Abstract:
  Recent observations of globular clusters (GCs) provide evidence that the
stellar initial mass function (IMF) may not be universal, suggesting
specifically that the IMF grows increasingly top-heavy with decreasing
metallicity and increasing gas density. Non-canonical IMFs can greatly affect
the evolution of GCs, mainly because the high end determines how many black
holes (BHs) form. Here we compute a new set of GC models, varying the IMF
within observational uncertainties. We find that GCs with top-heavy IMFs lose
most of their mass within a few Gyr through stellar winds and tidal stripping.
Heating of the cluster through BH mass segregation greatly enhances this
process. We show that, as they approach complete dissolution, GCs with
top-heavy IMFs can evolve into 'dark clusters' consisting of mostly BHs by
mass. In addition to producing more BHs, GCs with top-heavy IMFs also produce
many more binary BH (BBH) mergers. Even though these clusters are short-lived,
mergers of ejected BBHs continue at a rate comparable to, or greater than, what
is found for long-lived GCs with canonical IMFs. Therefore these clusters,
although they are no longer visible today, could still contribute significantly
to the local BBH merger rate detectable by LIGO/Virgo, especially for sources
with higher component masses well into the BH mass gap. We also report that one
of our GC models with a top-heavy IMF produces dozens of intermediate-mass
black holes (IMBHs) with masses $M>100\,{\rm M_\odot}$, including one with
$M>500\,{\rm M_\odot}$. Ultimately, additional gravitational wave observations
will provide strong constraints on the stellar IMF in old GCs and the formation
of IMBHs at high redshift.


Abstract:
  Within vacuum Weyl gravity, we obtain a solution by which, using different
choices of the conformal factor, we derive metrics describing (i)~a bounce of
the universe; (ii)~toroidal and spherical wormholes; and (iii)~a change in
metric signature. It is demonstrated that singularities occurring in these
systems are "masked". We give a simple explanation of the possibility of
masking the singularities within Weyl gravity. It is shown that in the first
and third cases the three-dimensional metrics form Ricci flows. The question of
the possible applicability of conformal Weyl gravity as some phenomenological
theory in an approximate description of quantum gravity is discussed.


Abstract:
  Semidefinite Programming (SDP) is a class of convex optimization programs
with vast applications in control theory, quantum information, combinatorial
optimization and operational research. Noisy intermediate-scale quantum (NISQ)
algorithms aim to make an efficient use of the current generation of quantum
hardware. However, optimizing variational quantum algorithms is a challenge as
it is an NP-hard problem that in general requires an exponential time to solve
and can contain many far from optimal local minima. Here, we present a current
term NISQ algorithm for SDP. The classical optimization program of our NISQ
solver is another SDP over a smaller dimensional ansatz space. We harness the
SDP based formulation of the Hamiltonian ground state problem to design a NISQ
eigensolver. Unlike variational quantum eigensolvers, the classical
optimization program of our eigensolver is convex, can be solved in polynomial
time with the number of ansatz parameters and every local minimum is a global
minimum. Further, we demonstrate the potential of our NISQ SDP solver by
finding the largest eigenvalue of up to $2^{1000}$ dimensional matrices and
solving graph problems related to quantum contextuality. We also discuss NISQ
algorithms for rank-constrained SDPs. Our work extends the application of NISQ
computers onto one of the most successful algorithmic frameworks of the past
few decades.


Abstract:
  The use of satellites to provide ubiquitous coverage and connectivity for
densely deployed Internet of Things (IoT) networks is expected to be a reality
in emerging 6G networks. Yet the low battery capacity of IoT nodes constitutes
a problem for their direct connectivity to satellites, which are located at
altitudes of up to 2000 km. In this paper, we propose a novel architecture
involving the use of reconfigurable intelligent surface (RIS) units to mitigate
the path loss associated with long transmission distances. These RIS units can
be placed on satellite reflectarrays, and, when used in broadcasting and
beamforming, they can provide significant gains in signal transmission. This
study shows that RIS-assisted satellites can provide up to 10^5 times higher
downlink and achievable uplink rates for IoT networks.


Abstract:
  In this paper, we introduce the notion of periodic safety, which requires
that the system trajectories periodically visit a subset of a forward-invariant
safe set, and utilize it in a multi-rate framework where a high-level planner
generates a reference trajectory that is tracked by a low-level controller
under input constraints. We introduce the notion of fixed-time barrier
functions which is leveraged by the proposed low-level controller in a
quadratic programming framework. Then, we design a model predictive control
policy for high-level planning with a bound on the rate of change for the
reference trajectory to guarantee that periodic safety is achieved. We
demonstrate the effectiveness of the proposed strategy on a simulation example,
where the proposed fixed-time stabilizing low-level controller shows successful
satisfaction of control objectives, whereas an exponentially stabilizing
low-level controller fails.


Abstract:
  Society is characterized by the presence of a variety of social norms:
collective patterns of sanctioning that can prevent miscoordination and
free-riding. Inspired by this, we aim to construct learning dynamics where
potentially beneficial social norms can emerge. Since social norms are
underpinned by sanctioning, we introduce a training regime where agents can
access all sanctioning events but learning is otherwise decentralized. This
setting is technologically interesting because sanctioning events may be the
only available public signal in decentralized multi-agent systems where reward
or policy-sharing is infeasible or undesirable. To achieve collective action in
this setting we construct an agent architecture containing a classifier module
that categorizes observed behaviors as approved or disapproved, and a
motivation to punish in accord with the group. We show that social norms emerge
in multi-agent systems containing this agent and investigate the conditions
under which this helps them achieve socially beneficial outcomes.


Abstract:
  Photon-assisted tunneling frequently provides detailed information on the
underlying charge-transfer process. In particular, the Tien-Gordon approach and
its extensions predict that the sideband spacing in bias voltage is a direct
fingerprint of the number of electrons transferred in a single tunneling event.
Here, we analyze photon-assisted tunneling into subgap states in
superconductors in the limit of small temperatures and bias voltages where
tunneling is dominated by resonant Andreev processes and does not conform to
the predictions of simple Tien-Gordon theory. Our analysis is based on a
systematic Keldysh calculation of the subgap conductance and provides a
detailed analytical understanding of photon-assisted tunneling into subgap
states, in excellent agreement with a recent experiment. We focus on tunneling
from superconducting electrodes and into Yu-Shiba-Rusinov states associated
with magnetic impurities or adatoms, but we also explicitly extend our results
to include normal-metal electrodes or other types of subgap states in
superconductors. In particular, we argue that photon-assisted Andreev
reflections provide a high-accuracy method to measure small, but nonzero
energies of subgap states which can be important for distinguishing
conventional subgap states from Majorana bound states.


Abstract:
  Computing market equilibria is a problem of both theoretical and applied
interest. Much research to date focuses on the case of static Fisher markets
with full information on buyers' utility functions and item supplies. Motivated
by real-world markets, we consider an online setting: individuals have linear,
additive utility functions; items arrive sequentially and must be allocated and
priced irrevocably. We define the notion of an online market equilibrium in
such a market as time-indexed allocations and prices which guarantee buyer
optimality and market clearance in hindsight. We propose a simple, scalable and
interpretable allocation and pricing dynamics termed as PACE. When items are
drawn i.i.d. from an unknown distribution (with a possibly continuous support),
we show that PACE leads to an online market equilibrium asymptotically. In
particular, PACE ensures that buyers' time-averaged utilities converge to the
equilibrium utilities w.r.t. a static market with item supplies being the
unknown distribution and that buyers' time-averaged expenditures converge to
their per-period budget. Hence, many desirable properties of market
equilibrium-based fair division such as no envy, Pareto optimality, and the
proportional-share guarantee are also attained asymptotically in the online
setting. Next, we extend the dynamics to handle quasilinear buyer utilities,
which gives the first online algorithm for computing first-price pacing
equilibria. Finally, numerical experiments on real and synthetic datasets show
that the dynamics converges quickly under various metrics.


Abstract:
  As a tool for capturing irregular temporal dependencies (rather than
resorting to binning temporal observations to construct time series), Hawkes
processes with exponential decay have seen widespread adoption across many
application domains, such as predicting the occurrence time of the next
earthquake or stock market spike. However, practical applications of Hawkes
processes face a noteworthy challenge: There is substantial and often
unquantified variance in decay parameter estimations, especially in the case of
a small number of observations or when the dynamics behind the observed data
suddenly change. We empirically study the cause of these practical challenges
and we develop an approach to surface and thereby mitigate them. In particular,
our inspections of the Hawkes process likelihood function uncover the
properties of the uncertainty when fitting the decay parameter. We thus propose
to explicitly capture this uncertainty within a Bayesian framework. With a
series of experiments with synthetic and real-world data from domains such as
"classical" earthquake modeling or the manifestation of collective emotions on
Twitter, we demonstrate that our proposed approach helps to quantify
uncertainty and thereby to understand and fit Hawkes processes in practice.


Abstract:
  The first step in most text simplification is to predict which words are
considered complex for a given target population before carrying out lexical
substitution. This task is commonly referred to as Complex Word Identification
(CWI) and it is often modelled as a supervised classification problem. For
training such systems, annotated datasets in which words and sometimes
multi-word expressions are labelled regarding complexity are required. In this
paper we analyze previous work carried out in this task and investigate the
properties of complex word identification datasets for English.


Abstract:
  This paper serves as a survey of recent advances in large margin training and
its theoretical foundations, mostly for (nonlinear) deep neural networks (DNNs)
that are probably the most prominent machine learning models for large-scale
data in the community over the past decade. We generalize the formulation of
classification margins from classical research to latest DNNs, summarize
theoretical connections between the margin, network generalization, and
robustness, and introduce recent efforts in enlarging the margins for DNNs
comprehensively. Since the viewpoint of different methods is discrepant, we
categorize them into groups for ease of comparison and discussion in the paper.
Hopefully, our discussions and overview inspire new research work in the
community that aim to improve the performance of DNNs, and we also point to
directions where the large margin principle can be verified to provide
theoretical evidence why certain regularizations for DNNs function well in
practice. We managed to shorten the paper such that the crucial spirit of large
margin learning and related methods are better emphasized.


Abstract:
  It is known that interference alignment (IA) plays an important role in
improving the degree of freedom (DoF) of multi-input and multi-output (MIMO)
systems. However, most of the traditional IA schemes suffer from the high
computational complexity and require the global and instantaneous channel state
information (CSI), both of which make them difficult to be extended to cellular
MIMO systems. To handle these issues, two new interference alignment schemes,
i.e., the retrospective interference regeneration (RIR) scheme and the
beamforming based distributed retrospective interference alignment (B-DRIA)
scheme, are proposed for cellular K-user MIMO downlink networks. For the RIR
scheme, it adopts interference elimination algorithm to erase redundant symbols
in inter-cell interference (ICI) signals, and then uses interference
regeneration algorithm to avoid secondary interference. The RIR scheme obtains
greater DoF gain than the retrospective interference alignment (RIA) scheme,
but incurs performance degradation when the transceiver antennas ratio (TAR)
approaches 1. Therefore, the B-DRIA scheme is further proposed. For the B-DRIA
scheme, the cellular beamforming matrix is introduced to eliminate the ICI, and
meanwhile distributed retrospective interference alignment algorithm is adopted
to align inter-user interference (IUI). The simulation results show that the
B-DRIA scheme obtains larger DoF than the RIR scheme locally. Specifically,
when TAR approaches 1, two schemes obtain the same DoF. While TAR approaches 2,
the DoF of the B-DRIA scheme is superior than the RIR scheme.


Abstract:
  Light in hyperbolic dispersion media is known to exhibit an intriguing
ray-like character. However, detailed understanding of ray-like excitations in
hyperbolic media is surprisingly limited, mostly based on numerical
simulations. In our work, we analytically describe the formation of multimodal
ray-like excitations in a planar slab of hyperbolic media. We demonstrate that
these rays have an approximately Lorentzian profile and that they propagate in
a zig-zagged manner. These rays acquire phase in a discrete fashion at every
internal reflection inside the slab and broaden at a rate which is proportional
to the rate of optical absorption. Moreover, based on this description, we
reveal a unique reflection mechnanism where destructive interefernce between
the different modes composing the ray can improve the strength of reflection at
an interface between a dielectric and a metalic substrate. This reflection
mechanism is highly asymmetric, occuring only when the ray is incident from the
side of the dielectric substrate to the side of the metallic substrate, and the
strength of reflection is shown to be directly related to the width of the ray.


Abstract:
  This article describes a formula for second variation of generalized
Einstein-Hilbert functional on Riemannian manifolds.


Abstract:
  A connected partition is a partition of the vertices of a graph into sets
that induce connected subgraphs. Such partitions naturally occur in many
application areas such as road networks, and image processing. We consider
Balanced Connected Partitions (BCP), where the two classical objectives for BCP
are to maximize the weight of the smallest, or minimize the weight of the
largest component. We study BCP on c-claw-free graphs, the class of graphs that
do not have $K_{1,c}$ as an induced subgraph, and present efficient
(c-1)-approximation algorithms for both objectives. In particular, due to the
(3-)claw-freeness of line graphs, this also implies a 2-approximations for the
edge-partition version of BCP in general graphs.
  In the 1970s Gy\H{o}ri and Lov\'{a}sz showed for natural numbers
$w_1,\dots,w_k$ where $\sum_i w_i$ is the vertex size, that if $G$ is
k-connected, then there exist a connected k-partition with part sizes
$w_1,\dots,w_k$. However, to this day no polynomial algorithm to compute such
partitions exists for k>4. Towards finding such a partition $T_1,\dots, T_k$,
we show how to efficiently compute connected partitions that at least
approximately meet the target weights, subject to the mild assumption that each
$w_i$ is greater than the weight of the heaviest vertex. In particular, we give
a 3-approximation for both the lower and the upper bounded version i.e. we
guarantee that each $T_i$ has weight at least $\frac{w_i}{3}$ or that each
$T_i$ has weight most $3w_i$, respectively. Also, we present a both-side
bounded version that produces a connected partition where each $T_i$ has size
at least $\frac{w_i}{3}$ and at most $\max(\{r,3\}) w_i$, where $r \geq 1$ is
the ratio between the largest and smallest value in $w_1, \dots, w_k$. In
particular for the balanced version, i.e.~$w_1=w_2=, \dots,=w_k$, this gives a
partition with $\frac{1}{3}w_i \leq w(T_i) \leq 3w_i$.


Abstract:
  Random fields are useful mathematical tools for representing natural
phenomena with complex dependence structures in space and/or time. In
particular, the Gaussian random field is commonly used due to its attractive
properties and mathematical tractability. However, this assumption seems to be
restrictive when dealing with counting data. To deal with this situation, we
propose a random field with a Poisson marginal distribution by considering a
sequence of independent copies of a random field with an exponential marginal
distribution as 'inter-arrival times' in the counting renewal processes
framework. Our proposal can be viewed as a spatial generalization of the
Poisson process.
  Unlike the classical hierarchical Poisson Log-Gaussian model, our proposal
generates a (non)-stationary random field that is mean square continuous and
with Poisson marginal distributions. For the proposed Poisson spatial random
field, analytic expressions for the covariance function and the bivariate
distribution are provided. In an extensive simulation study, we investigate the
weighted pairwise likelihood as a method for estimating the Poisson random
field parameters.
  Finally, the effectiveness of our methodology is illustrated by an analysis
of reindeer pellet-group survey data, where a zero-inflated version of the
proposed model is compared with zero-inflated Poisson Log-Gaussian and Poisson
Gaussian copula models. Supplementary materials for this article, include
technical proofs and R code for reproducing the work, are available as an
online supplement.


Abstract:
  Modeling networks as different graph types and researching on route finding
strategies, to avoid congestion in dense subnetworks via graph-theoretic
approaches, contributes to overall blocking probability reduction in networks.
Our main focus is to study methods for modeling congested subnetworks and graph
density measures, in order to identify routes that avoid dense subgraphs for
global congestion avoidance, along with covering related algorithmic issues.


Abstract:
  Heterogeneous presentation of a neurological disorder suggests potential
differences in the underlying pathophysiological changes that occur in the
brain. We propose to model heterogeneous patterns of functional network
differences using a demographic-guided attention (DGA) mechanism for recurrent
neural network models for prediction from functional magnetic resonance imaging
(fMRI) time-series data. The context computed from the DGA head is used to help
focus on the appropriate functional networks based on individual demographic
information. We demonstrate improved classification on 3 subsets of the ABIDE I
dataset used in published studies that have previously produced
state-of-the-art results, evaluating performance under a leave-one-site-out
cross-validation framework for better generalizeability to new data. Finally,
we provide examples of interpreting functional network differences based on
individual demographic variables.


Abstract:
  The spin reduced matrix defined by taking partial trace with the momentum
variable has been shown not to transform covariantly under Lorentz
transformations. In this paper, I suggest another representation space of the
Poincar\'e group, the elements of which are in a sense "apparent wave
functions". In this new Hilbert space, the naive spin observable
$\frac{1}{2}\boldsymbol{\tau}$ from nonrelativistic quantum mechanics on the
old Hilbert space is represented as the Newton-Wigner spin operator. Also on
this space, one can naturally associate to each state a Lorentz covariant spin
reduced density matrix, which is then given a new interpretation.


Abstract:
  We introduce a new class of Wasserstein-type distances specifically designed
to tackle questions concerning stability and convergence to equilibria for
kinetic equations. Thanks to these new distances, we improve some classical
estimates by Loeper and Dobrushin on Vlasov-type equations, and we present an
application to quasineutral limits.


Abstract:
  We consider the conjugate gradient algorithm applied to a general class of
spiked sample covariance matrices. The main result of the paper is that the
norms of the error and residual vectors at any finite step concentrate on
deterministic values determined by orthogonal polynomials with respect to a
deformed Marchenko--Pastur law. The first-order limits and fluctuations are
shown to be universal. Additionally, for the case where the bulk eigenvalues
lie in a single interval we show a stronger universality result in that the
asymptotic rate of convergence of the conjugate gradient algorithm only depends
on the support of the bulk, provided the spikes are well-separated from the
bulk. In particular, this shows that the classical condition number bound for
the conjugate gradient algorithm is pessimistic for spiked matrices.


Abstract:
  To prevent the spread of coronavirus disease 2019 (COVID-19), preliminary
temperature measurement and mask detection in public areas are conducted.
However, the existing temperature measurement methods face the problems of
safety and deployment. In this paper, to realize safe and accurate temperature
measurement even when a person's face is partially obscured, we propose a
cloud-edge-terminal collaborative system with a lightweight infrared
temperature measurement model. A binocular camera with an RGB lens and a
thermal lens is utilized to simultaneously capture image pairs. Then, a mobile
detection model based on a multi-task cascaded convolutional network (MTCNN) is
proposed to realize face alignment and mask detection on the RGB images. For
accurate temperature measurement, we transform the facial landmarks on the RGB
images to the thermal images by an affine transformation and select a more
accurate temperature measurement area on the forehead. The collected
information is uploaded to the cloud in real time for COVID-19 prevention.
Experiments show that the detection model is only 6.1M and the average
detection speed is 257ms. At a distance of 1m, the error of indoor temperature
measurement is about 3%. That is, the proposed system can realize real-time
temperature measurement in public areas.


Abstract:
  It is well known that positive values of redshift drift is a signature of
dark energy within the conventionally studied
Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) universe models. Here we show
-- without making assumptions on the metric tensor of the Universe -- that
redshift drift is a promising direct probe of violation of the strong energy
condition within the theory of general relativity.


Abstract:
  This paper presents an integrated system for performing precision harvesting
missions using a legged harvester. Our harvester performs a challenging task of
autonomous navigation and tree grabbing in a confined, GPS denied forest
environment. Strategies for mapping, localization, planning, and control are
proposed and integrated into a fully autonomous system. The mission starts with
a human mapping the area of interest using a custom-made sensor module.
Subsequently, a human expert selects the trees for harvesting. The sensor
module is then mounted on the machine and used for localization within the
given map. A planning algorithm searches for both an approach pose and a path
in a single path planning problem. We design a path following controller
leveraging the legged harvester's capabilities for negotiating rough terrain.
Upon reaching the approach pose, the machine grabs a tree with a
general-purpose gripper. This process repeats for all the trees selected by the
operator. Our system has been tested on a testing field with tree trunks and in
a natural forest. To the best of our knowledge, this is the first time this
level of autonomy has been shown on a full-size hydraulic machine operating in
a realistic environment.


Abstract:
  Primordial black holes (PBHs) produced in the early Universe have attracted
wide interest for their ability to constitute dark matter and explain the
compact binary coalescence. We propose a new mechanism of PBH production during
first-order phase transitions (PTs) and find that PBHs are naturally produced
during PTs model-independently. Because of the randomness of the quantum
tunneling, there always exists some probability that the vacuum decay is
postponed in a whole Hubble volume. Since the vacuum energy density remains
constant while radiation is quickly redshifted in the expanding Universe, the
postponed vacuum decay then results in overdense regions, which finally
collapse into PBHs as indicated by numerical simulations. Utilizing this result
one can obtain mutual predictions and constraints between PBHs and GWs from
PTs. The predicted mass function of PBHs is nearly monochromatic. We
investigate two typical cases and find that 1) PBHs from a PT constitute all
dark matter and GWs peak at $1$Hz, 2) PBHs from a PT can explain the
coalescence events observed by LIGO-Virgo collaboration, and meanwhile GWs can
explain the common-spectrum process detected by NANOGrav collaboration.


Abstract:
  We present a numerical method specifically designed for simulating
three-dimensional fluid--structure interaction (FSI) problems based on the
reference map technique (RMT). The RMT is a fully Eulerian FSI numerical method
that allows fluids and large-deformation elastic solids to be represented on a
single fixed computational grid. This eliminates the need for meshing complex
geometries typical in other FSI approaches, and greatly simplifies the coupling
between fluid and solids. We develop the first three-dimensional implementation
of the RMT, parallelized using the distributed memory paradigm, to simulate
incompressible FSI with neo-Hookean solids. As part of our new method, we
develop a new field extrapolation scheme that works efficiently in parallel.
Through representative examples, we demonstrate the method's accuracy and
convergence, as well as its suitability in investigating many-body and active
systems. The examples include settling of a mixture of heavy and buoyant soft
ellipsoids, lid-driven cavity flow containing a soft sphere, and swimmers
actuated via active stress.


Abstract:
  We study the multiplicity and concentration of complex valued solutions for a
fractional magnetic Schr\"odinger equation involving a scalar continuous
electric potential satisfying a local condition and a continuous nonlinearity
with subcritical growth. The main results are obtained by applying a
penalization technique, generalized Nehari manifold method and
Ljusternik-Schnirelman theory. We also prove a Kato's inequality for the
fractional magnetic Laplacian which we believe to be useful in the study of
other fractional magnetic problems.


Abstract:
  The set of strata of a reductive group can be viewed as an enlargement of the
set of unipotent classes. In this paper the notion of distinguished unipotent
class is extended to this larger set.


Abstract:
  Algebraic models for the reconstruction problem in X-ray computed tomography
(CT) provide a flexible framework that applies to many measurement geometries.
For large-scale problems we need to use iterative solvers, and we need stopping
rules for these methods that terminate the iterations when we have computed a
satisfactory reconstruction that balances the reconstruction error and the
influence of noise from the measurements. Many such stopping rules are
developed in the inverse problems communities, but they have not attained much
attention in the CT world. The goal of this paper is to describe and illustrate
four stopping rules that are relevant for CT reconstructions.


Abstract:
  The development of materials with specific structural properties is of huge
practical interest, for example, for medical applications or for the
development of light weight structures in aeronautics. In this article, we
combine shape optimization and homogenization for the optimal design of the
microstructure in scaffolds. Given the current microstructure, we apply the
isogeometric boundary element method to compute the effective tensor and to
update the microstructure by using the shape gradient in order to match the
desired effective tensor. Extensive numerical studies are presented to
demonstrate the applicability and feasibility of the approach.


Abstract:
  We present an approach to enhancing the realism of synthetic images. The
images are enhanced by a convolutional network that leverages intermediate
representations produced by conventional rendering pipelines. The network is
trained via a novel adversarial objective, which provides strong supervision at
multiple perceptual levels. We analyze scene layout distributions in commonly
used datasets and find that they differ in important ways. We hypothesize that
this is one of the causes of strong artifacts that can be observed in the
results of many prior methods. To address this we propose a new strategy for
sampling image patches during training. We also introduce multiple
architectural improvements in the deep network modules used for photorealism
enhancement. We confirm the benefits of our contributions in controlled
experiments and report substantial gains in stability and realism in comparison
to recent image-to-image translation methods and a variety of other baselines.


Abstract:
  In this short review we propose a critical assessment of the role of chaos
for the thermalization of Hamiltonian systems with high dimensionality. We
discuss this problem for both classical and quantum systems. A comparison is
made between the two situations: some examples from recent and past literature
are presented which support the point of view that chaos is not necessary for
thermalization. Finally, we suggest that a close analogy holds between the role
played by Kinchin's theorem for high-dimensional classical systems and the role
played by Von Neumann's theorem for many-body quantum systems.


Abstract:
  CONTEXT: [ABRIDGED]. For the Milky Way bulge, there are currently essentially
no measurements of carbon in un-evolved stars, hampering our abilities to
properly compare Galactic chemical evolution models to observational data for
this still enigmatic stellar population. AIMS: We aim to determine carbon
abundances for our sample of 91 microlensed bulge dwarf and subgiant stars.
Together with new determinations for oxygen this forms the first statistically
significant sample of bulge stars that have C and O abundances measured, and
for which the C abundances have not been altered by the nuclear burning
processes internal to the stars. METHODS: The analysis is based on
high-resolution spectra for a sample of 91 dwarf and subgiant stars that were
obtained during microlensing events when the brightnesses of the stars were
highly magnified. Carbon abundances were determined through spectral line
synthesis of five CI lines around 9100 A, and oxygen abundances using the three
OI lines at about 7770 A. [ABRIDGED] RESULTS: Carbon abundances was possible to
determine for 70 of the 91 stars in the sample and oxygen abundances for 88 of
the 91 stars in the sample. The [C/Fe] ratio evolves essentially in lockstep
with [Fe/H], centred around solar values at all [Fe/H]. The [O/Fe]-[Fe/H] trend
has an appearance very similar to that observed for other alpha-elements in the
bulge, [ABRIDGED]. When dividing the bulge sample into two sub-groups, one
younger than 8 Gyr and one older than 8 Gyr, the stars in the two groups follow
exactly the elemental abundance trends defined by the solar neighbourhood thin
and thick disks, respectively. Comparisons with recent models of Galactic
chemical evolution in the [C/O]-[O/H] plane shows that the models that best
match the data are the ones that have been calculated with the Galactic thin
and thick disks in mind. [ABRIDGED] ....


Abstract:
  We report on critical phenomena in the gravitational collapse of
electromagnetic waves. Generalizing earlier results that focused on dipole
electromagnetic waves, we here compare with quadrupole waves in axisymmetry. We
perform numerical simulations of dipole and quadrupole wave initial data,
fine-tuning both sets of data to the onset of black hole formation in order to
study the critical solution and related critical phenomena. We observe that
different multipole moments have different symmetries, indicating that the
critical solution for electromagnetic waves cannot be unique, at least not
globally. This is confirmed in our numerical simulations: while dipole data
lead to a single center of collapse, at the center of symmetry, quadrupole data
feature two separate centers of collapse on the symmetry axis, above and below
the center of symmetry -- reminiscent of similar findings reported for critical
collapse of vacuum gravitational waves. While the critical solution for neither
the dipole nor the quadrupole data is exactly self-similar, we find that their
approximate echoing periods appear to differ, as do the critical exponents. We
discuss whether the centers of collapse found for dipole and quadrupole data
might all have the same properties, which would suggest a "local uniqueness" of
the critical solution. Instead, we provide some evidence -- including the
differing echoing periods and critical exponents -- suggesting that the
critical solutions are distinct even locally. We speculate on the implications
of our findings for critical phenomena in the collapse of vacuum gravitational
waves, which share with electromagnetic waves the absence of a spherically
symmetric critical solution.


Abstract:
  Neutrinoless double-$\beta$ decay of $^{76}$Ge is searched for with germanium
detectors where source and detector of the decay are identical. For the success
of future experiments it is important to increase the mass of the detectors. We
report here on the characterization and testing of five prototype detectors
manufactured in inverted coaxial (IC) geometry from material enriched to 88% in
$^{76}$Ge. IC detectors combine the large mass of the traditional semi-coaxial
Ge detectors with the superior resolution and pulse shape discrimination power
of point contact detectors which exhibited so far much lower mass. Their
performance has been found to be satisfactory both when operated in vacuum
cryostat and bare in liquid argon within the GERDA setup. The measured
resolutions at the Q-value for double-$\beta$ decay of $^{76}$Ge
(Q$_{\beta\beta}$ = 2039 keV) are about 2.1 keV full width at half maximum in
vacuum cryostat. After 18 months of operation within the ultra-low background
environment of the GERmanium Detector Array (GERDA) experiment and an
accumulated exposure of 8.5 kg$\cdot$yr, the background index after analysis
cuts is measured to be $4.9^{+7.3}_{-3.4}\times 10^{-4}$ counts
/(keV$\cdot$kg$\cdot$yr) around Q$_{\beta\beta}$. This work confirms the
feasibility of IC detectors for the next-generation experiment LEGEND.


Abstract:
  Magnetic surveys are conventionally performed by scanning a domain with a
portable scalar magnetic sensor. Unfortunately, scalar magnetometers are
expensive, power consuming and bulky. In many applications, calibrated vector
magnetometers can be used to perform magnetic surveys. In recent years
algorithms based on artificial intelligence (AI) achieve state-of-the-art
results in many modern applications. In this work we investigate an AI
algorithm for the classical scalar calibration of magnetometers. A simple, low
cost method for performing a magnetic survey is presented. The method utilizes
a low power consumption sensor with an AI calibration procedure that improves
the common calibration methods and suggests an alternative to the conventional
technology and algorithms. The setup of the survey system is optimized for
quick deployment in-situ right before performing the magnetic survey. We
present a calibration method based on a procedure of rotating the sensor in the
natural earth magnetic field for an optimal time period. This technique can
deal with a constant field offset and non-orthogonality issues and does not
require any external reference. The calibration is done by finding an estimator
that yields the calibration parameters and produces the best geometric fit to
the sensor readings. A comprehensive model considering the physical,
algorithmic and hardware properties of the magnetometer of the survey system is
presented. The geometric ellipsoid fitting approach is parametrically tested.
The calibration procedure reduced the root-mean-squared noise from the order of
104 nT to less than 10 nT with variance lower than 1 nT in a complete 360
degrees rotation in the natural earth magnetic field.


Abstract:
  This work, Part II, together with its companion Part I develops a new
framework for stochastic functional Kolmogorov equations, which are nonlinear
stochastic differential equations depending on the current as well as the past
states. Because of the complexity of the problems, it is natural to divide our
contributions into two parts to answer a long-standing question in biology and
ecology. What are the minimal conditions for long-term persistence and
extinction of a population? Part I of our work provides characterization of
persistence, whereas in this part, extinction is the main focus. The main
techniques used in this paper are combination of the newly developed functional
It^o formula and a dynamical system approach. Compared to the study of
stochastic Kolmogorov systems without delays, the main difficulty is that
infinite dimensional systems have to be treated. The extinction is
characterized after investigating random occupation measures and examining
behavior of functional systems around boundaries. Our characterizations of
long-term behavior of the systems reduces to that of Kolmogorov systems without
delay when there is no past dependence. A number of applications are also
examined.


Abstract:
  Learning the structure of Bayesian networks and causal relationships from
observations is a common goal in several areas of science and technology. We
show that the prequential minimum description length principle (MDL) can be
used to derive a practical scoring function for Bayesian networks when flexible
and overparametrized neural networks are used to model the conditional
probability distributions between observed variables. MDL represents an
embodiment of Occam's Razor and we obtain plausible and parsimonious graph
structures without relying on sparsity inducing priors or other regularizers
which must be tuned. Empirically we demonstrate competitive results on
synthetic and real-world data. The score often recovers the correct structure
even in the presence of strongly nonlinear relationships between variables; a
scenario were prior approaches struggle and usually fail. Furthermore we
discuss how the the prequential score relates to recent work that infers causal
structure from the speed of adaptation when the observations come from a source
undergoing distributional shift.


Abstract:
  We analyze the energy transfer for solutions to the defocusing cubic
nonlinear Schr\"odinger (NLS) initial value problem on 2D irrational tori.
Moreover we complement the analytic study with numerical experimentation. As a
biproduct of our investigation we also prove that the quasi-resonant part of
the NLS initial value problem we consider, in both the focusing and defocusing
case, is globally well-posed for initial data of finite mass.


Abstract:
  Suppression of superconductivity due to the proximity effect between a
superconductor and a ferromagnet can be partially alleviated when a Cooper pair
simultaneously samples different directions of the short-range exchange field.
The superconductor's critical temperature, TC, is therefore expected to
partially recover when the ferromagnet is in a multi-domain state, as opposed
to a single-domain state. Here, we discuss series of experiments performed with
ferromagnet(Pt/Co)/spacer(IrMn and Pt)/superconductor(NbN) heterostructures. By
tuning the various parameters in play, e.g., superconducting coherence
length-to-thicknesses ratio, and domain sizes, we obtained up to 10 percent
recovery of the superconducting critical temperature {\Delta}TC/TC. This
large-scale recovery made novel investigations possible. In particular, from
the spacer thickness-dependence of {\Delta}TC/TC, it was possible to deduce the
characteristic length for Cooper pair penetration in an IrMn antiferromagnet.
This information is crucial for electronic transport, and up to now has been
difficult to access experimentally for antiferromagnets.


Abstract:
  We study here the observational constraints on single-field inflationary
models achievable with the next generation of CMB experiments. We particularly
focus on a Stage-IV like experiment and forecasts its constraints on
inflationary parameters in the context of $\alpha$-attractor inflation
comprising a large class of single-field models. To tailor our forecasts we use
as a fiducial model the results obtained with current CMB and LSS data,
assuming the $\alpha$-model a priori. We found that current CMB data are able
to place a tight bound on the ratio of the tensor-to-scalar ratio with the
alpha parameter $r/\alpha = 3.87^{+0.78}_{-0.94}\cdot 10^{-3}$ and on the
running of the scalar index $\alpha_S = -6.4^{+1.6}_{-1.3}\cdot 10^{-4}$ with a
value of the scalar index consistent with current constraints. In the
optimistic scenario of detection of primordial gravitational waves in the CMB
B-mode polarization power spectra, we found that CMB-S4 will be able to achieve
a $15\%$ bound on the value of the parameter $\alpha = 1.01^{+0.14}_{-0.18}$.
This bound clearly show the ability of CMB-S4 to constrain not only the energy
scale of inflation but also the shape of its potential. Enlarging the baseline
model to also include the neutrino sector merely reduce the accuracy of $5\%$
leading to $\alpha = 1.07^{+0.18}_{-0.23}$ so that our main conclusions are
still valid.


Abstract:
  Blind image deblurring is an important yet very challenging problem in
low-level vision. Traditional optimization based methods generally formulate
this task as a maximum-a-posteriori estimation or variational inference
problem, whose performance highly relies on the handcraft priors for both the
latent image and the blur kernel. In contrast, recent deep learning methods
generally learn, from a large collection of training images, deep neural
networks (DNNs) directly mapping the blurry image to the clean one or to the
blur kernel, paying less attention to the physical degradation process of the
blurry image. In this paper, we present a deep variational Bayesian framework
for blind image deblurring. Under this framework, the posterior of the latent
clean image and blur kernel can be jointly estimated in an amortized inference
fashion with DNNs, and the involved inference DNNs can be trained by fully
considering the physical blur model, together with the supervision of data
driven priors for the clean image and blur kernel, which is naturally led to by
the evidence lower bound objective. Comprehensive experiments are conducted to
substantiate the effectiveness of the proposed framework. The results show that
it can not only achieve a promising performance with relatively simple
networks, but also enhance the performance of existing DNNs for deblurring.


Abstract:
  Crystal structure prediction (CSP) has emerged as one of the most important
approaches for discovering new materials. CSP algorithms based on evolutionary
algorithms and particle swarm optimization have discovered a great number of
new materials. However, these algorithms based on ab initio calculation of free
energy are inefficient. Moreover, they have severe limitations in terms of
scalability. We recently proposed a promising crystal structure prediction
method based on atomic contact maps, using global optimization algorithms to
search for the Wyckoff positions by maximizing the match between the contact
map of the predicted structure and the contact map of the true crystal
structure. However, our previous contact map based CSP algorithms have two
major limitations: (1) the loss of search capability due to getting trapped in
local optima; (2) it only uses the connection of atoms in the unit cell to
predict the crystal structure, ignoring the chemical environment outside the
unit cell, which may lead to unreasonable coordination environments. Herein we
propose a novel multi-objective genetic algorithms for contact map-based
crystal structure prediction by optimizing three objectives, including contact
map match accuracy, the individual age, and the coordination number match.
Furthermore, we assign the age values to all the individuals of the GA and try
to minimize the age aiming to avoid the premature convergence problem. Our
experimental results show that compared to our previous CMCrystal algorithm,
our multi-objective crystal structure prediction algorithm (CMCrystalMOO) can
reconstruct the crystal structure with higher quality and alleviate the problem
of premature convergence.


Abstract:
  Partial groups are a natural generalization of discrete groups recently
introduced by Chermak in connection with the theory of fusion systems. In this
paper we develop an extension theory for partial groups based in the classical
theory of fibre bundles of simplicial sets.


Abstract:
  Highly r-process enhanced metal-poor stars (MP r-II, $\rm [Eu/Fe]>1$ and $\rm
[Fe/H]\lesssim-1.5$) have been observed in ultra-faint dwarf (UFD) galaxy,
specifically in Reticulum~II (Ret~II). The fact that only a few UFDs contain
such stars implies that the r-process site may reflect very rare, but
individually prolific events, such as neutron star mergers (NSMs). Considering
the relatively short star formation history (SFH) of UFDs, it is puzzling how
they could experience such a rare phenomenon. In this work, we show the results
of cosmological hydrodynamic zoom-in simulations of isolated UFDs
($M_{vir}\approx10^7-10^8$ solar mass and $M_{\ast}\approx10^3-10^4$ solar mass
at $z=0$) to explain the formation of MP r-II stars in UFDs. We employ a simple
toy model for NSM events, adopting parameters consistent with observations,
such as the NSM rate (1 per $M_{\ast}\approx10^5$ solar mass) and europium (Eu)
mass ($M_{Eu}\approx10^{-5}$ solar mass). We identify only one simulated galaxy
($ M_{vir}\approx4.6\times10^7$, $M_{\ast}\approx 3.4\times 10^3$ solar mass at
$z=0$) with abundances similar to Ret~II in a simulation volume that hosts
$\sim30$ UFD analogs, indicating that such abundances are possible but rare. By
exploring a range of key parameters, we demonstrate that the most important
factor in determining the formation of MP r-II stars in UFDs is how quickly
subsequent stars can be formed out of r-process enriched gas. We find that it
takes between 10 to 100~Myr to form the first and second burst of MP r-II
stars. Over this period, Eu-polluted gas maintains the required high abundance
ratios of $\rm [Eu/Fe]>1$.


Abstract:
  The climate emergency increasingly threatens our communities, ecosystems,
food production, health, and economy. It disproportionately impacts lower
income communities, communities of color, and the elderly. Assessments since
the 2018 IPCC 1.5 Celsius report show that current national and sub-national
commitments and actions are insufficient. Fortunately, a suite of solutions
exists now to mitigate the climate crisis if we initiate and sustain actions
today. California, which has a strong set of current targets in place and is
home to clean energy and high technology innovation, has fallen behind in its
climate ambition compared to a number of major governments. California, a
catalyst for climate action globally, can and should ramp up its leadership by
aligning its climate goals with the most recent science, coordinating actions
to make 2030 a point of significant accomplishment. This entails dramatically
accelerating its carbon neutrality and net-negative emissions goal from 2045 to
2030, including advancing clean energy and clean transportation standards, and
accelerating nature-based solutions on natural and working lands. It also means
changing its current greenhouse gas reduction goals both in the percentage and
the timing: cutting emissions by 80 percent (instead of 40 percent) below 1990
levels much closer to 2030 than 2050. These actions will enable California to
save lives, benefit underserved and frontline communities, and save trillions
of dollars. This rededication takes heed of the latest science, accelerating
equitable, job-creating climate policies. While there are significant
challenges to achieving these goals, California can establish policy now that
will unleash innovation and channel market forces, as has happened with solar,
and catalyze positive upward-scaling tipping points for accelerated global
climate action.


Abstract:
  Distributions of open source software packages dedicated to specific
programming languages facilitate software development by allowing software
projects to depend on the functionality provided by such reusable packages. The
health of a software project can be affected by the maturity of the packages on
which it depends. The version numbers of the used package releases provide an
indication of their maturity. Packages with a 0.y.z version number are commonly
assumed to be under initial development, suggesting that they are likely to be
less stable, and depending on them may be considered as less healthy. In this
paper, we empirically study, for four open source package distributions (Cargo,
npm, Packagist and RubyGems) to which extent 0.y.z package releases and >=1.0.0
package releases behave differently. We quantify the prevalence of 0.y.z
releases, we explore how long packages remain in the initial development stage,
we compare the update frequency of 0.y.z and >=1.0.0 package releases, we study
how often 0.y.z releases are required by other packages, we assess whether
semantic versioning is respected for dependencies towards them, and we compare
some characteristics of 0.y.z and >=1.0.0 package repositories hosted on
GitHub. Among others, we observe that package distributions are more permissive
than what semantic versioning dictates for 0.y.z releases, and that many of the
0.y.z releases can actually be regarded as mature packages. As a consequence,
the version number does not provide a good indication of the maturity of a
package release.


Abstract:
  We report the observation of matter-wave jet emission in a strongly
ferromagnetic spinor Bose-Einstein condensate of $^7$Li atoms. Directional
atomic beams with $|{F=1,m_F=1}\rangle$ and $|{F=1,m_F=-1}\rangle$ spin states
are generated from $|{F=1,m_F=0}\rangle$ state condensates, or vice versa. This
results from collective spin-mixing scattering events, where spontaneously
produced pairs of atoms with opposite momentum facilitates additional
spin-mixing collisions as they pass through the condensates. The matter-wave
jets of different spin states ($|{F=1,m_F=\pm1}\rangle$) can be a macroscopic
Einstein-Podolsky-Rosen state with spacelike separation. Its spin-momentum
correlations are studied by using the angular correlation function for each
spin state. Rotating the spin axis, the inter-spin and intra-spin momentum
correlation peaks display a high contrast oscillation, indicating collective
coherence of the atomic ensembles. We provide numerical calculations that
describe the experimental results at a quantitative level and can identify its
entanglement after 100~ms of a long time-of-flight.


Abstract:
  Observations in the lowest MWA band between $75-100$ MHz have the potential
to constrain the distribution of neutral hydrogen in the intergalactic medium
at redshift $\sim 13-17$. Using 15 hours of MWA data, we analyse systematics in
this band such as radio-frequency interference (RFI), ionospheric and wide
field effects. By updating the position of point sources, we mitigate the
direction independent calibration error due to ionospheric offsets. Our
calibration strategy is optimized for the lowest frequency bands by reducing
the number of direction dependent calibrators and taking into account radio
sources within a wider field of view. We remove data polluted by systematics
based on the RFI occupancy and ionospheric conditions, finally selecting 5.5
hours of the cleanest data. Using these data, we obtain two sigma upper limits
on the 21 cm power spectrum in the range of $0.1\lessapprox k \lessapprox 1
~\rm ~h~Mpc^{-1}$ and at $z$=14.2, 15.2 and 16.5, with the lowest limit being
$6.3\times 10^6 ~\rm mK^2$ at $\rm k=0.14 \rm ~h~Mpc^{-1}$ and at $z=15.2$ with
a possibility of a few \% of signal loss due to direction independent
calibration.


Abstract:
  Ageing of society and increase of time spent with chronic conditions
challenge the traditional long-term care model. Assistive technology and
eHealth are seen to play an important role when addressing these challenges.
One prominent example are patient self-management systems. These systems not
only transform the way patients with chronic conditions interact with the
healthcare system, but also change work practices of care providers. This
literature review addresses sociotechnical challenges of eHealth technologies
with a strong collaborative component. As a result, four themes are identified
and discussed.


Abstract:
  We uncover a highly nontrivial dependence of the spin-noise (SN) resonance
broadening induced by the intense probe beam. The measurements were performed
by probing the cell with cesium vapor at the wavelengths of the transition
${6}^2S_{1/2} \leftrightarrow {6}^2P_{3/2}$ ($\mathrm{D}_2$ line) with the
unresolved hyperfine structure of the excited state. The light-induced
broadening of the SN resonance was found to differ strongly at different slopes
of the $\mathrm{D}_2$ line and, generally, varied nonmonotonically with light
power. We discuss the effect in terms of the phenomenological Bloch equations
for the spin fluctuations and demonstrate that the SN broadening behavior
strongly depends on the relation between the pumping and excited-level decay
rates, the spin precession, and decoherence rates. To reconcile the puzzling
experimental results, we propose that the degree of optical perturbation of the
spin-system is controlled by the route of the excited-state relaxation of the
atom or, in other words, that the act of optical excitation of the atom does
not necessarily break down completely its ground-state coherence and continuity
of the spin precession. Spectral asymmetry of the effect, in this case, is
provided by the position of the "closed" transition $F = 4 \leftrightarrow F' =
5$ at the short-wavelength side of the line. This hypothesis, however, remains
to be proven by microscopic calculations.


Abstract:
  Languages emerge and change over time at the population level though
interactions between individual speakers. It is, however, hard to directly
observe how a single speaker's linguistic innovation precipitates a
population-wide change in the language, and many theoretical proposals exist.
We introduce a very general mathematical model that encompasses a wide variety
of individual-level linguistic behaviours and provides statistical predictions
for the population-level changes that result from them. This model allows us to
compare the likelihood of empirically-attested changes in definite and
indefinite articles in multiple languages under different assumptions on the
way in which individuals learn and use language. We find that accounts of
language change that appeal primarily to errors in childhood language
acquisition are very weakly supported by the historical data, whereas those
that allow speakers to change incrementally across the lifespan are more
plausible, particularly when combined with social network effects.


Abstract:
  This paper addresses the problem of mirror surface reconstruction, and
proposes a solution based on observing the reflections of a moving reference
plane on the mirror surface. Unlike previous approaches which require tedious
calibration, our method can recover the camera intrinsics, the poses of the
reference plane, as well as the mirror surface from the observed reflections of
the reference plane under at least three unknown distinct poses. We first show
that the 3D poses of the reference plane can be estimated from the reflection
correspondences established between the images and the reference plane. We then
form a bunch of 3D lines from the reflection correspondences, and derive an
analytical solution to recover the line projection matrix. We transform the
line projection matrix to its equivalent camera projection matrix, and propose
a cross-ratio based formulation to optimize the camera projection matrix by
minimizing reprojection errors. The mirror surface is then reconstructed based
on the optimized cross-ratio constraint. Experimental results on both synthetic
and real data are presented, which demonstrate the feasibility and accuracy of
our method.


Abstract:
  I discuss the scope and naturalness of the proton mass decomposition (or sum
rule) published in PRL74, 1071 (1995) and answer a few criticisms that appeared
recently in the literature, focusing particularly on its interpretation and the
quantum anomalous energy contribution. I comment on the so-called
frame-independent or invariant-mass decomposition from the trace of the
energy-momentum tensor. I stress the importance of measuring the quantum
anomalous energy through experiments. Finally, I point out a large discrepancy
in the scalar radius of the nucleon extracted from vector-meson productions and
lattice QCD calculations.


Abstract:
  Recent advances in wireless communication and solid-state circuits together
with the enormous demands of sensing ability have given rise to a new enabling
technology, integrated sensing and communications (ISAC). The ISAC captures two
main advantages over dedicated sensing and communication functionalities: 1)
Integration gain to efficiently utilize congested resources, and even, 2)
Coordination gain to balance dual-functional performance or/and perform mutual
assistance. Meanwhile, triggered by ISAC, we are also witnessing a paradigm
shift in the ubiquitous IoT architecture, in which the sensing and
communication layers are tending to converge into a new layer, namely, the
signaling layer.
  In this paper, we first attempt to introduce a definition of ISAC, analyze
the various influencing forces, and present several novel use cases. Then, we
complement the understanding of the signaling layer by presenting several key
benefits in the IoT era. We classify existing dominant ISAC solutions based on
the layers in which integration is applied. Finally, several challenges and
opportunities are discussed. We hope that this overview article will serve as a
primary starting point for new researchers and offer a bird's-eye view of the
existing ISAC-related advances from academia and industry, ranging from
solid-state circuitry, signal processing, and wireless communication to mobile
computing.


Abstract:
  Automatic segmentation of the musculoskeletal system in pediatric magnetic
resonance (MR) images is a challenging but crucial task for morphological
evaluation in clinical practice. We propose a deep learning-based regularized
segmentation method for multi-structure bone delineation in MR images, designed
to overcome the inherent scarcity and heterogeneity of pediatric data. Based on
a newly devised shape code discriminator, our adversarial regularization scheme
enforces the deep network to follow a learnt shape representation of the
anatomy. The novel shape priors based adversarial regularization (SPAR)
exploits latent shape codes arising from ground truth and predicted masks to
guide the segmentation network towards more consistent and plausible
predictions. Our contribution is compared to state-of-the-art regularization
methods on two pediatric musculoskeletal imaging datasets from ankle and
shoulder joints.


Abstract:
  This paper is devoted to Bresse systems, a robust model for circular beams,
given by a set of three coupled wave equations. The main objective is to
establish the existence of global attractors for dynamics of semilinear
problems with localized damping. In order to deal with localized damping a
unique continuation property (UCP) is needed. Therefore we also provide a
suitable UCP for Bresse systems. Our strategy is to set the problem in a
Riemannian geometry framework and see the system as a single equation with
different Riemann metrics. Then we perform Carleman-type estimates to get our
result.


Abstract:
  All light has structure, but only recently it has become possible to
construct highly controllable and precise potentials so that most laboratories
can harness light for their specific applications. In this chapter, we review
the emerging techniques for high-resolution and configurable optical trapping
of ultracold atoms. We focus on optical deflectors and spatial light modulators
in the Fourier and direct imaging configurations. These optical techniques have
enabled significant progress in studies of superfluid dynamics, single-atom
trapping, and underlie the emerging field of atomtronics. The chapter is
intended as a complete guide to the experimentalist for understanding,
selecting, and implementing the most appropriate optical trapping technology
for a given application. After introducing the basic theory of optical trapping
and image formation, we describe each of the above technologies in detail,
providing a guide to the fundamental operation of optical deflectors, digital
micromirror devices, and liquid crystal spatial light modulators. We also
describe the capabilities of these technologies for manipulation of trapped
ultracold atoms, where the potential is dynamically modified to enable
experiments, and where time-averaged potentials can realise more complex traps.
The key considerations when implementing time-averaged traps are described.


Abstract:
  We observe a novel type of shear banding in the rheology of thixotropic
yield-stress fluids that is due to the coupling of both non-locality and
thixotropy. The latter is known to lead to shear banding even in homogeneous
stress fields, but the bands observed in the presence of non-local effects
appear different as the shear rate varies continuously over the shear band.
Here, we introduce a simple non-local model for the shear banding (NL-SB), and
we implement it for the analysis of micron-scale rheo-MRI velocimetry
measurements of a milk microgel suspension in a cone-and-plate geometry. The
proposed NL-SB model accurately quantifies the cooperativity length and yields
values in the order of the aggregate size in the microgel.


Abstract:
  With web and mobile platforms becoming more prominent devices utilized in
data analysis, there are currently few systems which are not without flaw. In
order to increase the performance of these systems and decrease errors of data
oversimplification, we seek to understand how other programming languages can
be used across these platforms which provide data and type safety, as well as
utilizing concurrency to perform complex data manipulation tasks.


Abstract:
  Observations from dynamical systems often exhibit irregularities, such as
censoring, where values are recorded only if they fall within a certain range.
Censoring is ubiquitous in practice, due to saturating sensors,
limit-of-detection effects, and image-frame effects. In light of recent
developments on learning linear dynamical systems (LDSs), and on censored
statistics with independent data, we revisit the decades-old problem of
learning an LDS, from censored observations (Lee and Maddala (1985); Zeger and
Brookmeyer (1986)). Here, the learner observes the state $x_t \in \mathbb{R}^d$
if and only if $x_t$ belongs to some set $S_t \subseteq \mathbb{R}^d$. We
develop the first computationally and statistically efficient algorithm for
learning the system, assuming only oracle access to the sets $S_t$. Our
algorithm, Stochastic Online Newton with Switching Gradients, is a novel
second-order method that builds on the Online Newton Step (ONS) of Hazan et al.
(2007). Our Switching-Gradient scheme does not always use (stochastic)
gradients of the function we want to optimize, which we call "censor-aware"
function. Instead, in each iteration, it performs a simple test to decide
whether to use the censor-aware, or another "censor-oblivious" function, for
getting a stochastic gradient.
  In our analysis, we consider a "generic" Online Newton method, which uses
arbitrary vectors instead of gradients, and we prove an error-bound for it.
This can be used to appropriately design these vectors, leading to our
Switching-Gradient scheme. This framework significantly deviates from the
recent long line of works on censored statistics (e.g., Daskalakis et al.
(2018); Kontonis et al. (2019); Daskalakis et al. (2019)), which apply
Stochastic Gradient Descent (SGD), and their analysis reduces to establishing
conditions for off-the-shelf SGD-bounds.


Abstract:
  A new nonlinear integral equation (NLIE) describing the thermodynamics of the
Heisenberg spin chain is derived based on the t-W relation of the quantum
transfer matrices. The free energy of the system in a magnetic field is thus
obtained by solving the NLIE. This method can be generalized to other lattice
quantum integrable models. Taking the SU(3)-invariant quantum spin chain as an
example, we construct the corresponding NLIEs and compute the free energy. The
present results coincide exactly with those obtained via other methods
previously.


Abstract:
  The Spatial Convolution Model (SCM) analyzes allostery based on the spatial
evolution of the docking protein elastic media, whereby convolution of the
media in response to wave propagation is solved as a function of Z fluctuations
and backbone vibration modes. We show that although the elastic media is a
complex three-dimensional structure allostery behaves as if it occurs along a
stretched oscillating string, where inhomogeneities along the string effect
local entropies responsible for ligand binding and transduction of allosteric
waves. To identify inhomogeneities along the string, we ignored local density
and tension changes during wave propagation and resolved helix wave and
physical properties by applying molecular string and beam bending theories.
Importantly, we show that allostery occurs at three major scales and that
propagation of standing waves create a rolling entropy which drives entropy
transfers between fields. Conversion of resonance energy to quantum harmonic
oscillators allowed us to consider effects of damping and interactions with the
surrounding media as well as to model effects of residue interaction strength
on entropy transfer.


Abstract:
  We introduce a collection of recognizing textual entailment (RTE) datasets
focused on figurative language. We leverage five existing datasets annotated
for a variety of figurative language -- simile, metaphor, and irony -- and
frame them into over 12,500 RTE examples.We evaluate how well state-of-the-art
models trained on popular RTE datasets capture different aspects of figurative
language. Our results and analyses indicate that these models might not
sufficiently capture figurative language, struggling to perform pragmatic
inference and reasoning about world knowledge. Ultimately, our datasets provide
a challenging testbed for evaluating RTE models.


Abstract:
  It is well known that impurities play a central role in the linear and
nonlinear response of graphene at optical and terahertz frequencies. In this
work, we calculate the bands and intraband dipole connection elements for
nitrogen-doped monolayer graphene using a density functional tight binding
approach. Employing these results, we calculate the linear and nonlinear
response of the doped graphene to terahertz pulses using a density-matrix
approach in the length gauge. We present the results for the linear and
nonlinear mobility as well as third harmonic generation in graphene for
adsorbed and substitutional nitrogen doping for a variety of doping densities.
We show that the conduction bands are more parabolic in graphene structures
with substitutional nitrogen doping than for those with adsorbed nitrogen. As a
result, substitutional doping has a greater impact on the terahertz mobility
and nonlinear response of graphene than adsorbed nitrogen does.


Abstract:
  In todays world, where the need for security is paramount and biometric
access control systems are gaining mass acceptance due to their increased
reliability, research in this area is quite relevant. Also with the advent of
IOT devices and increased community support for cheap and small computers like
Raspberry Pi its convenient than ever to design a complete standalone system
for any purpose. This paper proposes a Facial Biometric System built on the
client-server paradigm using Raspberry Pi 3 model B running a novel local
descriptor based parallel algorithm. This paper also proposes an extended
version of Local Gradient Hexa Pattern with improved accuracy. The proposed
extended version of LGHP improved performance as shown in performance analysis.
Extended LGHP shows improvement over other state-of-the-art descriptors namely
LDP, LTrP, MLBP and LVP on the most challenging benchmark facial image
databases, i.e. Cropped Extended Yale-B, CMU-PIE, color-FERET, LFW, and
Ghallager database. Proposed system is also compared with various patents
having similar system design and intent to emphasize the difference and novelty
of the system proposed.


Abstract:
  Catastrophic forgetting undermines the effectiveness of deep neural networks
(DNNs) in scenarios such as continual learning and lifelong learning. While
several methods have been proposed to tackle this problem, there is limited
work explaining why these methods work well. This paper has the goal of better
explaining a popularly used technique for avoiding catastrophic forgetting:
quadratic regularization. We show that quadratic regularizers prevent
forgetting of past tasks by interpolating current and previous values of model
parameters at every training iteration. Over multiple training iterations, this
interpolation operation reduces the learning rates of more important model
parameters, thereby minimizing their movement. Our analysis also reveals two
drawbacks of quadratic regularization: (a) dependence of parameter
interpolation on training hyperparameters, which often leads to training
instability and (b) assignment of lower importance to deeper layers, which are
generally the place forgetting occurs in DNNs. Via a simple modification to the
order of operations, we show these drawbacks can be easily avoided, resulting
in 6.2% higher average accuracy at 4.5% lower average forgetting. Code
available at \url{https://github.com/EkdeepSLubana/QRforgetting}


Abstract:
  Video Question Answering is a task which requires an AI agent to answer
questions grounded in video. This task entails three key challenges: (1)
understand the intention of various questions, (2) capturing various elements
of the input video (e.g., object, action, causality), and (3) cross-modal
grounding between language and vision information. We propose Motion-Appearance
Synergistic Networks (MASN), which embed two cross-modal features grounded on
motion and appearance information and selectively utilize them depending on the
question's intentions. MASN consists of a motion module, an appearance module,
and a motion-appearance fusion module. The motion module computes the
action-oriented cross-modal joint representations, while the appearance module
focuses on the appearance aspect of the input video. Finally, the
motion-appearance fusion module takes each output of the motion module and the
appearance module as input, and performs question-guided fusion. As a result,
MASN achieves new state-of-the-art performance on the TGIF-QA and MSVD-QA
datasets. We also conduct qualitative analysis by visualizing the inference
results of MASN. The code is available at
https://github.com/ahjeongseo/MASN-pytorch.


Abstract:
  We propose to enhance the performance of localized plasmon structured
illumination microscopy (LP-SIM) via intensity correlations. LP-SIM uses
sub-wavelength illumination patterns to encode high spatial frequency
information. It can enhance the resolution up to three-fold before gaps in the
OTF support arise. For blinking fluorophores or for quantum antibunching an
intensity correlation analysis induces higher harmonics of the illumination
pattern and enlarges the effective OTF. This enables ultrahigh resolutions
without gaps in the OTF support, and thus a fully deterministic imaging scheme.
We present simulations that include shot and external noise and demonstrate the
resolution power under realistic photon budgets. The technique has potential in
light microscopy where low-intensity illumination is paramount while aiming for
high spatial but moderate temporal resolutions.


Abstract:
  We develop a geometric method to establish existence of equilibrium states
associated to H\"older potentials for center isometries (as are regular
elements of Anosov actions), in particular the entropy maximizing measure and
the SRB measure. In these cases it is established the uniqueness, and it is
given a characterization of the equilibrium states in terms in terms of their
disintegrations along stable and unstable foliations. It is proven also that
the resulting system is isomorphic to a Bernoulli scheme.


Abstract:
  Frost growth on cold surfaces is a transient process with coupled heat and
mass transfer. Due to multiple factors such as humidity, temperature, flow
velocity and constantly changing thermal properties as frost grows, precise
prediction can be challenging. Especially when the geometry of the frosting
surfaces gets complicated, it requires a balance of computing accuracy and
efficiency. In this work, a numerical model is developed to predict frost
growth considering the effect of the above parameters. Mixture model is adapted
to improve the computational efficiency and the unstructured grids add the
flexibility to extend the model to complex geometries. The predicted frost
growth rate matches well with the experimental data reported in the literature
under similar conditions. The model predicts reasonable growth trend of frost
as the surface temperature, air temperature, humidity and flow velocity vary.
The surface wettability effect is well captured at the early stage of frosting
and it shows a higher frost growth rate on surfaces with a higher wettability.


Abstract:
  The excitation of a black hole by infalling matter or radiation has been
studied for a long time, mostly in linear perturbation theory. In this paper we
study numerically the response of a Schwarzschild black hole to an incoming
gravitational wave pulse. We present a numerically well-posed initial boundary
value problem for the generalized conformal field equations in which a wave
profile for the ingoing wave is specified at an outer time-like boundary which
then hits an initially static and spherically symmetric black hole. The
non-linear interaction of the black hole with the gravitational wave leads to
scattered radiation moving back out. The clean separation between initial state
and incoming radiation makes this setup ideal to study scattering problems. The
use of the conformal field equations allows us to trace the response of the
black hole to null infinity where we can read off the scattered gravitational
waves and compute the Bondi-Sachs mass and the gravitational flux through
$\mathscr{I}$. In this way we check the Bondi-Sachs mass loss formula directly
on null infinity. We also comment on comparisons with quasinormal modes.


Abstract:
  We investigate strongly asymmetric self-assembled nanostructures with one of
dimensions reaching hundreds of nanometers. Close to the nanowire-like type of
confinement, such objects are sometimes assigned as one-dimensional in nature.
Here, we directly observe the spectrum of exciton excited states corresponding
to longitudinal quantization. This is based on probing the optical transitions
via polarization-resolved microphotoluminescence excitation ($\mu$PLE)
measurement performed on single nanostructures combined with theoretical
calculation of neutral and charged exciton optical properties. We successfully
probe absorption-like spectra for individual bright states forming the exciton
ground-state fine structure, as well as for the negatively charged exciton.
Confronting the calculated spectrum of excitonic absorption with $\mu$PLE
traces, we identify optical transitions involving states that contain carriers
at various excited levels related to the longest dimension. Based on
cross-polarized excitation-detection scheme, we show very well conserved spin
configuration during orbital relaxation of the exciton from a number of excited
states comparable to the quasi-resonant pumping via the optical phonon, and no
polarization memory for the trion, as theoretically expected.


Abstract:
  Throughout the COVID-19 pandemic, the most common symptom displayed by
patients has been a fever, leading to the use of temperature scanning as a
preemptive measure to detect potential carriers of the virus. Human employees
with handheld thermometers have been used to fulfill this task, however this
puts them at risk as they cannot be physically distanced and the sequential
nature of this method leads to great inconveniences and inefficiency. The
proposed solution is an autonomously navigating robot capable of conversing and
scanning people's temperature to detect fevers and help screen for COVID-19. To
satisfy this objective, the robot must be able to (1) navigate autonomously,
(2) detect and track people, and (3) get individuals' temperature reading and
converse with them if it exceeds 38{\deg}C. An autonomously navigating mobile
robot is used with a manipulator controlled using a face tracking algorithm,
and an end effector consisting of a thermal camera, smartphone, and chatbot.
The goal is to develop a functioning solution that performs the above tasks. In
addition, technical challenges encountered and their engineering solutions will
be presented, and recommendations will be made for enhancements that could be
incorporated when approaching commercialization.


Abstract:
  We prove a version of Temkin's local altered uniformization theorem. We show
that for any rig-smooth, quasi-compact and quasi-separated admissible formal
$\mathcal{O}_K$-model $\mathfrak{X}$, there is a finite extension $K'/K$ such
that $\mathfrak{X}_{\mathcal{O}_{K'}}$ locally admits a rig-\'etale morphism
$g\colon \mathfrak{X}' \to \mathfrak{X}_{\mathcal{O}_{K'}}$ and a
rig-isomorphism $h\colon \mathfrak{X}'' \to \mathfrak{X}'$ with $\mathfrak{X}'$
being a successive semi-stable curve fibration over $\mathcal{O}_{K'}$ and
$\mathfrak{X}''$ being a poly-stable formal $\mathcal{O}_{K'}$-scheme.
Moreover, $\mathfrak{X}'$ admits an action of a finite group $G$ such that
$g\colon \mathfrak{X}' \to \mathfrak{X}_{\mathcal{O}_{K'}}$ is $G$-invariant,
and the adic generic fiber $\mathfrak{X}'_{K'}$ becomes a $G$-torsor over its
quasi-compact open image $U=g_{K'}(\mathfrak{X}'_{K'})$. Also, we study
properties of the quotient map $\mathfrak{X}'/G \to
\mathfrak{X}_{\mathcal{O}_{K'}}$ and show that it can be obtained as a
composition of open immersions and rig-isomorphisms.


Abstract:
  The operating system is designed to manage the hardware and software
resources of a computer. With the development of quantum computing, the
management of quantum resources and cooperation between quantum systems and
other computing resources (e.g. CPU, GPU and FPGA etc.) become the key
challenge for the application of quantum computing to solve real world
problems. In this paper we propose a quantum operating system, Origin Pilot.
Origin Pilot includes the module of quantum task scheduling, quantum resource
management, quantum program compilation and qubits' automatic calibration. With
these modules, Origin Pilot can manage the quantum computing resources and
solve the multi-quantum processor scheduling problem. It can also allow the
parallel execution of multiple quantum programs and calibrate the quantum
resource effectively. Thus, the performance of resources is guaranteed and the
resource utilization is improved. By comparing the results with and without
Origin Pilot, we evaluate the impact on a quantum circuit's fidelity of qubits
mapping algorithm. We also evaluate the effectiveness of automatic calibration
and parallel execution of multi-quantum processors. Finally, Origin Pilot can
be easily customized for hybrid computing resources.


Abstract:
  We demonstrate for the first time the coexistence of a quantum-channel and
8x200 Gpbs 16-QAM optical channels with launching powers as high as -9dBm per
channel in a 2 km HC-NANF. Comparative analysis with single-mode fibre reveals
that the quantum channel could not be sustained at such power levels.


Abstract:
  We present an energy scaling function to predict, in a specific range, the
energy of bosonic trimers with large scattering lengths and finite range
interactions, which is validated by quantum Monte Carlo calculations using
microscopic Hamiltonians with two- and three-body potentials. The proposed
scaling function depends on the scattering length, effective range, and a
reference energy, which we chose as the trimer energy at unitarity. We obtained
the scaling function as a limit cycle from the solution of the renormalized
zero-range model with effective range corrections. We proposed a simple
parametrization of the energy scaling function. Besides the intrinsic interest
in theoretical and experimental investigations, this scaling function allows
one to probe Efimov physics with only the trimer ground states, which may open
opportunities to identify Efimov trimers whenever access to excited states is
limited.


Abstract:
  In this work, we continue the study of the bifurcations of the critical
points in a symmetric Caldera potential energy surface. In particular, we study
the influence of the depth of the potential on the trajectory behavior before
and after the bifurcations of the critical points. We observe two different
types of trajectory behavior: dynamical matching and the non-existence of
dynamical matching. Dynamical matching is a phenomenon that limits the way in
which a trajectory can exit the Caldera based solely on how it enters the
Caldera. Furthermore, we discuss two different types of symmetric Caldera
potential energy surface and the transition from the one type to the other
through the bifurcations of the critical points.


Abstract:
  Existing approaches to unsupervised object discovery (UOD) do not scale up to
large datasets without approximations that compromise their performance. We
propose a novel formulation of UOD as a ranking problem, amenable to the
arsenal of distributed methods available for eigenvalue problems and link
analysis. Through the use of self-supervised features, we also demonstrate the
first effective fully unsupervised pipeline for UOD. Extensive experiments on
COCO and OpenImages show that, in the single-object discovery setting where a
single prominent object is sought in each image, the proposed LOD (Large-scale
Object Discovery) approach is on par with, or better than the state of the art
for medium-scale datasets (up to 120K images), and over 37% better than the
only other algorithms capable of scaling up to 1.7M images. In the multi-object
discovery setting where multiple objects are sought in each image, the proposed
LOD is over 14% better in average precision (AP) than all other methods for
datasets ranging from 20K to 1.7M images. Using self-supervised features, we
also show that the proposed method obtains state-of-the-art UOD performance on
OpenImages. Our code is publicly available at https://github.com/huyvvo/LOD.


Abstract:
  Future wireless communications are largely inclined to deploy a massive
number of antennas at the base stations (BS) by exploiting energy-efficient and
environmentally friendly technologies. An emerging technology called dynamic
metasurface antennas (DMAs) is promising to realize such massive antenna arrays
with reduced physical size, hardware cost, and power consumption. This paper
aims to optimize the energy efficiency (EE) performance of DMAs-assisted
massive MIMO uplink communications. We propose an algorithmic framework for
designing the transmit precoding of each multi-antenna user and the DMAs tuning
strategy at the BS to maximize the EE performance, considering the availability
of the instantaneous and statistical channel state information (CSI),
respectively. Specifically, the proposed framework includes Dinkelbach's
transform, alternating optimization, and deterministic equivalent methods. In
addition, we obtain a closed-form solution to the optimal transmit signal
directions for the statistical CSI case, which simplifies the corresponding
transmission design. The numerical results show good convergence performance of
our proposed algorithms as well as considerable EE performance gains of the
DMAs-assisted massive MIMO uplink communications over the baseline schemes.


Abstract:
  We study $\infty$-Ground states in convex domains in the plane. In a polygon,
the points where an $\infty$-Ground state does not satisfy the $\infty$-Laplace
Equation are characterized: they are restricted to lie on specific curves,
which are acting as attracting (fictitious) streamlines. The gradient is
continuous outside these curves and no streamlines can meet there.


Abstract:
  Persistent homology has undergone significant development in recent years.
However, one outstanding challenge is to build a coherent statistical inference
procedure on persistent diagrams. In this paper, we first present a new lattice
path representation for persistent diagrams. We then develop a new exact
statistical inference procedure for lattice paths via combinatorial
enumerations. The lattice path method is applied to the topological
characterization of the protein structures of the COVID-19 virus. We
demonstrate that there are topological changes during the conformational change
of spike proteins.


Abstract:
  Computationally tractable methods are developed for centralized goal
assignment and planning of collision-free polynomial-in-time trajectories for
systems of multiple aerial robots. The method first assigns robots to goals to
minimize total time-in-motion based on initial trajectories. By coupling the
assignment and trajectory generation, the initial motion plans tend to require
only limited collision resolution. The plans are then refined by checking for
potential collisions and resolving them using either start time delays or
altitude assignment. Numerical experiments using both methods show significant
reductions in the total time required for agents to arrive at goals with only
modest additional computational effort in comparison to state-of-the-art prior
work, enabling planning for thousands of agents.


Abstract:
  With this work, we investigate the use of Reinforcement Learning (RL) for the
generation of spatial assemblies, by combining ideas from Procedural Generation
algorithms (Wave Function Collapse algorithm (WFC)) and RL for Game Solving.
WFC is a Generative Design algorithm, inspired by Constraint Solving. In WFC,
one defines a set of tiles/blocks and constraints and the algorithm generates
an assembly that satisfies these constraints. Casting the problem of generation
of spatial assemblies as a Markov Decision Process whose states transitions are
defined by WFC, we propose an algorithm that uses Reinforcement Learning and
Self-Play to learn a policy that generates assemblies that maximize objectives
set by the designer. Finally, we demonstrate the use of our Spatial Assembly
algorithm in Architecture Design.


Abstract:
  The Mann-Kendall test for trend has gained a lot of attention in a range of
disciplines, especially in the environmental sciences. One of the drawbacks of
the Mann-Kendall test when applied to real data is that no distinction can be
made between meaningful and non-meaningful differences in subsequent
observations. We introduce the concept of partial ties, which allows inferences
while accounting for (non)meaningful difference. We introduce the modified
statistic that accounts for such a concept and derive its variance estimator.
We also present analytical results for the behavior of the test in a class of
contiguous alternatives. Simulation results which illustrate the added value of
the test are presented. We apply our extended version of the test to some real
data concerning blood donation in Europe.


Abstract:
  Ground Penetrating Radar (GPR) is one of the most important non-destructive
evaluation (NDE) devices to detect subsurface objects (i.e., rebars, utility
pipes) and reveal the underground scene. The two biggest challenges in
GPR-based inspection are the GPR data collection and subsurface target imaging.
To address these challenges, we propose a robotic solution that automates the
GPR data collection process with a free motion pattern. It facilitates the 3D
metric GPR imaging by tagging the pose information with GPR measurement in
real-time. We also introduce a deep neural network (DNN) based GPR data
analysis method which includes a noise removal segmentation module to clear the
noise in GPR raw data and a DielectricNet to estimate the dielectric value of
subsurface media in each GPR B-scan data. We use both the field and synthetic
data to verify the proposed method. Experimental results demonstrate that our
proposed method can achieve better performance and faster processing speed in
GPR data collection and 3D GPR imaging than other methods.


Abstract:
  We consider the task of photo-realistic unconditional image generation
(generate high quality, diverse samples that carry the same visual content as
the image) on mobile platforms using Generative Adversarial Networks (GANs). In
this paper, we propose a novel approach to trade-off image generation accuracy
of a GAN for the energy consumed (compute) at run-time called Scale-Energy
Tradeoff GAN (SETGAN). GANs usually take a long time to train and consume a
huge memory hence making it difficult to run on edge devices. The key idea
behind SETGAN for an image generation task is for a given input image, we train
a GAN on a remote server and use the trained model on edge devices. We use
SinGAN, a single image unconditional generative model, that contains a pyramid
of fully convolutional GANs, each responsible for learning the patch
distribution at a different scale of the image. During the training process, we
determine the optimal number of scales for a given input image and the energy
constraint from the target edge device. Results show that with SETGAN's unique
client-server-based architecture, we were able to achieve a 56% gain in energy
for a loss of 3% to 12% SSIM accuracy. Also, with the parallel multi-scale
training, we obtain around 4x gain in training time on the server.


Abstract:
  Modern data centers suffer from immense power consumption. The erratic
behavior of internet traffic forces data centers to maintain excess capacity in
the form of idle servers in case the workload suddenly increases. As an idle
server still consumes a significant fraction of the peak energy, data center
operators have heavily invested in capacity scaling solutions. In simple terms,
these aim to deactivate servers if the demand is low and to activate them again
when the workload increases. To do so, an algorithm needs to strike a delicate
balance between power consumption, flow-time, and switching costs. Over the
last decade, the research community has developed competitive online algorithms
with worst-case guarantees. In the presence of historic data patterns,
prescription from Machine Learning (ML) predictions typically outperform such
competitive algorithms. This, however, comes at the cost of sacrificing the
robustness of performance, since unpredictable surges in the workload are not
uncommon. The current work builds on the emerging paradigm of augmenting
unreliable ML predictions with online algorithms to develop novel robust
algorithms that enjoy the benefits of both worlds.
  We analyze a continuous-time model for capacity scaling, where the goal is to
minimize the weighted sum of flow-time, switching cost, and power consumption
in an online fashion. We propose a novel algorithm, called Adaptive Balanced
Capacity Scaling (ABCS), that has access to black-box ML predictions, but is
completely oblivious to the accuracy of these predictions. In particular, if
the predictions turn out to be accurate in hindsight, we prove that ABCS is
$(1+\varepsilon)$-competitive. Moreover, even when the predictions are
inaccurate, ABCS guarantees a bounded competitive ratio. The performance of the
ABCS algorithm on a real-world dataset positively support the theoretical
results.


Abstract:
  Unmanned Aerial vehicles (UAVs) are widely used as network processors in
mobile networks, but more recently, UAVs have been used in Mobile Edge
Computing as mobile servers. However, there are significant challenges to use
UAVs in complex environments with obstacles and cooperation between UAVs. We
introduce a new multi-UAV Mobile Edge Computing platform, which aims to provide
better Quality-of-Service and path planning based on reinforcement learning to
address these issues. The contributions of our work include: 1) optimizing the
quality of service for mobile edge computing and path planning in the same
reinforcement learning framework; 2) using a sigmoid-like function to depict
the terminal users' demand to ensure a higher quality of service; 3) applying
synthetic considerations of the terminal users' demand, risk and geometric
distance in reinforcement learning reward matrix to ensure the quality of
service, risk avoidance, and the cost-savings. Simulations have shown the
effectiveness and feasibility of our platform, which can help advance related
researches.


Abstract:
  A convolutional neural network is used to align an orbital angular momentum
sorter in a transmission electron microscope. The method is demonstrated using
simulations and experimentally. As a result of its accuracy and speed, it
offers the possibility of real-time tuning of other electron optical devices
and electron beam shaping configurations.


Abstract:
  In this contribution, we investigate the interaction between electric and
magnetic fields with an electric quadrupole moment of a spinless particle
moving in an elastic medium which has a topological defect (screw dislocation).
By considering this interaction, the Schr\"odinger equation is exactly solved
by using the analytical method. Thus, the eigenfunction and energy eigenvalues
for two configurations are found. Meanwhile, by observing a shift in the
angular momentum quantum number, the energy eigenvalues and the wave function
of the system are modified, due to the screw dislocation in the medium.


Abstract:
  Spatiotemporal properties of two-dimensional (2D) Hall-magnetohydrodynamic
turbulence at intermediate plasma $\beta=2$ are studied by means of Fast
Iterative Filtering, a new technique for the decomposition of nonstationary
nonlinear signals. Results show that the magnetic energy at sub-ion scales is
concentrated in perturbations with frequencies smaller than the ion-cyclotron
(IC) frequency and with polarization properties that are incompatible with both
kinetic Alfv\'en waves (KAWs) and IC waves. At higher frequencies, we clearly
identify signatures of both whistler waves and KAWs, however their energetic
contribution to the magnetic power spectrum is negligible. We conclude that the
dynamics of 2D Hall-MHD turbulence at sub-ion scales is mainly driven by
localized intermittent structures, with no significant contribution of wavelike
fluctuations.


Abstract:
  We analyse errors of randomized explicit and implicit Euler schemes for
approximate solving of ordinary differential equations (ODEs). We consider
classes of ODEs for which the right-hand side functions satisfy Lipschitz
condition globally or only locally. Moreover, we assume that only inexact
discrete information, corrupted by some noise, about the right-hand side
function is available. Optimality and stability of explicit and implicit
randomized Euler algorithms are also investigated.


Abstract:
  Using the secondary Steenrod algebra, we compute all Adams $d_2$
differentials of the sphere up to the 140th stem, and all $d_2$'s of $C2$,
$C\eta$, $C\nu$, $C\sigma$ and $C2 \wedge C\sigma$ up to the 112th stem.
  This data allows us to compute hidden $2, \eta, \nu$ and $\sigma$ extensions
of the sphere that jump by one filtration, from which we resolve all remaining
unknown $d_2$, $d_3$, $d_4$ and $d_5$ differentials of the sphere up to the
95th stem.


Abstract:
  Mutual entrainment effects in cold neutron-proton mixtures are studied in the
framework of the self-consistent nuclear energy-density functional theory.
Exact expressions for the mass currents, valid for both homogeneous and
inhomogeneous systems, are directly derived from the time-dependent
Hartree-Fock equations with no further approximation. The equivalence with the
Fermi-liquid expression is also demonstrated. Focusing on neutron-star cores, a
convenient and simple analytical formulation of the entrainment matrix in terms
of the isovector effective mass is found, thus allowing to relate entrainment
phenomena in neutron stars to isovector giant dipole resonances in finite
nuclei. Results obtained with different functionals are presented. These
include the Brussels-Montreal functionals, for which unified equations of state
of neutron stars have been recently calculated.


Abstract:
  We obtain sharp inequalities between the large scale asymptotic of the $J$
functional with respect to the $d_1$ metric on the space of Kahler metrics.
Applications regarding the initial value problem for geodesic rays are
presented.