Abstract:
  We present results for the electronic and magnetic structure of Mn and Fe
clusters on Nb(110) surface, focusing on building blocks of atomic chains as
possible realizations of topological superconductivity. The magnetic ground
states of the atomic dimers and most of the monatomic chains are determined by
the nearest-neighbor isotropic interaction. The dependence on the
crystallographic direction as well as on the atomic coordination number is
analyzed via an orbital decomposition of this isotropic interaction based on
the spin-cluster expansion and the difference in the local density of states
between ferromagnetic and antiferromagnetic configurations. A spin-spiral
ground state is obtained for Fe chains along the [$1\overline{1}0$] direction
as a consequence of the frustration of the isotropic interactions. Here, a flat
spin-spiral dispersion relation is identified, which can stabilize spin spirals
with various wave vectors together with the magnetic anisotropy. This may lead
to the observation of spin spirals of different wave vectors and chiralities in
longer chains instead of a unique ground state.


Abstract:
  Highly symmetric networks can exhibit partly symmetry-broken states,
including clusters and chimera states, i.e., states of coexisting synchronized
and unsynchronized elements. We address the $\mathbb{S}_4$ permutation symmetry
of four globally coupled Stuart-Landau oscillators and uncover an
interconnected web of differently symmetric solutions. Among these are chaotic
$2\!-\!1\!-\!1$ minimal chimeras that arise from $2\!-\!1\!-\!1$ periodic
solutions in a period-doubling cascade, as well as fully asymmetric chaotic
states arising similarly from periodic $1\!-\!1\!-\!1\!-\!1$ solutions. A
backbone of equivariant pitchfork bifurcations mediate between the two
cascades, culminating in equivariant pitchforks of chaotic attractors.


Abstract:
  This paper presents a detailed quantitative evaluation of standardised
Decentralised Congestion Control (DCC) and packet dropping mechanisms for
Cellular V2X (C-V2X). Based on the identified shortcomings, an Access layer DCC
scheme, RRI adaptive, is then proposed. RRI adaptive accommodates the sidelink
scheduling mechanism Sensing Based Semi-Persistent Scheduling (SB-SPS),
eliminating incompatibilities between current standards and the scheduling
mechanism, to avoid unnecessary and reoccurring collisions. Two variants are
proposed; one is an evolution of the ETSI Reactive DCC mechanism and the other
aligns with the 3GPP approach based on channel occupancy ratio (CR). Both
approaches are compared with current ETSI and 3GPP standards and exhibit
improved performance. An evaluation of existing DCC standards and RRI Adaptive
to meet the Quality of Service (QoS) requirements of vehicular cooperative
awareness applications is also conducted.


Abstract:
  Extropy was introduced as a dual complement of the Shannon entropy. In this
investigation, we consider failure extropy and its dynamic version. Various
basic properties of these measures are presented. It is shown that the dynamic
failure extropy characterizes the distribution function uniquely. We also
consider weighted versions of these measures. Several virtues of the weighted
measures are explored. Finally, nonparametric estimators are introduced based
on the empirical distribution function.


Abstract:
  Context. Hot subdwarfs experienced strong mass loss on the Red Giant Branch
(RGB) and are now hot and small He-burning objects. Aims. In this project we
aim to perform a transit survey in all available light curves of hot subdwarfs
from space-based telescopes (Kepler, K2, TESS, and CHEOPS), with our
custom-made pipeline SHERLOCK, in order to determine the occurrence rate of
planets around these stars, as a function of orbital period and planetary
radius. Methods. In this first paper, we perform injection-and-recovery tests
of synthetic transits for a selection of representative Kepler, K2 and TESS
light curves, to determine which transiting bodies, in terms of object radius
and orbital period, we will be able to detect with our tools. We also provide
such estimates for CHEOPS data, which we analyze with the pycheops package.
Results. Transiting objects with a radius $\lesssim$ 1.0 $R_{\Earth}$ can be
detected in most of Kepler, K2 and CHEOPS targets for the shortest orbital
periods (1 d and below), reaching values as small as $\sim$0.3 $R_{\Earth}$ in
the best cases. Reaching sub-Earth-sized bodies is achieved only for the
brightest TESS targets, and the ones observed during a significant number of
sectors. We also give a series of representative results for farther and bigger
planets, for which the performances strongly depend on the target magnitude,
the length and the quality of the data. Conclusions. The TESS sample will
provide the most important statistics for the global aim of measuring the
planet occurrence rate around hot subdwarfs. The Kepler, K2 and CHEOPS data
will allow us to search for planetary remnants, i.e. very close and small
(possibly disintegrating) objects, which would have partly survived the
engulfment in their red giant host.


Abstract:
  We address the problem of inferring descriptions of system behavior using
Linear Temporal Logic (LTL) from a finite set of positive and negative
examples. Most of the existing approaches for solving such a task rely on
predefined templates for guiding the structure of the inferred formula. The
approaches that can infer arbitrary LTL formulas, on the other hand, are not
robust to noise in the data. To alleviate such limitations, we devise two
algorithms for inferring concise LTL formulas even in the presence of noise.
Our first algorithm infers minimal LTL formulas by reducing the inference
problem to a problem in maximum satisfiability and then using off-the-shelf
MaxSAT solvers to find a solution. To the best of our knowledge, we are the
first to incorporate the usage of MaxSAT solvers for inferring formulas in LTL.
Our second learning algorithm relies on the first algorithm to derive a
decision tree over LTL formulas based on a decision tree learning algorithm. We
have implemented both our algorithms and verified that our algorithms are
efficient in extracting concise LTL descriptions even in the presence of noise.


Abstract:
  We show that any surface of infinite type admits an ideal triangulation.
Furthermore, we show that a set of disjoint arcs can be completed into a
triangulation if and only if, as a set, they intersect every simple closed
curve a finite number of times.


Abstract:
  We show that, for any fixed weight, there is a natural system of Hodge
sheaves, whose Higgs field has no poles, arising from a flat projective family
of varieties parametrized by a regular complex base scheme, extending the
analogous classical result for smooth projective families due to Griffiths. As
an application, based on positivity of direct image sheaves, we establish a
criterion for base spaces of rational Gorenstein families to be of general
type. A key component of our arguments is centered around the construction of
derived categorical objects generalizing relative logarithmic forms for smooth
maps and their functorial properties.


Abstract:
  The present paper shows how one might model Everettian quantum mechanics
using hyperfinitely many worlds. A hyperfinite model allows one to consider
idealized measurements of observables with continuous-valued spectra where
different outcomes are associated with possibly infinitesimal probabilities.
One can also prove hyperfinite formulations of Everett's limiting
relative-frequency and randomness properties, theorems he considered central to
his formulation of quantum mechanics. This approach also provides a more
general framework in which to consider no-collapse formulations of quantum
mechanics more generally.


Abstract:
  Light field (LF) cameras provide rich spatio-angular visual representations
by sensing the visual scene from multiple perspectives and have recently
emerged as a promising technology to boost the performance of human-machine
systems such as biometrics and affective computing. Despite the significant
success of LF representation for constrained facial image analysis, this
technology has never been used for face and expression recognition in the wild.
In this context, this paper proposes a new deep face and expression recognition
solution, called CapsField, based on a convolutional neural network and an
additional capsule network that utilizes dynamic routing to learn hierarchical
relations between capsules. CapsField extracts the spatial features from facial
images and learns the angular part-whole relations for a selected set of 2D
sub-aperture images rendered from each LF image. To analyze the performance of
the proposed solution in the wild, the first in the wild LF face dataset, along
with a new complementary constrained face dataset captured from the same
subjects recorded earlier have been captured and are made available. A subset
of the in the wild dataset contains facial images with different expressions,
annotated for usage in the context of face expression recognition tests. An
extensive performance assessment study using the new datasets has been
conducted for the proposed and relevant prior solutions, showing that the
CapsField proposed solution achieves superior performance for both face and
expression recognition tasks when compared to the state-of-the-art.


Abstract:
  The development of large-scale quantum networks promises to bring a multitude
of technological applications as well as shed light on foundational topics,
such as quantum nonlocality. It is particularly interesting to consider
scenarios where sources within the network are statistically independent, which
leads to so-called network nonlocality, even when parties perform fixed
measurements. Here we promote certain parties to be trusted and introduce the
notion of network steering and network local hidden state (NLHS) models within
this paradigm of independent sources. In one direction, we show how results
from Bell nonlocality and quantum steering can be used to demonstrate network
steering. We further show that it is a genuinely novel effect, by exhibiting
unsteerable states that nevertheless demonstrate network steering, based upon
entanglement swapping, yielding a form of activation. On the other hand, we
provide no-go results for network steering in a large class of scenarios, by
explicitly constructing NLHS models.


Abstract:
  When observing task demonstrations, human apprentices are able to identify
whether a given task is executed correctly long before they gain expertise in
actually performing that task. Prior research into learning from demonstrations
(LfD) has failed to capture this notion of the acceptability of a task's
execution; meanwhile, temporal logics provide a flexible language for
expressing task specifications. Inspired by this, we present Bayesian
specification inference, a probabilistic model for inferring task specification
as a temporal logic formula. We incorporate methods from probabilistic
programming to define our priors, along with a domain-independent likelihood
function to enable sampling-based inference. We demonstrate the efficacy of our
model for inferring specifications, with over 90% similarity observed between
the inferred specification and the ground truth, both within a synthetic domain
and during a real-world table setting task.


Abstract:
  Telescopers for a function are linear differential (resp. difference)
operators annihilated by the definite integral (resp. definite sum) of this
function. They play a key role in Wilf-Zeilberger theory and algorithms for
computing them have been extensively studied in the past thirty years. In this
paper, we introduce the notion of telescopers for differential forms with
$D$-finite function coefficients. These telescopers appear in several areas of
mathematics, for instance parametrized differential Galois theory and mirror
symmetry. We give a sufficient and necessary condition for the existence of
telescopers for a differential form and describe a method to compute them if
they exist. Algorithms for verifying this condition are also given.


Abstract:
  The Cabibbo-suppressed decay $\Lambda_b^0\rightarrow\chi_{c1}p\pi^-$ is
observed for the first time using data from proton-proton collisions
corresponding to an integrated luminosity of 6fb$^{-1}$, collected with the
LHCb detector at a centre-of-mass energy of 13TeV. Evidence for the
$\Lambda_b^0\rightarrow\chi_{c2}p\pi^-$ decay is also found. Using the
$\Lambda_b^0\rightarrow\chi_{c1}pK^-$ decay as normalisation channel, the
ratios of branching fractions are measured to be $$\begin{array}{rcl} \frac{
\mathcal{B} (\Lambda_b^0\rightarrow\chi_{c1}p\pi^-)}{\mathcal{B}
(\Lambda_b^0\rightarrow\chi_{c1}pK^-)} & = & (6.59 \pm 1.01 \pm 0.22 ) \times
10^{-2} \,,
  \frac{\mathcal{B} (\Lambda_b^0\rightarrow\chi_{c2}p\pi^-)}{\mathcal{B}
(\Lambda_b^0\rightarrow\chi_{c1}p\pi^-)} & = & 0.95 \pm 0.30 \pm 0.04 \pm 0.04
\,,
  \frac{\mathcal{B} (\Lambda_b^0\rightarrow\chi_{c2}pK^-)}{\mathcal{B}
(\Lambda_b^0\rightarrow\chi_{c1}pK^-)} & = & 1.06 \pm 0.05 \pm 0.04 \pm 0.04
\,,\end{array}$$ where the first uncertainty is statistical, the second is
systematic and the third is due to the uncertainties in the branching fractions
of $\chi_{c1,2}\rightarrow J/\psi\gamma$ decays.


Abstract:
  A novel mechanism of boosting dark matter by cosmic neutrinos is proposed.
The new mechanism is so significant that the arriving flux of dark matter in
the mass window $1~{\rm keV} \lesssim m_{\rm DM} \lesssim 1~{\rm MeV}$ on Earth
can be enhanced by two to four orders of magnitude compared to one only by
cosmic electrons. Thereby we firstly derive conservative but still stringent
bounds and future sensitivity limits for such cosmic-neutrino-boosted dark
matter ($\nu$BDM) from advanced underground experiments such as Borexino,
PandaX, XENON1T, and JUNO.


Abstract:
  We study the continuity of half-plane capacity as a function of boundary
hulls with respect to the Carath\'eodory convergence. In particular, our
interest lies in the case that hulls are unbounded. Under the assumption that
every hull is contained in a fixed hull with finite imaginary part and finite
half-plane capacity, we show that the half-plane capacity is indeed continuous.
We also discuss the extension of this result to the case that the underlying
domain is finitely connected.


Abstract:
  Persuasion games are fundamental in economics and AI research and serve as
the basis for important applications. However, work on this setup assumes
communication with stylized messages that do not consist of rich human
language. In this paper we consider a repeated sender (expert) -- receiver
(decision maker) game, where the sender is fully informed about the state of
the world and aims to persuade the receiver to accept a deal by sending one of
several possible natural language reviews. We design an automatic expert that
plays this repeated game, aiming to achieve the maximal payoff. Our expert is
implemented within the Monte Carlo Tree Search (MCTS) algorithm, with deep
learning models that exploit behavioral and linguistic signals in order to
predict the next action of the decision maker, and the future payoff of the
expert given the state of the game and a candidate review. We demonstrate the
superiority of our expert over strong baselines, its adaptability to different
decision makers, and that its selected reviews are nicely adapted to the
proposed deal.


Abstract:
  Machine scheduling problems involving conflict jobs can be seen as a
constrained version of the classical scheduling problem, in which some jobs are
conflict in the sense that they cannot be proceeded simultaneously on different
machines. This conflict constraint naturally arises in several practical
applications and has recently received considerable attentions in the research
community. In fact, the problem is typically NP-hard (even for approximation)
and most of algorithmic results achieved so far have heavily relied on special
structures of the underlying graph used to model the conflict-job relation. Our
focus is on three objective functions: minimizing the makespan, minimizing the
weighted summation of the jobs' completion time, and maximizing the total
weights of completed jobs; the first two of which have been intensively studied
in the literature. For each objective function considered, we present several
mixed integer linear programming models and a constraint programming model,
from which we can solve the problems to optimality using dedicated solvers.
Binary search-based algorithms are also proposed to solve the makespan problem.
The results of numerical experiments performed on randomly generated data sets
with up to 32 jobs and 6 machines are reported and analysed to verify the
performance of the proposed methods.


Abstract:
  We consider Taylor dispersion for tracer particles in micro-fluidic planar
channels with strong confinement. In this context, the channel walls modify the
local diffusivity tensor and also interactions between the tracer particles and
the walls become important. We provide a simple and general formula for the
effective diffusion constant along the channel as well as the first non-trivial
finite time correction for arbitrary flows along the channel, arbitrary
interaction potentials with the walls and arbitrary expressions for the
diffusion tensor. The formula are in particular amenable to a straightforward
numerical implementation, rendering them extremely useful for comparison with
experiments. We present a number of applications, notably for systems which
have parabolically varying diffusivity profiles, to systems with attractive
interactions with the walls as well as electroosmotic flows between plates with
differing surface charges within the Debye-H\"uckel approximation.


Abstract:
  The oscillation model has been proposed as a theoretical framework for
describing user dynamics in online social networks. This model can model the
user dynamics generated by a particular network structure and allow its causal
relationships to be explicitly described. In this paper, by applying
perturbation theory to the fundamental equation of the oscillation model, we
confirm that we can explicitly trace, at least in principle, the changes in
user dynamics associated with changes in the network structure. Specifically,
we formulate perturbative expansions up to infinite order, by drawing on
inferences from regularities found in perturbative expansions; the accuracy of
perturbative expansions of finite order is evaluated by numerical experiments.


Abstract:
  The reflected entropy $S_R(A:B)$ of a density matrix $\rho_{AB}$ is a
bipartite correlation measure lower-bounded by the quantum mutual information
$I(A:B)$. In holographic states satisfying the quantum extremal surface
formula, where the reflected entropy is related to the area of the entanglement
wedge cross-section, there is often an order-$N^2$ gap between $S_R$ and $I$.
We provide an information-theoretic interpretation of this gap by observing
that $S_R - I$ is related to the fidelity of a particular Markov recovery
problem that is impossible in any state whose entanglement wedge cross-section
has a nonempty boundary; for this reason, we call the quantity $S_R - I$ the
Markov gap. We then prove that for time-symmetric states in pure AdS$_3$
gravity, the Markov gap is universally lower bounded by $\log(2)
\ell_{\text{AdS}}/2 G_N$ times the number of endpoints of the cross-section. We
provide evidence that this lower bound continues to hold in the presence of
bulk matter, and comment on how it might generalize above three bulk
dimensions. Finally, we explore the Markov recovery problem controlling $S_R -
I$ using fixed area states. This analysis involves deriving a formula for the
quantum fidelity -- in fact, for all the sandwiched R\'enyi relative entropies
-- between fixed area states with one versus two fixed areas, which may be of
independent interest. We discuss, throughout the paper, connections to the
general theory of multipartite entanglement in holography.


Abstract:
  Classification of features in a scene typically requires conversion of the
incoming photonic field into the electronic domain. Recently, an alternative
approach has emerged whereby passive structured materials can perform
classification tasks by directly using free-space propagation and diffraction
of light. In this manuscript, we present a theoretical and computational study
of such systems and establish the basic features that govern their performance.
We show that system architecture, material structure, and input light field are
intertwined and need to be co-designed to maximize classification accuracy. Our
simulations show that a single layer metasurface can achieve classification
accuracy better than conventional linear classifiers, with an order of
magnitude fewer diffractive features than previously reported. For a wavelength
{\lambda}, single layer metasurfaces of size with aperture density achieve ~96%
testing accuracy on the MNIST dataset, for an optimized distance ~ to the
output plane. This is enabled by an intrinsic nonlinearity in photodetection,
despite the use of linear optical metamaterials. Furthermore, we find that once
the system is optimized, the number of diffractive features is the main
determinant of classification performance. The slow asymptotic scaling with the
number of apertures suggests a reason why such systems may benefit from
multiple layer designs. Finally, we show a trade-off between the number of
apertures and fabrication noise.


Abstract:
  Einstein based his special theory of relativity on two postulates: (a)
physical laws appear the same in all inertial frames, and (b) the speed of
light in vacuum is an observer-independent constant. However, it is already
known that the principle of the constancy of the speed of light is redundant in
the construction of the special theory of relativity. Adhering to this idea, we
show here that the form of the Lagrangian for a free relativistic particle can
be derived if we consider a class of theories that remain invariant under a
duality transformation between space and time intervals. Therefore, the special
theory of relativity is interpreted as a theory that exhibits a duality
symmetry with space intervals and time intervals being dual to each other. The
Lorentz transformations then are simply deduced as the linear transformation
between inertial observers that leaves the (infinitesimal) action of the
relativistic particles unchanged.


Abstract:
  The remarkable recent progress in the precision of Lattice QCD computations
for a number of physical quantities relevant for flavour physics has motivated
the introduction of isospin-breaking effects, including in particular
electromagnetic corrections, to the computations. The isospin breaking
corrections are necessary to fully exploit this improved precision for the
determination of the fundamental parameters of the Standard Model, including
the CKM matrix elements, and to look for deviations from experimental
measurements which might signal the presence of new physics. Together with
colleagues from Rome, we have developed and implemented a framework for
including isospin-breaking corrections in leptonic decays
$P\to\ell\bar\nu_\ell(\gamma)$, where $P$ is a pseudoscalar meson and $\ell$ a
charged lepton, and the theoretical framework and numerical results are
reviewed below. The status of our studies to extend this framework to
semileptonic decays $P_1\to P_2\ell\bar\nu_\ell(\gamma)$, where $P_{1,2}$ are
pseudoscalar mesons, is also presented.


Abstract:
  Human motion prediction from historical pose sequence is at the core of many
applications in machine intelligence. However, in current state-of-the-art
methods, the predicted future motion is confined within the same activity. One
can neither generate predictions that differ from the current activity, nor
manipulate the body parts to explore various future possibilities. Undoubtedly,
this greatly limits the usefulness and applicability of motion prediction. In
this paper, we propose a generalization of the human motion prediction task in
which control parameters can be readily incorporated to adjust the forecasted
motion. Our method is compelling in that it enables manipulable motion
prediction across activity types and allows customization of the human movement
in a variety of fine-grained ways. To this aim, a simple yet effective
composite GAN structure, consisting of local GANs for different body parts and
aggregated via a global GAN is presented. The local GANs game in lower
dimensions, while the global GAN adjusts in high dimensional space to avoid
mode collapse. Extensive experiments show that our method outperforms
state-of-the-art. The codes are available at
https://github.com/herolvkd/AM-GAN.


Abstract:
  Recently, we systematically studied the basic theory of Bregman circumcenters
in another paper. In this work, we aim to investigate the application of
Bregman circumcenters.
  Here, we propose the forward Bregman monotonicity which is a generalization
of the powerful Fej\'er monotonicity, and show a weak convergence result of the
forward Bregman monotone sequence. We also naturally introduce the Bregman
circumcenter mappings associated with a finite set of operators. Then we
provide sufficient conditions for the sequence of iterations of the forward
Bregman circumcenter mapping to be forward Bregman monotone. Furthermore, we
prove that the sequence of iterations of the forward Bregman circumcenter
mapping weakly converges to a point in the intersection of the fixed point sets
of relevant operators, which reduces to the known weak convergence result of
the circumcentered method under the Euclidean distance. In addition, particular
examples are provided to illustrate the Bregman isometry and Browder's
demiclosedness principle, and our convergence result.


Abstract:
  In this paper, we consider the problem of determining the presence of a given
signal in a high-dimensional observation with unknown covariance matrix by
using an adaptive matched filter. Traditionally such filters are formed from
the sample covariance matrix of some given training data, but, as is
well-known, the performance of such filters is poor when the number of training
data $n$ is not much larger than the data dimension $p$. We thus seek a
covariance estimator to replace sample covariance. To account for the fact that
$n$ and $p$ may be of comparable size, we adopt the "large-dimensional
asymptotic model" in which $n$ and $p$ go to infinity in a fixed ratio. Under
this assumption, we identify a covariance estimator that is asymptotically
detection-theoretic optimal within a general shrinkage class inspired by C.
Stein, and we give consistent estimates for conditional false-alarm and
detection rate of the corresponding adaptive matched filter.


Abstract:
  In this paper, a novel and innovative methodology for feasible motion
planning in the multi-agent system is developed. On the basis of velocity
obstacles characteristics, the chance constraints are formulated in the
receding horizon control (RHC) problem, and geometric information of collision
cones is used to generate the feasible regions of velocities for the host
agent. By this approach, the motion planning is conducted at the velocity level
instead of the position level. Thus, it guarantees a safer collision-free
trajectory for the multi-agent system, especially for the systems with
high-speed moving agents. Moreover, a probability threshold of potential
collisions can be satisfied during the motion planning process. In order to
validate the effectiveness of the methodology, different scenarios for multiple
agents are investigated, and the simulation results clearly show that the
proposed approach can effectively avoid potential collisions with a collision
probability less than a specific threshold.


Abstract:
  We obtain the quadratic-in-spin terms of the conservative Hamiltonian
describing the interactions of a binary of spinning bodies in General
Relativity through $\mathcal{O}(G^2)$ and to all orders in velocity. Our
calculation extends a recently-introduced framework based on scattering
amplitudes and effective field theory to consider non-minimal coupling of the
spinning objects to gravity. At the order that we consider, we establish the
validity of the formula proposed in \cite{Bern:2020buy} that relates the
impulse and spin kick in a scattering event to the eikonal phase.


Abstract:
  The task of compression of data -- as stated by the source coding theorem --
is one of the cornerstones of information theory. Data compression usually
exploits statistical redundancies in the data according to its prior
distribution. Motivated by situations where one does not have access to the
statistics of data, but has some information about a transformation that is
going to be applied to it, we propose a novel method for data compression
called reverse compression. It is defined in such a way that works for both
classical and quantum information processes, and furthermore relies exclusively
on the channel to be used: all input data leading to indistinguishable outputs
is compressed to the same state, regardless of their prior distribution.
Moreover, this process can be characterized as a higher order operation within
the type of preprocessing. We also consider as an example the application of
the method to the classical and quantum erasure channel. The examples suggest
that noiseless reverse compression can take place only in trivial cases,
although meaningful instances of noisy reverse compression can exist.


Abstract:
  In this paper we consider a small-$x$ model for gluon GTMDs that we fit to
data on diffractive dijet production in electron-proton collisions obtained by
HERA's H1 Collaboration. Assuming a small number of free parameters, each with
a physical motivation, we are able to describe those data fairly well and with
this model we obtain predictions for the EIC for both electroproduction and
photoproduction which may allow to further test the underlying GTMD
description. In the general discussion of the impact parameter dependence we
recall some subtle issues related to localization of states, choice of frames,
and discuss what these aspects imply for the range of applicability of the
model.


Abstract:
  We analyse recently measured nonlinear photoemission spectra from Ag surfaces
that reveal resonances whose energies do not scale with the applied photon
energy but stay pinned to multiples of bulk plasmon energy $\hbar\omega_p$
above the Fermi level. To elucidate these unexpected and peculiar features we
investigate the spectra of plasmons generated in a solid by the optically
pumped electronic polarization and their effect on photoemission. By combining
quadratic response formalism for calculations of photoemission yield, a
nonperturbative approach to inelastic electron scattering, and first-principles
calculations for the electronic structure, we demonstrate the dependence of
probability amplitude for single- and multiplasmon excitations on the basic
parameters characterizing the photon pulse and the system. The resulting
multiexcitation spectrum evolves towards a truncated plasmonic coherent state.
Analogous concept is extrapolated to interpret plasmon generation by
multiphoton excited electronic polarization. Based on this we elaborate a
scenario that the thus created real plasmons act as supplementary
frequency-locked pump field for non-Einsteinian plasmonically assisted channels
of photoemission from metals. The established paradigm enables assignment and
assessment of the observed linear $\hbar\omega_p$- and nonlinear
$2\hbar\omega_p$-electron yields from Ag. Such effects may be exploited for
selective filtering of optical energy conversion in electronic systems.


Abstract:
  Malicious activities in cyberspace have gone further than simply hacking
machines and spreading viruses. It has become a challenge for a nations
survival and hence has evolved to cyber warfare. Malware is a key component of
cyber-crime, and its analysis is the first line of defence against attack. This
work proposes a novel deep boosted hybrid learning-based malware classification
framework and named as Deep boosted Feature Space-based Malware classification
(DFS-MC). In the proposed framework, the discrimination power is enhanced by
fusing the feature spaces of the best performing customized CNN architectures
models and its discrimination by an SVM for classification. The discrimination
capacity of the proposed classification framework is assessed by comparing it
against the standard customized CNNs. The customized CNN models are implemented
in two ways: softmax classifier and deep hybrid learning-based malware
classification. In the hybrid learning, Deep features are extracted from
customized CNN architectures and fed into the conventional machine learning
classifier to improve the classification performance. We also introduced the
concept of transfer learning in a customized CNN architecture based malware
classification framework through fine-tuning. The performance of the proposed
malware classification approaches are validated on the MalImg malware dataset
using the hold-out cross-validation technique. Experimental comparisons were
conducted by employing innovative, customized CNN, trained from scratch and
fine-tuning the customized CNN using transfer learning. The proposed
classification framework DFS-MC showed improved results, Accuracy: 98.61%,
F-score: 0.96, Precision: 0.96, and Recall: 0.96.


Abstract:
  Kidney transplantation can significantly enhance living standards for people
suffering from end-stage renal disease. A significant factor that affects graft
survival time (the time until the transplant fails and the patient requires
another transplant) for kidney transplantation is the compatibility of the
Human Leukocyte Antigens (HLAs) between the donor and recipient. In this paper,
we propose new biologically-relevant feature representations for incorporating
HLA information into machine learning-based survival analysis algorithms. We
evaluate our proposed HLA feature representations on a database of over 100,000
transplants and find that they improve prediction accuracy by about 1%, modest
at the patient level but potentially significant at a societal level. Accurate
prediction of survival times can improve transplant survival outcomes, enabling
better allocation of donors to recipients and reducing the number of
re-transplants due to graft failure with poorly matched donors.


Abstract:
  We report the discovery of 34 new open clusters and candidates as a result of
a systematic search carried out in 200 adjacent fields of 1x1 square degrees
area projected towards the Galactic bulge, using Gaia DR2 data. The objects
were identified and characterized by a joint analysis of their photometric,
kinematic and spatial distribution, which has been consistently used and proved
to be effective in our previous works. The discoveries were validated by
cross-referencing the objects position and astrometric parameters with the
available literature. Besides their coordinates and astrometric parameters, we
also provide sizes, ages, distances and reddening for the discovered objects.
In particular, 32 clusters are closer than 2 kpc from the Sun, which represents
an increment of nearly 39% of objects with astrophysical parameters determined
in the nearby inner disk. Although these objects fill an important gap in the
open clusters distribution along the Sagittarius arm, this arm, traced by known
clusters, appears to be interrupted, which may be an artifact due to the
incompleteness of the cluster census.


Abstract:
  This paper presents algorithms for the included-sums and excluded-sums
problems used by scientific computing applications such as the fast multipole
method. These problems are defined in terms of a $d$-dimensional array of $N$
elements and a binary associative operator~$\oplus$ on the elements. The
included-sum problem requires that the elements within overlapping boxes
cornered at each element within the array be reduced using $\oplus$. The
excluded-sum problem reduces the elements outside each box. The weak versions
of these problems assume that the operator $\oplus$ has an inverse $\ominus$,
whereas the strong versions do not require this assumption. In addition to
studying existing algorithms to solve these problems, we introduce three new
algorithms.
  The bidirectional box-sum (BDBS) algorithm solves the strong included-sums
problem in $\Theta(d N)$ time, asymptotically beating the classical summed-area
table (SAT) algorithm, which runs in $\Theta(2^d N)$ and which only solves the
weak version of the problem. Empirically, the BDBS algorithm outperforms the
SAT algorithm in higher dimensions by up to $17.1\times$.
  The \defn{box-complement} algorithm can solve the strong excluded-sums
problem in $\Theta(d N)$ time, asymptotically beating the state-of-the-art
corners algorithm by Demaine et al., which runs in $\Omega(2^d N)$ time. In 3
dimensions the box-complement algorithm empirically outperforms the corners
algorithm by about $1.4\times$ given similar amounts of space.
  The weak excluded-sums problem can be solved in $\Theta(d N)$ time by the
bidirectional box-sum complement (BDBSC) algorithm, which is a trivial
extension of the BDBS algorithm. Given an operator inverse $\ominus$, BDBSC can
beat box-complement by up to a factor of $4$.


Abstract:
  Brain networks are adaptively rewired continually, adjusting their topology
to bring about functionality and efficiency in sensory, motor and cognitive
tasks. In model neural network architectures, adaptive rewiring generates
complex, brain-like topologies. Present models, however, cannot account for the
emergence of complex directed connectivity structures. We tested a biologically
plausible model of adaptive rewiring in directed networks, based on two
algorithms widely used in distributed computing: advection and consensus. When
both are used in combination as rewiring criteria, adaptive rewiring shortens
path length and enhances connectivity. When keeping a balance between advection
and consensus, adaptive rewiring produces convergent-divergent units consisting
of convergent hub nodes, which collect inputs from pools of sparsely connected,
or local, nodes and project them via densely interconnected processing nodes
onto divergent hubs that broadcast output back to the local pools.
Convergent-divergent units operate within and between sensory, motor, and
cognitive brain regions as their connective core, mediating context-sensitivity
to local network units. By showing how these structures emerge spontaneously in
directed networks models, adaptive rewiring offers self-organization as a
principle for efficient information propagation and integration in the brain.


Abstract:
  Commonsense reasoning research has so far been limited to English. We aim to
evaluate and improve popular multilingual language models (ML-LMs) to help
advance commonsense reasoning (CSR) beyond English. We collect the Mickey
Corpus, consisting of 561k sentences in 11 different languages, which can be
used for analyzing and improving ML-LMs. We propose Mickey Probe, a
language-agnostic probing task for fairly evaluating the common sense of
popular ML-LMs across different languages. In addition, we also create two new
datasets, X-CSQA and X-CODAH, by translating their English versions to 15 other
languages, so that we can evaluate popular ML-LMs for cross-lingual commonsense
reasoning. To improve the performance beyond English, we propose a simple yet
effective method -- multilingual contrastive pre-training (MCP). It
significantly enhances sentence representations, yielding a large performance
gain on both benchmarks.


Abstract:
  For a conformally compact Poincar\'{e}-Einstein manifold $(X,g_+)$, we
consider two types of compactifications for it. One is $\bar{g}=\rho^2g_+$,
where $\rho$ is a fixed smooth defining function; the other is the adapted
(including Fefferman-Graham) compactification $\bar{g}_s=\rho^2_sg_+$ with a
continuous parameter $s>\frac{n}{2}$. In this paper, we mainly prove that for a
set of conformally compact Poincar\'{e}-Einstein manifolds $\{(X,
g_{+}^{(i)})\}$ with conformal infinity of positive Yamabe type,
$\{\bar{g}^{(i)}\}$ is compact in $C^{k,\alpha}(\overline{X})$ topology if and
only if $\{\bar{g}_s^{(i)}\}$ is compact in some $C^{l,\beta}(\overline{X})$
topology, provided that
$\bar{g}^{(i)}|_{TM}=\bar{g}_s^{(i)}|_{TM}=\hat{g}^{(i)}$ and $\hat{g}^{(i)}$
has positive scalar curvature for each $i$. See Theorem 1.1 and Corollary 1.1
for the exact relation of $(k,\alpha)$ and $(l,\beta)$.


Abstract:
  The present work constitutes a first step towards establishing a systematic
framework for treating variational problems that depend on a given input
function through a mixture of its derivatives of different orders in different
directions. For a fixed vector $\mathbf{a} := (a_1, \ldots, a_N) \in
\mathbb{N}^N$ and $u \colon \mathbb{R}^N \supset \Omega \to \mathbb{R}^n$ we
denote by $\nabla_{\mathbf{a}} u := (\partial^{\alpha} u)_{\langle \alpha,
\mathbf{a}^{-1} \rangle = 1}$ the matrix whose $i$-th row is composed of
derivatives $\partial^\alpha u^i$ of the $i$-th component of the map $u$, and
where the multi-indices $\alpha$ satisfy $\langle \alpha, \mathbf{a}^{-1}
\rangle = \sum_{j=1}^N \frac{\alpha_j}{a_j} = 1$. We study functionals of the
form $$ \mathrm{W}^{\mathbf{a},p}(\Omega;\mathbb{R}^n) \ni u \mapsto
\int_\Omega F(\nabla_{\mathbf{a}} u(x)) \, \mathrm{d} x,$$ where
$\mathrm{W}^{\mathbf{a},p}(\Omega; \mathbb{R}^n)$ is an appropriate Sobolev
space of mixed smoothness and $F$ is the integrand. We study existence of
minimisers of such functionals under prescribed Dirichlet boundary conditions.
We characterise coercivity, lower semicontiuity, and envelopes of relaxation of
such functionals, in terms of an appropriate generalisation of Morrey's
quasiconvexity.


Abstract:
  In this work, we propose a novel method for performing inertial aided
navigation, by using deep neural networks (DNNs). To date, most DNN inertial
navigation methods focus on the task of inertial odometry, by taking gyroscope
and accelerometer readings as input and regressing for integrated IMU poses
(i.e., position and orientation). While this design has been successfully
applied on a number of applications, it is not of theoretical performance
guarantee unless patterned motion is involved. This inevitably leads to
significantly reduced accuracy and robustness in certain use cases. To solve
this problem, we design a framework to compute observable IMU integration terms
using DNNs, followed by the numerical pose integration and sensor fusion to
achieve the performance gain. Specifically, we perform detailed analysis on the
motion terms in IMU kinematic equations, propose a dedicated network design,
loss functions, and training strategies for the IMU data processing, and
conduct extensive experiments. The results show that our method is generally
applicable and outperforms both traditional and DNN methods by wide margins.


Abstract:
  We use the wealth of deep archival optical spectroscopy on the GOODS-South
field from Keck, the VLT, and other facilities to select candidate
high-redshift Lyman continuum (LyC) leakers in the Hubble Deep UV Legacy Survey
(HDUV) dataset. We select sources at $2.35 < z < 3.05$, where the HST/WFC3
F275W filter probes only the redshifted LyC. We find five moderately
F275W-bright sources (four detected at $\gtrsim3\sigma$ significance) in this
redshift range. However, two of these show evidence in their optical spectra
for contamination by foreground galaxies along the line-of-sight. We then
perform an F275W error-weighted sum of the fluxes of all 129 galaxies at $2.35
< z < 3.05$ in both the GOODS-N and GOODS-S HDUV areas to estimate the total
ionizing flux. The result is dominated by just five candidate F275W-bright LyC
sources. Lastly, we examine the contributions to the metagalactic ionizing
background, finding that, at the sensitivity of the HDUV F275W data and
allowing for the effects of LyC transmission in the intergalactic medium (IGM),
star-forming galaxies can match the UV flux required to maintain an ionized IGM
at $z \sim 2.5$.


Abstract:
  We explore the chaotic dynamics and complexity of a neuro-system with respect
to variable synaptic weights in both noise free and noisy conditions. The
chaotic dynamics of the system is investigated by bifurcation analysis and 0-1
test. A multiscale complexity of the system is proposed based on the notion of
recurrence plot density entropy. Numerical results support the proposed
analysis. Impact of music on the aforesaid neuro-system has also been studied.
The analysis shows that inclusion of white noise even with a minimal strength
makes the neuro dynamics more complex, where as music signal keeps the dynamics
almost similar to that of the original system. This is properly interpreted by
the proposed multiscale complexity measure.


Abstract:
  Physics-based simulation for fluid flow in porous media is a computational
technology to predict the temporal-spatial evolution of state variables (e.g.
pressure) in porous media, and usually requires high computational expense due
to its nonlinearity and the scale of the study domain. This letter describes a
deep learning (DL) workflow to predict the pressure evolution as fluid flows in
large-scale 3D heterogeneous porous media. In particular, we apply feature
coarsening technique to extract the most representative information and perform
the training and prediction of DL at the coarse scale, and further recover the
resolution at the fine scale by 2D piecewise cubic interpolation. We validate
the DL approach that is trained from physics-based simulation data to predict
pressure field in a field-scale 3D geologic CO_2 storage reservoir. We evaluate
the impact of feature coarsening on DL performance, and observe that the
feature coarsening can not only decrease training time by >74% and reduce
memory consumption by >75%, but also maintains temporal error <1.5%. Besides,
the DL workflow provides predictive efficiency with ~1400 times speedup
compared to physics-based simulation.


Abstract:
  In this work, we present a learning-based pipeline to realise local
navigation with a quadrupedal robot in cluttered environments with static and
dynamic obstacles. Given high-level navigation commands, the robot is able to
safely locomote to a target location based on frames from a depth camera
without any explicit mapping of the environment. First, the sequence of images
and the current trajectory of the camera are fused to form a model of the world
using state representation learning. The output of this lightweight module is
then directly fed into a target-reaching and obstacle-avoiding policy trained
with reinforcement learning. We show that decoupling the pipeline into these
components results in a sample efficient policy learning stage that can be
fully trained in simulation in just a dozen minutes. The key part is the state
representation, which is trained to not only estimate the hidden state of the
world in an unsupervised fashion, but also helps bridging the reality gap,
enabling successful sim-to-real transfer. In our experiments with the
quadrupedal robot ANYmal in simulation and in reality, we show that our system
can handle noisy depth images, avoid dynamic obstacles unseen during training,
and is endowed with local spatial awareness.


Abstract:
  We introduce generalized hypergeometric Bernoulli numbers for Dirichlet
characters. We study their properties, including relations, expressions and
determinants. At the end in Appendix we derive first few expressions of these
numbers.


Abstract:
  The dependence of the fractal dimension of global attractors for the damped
3D Euler--Bardina equations on the regularization parameter $\alpha>0$ and
Ekman damping coefficient $\gamma>0$ is studied. We present explicit upper
bounds for this dimension for the case of the whole space, periodic boundary
conditions, and the case of bounded domain with Dirichlet boundary conditions.
The sharpness of these estimates when $\alpha\to0$ and $\gamma\to0$ (which
corresponds in the limit to the classical Euler equations) is demonstrated on
the 3D Kolmogorov flows on a torus.


Abstract:
  Boundary time crystals (BTC's) are non-equilibrium phases of matter occurring
in quantum systems in contact to an environment, for which a macroscopic
fraction of the many body system breaks time translation symmetry. We study
BTC's in collective $d$-level systems, focusing in the cases with $d=2$, $3$
and $4$. We find that BTC's appear in different forms for the different cases.
We first consider the model with collective $d=2$-level systems [presented in
Phys. Rev. Lett. $121$, $035301$ ($2018$)], whose dynamics is described by a
Lindblad master equation, and perform a throughout analysis of its phase
diagram and Jacobian stability for different interacting terms in the coherent
Hamiltonian. In particular, using perturbation theory for general (non
Hermitian) matrices we obtain analytically how a specific $\mathbb{Z}_2$
symmetry breaking Hamiltonian term destroys the BTC phase in the model. Based
on these results we define a $d=4$ model composed of a pair of collective
$2$-level systems interacting with each other. We show that this model support
richer dynamical phases, ranging from limit-cycles, period-doubling
bifurcations and a route to chaotic dynamics. The BTC phase is more robust in
this case, not annihilated by the former symmetry breaking Hamiltonian terms.
The model with collective $d=3$-level systems is defined similarly, as
competing pairs of levels, but sharing a common collective level. The dynamics
can deviate significantly from the previous cases, supporting phases with the
coexistence of multiple limit-cycles, closed orbits and a full degeneracy of
zero Lyapunov exponents.


Abstract:
  Potential applications of Zr/Al ODS alloys vests on the irradiation stability
of the Y4Zr3O12 dispersoids. Fundamental studies to identify the type of
defects are important in order to recognize pathways for damage alleviation. In
this context, studies relating to identification of point defects and their
clusters by in-situ ionoluminescence spectroscopy were taken up. The
ionoluminescence spectrum acquired during 100 keV He+ ion irradiation shows two
prominent bands, at 330 nm and 415 nm. Using density functional theory
calculations with HSE06 hybrid exchange correlation functional, the luminescent
bands have been identified to be arising due to native and irradiation induced
oxygen vacancy defects in charged and neutral configurations.


Abstract:
  Automatically generating medical reports for retinal images is one of the
promising ways to help ophthalmologists reduce their workload and improve work
efficiency. In this work, we propose a new context-driven encoding network to
automatically generate medical reports for retinal images. The proposed model
is mainly composed of a multi-modal input encoder and a fused-feature decoder.
Our experimental results show that our proposed method is capable of
effectively leveraging the interactive information between the input image and
context, i.e., keywords in our case. The proposed method creates more accurate
and meaningful reports for retinal images than baseline models and achieves
state-of-the-art performance. This performance is shown in several commonly
used metrics for the medical report generation task: BLEU-avg (+16%), CIDEr
(+10.2%), and ROUGE (+8.6%).


Abstract:
  We generalise the form factor bootstrap approach to integrable field theories
with U(1) symmetry to derive matrix elements of composite branch-point twist
fields associated with symmetry resolved entanglement entropies. The bootstrap
equations are solved for the free massive Dirac and complex boson theories,
which are the simplest theories with U(1) symmetry. We present the exact and
complete solution for the bootstrap, including vacuum expectation values and
form factors involving any type and arbitrarily number of particles. The
non-trivial solutions are carefully cross-checked by performing various limits
and by the application of the Delta-theorem. An alternative and compact
determination of the novel form factors is also presented. Based on the form
factors of the U(1) composite branch-point twist fields, we re-derive earlier
results showing entanglement equipartition for an interval in the ground state
of the two models.


Abstract:
  The detection and estimation of sinusoids is a fundamental signal processing
task for many applications related to sensing and communications. While
algorithms have been proposed for this setting, quantization is a critical, but
often ignored modeling effect. In wireless communications, estimation with low
resolution data converters is relevant for reduced power consumption in
wideband receivers. Similarly, low resolution sampling in imaging and spectrum
sensing allows for efficient data collection. In this work, we propose
SignalNet, a neural network architecture that detects the number of sinusoids
and estimates their parameters from quantized in-phase and quadrature samples.
We incorporate signal reconstruction internally as domain knowledge within the
network to enhance learning and surpass traditional algorithms in mean squared
error and Chamfer error. We introduce a worst-case learning threshold for
comparing the results of our network relative to the underlying data
distributions. This threshold provides insight into why neural networks tend to
outperform traditional methods and into the learned relationships between the
input and output distributions. In simulation, we find that our algorithm is
always able to surpass the threshold for three-bit data but often cannot exceed
the threshold for one-bit data. We use the learning threshold to explain, in
the one-bit case, how our estimators learn to minimize the distributional loss,
rather than learn features from the data.


Abstract:
  With the successful adoption of machine learning on electronic health records
(EHRs), numerous computational models have been deployed to address a variety
of clinical problems. However, due to the heterogeneity of EHRs, models trained
on different patient groups suffer from poor generalizability. How to mitigate
domain shifts between the source patient group where the model is built upon
and the target one where the model will be deployed becomes a critical issue.
In this paper, we propose a data augmentation method to facilitate domain
adaptation, which leverages knowledge from the source patient group when
training model on the target one. Specifically, adversarially generated samples
are used during domain adaptation to fill the generalization gap between the
two patient groups. The proposed method is evaluated by a case study on
different predictive modeling tasks on MIMIC-III EHR dataset. Results confirm
the effectiveness of our method and the generality on different tasks.


Abstract:
  We develop efficient, accurate, transferable, and interpretable machine
learning force fields for Au nanoparticles, based on data gathered from Density
Functional Theory calculations. We then use them to investigate the
thermodynamic stability of Au nanoparticles of different sizes (1 to 6 nm),
containing up to 6266 atoms, with respect to a solid-liquid phase change
through molecular dynamics simulations. We predict nanoparticle melting
temperatures in good agreement with respect to available experimental data.
Furthermore, we characterize in detail the solid to liquid phase change
mechanism employing an unsupervised learning scheme to categorize local atomic
environments. We thus provide a rigorous and data-driven definition of liquid
atomic arrangements in the inner and surface regions of a nanoparticle, and
employ it to show that melting initiates at the outer layers.


Abstract:
  SOTA coreference resolution produces increasingly impressive scores on the
OntoNotes benchmark. However lack of comparable data following the same scheme
for more genres makes it difficult to evaluate generalizability to open domain
data. This paper provides a dataset and comprehensive evaluation showing that
the latest neural LM based end-to-end systems degrade very substantially out of
domain. We make an OntoNotes-like coreference dataset called OntoGUM publicly
available, converted from GUM, an English corpus covering 12 genres, using
deterministic rules, which we evaluate. Thanks to the rich syntactic and
discourse annotations in GUM, we are able to create the largest human-annotated
coreference corpus following the OntoNotes guidelines, and the first to be
evaluated for consistency with the OntoNotes scheme. Out-of-domain evaluation
across 12 genres shows nearly 15-20% degradation for both deterministic and
deep learning systems, indicating a lack of generalizability or covert
overfitting in existing coreference resolution models.


Abstract:
  Let $s,t$ be natural numbers, and fix an $s$-core partition $\sigma$ and a
$t$-core partition $\tau$. Put $d=\gcd(s,t)$ and $m= lcm(s,t)$, and write
$N_{\sigma, \tau}(k)$ for the number of $m$-core partitions of length no
greater than $k$ whose $s$-core is $\sigma$ and $t$-core is $\tau$. We prove
that for $k$ large, $N_{\sigma, \tau}(k)$ is a quasipolynomial of period $m$
and degree $\frac{1}{d}(s-d)(t-d)$.


Abstract:
  This paper presents the beam dynamics systematic corrections and their
uncertainties for the Run-1 data set of the Fermilab Muon g-2 Experiment. Two
corrections to the measured muon precession frequency $\omega_a^m$ are
associated with well-known effects owing to the use of electrostatic quadrupole
(ESQ) vertical focusing in the storage ring. An average vertically oriented
motional magnetic field is felt by relativistic muons passing transversely
through the radial electric field components created by the ESQ system. The
correction depends on the stored momentum distribution and the tunes of the
ring, which has relatively weak vertical focusing. Vertical betatron motions
imply that the muons do not orbit the ring in a plane exactly orthogonal to the
vertical magnetic field direction. A correction is necessary to account for an
average pitch angle associated with their trajectories. A third small
correction is necessary because muons that escape the ring during the storage
time are slightly biased in initial spin phase compared to the parent
distribution. Finally, because two high-voltage resistors in the ESQ network
had longer than designed RC time constants, the vertical and horizontal
centroids and envelopes of the stored muon beam drifted slightly, but
coherently, during each storage ring fill. This led to the discovery of an
important phase-acceptance relationship that requires a correction. The sum of
the corrections to $\omega_a^m$ is 0.50 $\pm$ 0.09 ppm; the uncertainty is
small compared to the 0.43 ppm statistical precision of $\omega_a^m$.


Abstract:
  We study branching problem of the metaplectic representation of $Sp(2,
\mathbb R)$ under its principle subgroup $SL(2, \mathbb R)$. We find the
complete decomposition.


Abstract:
  Weighted Gaussian Curvature is an important measurement for images. However,
its conventional computation scheme has low performance, low accuracy and
requires that the input image must be second order differentiable. To tackle
these three issues, we propose a novel discrete computation scheme for the
weighted Gaussian curvature. Our scheme does not require the second order
differentiability. Moreover, our scheme is more accurate, has smaller support
region and computationally more efficient than the conventional schemes.
Therefore, our scheme holds promise for a large range of applications where the
weighted Gaussian curvature is needed, for example, image smoothing, cartoon
texture decomposition, optical flow estimation, etc.


Abstract:
  Superpixels are a useful representation to reduce the complexity of image
data. However, to combine superpixels with convolutional neural networks (CNNs)
in an end-to-end fashion, one requires extra models to generate superpixels and
special operations such as graph convolution. In this paper, we propose a way
to implicitly integrate a superpixel scheme into CNNs, which makes it easy to
use superpixels with CNNs in an end-to-end fashion. Our proposed method
hierarchically groups pixels at downsampling layers and generates superpixels.
Our method can be plugged into many existing architectures without a change in
their feed-forward path because our method does not use superpixels in the
feed-forward path but use them to recover the lost resolution instead of
bilinear upsampling. As a result, our method preserves detailed information
such as object boundaries in the form of superpixels even when the model
contains downsampling layers. We evaluate our method on several tasks such as
semantic segmentation, superpixel segmentation, and monocular depth estimation,
and confirm that it speeds up modern architectures and/or improves their
prediction accuracy in these tasks.


Abstract:
  Since its introduction over 50 years ago, the concept of Mosco convergence
has permeated through diverse areas of mathematics and applied sciences. These
include applied analysis, the theory of partial differential equations,
numerical analysis, and infinite dimensional constrained optimization, among
others. In this paper we explore some of the consequences of Mosco convergence
on applied problems that involve moving sets, with some historical accounts,
and modern trends and features. In particular, we focus on connections with
density of convex intersections, finite element approximations,
quasi-variational inequalities, and impulse problems.


Abstract:
  Machine Learning models should ideally be compact and robust. Compactness
provides efficiency and comprehensibility whereas robustness provides
resilience. Both topics have been studied in recent years but in isolation.
Here we present a robust model compression scheme which is independent of model
types: it can compress ensembles, neural networks and other types of models
into diverse types of small models. The main building block is the notion of
depth derived from robust statistics. Originally, depth was introduced as a
measure of the centrality of a point in a sample such that the median is the
deepest point. This concept was extended to classification functions which
makes it possible to define the depth of a hypothesis and the median
hypothesis. Algorithms have been suggested to approximate the median but they
have been limited to binary classification. In this study, we present a new
algorithm, the Multiclass Empirical Median Optimization (MEMO) algorithm that
finds a deep hypothesis in multi-class tasks, and prove its correctness. This
leads to our Compact Robust Estimated Median Belief Optimization (CREMBO)
algorithm for robust model compression. We demonstrate the success of this
algorithm empirically by compressing neural networks and random forests into
small decision trees, which are interpretable models, and show that they are
more accurate and robust than other comparable methods. In addition, our
empirical study shows that our method outperforms Knowledge Distillation on DNN
to DNN compression.


Abstract:
  The charge states of ions in dense plasmas fluctuate due to collisional
ionization and recombination. Here we show how, by modifying the ion
interaction potential, these fluctuations can mediate energy exchange between
the plasma electrons and ions. Moreover, we develop a theory for this novel
electron-ion energy transfer mechanism. Calculations using a random walk
approach for the fluctuations suggest that the energy exchange rate from charge
state fluctuations could be comparable to direct electron-ion collisions. This
mechanism is, however, predicted to exhibit a complex dependence on the
temperature and ionization state of the plasma, which could contribute to our
understanding of significant variation in experimental measurements of
equilibration times.


Abstract:
  We consider GMRES applied to discretisations of the high-frequency Helmholtz
equation with strong trapping; recall that in this situation the problem is
exponentially ill-conditioned through an increasing sequence of frequencies.
Under certain assumptions about the distribution of the eigenvalues, we prove
upper bounds on how the number of GMRES iterations grows with the frequency.
Our main focus is on boundary-integral-equation formulations of the exterior
Dirichlet and Neumann obstacle problems in 2- and 3-d; for these problems, we
investigate numerically the sharpness (in terms of dependence on frequency) of
both our bounds and various quantities entering our bounds. This paper is
therefore the first comprehensive study of the frequency-dependence of the
number of GMRES iterations for Helmholtz boundary-integral equations under
trapping.


Abstract:
  Regulating the activity of matrix metalloproteinases (MMPs) is a potential
strategy for osteoarthritis (OA) therapy, although delivering this effect in a
spatially and temporally localised fashion remains a challenge. Here, we report
an injectable and self-healing hydrogel enabling factor-free MMP regulation and
biomechanical competence in situ. The hydrogel is realised within one minute
upon room temperature coordination between hyaluronic acid (HA) and a
cell-friendly iron-glutathione complex in aqueous environment. The resultant
gel displayed up to 300% in shear strain and tolerance towards ATDC 5
chondrocytes, in line with the elasticity and biocompatibility requirements for
connective tissue application. Significantly enhanced inhibition of MMP-13
activity was achieved after 12 hours in vitro, compared with a commercial HA
injection (OSTENIL PLUS). Noteworthy, 24-hour incubation of a clinical synovial
fluid sample collected from a late-stage OA patient with the reported hydrogel
was still shown to downregulate synovial fluid MMP activity (100.0 +/- 17.6 %
--> 81.0 +/- 7.5 %), with at least comparable extent to the case of the OSTENIL
PLUS-treated SF group (100.0 +/- 17.6 % --> 92.3 +/- 27.3 %). These results
therefore open up new possibilities in the use of HA as both
mechanically-competent hydrogel as well as a mediator of MMP regulation for OA
therapy.


Abstract:
  We construct for every proper algebraic space over a ground field an Albanese
map to a para-abelian variety, which is unique up to unique isomorphism. This
holds in the absence of rational points or ample sheaves, and also for
reducible or non-reduced spaces, under the mere assumption that the structure
morphism is in Stein factorization. It also works under suitable assumptions in
families. In fact the treatment of the relative setting is crucial, even to
understand the situation over ground fields. This also ensures that Albanese
maps are equivariant with respect to actions of group schemes. Our approach
depends on the notion of families of para-abelian varieties, where each
geometric fiber admits the structure of an abelian variety, and
representability of tau-parts in relative Picard groups, together with
structure results on algebraic groups.


Abstract:
  We investigate the buckling and post-buckling properties of a hyperelastic
half-space coated by two hyperelastic layers when the composite structure is
subjected to a uniaxial compression. In the case of a half-space coated with a
{\it single} layer, it is known that when the shear modulus $\mu_f$ of the
layer is larger than the shear modulus $\mu_s$ of the half-space, a linear
analysis predicts the existence of a critical stretch and wave number, whereas
a weakly nonlinear analysis predicts the existence of a threshold value of the
modulus ratio $\mu_s/\mu_f\approx 0.57$ below which the buckling is
super-critical and above which the buckling is sub-critical. It is shown in
this paper that when another layer is added, a larger variety of behaviour can
be observed. For instance, buckling can occur at a preferred wavenumber
super-critically even if both layers are softer than the half-space although
the top layer would need to be harder than the bottom layer. When the shear
modulus of the bottom layer lies in a certain interval, the super-critical to
sub-critical transition can happen a number of times as the shear modulus of
the top layer is increased gradually. Thus, an extra layer imparts more
flexibility in producing wrinkling patterns with desired properties, and our
weakly nonlinear analysis provides a road map on the parameter regimes where
this can be achieved.


Abstract:
  In quantum communication systems, the precise estimation of the detector's
response to the incoming light is necessary to avoid security breaches. The
typical working regime uses a free-running single-photon avalanche diode in
combination with attenuated laser pulses at telecom wavelength for encoding
information. We demonstrate the validity of an analytical model for this regime
which considers the effects of dark counts and dead time on the measured count
rate. For the purpose of gaining a better understanding of these effects, the
photon detections were separated from the dark counts via a software-induced
gating mechanism. The model was verified by experimental data for mean photon
numbers covering three orders of magnitude as well as for laser repetition
frequencies below and above the inverse dead time. Consequently, our model
would be of interest for predicting the detector response not only in the field
of quantum communications, but also in any other quantum physics experiment
where high detection rates are needed.


Abstract:
  Isomorphs are curves in the phase diagram along which both structure and
dynamics to a good approximation are invariant. There are two main methods to
trace out isomorphs in both atomic and molecular systems, the configurational
adiabat method and the direct isomorph check method. We introduce and test a
new family of force based methods on three molecular models; the asymmetric
dumbbell model, the symmetric inverse power law dumbbell model, and the
Lewis-Wahnstr\"{o}m model of o-terphenyl. A unique feature of the force based
methods is that they only require a single configuration to trace out an
isomorph. The atomic force method was previously shown to work very well for
the Kob-Andersen binary Lennard-Jones mixture, but we show that it does not
work for molecular models. In contrast, we find that a new method based on
molecular forces works well for all three molecular models


Abstract:
  An approach is presented for the inversion of experimental in-plane,
co-polarized light scattering data in p and s polarization to obtain the
normalized surface-height autocorrelation function and the rms-roughness of a
two-dimensional randomly rough metal surface. The approach is based on an
expression, obtained on the basis of second-order phase perturbation theory,
for the contribution to the mean differential reflection coefficient from the
light scattered diffusely by the rough surface. The inversion scheme is
validated by using several sets of computer generated scattering data for rough
silver surfaces defined by Gaussian surface height correlation functions. The
reconstructions obtained by this approach are found to be rather accurate for
weakly rough surfaces illuminated by p- and s-polarized incident light; this is
also true in cases where the contributions to the input data from multiple
scattering of surface plasmon polaritons is not insignificant. Finally, the
inversion scheme is applied to experimental scattering data obtained for
characterized two-dimensional randomly rough gold surfaces, and the results
obtained in this way, compare favorably to what is obtained by directly
analyzing the surface morphology. Such results testify to the attractiveness of
the computationally efficient inversion scheme that we propose.


Abstract:
  Light bosonic scalars (e.g. axions) may form clouds around black holes via
superradiant instabilities, or via accretion if they form some component of the
dark matter. It has been suggested that their presence may lead to a
distinctive dephasing of the gravitational wave signal when a small compact
object spirals into a larger black hole. Motivated by this, we study
numerically the dynamical friction force on a black hole moving at relativistic
velocities in a background scalar field with an asymptotically homogeneous
energy density. We show that the relativistic scaling is analogous to that
found for supersonic collisional fluids, assuming an approximate expression for
the pressure correction which depends on the velocity and scalar mass. While we
focus on a complex scalar field, our results confirm the expectation that real
scalars would exert a force which oscillates between positive and negative
values in time with a frequency set by the scalar mass. The complex field
describes the time averaged value of this force, but in a real scalar the rapid
force oscillations could in principle leave an imprint on the trajectory. The
approximation we obtain can be used to inform estimates of dephasing in the
final stages of an extreme mass ratio inspiral.


Abstract:
  We promote the teaching of mass functions as an integral part of an
interdisciplinary science education. Mass functions characterize the frequency
distributions of objects with different masses on all cosmic scales. We intend
to enhance experiential learning of this concept with a creative LEGO brick
experiment for a diverse student audience. To our surprise, the LEGO mass
function is not only qualitatively but also quantitatively comparable to mass
functions found across the Universe. We also discuss the relation between
gravitation and mass distributions as a possible explanation for the continuity
of the universal mass function.


Abstract:
  In 2002 Robert Solovay proved that a subsystem BI of classical second order
arithmetic, with bar induction and arithmetical countable choice, can be
negatively interpreted in the neutral subsystem BSK of Kleene's intuitionistic
analysis FIM using Markov's Principle MP. Combining this result with Kleene's
formalized recursive realizability, he established (in primitive recursive
arithmetic PRA) that FIM + MP and BI have the same consistency strength.
  This historical note includes Solovay's original proof, with his permission,
and the additional observation that Markov's Principle can be weakened to a
double negation shift axiom consistent with Brouwer's creating subject
counterexamples.


Abstract:
  We present a new sufficient criterion to prove that a non-sofic
half-synchronized subshift is direct prime. The criterion is based on conjugacy
invariant properties of Fischer graphs of half-synchronized shifts. We use this
criterion to show as a new result that all n-Dyck shifts are direct prime, and
we also give new proofs of direct primeness of non-sofic beta-shifts and
non-sofic S-gap shifts. We also construct a class of non-sofic synchronized
direct prime subshifts which additionally admit reversible cellular automata
with all directions sensitive.


Abstract:
  Continuous biomarkers are common for disease screening and diagnosis. To
reach a dichotomous clinical decision, a threshold would be imposed to
distinguish subjects with disease from non-diseased individuals. Among various
performance metrics for a continuous biomarker, specificity at a controlled
sensitivity level (or vice versa) is often desirable for clinical utility since
it directly targets where the clinical test is intended to operate. Covariates,
such as age, race, and sample collection, could impact the controlled
sensitivity level in subpopulations and may also confound the association
between biomarker and disease status. Therefore, covariate adjustment is
important in such biomarker evaluation. In this paper, we suggest to adopt a
parsimonious quantile regression model for the diseased population, locally at
the controlled sensitivity level, and assess specificity with
covariate-specific control of the sensitivity. Variance estimates are obtained
from a sample-based approach and bootstrap. Furthermore, our proposed local
model extends readily to a global one for covariate adjustment for the receiver
operating characteristic (ROC) curve over the sensitivity continuum. We
demonstrate computational efficiency of this proposed method and restore the
inherent monotonicity in the estimated covariate-adjusted ROC curve. The
asymptotic properties of the proposed estimators are established. Simulation
studies show favorable performance of the proposal. Finally, we illustrate our
method in biomarker evaluation for aggressive prostate cancer.


Abstract:
  Blockchain has been praised for its capacity to hold data in a decentralized
and tamper-proof way. It also supports the execution of code through
blockchain's smart contracts, adding automation of actions to the network with
high trustability. However, as smart contracts are visible by anybody on the
network, the business data and logic may be at risk, thus companies could be
reluctant to use such technology. This paper aims to propose a pattern that
allows the execution of automatable legal contract clauses, where its execution
states are stored in an on-chain smart-contract and the logic needed to enforce
it wraps it off-chain. An engine completes this pattern by running a business
process that corresponds to the legal contract. We then propose a pattern-based
solution based on a real-life use case: transportation of refrigerated goods.
We argue that this pattern guarantees companies pseudonymity and data
confidentiality while ensuring that an audit trail can be reconstituted through
the blockchain smart-contract to identify misbehavior or errors. This paper
paves the way for a future possible implementation of the solution described,
as well as its evaluation.


Abstract:
  We introduce reflection properties of cardinals in which the attributes that
reflect are expressible by infinitary formulas whose lengths can be strictly
larger than the cardinal under consideration. This kind of generalized
reflection principle leads to the definitions of
$L_{\kappa^+,\kappa^+}$-indescribability and $\Pi^1_\xi$-indescribability of a
cardinal $\kappa$ for all $\xi<\kappa^+$. In this context, universal
$\Pi^1_\xi$ formulas exist, there is a normal ideal associated to
$\Pi^1_\xi$-indescribability and the notions of $\Pi^1_\xi$-indescribability
yield a strict hierarchy below a measurable cardinal. Additionally, given a
regular cardinal $\mu$, we introduce a diagonal version of Cantor's derivative
operator and use it to extend Bagaria's \cite{MR3894041} sequence
$langle\tau_\xi:\xi<\mu\rangle$ of derived topologies on $\mu$ to
$\langle\tau_\xi:\xi<\mu^+\rangle$. Finally, we prove that for all $\xi<\mu^+$,
if there is a stationary set of $\alpha<\mu$ that have a high enough degree of
indescribability, then there are stationarily-many $\alpha<\mu$ that are
nonisolated points in the space $(\mu,\tau_{\xi+1})$.


Abstract:
  We study the robust mean estimation problem in high dimensions, where less
than half of the datapoints can be arbitrarily corrupted. Motivated by
compressive sensing, we formulate the robust mean estimation problem as the
minimization of the $\ell_0$-`norm' of an \emph{outlier indicator vector},
under a second moment constraint on the datapoints. We further relax the
$\ell_0$-`norm' to the $\ell_p$-norm ($0<p\leq 1$) in the objective and prove
that the global minima for each of these objectives are order-optimal for the
robust mean estimation problem. Then we propose a computationally tractable
iterative $\ell_p$-minimization and hard thresholding algorithm that outputs an
order-optimal robust estimate of the population mean. Both synthetic and real
data experiments demonstrate that the proposed algorithm outperforms
state-of-the-art robust mean estimation methods. The source code will be made
available at GitHub.


Abstract:
  Objective: The purpose of this paper is to identify the largest cognitive
challenges faced by novices developing software in teams.
  Method: Using grounded theory, we conducted an ethnographic study for two
months following four ten person novice teams, consisting of computer science
students, developing software systems.
  Result: This paper identifies version control and merge operations as the
largest challenge faced by the novices. The literature studies reveal that
little research appears to have been carried out in the area of version control
from a user perspective.
  Limitations: A qualitative study on students is not applicable in all
contexts, but the result is credible and grounded in data and substantiated by
extant literature.
  Conclusion: We conclude that our findings motivate further research on
cognitive perspectives to guide improvement of software engineering and its
tools.


Abstract:
  In the light of recent experimental progress in determining the pressure and
shear distributions in the proton, these quantities are calculated in a model
with confined quarks supplemented by the pion field required by chiral
symmetry. The incorporation of the pion contributions is shown to account for
the long-range distributions, in general agreement with the experimentally
extracted quark contributions. The results of the model are also compared with
lattice QCD results at unphysically large quark mass.


Abstract:
  This study provides an efficient approach for using text data to calculate
patent-to-patent (p2p) technological similarity, and presents a hybrid
framework for leveraging the resulting p2p similarity for applications such as
semantic search and automated patent classification. We create embeddings using
Sentence-BERT (SBERT) based on patent claims. We leverage SBERTs efficiency in
creating embedding distance measures to map p2p similarity in large sets of
patent data. We deploy our framework for classification with a simple Nearest
Neighbors (KNN) model that predicts Cooperative Patent Classification (CPC) of
a patent based on the class assignment of the K patents with the highest p2p
similarity. We thereby validate that the p2p similarity captures their
technological features in terms of CPC overlap, and at the same demonstrate the
usefulness of this approach for automatic patent classification based on text
data. Furthermore, the presented classification framework is simple and the
results easy to interpret and evaluate by end-users. In the out-of-sample model
validation, we are able to perform a multi-label prediction of all assigned CPC
classes on the subclass (663) level on 1,492,294 patents with an accuracy of
54% and F1 score > 66%, which suggests that our model outperforms the current
state-of-the-art in text-based multi-label and multi-class patent
classification. We furthermore discuss the applicability of the presented
framework for semantic IP search, patent landscaping, and technology
intelligence. We finally point towards a future research agenda for leveraging
multi-source patent embeddings, their appropriateness across applications, as
well as to improve and validate patent embeddings by creating domain-expert
curated Semantic Textual Similarity (STS) benchmark datasets.


Abstract:
  It was shown recently that the heart of a twin cotorsion pair on an
extriangulated category is semi-abelian. In this article, we consider a special
kind of hearts of twin cotorsion pairs induced by $d$-cluster tilting
subcategories in extriangulated categories. We give a necessary and sufficient
condition for such hearts to be abelian. In particular, we also can see that
such hearts are hereditary. As an application, this generalizes the work by Liu
in an exact case, thereby providing new insights in a triangulated case.


Abstract:
  We establish enhanced bounds on Cheeger-Gromov rho-invariants for general
3-manifolds and yet stronger bounds for special classes of 3-manifold. As key
ingredients, we construct chain null-homotopies whose complexity is linearly
bounded by its boundary's. This result can be regarded as an algebraic
topological analogue of Gromov's conjecture for quantitative topology. The
author hopes for applications to various fields including the smooth knot
concordance group, quantitative topology, and complexity theory.


Abstract:
  The formation of positive ions of antihydrogen $\bar{\rm{H}}^+$ via the three
body reaction (i) $\rm{e}^+ + \rm{e}^- + \bar{\rm{H}} \rightarrow \rm{e}^- +
\bar{\rm{H}}^+$ is considered. In reaction (i), free positrons $\rm{e}^+$ are
incident on antihydrogen $\bar{\rm{H}}$ embedded in a gas of low-energy ($\sim
$ meV) electrons and, due to the positron-electron interaction, a positron is
attached to $\bar{\rm{H}}$ whereas an electron carries away the energy excess.
We compare reaction (i) with two radiative attachment mechanisms. One of them
is (ii) spontaneous radiative attachment, in which the ion is formed due to
spontaneous emission of a photon by a positron incident on $\bar{\rm{H}}$. The
other is (iii) two-center dileptonic attachment which takes place in the
presence of a neighboring atom B and in which an incident positron is attached
to $\bar{\rm{H}}$ via resonant transfer of energy to B with its subsequent
relaxation through spontaneous radiative decay. It is shown that reaction (i)
can strongly dominate over mechanisms (ii) and (iii) for positron energies
below $0.1$ eV. It is also shown that at the energies considered reaction (i)
will not be influenced by annihilation and that the reaction $\rm{e}^+ +
\rm{e}^+ + \bar{\rm{H}} \rightarrow \rm{e}^+ + \bar{\rm{H}}^+$ has a
vanishingly small rate compared to reaction (i).


Abstract:
  The eROSITA Final Equatorial-Depth Survey (eFEDS), covering ~140 square
degrees, was performed as part of the performance verification phase of the
eROSITA telescope on board of the Russian-German satellite
Spectrum-Roentgen-Gamma (SRG). In this paper we present the optical follow-up
of 542 X-ray extent selected galaxy group and cluster candidates providing
redshifts and cluster confirmation for the full sample. We use optical imaging
data from the Hyper Suprime-Cam Subaru Strategic Program and from the Legacy
Survey to run the cluster redshift and confirmation tool MCMF as well as the
optical cluster finder CAMIRA at the location of the X-ray candidates. While
providing redshift estimates for all 542 candidates, we construct an optically
confirmed sample of 477 clusters and groups with a residual contamination of
6%. Of these, 470 (98.5%) are confirmed using MCMF and 7 systems are added
through cross matching with spectroscopic group catalogs. Using observable to
observable scaling and the applied confirmation threshold, we predict 8 +/- 2
real systems have been excluded with the MCMF cut required to build this low
contamination sample. This number is in good agreement with the 7 systems
recovered through cross matching. Thus, we expect those 477 systems to include
>99% of all true clusters in the candidate list. Using an MCMF independent
method, we confirm the catalog contamination of the confirmed subsample to be 6
+/- 3% and find 17 +/- 3% contamination for the full X-ray sample. The
estimated contamination of the fulls sample is in agreement with MCMF dependent
estimate of ~17% and the expectation from dedicated X-ray simulations of ~20%.
We further present a sample of optically selected merging cluster candidates.


Abstract:
  The soft X-rays (SXRs: 90--150 $\r{A}$) are among the most interesting
spectral ranges to be investigated in the next generation of solar missions due
to their unique capability of diagnosing phenomena involving hot plasma with
temperatures up to 15~MK. Multilayer (ML) coatings are crucial for developing
SXR instrumentation, as so far they represent the only viable option for the
development of high-efficiency mirrors in this spectral range. However, the
current standard MLs are characterized by a very narrow spectral band which is
incompatible with the science requirements expected for a SXR spectrometer.
Nevertheless, recent advancement in the ML technology has made the development
of non-periodic stacks repeatable and reliable, enabling the manufacturing of
SXR mirrors with a valuable efficiency over a large range of wavelengths.
  In this work, after reviewing the state-of-the-art ML coatings for the SXR
range, we investigate the possibility of using M-fold and aperiodic stacks for
the development of multiband SXR spectrometers. After selecting a possible
choice of key spectral lines, some trade-off studies for an eight-bands
spectrometer are also presented and discussed, giving an evaluation of their
feasibility and potential performance.


Abstract:
  Tunneling atomic force microscopy (TUNA) was used at ambient conditions to
measure the current-voltage ($I$-$V$) characteristics at clean surfaces of
highly oriented graphite samples with Bernal and rhombohedral stacking orders.
The characteristic curves measured on Bernal-stacked graphite surfaces can be
understood with an ordinary self-consistent semiconductor modeling and quantum
mechanical tunneling current derivations. We show that the absence of a voltage
region without measurable current in the $I$-$V$ spectra is not a proof of the
lack of an energy band gap. It can be induced by a surface band bending due to
a finite contact potential between tip and sample surface. Taking this into
account in the model, we succeed to obtain a quantitative agreement between
simulated and measured tunnel spectra for band gaps $(12 \ldots 37)$\,meV, in
agreement to those extracted from the exponential temperature decrease of the
longitudinal resistance measured in graphite samples with Bernal stacking
order. In contrast, the surface of relatively thick graphite samples with
rhombohedral stacking reveals the existence of a maximum in the first
derivative $dI/dV$, a behavior compatible with the existence of a flat band.
The characteristics of this maximum are comparable to those obtained at low
temperatures with similar techniques.


Abstract:
  Notably innovative technologies will permit compact and affordable advanced
accelerators as the linear collider and X-ray free-electron lasers (XFELs) with
accelerating gradients over twice the value achieved with current technologies.
In particular XFEL is able to produce coherent X-ray pulses with peak
brightness 10 orders of magnitude greater than preceding approaches, which has
revolutionized numerous fields through imaging of the nanoscopic world at the
time and length scale of atom-based systems, that is of femtosecond and
Angstrom. There is a strong interest for combining these two fields, to form a
proper tool with the goal of producing a very compact XFEL in order to
investigate multi-disciplinary researches in chemistry, biology, materials
science, medicine and physics.
  In the framework of the Ultra -Compact XFEL project (UC-XFEL) under study at
the UCLA, an ultra high gradient higher harmonic RF accelerating structure for
the longitudinal space phase linearization is foreseen. To this aim, a Ka-Band
linearizer (34.2 GHz) with an integrated voltage of at least 15 MV working on
6th harmonic with respect to the main Linac frequency (5.712 GHz) is required.
We here present the electromagnetic design of a cold ultra compact Ka-band SW
linearizer, 8 cm long, working on pi mode with an ultra high accelerating
gradient (beyond 100 MV/m) and minimum surface electric field for minimizing
the probability of RF breakdown. Moreover, we discuss a TW option and compare
it with the initial SW structure, by means of main RF parameters as well as
beam-dynamics considerations. The numerical electromagnetic studies have been
performed by using the well known SuperFish, HFSS and CST.


Abstract:
  Modeling and predicting solar events, in particular, the solar ramping event
is critical for improving situational awareness for solar power generation
systems. Solar ramping events are significantly impacted by weather conditions
such as temperature, humidity, and cloud density. Discovering the correlation
between different locations and times is a highly challenging task since the
system is complex and noisy. We propose a novel method to model and predict
ramping events from spatial-temporal sequential solar radiation data based on a
spatio-temporal interactive Bernoulli process. We demonstrate the good
performance of our approach on real solar radiation datasets.


Abstract:
  Gas has been detected in many exoplanetary systems ($>$10 Myr), thought to be
released in the destruction of volatile-rich planetesimals orbiting in
exo-Kuiper belts. In this letter, we aim to explore whether gas is also
expected in the Kuiper belt (KB) in our Solar System. To quantify the gas
release in our Solar System, we use models for gas release that have been
applied to extrasolar planetary systems, as well as a physical model that
accounts for gas released due to the progressive internal warming of large
planetesimals. We find that only bodies larger than about 4 km can still
contain CO ice after 4.6 Gyr of evolution. This finding may provide a clue as
to why Jupiter-family comets, thought to originate in the Kuiper belt, are
deficient in CO compared to Oort-clouds comets. We predict that gas is still
produced in the KB right now at a rate of $2 \times 10^{-8}$ M$_\oplus$/Myr for
CO and orders of magnitude more when the Sun was younger. Once released, the
gas is quickly pushed out by the Solar wind. Therefore, we predict a gas wind
in our Solar System starting at the KB location and extending far beyond with
regards to the heliosphere with a current total CO mass of $\sim 2 \times
10^{-12}$ M$_\oplus$. We also predict the existence of a slightly more massive
atomic gas wind made of carbon and oxygen (neutral and ionized) with a mass of
$\sim 10^{-11}$ M$_\oplus$. We predict that gas is currently present in our
Solar System beyond the Kuiper belt and that although it cannot be detected
with current instrumentation, it could be observed in the future with an in
situ mission using an instrument similar to Alice on New Horizons with larger
detectors. Our model of gas release due to slow heating may also work for
exoplanetary systems and provide the first real physical mechanism for the gas
observations.


Abstract:
  We advance the theory of strong colorings over partitions, studying both
positive and negative Ramsey relations at the level of the uncountable.
  A correspondence between combinatorial properties of partitions and chain
conditions of natural forcing notions for destroying strong colorings over them
is uncovered and enables us to prove positive Ramsey relations for $\aleph_1$
from weak forms of Martin's Axiom, thereby answering two questions from
[CKS21]. Positive Ramsey relations for $\aleph_2$ and higher cardinals are
established as well and without making use of large cardinals.
  We also provide a group of pump-up theorems for strong colorings over
partitions. Some of them solve more problems from [CKS21].


Abstract:
  We report results of large scale ground state density matrix renormalization
group(DMRG) calculations on $t$-$t'$-$J$ cylinders with circumferences 6 and 8.
We determine a rough phase diagram which appears to approximate the 2D system.
While for many properties, positive and negative $t'$ values ($t'/t = \pm 0.2$)
appear to correspond to electron and hole doped cuprate systems, respectively,
the behavior of superconductivity itself shows an inconsistency between the
model and the materials. The $t'<0$ (hole doped) region shows
antiferromagnetism limited to very low doping, stripes more generally, and the
familiar Fermi-surface of the hole doped cuprates. However, we find $t'<0$
strongly suppresses superconductivity. The $t'>0$ (electron doped) region shows
the expected circular Fermi pocket of holes around the $(\pi,\pi)$ point and a
broad low-doped region of coexisting antiferromagnetism and $d$-wave pairing
with a strong triplet $p$ component at wave-vector $(\pi,\pi)$ induced by the
antiferromagnetism and $d$-wave pairing. The pairing for the electron low-doped
system with $t'>0$ is strong and unambiguous in the DMRG simulations. At larger
doping another broad region with stripes in addition to weaker $d$-wave pairing
and striped $p$-wave pairing appears. In a small doping region near $x=0.08$
for $t'\sim-0.2$, we find a new type of stripe involving unpaired holes located
predominantly on chains spaced three lattice spacings apart. The undoped
two-leg ladder regions in between mimic the short-ranged spin correlations seen
in two-leg Heisenberg ladders.


Abstract:
  Let $K_n$ denote the number of distinct values among the first $n$ terms of
an infinite exchangeable sequence of random variables $(X_1,X_2,\ldots)$. We
prove for $n=3$ that the extreme points of the convex set of all possible laws
of $K_3$ are those derived from i.i.d. sampling from discrete uniform
distributions and the limit case with $P(K_3=3)=1$, and offer a conjecture for
larger $n$. We also consider variants of the problem for finite exchangeable
sequences and exchangeable random partitions.


Abstract:
  We study the phase diagram of an extended parafermion chain, which, in
addition to terms coupling parafermions on neighbouring sites, also possesses
terms involving four sites. Via a Fradkin--Kadanoff transformation the
parafermion chain is shown to be equivalent to the non-chiral $\mathbb{Z}_3$
axial next-nearest neighbour Potts model. We discuss a possible experimental
realisation using hetero-nanostructures. The phase diagram contains several
gapped phases, including a topological phase where the system possesses three
(nearly) degenerate ground states, and a gapless Luttinger-liquid phase.


Abstract:
  Robots need to be able to work in multiple different environments. Even when
performing similar tasks, different behaviour should be deployed to best fit
the current environment. In this paper, We propose a new approach to
navigation, where it is treated as a multi-task learning problem. This enables
the robot to learn to behave differently in visual navigation tasks for
different environments while also learning shared expertise across
environments. We evaluated our approach in both simulated environments as well
as real-world data. Our method allows our system to converge with a 26%
reduction in training time, while also increasing accuracy.