  This article investigates the causality structure of financial time series.
We concentrate on three main approaches to measuring causality: linear Granger
causality, kernel generalisations of Granger causality (based on ridge
regression and the Hilbert--Schmidt norm of the cross-covariance operator) and
transfer entropy, examining each method and comparing their theoretical
properties, with special attention given to the ability to capture nonlinear
causality. We also present the theoretical benefits of applying non-symmetrical
measures rather than symmetrical measures of dependence. We apply the measures
to a range of simulated and real data. The simulated data sets were generated
with linear and several types of nonlinear dependence, using bivariate, as well
as multivariate settings. An application to real-world financial data
highlights the practical difficulties, as well as the potential of the methods.
We use two real data sets: (1) U.S. inflation and one-month Libor; (2) S$\&$P
data and exchange rates for the following currencies: AUDJPY, CADJPY, NZDJPY,
AUDCHF, CADCHF, NZDCHF. Overall, we reach the conclusion that no single method
can be recognised as the best in all circumstances, and each of the methods has
its domain of best applicability. We also highlight areas for improvement and
future research.


  Traditional visual place recognition (VPR), usually using standard cameras,
is easy to fail due to glare or high-speed motion. By contrast, event cameras
have the advantages of low latency, high temporal resolution, and high dynamic
range, which can deal with the above issues. Nevertheless, event cameras are
prone to failure in weakly textured or motionless scenes, while standard
cameras can still provide appearance information in this case. Thus, exploiting
the complementarity of standard cameras and event cameras can effectively
improve the performance of VPR algorithms. In the paper, we propose
FE-Fusion-VPR, an attention-based multi-scale network architecture for VPR by
fusing frames and events. First, the intensity frame and event volume are fed
into the two-stream feature extraction network for shallow feature fusion.
Next, the three-scale features are obtained through the multi-scale fusion
network and aggregated into three sub-descriptors using the VLAD layer.
Finally, the weight of each sub-descriptor is learned through the descriptor
re-weighting network to obtain the final refined descriptor. Experimental
results show that on the Brisbane-Event-VPR and DDD20 datasets, the Recall@1 of
our FE-Fusion-VPR is 29.26% and 33.59% higher than Event-VPR and
Ensemble-EventVPR, and is 7.00% and 14.15% higher than MultiRes-NetVLAD and
NetVLAD. To our knowledge, this is the first end-to-end network that goes
beyond the existing event-based and frame-based SOTA methods to fuse frame and
events directly for VPR.


  We study N=2, d=4 attractor equations for the quantum corrected two-moduli
prepotential $\mathcal{F}=st^2+i\lambda$, with $\lambda$ real, which is the
only correction which preserves the axion shift symmetry and modifies the
geometry. In the classical case the black hole effective potential is known to
have a flat direction. We found that in the presence of D0-D6 branes the black
hole potential exhibits a flat direction in the quantum case as well. It
corresponds to non-BPS $Z\neq 0$ solutions to the attractor equations. Unlike
the classical case, the solutions acquire non-zero values of the axion field.
For the cases of D0-D4 and D2-D6 branes the classical flat direction reduces to
separate critical points which turn out to have a vanishing axion field.


  We discuss the possibility of Mathieu group M24 acting as symmetry group on
the K3 elliptic genus as proposed recently by Ooguri, Tachikawa and one of the
present authors. One way of testing this proposal is to derive the twisted
elliptic genera for all conjugacy classes of M24 so that we can determine the
unique decomposition of expansion coefficients of K3 elliptic genus into
irreducible representations of M24. In this paper we obtain all the hitherto
unknown twisted elliptic genera and find a strong evidence of Mathieu
moonshine.


  We show that the effective potential for local composite operators is a
useful object in studing dynamical symmetry breaking by calculating the
effective potential for the local composite operators $\bar{\psi} \psi$ and
$\phi^2$ in the Gross-Neveu (GN) and O(N) models, respectively. Since the
effective potential for local composite operators can be calculated by using
the Cornwall-Jackiw-Tomboulis (CJT) effective potential in theory with
additional bare mass terms, we show that divergences in the effective potential
for local composite operators are the same as in the CJT effective potential.
We compare the results obtained with the results give by the auxiliary field
method.


  We summarize evidence that multiple supernovae exploded within 100 pc of
Earth in the past few Myr. These events had dramatic effects on the
heliosphere, compressing it to within ~20 au. We advocate for
cross-disciplinary research of nearby supernovae, including on interstellar
dust and cosmic rays. We urge for support of theory work, direct exploration,
and study of extrasolar astrospheres.


  We discuss the flavor conversion of supernova neutrinos in the three-flavor
mixing scheme of neutrinos. We point out that by neutrino observation from
supernova one can discriminate the inverted hierarchy of neutrino masses from
the normal one if s_{13}^2 \gsim a few \times 10^{-4}, irrespective of which
oscillation solution to the solar neutrino problem is realized in nature. We
perform an analysis of data of SN1987A and obtain a strong indication that the
inverted mass hierarchy is disfavored unless s_{13}^2 \lsim a few \times
10^{-4}.


  We introduce the task of 3D visual grounding in large-scale dynamic scenes
based on natural linguistic descriptions and online captured multi-modal visual
data, including 2D images and 3D LiDAR point clouds. We present a novel method,
dubbed WildRefer, for this task by fully utilizing the rich appearance
information in images, the position and geometric clues in point cloud as well
as the semantic knowledge of language descriptions. Besides, we propose two
novel datasets, i.e., STRefer and LifeRefer, which focus on large-scale
human-centric daily-life scenarios accompanied with abundant 3D object and
natural language annotations. Our datasets are significant for the research of
3D visual grounding in the wild and has huge potential to boost the development
of autonomous driving and service robots. Extensive experiments and ablation
studies demonstrate that our method achieves state-of-the-art performance on
the proposed benchmarks. The code is provided in
https://github.com/4DVLab/WildRefer.


  A lattice model is presented for the simulation of dynamics in polymeric
systems. Each polymer is represented as a chain of monomers, residing on a
sequence of nearest-neighbor sites of a face-centered-cubic lattice. The
polymers are self- and mutually avoiding walks: no lattice site is visited by
more than one polymer, nor revisited by the same polymer after leaving it. The
dynamics occurs through single-monomer displacements over one lattice spacing.
To demonstrate the high computational efficiency of the model, we simulate a
dense binary polymer mixture with repelling nearest-neighbor interactions
between the two types of polymers, and observe the phase separation over a long
period of time. The simulations consist of a total of 46,080 polymers, 100
monomers each, on a lattice with 13,824,000 sites, and an interaction strength
of 0.1 kT. In the final two decades of time, the domain-growth is found to be
d(t) ~ t^1/3, as expected for a so-called "Model B" system.


  We give a new presentation of the main result of Arunachalam, Bri\"et and
Palazuelos (SICOMP'19) and show that quantum query algorithms are characterized
by a new class of polynomials which we call Fourier completely bounded
polynomials. We conjecture that all such polynomials have an influential
variable. This conjecture is weaker than the famous Aaronson-Ambainis (AA)
conjecture (Theory of Computing'14), but has the same implications for
classical simulation of quantum query algorithms.
  We prove a new case of the AA conjecture by showing that it holds for
homogeneous Fourier completely bounded polynomials. This implies that if the
output of $d$-query quantum algorithm is a homogeneous polynomial $p$ of degree
$2d$, then it has a variable with influence at least $Var[p]^2$.
  In addition, we give an alternative proof of the results of Bansal, Sinha and
de Wolf (CCC'22 and QIP'23) showing that block-multilinear completely bounded
polynomials have influential variables. Our proof is simpler, obtains better
constants and does not use randomness.


  There is a wave of interest in using unsupervised neural networks for solving
differential equations. The existing methods are based on feed-forward
networks, {while} recurrent neural network differential equation solvers have
not yet been reported. We introduce an unsupervised reservoir computing (RC),
an echo-state recurrent neural network capable of discovering approximate
solutions that satisfy ordinary differential equations (ODEs). We suggest an
approach to calculate time derivatives of recurrent neural network outputs
without using backpropagation. The internal weights of an RC are fixed, while
only a linear output layer is trained, yielding efficient training. However, RC
performance strongly depends on finding the optimal hyper-parameters, which is
a computationally expensive process. We use Bayesian optimization to
efficiently discover optimal sets in a high-dimensional hyper-parameter space
and numerically show that one set is robust and can be used to solve an ODE for
different initial conditions and time ranges. A closed-form formula for the
optimal output weights is derived to solve first order linear equations in a
backpropagation-free learning process. We extend the RC approach by solving
nonlinear system of ODEs using a hybrid optimization method consisting of
gradient descent and Bayesian optimization. Evaluation of linear and nonlinear
systems of equations demonstrates the efficiency of the RC ODE solver.


  Quantum geometry defines the phase and amplitude distances between quantum
states. The phase distance is characterized by the Berry curvature and thus
relates to topological phenomena. The significance of the full quantum
geometry, including the amplitude distance characterized by the quantum metric,
has started to receive attention in the last few years. Various quantum
transport and interaction phenomena have been found to be critically influenced
by quantum geometry. For example, quantum geometry allows counterintuitive flow
of supercurrent in a flat band where single electrons are immobile. In this
Essay, I will discuss my view of the important open problems and future
applications of this research topic and will try to inspire the reader to come
up with further ideas. At its best, quantum geometry can open a new chapter in
band theory and lead to breakthroughs as transformative as room-temperature
superconductivity. However, first, more experiments directly showing the effect
of quantum geometry are needed. We also have to integrate quantum geometry
analysis in our most advanced numerical methods. Further, the ramifications of
quantum geometry should be studied in a wider range, including electric and
electromagnetic responses and interaction phenomena in free- and
correlated-electron materials, bosonic systems, optics, and other fields.


  The neutral triplet scalars (H^0, A^0) in the Higgs Triplet Model (HTM) are
difficult to detect at the LHC for the case of mass degeneracy with the singly
charged (H^+) and doubly charged (H^++) scalars.
  A non-zero value of a specific quartic coupling in the scalar potential
removes this degeneracy, and positive values of this coupling would give rise
to the mass hierarchy m_{A^0,H^0} > m_{H^+} > m_{H^++}.
  In this scenario the decays H^0-> H^+ W^* and A^0-> H^+ W^*, followed by H^+
-> H^{++} W^*, can proceed with large branching ratios, even for small mass
splittings among the triplet scalars.
  Such a cascade process would lead to production of H^{++}H^{--} accompanied
by several virtual W bosons, and would provide a way of observing H^0 and A^0
with a signature which is different from that of the conventional production
process for doubly charged scalars, q qbar -> \gamma^*, Z^* -> H^{++}H^{--}.
  Assuming the decay H^{++} to l^+ l^+, we quantify the magnitude of the
five-lepton and six-lepton signals in these channels, which have essentially
negligible backgrounds at the LHC.


  We address the problem of computing in the group of $\ell^k$-torsion rational
points of the jacobian variety of algebraic curves over finite fields, with a
view toward computing modular representations.


  Data availability is a bottleneck during early stages of development of new
capabilities for intelligent artificial agents. We investigate the use of text
generation techniques to augment the training data of a popular commercial
artificial agent across categories of functionality, with the goal of faster
development of new functionality. We explore a variety of encoder-decoder
generative models for synthetic training data generation and propose using
conditional variational auto-encoders. Our approach requires only direct
optimization, works well with limited data and significantly outperforms the
previous controlled text generation techniques. Further, the generated data are
used as additional training samples in an extrinsic intent classification task,
leading to improved performance by up to 5\% absolute f-score in low-resource
cases, validating the usefulness of our approach.


  In addition to the well known common properties such as small world and
community structures, recent empirical investigations suggest a universal
scaling law for the spatial structure of social networks. It is found that the
probability density distribution of an individual to have a friend at distance
$r$ scales as $P(r)\propto r^{-1}$. The basic principle that yields this
spatial scaling property is not yet understood. Here we propose a fundamental
origin for this law based on the concept of entropy. We show that this spatial
scaling law can result from maximization of information entropy, which means
individuals seek to maximize the diversity of their friendships. Such spatial
distribution can benefit individuals significantly in optimally collecting
information in a social network.


  We discuss the `memory effect' discovered in the 60's by Kovacs in
temperature shift experiments on glassy polymers, where the volume (or energy)
displays a non monotonous time behaviour. This effect is generic and is
observed on a variety of different glassy systems (including granular
materials). The aim of this paper is to discuss whether some microscopic
information can be extracted from a quantitative analysis of the `Kovacs hump'.
We study analytically two families of theoretical models: domain growth and
traps, for which detailed predictions of the shape of the hump can be obtained.
Qualitatively, the Kovacs effect reflects the heterogeneity of the system: its
description requires to deal not only with averages but with a full probability
distribution (of domain sizes or of relaxation times). We end by some
suggestions for a quantitative analysis of experimental results.


  Social norms have traditionally been difficult to quantify. In any particular
society, their sheer number and complex interdependencies often limit a
system-level analysis. One exception is that of the network of norms that
sustain the online Wikipedia community. We study the fifteen-year evolution of
this network using the interconnected set of pages that establish, describe,
and interpret the community's norms. Despite Wikipedia's reputation for
\textit{ad hoc} governance, we find that its normative evolution is highly
conservative. The earliest users create norms that both dominate the network
and persist over time. These core norms govern both content and interpersonal
interactions using abstract principles such as neutrality, verifiability, and
assume good faith. As the network grows, norm neighborhoods decouple
topologically from each other, while increasing in semantic coherence. Taken
together, these results suggest that the evolution of Wikipedia's norm network
is akin to bureaucratic systems that predate the information age.


  Detecting various types of stresses (nutritional, water, nitrogen, etc.) in
agricultural fields is critical for farmers to ensure maximum productivity.
However, stresses show up in different shapes and sizes across different crop
types and varieties. Hence, this is posed as an anomaly detection task in
agricultural images. Accurate anomaly detection in agricultural UAV images is
vital for early identification of field irregularities. Traditional supervised
learning faces challenges in adapting to diverse anomalies, necessitating
extensive annotated data. In this work, we overcome this limitation with
self-supervised learning using a masked image modeling approach. Masked
Autoencoders (MAE) extract meaningful normal features from unlabeled image
samples which produces high reconstruction error for the abnormal pixels during
reconstruction. To remove the need of using only ``normal" data while training,
we use an anomaly suppression loss mechanism that effectively minimizes the
reconstruction of anomalous pixels and allows the model to learn anomalous
areas without explicitly separating ``normal" images for training. Evaluation
on the Agriculture-Vision data challenge shows a mIOU score improvement in
comparison to prior state of the art in unsupervised and self-supervised
methods. A single model generalizes across all the anomaly categories in the
Agri-Vision Challenge Dataset


  Rotating Radio Transients (RRATs) are a new class of neutron stars discovered
through the emission of radio bursts. Eleven sources are known up to now, but
population studies predict these objects to be more numerous than the normal
radio pulsar population. Multiwavelength observations of these peculiar objects
are in progress to disentangle their spectral energy distribution, and then
study in detail their nature. In this review I report on the current state of
the art on these objects, and in particular on the results of new X-ray
observations.


  In this article, we propose a traffic rule inspired from nature, that
facilitates an elite agent to move efficiently through a crowd of inert agents.
When an object swims in a fluid medium or an intruder is forced through
granular matter, characteristic flow-fields are created around them. We show
that if inert agents, made small movements based on a traffic rule derived from
these characteristic flow-fields, they efficiently reorganize and transport
enough space for the elite to pass through. The traffic rule used in the
article is a dipole-field which satisfactorily captures the features of the
flow-fields around a moving intruder. We study the effectiveness of this dipole
traffic rule using numerical simulations in a 2D periodic domain, where one
self-propelled elite agent tries to move through a crowd of inert agents that
prefer to stay in a state of rest. Simulations are carried out for a wide range
of strengths of the traffic rule and packing density of the crowd. We
characterize and analyze four regions in the parameter space, free-flow, motion
due to cooperation, frozen and collective drift regions, and discuss the
consequence of the traffic rule in light of the collective behavior observed.
We believe that the proposed method can be of use in a swarm of robots working
in constrained environments.


  Probabilistic forecasting, i.e. estimating the probability distribution of a
time series' future given its past, is a key enabler for optimizing business
processes. In retail businesses, for example, forecasting demand is crucial for
having the right inventory available at the right time at the right place. In
this paper we propose DeepAR, a methodology for producing accurate
probabilistic forecasts, based on training an auto regressive recurrent network
model on a large number of related time series. We demonstrate how by applying
deep learning techniques to forecasting, one can overcome many of the
challenges faced by widely-used classical approaches to the problem. We show
through extensive empirical evaluation on several real-world forecasting data
sets accuracy improvements of around 15% compared to state-of-the-art methods.


  Consider a two-dimensional stratified solitary wave propagating through a
body of water that is bounded below by an impermeable ocean bed. In this work,
we study how such a wave can be reconstructed from data consisting of the wave
speed, upstream and downstream density profile, and the trace of the pressure
on the bed. First, we prove that this data uniquely determines the wave, both
in the (real) analytic and Sobolev regimes. Second, for waves that consist of
multiple layers of constant density immiscible fluids, we provide an exact
formula describing each of the interfaces in terms of the data. Finally, for
continuously stratified fluids, we detail a reconstruction scheme based on
approximation by layer-wise constant density flows.


  In this work, we develop a general perturbative procedure to find the
off-equatorial plane deflections in the weak deflection limit in general
stationary and axisymmetric spacetimes satisfying some common variable
separation conditions. Deflections of both null and timelike rays with the
finite distance effect of the source and detector taken into account are
obtained as dual series of $M/r_0$ and $r_0/r_{s,d}$. These deflections allow a
set of exact gravitational lensing equations from which the images' apparent
angular positions are solved. The method and general results are then applied
to the Kerr-Newmann, Kerr-Sen, and rotating Simpson-Visser spacetimes to study
the effect of the spin and characteristic (effective) charge of the spacetimes
and the source altitude on the deflection angles and image apparent angles. It
is found that in general, both the spacetime spin and charge only affect the
deflections from the second non-trivial order, while the source altitude
influences the deflection from the leading order. Because of these, it is found
that in gravitational lensing in realistic situations, it is hard to measure
the effects of the spacetime spin and charge from the images' apparent
locations. We also presented the off-equatorial deflections in the rotating
Bardeen, Hayward, Ghosh, and Tinchev black hole spacetimes.


  Video instance segmentation (VIS) aims at classifying, segmenting and
tracking object instances in video sequences. Recent transformer-based neural
networks have demonstrated their powerful capability of modeling
spatio-temporal correlations for the VIS task. Relying on video- or clip-level
input, they suffer from high latency and computational cost. We propose a
robust context fusion network to tackle VIS in an online fashion, which
predicts instance segmentation frame-by-frame with a few preceding frames. To
acquire the precise and temporal-consistent prediction for each frame
efficiently, the key idea is to fuse effective and compact context from
reference frames into the target frame. Considering the different effects of
reference and target frames on the target prediction, we first summarize
contextual features through importance-aware compression. A transformer encoder
is adopted to fuse the compressed context. Then, we leverage an
order-preserving instance embedding to convey the identity-aware information
and correspond the identities to predicted instance masks. We demonstrate that
our robust fusion network achieves the best performance among existing online
VIS methods and is even better than previously published clip-level methods on
the Youtube-VIS 2019 and 2021 benchmarks. In addition, visual objects often
have acoustic signatures that are naturally synchronized with them in
audio-bearing video recordings. By leveraging the flexibility of our context
fusion network on multi-modal data, we further investigate the influence of
audios on the video-dense prediction task, which has never been discussed in
existing works. We build up an Audio-Visual Instance Segmentation dataset, and
demonstrate that acoustic signals in the wild scenarios could benefit the VIS
task.


  We introduce a spin-1/2 model in three dimensions which is a generalization
of the well-known Kitaev model on a honeycomb lattice. Following Kitaev, we
solve the model exactly by mapping it to a theory of non-interacting fermions
in the background of a static Z_2 gauge field. The phase diagram consists of a
gapped phase and a gapless one, similar to the two-dimensional case.
Interestingly, unlike in the two-dimensional model, in the gapless phase the
gap vanishes on a contour in the k space. Furthermore, we show that the flux
excitations of the gauge field, due to some local constraints, form loop like
structures; such loops exist on a lattice formed by the plaquettes in the
original lattice and is topologically equivalent to the pyrochlore lattice.
Finally, we derive a low-energy effective Hamiltonian that can be used to study
the properties of the excitations in the gapped phase.


  The algebra of observables for identical particles on a line is formulated
starting from postulated basic commutation relations. A realization of this
algebra in the Calogero model was previously known. New realizations are
presented here in terms of differentiation operators and in terms of
SU(N)-invariant observables of the Hermitian matrix models. Some particular
structure properties of the algebra are briefly discussed.


  The main geometric ingredient of the closed string field theory are the
string vertices, the collections of string diagrams describing the elementary
closed string interactions, satisfying the quantum Batalian-Vilkovisky master
equation. They can be characterized using the Riemann surfaces endowed with the
metric solving the generalized minimal area problem. However, an adequately
developed theory of such Riemann surfaces is not available yet, and
consequently description of the string vertices via Riemann surfaces with the
minimal area metric fails to provide practical tools for performing
calculations. We describe an alternate construction of the string vertices
satisfying the Batalian-Vilkovisky master equation using Riemann surfaces
endowed with the metric having constant curvature $-1$ all over the surface. We
argue that this construction provides an approximately gauge invariant closed
string field theory.


  In this work we present a proof of the discreteness of the spectrum for
bosonic membrane, in a flat minkowski space. This may be useful to show the
quantum mechanical consistence of others bosonics extended models. This proof
includes the BRST residual symmetry and was directly performed over the
discretized membrane model. The BRST residual invariant effective action is
explicity constructed.


  We calculate the effects of a light, very weakly-coupled boson $X$ arising
from a spontaneously broken $U(1)_{B-L}$ symmetry on $\Delta N_{\rm eff}$ as
measured by the CMB and $Y_p$ from BBN. Our focus is the mass range $1 \; {\rm
eV} \lesssim m_X \lesssim 100 \; {\rm MeV}$; masses lighter than about an ${\rm
eV}$ have strong constraints from fifth-force law constraints, while masses
heavier than about 100 MeV are constrained by other probes. We do not assume
$X$ began in thermal equilibrium with the SM; instead, we allow $X$ to
freeze-in from its very weak interactions with the SM. We find $U(1)_{B-L}$ is
more strongly constrained by $\Delta N_{\rm eff}$ than previously considered.
The bounds arise from the energy density in electrons and neutrinos slowly
siphoned off into $X$ bosons, which become nonrelativistic, redshift as matter,
and then decay, dumping their slightly larger energy density back into the SM
bath causing $\Delta N_{\rm eff} > 0$. While some of the parameter space has
complementary constraints from stellar cooling, supernova emission, and
terrestrial experiments, we find future CMB observatories can access regions of
mass and coupling space not probed by any other method. In gauging
$U(1)_{B-L}$, we assume the $[U(1)_{B-L}]^3$ anomaly is canceled by
right-handed neutrinos, and so our $\Delta N_{\rm eff}$ calculations have been
carried out in two scenarios: neutrinos have Dirac masses, or, right-handed
neutrinos acquire Majorana masses. In the latter scenario, we comment on the
additional implications of thermalized right-handed neutrinos decaying during
BBN. We also briefly consider the possibility that $X$ decays into dark sector
states. If these states behave as radiation, we find weaker constraints,
whereas if they are massive, there are stronger constraints, though now from
$\Delta N_{\rm eff} < 0$.


  We present a theoretical study of an electronic quantum refrigerator based on
four quantum dots arranged in a square configuration, in contact with as many
thermal reservoirs. We show that the system implements the basic minimal
mechanism for acting as a self-contained quantum refrigerator, by demonstrating
heat extraction from the coldest reservoir and the cooling of the nearby
quantum-dot.


  The use of International Classification of Diseases (ICD) codes in healthcare
presents a challenge in selecting relevant codes as features for machine
learning models due to this system's large number of codes. In this study, we
compared several unsupervised feature selection methods for an ICD code
database of 49,075 coronary artery disease patients in Alberta, Canada.
Specifically, we employed Laplacian Score, Unsupervised Feature Selection for
Multi-Cluster Data, Autoencoder Inspired Unsupervised Feature Selection,
Principal Feature Analysis, and Concrete Autoencoders with and without ICD tree
weight adjustment to select the 100 best features from over 9,000 codes. We
assessed the selected features based on their ability to reconstruct the
initial feature space and predict 90-day mortality following discharge. Our
findings revealed that the Concrete Autoencoder methods outperformed all other
methods in both tasks. Furthermore, the weight adjustment in the Concrete
Autoencoder method decreased the complexity of features.


  We present a general protocol for stabilizer measurements and pumping in a
system of N superconducting qubits. We assume always-on, equal dispersive
couplings to a single mode of a high-Q microwave resonator in the ultra-strong
dispersive limit where the dispersive shifts largely exceed the spectral
linewidth. In this limit, we show how to map the two eigenvalues of an
arbitrary weight M < N Pauli operator, onto two quasi-orthogonal coherent
states of the cavity. Together with a fast cavity readout, this enables the
efficient measurement of stabilizer operators.


  This study proposes a general, scalable method to learn control-oriented
thermal models of buildings that could enable wide-scale deployment of
cost-effective predictive controls. An Unscented Kalman Filter augmented for
parameter and disturbance estimation is shown to accurately learn and predict a
building's thermal response. Recent studies of heating, ventilating, and air
conditioning (HVAC) systems have shown significant energy savings with advanced
model predictive control (MPC). A scalable cost-effective method to readily
acquire accurate, robust models of individual buildings' unique thermal
envelopes has historically been elusive and hindered the widespread deployment
of prediction-based control systems. Continuous commissioning and lifetime
performance of these thermal models requires deployment of on-line data-driven
system identification and parameter estimation routines. We propose a novel
gray-box approach using an Unscented Kalman Filter based on a multi-zone
thermal network and validate it with EnergyPlus simulation data. The filter
quickly learns parameters of a thermal network during periods of known or
constrained loads and then characterizes unknown loads in order to provide
accurate 24+ hour energy predictions. This study extends our initial
investigation by formalizing parameter and disturbance estimation routines and
demonstrating results across a year-long study.


  The assessment of seismic risk in urban areas with high seismicity is
certainly one of the most important problems that territorial managers have to
face. A reliable evaluation of this risk is the basis for the design of both
specific seismic improvement interventions and emergency management plans.
Unappropriate seismic risk assessments may provide misleading results and
induce bad decisions with relevant economic and social impact. The seismic risk
in urban areas is mainly linked to three factors, namely, "hazard", "exposure"
and "vulnerability". Hazard measures the potential of an earthquake to produce
harm; exposure evaluates the amount of population exposed to harm;
vulnerability represents the proneness of considered buildings to suffer
damages in case of an earthquake. Estimates of such factors may not always
coincide with the perceived risk of the resident population. The propensity to
implement structural seismic improvement interventions aimed at reducing the
vulnerability of buildings depends significantly on the perceived risk. This
paper investigates on the difference between objective and perceived risk and
highlights some critical issues. The aim of this study is to calibrate
opportune policies, which allow addressing the most appropriate seismic risk
mitigation options with reference to current levels of perceived risk. We
propose the introduction of a Seismic Policy Prevention index (SPPi). This
methodology is applied to a case-study focused on a densely populated district
of the city of Catania (Italy).


  Replication of SARS-CoV2 virions is an error-prone process which may
eventually generate a percentage of impaired protein copies with complete lack
of functionality. For instance, after RNA mis-replication, a very premature
stop codon (UAG, UAA, UGA) coding for a prematurely truncated
(nonsense-mutated) spike protein may occur.
  In the natural virus replication process via cell infection, the nonsense
genomes are corrected by the proofreading enzymes of the virus, strongly
penalized by natural selection and condemned to a very short life by the host
cell's mRNA watching mechanisms. However, for the very long spike genome of
1273 codons, a truncated non-functional spike protein may potentially still
occur with a high frequency, even in presence of a low mutation rate per single
nucleotide.
  With this paper, a hi-fidelity post-processing of SARS-CoV2 spike sequences
is provided: in ex-vivo samples from patients, an impressively high rate of
26\% of prematurely-stopped (nonsense-mutated) spike genomes sequences due to
insertions/deletions is found, compared with a 9.7\% obtained from in-vitro
cell culture.
  A general warning on the possible high rate of prematurely-stopped spike
protein sequences is also raised for "artificial" de novo DNA synthesis
processes of SARS-CoV2 spike genomes with no associated natural
proofreading/selection, possibly including vaccine preparations.
  Finally, a metric based on the ratio between prematurely stopped and "normal"
genomes is proposed as a potential host-independent variant-watching tool, able
to classify the infectivity of new spike mutations.


  The statistical properties of the ultra-luminous X-ray source (ULX)
populations extracted from the ROSAT HRI survey of X-ray point sources in
nearby galaxies in Paper I are studied to reveal connections between the ULX
phenomenon and survey galaxy properties. The $logN$--$logS$ relation is used to
calculate and remove the influence of false ULXs from the background and/or
foreground objects. Study of the luminosity functions shows that the regular
ULXs below $10^{40}$ erg/sec are an extension of the ordinary HMXB/LMXB
population below $10^{39}$ erg/sec in the late-type galaxies, and that the
extreme ULXs above $10^{40}$ erg/sec might be a different population from the
regular ULXs. This survey confirms statistically that the ULX phenomenon is
closely connected to star formation activities, since ULXs preferentially occur
in late-type galaxies rather than in early-typTwo ULX populations, the
HMXB-like ULXs as an extension of the ordinary HMXB population associated with
the young stellar population and the LMXB-like ULXs as an extension of the
ordinary LMXB population associated with the old stellar population, are both
required to account for the total ULX population. It is found that the
LMXB-like ULXs dominate the ULX population at low star formation rates, while
HMXB-like ULXs dominate at high star formation rates. However, an accurate
quantitative description of the relative fractions of HMXB-like ULXs and
LMXB-like ULXs is impossible in the current HRI survey due to the source
blending effects, and a Chandra survey of the X-ray point sources in nearby
galaxies would be required for this purpose. e galaxies, and ULXs in late-type
galaxies tend to trace the spiral arms.


  A new type of magnetically frustrated lattice is found in the layered
fluoride NaBa2Mn3F11. A kagome-type array of regular triangles composed of Mn2+
ions (spin 5/2) deforms so as to generate the next-nearest-neighbor interaction
J2 between three out of six spins in the hexagon of a normal kagome lattice, in
addition to the nearest-neighbor interaction J1. As a function of |J2/J1|, this
lattice can interconnect the kagome (J2 = 0) and the triangular (J2 = J1)
lattices and thus is called the kagome-triangular (KT) lattice. Magnetic
susceptibility and heat capacity measurements performed on a polycrystalline
sample of NaBa2Mn3F11 show an intensive short-range antiferromagnetic
correlation below 14 K probably due to the specific magnetic frustration of the
KT lattice. In addition, a long-range order at 2.0 K is observed, which is
significantly low compared with the antiferromagnetic Weiss temperature of 32.3
K. Theoretical considerations of Heisenberg spins in the KT lattice reveal
unique non-coplanar magnetic orders in the case of ferromagnetic J1 and
antiferromagnetic J2. NaBa2Mn3F11 may actually exist in this regime according
to the results of our analysis based on classical Monte-Carlo simulation.


  We survey existing approaches to the formal verification of statecharts using
model checking. Although the semantics and subset of statecharts used in each
approach varies considerably, along with the model checkers and their
specification languages, most approaches rely on translating the hierarchical
structure into the flat representation of the input language of the model
checker. This makes model checking difficult to scale to industrial models, as
the state space grows exponentially with flattening. We look at current
approaches to model checking hierarchical structures and find that their
semantics is significantly different from statecharts. We propose to address
the problem of state space explosion using a combination of techniques, which
are proposed as directions for further research.


  This paper studies carbon taxes effectiveness to induce a transition to
cleaner production when a firm faces different technologies and demands. To
determine carbon taxes effectiveness, we propose a framework based on a
strategic capacity planning under carbon taxes model, that consider proper
perfomance measures. The model, which is formulated as a mixed integer linear
problem (MILP), considers issues that previous work have not studied jointly,
such as machine replacement, workforce planning, and maintenance. The
effectiveness measures consider levels of clean production and periods to reach
a technological transition. Our computational experiments, based on a real
case, have shown that carbon taxes by themselves do not necessarily induce a
transition to clean production, since their effectiveness depends on the
available technology relationship and the demand magnitude.


  In order to support a variety of missions and deal with different flight
environments, drone control programs typically provide configurable control
parameters. However, such a flexibility introduces vulnerabilities. One such
vulnerability, referred to as range specification bugs, has been recently
identified. The vulnerability originates from the fact that even though each
individual parameter receives a value in the recommended value range, certain
combinations of parameter values may affect the drone physical stability. In
this paper we develop a novel learning-guided search system to find such
combinations, that we refer to as incorrect configurations. Our system applies
metaheuristic search algorithms mutating configurations to detect the
configuration parameters that have values driving the drone to unstable
physical states. To guide the mutations, our system leverages a machine
learning predictor as the fitness evaluator. Finally, by utilizing
multi-objective optimization, our system returns the feasible ranges based on
the mutation search results. Because in our system the mutations are guided by
a predictor, evaluating the parameter configurations does not require
realistic/simulation executions. Therefore, our system supports a comprehensive
and yet efficient detection of incorrect configurations. We have carried out an
experimental evaluation of our system. The evaluation results show that the
system successfully reports potentially incorrect configurations, of which over
85% lead to actual unstable physical states.


  We present experimental results on the measurement of fidelity decay under
contrasting system dynamics using a nuclear magnetic resonance quantum
information processor. The measurements were performed by implementing a
scalable circuit in the model of deterministic quantum computation with only
one quantum bit. The results show measurable differences between regular and
complex behaviour and for complex dynamics are faithful to the expected
theoretical decay rate. Moreover, we illustrate how the experimental method can
be seen as an efficient way for either extracting coarse-grained information
about the dynamics of a large system, or measuring the decoherence rate from
engineered environments.


  We investigate the generation of entanglement through a quantum dot molecule
under the influence of vibrational phonon modes in a bias voltage junction. The
molecular quantum dot system is realized by coupled quantum dots inside a
suspended carbon nanotube. We consider the dynamical entanglement as a function
of bias voltage and temperature by taking into account the electron-phonon
interaction. In order to generate the robust entanglement between quantum dots
and preserve it to reach the maximal achievable amount steadily, we introduce
an asymmetric coupling protocol and apply the easy tunable bias voltage-driven
field. For an oscillating bias voltage, the time-varying entanglement can
periodically reach the maximum revival. In thermal entanglement dynamics, the
phenomena of thermal entanglement degradation and thermal entanglement revival
are observed which are intensively affected by the strength of phonon
decoherence. The revival of entanglement shows a larger value for a higher
phonon coupling.


  Heart rate variability (HRV) is a practical and noninvasive measure of
autonomic nervous system activity, which plays an essential role in
cardiovascular health. However, using HRV to assess physiology status is
challenging. Even in clinical settings, HRV is sensitive to acute stressors
such as physical activity, mental stress, hydration, alcohol, and sleep.
Wearable devices provide convenient HRV measurements, but the irregularity of
measurements and uncaptured stressors can bias conventional analytical methods.
To better interpret HRV measurements for downstream healthcare applications, we
learn a personalized diurnal rhythm as an accurate physiological indicator for
each individual. We develop Physiological Multitask-Learning (PhysioMTL) by
harnessing Optimal Transport theory within a Multitask-learning (MTL)
framework. The proposed method learns an individual-specific predictive model
from heterogeneous observations, and enables estimation of an optimal transport
map that yields a push forward operation onto the demographic features for each
task. Our model outperforms competing MTL methodologies on unobserved
predictive tasks for synthetic and two real-world datasets. Specifically, our
method provides remarkable prediction results on unseen held-out subjects given
only $20\%$ of the subjects in real-world observational studies. Furthermore,
our model enables a counterfactual engine that generates the effect of acute
stressors and chronic conditions on HRV rhythms.


  Quarkonia have been studied in different collision system and energy in order
to understand the effects of the hot and dense medium created in heavy-ion
collisions. CMS is well suited to measure quarkonia decays to muons given the
muon identification and charged particle tracking capability. We report here
prompt, non-prompt J/\psi, and \Upsilon\ production measured by the CMS
experiment in pp collisions at \sqrt{s}=7 TeV. In addition, the J/\psi\ and
\Upsilon\ production in PbPb at \sqrt{s_{NN}}=2.76 TeV and pp collisions at the
same per nucleon energy are measured and compared. Prompt and non-prompt
J/\psi\ contributions are separated for the first time in heavy-ion collisions,
as is the ground from the excited states in the \Upsilon\ family. Suppression
in PbPb at \sqrt{s_{NN}}=2.76 TeV is quantified for prompt J/\psi, B->J/\psi,
and \Upsilon(1S), as well as the relative suppression of \Upsilon(2S+3S)
compared to \Upsilon(1S).


  Semiconductor devices favor high carrier mobility for reduced Joule heating
and high thermal conductivity for rapid heat dissipation. The ability to
accurately characterize the motion of charge carriers and heat carriers is
necessary to improve the performance of electronic devices. However, the
conventional approaches of measuring carrier mobility and thermal conductivity
require separate and independent measurement techniques. These techniques often
involve invasive probing, such as depositing thin metal films on the sample as
Ohmic contacts for characterizing electrical transport or as optical
transducers for characterizing thermal transport, which becomes more cumbersome
as the geometry of the semiconductor devices becomes small and complicated.
Here we demonstrate a non-contact frequency-domain pump-probe method that
requires no sample pretreatment to simultaneously probe carrier and phonon
transport. We find that the optical reflectance depends on both excess carriers
and phonons in response to exposure to a modulated continuous-wave pump laser
source. By modeling the ambipolar diffusion of photo-induced excess carriers,
energy transfer between electrons and phonons, and phonon diffusion, we are
able to extract temperature-dependent electrical and thermal transport
coefficients in Si, Ge, SiGe and GaAs. The intrinsically weak perturbation from
the continuous-wave pump laser avoids invoking the highly nonequilibrium
transport regime that may arise when using a pulsed laser and allows for
accurate assessment of electrical transport in actual devices. Our approach
provides a convenient and accurate platform for the study of the charge
transport and energy dissipation in semiconductors.


  We present an analysis of high-resolution spectroscopic observations of
Polaris B, the optical companion of the Polaris Ab system. The star has a
radial velocity V_r of -16.6km/s to -18.9km/s, and a projected rotational
velocity vsini=110 km/s. The derived atmospheric parameters are: Teff=6900K;
logg=4.3; V_t=2.5km/s. Polaris B has elemental abundances generally similar to
those of the Cepheid Polaris A (Usenko et al. 2005a), although carbon, sodium
and magnesium are close to the solar values. At a spectral type of F3V Polaris
B has a luminosity of 3.868L_sun, an absolute magnitude of +3.30mag, and a
distance of 109.5pc. The mass of the star is estimated to be 1.39M_sun, close
to a mass of 1.38+/-0.61M_sun for the recently-resolved orbital periods
companion Polaris Ab observed by Evans et al. (2007).


  Objective: Murmurs are abnormal heart sounds, identified by experts through
cardiac auscultation. The murmur grade, a quantitative measure of the murmur
intensity, is strongly correlated with the patient's clinical condition. This
work aims to estimate each patient's murmur grade (i.e., absent, soft, loud)
from multiple auscultation location phonocardiograms (PCGs) of a large
population of pediatric patients from a low-resource rural area. Methods: The
Mel spectrogram representation of each PCG recording is given to an ensemble of
15 convolutional residual neural networks with channel-wise attention
mechanisms to classify each PCG recording. The final murmur grade for each
patient is derived based on the proposed decision rule and considering all
estimated labels for available recordings. The proposed method is
cross-validated on a dataset consisting of 3456 PCG recordings from 1007
patients using a stratified ten-fold cross-validation. Additionally, the method
was tested on a hidden test set comprised of 1538 PCG recordings from 442
patients. Results: The overall cross-validation performances for patient-level
murmur gradings are 86.3% and 81.6% in terms of the unweighted average of
sensitivities and F1-scores, respectively. The sensitivities (and F1-scores)
for absent, soft, and loud murmurs are 90.7% (93.6%), 75.8% (66.8%), and 92.3%
(84.2%), respectively. On the test set, the algorithm achieves an unweighted
average of sensitivities of 80.4% and an F1-score of 75.8%. Conclusions: This
study provides a potential approach for algorithmic pre-screening in
low-resource settings with relatively high expert screening costs.
Significance: The proposed method represents a significant step beyond
detection of murmurs, providing characterization of intensity which may provide
a enhanced classification of clinical outcomes.


  On 12 February 2020 the Royal Statistical Society hosted a meeting to discuss
the forthcoming paper ``Graphical models for extremes'' by Sebastian Engelke
and Adrien Hitz [arXiv:1812.01734]. This short note is a supplement to my
discussion contribution. It contains the proofs. It is shown that the
traditional notion of extremal independence agrees with the newly introduced
notion of extremal independence, which subsequently allows for a meaningful
interpretation of disconnected graphs in the context of the discussion paper.
The notation and references used in this note are adopted from the discussion
paper.


  We report optical, radio and X-ray observations of the Narrow-Line Seyfert 1
galaxy RX J1702.5+3247. The soft (0.1-2 keV) X-ray flux, measured by BeppoSAX,
is characterized by strong variability on short time scales (< 500 sec). The
most extreme amplitude variations require a radiative efficiency exceeding the
maximum for a Kerr black hole, implying the presence of relativistic effects. A
comparison with archival ROSAT data reveals long term temporal and spectral
variability. The 0.1-10 keV spectrum is equally well fitted either by an
ionized reflection disk model, or by a broken power law plus a Gaussian line,
consistent with a hydrogen-like iron line at 6.97 keV from a highly ionized
accretion disk.


  Encoder-decoder neural networks (EDNN) condense information most relevant to
the output of the feedforward network to activation values at a bottleneck
layer. We study the use of this architecture in emulation and interpretation of
simulated X-ray spectroscopic data with the aim to identify key structural
characteristics for the spectra, previously studied using emulator-based
component analysis (ECA). We find an EDNN to outperform ECA in covered target
variable variance, but also discover complications in interpreting the latent
variables in physical terms. As a compromise of the benefits of these two
approaches, we develop a network where the linear projection of ECA is used,
thus maintaining the beneficial characteristics of vector expansion from the
latent variables for their interpretation. These results underline the
necessity of information recovery after its condensation and identification of
decisive structural degrees for the output spectra for a justified
interpretation.


  Some recent ZEUS results on the photoproduction of open charm and possible
implications for photon structure are discussed.


  To discuss one-photon polarization states we find an explicit form of the
Wigner's little group element in the massless case for arbitrary Lorentz
transformation. As is well known, when analyzing the transformation properties
of the physical states, only the value of the phase factor is relevant. We show
that this phase factor depends only on the direction of the momentum
$\vec{k}/|\vec{k}|$ and does not depend on the frequency $k^0$. Finally, we use
this observation to discuss the transformation properties of the linearly
polarized photons and the corresponding reduced density matrix. We find that
they transform properly under Lorentz group.


  Representation learning on text-attributed graphs (TAGs) is vital for
real-world applications, as they combine semantic textual and contextual
structural information. Research in this field generally consist of two main
perspectives: local-level encoding and global-level aggregating, respectively
refer to textual node information unification (e.g., using Language Models) and
structure-augmented modeling (e.g., using Graph Neural Networks). Most existing
works focus on combining different information levels but overlook the
interconnections, i.e., the contextual textual information among nodes, which
provides semantic insights to bridge local and global levels. In this paper, we
propose GraphBridge, a multi-granularity integration framework that bridges
local and global perspectives by leveraging contextual textual information,
enhancing fine-grained understanding of TAGs. Besides, to tackle scalability
and efficiency challenges, we introduce a graphaware token reduction module.
Extensive experiments across various models and datasets show that our method
achieves state-of-theart performance, while our graph-aware token reduction
module significantly enhances efficiency and solves scalability issues.


  We study the dual equivalence between the nonlinear generalization of the
self-dual ($NSD_{B\wedge F}$) and the topologically massive $B\wedge F$ models
with particular emphasis on the nonlinear electrodynamics proposed by Born and
Infeld. This is done through a dynamical gauge embedding of the nonlinear
self-dual model yielding to a gauge invariant and dynamically equivalent
theory. We clearly show that nonpolinomial $NSD_{B\wedge F}$ models can be
mapped, through a properly defined duality transformation, into $TM_{B\wedge
F}$ actions. The general result obtained is then particularized for a number of
examples, including the Born-Infeld-BF (BIBF) model that has experienced a
revival in the recent literature.


  This paper is a small step towards the goal of constructing a coherent theory
of physic and mathematics together. It is based on two ideas, the localization
of mathematical systems in space or space time, and the separation of the
concepts of number from number value. The separation of number from number
value along with the freedom of choice of number values at different points of
space or space time enables the introduction of a space or space time dependent
number valuation field. The presence of a location dependent number value field
affects theoretical descriptions of many physical and geometric quantities. A
simple geometric example is worked out in detail, that of the length of a path.
  The localization of mathematical systems and the separation of number from
number value or meaning both emphasize the role of observers. The separation of
number from number value shows the role of observers in that value or meaning
are conscious observer related concepts. Nothing, including numbers, has value
or meaning to an unconscious observer. The localization of mathematical systems
also shows the role of observers in that the mathematics that is potentially
available to an observer is that at the same position as is the observer. This
represents the mathematical knowledge that can reside in an observers brain. As
an observer moves in space or space time, the mathematical knowledge
potentially available to the observer is the collection of mathematical systems
at the same location as the observer. It is hoped that this work, which was
begun in 2010, will lead to a better understanding of the relation between the
foundations of mathematics and physics, and the role that observers play in
this relation.


  The cross sections for the reactions of the strange production p + p -> p +
Lambda + K^+ and p + p -> p + Sigma^0 + K^+ near thresholds of the final states
p Lambda K^+ and p Sigma^0 K^+ are calculated in the effective Lagrangian
approach. Our approach is based on the dominant contribution of the one-pion
exchange and strong interaction of the colliding protons in the initial state.
The theoretical values of the cross sections agree reasonably well with the
experimental data. The polarization properties of the Lambda and Sigma^0
hyperons are discussed.


  Currently, most bone implants used in orthopedics and traumatology are
non-degradable and may need to be surgically removed later on e.g. in the case
of children. This removal is associated with health risks which could be
minimized by using biodegradable implants. Therefore, research on
magnesium-based implants is ongoing, which can be objectively quantified
through synchrotron radiation microtomography and subsequent image analysis. In
order to evaluate the suitability of these materials, e.g. their stability over
time, accurate pixelwise segmentations of these high-resolution scans are
necessary. The fully-convolutional U-Net architecture achieves a Dice
coefficient of 0.750 +/- 0.102 when trained with a small dataset with dense
expert annotations. However, extending the learning to larger databases would
require prohibitive annotation efforts. Hence, in this work we implemented and
compared new training methods that require only a small fraction of manually
annotated pixels. While directly training on these scribble annotation
deteriorates the segmentation quality by 26.8 percentage points, our new random
walk-based semi-automatic target achieves the same Dice overlap as a dense
supervision, and thus offers a more promising approach for sparse annotations.


  In this paper we propose the first non-parametric Bayesian model using
Gaussian Processes to make inference on Poisson Point Processes without
resorting to gridding the domain or to introducing latent thinning points.
Unlike competing models that scale cubically and have a squared memory
requirement in the number of data points, our model has a linear complexity and
memory requirement. We propose an MCMC sampler and show that our model is
faster, more accurate and generates less correlated samples than competing
models on both synthetic and real-life data. Finally, we show that our model
easily handles data sizes not considered thus far by alternate approaches.


  We study Graver test sets for families of linear multi-stage stochastic
integer programs with varying number of scenarios. We show that these test sets
can be decomposed into finitely many ``building blocks'', independent of the
number of scenarios, and we give an effective procedure to compute these
building blocks. The paper includes an introduction to Nash-Williams' theory of
better-quasi-orderings, which is used to show termination of our algorithm. We
also apply this theory to finiteness results for Hilbert functions.


  Determining when two individuals are within close distance is key to contain
a pandemic, e.g., to alert individuals in real-time and trace their social
contacts. Common approaches rely on either Bluetooth Low Energy (BLE) or
ultra-wideband (UWB) radios, that nonetheless strike opposite tradeoffs for
energy efficiency vs. accuracy of distance estimates.
  Janus reconciles these dimensions with a dual-radio protocol enabling
efficient and accurate social contact detection. Measurements show that Janus
achieves weeks to months of autonomous operation, depending on the
configuration. Several large-scale campaigns in real-world contexts confirm its
reliability and practical usefulness in enabling insightful analysis of contact
data.


  We examine the distribution of axis ratios of a large sample of disk galaxies
hosting type 2 AGNs selected from the Sloan Digital Sky Survey and compare it
with a well-defined control sample of non-active galaxies. We find them
significantly different, where the type 2 AGNs show both an excess of edge-on
objects and deficit of round objects. This systematical bias can not be
explained by a nuclear obscurer oriented randomly with respect to the stellar
disk. However, a nuclear obscurer coplanar with the stellar disk also does not
fit the data very well. By assuming that the nuclear obscurer having an opening
angle of ~60 degree, we find the observed axis ratio distribution can be nicely
reproduced by a mean tilt angle of ~30 degree between the nuclear obscurer and
the stellar disk.


  Automatic medical image classification is a very important field where the
use of AI has the potential to have a real social impact. However, there are
still many challenges that act as obstacles to making practically effective
solutions. One of those is the fact that most of the medical imaging datasets
have a class imbalance problem. This leads to the fact that existing AI
techniques, particularly neural network-based deep-learning methodologies,
often perform poorly in such scenarios. Thus this makes this area an
interesting and active research focus for researchers. In this study, we
propose a novel loss function to train neural network models to mitigate this
critical issue in this important field. Through rigorous experiments on three
independently collected datasets of three different medical imaging domains, we
empirically show that our proposed loss function consistently performs well
with an improvement between 2%-10% macro f1 when compared to the baseline
models. We hope that our work will precipitate new research toward a more
generalized approach to medical image classification.


  In this paper, we present an effective deep prediction framework based on
robust recurrent neural networks (RNNs) to predict the likely therapeutic
classes of medications a patient is taking, given a sequence of diagnostic
billing codes in their record. Accurately capturing the list of medications
currently taken by a given patient is extremely challenging due to undefined
errors and omissions. We present a general robust framework that explicitly
models the possible contamination through overtime decay mechanism on the input
billing codes and noise injection into the recurrent hidden states,
respectively. By doing this, billing codes are reformulated into its temporal
patterns with decay rates on each medical variable, and the hidden states of
RNNs are regularised by random noises which serve as dropout to improved RNNs
robustness towards data variability in terms of missing values and multiple
errors. The proposed method is extensively evaluated on real health care data
to demonstrate its effectiveness in suggesting medication orders from
contaminated values.


  Young planets embedded in their protoplanetary disk interact gravitationally
with it leading to energy and angular momentum exchange. This interaction
determines the evolution of the planet through changes to the orbital
parameters. We investigate changes in the orbital elements of a 20 Earth--mass
planet due to the torques from the disk. We focus on the non-linear evolution
of initially non-vanishing eccentricity $e$ and/or inclination $i$. We treat
the disk as a two- or three-dimensional viscous fluid and perform
hydrodynamical simulations with an embedded planet. We find rapid exponential
decay of the planet orbital eccentricity and inclination for small initial
values of $e$ and $i$, in agreement with linear theory. For larger values of $e
> 0.1$ the decay time increases and the decay rate scales as $\dot{e} \propto
e^{-2}$, consistent with existing theoretical models. For large inclinations
($i$ > 6 deg) the inclination decay rate shows an identical scaling $di/dt
\propto i^{-2}$. We find an interesting dependence of the migration on the
eccentricity. In a disk with aspect ratio $H/r=0.05$ the migration rate is
enhanced for small non-zero eccentricities ($e < 0.1$), while for larger values
we see a significant reduction by a factor of $\sim 4$. We find no indication
for a reversal of the migration for large $e$, although the torque experienced
by the planet becomes positive when $e \simeq 0.3$. This inward migration is
caused by the persisting energy loss of the planet.
  For non gap forming planets, eccentricity and inclination damping occurs on a
time scale that is very much shorter than the migration time scale. The results
of non linear hydrodynamic simulations are in very good agreement with linear
theory for small $e$ and $i$.


  Multiple Instance Learning (MIL) is a weak supervision learning paradigm that
allows modeling of machine learning problems in which labels are available only
for groups of examples called bags. A positive bag may contain one or more
positive examples but it is not known which examples in the bag are positive.
All examples in a negative bag belong to the negative class. Such problems
arise frequently in fields of computer vision, medical image processing and
bioinformatics. Many neural network based solutions have been proposed in the
literature for MIL, however, almost all of them rely on introducing specialized
blocks and connectivity in the architectures. In this paper, we present a novel
and effective approach to Multiple Instance Learning in neural networks.
Instead of making changes to the architectures, we propose a simple bag-level
ranking loss function that allows Multiple Instance Classification in any
neural architecture. We have demonstrated the effectiveness of our proposed
method for popular MIL benchmark datasets. In addition, we have tested the
performance of our method in convolutional neural networks used to model an MIL
problem derived from the well-known MNIST dataset. Results have shown that
despite being simpler, our proposed scheme is comparable or better than
existing methods in the literature in practical scenarios. Python code files
for all the experiments can be found at https://github.com/amina01/ESMIL.


  Higgs physics beyond the Standard Model (SM) is presented in the context of
an underlying strong dynamics of electroweak symmetry breaking (EWSB) as given
by composite Higgs models. Subsequently, the study of New Physics (NP) effects
in a more model-independent way through the effective Lagrangian approach is
briefly sketched before moving on to the investigation of NP through Higgs
coupling measurements. Depending on the precision on the extracted couplings,
NP scales up to the TeV range can be probed at the high-luminosity option of
the LHC, if the coupling deviations arise from mixing effects or from some
underlying strong dynamics.


  Let $G(V,E)$ be a directed graph with $n$ vertices and $m$ edges. The edges
$E$ of $G$ are divided into two types: $E_F$ and $E_P$. Each edge of $E_F$ has
a fixed price. The edges of $E_P$ are the priceable edges and their price is
not fixed a priori. Let $r$ be a vertex of $G$. For an assignment of prices to
the edges of $E_P$, the revenue is given by the following procedure: select a
shortest path tree $T$ from $r$ with respect to the prices (a tree of cheapest
paths); the revenue is the sum, over all priceable edges $e$, of the product of
the price of $e$ and the number of vertices below $e$ in $T$.
  Assuming that $k=|E_P|\ge 2$ is a constant, we provide a data structure whose
construction takes $O(m+n\log^{k-1} n)$ time and with the property that, when
we assign prices to the edges of $E_P$, the revenue can be computed in
$(\log^{k-1} n)$. Using our data structure, we save almost a linear factor when
computing the optimal strategy in the Stackelberg shortest paths tree game of
[D. Bil{\`o} and L. Gual{\`a} and G. Proietti and P. Widmayer. Computational
aspects of a 2-Player Stackelberg shortest paths tree game. Proc. WINE 2008].


  This book deals with the theory of generalized algebraic transformations,
which is elaborated with the aim to provide a relatively simple theoretical
tool that enables an exact treatment of diverse more complex
lattice-statistical models. In addition to a brief historical account on the
developments of this exact mapping method, the versatility of generalized
algebraic transformations will be convincingly evidenced when providing exact
results for two different families of exactly solvable models. The family of
exactly solved Ising models brings a deeper insight into various aspects
closely associated especially with phase transitions and critical phenomena.
The second class of exactly solved Ising-Heisenberg models sheds light on
striking quantum manifestations of spontaneously long-range ordered systems,
which are closely connected with a mutual interplay between quantum and
cooperative phenomena.


  Aligning protein interaction networks (PPI) of two or more organisms consists
of finding a mapping of the nodes (proteins) of the networks that captures
important structural and functional associations (similarity). It is a well
studied but difficult problem. It is provably NP-hard in some instances thus
computationally very demanding. The problem comes in several versions: global
versus local alignment; pairwise versus multiple alignment; one-to-one versus
many-to-many alignment. Heuristics to address the various instances of the
problem abound and they achieve some degree of success when their performance
is measured in terms of node and/or edges conservation. However, as the
evolutionary distance between the organisms being considered increases the
results tend to degrade. Moreover, poor performance is achieved when the
considered networks have remarkably different sizes in the number of nodes
and/or edges. Here we address the challenge of analyzing and comparing
different approaches to global network alignment, when a one-to-one mapping is
sought. We consider and propose various measures to evaluate the agreement
between alignments obtained by existing approaches. We show that some such
measures indicate an agreement that is often about the same than what would be
obtained by chance. That tends to occur even when the mappings exhibit a good
performance based on standard measures.


  A recent study applied frequentist survival analysis methods to a subset of
the Software Heritage Graph and determined which attributes of an OSS project
contribute to its health. This paper serves as an exact replication of that
study. In addition, Bayesian survival analysis methods were applied to the same
dataset, and an additional project attribute was studied to serve as a
conceptual replication. Both analyses focus on the effects of certain
attributes on the survival of open-source software projects as measured by
their revision activity. Methods such as the Kaplan-Meier estimator, Cox
Proportional-Hazards model, and the visualization of posterior survival
functions were used for each of the project attributes. The results show that
projects which publish major releases, have repositories on multiple hosting
services, possess a large team of developers, and make frequent revisions have
a higher likelihood of survival in the long run. The findings were similar to
the original study; however, a deeper look revealed quantitative
inconsistencies.


  Recent advances in nanotechnologies have prompted the need for tools to
accurately and non invasively manipulate individual nanoobjects. Among the
possible strategies, optical forces have been widely used to enable nano
optical tweezers capable of trapping or moving a specimen with unprecedented
accuracy. Here, we propose an architecture consisting of a nanotip excited with
a plasmonic vortex enabling effective dynamical control of nanoparticles in
three dimensions. The optical field generated by the structure can be used to
manipulate single dielectric nanoparticles acting on the total angular momentum
of light used to illuminate the structure. We demonstrate that it is possible
to stably trap or force the beaming of the particle from specific points, thus
enabling a new platform for nanoparticle manipulation and sorting.


  With numerical calculation of coupled Bloch equations, we have simulated the
spin dynamics of nuclear magnetic resonance gyroscope based on alkali
metal-noble gas hybrid trispin system. From the perspective of damping harmonic
oscillator, a thorough analysis of the response dynamics is demonstrated. The
simulation results shows a linear increasing response of gyroscope signal while
the noblge gas nuclear spin magnetization and alkali atomic spin lifetime
parameters are at the over damping condition. An upper limit of response is
imposed on the NMR gyroscope signal due to the inherent dynamics of the hybrid
trispin system. The results agrees with present available experimental results
and provide useful guidings for future experiments.


  Computational modeling is usually applied to aid experimental exploration of
advanced materials to better understand the fundamental plasticity mechanisms
during mechanical testing. In this work, we perform Molecular dynamics (MD)
simulations to emulate experimental room temperature spherical-nanoindentation
of crystalline W matrices by different interatomic potentials: EAM, modified
EAM, and a recently developed machine learned based tabulated Gaussian
approximation potential (tabGAP) for describing the interaction of W-W. Results
show similarities between load displacements and stress-strain curves,
regardless of the numerical model. However, a discrepancy is observed at early
stages of the elastic to plastic deformation transition showing different
mechanisms for dislocation nucleation and evolution, that is attributed to the
difference of Burgers vector magnitudes, stacking fault and dislocation glide
energies. Besides, contact pressure is investigated by considering large
indenters sizes that provides a detailed analysis of screw and edge
dislocations during loading process. Furthermore, the glide barrier of this
kind of dislocations are reported for all the interatomic potentials showing
that tabGAP model presents the most accurate results with respect to density
functional theory calculations and a good qualitative agreement with reported
experimental data


  The absence of a band-gap in graphene limits the gate modulation of its
electron conductivity, both in regular graphene as well as in PN junctions,
where electrostatic barriers prove transparent to Klein tunneling. We
demonstrate a novel way to directly open a gate-tunable transmission gap across
graphene PN junctions (GPNJ) by introducing an additional barrier in the middle
that replaces Klein tunneling with regular tunneling, allowing us to
electrostatically modulate the current by several orders of magnitude. The gap
arises by angularly sorting electrons by their longitudinal energy and
filtering out the hottest, normally incident electrons with the tunnel barrier,
and the rest through total internal reflection. Using analytical and atomistic
numerical studies of quantum transport, we show that the complete filtering of
all incident electrons causes the GPNJ to act as a novel metamaterial with a
unique gate-tunable transmission-gap that generates a sharp non-thermal
switching of electrons. In fact, the transmission gap gradually diminishes to
zero as we electrostatically reduce the voltage gradient across the junction
towards the homogeneous doping limit. The resulting gate tunable
metal-insulator transition enables the electrons to overcome the classic room
temperature switching limit of kTln10/q = 60mV/decade for subthreshold
conduction.


  A micro-canonical treatment is used to study particle production in pp
collisions. First this micro-canonical treatment is compared to some canonical
ones. Then proton, antiproton and pion 4(\pi) multiplicities from proton-proton
collisions at various center of mass energies are used to fix the
micro-canonical parameters (E) and (V). The dependences of the micro-canonical
parameters on the collision energy are parameterised for the further study of
pp reactions with this micro-canonical treatment.


  In this article we use semigroupoids to describe a notion of algebraic
bundles, mostly motivated by Fell ($C^*$-algebraic) bundles, and the sectional
algebras associated to them. As the main motivational example, Steinberg
algebras may be regarded as the sectional algebras of trivial (direct product)
bundles. Several theorems which relate geometric and algebraic constructions -
via the construction of a sectional algebra - are widely generalized: Direct
products bundles by semigroupoids correspond to tensor products of algebras;
Semidirect products of bundles correspond to "na\"ive" crossed products of
algebras; Skew products of graded bundles correspond to smash products of
graded algebras; Quotient bundles correspond to quotient algebras. Moreover,
most of the results hold in the non-Hausdorff setting. In the course of this
work, we generalize the definition of smash products to groupoid graded
algebras.
  As an application, we prove that whenever $\theta$ is a $\land$-preaction of
a discrete inverse semigroupoid $S$ on an ample (possibly non-Hausdorff)
groupoid $\mathcal{G}$, the Steinberg algebra of the associated groupoid of
germs is naturally isomorphic to a crossed product of the Steinberg algebra of
$\mathcal{G}$ by $S$. This is a far-reaching generalization of analogous
results which had been proven in particular cases.


  LS 5039 is a well-known $\gamma$-ray binary system which consists of an
unknown compact object and a massive companion O star. It shows rather stable
emissions at high energies over years and hence serves as an ideal laboratory
to investigate the emission mechanism for such peculiar systems which emit
prominent $\gamma$-rays. To this end, we take the orbital phase resolved energy
spectrum as observed by \fermi over 10 years. We divide the orbit into four
orbital phases, each with an orbital phase range of 0.25, centered at 0.00,
0.25, 0.50 and 0.75 respectively, where the phase 0.0 is the periastron and
phase 0.5 is the apastron. The phases around 0.25 and 0.75 are symmetric and
hence are supposed to have identical local acceleration environment. The
spectral analysis shows that, the \fermi spectra are largely different from
these two symmetric orbital phases: the emission from orbital phase 0.25 turns
out to be significantly stronger than that from 0.75. This result does not fit
a scenario that $\gamma$-rays are Doppler boosted emission from bow shock tails
if LS 5039 has a shock configuration similar to PSR B1259-63, and indicates
that the inverse Compton scatterings between the shock accelerated plasma and
the stellar particle environment is the underline procedure. We also find that
the previous report for a disappearance of the orbital modulation at 3--20 GeV
is due to the similar spectral turn-over energies of the different orbital
phases. The spectral properties of periastron and apastron regions are
addressed in the context of the measurements in phase regions around 0.25 and
0.75.


  A short survey of the role of unitarity for hard diffractive cross-sections
is given


  We theoretically investigate the nature of the state at quarter filled lowest
Landau level and predict that, as the quantum well width is increased, a
transition occurs from the composite fermion Fermi sea into a novel non-Abelian
fractional quantum Hall state that is topologically equivalent to $f$-wave
pairing of composite fermions. This state is topologically distinct from the
familiar $p$-wave paired Pfaffian state. We compare our calculated phase
diagram with experiments and make predictions for many observable quantities.


  This paper takes new look on language and knowledge modelling for corpus
linguistics. Using ideas of Chaitin, a line of argument is made against
language/knowledge separation in Natural Language Processing. A simplistic
model, that generalises approaches to language and knowledge, is proposed. One
of hypothetical consequences of this model is Strong AI.


  We present an explicit characterization for the real, continuous, isotropic
and strictly positive definite kernels on a product of compact two-point
homogeneous spaces, in the cases in which at least one of the spaces is a
sphere of dimension greater than 1 and the other is not a circle. The result
complements similar characterizations previously obtained for products of high
dimensional spheres.


  The ultrafast switching of magnetization in multiferroic materials by a
femtosecond laser could provide various advantages in photonics and magnonics.
An efficient approach to control the light matter interaction is the modulation
of ultrafast coherent magnons and phonons in the high frequency range.
Spontaneous Raman and infrared spectra reveal the excitation of magnons and
optical phonons in multiferroic BiFeO3 in the sub few terahertz range. However,
coherent control of such quasiparticles has not been achieved yet. In this
study, we demonstrate that linearly polarized laser pulses simultaneously
excite coherent magnons out of plane and in plane cyclone modes and optical
phonon E mode in BiFeO3. Experimental results in conjugation with
phenomenological theory, by considering three uniformly distributed magnetic
domains reveal that impulsive stimulated Raman scattering is responsible for
the generation of coherent magnons and phonons in BiFeO3. The observation of
these terahertz magnon and optical phonon modes paves the way for the
development of ultrafast magneto electro optical devices.


  We show that countable increasing unions preserve a large family of
well-studied covering properties, which are not necessarily sigma-additive.
Using this, together with infinite-combinatorial methods and simple forcing
theoretic methods, we explain several phenomena, settle problems of Just,
Miller, Scheepers and Szeptycki [COC2], Gruenhage and Szeptycki [FUfin], Tsaban
and Zdomskyy [SFT], and Tsaban [o-bdd, OPiT], and construct topological groups
with very strong combinatorial properties.


  The paper is devoted to generic translation flows corresponding to Abelian
differentials with one zero of order two on flat surfaces of genus two. These
flows are weakly mixing by the Avila-Forni theorem. Our main result gives first
quantitative estimates on their spectrum, establishing the Hoelder property for
the spectral measures of Lipschitz functions. The proof proceeds via uniform
estimates of twisted Birkhoff integrals in the symbolic framework of random
Markov compacta and arguments of Diophantine nature in the spirit of Salem,
Erdos and Kahane.


  Renormalization scheme uncertainties in the next-next-to-leading order QCD
predictions are discussed. To obtain an estimate of these uncertainties it is
proposed to compare predictions in all schemes that do not have unnaturally
large expansion coefficients. A concrete prescription for eliminating the
unnatural schemes is given, based on the requirement that large cancellations
in the expression for the characteristic renormalization scheme invariant
should be avoided. As an example the QCD corrections to the Bjorken sum rule
are considered. The importance of the next-next-to-leading order corrections
for a proper evaluation of perturbative QCD predictions is emphasized.


  It is shown that charged hadron multiplicity distributions in lepton-nucleon
scattering are fairly well described by the modified negative binomial
distribution in the energy range from 3-4 to 220 GeV. The energy behaviour of
the parameter k is similar to the dependence observed for e+e- annihilation.


  In 1996, Huisken-Yau proved that every three-dimensional Riemannian manifold
can be uniquely foliated near infinity by stable closed surfaces of constant
mean curvature (CMC) if it is asymptotically equal to the (spatial)
Schwarzschild solution. Using their method, Rigger proved the same theorem for
Riemannian manifolds being asymptotically equal to the (spatial)
(Schwarzschild-)Anti-de Sitter solution. This was generalized to asymptotically
hyperbolic manifolds by Neves-Tian, Chodosh, and the author at a later stage.
In this work, we prove the reverse implication as the author already did in the
Euclidean setting, i.e. any three-dimensional Riemannian manifold is
asymptotically hyperbolic if it (and only if) possesses a CMC-cover satisfying
certain geometric curvature estimates, a uniqueness property, and each surface
has controlled instability. As toy application of these geometric
characterizations of asymptotically Euclidean and hyperbolic manifolds, we
present a method for replacing an asymptotically hyperbolic by an
asymptotically Euclidean end and apply this method to prove that the Hawking
mass of the CMC-surfaces is bounded by their limit being the total mass of the
asymptotically hyperbolic manifold, where equality holds only for the t=0-slice
of the (Schwarzschild-)Anti-de Sitter spacetime.


  We investigate the three-dimensional flow around and through a porous screen
for various porosities at high Reynolds number Re = O(10000). Historically, the
study of this problem was focused on two-dimensional cases and for a screen
spanning completely or partially a channel. Since many recent problems have
involved a porous object in a three-dimensional free flow, we present a
three-dimensional model for porous screens, initially based on Koo & James
(1973) and Steiros & Hultmark (2018), accounting for viscous effects at the
vicinity of the screen, from which we can derive velocities, pressure
distribution as well as aerodynamic forces. We then characterize experimentally
the aerodynamic drag coefficient for a porous square screen, composed of
fibers, immersed in a laminar air flow with different angles of attack. We test
various fiber diameters to explore the effect of the space between the pores on
the drag force. The drag prediction from the model is in good agreement with
our experimental results. Our theoretical and experimental results suggest that
for high solidity, a homogeneous porous screens composed of fibers can have a
higher drag coefficient than a flat plate with the same dimensions. We also
show that local viscous effects are important: at the same solidity and with
the same air flow, the drag coefficient strongly depends on the Reynolds number
based on the fiber diameter. The model, taking into account three-dimensional
effects and the shape of the porous screen, may have many applications
including the prediction of water collection efficiency for fog harvesters.


  The learning efficiency and generalization ability of an intelligent agent
can be greatly improved by utilizing a useful set of skills. However, the
design of robot skills can often be intractable in real-world applications due
to the prohibitive amount of effort and expertise that it requires. In this
work, we introduce Skill Learning In Diversified Environments (SLIDE), a method
to discover generalizable skills via automated generation of a diverse set of
tasks. As opposed to prior work on unsupervised discovery of skills which
incentivizes the skills to produce different outcomes in the same environment,
our method pairs each skill with a unique task produced by a trainable task
generator. To encourage generalizable skills to emerge, our method trains each
skill to specialize in the paired task and maximizes the diversity of the
generated tasks. A task discriminator defined on the robot behaviors in the
generated tasks is jointly trained to estimate the evidence lower bound of the
diversity objective. The learned skills can then be composed in a hierarchical
reinforcement learning algorithm to solve unseen target tasks. We demonstrate
that the proposed method can effectively learn a variety of robot skills in two
tabletop manipulation domains. Our results suggest that the learned skills can
effectively improve the robot's performance in various unseen target tasks
compared to existing reinforcement learning and skill learning methods.


  Understanding the origins of volunteerism and free-riding is crucial in
collective action situations where a sufficient number of cooperators is
necessary to achieve shared benefits, such as in vaccination campaigns and
social change movements. Despite the importance of attaining behavior
consistency for successful collective action, the theoretical mechanisms behind
this process remain largely elusive. Here, we address this issue by studying
the evolutionary dynamics of individual cooperativity levels in multi-round
threshold public goods games. By using adaptive dynamics, we explore how
individual behavior responses are shaped by incentives, with and without
rewards or punishment. We demonstrate that rewarding consistent cooperators can
lead to the emergence of two distinct populations: volunteers who consistently
cooperate and free-riders who consistently defect. In contrast, punishing
consistent defectors does not lead to similar evolutionary branching. Our
results help offer insights into designing effective interventions that promote
collective action and address collective risk dilemmas ranging from climate
mitigation to pandemic control.


  We study different models of radiating slowly rotating bodies up to the first
order in the angular velocity. It is shown that up to this order the evolution
of the eccentricity is highly model-dependent even for very compact objects.


  We derive a mean-field description for two-dimensional (2D) interacting Bose
gases at arbitrary temperatures. We find that genuine Bose-Einstein
condensation with long-range coherence only survives at zero temperature. At
finite temperatures, many-body pairing effects included in our mean-field
theory introduce a finite amplitude for the pairing density, which results in a
finite superfluid density. We incorporate Berenzinskii-Kosterlitz-Thouless
(BKT) physics into our model by considering the phase fluctuations of our
pairing field. This then leads to the result that the superfluid phase is only
stable below the BKT temperature due to these phase fluctuations. In the weakly
interacting regime at low temperature we compare our theory to previous results
from perturbative calculations, renormalization group calculations as well as
Monte Carlo simulations. We present a finite-temperature phase diagram of 2D
Bose gases. One signature of the finite amplitude of the pairing density field
is a two-peak structure in the single-particle spectral function, resembling
that of the pseudogap phase in 2D attractive Fermi gases.


  Acoustic flow velocity and direction meters from the IST series, which can be
integrated into various measuring systems, are considered. The simulation of
the flow profile in the measuring tube was carried out depending on the free
flow velocity. A measurement method has been developed that uses the transit
time of repeated acoustic signals reflected from the transducers, which makes
it possible to take into account time hardware delays and a change in the
length of the measuring base, which affect the determination of the speed of
sound and flow, while increasing the accuracy of measuring the sound speed
parameters with simultaneous measuring the flow velocity and temperatures for
liquid media of different densities.


  Conversational search applications offer the prospect of improved user
experience in information seeking via agent support. However, it is not clear
how searchers will respond to this mode of engagement, in comparison to a
conventional user-driven search interface, such as those found in a standard
web search engine. We describe a laboratory-based study directly comparing user
behaviour for a conventional search interface (CSI) with that of an
agent-mediated multiview conversational search interface (MCSI) which extends
the CSI. User reaction and search outcomes of the two interfaces are compared
using implicit evaluation using five analysis methods: claiming to have a
better search experience in contrast to a corresponding standard search
interface.


  We present a multirate method that is particularly suited for integrating the
systems of Ordinary Differential Equations (ODEs) that arise in step models of
surface evolution. The surface of a crystal lattice, that is slightly miscut
from a plane of symmetry, consists of a series of terraces separated by steps.
Under the assumption of axisymmetry, the step radii satisfy a system of ODEs
that reflects the steps' response to step line tension and step-step
interactions. Two main problems arise in the numerical solution of these
equations. First, the trajectory of the innermost step can become singular,
resulting in a divergent step velocity. Second, when a step bunching
instability arises, the motion of steps within a bunch becomes very strongly
stable, resulting in "local stiffness". The multirate method introduced in this
paper ensures that small time steps are taken for singular and locally stiff
components, while larger time steps are taken for the remaining ones. Special
consideration is given to the construction of high order interpolants during
run time which ensures fourth order accuracy of scheme for components of the
solution sufficiently far away from singular trajectories.


  Ensuring privacy of users of social networks is probably an unsolvable
conundrum. At the same time, an informed use of the existing privacy options by
the social network participants may alleviate - or even prevent - some of the
more drastic privacy-averse incidents. Unfortunately, recent surveys show that
an average user is either not aware of these options or does not use them,
probably due to their perceived complexity. It is therefore reasonable to
believe that tools assisting users with two tasks: 1) understanding their
social net behavior in terms of their privacy settings and broad privacy
categories, and 2)recommending reasonable privacy options, will be a valuable
tool for everyday privacy practice in a social network context. This paper
presents YourPrivacyProtector, a recommender system that shows how simple
machine learning techniques may provide useful assistance in these two tasks to
Facebook users. We support our claim with empirical results of application of
YourPrivacyProtector to two groups of Facebook users.


  We introduce a three-step framework to determine at which pitches Major
League batters should swing. Unlike traditional plate discipline metrics, which
implicitly assume that all batters should always swing at (resp. take) pitches
inside (resp. outside) the strike zone, our approach explicitly accounts not
only for the players and umpires involved in the pitch but also in-game
contextual information like the number of outs, the count, baserunners, and
score. We first fit flexible Bayesian nonparametric models to estimate (i) the
probability that the pitch is called a strike if the batter takes the pitch;
(ii) the probability that the batter makes contact if he swings; and (iii) the
number of runs the batting team is expected to score following each pitch
outcome (e.g. swing and miss, take a called strike, etc.). We then combine
these intermediate estimates to determine whether swinging increases the
batting team's run expectancy. Our approach enables natural uncertainty
propagation so that we can not only determine the optimal swing/take decision
but also quantify our confidence in that decision. We illustrate our framework
using a case study of pitches faced by Mike Trout in 2019.


  We discuss the theoretical modelling of x-ray photoelectron diffraction (XPD)
with hard x-ray excitation at up to 20 keV, using the dynamical theory of
electron diffraction to illustrate the characteristic aspects of diffraction
patterns resulting from such localized emission sources in a multi-layer
crystal.
  We show via dynamical calculations for diamond, Si, and Fe that the dynamical
theory well predicts available current data for lower energies around 1 keV,
and that the patterns for energies above about 1 keV are dominated by Kikuchi
bands which are created by the dynamical scattering of electrons from lattice
planes. The origin of the fine structure in such bands is discussed from the
point of view of atomic positions in the unit cell. The profiles and positions
of the element-specific photoelectron Kikuchi bands are found to be sensitive
to lattice distortions (e.g. a 1% tetragonal distortion) and the position of
impurities or dopants with respect to lattice sites. We also compare the
dynamical calculations to results from a cluster model that is more often used
to describe lower-energy XPD. We conclude that hard XPD (HXPD) should be
capable of providing unique bulk-sensitive structural information for a wide
variety of complex materials in future experiments.