  A number of high-level languages and libraries have been proposed that offer
novel and simple to use abstractions for concurrent, asynchronous, and
distributed programming. The execution models that realise them, however, often
change over time---whether to improve performance, or to extend them to new
language features---potentially affecting behavioural and safety properties of
existing programs. This is exemplified by SCOOP, a message-passing approach to
concurrent object-oriented programming that has seen multiple changes proposed
and implemented, with demonstrable consequences for an idiomatic usage of its
core abstraction. We propose a semantics comparison workbench for SCOOP with
fully and semi-automatic tools for analysing and comparing the state spaces of
programs with respect to different execution models or semantics. We
demonstrate its use in checking the consistency of properties across semantics
by applying it to a set of representative programs, and highlighting a
deadlock-related discrepancy between the principal execution models of SCOOP.
Furthermore, we demonstrate the extensibility of the workbench by generalising
the formalisation of an execution model to support recently proposed extensions
for distributed programming. Our workbench is based on a modular and
parameterisable graph transformation semantics implemented in the GROOVE tool.
We discuss how graph transformations are leveraged to atomically model
intricate language abstractions, how the visual yet algebraic nature of the
model can be used to ascertain soundness, and highlight how the approach could
be applied to similar languages.


  Lepton number violation (LNV) mediated by short range operators can manifest
itself in both neutrinoless double beta decay and in processes with same sign
dilepton final states at the LHC. We derive limits from existing LHC data at
$\sqrt{s}=8$ TeV and compare the discovery potential of the forthcoming
$\sqrt{s}=14$ TeV phase of the LHC with the sensitivity of current and future
neutrinoless double beta decay experiments, assuming the short-range part of
the decay amplitude dominates. We focus on the first of two possible topologies
triggered by one fermion and two bosons in the intermediate state. In all
cases, except for the pure leptoquark mechanism, the LHC will be more sensitive
than neutrinoless double beta decay in the future. In addition, we propose to
search for a charge asymmetry in the final state leptons and to use different
invariant mass peaks as a possibility to discriminate the various possible
mechanisms for LNV signals at the LHC.


  When a light pulse is reflected from a mirror, energy and momentum are
exchanged between the electromagnetic field and the material medium. The
resulting change in the energy of the reflected photons is directly related to
their Doppler shift arising from the change in the state of motion of the
mirror. Similarly, the Doppler shift of photons that enter an absorber is
intimately tied to the kinetic energy and momentum acquired by the absorber in
its interaction with the incident light. The argument from the Doppler shift
yields expressions for the exchanged energy and momentum that are identical
with those obtained from Maxwell's equations and the Lorentz law of force,
despite the fact that the physical bases of the two methods are fundamentally
different. Here we apply the Doppler shift argument to a submerged partial
reflector (one that absorbs a fraction of the incident light), deducing in the
process the magnitude of the photon momentum within the submerging medium. We
also discuss the case of the submerging medium having a negative refractive
index, and show the absence of the so-called "reversed" Doppler shift when the
reflector is detached from the negative-index medium.


  We study two systems of tangle equations that arise when modeling the action
of the Integrase family of proteins on DNA. These two systems--direct and
inverted repeats--correspond to two different possibilities for the initial DNA
sequence. We present one new class of solutions to the tangle equations. In the
case of inverted repeats we prove that any solution not in these, or 2
previously known classes, would have to belong to one specific class. In the
case of direct repeats we prove that the three classes are are the only
solutions possible.


  Analytic methods for Abelian projection are developed, and a number of
results related to string tension measurements are obtained. It is proven that
even without gauge fixing, Abelian projection yields string tensions of the
underlying non-Abelian theory. Strong arguments are given for similar results
in the case where gauge fixing is employed. The subgroup used for projection
need only contain the center of the gauge group, and need not be Abelian. While
gauge fixing is shown to be in principle unnecessary for the success of Abelian
projection, it is computationally advantageous for the same reasons that
improved operators, e.g., the use of fat links, are advantageous in Wilson loop
measurements.


  Converting quad meshes to smooth manifolds, guided subdivision offers a way
to combine the good highlight line distributions of recent G-spline
constructions with the refinability of subdivision surfaces. Specifically, we
present a C2 subdivision algorithm of polynomial degree bi-6 and a curvature
bounded algorithm of degree bi-5. We prove that the common eigen-structure of
this class of subdivision algorithms is determined by their guide and
demonstrate that the eigenspectrum (speed of contraction) can be adjusted
without harming the shape.


  The applicability of the swarm robots to perform foraging tasks is inspired
by their compact size and cost. A considerable amount of energy is required to
perform such tasks, especially if the tasks are continuous and/or repetitive.
Real-world situations in which robots perform tasks continuously while staying
alive (survivability) and maximizing production (performance) require energy
awareness. This paper proposes an energy-conscious distributed task allocation
algorithm to solve continuous tasks (e.g., unlimited foraging) for cooperative
robots to achieve highly effective missions. We consider efficiency as a
function of the energy consumed by the robot during exploration and collection
when food is returned to the collection bin. Finally, the proposed
energy-efficient algorithm minimizes the total transit time to the charging
station and time consumed while recharging and maximizes the robot's lifetime
to perform maximum tasks to enhance the overall efficiency of collaborative
robots. We evaluated the proposed solution against a typical greedy
benchmarking strategy (assigning the closest collection bin to the available
robot and recharging the robot at maximum) for efficiency and performance in
various scenarios. The proposed approach significantly improved performance and
efficiency over the baseline approach.


  Stars pulsating in three radial modes are very rare; only three examples are
known in the Galaxy. These stars are very useful since their periods may be
measured very precisely, and this will constrain the global stellar parameters
and the models of the star's interior. The purpose of this paper is to present
a new example of the class of triple-mode radial pulsators. A search for
candidate multi-mode pulsators was carried out in public survey data.
Time-series photometry of one of the candidates, GSC 762-110, was performed.
GSC 762-110 was found to be a triple-mode radial pulsator, with a fundamental
period of 0.1945d and period ratios of 0.7641 and 0.8012. In addition two
non-radial modes were found, for which the amplitude has diminished
considerably over the last few years.


  We present a computational framework for the simulation of blood flow with
fully resolved red blood cells (RBCs) using a modular approach that consists of
a lattice Boltzmann solver for the blood plasma, a novel finite element based
solver for the deformable bodies and an immersed boundary method for the
fluid-solid interaction. For the RBCs, we propose a nodal projective FEM
(npFEM) solver which has theoretical advantages over the more commonly used
mass-spring systems (mesoscopic modeling), such as an unconditional stability,
versatile material expressivity, and one set of parameters to fully describe
the behavior of the body at any mesh resolution. At the same time, the method
is substantially faster than other FEM solvers proposed in this field, and has
an efficiency that is comparable to the one of mesoscopic models. At its core,
the solver uses specially defined potential energies, and builds upon them a
fast iterative procedure based on quasi-Newton techniques. For a known
material, our solver has only one free parameter that demands tuning, related
to the body viscoelasticity. In contrast, state-of-the-art solvers for
deformable bodies have more free parameters, and the calibration of the models
demands special assumptions regarding the mesh topology, which restrict their
generality and mesh independence. We propose as well a modification to the
potential energy proposed by Skalak et al. 1973 for the red blood cell
membrane, which enhances the strain hardening behavior at higher deformations.
Our viscoelastic model for the red blood cell, while simple enough and
applicable to any kind of solver as a post-convergence step, can capture
accurately the characteristic recovery time and tank-treading frequencies. The
framework is validated using experimental data, and it proves to be scalable
for multiple deformable bodies.


  The spectrum of bound and scattering states of the one dimensional Dirac
Hamiltonian describing fermions distorted by a static background built from two
Dirac delta potentials is studied. A distinction will be made between
mass-spike and electrostatic Dirac delta-potentials. The second quantisation is
then performed to promote the relativistic quantum mechanical problem to a
relativistic quantum field theory and study the quantum vacuum interaction
energy for fermions confined between opaque plates. The work presented here is
a continuation of [Guilarte et al 2019 Front. Phys.7 109].


  Medical image segmentation is inherently an ambiguous task due to factors
such as partial volumes and variations in anatomical definitions. While in most
cases the segmentation uncertainty is around the border of structures of
interest, there can also be considerable inter-rater differences. The class of
conditional variational autoencoders (cVAE) offers a principled approach to
inferring distributions over plausible segmentations that are conditioned on
input images. Segmentation uncertainty estimated from samples of such
distributions can be more informative than using pixel level probability
scores. In this work, we propose a novel conditional generative model that is
based on conditional Normalizing Flow (cFlow). The basic idea is to increase
the expressivity of the cVAE by introducing a cFlow transformation step after
the encoder. This yields improved approximations of the latent posterior
distribution, allowing the model to capture richer segmentation variations.
With this we show that the quality and diversity of samples obtained from our
conditional generative model is enhanced. Performance of our model, which we
call cFlow Net, is evaluated on two medical imaging datasets demonstrating
substantial improvements in both qualitative and quantitative measures when
compared to a recent cVAE based model.


  Report on the NSF Workshop on Formal Methods for Security, held 19-20
November 2015.


  Neural networks promote a distributed representation with no clear place for
symbols. Despite this, we propose that symbols are manufactured simply by
training a sparse random noise as a self-sustaining attractor in a feedback
spiking neural network. This way, we can generate many of what we shall call
prime attractors, and the networks that support them are like registers holding
a symbolic value, and we call them registers. Like symbols, prime attractors
are atomic and devoid of any internal structure. Moreover, the winner-take-all
mechanism naturally implemented by spiking neurons enables registers to recover
a prime attractor within a noisy signal. Using this faculty, when considering
two connected registers, an input one and an output one, it is possible to bind
in one shot using a Hebbian rule the attractor active on the output to the
attractor active on the input. Thus, whenever an attractor is active on the
input, it induces its bound attractor on the output; even though the signal
gets blurrier with more bindings, the winner-take-all filtering faculty can
recover the bound prime attractor. However, the capacity is still limited. It
is also possible to unbind in one shot, restoring the capacity taken by that
binding. This mechanism serves as a basis for working memory, turning prime
attractors into variables. Also, we use a random second-order network to
amalgamate the prime attractors held by two registers to bind the prime
attractor held by a third register to them in one shot, de facto implementing a
hash table. Furthermore, we introduce the register switch box composed of
registers to move the content of one register to another. Then, we use spiking
neurons to build a toy symbolic computer based on the above. The technics used
suggest ways to design extrapolating, reusable, sample-efficient deep learning
networks at the cost of structural priors.


  We report on measurements of the time-dependent CP violating observables in
$B^0_s\rightarrow D^{\mp}_s K^{\pm}$ decays using a dataset corresponding to
1.0 fb$^{-1}$ of pp collisions recorded with the LHCb detector. We find the CP
violating observables $C_f=0.53\pm0.25\pm0.04$,
$A^{\Delta\Gamma}_f=0.37\pm0.42\pm0.20$,
$A^{\Delta\Gamma}_{\bar{f}}=0.20\pm0.41\pm0.20$, $S_f=-1.09\pm0.33\pm0.08$,
$S_{\bar{f}}=-0.36\pm0.34\pm0.08$, where the uncertainties are statistical and
systematic, respectively. Using these observables together with a recent
measurement of the $B^0_s$ mixing phase $-2\beta_s$ leads to the first
extraction of the CKM angle $\gamma$ from $B^0_s \rightarrow D^{\mp}_s K^{\pm}$
decays, finding $\gamma$ = (115$_{-43}^{+28}$)$^\circ$ modulo 180$^\circ$ at
68% CL, where the error contains both statistical and systematic uncertainties.


  In this paper we discuss the numerical solution on a simple 2D domain of the
Helmoltz equation with mixed boundary conditions. The so called radiation
problem depends on the wavenumber constant parameter k and it is inspired here
by medical applications, where a transducer emits a pulse at a given frequency.
This problem has been successfully solved in the past with the classical Finite
Element Method (FEM) for relative small values of k. But in modern applications
the values of k can be of order of thousands and FEM faces up several numerical
difficulties. To overcome these difficulties we solve the radiation problem
using the Isogeometric Analysis (IgA), a kind of generalization of FEM.
Starting with the variational formulation of the radiation problem, we show
with details how to apply the isogeometric approach in order to compute the
coefficients of the approximated solution of radiation problem in terms of the
B-spline basis functions. Our implementation of IgA using GeoPDEs software
shows that isogeometric approach is superior than FEM, since it is able to
reduce substantially the pollution error, especially for high values of k,
producing additionally smoother solutions which depend on less degrees of
freedom.


  The use of machine-learning techniques has grown in numerous research areas.
Currently, it is also widely used in statistics, including the official
statistics for data collection (e.g. satellite imagery, web scraping and text
mining, data cleaning, integration and imputation) but also for data analysis.
However, the usage of these methods in survey sampling including small area
estimation is still very limited. Therefore, we propose a predictor supported
by these algorithms which can be used to predict any population or
subpopulation characteristics based on cross-sectional and longitudinal data.
Machine learning methods have already been shown to be very powerful in
identifying and modelling complex and nonlinear relationships between the
variables, which means that they have very good properties in case of strong
departures from the classic assumptions. Therefore, we analyse the performance
of our proposal under a different set-up, in our opinion of greater importance
in real-life surveys. We study only small departures from the assumed model, to
show that our proposal is a good alternative in this case as well, even in
comparison with optimal methods under the model. What is more, we propose the
method of the accuracy estimation of machine learning predictors, giving the
possibility of the accuracy comparison with classic methods, where the accuracy
is measured as in survey sampling practice. The solution of this problem is
indicated in the literature as one of the key issues in integration of these
approaches. The simulation studies are based on a real, longitudinal dataset,
freely available from the Polish Local Data Bank, where the prediction problem
of subpopulation characteristics in the last period, with "borrowing strength"
from other subpopulations and time periods, is considered.


  We describe the foundation of the log minimal model program for log canonical
pairs according to Ambro's idea. We generalize Koll\'ar's vanishing and
torsion-free theorems for embedded simple normal crossing pairs. Then we prove
the cone and contraction theorems for quasi-log varieties, especially, for log
canonical pairs.


  HD 21190 is a known $\delta$ Scuti star showing Ap star characteristics and a
variability period of 3.6 h discovered by the Hipparcos mission. Using Gaia DR1
data for an astrometric analysis, it was recently suggested that HD 21190 forms
a physical binary system with the companion CPD -83$^{\circ}$ 64B. An
atmospheric chemical analysis based on HARPS observations revealed the presence
of overabundances of heavy and rare-earth elements, which are typically
observed in chemically peculiar stars with large-scale organized magnetic
fields. Previous observations of HD 21190 indicated a magnetic field strength
of a few hundred Gauss. The presence of a magnetic field in CPD -83$^{\circ}$
64B remained unexplored. In this work, we reanalyse this system using Gaia DR2
data and present our search for the magnetic field in both stars based on
multi-epoch HARPSpol high-resolution and FORS 2 low-resolution
spectropolarimetric observations. The Gaia DR2 results clearly indicate that
the two star are not physically associated. A magnetic field detection at a
significance level of more than 6$\sigma$ ($\left< B_{\rm z}\right>_{\rm
all}=230\pm38$ G) was achieved for the $\delta$ Scuti variable HD 21190 in FORS
2 observations using the entire spectrum for the measurements. The magnetic
field appears to be stronger in CPD -83$^{\circ}$ 64B. The highest value for
the longitudinal magnetic field in CPD -83$^{\circ}$ 64B, $\left< B_{\rm
z}\right>_{\rm all}=509\pm104$ G, is measured at a significance level of
4.9$\sigma$. Furthermore, the high-resolution HARPS\-pol observations of this
component indicate the presence of pulsational variability on a time scale of
tens of minutes.


  We construct new examples of Einstein metrics by perturbing the conformal
infinity of geometrically finite hyperbolic metrics and by applying the inverse
function theorem in suitable weighted H\"older spaces.


  Measuring the centroid of a spectral line is a common problem in astronomy.
Many methods have been devised to overcome limitations due to either noise in
the spectra or asymmetric profiles, the most common of which are the intensity
weighted averages (first moment) or fits of analytical (typically Gaussian)
profiles. If the spectral line can be considered a single component, we
demonstrate that a simple quadratic fit to the pixel of maximum intensity and
its two neighboring pixels provides a robust measure of the line centroid. This
approach allows for a sub-velocity resolution precision on the line centroid,
without be biases by noise or asymmetric features in the line profile and
outperforming traditional methods in most situations.


  Current Deep Neural Networks are vulnerable to adversarial examples, which
alter their predictions by adding carefully crafted noise. Since human eyes are
robust to such inputs, it is possible that the vulnerability stems from the
standard way of processing inputs in one shot by processing every pixel with
the same importance. In contrast, neuroscience suggests that the human vision
system can differentiate salient features by (1) switching between multiple
fixation points (saccades) and (2) processing the surrounding with a
non-uniform external resolution (foveation). In this work, we advocate that the
integration of such active vision mechanisms into current deep learning systems
can offer robustness benefits. Specifically, we empirically demonstrate the
inherent robustness of two active vision methods - GFNet and FALcon - under a
black box threat model. By learning and inferencing based on downsampled
glimpses obtained from multiple distinct fixation points within an input, we
show that these active methods achieve (2-3) times greater robustness compared
to a standard passive convolutional network under state-of-the-art adversarial
attacks. More importantly, we provide illustrative and interpretable
visualization analysis that demonstrates how performing inference from distinct
fixation points makes active vision methods less vulnerable to malicious
inputs.


  Quantum computers can accurately compute ground state energies using phase
estimation, but this requires a guiding state which has significant overlap
with the true ground state.For large molecules and extended materials, it
becomes difficult to find guiding states with good ground state overlap for
growing molecule sizes. Additionally, the required number of qubits and quantum
gates may become prohibitively large. One approach for dealing with these
challenges is to use a quantum embedding method, which allows a reduction to
one or multiple smaller quantum cores embedded in a larger quantum region. In
such situations it is unclear how the embedding method affects the hardness of
constructing good guiding states. In this work, we therefore investigate the
preparation of guiding states in the context of quantum embedding methods. We
extend previous work on quantum impurity problems, a framework in which we can
rigorously analyze the embedding of a subset of orbitals. While there exist
results for optimal active orbital space selection in terms of energy
minimization, we rigorously demonstrate how the same principles can be used to
define selected orbital spaces for state preparation in terms of the overlap
with the ground state. Moreover, we perform numerical studies of molecular
systems relevant to biochemistry, one field in which quantum embedding methods
are required due to the large size of biomacromolecules such as proteins and
nucleic acids. We investigate two different embedding strategies which can
exhibit qualitatively different orbital entanglement. In all cases we
demonstrate that the easy-to-obtain mean-field state will have a sufficiently
high overlap with the target state to perform quantum phase estimation.


  We analyze the five-dimensional AdS gravity coupled to a gauge field and a
charged scalar field. Under a Scherk-Schwarz compactification, we show that the
system undergoes a superconductor/insulator transition at zero temperature in
2+1 dimensions as we change the chemical potential. By taking into account a
confinement/deconfinement transition, the phase diagram turns out to have a
rich structure. We will observe that it has a similarity with the RVB
(resonating valence bond) approach to high-Tc superconductors via an emergent
gauge symmetry.


  During the last five years of his life, 1874-79, James Clerk Maxwell was
absorbed in editing the electrical researches of Henry Cavendish, performed 100
years earlier. This endeavour is often assumed to be a work of duty to the
Cavendish family, and an unfortunate waste of Maxwell's time. By looking at the
history of Cavendish's papers, and the editorial choices that Maxwell made,
this paper questions this assumption, considering the importance of Cavendish's
experiments in Maxwell's electrical programme, and the implications that he may
have derived for developing a doctrine of experimental method.


  Observing nuclear neutrinoless double beta (0vbb) decay would be a
revolutionary result in particle physics. Observing such a decay would prove
that the neutrinos are their own antiparticles, help to study the absolute mass
of neutrinos, explore the origin of their mass, and may explain the
matter-antimatter asymmetry in our universe by lepton number violation.
  We propose developing a time projection chamber (TPC) using high-pressure
82SeF6 gas and top-metal silicon sensors for read-out in the China Jinping
Underground Laboratory (CJPL) to search for neutrinoless double beta decay of
82Se, called the NvDEx experiment. Besides being located at CJPL with the
world's thickest rock shielding, NvDEx combines the advantages of the high Qbb
(2.996 MeV) of 82Se and the TPC's ability to distinguish signal and background
events using their different topological characteristics. This makes NvDEx
unique, with great potential for low-background and high-sensitivity 0vbb
searches.
  NvDEx-100, a NvDEx experiment phase with 100 kg of SeF6 gas, is being built,
with plans to complete installation at CJPL by 2025. This report introduces
0vbb physics, the NvDEx concept and its advantages, and the schematic design of
NvDEx-100, its subsystems, and background and sensitivity estimation.


  Mergers of binary neutron stars create conditions of supranuclear density
$n\gtrsim n_{\rm nuc}\simeq 0.17 {\rm fm}^{-3}$ and moderate temperature
$50\lesssim T \lesssim 90 {\rm MeV}$. These events thus probe a sensitive
region of the density-temperature phase diagram of QCD matter. We study photon
production by the QCD conformal anomaly for a signal of a possible transition
to quark degrees of freedom during the merger. We discuss energy loss due to
photon radiation as a cooling mechanism that is sensitive to the bulk viscosity
and thermal conductivity of the quark matter.


  It is shown that the non-trivial cocycles on simple Lie algebras may be used
to introduce antisymmetric multibrackets which lead to higher-order Lie
algebras, the definition of which is given. Their generalised Jacobi identities
turn out to be satisfied by the antisymmetric tensors (or higher-order
`structure constants') which characterise the Lie algebra cocycles. This
analysis allows us to present a classification of the higher-order simple Lie
algebras as well as a constructive procedure for them. Our results are
synthesised by the introduction of a single, complete BRST operator associated
with each simple algebra.


  We compare different neural network architectures for Machine Learning (ML)
algorithms designed to identify the neutrino interaction vertex position in the
MINERvA detector. The architectures developed and optimized by hand are
compared with the architectures developed in an automated way using the package
"Multi-node Evolutionary Neural Networks for Deep Learning" (MENNDL), developed
at Oak Ridge National Laboratory (ORNL). The two architectures resulted in a
similar performance which suggests that the systematics associated with the
optimized network architecture are small. Furthermore, we find that while the
domain expert hand-tuned network was the best performer, the differences were
negligible and the auto-generated networks performed well. There is always a
trade-off between human, and computer resources for network optimization and
this work suggests that automated optimization, assuming resources are
available, provides a compelling way to save significant expert time.


  Covering model provides a general framework for granular computing in that
overlapping among granules are almost indispensable. For any given covering,
both intersection and union of covering blocks containing an element are
exploited as granules to form granular worlds at different abstraction levels,
respectively, and transformations among these different granular worlds are
also discussed. As an application of the presented multi-granular perspective
on covering, relational interpretation and axiomization of four types of
covering based rough upper approximation operators are investigated, which can
be dually applied to lower ones.


  Fast and reliable K-Nearest Neighbor Graph algorithms are more important than
ever due to their widespread use in many data processing techniques. This paper
presents a runtime optimized C implementation of the heuristic "NN-Descent"
algorithm by Wei Dong et al. for the l2-distance metric. Various implementation
optimizations are explained which improve performance for low-dimensional as
well as high dimensional datasets. Optimizations to speed up the selection of
which datapoint pairs to evaluate the distance for are primarily impactful for
low-dimensional datasets. A heuristic which exploits the iterative nature of
NN-Descent to reorder data in memory is presented which enables better use of
locality and thereby improves the runtime. The restriction to the l2-distance
metric allows for the use of blocked distance evaluations which significantly
increase performance for high dimensional datasets. In combination the
optimizations yield an implementation which significantly outperforms a widely
used implementation of NN-Descent on all considered datasets. For instance, the
runtime on the popular MNIST handwritten digits dataset is halved.


  Automatic image-based pavement distress detection and recognition are vital
for pavement maintenance and management. However, existing deep learning-based
methods largely omit the specific characteristics of pavement images, such as
high image resolution and low distress area ratio, and are not end-to-end
trainable. In this paper, we present a series of simple yet effective
end-to-end deep learning approaches named Weakly Supervised Patch Label
Inference Networks (WSPLIN) for efficiently addressing these tasks under
various application settings. WSPLIN transforms the fully supervised pavement
image classification problem into a weakly supervised pavement patch
classification problem for solutions. Specifically, WSPLIN first divides the
pavement image under different scales into patches with different collection
strategies and then employs a Patch Label Inference Network (PLIN) to infer the
labels of these patches to fully exploit the resolution and scale information.
Notably, we design a patch label sparsity constraint based on the prior
knowledge of distress distribution and leverage the Comprehensive Decision
Network (CDN) to guide the training of PLIN in a weakly supervised way.
Therefore, the patch labels produced by PLIN provide interpretable intermediate
information, such as the rough location and the type of distress. We evaluate
our method on a large-scale bituminous pavement distress dataset named CQU-BPDD
and the augmented Crack500 (Crack500-PDD) dataset, which is a newly constructed
pavement distress detection dataset augmented from the Crack500. Extensive
results demonstrate the superiority of our method over baselines in both
performance and efficiency. The source codes of WSPLIN are released on
https://github.com/DearCaat/wsplin.


  Low temperature specific heat has been measured for an overdoped
$La_{2-x}Sr_xCuO_4$ (x = 0.22) single crystal. The quasiparticle density of
states (DOS) in the mixed state is found to deviate from the predicted scaling
law $C_{vol}=Hf(T/H^{1/2})$. However, this scaling behavior is nicely
reconciled if one considers the normal core region ($\xi \approx$ 21$\AA$)
which gives small contribution to the total DOS. The radius of the core and
other parameters derived are consistent with reported values. Our results
suggest that there is no zero-bias conductance peak (ZBCP), which is predicted
by the simple Bogoliubov de-Gennes theory in the vortex core of a d-wave
superconductor.


  In this paper we deal with a finite abelian group $G$ and the abstract
Fourier transform ${\mathcal F}:{\mathbb C}^G\to {\mathbb C}^\hat{G}$. Then, we
consider $\tilde{j}\circ {\mathcal F}:{\mathbb C}^G\to {\mathbb C}^\hat{G}$
where $\tilde j:{\mathbb C}^\hat{G}\to {\mathbb C}^G$ is defined by the
composition with a bijection $j:G\to \hat{G}$. ($\tilde j$ is a pullback of
$j$.)
  In particular, we show that $(\tilde{j}\circ {\mathcal F})^2$ is a
permutation if and only if $j$ is a group isomorphism. Then, we study how the
spectra of $\tilde{j}\circ{\mathcal F}$ depends on the isomorphism $j$.


  Application of current and near-term quantum hardware to the electronic
structure problem is highly limited by qubit counts, coherence times, and gate
fidelities. To address these restrictions within the variational quantum
eigensolver (VQE) framework, many recent contributions have suggested dressing
the electronic Hamiltonian to include a part of electron correlation, leaving
the rest to be accounted by VQE state preparation. We present a new dressing
scheme that combines preservation of the Hamiltonian hermiticity and an exact
quadratic truncation of the Baker-Campbell-Hausdorff expansion. The new
transformation is constructed as the exponent of an involutory linear
combination (ILC) of anti-commuting Pauli products. It incorporates important
strong correlation effects in the dressed Hamiltonian and can be viewed as a
classical preprocessing step alleviating the resource requirements of the
subsequent VQE application. The assessment of the new computational scheme for
electronic structure of the LiH, H$_2$O, and N$_2$ molecules shows significant
increase in efficiency compared to conventional qubit coupled cluster
dressings.


  In dimension $n\geq 3$, we prove a local uniqueness result for the potentials
$q$ of the Schr\"odinger equation $-\Delta u+qu=0$ from partial boundary data.
More precisely, we show that potentials $q_1,q_2\in L^\infty$ with positive
essential infima can be distinguished by local boundary data if there is a
neighborhood of a boundary part where $q_1\geq q_2$ and $q_1\not\equiv q_2$.


  Method for detection and visualization of trends, periodicities, local
peculiarities in measurement series (dL-method) based on DFA technology
(Detrended fluctuation analysis) is proposed. The essence of the method lies in
reflecting the values of absolute deviation of measurement accumulation series
points from the respective values of linear approximation. It is shown that
dL-method in some cases allows better determination of local peculiarities than
wavelet-analysis. Easy-to-realize approach that is proposed can be used in the
analysis of time series in such fields as economics and sociology.


  We calculate the group $\kappa_2$ of exotic elements in the $K(2)$-local
Picard group at the prime $2$ and find it is a group of order $2^9$ isomorphic
to $(\mathbb{Z}/8)^2 \times (\mathbb{Z}/2)^3$. In order to do this we must
define and exploit a variety of different ways of constructing elements in the
Picard group, and this requires a significant exploration of the theory. The
most innovative technique, which so far has worked best at the prime $2$, is
the use of a $J$-homomorphism from the group of real representations of finite
quotients of the Morava stabilizer group to the $K(n)$-local Picard group.


  As a part of the Data-Centric AI Competition, we propose a data-centric
approach to improve the diversity of the training samples by iterative
sampling. The method itself relies strongly on the fidelity of augmented
samples and the diversity of the augmentation methods. Moreover, we improve the
performance further by introducing more samples for the difficult classes
especially providing closer samples to edge cases potentially those the model
at hand misclassifies.


  The conchoid of a plane curve $C$ is constructed using a fixed circle $B$ in
the affine plane. We generalize the classical definition so that we obtain a
conchoid from any pair of curves $B$ and $C$ in the projective plane. We
present two definitions, one purely algebraic through resultants and a more
geometric one using an incidence correspondence in $\PP^2 \times \PP^2$. We
prove, among other things, that the conchoid of a generic curve of fixed degree
is irreducible, we determine its singularities and give a formula for its
degree and genus. In the final section we return to the classical case: for any
given curve $C$ we give a criterion for its conchoid to be irreducible and we
give a procedure to determine when a curve is the conchoid of another.


  A new class of disk MHD equilibrium solutions is described, which is valid
within the standard local (``shearing sheet'') approximation scheme. These
solutions have the following remarkable property: velocity streamlines and
magnetic lines of force rotate rigidly, even in the presence of differential
rotation. This situation comes about because the Lorentz forces acting upon
modified epicycles compel fluid elements to follow magnetic lines of force.
Field line (and streamline) configurations may be elliptical or hyperbolic,
prograde or retrograde. These structures have previously known hydrodynamical
analogs: the ``planet'' solutions described by Goodman, Narayan, & Goldreich.
  The primary focus of this investigation is configurations in the disk plane.
A related family of solutions lying in a vertical plane is briefly discussed;
other families of solutions may exist. Whether these MHD structures are stable
is not yet known, but could readily be determined by three-dimensional
simulations. If stable or quasi-stable, these simple structures may find
important applications in both accretion and galactic disks.


  Computing the directed path-width of a directed graph is an NP-hard problem.
Even for digraphs of maximum semi-degree 3 the problem remains hard. We propose
a decomposition of an input digraph G=(V,A) by a number k of sequences with
entries from V, such that (u,v) in A if and only if in one of the sequences
there is an occurrence of u appearing before an occurrence of v. We present
several graph theoretical properties of these digraphs. Among these we give
forbidden subdigraphs of digraphs which can be defined by k=1 sequence, which
is a subclass of semicomplete digraphs. Given the decomposition of digraph G,
we show an algorithm which computes the directed path-width of G in time
O(k\cdot (1+N)^k), where N denotes the maximum sequence length. This leads to
an XP-algorithm w.r.t. k for the directed path-width problem. Our result
improves the algorithms of Kitsunai et al. for digraphs of large directed
path-width which can be decomposed by a small number of sequence.


  Recently, stochastic rounding (SR) has been implemented in specialized
hardware but most current computing nodes do not yet support this rounding
mode. Several works empirically illustrate the benefit of stochastic rounding
in various fields such as neural networks and ordinary differential equations.
For some algorithms, such as summation, inner product or matrixvector
multiplication, it has been proved that SR provides probabilistic error bounds
better than the traditional deterministic bounds. In this paper, we extend this
theoretical ground for a wider adoption of SR in computer architecture. First,
we analyze the biases of the two SR modes: SR-nearness and SR-up-or-down. We
demonstrate on a case-study of Euler's forward method that IEEE-754 default
rounding modes and SR-up-or-down accumulate rounding errors across iterations
and that SR-nearness, being unbiased, does not. Second, we prove a O($\sqrt$ n)
probabilistic bound on the forward error of Horner's polynomial evaluation
method with SR, improving on the known deterministic O(n) bound.


  The properties of strange quark stars are studied within the quasi-particle
model. Taking into account the chemical equilibrium and charge neutrality, the
EOS of $ (2+1) $-flavor quark matter is obtained. We illustrate the parameter
spaces with constraints from two aspects: the one is based on the astronomical
results of PSR J$ 0740+6620 $ and GW$ 170817 $, and another is based on the
constraints proposed from the theoretical study of compact star that the EOS
must ensure the tidal deformability $ \varLambda_{1.4}=190^{+390}_{-120} $ and
support a maximum mass above $ 1.97~M_{\odot} $. It is found that the both
types of constraints can not restrict the parameter space of quasi-particle
model in a reliable region and thus we conclude that the small compact star
cannot be strange quark star.


  Since satellite systems are playing an increasingly important role in our
civilization, their security and privacy weaknesses are more and more
concerned. For example, prior work demonstrates that the communication channel
between maritime VSAT and ground segment can be eavesdropped on using
consumer-grade equipment. The stream decoder GSExtract developed in this prior
work performs well for most packets but shows incapacity for corrupted streams.
We discovered that such stream corruption commonly exists in not only Europe
and North Atlantic areas but also Asian areas. In our experiment, using
GSExtract, we are only able to decode 2.1\% satellite streams we eavesdropped
on in Asia.
  Therefore, in this work, we propose to use a contrastive learning technique
with data augmentation to decode and recover such highly corrupted streams.
Rather than rely on critical information in corrupted streams to search for
headers and perform decoding, contrastive learning directly learns the features
of packet headers at different protocol layers and identifies them in a stream
sequence. By filtering them out, we can extract the innermost data payload for
further analysis. Our evaluation shows that this new approach can successfully
recover 71-99\% eavesdropped data hundreds of times faster speed than
GSExtract. Besides, the effectiveness of our approach is not largely damaged
when stream corruption becomes more severe.


  We discuss and analyze a neural network architecture, that enables learning a
model class for a set of different data samples rather than just learning a
single model for a specific data sample. In this sense, it may help to reduce
the overfitting problem, since, after learning the model class over a larger
data sample consisting of such different data sets, just a few parameters need
to be adjusted for modeling a new, specific problem. After analyzing the method
theoretically and by regression examples for different one-dimensional
problems, we finally apply the approach to one of the standard problems asset
managers and banks are facing: the calibration of spread curves. The presented
results clearly show the potential that lies within this method. Furthermore,
this application is of particular interest to financial practitioners, since
nearly all asset managers and banks which are having solutions in place may
need to adapt or even change their current methodologies when ESG ratings
additionally affect the bond spreads.


  This paper reviews several uses of harmonic information in the synthesis of
mechanisms and shows that such information can be put to even greater use in
this field. Results are presented for both single and multi-degree of freedom
systems which support this claim. In both cases, the inclusion of harmonic
information into the objective function aids the search to locate high quality
solutions.


  The paper describes an application of the tree classification method Random
Forest (RF), as used in the analysis of data from the ground-based gamma
telescope MAGIC. In such telescopes, cosmic gamma-rays are observed and have to
be discriminated against a dominating background of hadronic cosmic-ray
particles. We describe the application of RF for this gamma/hadron separation.
The RF method often shows superior performance in comparison with traditional
semi-empirical techniques. Critical issues of the method and its implementation
are discussed. An application of the RF method for estimation of a continuous
parameter from related variables, rather than discrete classes, is also
discussed.


  We consider various $A_{\infty}$-algebras of differential (super)forms, which
are related to gauge theories and demonstrate explicitly how certain
reformulations of gauge theories lead to the transfer of the corresponding
$A_{\infty}$-structures. In $N=2$ 3D space we construct the homotopic
counterpart of the de Rham complex, which is related to the superfield
formulation of the $N=2$ Chern-Simons theory.


  Neural Networks (NN), although successfully applied to several Artificial
Intelligence tasks, are often unnecessarily over-parametrised. In edge/fog
computing, this might make their training prohibitive on resource-constrained
devices, contrasting with the current trend of decentralising intelligence from
remote data centres to local constrained devices. Therefore, we investigate the
problem of training effective NN models on constrained devices having a fixed,
potentially small, memory budget. We target techniques that are both
resource-efficient and performance effective while enabling significant network
compression. Our Dynamic Hard Pruning (DynHP) technique incrementally prunes
the network during training, identifying neurons that marginally contribute to
the model accuracy. DynHP enables a tunable size reduction of the final neural
network and reduces the NN memory occupancy during training. Freed memory is
reused by a \emph{dynamic batch sizing} approach to counterbalance the accuracy
degradation caused by the hard pruning strategy, improving its convergence and
effectiveness. We assess the performance of DynHP through reproducible
experiments on three public datasets, comparing them against reference
competitors. Results show that DynHP compresses a NN up to $10$ times without
significant performance drops (up to $3.5\%$ additional error w.r.t. the
competitors), reducing up to $80\%$ the training memory occupancy.


  We analize the role played by the thermal $f_0(500)$ state or $\sigma$ in
chiral symmetry restoration. The temperature corrections to the spectral
properties of that state are included in order to provide a better description
of the scalar susceptibility $\chi_S$ around the transition region. We use the
Linear Sigma Model to establish the relation between $\chi_S$ and the $\sigma$
propagator, which is used as a benchmark to test the approach where $\chi_S$ is
saturated by the $f_0(500)$ inverse self-energy. Within such saturation
approach, a peak for $\chi_S$ around the chiral transition is obtained when
considering the $f_0(500)$ generated as a $\pi\pi$ scattering pole within
Unitarized Chiral Perturbation Theory at finite temperature. That approach
yields results complying with lattice data when the uncertainties of the
low-energy constants are taken into account. Those uncertainties and the
unitarization method are used to check the robustness of this approximation.
Finally, we will discuss some recent results within the chiral lagrangian
framework related to the topological susceptibility and its connection with
chiral and $U_A(1)$ restoration.


  It is shown that the Hamiltonian version of the Duffin-Kemmer-Petiau theory
with electromagnetic coupling brings about a source term at the current. It is
also shown that such a source term disappears from the scenario if one uses the
correct physical form for the Duffin-Kemmer-Petiau field, regardless the choice
for representing the Duffin-Kemmer-Petiau matrices. This result is used to fix
the ambiguity in the electromagnetic coupling in the Duffin-Kemmer-Petiau
theory. Moreover, some widespread misconceptions about the Hermiticity in the
Duffin-Kemmer-Petiau theory are discussed.


  We calculate the production of exotic electromagnetic bound systems, which ia
an consisting of a ($l^{+}l^{-}$) bound state system such as positronium,
dimuonium, and ditauonium, by photon-photon interaction with the equivalent
photon approximation in ultra-peripheral heavy ion collisions considering the
nuclear form factor. The numerical results demonstrate that the experimental
study of positronium, dimuonium, and ditauonium in ultra-peripheral collisions
is feasible at Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider
(LHC) energies.


  The hybrid power system state estimation problem requires computing the state
of the power network using data from both legacy and phasor measurements.
Recent research has shown that the normal equations approach in complex
variables is computationally advantageous, particularly in the presence of
phasor measurement values, and that its software implementation is best suited
to modern processors that employ single instruction multiple data (SIMD)
processor extensions. The complex normal equations approach is however not
ideal for handling zero injection measurements, as it requires their modeling
as pseudo-measurements with high weights. This paper employs Wirtinger calculus
for extending the complex normal equations approach to include equality
constraints, and contrasts it with two previously published implementations:
the normal equations approach in complex variables and the hybrid equality
constrained state estimator in real variables. Numerical results are reported
on transmission networks having up to 9241 nodes; they show that the complex
variable equality constrained hybrid state estimator exhibits superior
performance as compared to the above two techniques in terms of both
computational time and accuracy. Moreover, the execution time on the largest
network is less than 300 ms, which makes the proposed implementation
commensurate with the requirements of real-time applications.


  We provide an extensive study of the lifetimes of singly charmed baryons and
mesons, within the heavy quark expansion with all known corrections included. A
special attention is devoted to the choice of the charm mass and wavefunctions
of heavy baryons. We give our predictions for lifetimes, lifetime ratios, and
semileptonic branching ratios of singly charmed baryons. Our results
accommodate the experimentally-favoured hierarchy of singly charmed baryon
lifetimes \begin{eqnarray*} \tau\left(\Xi_c^{0}\right) <
\tau\left(\Lambda_c^{+}\right)< \tau\left(\Omega_c^{0}\right) <
\tau\left(\Xi_c^{+}\right)\, \end{eqnarray*} in contrast to earlier theoretical
findings. Predictions for charmed meson lifetimes and semileptonic decay rates
are in agreement with a recent comprehensive study and experimental results
within uncertainties.


  We examine the lepton flavor-changing processes in R-parity violating MSSM.
First, we update the constraints on the relevant R-violating couplings by using
the latest data on the rare decays l_i->l_j \gamma. We find that the updated
constraints are much stronger than the old ones from rare Z-decays at LEP. Then
we calculate the processes Z->l_i l_j and \gamma\gamma->l_i l_j. We find that
with the updated constraints the R-violating couplings can still enhance the
rates of these processes to the sensitivity of GigaZ and photon-photon
collision options of the ILC.


  We study dynamical systems arising from word maps on simple groups. We
develop a geometric method based on the classical trace map for investigating
periodic points of such systems. These results lead to a new approach to the
search of Engel-like sequences of words in two variables which characterize
finite solvable groups. They also give rise to some new phenomena and concepts
in the arithmetic of dynamical systems.


  We present a novel approach for the detection of events in systems of
ordinary differential equations. The new method combines the unique features of
Taylor integrators with state-of-the-art polynomial root finding techniques to
yield a novel algorithm ensuring strong event detection guarantees at a modest
computational overhead. Detailed tests and benchmarks focused on problems in
astrodynamics and celestial mechanics (such as collisional N-body systems,
spacecraft dynamics around irregular bodies accounting for eclipses,
computation of Poincare' sections, etc.) show how our approach is superior in
both performance and detection accuracy to strategies commonly employed in
modern numerical integration works. The new algorithm is available in our open
source Taylor integration package heyoka.


  Let ${\cal H}(P,C_3)$ be the set of order homomorphisms from a poset $P$ to
the chain $C_3 = 1 < 2 < 3$. We develop a recursive approach for the
calculation of the cardinality of ${\cal H}(P,C_3)$, and we apply it on several
types of posets, including $P = C_3 \times C_3 \times C_k$ and $P = {\cal
H}(C_k, C_3)$; for the latter poset $P$, we derive a direct formula for $\#
{\cal H} ( P, C_3 )$.


  The Past Extragradient (PEG) [Popov, 1980] method, also known as the
Optimistic Gradient method, has known a recent gain in interest in the
optimization community with the emergence of variational inequality
formulations for machine learning. Recently, in the unconstrained case,
Golowich et al. [2020] proved that a $O(1/N)$ last-iterate convergence rate in
terms of the squared norm of the operator can be achieved for Lipschitz and
monotone operators with a Lipschitz Jacobian. In this work, by introducing a
novel analysis through potential functions, we show that (i) this $O(1/N)$
last-iterate convergence can be achieved without any assumption on the Jacobian
of the operator, and (ii) it can be extended to the constrained case, which was
not derived before even under Lipschitzness of the Jacobian. The proof is
significantly different from the one known from Golowich et al. [2020], and its
discovery was computer-aided. Those results close the open question of the last
iterate convergence of PEG for monotone variational inequalities.


  As part of a search for Wolf-Rayet (WR) stars in the Magellanic Clouds, we
have discovered a new type of WR star in the Large Magellanic Cloud (LMC).
These stars have both strong emission lines, as well as He ii and Balmer
absorption lines and spectroscopically resemble a WN3 and O3V binary pair.
However, they are visually too faint to be WN3+O3V binary systems. We have
found nine of these WN3/O3s, making up ~6% of the population of LMC WRs. Using
CMFGEN, we have successfully modeled their spectra as single stars and have
compared the physical parameters with those of more typical LMC WNs. Their
temperatures are around 100,000 K, a bit hotter than the majority of WN stars
(by around 10,000 K) although a few hotter WNs are known. The abundances are
what you would expect for CNO equilibrium. However, most anomalous are their
mass-loss rates which are more like that of an O-type star than a WN star.
While their evolutionary status is uncertain, their low mass-loss rates and
wind velocities suggest that they are not products of homogeneous evolution. It
is possible instead that these stars represent an intermediate stage between O
stars and WNs. Since WN3/O3 stars are unknown in the Milky Way, we suspect that
their formation depends upon metallicity, and we are investigating this further
by a deep survey in M33, which posses a metallicity gradient.


  By pulsed s-shell resonant excitation of a single quantum dot-micropillar
system, we generate long streams of a thousand of near transform-limited single
photons with high mutual indistinguishability. Hong-Ou-Mandel interference of
two photons are measured as a function of their emission time separation
varying from 13 ns to 14.7 {\mu}s, where the visibility slightly drops from
95.9(2)% to a plateau of 92.1(5)% through a slow dephasing process occurring at
time scale of 0.7 {\mu}s. Temporal and spectral analysis reveal the pulsed
resonance fluorescence single photons are close to transform limit, which are
readily useful for multi-photon entanglement and interferometry experiments.


  Inferring the substitutable and complementary products for a given product is
an essential and fundamental concern for the recommender system. To achieve
this, existing approaches take advantage of the knowledge graphs to learn more
evidences for inference, whereas they often suffer from invalid reasoning for
lack of elegant decision making strategies. Therefore, we propose a novel
Knowledge-Aware Path Reasoning (KAPR) model which leverages the dynamic policy
network to make explicit reasoning over knowledge graphs, for inferring the
substitutable and complementary relationships. Our contributions can be
highlighted as three aspects. Firstly, we model this inference scenario as a
Markov Decision Process in order to accomplish a knowledge-aware path reasoning
over knowledge graphs. Secondly,we integrate both structured and unstructured
knowledge to provide adequate evidences for making accurate decision-making.
Thirdly, we evaluate our model on a series of real-world datasets, achieving
competitive performance compared with state-of-the-art approaches. Our code is
released on https://gitee.com/yangzijing flower/kapr/tree/master.


  Summary: Longer sequencing reads, with at least 200 bases per template are
now common. While traditional aligners have adopted new strategies to improve
the mapping of longer reads, aligners specific to bisulfite-sequencing were
optimized when much shorter reads were the norm. We sought to perform the first
comparison using longer reads to determine which aligners were most accurate
and efficient and to evaluate a novel software tool, bwa-meth, built on a
traditional mapper that supports insertions, deletions and clipped alignments.
We gauge accuracy by comparing the number of on and off-target reads from a
targeted sequencing project and by simulations. Availability and
Implementation: The benchmarking scripts and the bwa-meth software are
available at https://github/com/brentp/bwa-meth/ under the MIT License.


  The mass spectrum and strong decays of the X(1870) and $\eta_2(1870)$ are
analyzed. Our results indicate that X(1870) and $\eta_2(1870)$ are the two
different resonances. The narrower X(1870) seems likely a good hybrid
candidate. We support the $\eta_2(1870)$ as the $\eta_2(2^1D_2)$ quarkonium. We
suggest to search the isospin partner of X(1870) in the channels of
$J/\psi\rightarrow\rho f_0(980)\pi$ and $J/\psi\rightarrow\rho b_1(1235)\pi$ in
the future. The latter channel is very important for testing the hybrid
scenario.


  Off-Policy Estimation (OPE) methods allow us to learn and evaluate
decision-making policies from logged data. This makes them an attractive choice
for the offline evaluation of recommender systems, and several recent works
have reported successful adoption of OPE methods to this end. An important
assumption that makes this work is the absence of unobserved confounders:
random variables that influence both actions and rewards at data collection
time. Because the data collection policy is typically under the practitioner's
control, the unconfoundedness assumption is often left implicit, and its
violations are rarely dealt with in the existing literature.
  This work aims to highlight the problems that arise when performing
off-policy estimation in the presence of unobserved confounders, specifically
focusing on a recommendation use-case. We focus on policy-based estimators,
where the logging propensities are learned from logged data. We characterise
the statistical bias that arises due to confounding, and show how existing
diagnostics are unable to uncover such cases. Because the bias depends directly
on the true and unobserved logging propensities, it is non-identifiable. As the
unconfoundedness assumption is famously untestable, this becomes especially
problematic. This paper emphasises this common, yet often overlooked issue.
Through synthetic data, we empirically show how na\"ive propensity estimation
under confounding can lead to severely biased metric estimates that are allowed
to fly under the radar. We aim to cultivate an awareness among researchers and
practitioners of this important problem, and touch upon potential research
directions towards mitigating its effects.


  Magnetic shape memory alloys exhibit a hierarchically twinned microstructure,
which has been well examined in epitaxial Ni-Mn-Ga films. Here we analyze
consequences of this twin within twins microstructure on the magnetic domain
pattern. Atomic and magnetic force microscopy are used to probe the correlation
between the martensitic microstructure and magnetic domains. We examine
consequences of different twin boundary orientations with respect to the
substrate normal as well as variant boundaries between differently aligned
twinned laminates. A detailed micromagnetic analysis is given which describes
the influence of the finite film thickness on the formation of magnetic band
domains in these multiferroic materials.


  We investigate the charge and heat electronic noise in a generic two-terminal
mesoscopic conductor in the absence of the corresponding charge and heat
currents. Despite these currents being zero, shot noise is generated in the
system. We show that, irrespective of the conductor's details and the specific
nonequilibrium conditions, the charge shot noise never exceeds its thermal
counterpart, thus establishing a general bound. Such a bound does not exist in
the case of heat noise, which reveals a fundamental difference between charge
and heat transport under zero-current conditions.


  We consider a non-selfadjoint $h$-differential model operator $P_h$ in the
semiclassical limit ($h\rightarrow 0$) subject to small random perturbations.
Furthermore, we let the coupling constant $\delta$ be
$\exp\{-\frac{1}{Ch}\}\leq \delta \ll h^{\kappa}$ for constants $C,\kappa>0$
suitably large. Let $\Sigma$ be the closure of the range of the principal
symbol. Previous results on the same model by Hager, Bordeaux-Montrieux and
Sj\"ostrand show that if $\delta \gg\exp\{-\frac{1}{Ch}\}$ there is, with a
probability close to $1$, a Weyl law for the eigenvalues in the interior of the
of the pseudospectrum up to a distance $\gg\left(-h\ln{\delta
h}\right)^{\frac{2}{3}}$ to the boundary of $\Sigma$. We study the intensity
measure of the random point process of eigenvalues and prove an $h$-asymptotic
formula for the average density of eigenvalues. With this we show that there
are three distinct regions of different spectral behavior in $\Sigma$: The
interior of the of the pseudospectrum is solely governed by a Weyl law, close
to its boundary there is a strong spectral accumulation given by a tunneling
effect followed by a region where the density decays rapidly.


  In \cite{davis19}, the author constructed an elliptic version of the
Grothendieck-Springer resolution for the stack $\mathrm{Bun}_G$ of principal
bundles under a simply connected simple group $G$ on an elliptic curve $E$.
This is a simultaneous log resolution of a map from $\mathrm{Bun}_G$ to the
union of the coarse moduli space of semistable $G$-bundles and a single stacky
point. In this paper, we study singularities, resolutions and deformations
coming from subregular slices of this elliptic Grothendieck-Springer
resolution. More precisely, we construct explicit slices of $\mathrm{Bun}_G$
through all subregular unstable bundles, for every $G$. For $G \neq SL_2$, we
describe the pullbacks of the elliptic Grothendieck-Springer resolution to
these slices as concrete varieties, extending and refining earlier work of I.
Grojnowski and N. Shepherd-Barron, who related these varieties to del Pezzo
surfaces in type $E$. We use the resolutions to identify the singularities of
the unstable locus of the subregular slices, and prove that that the extended
coarse moduli space map gives deformations that are miniversal among
torus-equivariant deformations with appropriate weights.


  We analyse the behavior of the thermal conductivity, $\kappa(H)$, in the
vortex state of a quasi-two-dimensional d-wave superconductor when both the
heat current and the applied magnetic field are in the basal plane. At low
temperature the effect of the field is accounted for in a semiclassical
approximation, via a Doppler shift in the spectrum of the nodal quasiparticles.
In that regime $\kappa(H)$ exhibits twofold oscillations as a function of the
angle between the direction of the field in the plane and the direction of the
heat current, in agreement with experiment.


  Mixture-of-Experts (MoE) has emerged as a favorable architecture in the era
of large models due to its inherent advantage, i.e., enlarging model capacity
without incurring notable computational overhead. Yet, the realization of such
benefits often results in ineffective GPU memory utilization, as large portions
of the model parameters remain dormant during inference. Moreover, the memory
demands of large models consistently outpace the memory capacity of
contemporary GPUs. Addressing this, we introduce SiDA-MoE
($\textbf{S}$parsity-$\textbf{i}$nspired $\textbf{D}$ata-$\textbf{A}$ware), an
efficient inference approach tailored for large MoE models. SiDA-MoE
judiciously exploits both the system's main memory, which is now abundant and
readily scalable, and GPU memory by capitalizing on the inherent sparsity on
expert activation in MoE models. By adopting a data-aware perspective, SiDA-MoE
achieves enhanced model efficiency with a neglectable performance drop.
Specifically, SiDA-MoE attains a remarkable speedup in MoE inference with up to
$3.93\times$ throughput increasing, up to $72\%$ latency reduction, and up to
$80\%$ GPU memory saving with down to $1\%$ performance drop. This work paves
the way for scalable and efficient deployment of large MoE models, even with
constrained resources. Code is available at:
https://github.com/timlee0212/SiDA-MoE.


  We investigate the dynamical control of the heat flux exchanged in near-field
regime between a membrane made with a phase-change material and a substrate
when the temperature of the membrane is tuned around its critical value. We
show that in interaction with an external source of thermal radiation, this
system is multistable and behaves as a thermal transistor, being able to
dynamically modulate and even amplify super-Planckian heat fluxes. This
behavior could be used to dynamically control heat fluxes exchanged at the
nanoscale in systems out of thermal equilibrium and to process thermal
information employing suspended membranes.


  The purpose of this paper is to present a systemic study of some families of
multiple q-Genocchi and euler numbers by using multivariate q-Volkenborn
integral. From the studies of those q-Genocchi numbers and polynomials of
higher order we derive some interesting identities related to q-Genocchi
numbers and polynomials of higher order.


  Objective: Despite numerous studies proposed for audio restoration in the
literature, most of them focus on an isolated restoration problem such as
denoising or dereverberation, ignoring other artifacts. Moreover, assuming a
noisy or reverberant environment with limited number of fixed
signal-to-distortion ratio (SDR) levels is a common practice. However,
real-world audio is often corrupted by a blend of artifacts such as
reverberation, sensor noise, and background audio mixture with varying types,
severities, and duration. In this study, we propose a novel approach for blind
restoration of real-world audio signals by Operational Generative Adversarial
Networks (Op-GANs) with temporal and spectral objective metrics to enhance the
quality of restored audio signal regardless of the type and severity of each
artifact corrupting it. Methods: 1D Operational-GANs are used with generative
neuron model optimized for blind restoration of any corrupted audio signal.
Results: The proposed approach has been evaluated extensively over the
benchmark TIMIT-RAR (speech) and GTZAN-RAR (non-speech) datasets corrupted with
a random blend of artifacts each with a random severity to mimic real-world
audio signals. Average SDR improvements of over 7.2 dB and 4.9 dB are achieved,
respectively, which are substantial when compared with the baseline methods.
Significance: This is a pioneer study in blind audio restoration with the
unique capability of direct (time-domain) restoration of real-world audio
whilst achieving an unprecedented level of performance for a wide SDR range and
artifact types. Conclusion: 1D Op-GANs can achieve robust and computationally
effective real-world audio restoration with significantly improved performance.
The source codes and the generated real-world audio datasets are shared
publicly with the research community in a dedicated GitHub repository1.


  String inspired models with millimeter scale extra dimensions provide a
natural way to understand an ultralight sterile neutrino needed for a
simultaneous explanation of the solar, atmospheric and LSND neutrino
oscillation results. The sterile neutrino is the bulk neutrino ($\nu_B$)
postulated to exist in these models, and it becomes ultralight in theories that
prevent the appearance of its direct mass terms. Its Kaluza-Klein (KK) states
then add new oscillation channels for the electron neutrino emitted from the
solar core. We show that successive MSW transitions of solar $\nu_e$ to the
lower lying KK modes of $\nu_B$ in conjunction with vacuum oscillations between
the $\nu_e$ and the zero mode of $\nu_B$ provide a new way to fit the solar
neutrino data. Using just the average rates from the three types of solar
experiments, we predict the Super-Kamiokande spectrum with 73\% probability,
but dips characteristic of the 0.06 mm extra dimension should be seen in the
SNO spectrum. We discuss both intermediate and low string scale models where
the desired phenomenology can emerge naturally.


  Strong coupling of electromagnetic cavity fields with antiferromagnetic spin
waves in hematite ($\alpha$-Fe$_2$O$_3$) was achieved above room temperature. A
cube of hematite was placed in a metallic tube and transmission was measured,
using a continuous-wave THz spectrometer. Spectra, collected as a function of
temperature, reveal the formation of magnetic polaritons.


  On-ramp merging areas are typical bottlenecks in the freeway network, since
merging on-ramp vehicles may cause intensive disturbances on the mainline
traffic flow and lead to various negative impacts on traffic efficiency and
safety. The connected and autonomous vehicles (CAVs), with their capabilities
of real-time communication and precise motion control, hold a great potential
to facilitate ramp merging operation through enhanced coordination strategies.
This paper presents a comprehensive review of the existing ramp merging
strategies leveraging CAVs, focusing on the latest trends and developments in
the research field. The review comprehensively covers 44 papers recently
published in leading transportation journals. Based on the application context,
control strategies are categorized into three categories: merging into
sing-lane freeways with total CAVs, merging into sing-lane freeways with mixed
traffic flows, and merging into multilane freeways. Relevant literature is
reviewed regarding the required technologies, control decision level, applied
methods, and impacts on traffic performance. More importantly, we identify the
existing research gaps and provide insightful discussions on the potential and
promising directions for future research based on the review, which facilitates
further advancement in this research topic.


  The theory of low-frequency internal gravity waves (IGWs) is readdressed in
the stable stratified weakly ionized Earth's ionosphere. The formation of
dipolar vortex structures and their dynamical evolution, as well as, the
emergence of chaos in the wave-wave interactions are studied both in presence
and absence of the Pedersen conductivity. The latter is shown to inhibit the
formation of solitary vortices and the onset of chaos.


  Anisotropic lattices are an efficient means of studying the glueballs of QCD,
however problems arise with simulations of the lightest, scalar state. The mass
is strongly dependent on the lattice spacing, even when a mean-field improved
gluon action is used. The nature and cause of these errors are discussed and
the scaling properties of the scalar from different lattice actions are
presented.


  We provide methods to compute the colored HOMFLY polynomials of knots and
links with symmetric representations based on the linear skein theory. By using
diagrammatic calculations, several formulae for the colored HOMFLY polynomials
are obtained. As an application, we calculate some examples for hyperbolic
knots and links, and we study a generalization of the volume conjecture by
means of numerical calculations. In these examples, we observe that asymptotic
behaviors of invariants seem to have relations to the volume conjecure.


  Let $\FF$ be a set of real-valued functions on a set $\XX$ and let $S:\FF \to
\GG$ be an arbitrary mapping. We consider the problem of making inference about
$S(f)$, with $f\in\FF$ unknown, from a finite set of pointwise evaluations of
$f$. We are mainly interested in the problems of approximation and
optimization. In this article, we make a brief review of results concerning
average error bounds of Bayesian search methods that use a random process prior
about $f$.


  The X2.2-class solar flare of February 15, 2011, produced a powerful sunquake
event, representing a helioseismic response to the flare impact in the solar
photosphere, which was observed with the HMI instrument on the Solar Dynamics
Observatory (SDO). The impulsively excited acoustic waves formed a compact
wavepacket traveling through the solar interior and appearing on the surface as
expanding wave ripples. The initial flare impacts were observed in the form of
compact and rapid variations of the Doppler velocity, line-of-sight magnetic
field and continuum intensity. These variations formed a typical two-ribbon
flare structure, and are believed to be associated with thermal and
hydrodynamic effects of high-energy particles heating the lower atmosphere. The
analysis of the SDO/HMI and X-ray data from the Ramaty High Energy Solar
Spectroscopic Imager (RHESSI) shows that the helioseismic waves were initiated
by the photospheric impact in the early impulsive phase, observed prior to the
hard X-ray (50-100 keV) impulse, and were probably associated with atmospheric
heating by relatively low-energy electrons (~6-50 keV) and heat flux transport.
The impact caused a short motion in the sunspot penumbra prior to the
appearance of the helioseismic wave. It is found that the helioseismic wave
front traveling through a sunspot had a lower amplitude and was significantly
delayed relative to the front traveling outside the spot. These observations
open new perspectives for studying the flare photospheric impacts and for using
the flare-excited waves for sunspot seismology.


  Entropic Dynamics (ED) is an inference-based framework that seeks to
construct dynamical theories of physics without assuming the conventional
formalism --- the Hamiltonians, Poisson brackets, Hilbert spaces, etc. ---
typically associated with physics. In this work we develop an ED of scalar
fields that is both quantum and manifestly covariant. The framework for
accomplishing this is inspired by the covariant methods of Dirac, Teitelboim,
and Kuchar. In addition to the ostensible result of a covariant quantum ED, we
also show how the covariance requirement of path independence proposed by
Teitelboim is sufficient for a derivation of Hamiltonian dynamics and also
provides a proof of uniqueness for the quantum potential that leads to quantum
theory.


  Interacting quantum spin models are remarkably useful for describing
different types of physical, chemical, and biological systems. Significant
understanding of their equilibrium properties has been achieved to date,
especially for the case of spin models with short-range couplings. However,
progress towards the development of a comparable understanding in long-range
interacting models, in particular out-of-equilibrium, remains limited. In a
recent work, we proposed a semiclassical numerical method to study spin models,
the discrete truncated Wigner approximation (DTWA), and demonstrated its
capability to correctly capture the dynamics of one- and two-point correlations
in one dimensional (1D) systems. Here we go one step forward and use the DTWA
method to study the dynamics of correlations in 2D systems with many spins and
different types of long-range couplings, in regimes where other numerical
methods are generally unreliable. We compute spatial and time-dependent
correlations for spin-couplings that decay with distance as a power-law and
determine the velocity at which correlations propagate through the system.
Sharp changes in the behavior of those velocities are found as a function of
the power-law decay exponent. Our predictions are relevant for a broad range of
systems including solid state materials, atom-photon systems and ultracold
gases of polar molecules, trapped ions, Rydberg, and magnetic atoms. We
validate the DTWA predictions for small 2D systems and 1D systems, but
ultimately, in the spirt of quantum simulation, experiments will be needed to
confirm our predictions for large 2D systems.


  Green Pea galaxies are low-redshift starburst dwarf galaxies, with properties
similar to those of the high-redshift galaxies that reionized the Universe. We
report the first mapping of the spatial distribution of atomic hydrogen (HI) in
and around a Green Pea, GP J0213+0056 at z=0.0399, using the Giant Metrewave
Radio Telescope (GMRT). Like many Green Peas, GP J0213+0056 shows strong HI 21
cm emission in single-dish spectroscopy, strong Ly-alpha emission, and a high
[OIII]$\lambda$5007/[OIII]$\lambda$3727 luminosity ratio, O32 $\approx$ 8.8,
consistent with a high leakage of Lyman-continuum radiation. Our GMRT HI 21 cm
images show that the HI 21 cm emission in the field of GP J0213+0056 arises
from an extended broken-ring structure around the Green Pea, with the strongest
emission coming from a region between GP J0213+0056 and a companion galaxy
lying $\approx$ 4.7 kpc away, and little HI 21cm emission coming from the Green
Pea itself. We find that the merger between GP J0213+0056 and its companion is
likely to have triggered the starburst, and led to a disturbed HI spatial and
velocity distribution, which in turn allowed Ly-alpha (and, possibly,
Lyman-continuum) emission to escape the Green Pea. Our results suggest that
such mergers, and the resulting holes in the HI distribution, are a natural way
to explain the tension between the requirements of cold gas to fuel the
starburst and the observed leakage of Ly-alpha and Lyman-continuum emission in
Green Pea galaxies and their high-redshift counterparts.


  We find significant fluctuations of angular momentum within the convective
helium shell of a pre-collapse massive star - a core-collapse supernova
progenitor - which may facilitate the formation of accretion disks and jets
that can explode the star. The convective flow in our model of an evolved
M_ZAMS=15Msun star, computed with the sub-sonic hydrodynamic solver MAESTRO,
contains entire shells with net angular momentum in different directions. This
phenomenon may have important implications for the late evolutionary stages of
massive stars, and for the dynamics of core-collapse.


  As attribution-based explanation methods are increasingly used to establish
model trustworthiness in high-stakes situations, it is critical to ensure that
these explanations are stable, e.g., robust to infinitesimal perturbations to
an input. However, previous works have shown that state-of-the-art explanation
methods generate unstable explanations. Here, we introduce metrics to quantify
the stability of an explanation and show that several popular explanation
methods are unstable. In particular, we propose new Relative Stability metrics
that measure the change in output explanation with respect to change in input,
model representation, or output of the underlying predictor. Finally, our
experimental evaluation with three real-world datasets demonstrates interesting
insights for seven explanation methods and different stability metrics.


  We present Spitzer IRAC images of the HH 111 outflow, that show a wealth of
condensations/knots in both jet and counterjet. Studying the positional
distribution of these knots, we find very suggestive evidence of a mirror
symmetric pattern in the jet/counterjet flow. We model this pattern as the
result of an orbital motion of the jet source around a binary companion. From a
fit of an analytic, ballistic model to the observed path of the HH 111 system,
we find that the motion in a binary with two approx. 1 Msolar stars (one of
them being the HH 111 source), in a circular orbit with a separation of approx.
186 AU would produce the mirror symmetric pattern seen in the outflow.


  Pseudo-one dimensional (pseudo-1D) materials are a new-class of materials
where atoms are arranged in chain like structures in two-dimensions (2D).
Examples include recently discovered black phosphorus, ReS2 and ReSe2 from
transition metal dichalcogenides, TiS3 and ZrS3 from transition metal
trichalcogenides and most recently GaTe. The presence of structural anisotropy
impacts their physical properties and leads to direction dependent light-matter
interactions, dichroic optical responses, high mobility channels, and
anisotropic thermal conduction. Despite the numerous reports on the vapor phase
growth of isotropic TMDCs and post transition metal chalcogenides such as MoS2
and GaSe, the synthesis of pseudo-1D materials is particularly difficult due to
the anisotropy in interfacial energy, which stabilizes dendritic growth rather
than single crystalline growth with well-defined orientation. The growth of
monoclinic GaTe has been demonstrated on flexible mica substrates with superior
photodetecting performance. In this work, we demonstrate that pseudo-1D
monoclinic GaTe layers can be synthesized on a variety of other substrates
including GaAs (111), Si (111) and c-cut sapphire by physical vapor transport
techniques. High resolution transmission electron microscopy (HRTEM)
measurements, together with angle resolved micro-PL and micro-Raman techniques,
provide for the very first time atomic scale resolution experiments on
pseudo-1D structures in monoclinic GaTe and anisotropic properties.
Interestingly, GaTe nanomaterials grown on sapphire exhibit highly efficient
and narrow localized emission peaks below the band gap energy, which are found
to be related to select types of line and point defects as evidenced by PL and
Raman mapping scans. It makes the samples grown on sapphire more prominent than
those grown on GaAs and Si, which demonstrate more regular properties.


  We present sufficient conditions for the exact controllability in projection
of the linear Schr{\"o}dinger equations in the case where the spectrum of the
free Hamiltonian is pure point. We consider the general case in which the
Hamiltonian may be nonlinear with respect to the control. The controllability
result applies, in particular, to Schr{\"o}dinger equations with a
polarizability term.


  We geometrically study the Legendre duality relation that plays an important
role in statistical physics with the standard or generalized entropies. For
this purpose, we introduce dualistic structure defined by information geometry,
and discuss concepts arising in generalized thermostatistics, such as relative
entropies, escort distributions and modified expectations. Further, a possible
generalization of these concepts in a certain direction is also considered.
Finally, as an application of such a geometric viewpoint, we briefly
demonstrate several new results on a behavior of the solution to the nonlinear
diffusion equation called the {\em porous medium equation}.


  Scene text removal aims to remove the text and fill the regions with
perceptually plausible background information in natural images. It has
attracted increasing attention due to its various applications in privacy
protection, scene text retrieval, and text editing. With the development of
deep learning, the previous methods have achieved significant improvements.
However, most of the existing methods seem to ignore the large perceptive
fields and global information. The pioneer method can get significant
improvements by only changing training data from the cropped image to the full
image. In this paper, we present a single-stage multi-scale network MSLKANet
for scene text removal in full images. For obtaining large perceptive fields
and global information, we propose multi-scale large kernel attention (MSLKA)
to obtain long-range dependencies between the text regions and the backgrounds
at various granularity levels. Furthermore, we combine the large kernel
decomposition mechanism and atrous spatial pyramid pooling to build a large
kernel spatial pyramid pooling (LKSPP), which can perceive more valid pixels in
the spatial dimension while maintaining large receptive fields and low cost of
computation. Extensive experimental results indicate that the proposed method
achieves state-of-the-art performance on both synthetic and real-world datasets
and the effectiveness of the proposed components MSLKA and LKSPP.


  A relativistic quantum mechanics is studied for bound hadronic systems in the
framework of the Point Form Relativistic Hamiltonian Dynamics. Negative energy
states are introduced taking into account the restrictions imposed by a correct
definition of the Poincar\'e group generators. We obtain nonpathological,
manifestly covariant wave equations that dynamically contain the contributions
of the negative energy states. Auxiliary negative energy states are also
introduced, specially for studying the interactions of the hadronic systems
with external probes.


  In this note, we clarify a point concerning the power divergence in lattice
calculations of $\Delta I = 1/2$ $K\to\pi\pi$ decay amplitudes. There have been
worries that this divergence might show up in the Minkowski amplitudes at
$M_{K}=M_{\pi}$ with all the mesons at rest. Here we demonstrate, via an
explicit calculation in leading-order Chiral Perturbation Theory, that the
power divergence is absent at the above kinematic point, as predicted by CPS
symmetry.


  Hidden Markov models (HMMs) are commonly used to model animal movement data
and infer aspects of animal behavior. An HMM assumes that each data point from
a time series of observations stems from one of $N$ possible states. The states
are loosely connected to behavioral modes that manifest themselves at the
temporal resolution at which observations are made. However, due to advances in
tag technology, data can be collected at increasingly fine temporal
resolutions. Yet, inferences at time scales cruder than those at which data are
collected, and which correspond to larger-scale behavioral processes, are not
yet answered via HMMs. We include additional hierarchical structures to the
basic HMM framework in order to incorporate multiple Markov chains at various
time scales. The hierarchically structured HMMs allow for behavioral inferences
at multiple time scales and can also serve as a means to avoid coarsening data.
Our proposed framework is one of the first that models animal behavior
simultaneously at multiple time scales, opening new possibilities in the area
of animal movement modeling. We illustrate the application of hierarchically
structured HMMs in two real-data examples: (i) vertical movements of harbor
porpoises observed in the field, and (ii) garter snake movement data collected
as part of an experimental design.


  Differential cross sections are presented for the prompt and non-prompt
production of the hidden-charm states $X(3872)$ and $\psi(2S)$, in the decay
mode $J/\psi \pi^+\pi^-$, measured using 11.4 fb$^{-1}$ of $pp$ collisions at
$\sqrt{s} = 8$ TeV by the ATLAS detector at the LHC. The ratio of
cross-sections $X(3872)/\psi(2S)$ is also given, separately for prompt and
non-prompt components, as well as the non-prompt fractions of $X(3872)$ and
$\psi(2S)$. Assuming independent single effective lifetimes for non-prompt
$X(3872)$ and $\psi(2S)$ production gives $R_B = \frac{\mathcal{B}(B
\rightarrow X(3872)\textrm{ + any}) \mathcal{B}(X(3872 \rightarrow
J/\psi\pi^+\pi^-)}{\mathcal{B}(B \rightarrow \psi(2S)\textrm{ + any})
\mathcal{B}(\psi(2S) \rightarrow J/\psi\pi^+\pi^-)} = (3.95 \pm 0.32
\mathrm{(stat)} \pm 0.08\mathrm{(sys)}) \times 10^{-2}$, while separating
short- and long-lived contributions, assuming that the short-lived component is
due to $B_c$ decays, gives $R_B = (3.57 \pm 0.33\mathrm{(stat)} \pm
0.11\mathrm{(sys)}) \times 10^{-2}$, with the fraction of non-prompt $X(3872)$
produced via $B_c$ decays for $p_\mathrm{T}(X(3872)) > 10$ GeV being $(25 \pm
13\mathrm{(stat)} \pm 2\mathrm{(sys)} \pm 5\mathrm{(spin)})\%$. The
distributions of the dipion invariant mass in the $X(3872)$ and $\psi(2S)$
decays are also measured and compared to theoretical predictions.


  This paper presents FauxPy, a fault localization tool for Python programs.
FauxPy supports seven well-known fault localization techniques in four
families: spectrum-based, mutation-based, predicate switching, and stack trace
fault localization. It is implemented as plugin of the popular Pytest testing
framework, but also works with tests written for Unittest and Hypothesis (two
other popular testing frameworks). The paper showcases how to use FauxPy on two
illustrative examples, and then discusses its main features and capabilities
from a user's perspective. To demonstrate that FauxPy is applicable to analyze
Python projects of realistic size, the paper also summarizes the results of an
extensive experimental evaluation that applied FauxPy to 135 real-world bugs
from the BugsInPy curated collection. To our knowledge, FauxPy is the first
open-source fault localization tool for Python that supports multiple fault
localization families.


  A molecular wire coupled to two electron reservoirs is investigated within a
tight-binding approach including spin and Coulomb interaction. Under the
assumption of weak coupling to the electron reservoirs a quantum master
equation can be derived for the electron transport through the wire. Motivated
by the phenomenon of coherent destruction of tunneling for monochromatic laser
fields, the influence of Gaussian laser pulses on the transport through the
wires is studied. For situations in which the maximum amplitude of the electric
field fulfills the conditions for the destructive quantum effect the average
current through the system can be suppressed even for a wire consisting of only
one site. Turning on the electron correlation does not destroy the suppression
of the current by the laser.


  Listing all k-cliques is a fundamental problem in graph mining, with
applications in finance, biology, and social network analysis. However, owing
to the exponential growth of the search space as k increases, listing all
k-cliques is algorithmically challenging. DDegree and DDegCol are the
state-of-the-art algorithms that exploit ordering heuristics based on degree
ordering and color ordering, respectively. Both DDegree and DDegCol induce high
time and space overhead for set intersections cause they construct and maintain
all induced subgraphs. Meanwhile, it is non-trivial to implement the data level
parallelism to further accelerate on DDegree and DDegCol.
  In this paper, we propose two efficient algorithms SDegree and BitCol for
k-clique listing. We mainly focus on accelerating the set intersections for
k-clique listing. Both SDegree and BitCol exploit the data level parallelism
for further acceleration with single instruction multiple data (SIMD) or vector
instruction sets. Furthermore, we propose two preprocessing techniques Pre-Core
and Pre-List, which run in linear time. The preprocessing techniques
significantly reduce the size of the original graph and prevent exploring a
large number of invalid nodes. In the theoretical analysis, our algorithms have
a comparable time complexity and a slightly lower space complexity than the
state-of-the-art algorithms. The comprehensive experiments reveal that our
algorithms outperform the state-of-the-art algorithms by 3.75x for degree
ordering and 5.67x for color ordering on average.


  We construct a new two-dimensional N=8 supersymmetric mechanics with
nonlinear chiral supermultiplet. Being intrinsically nonlinear this multiplet
describes 2 physical bosonic and 8 fermionic degrees of freedom. We construct
the most general superfield action of the sigma-model type and propose its
simplest extension by a Fayet-Iliopoulos term. The most interesting property of
the constructed system is a new type of geometry in the bosonic subsector,
which is different from the special Kahler one characterizing the case of the
linear chiral N=8 supermultiplet.


  The Relevance Feedback (RF) process relies on accurate and real-time
relevance estimation of feedback documents to improve retrieval performance.
Since collecting explicit relevance annotations imposes an extra burden on the
user, extensive studies have explored using pseudo-relevance signals and
implicit feedback signals as substitutes. However, such signals are indirect
indicators of relevance and suffer from complex search scenarios where user
interactions are absent or biased.
  Recently, the advances in portable and high-precision brain-computer
interface (BCI) devices have shown the possibility to monitor user's brain
activities during search process. Brain signals can directly reflect user's
psychological responses to search results and thus it can act as additional and
unbiased RF signals. To explore the effectiveness of brain signals in the
context of RF, we propose a novel RF framework that combines BCI-based
relevance feedback with pseudo-relevance signals and implicit signals to
improve the performance of document re-ranking. The experimental results on the
user study dataset show that incorporating brain signals leads to significant
performance improvement in our RF framework. Besides, we observe that brain
signals perform particularly well in several hard search scenarios, especially
when implicit signals as feedback are missing or noisy. This reveals when and
how to exploit brain signals in the context of RF.


  Zoonotic disease transmission between animals and humans is a growing risk
and the agricultural context acts as a likely point of transition, with
individual heterogeneity acting as an important contributor. Thus,
understanding the dynamics of disease spread in the wildlife-livestock
interface is crucial for mitigating these risks of transmission. Specifically,
the interactions between pigeons and in-door cows at dairy farms can lead to
significant disease transmission and economic losses for farmers; putting
livestock, adjacent human populations, and other wildlife species at risk. In
this paper, we propose a novel spatio-temporal multi-pathogen model with
continuous spatial movement. The model expands on the
Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) framework and accounts for
both within-species and cross-species transmission of pathogens, as well as the
exploration-exploitation movement dynamics of pigeons, which play a critical
role in the spread of infection agents. In addition to model formulation, we
also implement it as an agent-based simulation approach and use empirical field
data to investigate different biologically realistic scenarios, evaluating the
effect of various parameters on the epidemic spread. Namely, in agreement with
theoretical expectations, the model predicts that the heterogeneity of the
pigeons' movement dynamics can drastically affect both the magnitude and
stability of outbreaks. In addition, joint infection by multiple pathogens can
have an interactive effect unobservable in single-pathogen SIR models,
reflecting a non-intuitive inhibition of the outbreak. Our findings highlight
the impact of heterogeneity in host behavior on their pathogens and allow
realistic predictions of outbreak dynamics in the multi-pathogen
wildlife-livestock interface with consequences to zoonotic diseases in various
systems.


  Triplet networks are widely used models that are characterized by good
performance in classification and retrieval tasks. In this work we propose to
train a triplet network by putting it as the discriminator in Generative
Adversarial Nets (GANs). We make use of the good capability of representation
learning of the discriminator to increase the predictive quality of the model.
We evaluated our approach on Cifar10 and MNIST datasets and observed
significant improvement on the classification performance using the simple k-nn
method.


  This paper aims to describe the behavior of diffeological differential
1-forms under the operation of gluing of diffeological spaces along a smooth
map. In the diffeological context, two constructions regarding diffeological
forms are available, that of the vector space \Omega^1(X) of all 1-forms, and
that of the (pseudo-)bundle \Lambda^1(X) of values of 1-forms. We describe the
behavior of the former under an arbitrary gluing of two diffeological spaces,
while for the latter, we limit ourselves to the case of gluing along a
diffeomorphism.


  This note gives an example of closed smooth manifolds $M$ and $N$ for which
the rank of $M\times N$ is strictly greater than $rank M + rank N$.


  In (online) learning theory the concepts of sparsity, variance and curvature
are well-understood and are routinely used to obtain refined regret and
generalization bounds. In this paper we further our understanding of these
concepts in the more challenging limited feedback scenario. We consider the
adversarial multi-armed bandit and linear bandit settings and solve several
open problems pertaining to the existence of algorithms with favorable regret
bounds under the following assumptions: (i) sparsity of the individual losses,
(ii) small variation of the loss sequence, and (iii) curvature of the action
set. Specifically we show that (i) for $s$-sparse losses one can obtain
$\tilde{O}(\sqrt{s T})$-regret (solving an open problem by Kwon and Perchet),
(ii) for loss sequences with variation bounded by $Q$ one can obtain
$\tilde{O}(\sqrt{Q})$-regret (solving an open problem by Kale and Hazan), and
(iii) for linear bandit on an $\ell_p^n$ ball one can obtain $\tilde{O}(\sqrt{n
T})$-regret for $p \in [1,2]$ and one has $\tilde{\Omega}(n \sqrt{T})$-regret
for $p>2$ (solving an open problem by Bubeck, Cesa-Bianchi and Kakade). A key
new insight to obtain these results is to use regularizers satisfying more
refined conditions than general self-concordance


  The production rate of soft real photons or soft lepton pairs by a hot QCD
plasma is dominated by strong collinear divergences. As a consequence, it
appears that the effective theory based on the resummation of hard thermal
loops fails to handle properly these light-cone sensitive processes since some
formally higher order diagrams are in reality the dominant ones.