhibited a
universal mass scaling similar to the one in classical general relativity. In
particular, no evidence of a mass gap appeared as had been suggested by
previous studies. The lack of a mass gap indicated the possible existence of a
self-similar critical solution as in general relativity. Here we provide
further evidence for its existence. Using an adaptive mesh refinement code, we
show that "echoes" arise as a result of the discrete self-similarity in
space-time. We also show the existence of "wiggles" in the mass scaling
relation, as in the classical theory. The results from the semi-classical
theory agree well with those of classical general relativity unless one takes
unrealistically large values for the polymerization parameter.


Abstract:
  Nuclear-powered X-ray millisecond pulsars are the third type of millisecond
pulsars, which are powered by thermonuclear fusion processes. The corresponding
brightness oscillations, known as burst oscillations, are observed during some
thermonuclear X-ray bursts, when the burning and cooling accreted matter gives
rise to an azimuthally asymmetric brightness pattern on the surface of the
spinning neutron star. Apart from providing neutron star spin rates, this X-ray
timing feature can be a useful tool to probe the fundamental physics of neutron
star interior and surface. This chapter presents an overview of the relatively
new field of nuclear-powered X-ray millisecond pulsars.


Abstract:
  It was recently shown that wavepackets with skewed momentum distribution
exhibit a boomerang-like dynamics in the Anderson model due to Anderson
localization: after an initial ballistic motion, they make a U-turn and
eventually come back to their starting point. In this paper, we study the
robustness of the quantum boomerang effect in various kinds of disordered and
dynamical systems: tight-binding models with pseudo-random potentials, systems
with band random Hamiltonians, and the kicked rotor. Our results show that the
boomerang effect persists in models with pseudo-random potentials. It is also
present in the kicked rotor, although in this case with a specific dependency
on the initial state. On the other hand, we find that random hopping processes
inhibit any drift motion of the wavepacket, and consequently the boomerang
effect. In particular, if the random nearest-neighbor hopping amplitudes have
zero average, the wavepacket remains in its initial position.


Abstract:
  This article is a response to the continued assumption, cited even in reports
and reviews of recent experimental breakthroughs and advances in theoretical
methods, that the antiJaynes-Cummings (AJC) interaction is an intractable
energy non-conserving component of the quantum Rabi model (QRM). We present
three key features of QRM dynamics : (a) the AJC interaction component has a
conserved excitation number operator and is exactly solvable (b) QRM dynamical
space consists of a rotating frame (RF) dominated by an exactly solved
Jaynes-Cummings (JC) interaction specified by a conserved JC excitation number
operator which generates the U(1) symmetry of RF and a correlated
counterrotating frame (CRF) dominated by an exactly solved antiJaynes-Cummings
(AJC) interaction specified by a conserved AJC excitation number operator which
generates the U(1) symmetry of CRF.


Abstract:
  Smart homes are one of the most promising applications of the emerging
Internet of Things (IoT) technology. With the growing number of IoT related
devices such as smart thermostats, smart fridges, smart speaker, smart light
bulbs and smart locks, smart homes promise to make our lives easier and more
comfortable. However, the increased deployment of such smart devices brings an
increase in potential security risks and home privacy breaches. In order to
overcome such risks, Intrusion Detection Systems are presented as pertinent
tools that can provide network-level protection for smart devices deployed in
home environments. These systems monitor the network activities of the smart
home-connected de-vices and focus on alerting suspicious or malicious activity.
They also can deal with detected abnormal activities by hindering the impostors
in accessing the victim devices. However, the employment of such systems in the
context of a smart home can be challenging due to the devices hardware
limitations, which may restrict their ability to counter the existing and
emerging attack vectors. Therefore, this paper proposes an experimental
comparison between the widely used open-source NIDSs namely Snort, Suricata and
Bro IDS to find the most appropriate one for smart homes in term of detection
accuracy and resources consumption including CP and memory utilization.
Experimental Results show that Suricata is the best performing NIDS for smart
homes


Abstract:
  Time-harmonic electromagnetic waves in vacuum are described by the Helmholtz
equation $\Delta u+\omega ^{2}u=0 $ for $ (x,y,z) \in \mathbb{R}^3 $. For the
evolution of such waves along the $z$-axis a Schr\"odinger equation can be
derived through a multiple scaling ansatz. It is the purpose of this paper to
justify this formal approximation by proving bounds between this formal
approximation and true solutions of the original system. The challenge of the
presented validity analysis is the fact that the Helmholtz equation is
ill-posed as an evolutionary system along the $z$-axis.


Abstract:
  Using detailed synchrotron diffraction, magnetization, thermodynamic and
transport measurements, we investigate the relationship between the mixed
valence of Ir, lattice strain and the resultant structural and magnetic ground
states in the geometrically frustrated triple perovskite iridate
Ba$_{3}$NaIr$_{2}$O$_{9}$. We observe a complex interplay between lattice
strain and structural phase co-existence, which is in sharp contrast to what is
typically observed in this family of compounds. The low temperature magnetic
ground state is characterized by the absence of long range order, and points
towards the condensation of a cluster glass state from an extended regime of
short range magnetic correlations.


Abstract:
  Modulo-wrapping receivers have attracted interest in several areas of digital
communications, including precoding and lattice coding. The asymptotic capacity
and error performance of the modulo AWGN channel have been well established.
However, due to underlying assumptions of the asymptotic analyses, these
findings might not always be realistic in physical world applications, which
are often dimension- or delay-limited. In this work, the optimum ways to
achieve minimum probability of error for binary signaling through a scalar
modulo AWGN channel is examined under different scenarios where the receiver
has access to full or partial information. In case of partial information at
the receiver, an iterative estimation rule is proposed to reduce the error
rate, and the performance of different estimators are demonstrated in simulated
experiments.


Abstract:
  We estimate the chirality of the cosmological medium due to parity violating
decays of standard model particles, focusing on the example of tau leptons. The
non-trivial chirality is however too small to make a significant contribution
to the cosmological magnetic field via the chiral-magnetic effect.


Abstract:
  For a bipartite graph $G$ with parts $X$ and $Y$, an $X$-interval coloring is
a proper edge coloring of $G$ by integers such that the colors on the edges
incident to any vertex in $X$ form an interval. Denote by $\chi'_{int}(G,X)$
the minimum $k$ such that $G$ has an $X$-interval coloring with $k$ colors. The
author and Toft conjectured [Discrete Mathematics 339 (2016), 2628--2639] that
there is a polynomial $P(x)$ such that if $G$ has maximum degree at most
$\Delta$, then $\chi'_{int}(G,X) \leq P(\Delta)$. In this short note, we prove
this conjecture; in fact, we prove that a cubic polynomial suffices. We also
deduce some improved upper bounds on $\chi'_{int}(G,X)$ for bipartite graphs
with small maximum degree.


Abstract:
  In pursuit of explainability, we develop generative models for sequential
data. The proposed models provide state-of-the-art classification results and
robust performance for speech phone classification. We combine modern neural
networks (normalizing flows) and traditional generative models (hidden Markov
models - HMMs). Normalizing flow-based mixture models (NMMs) are used to model
the conditional probability distribution given the hidden state in the HMMs.
Model parameters are learned through judicious combinations of time-tested
Bayesian learning methods and contemporary neural network learning methods. We
mainly combine expectation-maximization (EM) and mini-batch gradient descent.
The proposed generative models can compute likelihood of a data and hence
directly suitable for maximum-likelihood (ML) classification approach. Due to
structural flexibility of HMMs, we can use different normalizing flow models.
This leads to different types of HMMs providing diversity in data modeling
capacity. The diversity provides an opportunity for easy decision fusion from
different models. For a standard speech phone classification setup involving 39
phones (classes) and the TIMIT dataset, we show that the use of standard
features called mel-frequency-cepstral-coeffcients (MFCCs), the proposed
generative models, and the decision fusion together can achieve $86.6\%$
accuracy by generative training only. This result is close to state-of-the-art
results, for examples, $86.2\%$ accuracy of PyTorch-Kaldi toolkit [1], and
$85.1\%$ accuracy using light gated recurrent units [2]. We do not use any
discriminative learning approach and related sophisticated features in this
article.


Abstract:
  Explaining the predictions of opaque machine learning algorithms is an
important and challenging task, especially as complex models are increasingly
used to assist in high-stakes decisions such as those arising in healthcare and
finance. Most popular tools for post-hoc explainable artificial intelligence
(XAI) are either insensitive to context (e.g., feature attributions) or
difficult to summarize (e.g., counterfactuals). In this paper, I introduce
\emph{rational Shapley values}, a novel XAI method that synthesizes and extends
these seemingly incompatible approaches in a rigorous, flexible manner. I
leverage tools from decision theory and causal modeling to formalize and
implement a pragmatic approach that resolves a number of known challenges in
XAI. By pairing the distribution of random variables with the appropriate
reference class for a given explanation task, I illustrate through theory and
experiments how user goals and knowledge can inform and constrain the solution
set in an iterative fashion. The method compares favorably to state of the art
XAI tools in a range of quantitative and qualitative comparisons.


Abstract:
  We show that there are 4 infinite families of lattice equable kites, given by
corresponding Pell or Pell-like equations, but up to Euclidean motions, there
are exactly 5 lattice equable trapezoids (2 isosceles, 2 right, 1 singular) and
4 lattice equable cyclic quadrilaterals. We also show that, with one exception,
the interior diagonals of lattice equable quadrilaterals are irrational.


Abstract:
  A major challenge in the study of cryptography is characterizing the
necessary and sufficient assumptions required to carry out a given
cryptographic task. The focus of this work is the necessity of a broadcast
channel for securely computing symmetric functionalities (where all the parties
receive the same output) when one third of the parties, or more, might be
corrupted. Assuming all parties are connected via a peer-to-peer network, but
no broadcast channel (nor a secure setup phase) is available, we prove the
following characterization:
  1) A symmetric $n$-party functionality can be securely computed facing
$n/3\le t<n/2$ corruptions (\ie honest majority), if and only if it is
\emph{$(n-2t)$-dominated}; a functionality is $k$-dominated, if \emph{any}
$k$-size subset of its input variables can be set to \emph{determine} its
output.
  2) Assuming the existence of one-way functions, a symmetric $n$-party
functionality can be securely computed facing $t\ge n/2$ corruptions (\ie no
honest majority), if and only if it is $1$-dominated and can be securely
computed with broadcast.
  It follows that, in case a third of the parties might be corrupted, broadcast
is necessary for securely computing non-dominated functionalities (in which
"small" subsets of the inputs cannot determine the output), including, as
interesting special cases, the Boolean XOR and coin-flipping functionalities.


Abstract:
  We consider Shimura varieties for orthogonal or spin groups acting on
hermitian symmetric domains of type IV. We give regular p-adic integral models
for these varieties over odd primes p at which the level subgroup is the
connected stabilizer of a vertex lattice in the orthogonal space. Our
construction is obtained by combining results of Kisin and the first author
with an explicit presentation and resolution of a corresponding local model.


Abstract:
  To indirectly study the internal structure of giant clumps in main sequence
galaxies at $z \sim 1-3$, we target very turbulent and gas-rich local analogues
from the DYNAMO sample with the Hubble Space Telescope, over a wavelength range
of $\sim 200-480$ nm. We present a catalog of 58 clumps identified in six
DYNAMO galaxies, including the WFC3/UVIS F225W, F336W, and F467M photometry
where the ($225-336$) and ($336-467$) colours are sensitive to extinction and
stellar population age respectively. We measure the internal colour gradients
of clumps themselves to study their age and extinction properties. We find a
marked colour trend within individual clumps, where the resolved colour
distributions show that clumps generally have bluer ($336-467$) colours
(denoting very young ages) in their centers than at their edges, with little
variation in the ($225-336$) colour associated with extinction. Furthermore, we
find that clumps whose colours suggest they are older, are preferentially
located closer toward the centers of their galaxies, and we find no young
clumps at small galactocentric distances. Both results are consistent with
simulations of high-redshift star forming systems that show clumps form via
violent disk instability, and through dynamic processes migrate to the centers
of their galaxies to contribute to bulge growth on timescales of a few 100 Myr,
while continually forming stars in their centers. When we compare the DYNAMO
clumps to those in these simulations, we find the best agreement with the
long-lived clumps.


Abstract:
  In this paper, we analyse the causal aspects of evolving marginally trapped
surfaces in a D-dimensional spherically symmetric spacetime, sourced by perfect
fluid with a cosmological constant. The norm of the normal to the marginally
trapped tube is shown to be the product of lie derivatives of the expansion
parameter of future outgoing null rays along the incoming and outgoing null
directions. We obtain a closed form expression for this norm in terms of
principal density, pressure, areal radius and cosmological constant. For the
case of a homogeneous fluid distribution, we obtain a simple formula for
determining the causal nature of the evolving horizons. We obtain the causal
phase portraits and highlight the critical radius. We identify many solutions
where the causal signature of the marginally trapped tube or marginally
anti-trapped tube is always null despite having an evolving area. These
solutions don't comply with the standard inner and outer horizon classification
for degenerate horizons. we propose an alternate prescription for this
classification of these degenerate horizons.


Abstract:
  We examine the feasibility of the Bell test (i.e., detecting a violation of
the Bell inequality) with the ATLAS detector in Large Hadron Collider (LHC) at
CERN through the flavor entanglement between the B mesons. After addressing the
possible issues that arise associated with the experiment and how they may be
treated based on an analogy with conventional Bell tests, we show in our
simulation study that under realistic conditions (expected from the LHC Run 3
operation) the Bell test is feasible under mild assumptions. The definitive
factor for this promising result lies primarily in the fact that the ATLAS
detector is capable of measuring the decay times of the B mesons independently,
which was not available in the previous experiment with the Belle detector at
KEK. This result suggests the possibility of the Bell test in much higher
energy domains and may open up a new arena for experimental studies of quantum
foundations.


Abstract:
  Our contributions with this paper are twofold. First, we elucidate the
methodological requirements for a risk framework of custodial operations and
argue for the value of this type of risk model as complementary with
cryptographic and blockchain security models. Second, we present a risk model
in the form of a library of attack-trees for Revault -- an open-source custody
protocol. The model can be used by organisations as a risk quantification
framework for a thorough security analysis in their specific deployment
context. Our work exemplifies an approach that can be used independent of which
custody protocol is being considered, including complex protocols with multiple
stakeholders and active defence infrastructure.


Abstract:
  Obtaining high-quality parallel corpora is of paramount importance for
training NMT systems. However, as many language pairs lack adequate
gold-standard training data, a popular approach has been to mine so-called
"pseudo-parallel" sentences from paired documents in two languages. In this
paper, we outline some problems with current methods, propose computationally
economical solutions to those problems, and demonstrate success with novel
methods on the Tatoeba similarity search benchmark and on a downstream task,
namely NMT. We uncover the effect of resource-related factors (i.e. how much
monolingual/bilingual data is available for a given language) on the optimal
choice of bitext mining approach, and echo problems with the oft-used BUCC
dataset that have been observed by others. We make the code and data used for
our experiments publicly available.


Abstract:
  In this paper we consider the massive scalar perturbation on the top of a
small spinning-like black hole in context of Einstein-bumblebee modified
gravity in order to probe the role of spontaneous Lorentz symmetry breaking on
the superradiance scattering and corresponding instability. We show that at the
low-frequency limit of the scalar wave the superradiance scattering will be
enhanced with the Lorentz-violating parameter $\alpha<0$ and will be weakened
with $\alpha>0$. Moreover, by addressing the black hole bomb issue, we extract
an improved bound in the instability regime indicating that $\alpha<0$
increases the parameter space of the scalar field instability, while $\alpha>0$
decreases it.


Abstract:
  We highlight new results on the localization number of a graph, a parameter
derived from the localization graph searching game. After introducing the game
and providing an overview of existing results, we describe recent results on
the localization number. We describe bounds or exact values of the localization
number of incidence graphs of designs, polarity graphs, and Kneser graphs.


Abstract:
  One of the most critical tasks for startups is to validate their business
model. Therefore, entrepreneurs try to collect information such as feedback
from other actors to assess the validity of their assumptions and make
decisions. However, previous work on decisional guidance for business model
validation provides no solution for the highly uncertain and complex context of
earlystage startups. The purpose of this paper is, thus, to develop design
principles for a Hybrid Intelligence decision support system (HI-DSS) that
combines the complementary capabilities of human and machine intelligence. We
follow a design science research approach to design a prototype artifact and a
set of design principles. Our study provides prescriptive knowledge for HI-DSS
and contributes to previous work on decision support for business models, the
applications of complementary strengths of humans and machines for making
decisions, and support systems for extremely uncertain decision-making
problems.


Abstract: We compute the Chow groups of smooth Gushel-Mukai varieties of dimension $5$.


Abstract:
  We study the distribution of the Frobenius traces on $K3$ surfaces. We
compare experimental data with the predictions made by the Sato--Tate
conjecture, i.e. with the theoretical distributions derived from the theory of
Lie groups assuming equidistribution. Our sample consists of generic $K3$
surfaces, as well as of such having real and complex multiplication. We report
evidence for the Sato--Tate conjecture for the surfaces considered.


Abstract:
  In this work, the anisotropic variant of the quantum Rabi model with
different coupling strengths of the rotating and counter-rotating wave terms is
studied by the Bogoliubov operator approach. The anisotropy preserves the
parity symmetry of the original model. We derive the corresponding
$G$-function, which yields both the regular and exceptional eigenvalues. The
exceptional eigenvalues correspond to the crossing points of two energy levels
with different parities and are doubly degenerate. We find analytically that
the ground-state and the first excited state can cross several times,
indicating multiple first-order phase transitions as function of the coupling
strength. These crossing points are related to manifest parity symmetry of the
Hamiltonian, in contrast to the level crossings in the asymmetric quantum Rabi
model which are caused by a hidden symmetry.


Abstract:
  We study the connection between risk aversion, number of consumers and
uniqueness of equilibrium. We consider an economy with two goods and $c$
impatience types, where each type has additive separable preferences with HARA
Bernoulli utility function,
$u_H(x):=\frac{\gamma}{1-\gamma}\left(b+\frac{a}{\gamma}x\right)^{1-\gamma}$.
We show that if $\gamma\in \left(1, \frac{c}{c-1}\right]$, the equilibrium is
unique. Moreover, the methods used, involving Newton's symmetric polynomials
and Descartes' rule of signs, enable us to offer new sufficient conditions for
uniqueness in a closed-form expression highlighting the role played by
endowments, patience and specific HARA parameters. Finally, new necessary and
sufficient conditions in ensuring uniqueness are derived for the particular
case of CRRA Bernoulli utility functions with $\gamma =3$.


Abstract:
  Saliency maps have shown to be both useful and misleading for explaining
model predictions especially in the context of images. In this paper, we
perform sanity checks for text modality and show that the conclusions made for
image do not directly transfer to text. We also analyze the effects of the
input multiplier in certain saliency maps using similarity scores,
max-sensitivity and infidelity evaluation metrics. Our observations reveal that
the input multiplier carries input's structural patterns in explanation maps,
thus leading to similar results regardless of the choice of model parameters.
We also show that the smoothness of a Neural Network (NN) function can affect
the quality of saliency-based explanations. Our investigations reveal that
replacing ReLUs with Softplus and MaxPool with smoother variants such as
LogSumExp (LSE) can lead to explanations that are more reliable based on the
infidelity evaluation metric.


Abstract:
  The main result provide a common generalization for Ramsey-type theorems
concerning finite colorings of edge sets of complete graphs with vertices in
infinite semigroups. We capture the essence of theorems proved in different
fields: for natural numbers due to Milliken--Tylor, Deuber--Hindman,
Bergelson--Hindman, for combinatorial covering properties due to Scheepers and
Tsaban, and local properties in function spaces due to Scheepers. To this end,
we use idempotent ultrafilters in the \v{C}ech--Stone compactifications of
discrete infinite semigroups and topological games. The research is motivated
by the recent breakthrough work of Tsaban about colorings and the Menger
covering property.


Abstract:
  Quantum effects fundamentally engender exotic physical phenomena in
macroscopic systems, which advance next-generation technological applications.
Rotational tunneling that represents the quantum phenomenon of the librational
motion of molecules is ubiquitous in hydrogen-contained materials. However, its
direct manifestation in realizing macroscopic physical properties is elusive.
Here we report an observation of reentrant ferroelectricity under low pressure
that is mediated by the rotational tunneling of ammonium ions in molecule-based
(NH$_4$)$_2$FeCl$_5 \cdot$H$_2$O. Applying a small pressure leads to a
transition from spin-driven ferroelectricity to paraelectricity coinciding with
the stabilization of a collinear magnetic phase. Such a transition is
attributed to the hydrogen bond fluctuations via the rotational tunneling of
ammonium groups as supported by theoretical calculations. Higher pressure lifts
the quantum fluctuations and leads to a reentrant ferroelectric phase
concomitant with another incommensurate magnetic phase. These results
demonstrate that the rotational tunneling emerges as a new route to control
magnetic-related properties in soft magnets, opening avenues for designing
multi-functional materials and realizing potential quantum control.


Abstract:
  How to obtain good value estimation is one of the key problems in
Reinforcement Learning (RL). Current value estimation methods, such as DDPG and
TD3, suffer from unnecessary over- or underestimation bias. In this paper, we
explore the potential of double actors, which has been neglected for a long
time, for better value function estimation in continuous setting. First, we
uncover and demonstrate the bias alleviation property of double actors by
building double actors upon single critic and double critics to handle
overestimation bias in DDPG and underestimation bias in TD3 respectively. Next,
we interestingly find that double actors help improve the exploration ability
of the agent. Finally, to mitigate the uncertainty of value estimate from
double critics, we further propose to regularize the critic networks under
double actors architecture, which gives rise to Double Actors Regularized
Critics (DARC) algorithm. Extensive experimental results on challenging
continuous control tasks show that DARC significantly outperforms
state-of-the-art methods with higher sample efficiency.


Abstract:
  How often is a quintic polynomial solvable by radicals? We establish that the
number of such polynomials, monic and irreducible with integer coefficients in
$[-H,H]$, is $O(H^{3.91})$. More generally, we show that if $n \ge 3$ and $n
\notin \{ 7, 8, 10 \}$ then there are $O(H^{n-1.017})$ monic, irreducible
polynomials of degree $n$ with integer coefficients in $[-H,H]$ and Galois
group not containing $A_n$. Save for the alternating group and degrees
$7,8,10$, this establishes a 1936 conjecture of van der Waerden.


Abstract:
  Epitaxial growth on a surface vicinal to a high-symmetry crystallographic
plane occurs through the propagation of atomic steps, a process called
step-flow growth. In some instances, the steps tend to form close groups (or
bunches), a phenomenon termed step bunching, which corresponds to an
instability of the equal-spacing step propagation. Over the last fifty years,
various mechanisms have been proposed to explain step bunching, the most
prominent of which are the inverse Ehrlich-Schwoebel effect (i.e., the
asymmetry which favors the attachment of adatoms from the upper terrace),
elastically mediated interactions between steps (in heteroepitaxy), step
permeability (in electromigration-controlled growth), and the chemical effect
(which couples the diffusion fields on all terraces). Beyond the discussion of
the influence of each of these mechanisms taken independently on the propensity
to bunching, we propose a unified treatment of the effect of these mechanisms
on the onset of the bunching instability, which also accounts for their
interplay. This is done in the setting of the so-called quasistatic
approximation, which by permitting mostly analytical treatment, offers a clear
view of the influence on stability of the combined mechanisms. In particular,
we find that the Ehrlich-Schwoebel effect, elastic step-interactions and the
chemical effect combine in a quasi-additive fashion, whereas step permeability
is neither stabilizing nor destabilizing per se but changes the relative
influence of the three aforementioned mechanisms. In a companion paper, we
demonstrate and discuss the importance of another mechanism, which we call the
dynamics effect, that emerges when relaxing the simplifying but questionable
quasistatic approximation.


Abstract:
  Deep reinforcement learning (DRL) has recently shown its success in tackling
complex combinatorial optimization problems. When these problems are extended
to multiobjective ones, it becomes difficult for the existing DRL approaches to
flexibly and efficiently deal with multiple subproblems determined by weight
decomposition of objectives. This paper proposes a concise meta-learning-based
DRL approach. It first trains a meta-model by meta-learning. The meta-model is
fine-tuned with a few update steps to derive submodels for the corresponding
subproblems. The Pareto front is built accordingly. The computational
experiments on multiobjective traveling salesman problems demonstrate the
superiority of our method over most of learning-based and iteration-based
approaches.


Abstract:
  Let $E^*$ be a finite complex of locally free sheaves on a complex manifold
$X$. We prove that to every connection of type $(1,0)$ on $E^*$ it is
canonically associated an $L_{\infty}$ morphism $g\colon A^{0,
*}_X(\mathcal{H}om^*_{O_X}(E^*,E^*))\to \dfrac{A^{*,*}_X}{A^{\ge 2,*}_X}[2]$
that lifts the 1-component of Buchweitz-Flenner semiregularity map. An
application to deformations of coherent sheaves on projective manifolds is
given.


Abstract:
  Single image generative models perform synthesis and manipulation tasks by
capturing the distribution of patches within a single image. The classical (pre
Deep Learning) prevailing approaches for these tasks are based on an
optimization process that maximizes patch similarity between the input and
generated output. Recently, however, Single Image GANs were introduced both as
a superior solution for such manipulation tasks, but also for remarkable novel
generative tasks. Despite their impressiveness, single image GANs require long
training time (usually hours) for each image and each task. They often suffer
from artifacts and are prone to optimization issues such as mode collapse. In
this paper, we show that all of these tasks can be performed without any
training, within several seconds, in a unified, surprisingly simple framework.
We revisit and cast the "good-old" patch-based methods into a novel
optimization-free framework. We start with an initial coarse guess, and then
simply refine the details coarse-to-fine using patch-nearest-neighbor search.
This allows generating random novel images better and much faster than GANs. We
further demonstrate a wide range of applications, such as image editing and
reshuffling, retargeting to different sizes, structural analogies, image
collage and a newly introduced task of conditional inpainting. Not only is our
method faster ($\times 10^3$-$\times 10^4$ than a GAN), it produces superior
results (confirmed by quantitative and qualitative evaluation), less artifacts
and more realistic global structure than any of the previous approaches
(whether GAN-based or classical patch-based).


Abstract:
  We provide a semigroup approach to the viscous Hamilton-Jacobi equation. It
turns out that exponential Orlicz hearts are suitable spaces to handle the
(quadratic) non-linearity of the Hamiltonian. Based on an abstract extension
result for nonlinear semigroups on spaces of continuous functions, we represent
the solution of the viscous Hamilton-Jacobi equation as a strongly continuous
convex semigroup on an exponential Orlicz heart. As a result, the solution
depends continuously on the initial data. We further determine the symmetric
Lipschitz set which is invariant under the semigroup. This automatically yields
a priori estimates and regularity in Sobolev spaces. In particular, on the
domain restricted to the symmetric Lipschitz set, the generator can be
explicitly determined and linked with the viscous Hamilton-Jacobi equation.


Abstract:
  Searching for a more compact network width recently serves as an effective
way of channel pruning for the deployment of convolutional neural networks
(CNNs) under hardware constraints. To fulfill the searching, a one-shot
supernet is usually leveraged to efficiently evaluate the performance
\wrt~different network widths. However, current methods mainly follow a
\textit{unilaterally augmented} (UA) principle for the evaluation of each
width, which induces the training unfairness of channels in supernet. In this
paper, we introduce a new supernet called Bilaterally Coupled Network (BCNet)
to address this issue. In BCNet, each channel is fairly trained and responsible
for the same amount of network widths, thus each network width can be evaluated
more accurately. Besides, we leverage a stochastic complementary strategy for
training the BCNet, and propose a prior initial population sampling method to
boost the performance of the evolutionary search. Extensive experiments on
benchmark CIFAR-10 and ImageNet datasets indicate that our method can achieve
state-of-the-art or competing performance over other baseline methods.
Moreover, our method turns out to further boost the performance of NAS models
by refining their network widths. For example, with the same FLOPs budget, our
obtained EfficientNet-B0 achieves 77.36\% Top-1 accuracy on ImageNet dataset,
surpassing the performance of original setting by 0.48\%.


Abstract:
  We propose here - for the first time in the literature, to our best knowledge
- an electroweak unification based on the $SU(5)_{L}\times U(1)_{Y}$ gauge
group. The spontaneous symmetry breaking takes place in the manner
$SU(5)_{L}\times U(1)_{Y} \rightarrow U(1)_{em}$, due to a particular Higgs
sector consisting of five scalar quintuplets. Each scalar quintuplet acquires
its own vacuum expectation value, by means of a proper parametrization which is
worked out once the overall vacuum expectation value in the model is
established. The decoupling of the low energy regime (corresponding to the
Standard Model) from the high scale (required by out model here) is
straightforwardly achieved in order to preserve the consistency with the
present experimental data. Finally, a promising phenomenological outcome is
derived by simply tuning a single free parameter. Our results include, besides
a viable one-parameter mass spectrum, also the prediction of precisely three
generations in the fermion sector and the electric charge quantization.


Abstract:
  We study strategic interactions between firms with heterogeneous beliefs
about future climate impacts. To that end, we propose a Cournot-type
equilibrium model where firms choose mitigation efforts and production
quantities such as to maximize the expected profits under their subjective
beliefs. It is shown that optimal mitigation efforts are increased by the
presence of uncertainty and act as substitutes; i.e., one firm's lack of
mitigation incentivizes others to act more decidedly, and vice versa.


Abstract:
  Several of the latest GAN-based vocoders show remarkable achievements,
outperforming autoregressive and flow-based competitors in both qualitative and
quantitative measures while synthesizing orders of magnitude faster. In this
work, we hypothesize that the common factor underlying their success is the
multi-resolution discriminating framework, not the minute details in
architecture, loss function, or training strategy. We experimentally test the
hypothesis by evaluating six different generators paired with one shared
multi-resolution discriminating framework. For all evaluative measures with
respect to text-to-speech syntheses and for all perceptual metrics, their
performances are not distinguishable from one another, which supports our
hypothesis.


Abstract:
  In this perspective piece, I benchmark gallium arsenide, silicon, and
germanium as material platforms for gate-defined quantum dot spin qubits. I
focus on materials stacks, quantum dot architectures, bandstructure properties
and qualifiers for disorder from electrical transport. This brief note is far
from being exhaustive and should be considered a first introduction to the
materials challenges and opportunities towards a larger spin qubit quantum
processor.


Abstract:
  Clustering using deep neural network models have been extensively studied in
recent years. Among the most popular frameworks are the VAE and GAN frameworks,
which learns latent feature representations of data through encoder / decoder
neural net structures. This is a suitable base for clustering tasks, as the
latent space often seems to effectively capture the inherent essence of data,
simplifying its manifold and reducing noise. In this article, the VAE framework
is used to investigate how probability function gradient ascent over data
points can be used to process data in order to achieve better clustering.
Improvements in classification is observed comparing with unprocessed data,
although state of the art results are not obtained. Processing data with
gradient descent however results in more distinct cluster separation, making it
simpler to investigate suitable hyper parameter settings such as the number of
clusters. We propose a simple yet effective method for investigating suitable
number of clusters for data, based on the DBSCAN clustering algorithm, and
demonstrate that cluster number determination is facilitated with gradient
processing. As an additional curiosity, we find that our baseline model used
for comparison; a GMM on a t-SNE latent space for a VAE structure with weight
one on reconstruction during training (autoencoder), yield state of the art
results on the MNIST data, to our knowledge not beaten by any other existing
model.


Abstract:
  We studied the impact of field heterogeneity on entrainment in a system of
uniformly interacting phase oscillators. Field heterogeneity is shown to induce
dynamical heterogeneity in the system. In effect, the heterogeneous field
partitions the system into interacting groups of oscillators that feel the same
local field strength and phase. Based on numerical and analytical analysis of
the explicit dynamical equations derived from the periodically forced Kuramoto
model, we found that the heterogeneous field can disrupt entrainment at
different field frequencies when compared to the homogeneous field. This
transition occurs when the phase- and frequency-locked synchronization between
groups of oscillators is broken at a critical field frequency, causing each
group to enter a new dynamical state (disrupted state). Strikingly, it is shown
that disrupted dynamics can differ between groups.


Abstract:
  Large-scale machine learning and data mining methods routinely distribute
computations across multiple agents to parallelize processing. The time
required for the computations at the agents is affected by the availability of
local resources and/or poor channel conditions giving rise to the "straggler
problem". As a remedy to this problem, we employ Unequal Error Protection (UEP)
codes to obtain an approximation of the matrix product in the distributed
computation setting to provide higher protection for the blocks with higher
effect on the final result. We characterize the performance of the proposed
approach from a theoretical perspective by bounding the expected reconstruction
error for matrices with uncorrelated entries. We also apply the proposed coding
strategy to the computation of the back-propagation step in the training of a
Deep Neural Network (DNN) for an image classification task in the evaluation of
the gradients. Our numerical experiments show that it is indeed possible to
obtain significant improvements in the overall time required to achieve the DNN
training convergence by producing approximation of matrix products using UEP
codes in the presence of stragglers.


Abstract:
  Classes of branched surfaces extend the classes of surfaces or 2-dimensional
manifolds satisfying suitable properties and defined in various manners. Reeb
spaces of smooth maps of suitable classes into surfaces whose codimensions are
negative are regarded as branched surfaces. They are the spaces of all
connected components of preimages and natural quotient spaces of the manifolds
of the domains. They are defined for general smooth maps and important
topological objects in differential topology. They also play important roles in
applied or applications of mathematics such as projections in data analysis and
visualizations.
  The present paper concerns global topologies of branched surfaces and
explicit construction of canonically obtained maps from the branched surfaces
into surfaces of the targets via fundamental operations. The class of these
induced maps extends the class of smooth immersions of compact surfaces into
surfaces with no boundaries. It is also regarded as a variant of the class of
so-called generic smooth maps between these surfaces. We study so-called
"geography" of such maps as a natural, important and new study and also study
global topological properties of the branched surfaces such as embeddability
into 3-dimensional closed and connected manifolds as a well-known variant of
embeddability of graphs into surfaces.
  The author has also believed that understanding global topologies of Reeb
spaces of suitable smooth maps are important and obtained several Reeb spaces
with information on global algebraic topological or differential topological
properties. The present study can be also regarded as a study on these objects
in 2-dimensional cases for {\it simple} fold maps, generalized versions of
so-called Morse functions and can be seen a new work motivated by various
theory of Morse functions and higher dimensional variants.


Abstract:
  A thorough investigation of local structure, influencing macroscopic
properties of the solid is of potential interest. We investigated the local
structure of GaN nanowires (NWs) with different native defect concentration
synthesized by the chemical vapor deposition technique. Extended X-ray
absorption fine structure (EXAFS) analysis and semi-empirical and the density
functional theory (DFT) calculations were used to address the effect of dopant
incorporation along with other defects on the co-ordination number and bond
length values. The decrease of the bond length values along preferential
crystal axes in the local tetrahedral structure of GaN emphasizes the preferred
lattice site for oxygen doping. The preferential bond length contraction is
corroborated by the simulations. We have also studied the impact on the local
atomic configuration of GaN NWs with Al incorporation. AlxGa1-xN NWs are
synthesized via novel ion beam techniques of ion beam mixing and
post-irradiation diffusion process. The change in the local tetrahedral
structure of GaN with Al incorporation is investigated by EXAFS analysis. The
analysis provides a clear understanding of choosing a suitable process for
ternary III-nitride random alloy formation. The local structure study with the
EXAFS analysis is corroborated with the observed macroscopic properties studied
using Raman spectroscopy.


Abstract:
  Let $V$ be a vector space with countable dimension over a field, and let $u$
be an endomorphism of it which is locally finite, i.e. $(u^k(x))_{k \geq 0}$ is
linearly dependent for all $x$ in $V$. We give several necessary and sufficient
conditions for the decomposability of $u$ into the sum of two square-zero
endomorphisms. Moreover, if $u$ is invertible, we give necessary and sufficient
conditions for the decomposability of $u$ into the product of two involutions,
as well as for the decomposability of $u$ into the product of two unipotent
endomorphisms of index $2$. Our results essentially extend the ones that are
known in the finite-dimensional setting.
  In particular, we obtain that every strictly upper-triangular infinite matrix
with entries in a field is the sum of two square-zero infinite matrices
(potentially non-triangular, though), and that every upper-triangular infinite
matrix (with entries in a field) with only $\pm 1$ on the diagonal is the
product of two involutory infinite matrices.


Abstract:
  In this paper, we explore the dynamics of a Hamiltonian system after a double
van der Waals potential energy surface degenerates into a single well. The
energy of the system is increased from the bottom of the potential well up to
the dissociation energy, which occurs when the system becomes open. In
particular, we study the bifurcations of the basic families of periodic orbits
of this system as the energy increases using Lagrangian descriptors and
Poincar\'e maps. We investigate the capability of Lagrangian descriptors to
find periodic orbits of bifurcating families for the case of resonant,
saddle-node and pitchfork bifurcations.


Abstract:
  In this paper, we propose a novel approach for the transcription of speech
conversations with natural speaker overlap, from single channel recordings. We
propose a combination of a speaker diarization system and a hybrid automatic
speech recognition (ASR) system with speaker activity assisted acoustic model
(AM). An end-to-end neural network system is used for speaker diarization. Two
architectures, (i) input conditioned AM, and (ii) gated features AM, are
explored to incorporate the speaker activity information. The models output
speaker specific senones. The experiments on Switchboard telephone
conversations show the advantage of incorporating speaker activity information
in the ASR system for recordings with overlapped speech. In particular, an
absolute improvement of $11\%$ in word error rate (WER) is seen for the
proposed approach on natural conversation speech with automatic diarization.


Abstract:
  Recent studies have provided both empirical and theoretical evidence
illustrating that heavy tails can emerge in stochastic gradient descent (SGD)
in various scenarios. Such heavy tails potentially result in iterates with
diverging variance, which hinders the use of conventional convergence analysis
techniques that rely on the existence of the second-order moments. In this
paper, we provide convergence guarantees for SGD under a state-dependent and
heavy-tailed noise with a potentially infinite variance, for a class of
strongly convex objectives. In the case where the $p$-th moment of the noise
exists for some $p\in [1,2)$, we first identify a condition on the Hessian,
coined '$p$-positive (semi-)definiteness', that leads to an interesting
interpolation between positive semi-definite matrices ($p=2$) and diagonally
dominant matrices with non-negative diagonal entries ($p=1$). Under this
condition, we then provide a convergence rate for the distance to the global
optimum in $L^p$. Furthermore, we provide a generalized central limit theorem,
which shows that the properly scaled Polyak-Ruppert averaging converges weakly
to a multivariate $\alpha$-stable random vector. Our results indicate that even
under heavy-tailed noise with infinite variance, SGD can converge to the global
optimum without necessitating any modification neither to the loss function or
to the algorithm itself, as typically required in robust statistics. We
demonstrate the implications of our results to applications such as linear
regression and generalized linear models subject to heavy-tailed data.


Abstract:
  During the research cruise AL547 with RV ALKOR (October 20-31, 2020), a
collaborative underwater network of ocean observation systems was deployed in
Boknis Eck (SW Baltic Sea, German exclusive economic zone (EEZ)) in the context
of the project ARCHES (Autonomous Robotic Networks to Help Modern Societies).
This network was realized via a Digital Twin Prototype approach. During that
period different scenarios were executed to demonstrate the feasibility of
Digital Twins in an extreme environment such as underwater. One of the
scenarios showed the collaboration of stage IV Digital Twins with their
physical counterparts on the seafloor. This way, we address the research
question, whether Digital Twins represent a feasible approach to operate mobile
ad hoc networks for ocean and coastal observation.


Abstract:
  This paper introduces an adaptive model-free deep reinforcement approach that
can recognize and adapt to the diurnal patterns in the ride-sharing environment
with car-pooling. Deep Reinforcement Learning (RL) suffers from catastrophic
forgetting due to being agnostic to the timescale of changes in the
distribution of experiences. Although RL algorithms are guaranteed to converge
to optimal policies in Markov decision processes (MDPs), this only holds in the
presence of static environments. However, this assumption is very restrictive.
In many real-world problems like ride-sharing, traffic control, etc., we are
dealing with highly dynamic environments, where RL methods yield only
sub-optimal decisions. To mitigate this problem in highly dynamic environments,
we (1) adopt an online Dirichlet change point detection (ODCP) algorithm to
detect the changes in the distribution of experiences, (2) develop a Deep Q
Network (DQN) agent that is capable of recognizing diurnal patterns and making
informed dispatching decisions according to the changes in the underlying
environment. Rather than fixing patterns by time of week, the proposed approach
automatically detects that the MDP has changed, and uses the results of the new
model. In addition to the adaptation logic in dispatching, this paper also
proposes a dynamic, demand-aware vehicle-passenger matching and route planning
framework that dynamically generates optimal routes for each vehicle based on
online demand, vehicle capacities, and locations. Evaluation on New York City
Taxi public dataset shows the effectiveness of our approach in improving the
fleet utilization, where less than 50% of the fleet are utilized to serve the
demand of up to 90% of the requests, while maximizing profits and minimizing
idle times.


Abstract:
  There is a growing concern that e-commerce platforms are amplifying
vaccine-misinformation. To investigate, we conduct two-sets of algorithmic
audits for vaccine misinformation on the search and recommendation algorithms
of Amazon -- world's leading e-retailer. First, we systematically audit
search-results belonging to vaccine-related search-queries without logging into
the platform -- unpersonalized audits. We find 10.47% of search-results promote
misinformative health products. We also observe ranking-bias, with Amazon
ranking misinformative search-results higher than debunking search-results.
Next, we analyze the effects of personalization due to account-history, where
history is built progressively by performing various real-world user-actions,
such as clicking a product. We find evidence of filter-bubble effect in
Amazon's recommendations; accounts performing actions on misinformative
products are presented with more misinformation compared to accounts performing
actions on neutral and debunking products. Interestingly, once user clicks on a
misinformative product, homepage recommendations become more contaminated
compared to when user shows an intention to buy that product.


Abstract:
  With the advent of deep learning, the number of works proposing new methods
or improving existent ones has grown exponentially in the last years. In this
scenario, "very deep" models were emerging, once they were expected to extract
more intrinsic and abstract features while supporting a better performance.
However, such models suffer from the gradient vanishing problem, i.e.,
backpropagation values become too close to zero in their shallower layers,
ultimately causing learning to stagnate. Such an issue was overcome in the
context of convolution neural networks by creating "shortcut connections"
between layers, in a so-called deep residual learning framework. Nonetheless, a
very popular deep learning technique called Deep Belief Network still suffers
from gradient vanishing when dealing with discriminative tasks. Therefore, this
paper proposes the Residual Deep Belief Network, which considers the
information reinforcement layer-by-layer to improve the feature extraction and
knowledge retaining, that support better discriminative performance.
Experiments conducted over three public datasets demonstrate its robustness
concerning the task of binary image classification.


Abstract:
  Nanowires (NWs) with their quasi-one-dimensionality often present different
structural and opto-electronic properties than their thin-film counterparts.
The thinner they are the larger these differences are, in particular in the
carrier-phonon scattering and thermal conductivity. In this work, we present
femtosecond transient absorbance measurements on GaAs0.8P0.2 NWs of two
different diameters, 36 and 51 nm. The results show that thinner NWs sustain
the hot-carriers at a higher temperature for longer times than thicker NWs. We
explain the observation suggesting that in thinner NWs, the build-up of a
hot-phonon bottleneck is easier than in thicker NWs because of the increased
phonon scattering at the NW sidewalls which facilitates the build-up of a large
phonon density. The large number of optical phonons emitted during the carrier
relaxation processes generate a non-equilibrium population of acoustic phonons
that propagates less efficiently in thin NWs. This makes the possible
acoustic-to-optical phonon up-conversion process easier, which prolongs the LO
phonon lifetime resulting in the slowdown of the carrier cooling. The important
observation that the carrier temperature in thin NWs is higher than in thick
NWs already at the beginning of the hot carrier regime suggests that the
phonon-mediated scattering processes in the non-thermal regime play a major
role at least for the carrier densities investigated here (8x1018-4x1019 cm-3).
Our results also suggest that the boundary scattering of phonons at crystal
defects is negligible compared to the surface scattering at the NW sidewalls.


Abstract:
  The uncertainty principle states that a measurement inevitably disturbs the
system, while it is often supposed that a quantum system is not disturbed
without state change. Korzekwa, Jennings, and Rudolph [Phys. Rev. A 89, 052108
(2014)] pointed out a conflict between those two views, and concluded that
state-dependent formulations of error-disturbance relations are untenable.
Here, we reconcile the conflict by showing that a quantum system is disturbed
without state change, in favor of the recently obtained universally valid
state-dependent error-disturbance relations.


Abstract:
  This work focuses on comparing different solutions for machine translation on
low resource language pairs, namely, with zero-shot transfer learning and
unsupervised machine translation. We discuss how the data size affects the
performance of both unsupervised MT and transfer learning. Additionally we also
look at how the domain of the data affects the result of unsupervised MT. The
code to all the experiments performed in this project are accessible on Github.


Abstract:
  This paper introduces properties and relations for proximal homotopy as well
as descriptive proximal homotopy. A number of results are given for the
homotopy between Lodato-proximally continuous maps and for the homotopy between
descriptive Lodato proximally continuous maps. This paper also introduces
homotopic cycles in conjunction with paths in either proximity spaces in
general or in descriptive proximity spaces. Three main results in this paper
are (1) that the product of descriptive Lodata proximity (dlp) spaces is also a
dlp space, (2) every descriptive dlp relation is an equivalence relation and
(3) every homotopic cycle has a free group representation.


Abstract:
  The analytical theory of our earlier study (Mortensen et al. (2021),
Mathematical Medicine and Biology, 38(1), pp. 106-131) is extended to address
the outstanding cases of fibroblast barrier distribution and myocyte strait
distribution. In particular, closed-form approximations to the resting membrane
potential and to the critical parameter values for propagation are derived for
these two non-uniform fibroblast distributions and are in good agreement with
numerical estimates.


Abstract:
  Additive robotic construction of building-scale discrete bar structures, such
as trusses and space frames, is increasingly attractive due to the potential
improvements in efficiency, safety, and design possibilities. However,
programming complex robots, such as manipulators with seven degrees of freedom,
to successfully complete construction tasks can be tedious, challenging, or
impossible for a human to do manually. Namely, the structure must be
constructed in a sequence that preserves structural properties, such as
stiffness, at each step. At the same time, this sequence must allow for the
robot to precisely manipulate elements within the in-progress structure while
respecting geometric constraints that, for example, ensure the robot does not
collide with what it has built. In this work, we present an automated and newly
generalized planning approach for jointly finding a construction sequence and
robot motion plan for additive construction that satisfies these requirements.
Our approach can be applied in a variety of additive construction processes,
and we demonstrate it specifically on spatial extrusion and discrete bar
assembly in this paper. We demonstrate the effectiveness of our approach on
several simulated and real-world extrusion and assembly tasks, including a
human-scale physical prototype, for which our algorithm is deployed for the
first time to plan the assembly of a complicated double tangent bar system
design.


Abstract:
  Although previous research on Aspect-based Sentiment Analysis (ABSA) for
Indonesian reviews in hotel domain has been conducted using CNN and XGBoost,
its model did not generalize well in test data and high number of OOV words
contributed to misclassification cases. Nowadays, most state-of-the-art results
for wide array of NLP tasks are achieved by utilizing pretrained language
representation. In this paper, we intend to incorporate one of the foremost
language representation model, BERT, to perform ABSA in Indonesian reviews
dataset. By combining multilingual BERT (m-BERT) with task transformation
method, we manage to achieve significant improvement by 8% on the F1-score
compared to the result from our previous study.


Abstract:
  We report a Karl G. Jansky Very Large Array (JVLA) search for redshifted
CO(1-0) or CO(2-1) emission, and a Hubble Space Telescope Wide Field Camera~3
(HST-WFC3) search for rest-frame near-ultraviolet (NUV) stellar emission, from
seven HI-selected galaxies associated with high-metallicity ([M/H]~$\geq -1.3$)
damped Ly$\alpha$ absorbers (DLAs) at $z\approx 4$. The galaxies were earlier
identified by ALMA imaging of their [CII]~158$\mu$m emission. We also used the
JVLA to search for CO(2-1) emission from the field of a low-metallicity
([M/H]~$=-2.47$) DLA at $z\approx 4.8$. No statistically significant CO
emission is detected from any of the galaxies, yielding upper limits of
$M_{mol}<(7.4 - 17.9)\times 10^{10}\times (\alpha_{CO}/4.36) M_\odot$ on their
molecular gas mass. We detect rest-frame NUV emission from four of the seven
[CII]~158$\mu$m-emitting galaxies, the first detections of the stellar
continuum from HI-selected galaxies at $z\gtrsim 4$. The HST-WFC3 images yield
typical sizes of the stellar continua of $\approx 2-4$~kpc and inferred
dust-unobscured star-formation rates (SFRs) of $\approx 5.0-17.5 M_\odot$/yr,
consistent with, or slightly lower than, the total SFRs estimated from the
far-infrared (FIR) luminosity. We further stacked the CO(2-1) emission signals
of six [CII]~158$\mu$m-emitting galaxies in the image plane. Our non-detection
of CO(2-1) emission in the stacked image yields the limit $M_{mol}<4.1 \times
10^{10}\times (\alpha_{CO}/4.36) M_\odot$ on the average molecular gas mass of
the six galaxies. Our molecular gas mass estimates and NUV SFR estimates in
HI-selected galaxies at $z\approx 4$ are consistent with those of main-sequence
galaxies with similar [CII]~158$\mu$m and FIR luminosities at similar
redshifts. However, the NUV emission in the HI-selected galaxies appears more
extended than that in main-sequence galaxies at similar redshifts.


Abstract:
  In a recent paper by Jafarov, Nagiyev, Oste and Van der Jeugt (2020 {\sl J.\
Phys.\ A} {\bf 53} 485301), a confined model of the non-relativistic quantum
harmonic oscillator, where the effective mass and the angular frequency are
dependent on the position, was constructed and it was shown that the
confinement parameter gets quantized. By using a point canonical transformation
starting from the constant-mass Schr\"odinger equation for the Rosen-Morse II
potential, it is shown here that similar results can be easily obtained without
quantizing the confinement parameter. In addition, an extension to a confined
shifted harmonic oscillator directly follows from the same point canonical
transformation.


Abstract:
  We describe the design of a soldered sapphire optical viewport, useful for
spectroscopic applications of samples at high temperatures and high pressures.
The sapphire window is bonded via active soldering to a metal flange with a
structure of two c-shaped rings made of different metallic materials in
between, as to mitigate thermally induced stress. A spectroscopic cell equipped
with two of the optical viewports has been successfully operated with alkali
metals in a noble gas environment at temperatures in the range $20\,${\deg}C to
$450\,${\deg}C at noble gas pressures from $10^{-6}\,$mbar to $330\,$bar. At
the upper pressure range, we observe a leakage rate smaller than our readout
accuracy of $30\,$mbar per day.


Abstract:
  We propose new width-based planning and learning algorithms inspired from a
careful analysis of the design decisions made by previous width-based planners.
The algorithms are applied over the Atari-2600 games and our best performing
algorithm, Novelty guided Critical Path Learning (N-CPL), outperforms the
previously introduced width-based planning and learning algorithms $\pi$-IW(1),
$\pi$-IW(1)+ and $\pi$-HIW(n, 1). Furthermore, we present a taxonomy of the
Atari-2600 games according to some of their defining characteristics. This
analysis of the games provides further insight into the behaviour and
performance of the algorithms introduced. Namely, for games with large
branching factors, and games with sparse meaningful rewards, N-CPL outperforms
$\pi$-IW, $\pi$-IW(1)+ and $\pi$-HIW(n, 1).


Abstract:
  Human social behavior plays a crucial role in how pathogens like SARS-CoV-2
or fake news spread in a population. Social interactions determine the contact
network among individuals, while spreading, requiring individual-to-individual
transmission, takes place on top of the network. Studying the topological
aspects of a contact network, therefore, not only has the potential of leading
to valuable insights into how the behavior of individuals impacts spreading
phenomena, but it may also open up possibilities for devising effective
behavioral interventions. Because of the temporal nature of interactions -
since the topology of the network, containing who is in contact with whom,
when, for how long, and in which precise sequence, varies (rapidly) in time -
analyzing them requires developing network methods and metrics that respect
temporal variability, in contrast to those developed for static (i.e.,
time-invariant) networks. Here, by means of event mapping, we propose a method
to quantify how quickly agents mingle by transforming temporal network data of
agent contacts. We define a novel measure called 'contact sequence centrality',
which quantifies the impact of an individual on the contact sequences,
reflecting the individual's behavioral potential for spreading. Comparing
contact sequence centrality across agents allows for ranking the impact of
agents and identifying potential 'behavioral super-spreaders'. The method is
applied to social interaction data collected at an art fair in Amsterdam. We
relate the measure to the existing network metrics, both temporal and static,
and find that (mostly at longer time scales) traditional metrics lose their
resemblance to contact sequence centrality. Our work highlights the importance
of accounting for the sequential nature of contacts when analyzing social
interactions.


Abstract:
  We propose deep neural network algorithms to calculate efficient frontier in
some Mean-Variance and Mean-CVaR portfolio optimization problems. We show that
we are able to deal with such problems when both the dimension of the state and
the dimension of the control are high. Adding some additional constraints, we
compare different formulations and show that a new projected feedforward
network is able to deal with some global constraints on the weights of the
portfolio while outperforming classical penalization methods. All developed
formulations are compared in between. Depending on the problem and its
dimension, some formulations may be preferred.


Abstract:
  We show that the assumption of a weak form of the Hardy-Littlewood conjecture
on the Goldbach problem suffices to disprove the possible existence of
exceptional zeros of Dirichlet L-functions. This strengthens a result of the
authors named in the title.


Abstract:
  Imaging in clinical routine is subject to changing scanner protocols,
hardware, or policies in a typically heterogeneous set of acquisition hardware.
Accuracy and reliability of deep learning models suffer from those changes as
data and targets become inconsistent with their initial static training set.
Continual learning can adapt to a continuous data stream of a changing imaging
environment. Here, we propose a method for continual active learning on a data
stream of medical images. It recognizes shifts or additions of new imaging
sources - domains -, adapts training accordingly, and selects optimal examples
for labelling. Model training has to cope with a limited labelling budget,
resembling typical real world scenarios. We demonstrate our method on
T1-weighted magnetic resonance images from three different scanners with the
task of brain age estimation. Results demonstrate that the proposed method
outperforms naive active learning while requiring less manual labelling.


Abstract:
  We investigate the large-scale clustering of the final spectroscopic sample
of quasars from the recently completed extended Baryon Oscillation
Spectroscopic Survey (eBOSS). The sample contains $343708$ objects in the
redshift range $0.8<z<2.2$ and $72667$ objects with redshifts $2.2<z<3.5$,
covering an effective area of $4699~{\rm deg}^{2}$. We develop a neural
network-based approach to mitigate spurious fluctuations in the density field
caused by spatial variations in the quality of the imaging data used to select
targets for follow-up spectroscopy. Simulations are used with the same angular
and radial distributions as the real data to estimate covariance matrices,
perform error analyses, and assess residual systematic uncertainties. We
measure the mean density contrast and cross-correlations of the eBOSS quasars
against maps of potential sources of imaging systematics to address algorithm
effectiveness, finding that the neural network-based approach outperforms
standard linear regression. Stellar density is one of the most important
sources of spurious fluctuations, and a new template constructed using data
from the Gaia spacecraft provides the best match to the observed quasar
clustering. The end-product from this work is a new value-added quasar
catalogue with the improved weights to correct for nonlinear imaging systematic
effects, which will be made public. Our quasar catalogue is used to measure the
local-type primordial non-Gaussianity in our companion paper, Mueller et al. in
preparation.


Abstract:
  Advancements in heterogeneous computing technologies enable the significant
potential of virtual reality (VR) applications. To offer the best user
experience (UX), a system should adopt an untethered, wireless-network-based
architecture to transfer VR content between the user and the content generator.
However, modern wireless network technologies make implementing such an
architecture challenging, as VR applications require superior video quality --
with high resolution, high frame rates, and very low latency.
  This paper presents OpenUVR, an open-source framework that uses commodity
hardware components to satisfy the demands of interactive, real-time VR
applications. OpenUVR significantly improves UX through a redesign of the
system stack and addresses the most time-sensitive issues associated with
redundant memory copying in modern computing systems. OpenUVR presents a
cross-layered VR datapath to avoid redundant data operations and computation
among system components, OpenUVR customizes the network stack to eliminate
unnecessary memory operations incurred by mismatching data formats in each
layer, and OpenUVR uses feedback from mobile devices to remove memory buffers.
  Together, these modifications allow OpenUVR to reduce VR application delays
to 14.32 ms, meeting the 20 ms minimum latency in avoiding motion sickness. As
an open-source system that is fully compatible with commodity hardware, OpenUVR
offers the research community an opportunity to develop, investigate, and
optimize applications for untethered, high-performance VR architectures.