  Adaptation to environmental change is a common property of biological
systems. Cells initially respond to external changes in the environment, but
after some time, they regain their original state. By considering an element
consisting of two variables that show such adaptation dynamics, we studied a
coupled dynamical system containing such elements to examine the diverse
dynamics in the system and classified the behaviors on the basis of the network
structure that determined the interaction among elements. For a system with two
elements, two types of behaviors, perfect adaptation and simple oscillation,
were observed. For a system with three elements, in addition to these two
types, novel types of dynamics, namely, rapid burst-type oscillation and a slow
cycle, were discovered; depending on the initial conditions, these novel types
of dynamics coexisted. These behaviors are a result of the characteristic
dynamics of each element, i.e., fast response and slow adaptation processes.
The behaviors depend on the network structure (in specific, a combination of
positive or negative feedback among elements). Cooperativity among elements due
to a positive feedback loop leads to simple oscillation, whereas frustration
involving alternating positive and negative interactions among elements leads
to the coexistence of rapid bursting oscillation and a slow cycle. These
behaviors are classified on the basis of the frustration indices defined by the
network structure. The period of the slow cycle is much longer than the
original adaptation time scale, while the burst-type oscillation is a continued
response that does not involve any adaptation. We briefly discuss the universal
applicability of our results to a network of a larger number of elements and
their possible relevance to biological systems.


  An analysis of 3-flavour neutrino oscillations inside the Earth is presented
in terms of the oscillograms -- contour plots of oscillation probabilities in
the plane neutrino energy -- nadir angle. Special attention is paid to CP
violation in neutrino oscillations in the Earth.


  Recent models of galactic chemical evolution account for updated evolutionary
models of massive stars (with special emphasis on stellar winds) and for the
effects of intermediate mass and massive binaries. The results are summarised.
We also present a critical discussion on possible effects of stellar rotation
on overall galactic chemical evolutionary simulations.


  After an introduction in which we review the fundamental difficulty in
constructing lattice chiral gauge theories, we discuss the analytic and
numerical evidence that abelian lattice chiral gauge theories can be
non-perturbatively constructed through the gauge-fixing approach. While a
complete non-abelian extension is still under construction, we also show how
fermion-number violating processes are realized in this approach.


  Face presentation attack detection plays a critical role in the modern face
recognition pipeline. A face presentation attack detection model with good
generalization can be obtained when it is trained with face images from
different input distributions and different types of spoof attacks. In reality,
training data (both real face images and spoof images) are not directly shared
between data owners due to legal and privacy issues. In this paper, with the
motivation of circumventing this challenge, we propose a Federated Face
Presentation Attack Detection (FedPAD) framework that simultaneously takes
advantage of rich fPAD information available at different data owners while
preserving data privacy. In the proposed framework, each data center locally
trains its own fPAD model. A server learns a global fPAD model by iteratively
aggregating model updates from all data centers without accessing private data
in each of them. To equip the aggregated fPAD model in the server with better
generalization ability to unseen attacks from users, following the basic idea
of FedPAD, we further propose a Federated Generalized Face Presentation Attack
Detection (FedGPAD) framework. A federated domain disentanglement strategy is
introduced in FedGPAD, which treats each data center as one domain and
decomposes the fPAD model into domain-invariant and domain-specific parts in
each data center. Two parts disentangle the domain-invariant and
domain-specific features from images in each local data center, respectively. A
server learns a global fPAD model by only aggregating domain-invariant parts of
the fPAD models from data centers and thus a more generalized fPAD model can be
aggregated in server. We introduce the experimental setting to evaluate the
proposed FedPAD and FedGPAD frameworks and carry out extensive experiments to
provide various insights about federated learning for fPAD.


  Combining CNN with CRF for modeling dependencies between pixel labels is a
popular research direction. This task is far from trivial, especially if
end-to-end training is desired. In this paper, we propose a novel simple
approach to CNN+CRF combination. In particular, we propose to simulate a CRF
regularizer with a trainable module that has standard CNN architecture. We call
this module a CRF Simulator. We can automatically generate an unlimited amount
of ground truth for training such CRF Simulator without any user interaction,
provided we have an efficient algorithm for optimization of the actual CRF
regularizer. After our CRF Simulator is trained, it can be directly
incorporated as part of any larger CNN architecture, enabling a seamless
end-to-end training. In particular, the other modules can learn parameters that
are more attuned to the performance of the CRF Simulator module. We demonstrate
the effectiveness of our approach on the task of salient object segmentation
regularized with the standard binary CRF energy. In contrast to previous work
we do not need to develop and implement the complex mechanics of optimizing a
specific CRF as part of CNN. In fact, our approach can be easily extended to
other CRF energies, including multi-label. To the best of our knowledge we are
the first to study the question of whether the output of CNNs can have
regularization properties of CRFs.


  We present a Python tool to generate a standard dataset from solar images
that allows for user-defined selection criteria and a range of pre-processing
steps. Our Python tool works with all image products from both the Solar and
Heliospheric Observatory (SoHO) and Solar Dynamics Observatory (SDO) missions.
We discuss a dataset produced from the SoHO mission's multi-spectral images
which is free of missing or corrupt data as well as planetary transits in
coronagraph images, and is temporally synced making it ready for input to a
machine learning system. Machine-learning-ready images are a valuable resource
for the community because they can be used, for example, for forecasting space
weather parameters. We illustrate the use of this data with a 3-5 day-ahead
forecast of the north-south component of the interplanetary magnetic field
(IMF) observed at Lagrange point one (L1). For this use case, we apply a deep
convolutional neural network (CNN) to a subset of the full SoHO dataset and
compare with baseline results from a Gaussian Naive Bayes classifier.


  At the end of the nineteenth century \'Edouard Branly discovered that the
electrical resistance of a granular metallic conductor could drop by several
orders of magnitude when excited by the electromagnetic field emitted by a
distant electrical spark [ ]. Despite the fact that this effect was used to
detect radio waves in the early days of wireless telegraphy and more recently,
studied in the field of granular materials, no satisfactory explanation of the
physical origin of the effect has been proposed. In this contribution, we
relate the Branly effect to the induced tunnelling effect first described by
Fran\c{c}ois Bardou and Dominique Boos\'e [ ].


  Automatic speech recognition can potentially benefit from the lip motion
patterns, complementing acoustic speech to improve the overall recognition
performance, particularly in noise. In this paper we propose an audio-visual
fusion strategy that goes beyond simple feature concatenation and learns to
automatically align the two modalities, leading to enhanced representations
which increase the recognition accuracy in both clean and noisy conditions. We
test our strategy on the TCD-TIMIT and LRS2 datasets, designed for large
vocabulary continuous speech recognition, applying three types of noise at
different power ratios. We also exploit state of the art Sequence-to-Sequence
architectures, showing that our method can be easily integrated. Results show
relative improvements from 7% up to 30% on TCD-TIMIT over the acoustic modality
alone, depending on the acoustic noise level. We anticipate that the fusion
strategy can easily generalise to many other multimodal tasks which involve
correlated modalities. Code available online on GitHub:
https://github.com/georgesterpu/Sigmedia-AVSR


  The cutoff phenomena for Markovian dynamics have been observed and rigorously
verified for a multitude of models, particularly for Glauber-type dynamics on
spin systems. However, prior studies have barely considered irreversible
chains. In this work, the cutoff phenomenon of certain cyclic dynamics are
studied on the hypercube $\Sigma_{n} = Q^{V_{n}}$, where $Q = \{1, 2, 3\}$ and
$V_{n} = \{1,...,n\}$. The main feature of these dynamics is the fact that they
are represented by an irreversible Markov chain. Based on the coupling
modifications suggested in a previous study of the cutoff phenomenon for the
Curie-Weiss-Potts model, a comprehensive proof is presented.


  Distant supervision has become the standard method for relation extraction.
However, even though it is an efficient method, it does not come at no
cost---The resulted distantly-supervised training samples are often very noisy.
To combat the noise, most of the recent state-of-the-art approaches focus on
selecting one-best sentence or calculating soft attention weights over the set
of the sentences of one specific entity pair. However, these methods are
suboptimal, and the false positive problem is still a key stumbling bottleneck
for the performance. We argue that those incorrectly-labeled candidate
sentences must be treated with a hard decision, rather than being dealt with
soft attention weights. To do this, our paper describes a radical solution---We
explore a deep reinforcement learning strategy to generate the false-positive
indicator, where we automatically recognize false positives for each relation
type without any supervised information. Unlike the removal operation in the
previous studies, we redistribute them into the negative examples. The
experimental results show that the proposed strategy significantly improves the
performance of distant supervision comparing to state-of-the-art systems.


  In this paper we extend the constructions of Boava and Exel to present the
C*-algebra associated with an injective endomorphism of a group with finite
cokernel as a partial group algebra and consequently as a partial crossed
product. With this representation we present another way to study such
C*-algebras, only using tools from partial crossed products.


  The large dimension ($D$) limit of general relativity has been used in
problems involving black holes as an analytical approximation tool. Further it
has been proposed that both linear and nonlinear problems involving black holes
can be systematically studied in a $1/D$ expansion. Certain quasinormal modes
of higher-dimensional Schwarzschild black holes with $\omega \sim
\mathcal{O}(1)$ were studied in the large $D$ limit using a $1/D$ expansion for
the mode function.
  In this paper, we revisit this linear perturbation problem and obtain an
analytical equation for the vector quasinormal modes $\omega \sim
\mathcal{O}(1)$ in the large $D$ limit, without using a $1/D$ expansion for the
mode function. This can be used to compute quasinormal modes to next to leading
order in $1/D$. We also compute vector and scalar quasinormal modes with
$\omega \sim \mathcal{O}(D)$ in the Laplace transform approach in the large $D$
limit. We discuss the useful features of this approach specifically in the
large $D$ limit.


  A large fraction of stars forming in our galaxy are born within clusters
embedded in giant molecular clouds. In these environments, the background UV
radiation fields impinging upon circumstellar disks can often dominate over the
radiation fields produced by each disk's central star. As a result, this
background radiation can drive the evaporation of circumstellar disks and lead
to the loss of planet forming potential within a cluster. This paper presents a
detailed analysis of this process for clusters whose stellar membership falls
within the range $100 \le N \le 1000$. For these intermediate-sized clusters,
the background UV field is often dominated by the most massive stellar member.
Due to the steep slope of the initial mass function, the amount of background
UV light that bathes clusters of similar size displays significant variance. As
a result, we perform a statistical analysis of this problem by calculating
distributions of FUV flux values impinging upon star/disk systems for several
cluster scenarios. We find that in the absence of dust attenuation, giant
planet formation would likely be inhibited in approximately half of systems
forming within intermediate-sized clusters regardless of stellar membership. In
contrast, the presence of dust can significantly lower this value, with the
effect considerably more pronounced in more populated clusters.


  As a contribution to an eventual solution of the problem of determination of
the maximal subgroups of the Monster we show that there is a unique conjugacy
class of subgroups isomorphic to $PSU_3(8)$. The argument depends on some
computations in various subgroups, but not on computations in the Monster
itself.


  We investigate the excitonic spectrum of MoS$_2$ monolayers and calculate its
optical absorption properties over a wide range of energies. Our approach takes
into account the anomalous screening in two dimensions and the presence of a
substrate, both cast by a suitable effective Keldysh potential. We solve the
Bethe-Salpeter equation using as a basis a Slater-Koster tight-binding model
parameterized to fit ab initio MoS$_2$ band structure calculations. The
resulting optical conductivity is in good quantitative agreement with existing
measurements up to ultraviolet energies. We establish that the electronic
contributions to the C excitons arise not from states in the vicinity of the
$\Gamma$ point, but from a set of $k$-points over extended portions of the
Brillouin zone. Our results reinforce the advantages of approaches based on
effective models to expeditiously explore the properties and tunability of
excitons in TMD systems.


  Curriculum Analytics (CA) studies curriculum structure and student data to
ensure the quality of educational programs. One desirable property of courses
within curricula is that they are not unexpectedly more difficult for students
of different backgrounds. While prior work points to likely variations in
course difficulty across student groups, robust methodologies for capturing
such variations are scarce, and existing approaches do not adequately decouple
course-specific difficulty from students' general performance levels. The
present study introduces Differential Course Functioning (DCF) as an Item
Response Theory (IRT)-based CA methodology. DCF controls for student
performance levels and examines whether significant differences exist in how
distinct student groups succeed in a given course. Leveraging data from over
20,000 students at a large public university, we demonstrate DCF's ability to
detect inequities in undergraduate course difficulty across student groups
described by grade achievement. We compare major pairs with high co-enrollment
and transfer students to their non-transfer peers. For the former, our findings
suggest a link between DCF effect sizes and the alignment of course content to
student home department motivating interventions targeted towards improving
course preparedness. For the latter, results suggest minor variations in
course-specific difficulty between transfer and non-transfer students. While
this is desirable, it also suggests that interventions targeted toward
mitigating grade achievement gaps in transfer students should encompass
comprehensive support beyond enhancing preparedness for individual courses. By
providing more nuanced and equitable assessments of academic performance and
difficulties experienced by diverse student populations, DCF could support
policymakers, course articulation officers, and student advisors.


  This review summarizes the coherent structures (CS) based on two-point
correlations and their applications, with a focus on the interpretation of
statistic CS and their characteristics. We review studies on this topic, which
have attracted attention in recent years, highlighting improvements,
expansions, and promising future directions for two-point statistics of CS in
turbulent flow. The CS is one of typical structures of turbulent flow,
transporting energy from large-scale to small-scale structures. To investigate
the CS in turbulent flow, a large amount of two-point correlation techniques
for CS identification and visualization have been, and are currently being,
intensively studied by researchers. Two-point correlations with examples and
comparisons between different methods are briefly reviewed at first. Some of
the uses of correlations in both Eulerian and Lagrangian frames of reference to
obtain their properties at consecutive spatial locations and time events are
surveyed. Two-point correlations, involving space-time correlations, two-point
spatial correlations, and cross correlations, as essential to theories and
models of turbulence and for the analyses of experimental and numerical
turbulence data are then discussed. The velocity-vorticity correlation
structure (VVCS) as one of the statistical CS based on two-point correlations
is reiterated in detail. Finally, we summarize the current understanding of
two-point correlations of turbulence and conclude with future issues for this
field.


  In this work we derive the interface exchange boundary conditions for the
classical linear dynamics of magnetization in ferromagnetic layers with the
interface Dzyaloshinskii-Moriya interaction (IDMI). We show that IDMI leads to
pinning of dynamic magnetization at the interface. An unusual peculiarity of
the IDMI-based pinning is that its scales as the spin-wave wave number. We
incorporate these boundary conditions into an existing numerical model for the
dynamics of the Damon-Eshbach spin wave in ferromagnetic films. IDMI affects
the dispersion and the frequency non-reciprocity of the traveling Damon-Eshbach
spin wave. For a broad range of film thicknesses L and wave numbers the results
of the numerical simulations of the spin wave dispersion are in a good
agreement with a simple analytical expression which shows that the contribution
of IDMI to the dispersion scales as 1/L, similarly to the effect of other types
of interfacial anisotropy. Suggestions to experimentalists how to detect the
presence of IDMI in a spin wave experiment are given.


  Adversarially robust machine learning has received much recent attention.
However, prior attacks and defenses for non-parametric classifiers have been
developed in an ad-hoc or classifier-specific basis. In this work, we take a
holistic look at adversarial examples for non-parametric classifiers, including
nearest neighbors, decision trees, and random forests. We provide a general
defense method, adversarial pruning, that works by preprocessing the dataset to
become well-separated. To test our defense, we provide a novel attack that
applies to a wide range of non-parametric classifiers. Theoretically, we derive
an optimally robust classifier, which is analogous to the Bayes Optimal. We
show that adversarial pruning can be viewed as a finite sample approximation to
this optimal classifier. We empirically show that our defense and attack are
either better than or competitive with prior work on non-parametric
classifiers. Overall, our results provide a strong and broadly-applicable
baseline for future work on robust non-parametrics. Code available at
https://github.com/yangarbiter/adversarial-nonparametrics/ .


  The underwater acoustic channel is characterized by a path loss that depends
not only on the transmission distance, but also on the signal frequency.
Signals transmitted from one user to another over a distance $l$ are subject to
a power loss of $l^{-\alpha}{a(f)}^{-l}$. Although a terrestrial radio channel
can be modeled similarly, the underwater acoustic channel has different
characteristics. The spreading factor $\alpha$, related to the geometry of
propagation, has values in the range $1 \leq \alpha \leq 2$. The absorption
coefficient $a(f)$ is a rapidly increasing function of frequency: it is three
orders of magnitude greater at 100 kHz than at a few Hz. Existing results for
capacity of wireless networks correspond to scenarios for which $a(f) = 1$, or
a constant greater than one, and $\alpha \geq 2$. These results cannot be
applied to underwater acoustic networks in which the attenuation varies over
the system bandwidth. We use a water-filling argument to assess the minimum
transmission power and optimum transmission band as functions of the link
distance and desired data rate, and study the capacity scaling laws under this
model.


  Stationarity is a very general, qualitative assumption, that can be assessed
on the basis of application specifics. It is thus a rather attractive
assumption to base statistical analysis on, especially for problems for which
less general qualitative assumptions, such as independence or finite memory,
clearly fail. However, it has long been considered too general to allow for
statistical inference to be made. One of the reasons for this is that rates of
convergence, even of frequencies to the mean, are not available under this
assumption alone. Recently, it has been shown that, while some natural and
simple problems such as homogeneity, are indeed provably impossible to solve if
one only assumes that the data is stationary (or stationary ergodic), many
others can be solved using rather simple and intuitive algorithms. The latter
problems include clustering and change point estimation. In this volume I
summarize these results. The emphasis is on asymptotic consistency, since this
the strongest property one can obtain assuming stationarity alone. While for
most of the problems for which a solution is found this solution is
algorithmically realizable, the main objective in this area of research, the
objective which is only partially attained, is to understand what is possible
and what is not possible to do for stationary time series. The considered
problems include homogeneity testing, clustering with respect to distribution,
clustering with respect to independence, change-point estimation, identity
testing, and the general question of composite hypotheses testing. For the
latter problem, a topological criterion for the existence of a consistent test
is presented. In addition, several open questions are discussed.


  A nucleon-nucleus interaction model has been applied to ascertain the
underlying character of the negative-parity spectra of four isobars of mass
seven, from neutron-- to proton--emitter driplines. With one single nuclear
potential defined by a simple coupled-channel model, a multichannel algebraic
scattering approach (MCAS) has been used to determine the bound and resonant
spectra of the four nuclides, of which ^7He and ^7B are particle unstable.
Incorporation of Pauli blocking in the model enables a description of all known
spin-parity states of the mass-7 isobars. We have also obtained spectra of
similar quality by using a large space no-core shell model. Additionally, we
have studied ^7Li and ^7Be using a dicluster model. We have found a
dicluster-model potential that can reproduce the lowest four states of the two
nuclei, as well as the relevant low-energy elastic scattering cross sections.
But, with this model, the rest of the energy spectra cannot be obtained.


  We apply the crystal basis theory for Fock spaces over quantum affine
algebras to the modular representations of the cyclotomic Hecke algebras of
type $G(p,p,n)$. This yields a classification of simple modules over these
cyclotomic Hecke algebras in the non-separated case, generalizing our previous
work [J. Hu, J. Algebra 267 (2003) 7-20]. The separated case was completed in
[J. Hu, J. Algebra 274 (2004) 446--490]. Furthermore, we use Naito--Sagaki's
work [S. Naito & D. Sagaki, J. Algebra 251 (2002) 461--474] on
Lakshmibai--Seshadri paths fixed by diagram automorphisms to derive explicit
formulas for the number of simple modules over these Hecke algebras. These
formulas generalize earlier results of [M. Geck, Represent. Theory 4 (2000)
370-397] on the Hecke algebras of type $D_n$ (i.e., of type $G(2,2,n)$).


  To some extent, graph evolutionary mechanisms can be explained by its
spectra. Here, we are interested in two graph operations, namely, motif
(subgraph) doubling and attachment that are biologically relevant. We
investigate how these two processes affect the spectrum of the normalized graph
Laplacian. A high (algebraic) multiplicity of the eigenvalues $1, 1\pm 0.5,
1\pm \sqrt{0.5}$ and others has been observed in the spectrum of many real
networks. We attempt to explain the production of distinct eigenvalues by motif
doubling and attachment. Results on the eigenvalue $1$ are discussed
separately.


  The outage of a transmission line may change the system phase angle
differences to the point that the system experience stress conditions. Hence,
the angle differences for post-contingency condition of a transmission lines
should be predicted in real time operation. However, online line-based phase
angle difference monitoring and prediction for power system stress assessment
is not a universal operating practice yet. Thus, in this paper, an online power
system stress assessment framework is proposed by developing a convolutional
neural network (CNN) module trained through Deep Learning approach. In the
proposed framework, the continuously streaming system phase angle data, driven
from phasor measurement units (PMUs) or a state estimator (SE), is used to
construct power system stress indices adaptive to the structure parameters of
the CNN module. Using this approach, any hidden patterns between phase angles
of buses and system stress conditions are revealed at low computation cost
while yielding accurate stress status and the severity of the stress. The
effectiveness and scalability of the proposed method has been verified on the
IEEE 118-bus and more importantly, on the PJM Interconnection system. Moreover,
outperformance of the proposed method is verified by comparing the results with
artificial neural network (ANN) and decision tree (DT).


  In the paper we describe basin of attraction and the Siegel discs of the
$p$-adic dynamical system $f(x)=x^{2n+1}+ax^{n+1}$ over complex $p$-adic field.


  Citation based approaches have seen good progress for recommending research
papers using citations in the paper. Citation proximity analysis which uses the
in-text citation proximity to find relatedness between two research papers is
better than co-citation analysis and bibliographic analysis. However, one
common problem which exists in each approach is that paper should be well
cited. If documents are not cited properly or not cited at all, then using
these approaches will not be helpful. To overcome the problem, this paper
discusses the approach Virtual Citation Proximity (VCP) which uses Siamese
Neural Network along with the notion of citation proximity analysis and
content-based filtering. To train this model, the actual distance between the
two citations in a document is used as ground truth, this distance is the word
count between the two citations. VCP is trained on Wikipedia articles for which
the actual word count is available which is used to calculate the similarity
between the documents. This can be used to calculate relatedness between two
documents in a way they would have been cited in the proximity even if the
documents are uncited. This approach has shown a great improvement in
predicting proximity with basic neural networks over the approach which uses
the Average Citation Proximity index value as the ground truth. This can be
improved by using a complex neural network and proper hyper tuning of
parameters.


  State-of-the-art Ni/Ti supermirror neutron optics have limited reflected
intensity and a restricted neutron energy range due to the interface width.
Incorporating low-neutron-absorbing 11B4C enhances reflectivity and allows for
thinner layers to be deposited, with which more efficient supermirrors with
higher m-values can be realized. However, incorporating 11B4C reduces the
optical contrast, limiting the attainable reflectivity at low scattering
vectors, making this approach infeasible. This study explores various
approaches to optimize the material design of 11B4C-containing Ni/Ti
supermirrors to maintain high reflectivity at low scattering vectors and
achieve low interface widths at large scattering vectors. The scattering length
density contrast versus interface width is investigated for multilayer periods
of 30 {\AA}, 48 {\AA}, and 84 {\AA}, for designs involving pure Ni/Ti
multilayers, multilayers with 11B4C co-deposited in Ni and Ti layers,
multilayers with 11B4C co-deposited only in Ni layers, and multilayers with
11B4C as thin interlayers between Ni and Ti layers. Our results suggest that a
depth-graded hybrid material design by incorporating 11B4C inside the Ni and Ti
layers, below approximately 26 {\AA}, and introducing 1.5 {\AA} 11B4C
interlayers between the thicker Ni and Ti layers can achieve a higher
reflectivity than state-of-the-art Ni/Ti multilayers over the entire scattering
vector range.


  Optically-transparent immersion liquids with refractive index (n ~ 1.77) to
match sapphire-based aplanatic numerical aperture increasing lens (aNAIL) are
necessary for achieving deep 3D imaging with high spatial resolution. We report
that antimony tribromide (SbBr$_{3}$) salt dissolved in liquid diiodomethane
(CH$_{2}$I$_{2}$) provides a new high refractive index immersion liquid for
optics applications. The refractive index is tunable from n = 1.74 (pure) to n
= 1.873 (saturated), by adjusting either salt concentration or temperature;
this allows it to match (or even exceed) the refractive index of sapphire.
Importantly, the solution gives excellent light transmittance in the
ultraviolet to near-infrared range, an improvement over commercially-available
immersion liquids. This refractive index matched immersion liquid formulation
has enabled us to develop a sapphire-based aNAIL objective that has both high
numerical aperture (NA = 1.17) and long working distance (WD = 12 mm). This
opens up new possibilities for deep 3D imaging with high spatial resolution.


  We introduce a novel real-time formulation of lattice NRQCD designed for
simulations in the background of an highly occupied gluon field. By evolving
quarks in the background of a dynamically evolving gluon field we computed the
time-evolution of heavy-quarkonium spectral functions as well as the static and
for finitely heavy quarks generalised potential. We conclude that the back
reaction of the quarks is necessary for any binding process. Here we discuss
the methodology, our results and the origin of the absence of a binding
process.


  With Artificial Intelligence (AI) increasingly permeating various aspects of
society, including healthcare, the adoption of the Transformers neural network
architecture is rapidly changing many applications. Transformer is a type of
deep learning architecture initially developed to solve general-purpose Natural
Language Processing (NLP) tasks and has subsequently been adapted in many
fields, including healthcare. In this survey paper, we provide an overview of
how this architecture has been adopted to analyze various forms of data,
including medical imaging, structured and unstructured Electronic Health
Records (EHR), social media, physiological signals, and biomolecular sequences.
Those models could help in clinical diagnosis, report generation, data
reconstruction, and drug/protein synthesis. We identified relevant studies
using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA) guidelines. We also discuss the benefits and limitations of using
transformers in healthcare and examine issues such as computational cost, model
interpretability, fairness, alignment with human values, ethical implications,
and environmental impact.


  Recent research suggests that the supercritical state consists of liquidlike
and gaslike states where particle dynamics and key system properties are
qualitatively different. We report experimental evidence of the structural
crossover in supercritical neon at pressure and temperature conditions
significantly exceeding the critical point values: 250$\,P_c$ and 6.6$\,T_c$.
The experimental results show a crossover of the medium-range order structure
evidenced by the change of the structure factor with pressure. We also observe
the crossover of the short-range order structure indicated by changes in the
coordination number. The relative width of the crossover is fairly narrow and
is smaller than 10-12 \% in pressure and temperature. By comparing our
experimental results with molecular dynamics simulations, we suggest that the
observed crossover can be attributed to the Frenkel line and discuss the
relationship between the structural crossover and qualitative changes of
dynamical and thermodynamic properties of supercritical matter.


  We describe the spectrum of weighted $d$-isomorphisms of Banach lattices
restricted on closed subspaces that are "rich" enough to preserve some "memory"
of the order structure of the original lattice. The examples include (but are
not limited to) weighted isometries of Hardy spaces on the polydisk and unit
ball in $\mathds{C}^n$.


  A number of emission and absorption features are expected to be visible in
high energy resolution X-ray spectra of type 1 AGN with ionized gas along the
line of sight (so called "warm absorbers"). Emission strongly depends on the
geometrical configuration of the gas, while absorption along the line of sight
does not. Absorption features include photoelectric absorption K and L edges
along with many strong K$\alpha$, K$\beta$ and L resonance absorption lines
from the most abundant elements. We present detailed simulations of our
``photoelectric + resonant absorption'' model with the high energy resolution
gratings and calorimeters of AXAF, XMM and Constellation-X, and discuss the
relevant physics which can be addressed with the new generation of X-ray
spectrometers.


  In the famous textbook written by Landau and Lifshitz all the vacuum metrics
of the general theory of relativity are derived, which depend on one coordinate
in the absence of a cosmological constant. Unfortunately, when considering
these solutions, the authors missed some of the possible solutions discussed in
this article. An exact solution is demonstrated, which is absent in the book by
Landau and Lifshitz. It describes space-time with a gravitational wave of zero
frequency. It is shown that there are no other solutions of this type than
listed above and Minkowski's metrics. The list of vacuum metrics that depend on
one coordinate is not complete without the solution provided in this paper.


  We consider the following stochastic partial differential equation,
\begin{align*} &dY_t=L^\ast Y_tdt+A^\ast Y_t\cdot dB_t\\ &Y_0=\psi,
\end{align*} associated with a stochastic flow $\{X(t,x)\}$, for $t \geq 0$, $x
\in \mathbb{R}^d$, as in [Rajeev \& Thangavelu, \emph{{Probabilistic
representations of solutions of the forward
  equations}}, Potential Anal. \textbf{28} (2008), no.~2, 139--162]. We show
that the strong solutions constructed there are `locally of compact support'.
Using this notion,we define the mild solutions of the above equation and show
the equivalence between strong and mild solutions in the multi Hilbertian space
$\mathcal{S}^\prime$. We show uniqueness of solutions in the case when $\psi$
is smooth via the `monotonicity inequality' for $(L^\ast,A^\ast)$, which is a
known criterion for uniqueness.


  I discuss how global QCD fits of parton distribution functions can make the
somewhat separated fields of high-energy particle physics and lower energy
hadronic and nuclear physics interact to the benefit of both. I review specific
examples of this interplay from recent works of the CTEQ-Jefferson Lab
collaboration, including hadron structure at large parton momentum and gauge
boson production at colliders. I devote particular attention to quantifying
theoretical uncertainties arising in the treatment of large partonic momentum
contributions to deep inelastic scattering observables, and to discussing the
experimental progress needed to reduce these.


  We present Chandra HETGS spectroscopy of the Be/X-ray binary 1A 0535+262
obtained during the 2009/2010 giant outburst. These are the first CCD grating
spectra of this type of system during a giant outburst. Our spectra reveal a
number of lines including a narrow Fe K_alpha emission line with a FWHM of ~
5000 km s^-1. For the first time, we detect the presence of a highly ionized
outflow in a Be/X-ray binary. Assuming that the line is He-like Fe XXV, fits
with a simple Gaussian imply an outflow velocity of ~ 1500 km s^-1. However,
self-consistent photoionization modeling with XSTAR suggests that Fe XXIII-XXIV
must also contribute. In this case, an outflow velocity of ~ 3000 km s^-1 is
implied. These results are discussed in the context of the accretion flow in
Be-star, neutron star, and black hole X-ray binaries.


  In spite of the improvements achieved by the several denoising algorithms
over the years, many of them still fail at preserving the fine details of the
image after denoising. This is as a result of the smooth-out effect they have
on the images. Most neural network-based algorithms have achieved better
quantitative performance than the classical denoising algorithms. However, they
also suffer from qualitative (visual) performance as a result of the smooth-out
effect. In this paper, we propose an algorithm to address this shortcoming. We
propose a deep collaborative filtering-based (Deep-CoFiB) algorithm for image
denoising. This algorithm performs collaborative denoising of image patches in
the sparse domain using a set of optimized neural network models. This results
in a fast algorithm that is able to excellently obtain a trade-off between
noise removal and details preservation. Extensive experiments show that the
DeepCoFiB performed quantitatively (in terms of PSNR and SSIM) and
qualitatively (visually) better than many of the state-of-the-art denoising
algorithms.


  Many systems today distribute trust across multiple parties such that the
system provides certain security properties if a subset of the parties are
honest. In the past few years, we have seen an explosion of academic and
industrial cryptographic systems built on distributed trust, including secure
multi-party computation applications (e.g., private analytics, secure learning,
and private key recovery) and blockchains. These systems have great potential
for improving security and privacy, but face a significant hurdle on the path
to deployment. We initiate study of the following problem: a single
organization is, by definition, a single party, and so how can a single
organization build a distributed-trust system where corruptions are
independent? We instead consider an alternative formulation of the problem:
rather than ensuring that a distributed-trust system is set up correctly by
design, what if instead, users can audit a distributed-trust deployment? We
propose a framework that enables a developer to efficiently and cheaply set up
any distributed-trust system in a publicly auditable way. To do this, we
identify two application-independent building blocks that we can use to
bootstrap arbitrary distributed-trust applications: secure hardware and an
append-only log. We show how to leverage existing implementations of these
building blocks to deploy distributed-trust systems, and we give
recommendations for infrastructure changes that would make it easier to deploy
distributed-trust systems in the future.


  We discuss a method to transform the covariant Fokker action into an implicit
two-degree-of-freedom Hamiltonian for the electromagnetic two-body problem with
arbitrary masses. This dynamical system appeared 100 years ago and it was
popularized in the 1940's by the still incomplete Wheeler and Feynman program
to quantize it as a means to overcome the divergencies of perturbative QED. Our
finite-dimensional implicit Hamiltonian is closed and involves no series
expansions. The Hamiltonian formalism is then used to motivate an EBK
quantization based on the classical trajectories with a non-perturbative
formula that predicts energies free of infinities.


  We present the polarization profiles of 22 pulsars in the globular cluster 47
Tucanae using observations from the MeerKAT radio telescope at UHF-band
(544-1088 MHz) and report precise values of dispersion measure (DM) and
rotation measure (RM). We use these measurements to investigate the presence of
turbulence in electron density and magnetic fields. The structure function of
DM shows a break at $\sim 30$ arcsec ($\sim 0.6$ pc at the distance of 47
Tucanae) that suggests the presence of turbulence in the gas in the cluster
driven by the motion of wind-shedding stars. On the other hand, the structure
function of RM does not show evidence of a break. This non-detection could be
explained either by the limited number of pulsars or by the effects of the
intervening gas in the Galaxy along the line of sight. Future pulsar
discoveries in the cluster could help confirm the presence and localise the
turbulence.


  The cross-Wigner distribution $W(f,g)$ of two functions or temperate
distributions $f,g$ is a fundamental tool in quantum mechanics and in signal
analysis. Usually, in applications in time-frequency analysis $f$ and $g$
belong to some modulation space and it is important to know which modulation
spaces $W(f,g)$ belongs to. Although several particular sufficient conditions
have been appeared in this connection, the general problem remains open. In the
present paper we solve completely this issue by providing the full range of
modulation spaces in which the continuity of the cross-Wigner distribution
$W(f,g)$ holds, as a function of $f,g$. The case of weighted modulation spaces
is also considered. The consequences of our results are manifold: new bounds
for the short-time Fourier transform and the ambiguity function, boundedness
results for pseudodifferential (in particular, localization) operators and
properties of the Cohen class.


  I give a brief overview of recent results from lattice QCD calculations which
are relevant for the phenomenology of the Standard Model. I discuss, in
particular, the lattice determination of light quark masses and the calculation
of those hadronic quantities, such as semileptonic form factors, decay
constants and B-parameters, which are of particular interest for the analysis
of the CKM mixing matrix and the origin of CP violation.


  We construct a finitely-presented group such that its Vogel-Levine
localization is not transfinitely nilpotent. This answers a problem of J. P.
Levine.


  Human action recognition has drawn a lot of attention in the recent years due
to the research and application significance. Most existing works on action
recognition focus on learning effective spatial-temporal features from videos,
but neglect the strong causal relationship among the precondition, action and
effect. Such relationships are also crucial to the accuracy of action
recognition. In this paper, we propose to model the causal relationships based
on the precondition and effect to improve the performance of action
recognition. Specifically, a Cycle-Reasoning model is proposed to capture the
causal relationships for action recognition. To this end, we annotate
precondition and effect for a large-scale action dataset. Experimental results
show that the proposed Cycle-Reasoning model can effectively reason about the
precondition and effect and can enhance action recognition performance.


  Obtaining syntactic parses is a crucial part of many NLP pipelines. However,
most of the world's languages do not have large amounts of syntactically
annotated corpora available for building parsers. Syntactic projection
techniques attempt to address this issue by using parallel corpora consisting
of resource-poor and resource-rich language pairs, taking advantage of a parser
for the resource-rich language and word alignment between the languages to
project the parses onto the data for the resource-poor language. These
projection methods can suffer, however, when the two languages are divergent.
In this paper, we investigate the possibility of using small, parallel,
annotated corpora to automatically detect divergent structural patterns between
two languages. These patterns can then be used to improve structural projection
algorithms, allowing for better performing NLP tools for resource-poor
languages, in particular those that may not have large amounts of annotated
data necessary for traditional, fully-supervised methods. While this detection
process is not exhaustive, we demonstrate that common patterns of divergence
can be identified automatically without prior knowledge of a given language
pair, and the patterns can be used to improve performance of projection
algorithms.


  Albeit of crucial interest for both financial practitioners and researchers,
market-implied volatility data of European swaptions often exhibit large
portions of missing quotes due to illiquidity of the various underlying
swaption instruments. In this case, standard stochastic interpolation tools
like the common SABR model often cannot be calibrated to observed implied
volatility smiles, due to data being only available for the at-the-money quote
of the respective underlying swaption. Here, we propose to infer the geometry
of the full unknown implied volatility cube by learning stochastic latent
representations of implied volatility cubes via variational autoencoders,
enabling inference about the missing volatility data conditional on the
observed data by an approximate Gibbs sampling approach. Imputed estimates of
missing quotes can afterwards be used to fit a standard stochastic volatility
model. Since training data for the employed variational autoencoder model is
usually sparsely available, we test the robustness of the approach for a model
trained on synthetic data on real market quotes and we show that SABR
interpolated volatilites calibrated to reconstructed volatility cubes with
artificially imputed missing values differ by not much more than two basis
points compared to SABR fits calibrated to the complete cube. Moreover, we show
how the imputation can be used to successfully set up delta-neutral portfolios
for hedging purposes.


  We report on the first results of a search for optical-wavelength photons
mixing with hypothetical hidden-sector paraphotons in the mass range between
10^-5 and 10^-2 electron volts for a mixing parameter greater than 10^-7. This
was a generation-regeneration experiment using the "light shining through a
wall" technique in which regenerated photons are searched for downstream of an
optical barrier that separates it from an upstream generation region. The new
limits presented here are approximately three times more sensitive to this
mixing than the best previous measurement. The present results indicate no
evidence for photon-paraphoton mixing for the range of parameters investigated.


  First identified from the HATNet wide-field photometric survey, these
candidate transiting planets were then followed-up with a variety of
photometric observations. Determining the planetary nature of the objects and
characterizing the parameters of the systems were mainly done with the SOPHIE
spectrograph at the 1.93m telescope at OHP and the TRES spectrograph at the
1.5m telescope at FLWO. HAT-P-42b and HAT-P-43b are typical hot Jupiters on
circular orbits around early-G/late-F main sequence host stars, with periods of
4.641876\pm0.000032 and 3.332688\pm0.000016 days, masses of 0.975\pm0.126 and
0.660\pm0.083 Mjup, and radii of 1.277\pm0.149 and 1.283+0.057-0.034 Rjup,
respectively. These discoveries increase the sample of planets with measured
mean densities, which is needed to constrain theories of planetary interiors
and atmospheres. Moreover, their hosts are relatively bright (V < 13.5)
facilitating further follow-up studies.


  We study the state transfer through quantum walks placed on a bounded
one-dimensional path. We first consider continuous-time quantum walks from a
Gaussian state. We find such a state when superposing centered on the starting
and antipodal positions preserves a high fidelity for a long time and when sent
on large circular graphs. Furthermore, it spreads with a null group velocity.
We also explore discrete-time quantum walks to evaluate the qubit fidelity
throughout the walk. In this case, the initial state is a product of states
between a qubit and a Gaussian superposition of position states. Then, we add
two $\sigma_x$ gates to confine this delocalized qubit. We also find that this
bounded system dynamically enables periodic recovery of the initial separable
state. We outline some applications of our results in dynamic graphs and
propose quantum circuits to implement them based on the available literature.


  We derive a generalized version of the work fluctuation theorem for
nonequilibrium systems with spatio-temporal temperature fluctuations. For
chi-square distributed inverse temperature we obtain a generalized fluctuation
theorem based on q-exponentials, whereas for other temperature distributions
more complicated formulae arise. Since q-exponentials have a power law decay,
the decay rate in this generalized fluctuation theorem is much slower than the
conventional exponential decay. This implies that work fluctuations can be of
relevance for the design of micro and nano structures, since the work done on
the system is relatively much larger than in the conventional fluctuation
theorem.


  In this paper we use the latest corrections to the Newton-Einstein secular
perihelion rates of some planets of the Solar System, phenomenologically
estimated with the EPM2004 ephemerides by the Russian astronomer E.V. Pitjeva,
to put severe constraints on the range parameter lambda characterizing the
Yukawa-like modifications of the Newtonian inverse-square law of gravitation.
It turns out that the range cannot exceed about one tenth of an Astronomical
Unit. We assumed neither equivalence principle violating effects nor spatial
variations of $\alpha$ and $\lambda$. This finding may have important
consequences on all the modified theories of gravity involving Yukawa-type
terms with range parameters much larger than the Solar System size. However,
caution is advised since we, currently have at our disposal only the periehlion
extra-rates estimated by Pitjeva: if and when other groups will estimate their
own corrections to the secular motion of perihelia, more robust and firm tests
may be conducted.


  Holocene (the last 12,000 years) temperature variation, including the
transition out of the last Ice Age to a warmer climate, is reconstructed at
multiple locations in southern Finland, Sweden and Estonia based on pollen
fossil data from lake sediment cores. A novel Bayesian statistical approach is
proposed that allows the reconstructed temperature histories to interact
through shared environmental response parameters and spatial dependence. The
prior distribution for past temperatures is partially based on numerical
climate simulation. The features in the reconstructions are consistent with the
quantitative climate reconstructions based on more commonly used reconstruction
techniques. The results suggest that the novel spatio-temporal approach can
provide quantitative reconstructions that are smoother, less uncertain and
generally more realistic than the site-specific individual reconstructions.


  Mediated semi-quantum key distribution involves the use of two end-users who
have very restricted, almost classical, capabilities, who wish to establish a
shared secret key using the help of a fully-quantum server who may be
adversarial. In this paper, we introduce a new mediated semi-quantum key
distribution protocol, extending prior work, which has asymptotically perfect
efficiency. Though this comes at the cost of decreased noise tolerance, our
protocol is backwards compatible with prior work, so users may easily switch to
the old (normally less efficient) protocol if the noise level is high enough to
justify it. To prove security, we show an interesting reduction from the
mediated semi-quantum scenario to a fully-quantum entanglement based protocol
which may be useful when proving the security of other multi-user QKD
protocols.


  We explore some of the constraints imposed by positivity of the QCD measure
(Weingarten's inequalities) on the parameters defining chiral perturbation
theory. We find, in particular, that $2 m_q (\mu)\leq B_0(\mu) Z(\mu)$. The use
of further properties of the exact fermion propagator yields information on
some higher order parameters.


  We investigate periodical oscillations in the conductance of suspended Au and
Pt atomic chains during elongation under mechanical stress. Analysis of
conductance and shot noise measurements reveals that the oscillations are
mainly related to variations in a specific conduction channel as the chain
undergoes transitions between zigzag and linear atomic configurations. The
calculated local electronic structure shows that the oscillations originate
from varying degrees of hybridization between the atomic orbitals along the
chain as a function of the zigzag angle. These variations are highly dependent
on the directionality and symmetry of the relevant orbitals, in agreement with
the order-of-magnitude difference between the Pt and Au oscillation amplitudes
observed in experiment. Our results demonstrate that the sensitivity of
conductance to structural variations can be controlled by designing
atomic-scale conductors in view of the directional interactions between atomic
orbitals.


  Behavior-driven development (BDD) is an Agile testing methodology fostering
collaboration among developers, QA analysts, and stakeholders. In this
manuscript, we propose a novel approach to enhance BDD practices using large
language models (LLMs) to automate acceptance test generation. Our study uses
zero and few-shot prompts to evaluate LLMs such as GPT-3.5, GPT-4, Llama-2-13B,
and PaLM-2. The paper presents a detailed methodology that includes the
dataset, prompt techniques, LLMs, and the evaluation process. The results
demonstrate that GPT-3.5 and GPT-4 generate error-free BDD acceptance tests
with better performance. The few-shot prompt technique highlights its ability
to provide higher accuracy by incorporating examples for in-context learning.
Furthermore, the study examines syntax errors, validation accuracy, and
comparative analysis of LLMs, revealing their effectiveness in enhancing BDD
practices. However, our study acknowledges that there are limitations to the
proposed approach. We emphasize that this approach can support collaborative
BDD processes and create opportunities for future research into automated BDD
acceptance test generation using LLMs.


  Melodic similarity measurement is of key importance in music information
retrieval. In this paper, we use geometric matching techniques to measure the
similarity between two melodies. We represent music as sets of points or sets
of horizontal line segments in the Euclidean plane and propose efficient
algorithms for optimization problems inspired in two operations on melodies;
linear scaling and audio compression. In the scaling problem, an incoming query
melody is scaled forward until the similarity measure between the query and a
reference melody is minimized. The compression problem asks for a subset of
notes of a given melody such that the matching cost between the selected notes
and the reference melody is minimized.


  Let $f(\mathbf z)$ be an analytic function defined in the neighborhood of the
origin of $\mathbb C^n$ which have some Newton degenerate faces. We generalize
the Varchenko formula for the zeta function of the Milnor fibration of a Newton
non-degenerate function $f$ to this case. As an application, we give an example
of a pair of hypersurfaces with the same Newton boundary and the same zeta
function with different tangent cones.


  For observed galactic microlensing events only one fit is usually presented,
though, especially for a binary lens, several fits may be possible. This has
been shown for the MACHO LMC#1 event (Dominik & Hirshfeld 1996). Here I discuss
the strong binary lens events OGLE#7 and DUO#2. It is shown that several models
with a large variety of parameters are in accordance with the photometric data.
For most of the fits, 1-sigma-bounds on the fit parameters are given. The
variation of the parameters within the bounds is in some cases considerable. It
is likely that other binary lens systems which will occur will have properties
similar to the discussed events.


  The use of Deep Learning in the medical field is hindered by the lack of
interpretability. Case-based interpretability strategies can provide intuitive
explanations for deep learning models' decisions, thus, enhancing trust.
However, the resulting explanations threaten patient privacy, motivating the
development of privacy-preserving methods compatible with the specifics of
medical data. In this work, we analyze existing privacy-preserving methods and
their respective capacity to anonymize medical data while preserving
disease-related semantic features. We find that the PPRL-VGAN deep learning
method was the best at preserving the disease-related semantic features while
guaranteeing a high level of privacy among the compared state-of-the-art
methods. Nevertheless, we emphasize the need to improve privacy-preserving
methods for medical imaging, as we identified relevant drawbacks in all
existing privacy-preserving approaches.


  We present ArTST, a pre-trained Arabic text and speech transformer for
supporting open-source speech technologies for the Arabic language. The model
architecture follows the unified-modal framework, SpeechT5, that was recently
released for English, and is focused on Modern Standard Arabic (MSA), with
plans to extend the model for dialectal and code-switched Arabic in future
editions. We pre-trained the model from scratch on MSA speech and text data,
and fine-tuned it for the following tasks: Automatic Speech Recognition (ASR),
Text-To-Speech synthesis (TTS), and spoken dialect identification. In our
experiments comparing ArTST with SpeechT5, as well as with previously reported
results in these tasks, ArTST performs on a par with or exceeding the current
state-of-the-art in all three tasks. Moreover, we find that our pre-training is
conducive for generalization, which is particularly evident in the low-resource
TTS task. The pre-trained model as well as the fine-tuned ASR and TTS models
are released for research use.


  We present the identification of 13 DB + dM binaries and 2 DC + dM binaries
from the Sloan Digital Sky Survey (SDSS). Before the SDSS only 2 DB + dM
binaries and 1 DC + dM binary were known. At least three, possibly 8, of the
new DB + dM binaries seem to have white dwarf temperatures well above 30000 K
which would place them in the so called DB-gap. Finding these DB white dwarfs
in binaries may suggest that they have formed through a different evolutionary
channel than the ones in which DA white dwarfs transform into DB white dwarfs
due to convection in the upper layers.


  ASCA data are used to obtain two-dimensional gas temperature maps of A2256,
A2319, A2163 and A665. In all four, the temperature decreases significantly at
off-center distances of ~1 h^{-1} Mpc. Central regions of the two nearer
clusters A2256 and A2319 appear largely isothermal except for the cooler spots
coincident with the subunits in their X-ray surface brightness. Although the
existence of this substructure may suggest ongoing merger activity, no
asymmetric features in the temperature distribution resembling those in the
hydrodynamic merger simulations are apparent. In the outer parts of the
clusters, the temperature declines symmetrically with radius. In A2256 and
A2319, it follows a polytropic slope with gamma~1.3-1.5. This is somewhat
steeper than the simulations predict for a flat CDM universe and is closer to
the open universe predictions (Evrard etal 1996). The temperature drop is more
prominent in the outer regions of A2163 and A665 and appears even steeper than
adiabatic (although not inconsistent with it). If the gas in the outskirts of
these two clusters is indeed as cool as we measure, the cluster atmospheres
should be convectively unstable and transient. Also, such a steep temperature
profile could not possibly emerge if the gas was heated only via the release of
its own gravitational energy during infall. This may indicate the presence of
an additional heat source in the inner cluster, such as merger shocks
transferring energy from the dark matter to the gas.


  Measurements of transverse polarization of Lambda hyperons produced in high
energy pp collisions may help to address several open issues about Lambda
production and polarization mechanisms, such as the amount of SU(3) breaking,
the importance of gluons and sea quarks, and the origin of spontaneous Lambda
polarization. The process p + p -> Lambda^\uparrow + jet + X at midrapidity is
ideally suited for this purpose, for instance at LHC's ALICE experiment. New
expressions and predictions are presented for the transverse Lambda
polarization in this process, within a factorized description which involves
transverse momentum and spin dependence in the fragmentation process.
Uncertainties from the unpolarized Lambda fragmentation functions, due to the
unknown magnitude of SU(3) breaking and the apparent inconsistency between pp
and e^+ e^- data, are investigated.


  Human pose estimation (HPE) is a classical task in computer vision that
focuses on representing the orientation of a person by identifying the
positions of their joints. We design a lighterversion of the stacked hourglass
network with minimal loss in performance of the model. The lightweight
2-stacked hourglass has a reduced number of channels with depthwise separable
convolutions, residual connections with concatenation, and residual connections
between the necks of the hourglasses. The final model has a marginal drop in
performance with 79% reduction in the number of parameters and a similar drop
in MAdds


  Graph convolutions have gained popularity due to their ability to efficiently
operate on data with an irregular geometric structure. However, graph
convolutions cause over-smoothing, which refers to representations becoming
more similar with increased depth. However, many different definitions and
intuitions currently coexist, leading to research efforts focusing on
incompatible directions. This paper attempts to align these directions by
showing that over-smoothing is merely a special case of power iteration. This
greatly simplifies the existing theory on over-smoothing, making it more
accessible. Based on the theory, we provide a novel comprehensive definition of
rank collapse as a generalized form of over-smoothing and introduce the
rank-one distance as a corresponding metric. Our empirical evaluation of 14
commonly used methods shows that more models than were previously known suffer
from this issue.


  Multipoles are paramount for describing electromagnetic fields in many areas
of nanoscale optics, playing an essential role for the design of devices in
plasmonics and all-dielectric nanophotonics. Challenging the traditional
division into electric and magnetic moments, toroidal moments have been
proposed as a physically distinct family of multipoles with significant
contributions to the properties of matter. However, the apparent impossibility
of separately measuring their response sheds doubt on their true physical
significance. Here, we confirm the possibility of selectively exciting toroidal
moments without any other multipole. We develop a set of general conditions
that any current distribution must fulfill to be entirely described by toroidal
moments, and prove our results in an analytically solvable case. Our new theory
allows us to design and verify experimentally for the first time an artificial
structure supporting a pure, broadband toroidal dipole response in the complete
absence of the electric dipole and other 'ordinary' multipole contributions. In
addition, we propose a structure capable of supporting a novel type of
nonradiating source, a 'toroidal anapole', originating from the destructive
interference of the toroidal dipole with the unconventional electromagnetic
sources known as mean square radii. The results in this work provide conclusive
evidence on the independent excitation of toroidal moments in electrodynamics.


  We demonstrate that in the framework of standard general relativity
polytropic spheres with properly fixed polytropic index $n$ and relativistic
parameter $\sigma$, giving ratio of the central pressure $p_\mathrm{c}$ to the
central energy density $\rho_\mathrm{c}$, can contain region of trapped null
geodesics. Such trapping polytropes can exist for $n > 2.138$ and they are
generally much more extended and massive than the observed neutron stars. We
show that in the $n$--$\sigma$ parameter space the region of allowed trapping
increase with polytropic index for interval of physical interest $2.138 < n <
4$. Space extension of the region of trapped null geodesics increases with both
increasing $n$ and $\sigma > 0.677$ from the allowed region. In order to relate
the trapping phenomenon to astrophysically relevant situations, we restrict
validity of the polytropic configurations to their extension $r_\mathrm{extr}$
corresponding to the gravitational mass $M \sim 2M_{\odot}$ of the most massive
observed neutron stars. Then for the central density $\rho_\mathrm{c} \sim
10^{15}$~g\,cm$^{-3}$ the trapped regions are outside $r_\mathrm{extr}$ for all
values of $2.138 < n < 4$, for the central density $\rho_\mathrm{c} \sim 5
\times 10^{15}$~g\,cm$^{-3}$ the whole trapped regions are located inside of
$r_\mathrm{extr}$ for $2.138 < n < 3.1$, while for $\rho_\mathrm{c} \sim
10^{16}$~g\,cm$^{-3}$ the whole trapped regions are inside of $r_\mathrm{extr}$
for all values of $2.138 < n < 4$, guaranteeing astrophysically plausible
trapping for all considered polytropes. The region of trapped null geodesics is
located closely to the polytrope centre and could have relevant influence on
cooling of such polytropes or for binding of gravitational waves in their
interior.


  We performed a systematic study on the properties of CeFeAs$_{1-x}$P$_{x}$O
($0\leq x\leq 1$) by electrical resistivity, magnetization and specific heat
measurements. The c-axis lattice constant decreases significantly with
increasing P content, suggesting a remarkable chemical pressure. The Fe-3d
electrons show the enhanced metallic behavior upon P-doping and undergo a
magnetic quantum phase transition around $x \approx 0.4$. Meanwhile, the Ce-4f
electrons develop a ferromagnetic order near the same doping level. The
ferromagnetic order is vanishingly small around $x=0.9$. The data suggest a
heavy-fermion-like behavior as $x\geq 0.95$. No superconductivity is observed
down to 2 K. Our results show the ferromagnetic ordered state as an
intermediate phase intruding between the antiferromagnetic bad metal and the
nonmagnetic heavy fermion metal and support the cerium-containing iron
pnictides as a unique layered Kondo lattice system.


  Federated clustering, an essential extension of centralized clustering for
federated scenarios, enables multiple data-holding clients to collaboratively
group data while keeping their data locally. In centralized scenarios,
clustering driven by representation learning has made significant advancements
in handling high-dimensional complex data. However, the combination of
federated clustering and representation learning remains underexplored. To
bridge this, we first tailor a cluster-contrastive model for learning
clustering-friendly representations. Then, we harness this model as the
foundation for proposing a new federated clustering method, named
cluster-contrastive federated clustering (CCFC). Benefiting from representation
learning, the clustering performance of CCFC even double those of the best
baseline methods in some cases. Compared to the most related baseline, the
benefit results in substantial NMI score improvements of up to 0.4155 on the
most conspicuous case. Moreover, CCFC also shows superior performance in
handling device failures from a practical viewpoint.


  In recent years, convolutional neural networks (CNN) have played an important
role in the field of deep learning. Variants of CNN's have proven to be very
successful in classification tasks across different domains. However, there are
two big drawbacks to CNN's: their failure to take into account of important
spatial hierarchies between features, and their lack of rotational invariance.
As long as certain key features of an object are present in the test data,
CNN's classify the test data as the object, disregarding features' relative
spatial orientation to each other. This causes false positives. The lack of
rotational invariance in CNN's would cause the network to incorrectly assign
the object another label, causing false negatives. To address this concern,
Hinton et al. propose a novel type of neural network using the concept of
capsules in a recent paper. With the use of dynamic routing and reconstruction
regularization, the capsule network model would be both rotation invariant and
spatially aware. The capsule network has shown its potential by achieving a
state-of-the-art result of 0.25% test error on MNIST without data augmentation
such as rotation and scaling, better than the previous baseline of 0.39%. To
further test out the application of capsule networks on data with higher
dimensionality, we attempt to find the best set of configurations that yield
the optimal test error on CIFAR10 dataset.


  The BCS equations are the centerpiece of the microscopic description of
superconductivity. Their space discretization yields a system of coupled
ordinary differential equations. In this work, we come up with fast time
evolution schemes based on a splitting approach. One of the schemes only
requires basic operations. For the physically important case of the BCS
equations for a contact interaction potential, the computational cost of the
schemes increases only linearly with the dimension of the space discretization.
Their accuracy is demonstrated in extensive numerical experiments. These
experiments also show that the physical energy of the system is preserve up to
very small errors.


  We present a preliminary study of Schur norms $\|M\|_{S}=\max\{ \|M\circ C\|:
\|C\|=1\}$, where M is a matrix whose entries are $\pm1$, and $\circ$ denotes
the entrywise (i.e., Schur or Hadamard) product of the matrices. We show that,
if such a matrix M is n-by-n, then its Schur norm is bounded by $\sqrt{n}$, and
equality holds if and only if it is a Hadamard matrix. We develop a numerically
efficient method of computing Schur norms, and as an application of our results
we present several almost Hadamard matrices that are better than were
previously known.


  When launching or landing a spacecraft on the regolith of a terrestrial
surface, special attention needs to be paid to the rocket exhaust cratering
effects. If the effects are not controlled, the rocket cratering could damage
the spacecraft or other surrounding hardware. The cratering effects of a rocket
landing a planet's surface are not understood well, especially for the lunar
case with the plume expanding in vacuum. As a result, the blast effects cannot
be estimated sufficiently using analytical theories. It is necessary to develop
physics-based simulation tools in order to calculate mission-essential
parameters. In this work we test out the scaling laws of the physics in regard
to growth rate of the crater depth. This will provide the physical insight
necessary to begin the physics-based modeling.


  We analyze the light scattered by a single InAs quantum dot interacting with
a resonant continuous-wave laser. High resolution spectra reveal clear
distinctions between coherent and incoherent scattering, with the laser
intensity spanning over four orders of magnitude. We find that the fraction of
coherently scattered photons can approach unity under sufficiently weak or
detuned excitation, ruling out pure dephasing as a relevant decoherence
mechanism. We show how spectral diffusion shapes spectra, correlation
functions, and phase-coherence, concealing the ideal radiatively-broadened
two-level system described by Mollow.


  An infinite square well with a discontinuous step is one of the simplest
systems to exhibit non-Newtonian ray-splitting periodic orbits in the
semiclassical limit. This system is analyzed using both time-independent
perturbation theory (PT) and periodic-orbit theory and the approximate formulas
for the energy eigenvalues derived from these two approaches are compared. The
periodic orbits of the system can be divided into classes according to how many
times they reflect from the potential step. Different classes of orbits
contribute to different orders of PT. The dominant term in the second-order PT
correction is due to non-Newtonian orbits that reflect from the step exactly
once. In the limit in which PT converges the periodic-orbit theory results
agree with those of PT, but outside of this limit the periodic-orbit theory
gives much more accurate results for energies above the potential step.


  The dynamical structure of the Kuiper belt can be used as a clue to the
formation and evolution of the Solar System, planetary systems in general, and
Neptune's early orbital history in particular. The problem is best addressed by
forward modeling where different initial conditions and Neptune's orbital
evolutions are tested, and the model predictions are compared to orbits of
known Kuiper belt objects (KBOs). It has previously been established that
Neptune radially migrated, by gravitationally interacting with an outer disk of
planetesimals, from the original radial distance r < 25 au to its current orbit
at 30 au. Here we show that the migration models with a very low orbital
eccentricity of Neptune e_N < 0.05 do not explain KBOs with semimajor axes 50 <
a < 60 au, perihelion distances q > 35 au and inclinations i < 10 deg. If e_N <
0.05 at all times, the Kozai cycles control the implantation process and the
orbits with q > 35 au end up having, due to the angular momentum's z-component
conservation, i > 10 deg. Better results are obtained when Neptune's
eccentricity is excited to e_N ~ 0.1 and subsequently damped by dynamical
friction. The low-e & low-i orbits at 50-60 au are produced in this model when
KBOs are lifted from the scattered disk by secular cycles -- mainly the apsidal
resonance nu_8 -- near various mean motion resonances. These results give
support to a (mild) dynamical instability that presumably excited the orbits of
giant planets during Neptune's early migration.


  The development of scientific software is often a partnership between domain
scientists and scientific software engineers. It is especially important to
embrace these collaborations when developing advanced scientific software,
where sustainability, reproducibility, and extensibility are important. In the
ideal case, as discussed in this manuscript, this brings together teams
composed of the world's foremost scientific experts in a given field with
seasoned software developers experienced in forming highly collaborative teams
working on software to further scientific research.


  We consider the $[q+1,q-3,5]_q3$ generalized doubly-extended Reed-Solomon
code of codimension $4$ as the code associated with the twisted cubic in the
projective space $\mathrm{PG}(3,q)$. Basing on the point-plane incidence matrix
of $\mathrm{PG}(3,q)$, we obtain the number of weight 3 vectors in all the
cosets of the considered code. This allows us to classify the cosets by their
weight distributions and to obtain these distributions. The weight of a coset
is the smallest Hamming weight of any vector in the coset. For the cosets of
equal weight having distinct weight distributions, we prove that the difference
between the $w$-th components, $3<w\le q+1$, of the distributions is uniquely
determined by the difference between the $3$-rd components. This implies an
interesting (and in some sense unexpected) symmetry of the obtained
distributions.


  The geometric motivic Poincar\'e series of a variety, which was introduced by
Denef and Loeser, takes into account the classes in the Grothendieck ring of
the sequence of jets of arcs in the variety. Denef and Loeser proved that this
series has a rational form. We describe it in the case of an affine toric
variety of arbitrary dimension. The result, which provides an explicit set of
candidate poles, is expressed in terms of the sequence of Newton polyhedra of
certain monomial ideals, which we call logarithmic jacobian ideals, associated
to the modules of differential forms with logarithmic poles outside the torus
of the toric variety.


  We study the physics potential of the detection of the Cosmic Neutrino
Background via neutrino capture on tritium, taking the proposed PTOLEMY
experiment as a case study. With the projected energy resolution of $\Delta
\sim$ 0.15 eV, the experiment will be sensitive to neutrino masses with
degenerate spectrum, $m_1 \simeq m_2 \simeq m_3 = m_\nu \gtrsim 0.1$ eV. These
neutrinos are non-relativistic today; detecting them would be a unique
opportunity to probe this unexplored kinematical regime. The signature of
neutrino capture is a peak in the electron spectrum that is displaced by $2
m_{\nu}$ above the beta decay endpoint. The signal would exceed the background
from beta decay if the energy resolution is $\Delta \lesssim 0.7 m_\nu $.
Interestingly, the total capture rate depends on the origin of the neutrino
mass, being $\Gamma^{\rm D} \simeq 4$ and $\Gamma^{\rm M} \simeq 8$ events per
year (for a 100 g tritium target) for unclustered Dirac and Majorana neutrinos,
respectively. An enhancement of the rate of up to $\mathcal{O}(1)$ is expected
due to gravitational clustering, with the unique potential to probe the local
overdensity of neutrinos. Turning to more exotic neutrino physics, PTOLEMY
could be sensitive to a lepton asymmetry, and reveal the eV-scale sterile
neutrino that is favored by short baseline oscillation searches. The experiment
would also be sensitive to a neutrino lifetime on the order of the age of the
universe and break the degeneracy between neutrino mass and lifetime which
affects existing bounds.


  Accumulating evidence indicates that the capacity to integrate information in
the brain is a prerequisite for consciousness. Integrated Information Theory
(IIT) of consciousness provides a mathematical approach to quantifying the
information integrated in a system, called integrated information, $\Phi$.
Integrated information is defined theoretically as the amount of information a
system generates as a whole, above and beyond the sum of the amount of
information its parts independently generate. IIT predicts that the amount of
integrated information in the brain should reflect levels of consciousness.
Empirical evaluation of this theory requires computing integrated information
from neural data acquired from experiments, although difficulties with using
the original measure $\Phi$ precludes such computations. Although some
practical measures have been previously proposed, we found that these measures
fail to satisfy the theoretical requirements as a measure of integrated
information. Measures of integrated information should satisfy the lower and
upper bounds as follows: The lower bound of integrated information should be 0
when the system does not generate information (no information) or when the
system comprises independent parts (no integration). The upper bound of
integrated information is the amount of information generated by the whole
system and is realized when the amount of information generated independently
by its parts equals to 0. Here we derive the novel practical measure $\Phi^*$
by introducing a concept of mismatched decoding developed from information
theory. We show that $\Phi^*$ is properly bounded from below and above, as
required, as a measure of integrated information. We derive the analytical
expression $\Phi^*$ under the Gaussian assumption, which makes it readily
applicable to experimental data.


  Direct and alternating current iontophoresis and electro-osmosis
methodologies have provided new methods of transcutaneous drug delivery. A
byproduct of such methods is lowering the electrical impedance of the electrode
to skin contact, as conductive ions permeate the stratum corneum, the primary
resistive layer of the skin. We developed a method for adapting iontophoresis
to condition the electrode to skin contact, both for electrophysiological
recording and electrical stimulation of body tissues. By utilizing direct
current to treat electrodes with high impedance we show the effectiveness of
iontopheresis as a driving force for permeation of ionic electrolyte into the
skin barrier. We applied direct current (DC) levels of 50 {\mu}A to electrodes
on the human head for 30 seconds with paste (Nihon Kohden Elefix) electrolyte.
Typically immediately after DC treatment conditioning there was an impedance
drop of 10-30%. The effect was lasting over several hours, with the paste
electrolyte. These results demonstrate the feasibility of DC conditioning to
reduce the set time of electrolytic solutions and to maintain good skin contact
during extended recording or stimulation sessions.


  There is recent interest in finding a potential formulation for Stochastic
Partial Differential Equations (SPDEs). The rationale behind this idea lies in
obtaining all the dynamical information of the system under study from one
single expression. In this Letter we formally provide a general Lagrangian
formalism for SPDEs using the Hojman et al. method. We show that it is possible
to write the corresponding effective potential starting from an s-equivalent
Lagrangean, and that this potential is able to reproduce all the dynamics of
the system, once a special differential operator has been applied. This
procedure can be used to study the complete time evolution and spatial
inhomogeneities of the system under consideration, and is also suitable for the
statistical mechanics description of the problem. Keywords: stochastic partial
differential equations, variational formulation, effective potential. PACS:
45.20.Jj; 02.50.-r; 02.50.Ey.


  The aim of this work is to use some categories of Actor-Network Theory (ANT)
developed by Bruno Latour in the book Reassembling the Social -- An
Introduction to Actor-Network Theory to analyze a current controversy that
occurs in the area of cosmology: the dark matter existence and its
characteristics. The central idea of this article is to try to decipher which
type of agent is the dark matter and open the discussion about this topic from
a sociological perspective.


  The Bianchi IX cosmological model is analyzed in a generalized uncertainty
principle framework. The Arnowitt-Deser-Misner reduction of the dynamics is
performed and a time-coordinate, namely the volume of the Universe, naturally
arises. Such a variable is treated in the ordinary way while the anisotropies
(the physical degrees of freedom) are described by a deformed Heisenberg
algebra. The analysis of the model (passing through Bianchi I and II) is
performed at classical level by studying the modifications induced on the
symplectic geometry by the deformed algebra. We show that, the Universe can not
isotropize because of the deformed Kasner dynamics, the triangular allowed
domain is asymptotically stationary with respect to the particle (Universe) and
its bounces against the walls are not interrupted by the deformed effects.
Furthermore, no reflection law can be in general obtained since the Bianchi II
model is no longer analytically integrable. This way, the deformed Mixmaster
Universe can be still considered a chaotic system.


  We propose a discontinuous least squares finite element method for solving
the Helmholtz equation. The method is based on the L2 norm least squares
functional with the weak imposition of the continuity across the interior faces
as well as the boundary conditions. We minimize the functional over the
discontinuous polynomial spaces to seek numerical solutions. The wavenumber
explicit error estimates to our method are established. The optimal convergence
rate in the energy norm with respect to a fixed wavenumber is attained. The
least squares functional can naturally serve as a posteriori estimator in the
h-adaptive procedure. It is convenient to implement the code due to the usage
of discontinuous elements. Numerical results in two and three dimensions are
presented to verify the error estimates.


  DAMON leverages manifold learning and variational autoencoding to achieve
obstacle avoidance, allowing for motion planning through adaptive graph
traversal in a pre-learned low-dimensional hierarchically-structured manifold
graph that captures intricate motion dynamics between a robotic arm and its
obstacles. This versatile and reusable approach is applicable to various
collaboration scenarios.
  The primary advantage of DAMON is its ability to embed information in a
low-dimensional graph, eliminating the need for repeated computation required
by current sampling-based methods. As a result, it offers faster and more
efficient motion planning with significantly lower computational overhead and
memory footprint. In summary, DAMON is a breakthrough methodology that
addresses the challenge of dynamic obstacle avoidance in robotic systems and
offers a promising solution for safe and efficient human-robot collaboration.
  Our approach has been experimentally validated on a 7-DoF robotic manipulator
in both simulation and physical settings. DAMON enables the robot to learn and
generate skills for avoiding previously-unseen obstacles while achieving
predefined objectives. We also optimize DAMON's design parameters and
performance using an analytical framework. Our approach outperforms mainstream
methodologies, including RRT, RRT*, Dynamic RRT*, L2RRT, and MpNet, with 40\%
more trajectory smoothness and over 65\% improved latency performance, on
average.


  In a recent work arXiv:2108.08835, Kong, Wen and Zheng use Ising chain to
show that the macroscopic observables in a 1d (space dimension) gapped quantum
phase form an enriched fusion category. In this work, we generalize their
results to the phases with finite abelian onsite symmetries. By carefully
analyzing the observables on both the symmetry preserving and symmetry breaking
cases, we explicitly show that the spacetime observables of these 1d phases
form fusion categories enriched in their monoidal centers. These are actually
concrete examples of the holographic duality based on topological wick
rotation. Our categorical descriptions also reproduce an existing
classification result and shed light on a unified definition of all quantum
phases.


  We study relations between Rauzy classes coming from an interval exchange map
and the corresponding connected components of strata of the moduli space of
Abelian differentials. This gives a criterion to decide whether two
permutations are in the same Rauzy class or not, without actually computing
them. We prove a similar result for Rauzy classes corresponding to quadratic
differentials.


  The expression of temperature distribution along a film thickness is derived
and distribution of temperature in the film as the substrate is heated is
shown. The variation of film resistance with different substrate temperature is
calculated and the existence of temperature gradient along the film thickness
with finite thermal conductivity leads to hysteresis like behaviour on
heating-cooling the film.


  This paper presents a new class of high order linear ImEx multistep schemes
with large regions of unconditional stability. Unconditional stability is a
desirable property of a time stepping scheme, as it allows the choice of time
step solely based on accuracy considerations. Of particular interest are
problems for which both the implicit and explicit parts of the ImEx splitting
are stiff. Such splittings can arise, for example, in variable-coefficient
problems, or the incompressible Navier-Stokes equations. To characterize the
new ImEx schemes, an unconditional stability region is introduced, which plays
a role analogous to that of the stability region in conventional multistep
methods. Moreover, computable quantities (such as a numerical range) are
provided that guarantee an unconditionally stable scheme for a proposed
implicit-explicit matrix splitting. The new approach is illustrated with
several examples. Coefficients of the new schemes up to fifth order are
provided.


  We classify groups generated by powers of 2 Dehn twists which are 1) free or
2) have no ``unexpected'' reducible elements. We give some sufficient
conditions in the case of groups generated by powers of more than two twists.


  We study nuclear modification factors for single $D$ meson and semileptonic
decay lepton $l$ ($=e,\mu$) production in minimum bias proton-nucleus (p$A$)
collisions at the LHC in the color-glass-condensate (CGC) framework at leading
order in strong coupling. In our numerical computations, transverse momentum
($k_\perp$) dependent multi-point Wilson line correlators are employed for
describing target nucleus for p$A$ and proton for pp. The projectile proton is
treated with unintegrated gluon distribution function, which is also
$k_\perp$-dependent. The rapidity evolutions of these functions in the small
Bjorken $x$ region are taken into account by solving running coupling
Balitsky-Kovchegov (BK) equation at leading logarithmic accuracy. For
simplicity, we employ Kartvelishvili's type fragmentation function and a simple
model for lepton energy distribution from seileptonic decay, respectively, to
compute differential cross sections for $D$ and $l$ production. The gluon
saturation scale inside the heavy nucleus is enhanced and dependent on $x$,
which we take into account by replacing the initial saturation scale in the BK
equation with a larger value for the heavy nucleus. We show that the saturation
effect leads to perceptible nuclear suppression of $D$ production at forward
rapidity. Our numerical results predict similar nuclear suppressions in p$A$
collisions for forward $l$ production at lower transverse momentum
$p_\perp<2\;{\rm GeV}$. Numerical tables on the nuclear modifications of $D$
and $l$ are listed in this note.


  Several directions of the demand of decay data in nuclear astrophysics are
discussed for radionuclides near the valley of nuclear stability. The current
decay data evaluation results have been presented for a number of radionuclides
of astrophysical interest. An extended list of such nuclides is offered for
their nuclear characteristics to be further evaluated by the Decay Data
Evaluation Project cooperation participants.


  Pythonic code is idiomatic code that follows guiding principles and practices
within the Python community. Offering performance and readability benefits,
Pythonic code is claimed to be widely adopted by experienced Python developers,
but can be a learning curve to novice programmers. To aid with Pythonic
learning, we create an automated tool, called Teddy, that can help checking the
Pythonic idiom usage. The tool offers a prevention mode with Just-In-Time
analysis to recommend the use of Pythonic idiom during code review and a
detection mode with historical analysis to run a thorough scan of idiomatic and
non-idiomatic code. In this paper, we first describe our tool and an evaluation
of its performance. Furthermore, we present a case study that demonstrates how
to use Teddy in a real-life scenario on an Open Source project. An evaluation
shows that Teddy has high precision for detecting Pythonic idiom and
non-Pythonic code. Using interactive visualizations, we demonstrate how novice
programmers can navigate and identify Pythonic idiom and non-Pythonic code in
their projects. Our video demo with the full interactive visualizations is
available at https://youtu.be/vOCQReSvBxA.


  This letter investigates the physical layer security in a wireless
cooperative network where communication is assisted by a full-duplex (FD)
untrusted relay in the presence of multiple external eavesdroppers. A
cluster-based colluding eavesdropping setting is considered, where illegitimate
nodes with common interests are grouped in a cluster. In order to confuse the
different eavesdropping clusters, we consider artificial-noise-aided
beamforming at the source node. Moreover, FD relay jamming is adopted to
improve the system's security. To maintain secure communications against the
untrusted relay node, a FD destination jamming scheme is adopted. Our proposed
scheme is designed based on the channel state information of the legitimate
nodes only. Numerical results show that the optimal power allocation factor
between data and artificial noise depends on the total number of antennas of
the different colluding eavesdropping clusters.


  Gravitational-wave observations of inspiralling binary neutron star systems
can be used to measure the neutron-star equation of state (EOS) through the
tidally induced shift in the waveform phase that depends on the tidal
deformability parameter $\lambda$. Previous work has shown that $\lambda$, a
function of the neutron-star EOS and mass, is measurable by Advanced LIGO for a
single event when including tidal information up to the merger frequency. In
this work, we describe a method for stacking measurements of $\lambda$ from
multiple inspiral events to measure the EOS. We use Markov chain Monte Carlo
simulations to estimate the parameters of a 4-parameter piecewise polytrope EOS
that matches theoretical EOS models to a few percent. We find that, for
"realistic" event rates ($\sim 40$ binary neutron star inspiral events per year
with signal-to-noise ratio $> 8$ in a single Advanced LIGO detector), combining
a year of gravitational-wave data from a three-detector network with the
constraints from causality and recent high mass neutron-star measurements, the
EOS above nuclear density can be measured to better than a factor of two in
pressure in most cases. We also find that in the mass range
$1M_\odot$--$2M_\odot$, the neutron-star radius can be measured to better than
$\pm 1$ km and the tidal deformability can be measured to better than $\pm 1
\times 10^{36}$ g cm$^2$ s$^2$ (10%--50% depending on the EOS and mass). The
overwhelming majority of this information comes from the loudest $\sim 5$
events. Current uncertainties in the post-Newtonian waveform model, however,
lead to systematic errors in the EOS measurement that are as large as the
statistical errors, and more accurate waveform models are needed to minimize
this error.


  We present StyleMamba, an efficient image style transfer framework that
translates text prompts into corresponding visual styles while preserving the
content integrity of the original images. Existing text-guided stylization
requires hundreds of training iterations and takes a lot of computing
resources. To speed up the process, we propose a conditional State Space Model
for Efficient Text-driven Image Style Transfer, dubbed StyleMamba, that
sequentially aligns the image features to the target text prompts. To enhance
the local and global style consistency between text and image, we propose
masked and second-order directional losses to optimize the stylization
direction to significantly reduce the training iterations by 5 times and the
inference time by 3 times. Extensive experiments and qualitative evaluation
confirm the robust and superior stylization performance of our methods compared
to the existing baselines.


  Predictive process monitoring is concerned with the analysis of events
produced during the execution of a business process in order to predict as
early as possible the final outcome of an ongoing case. Traditionally,
predictive process monitoring methods are optimized with respect to accuracy.
However, in environments where users make decisions and take actions in
response to the predictions they receive, it is equally important to optimize
the stability of the successive predictions made for each case. To this end,
this paper defines a notion of temporal stability for binary classification
tasks in predictive process monitoring and evaluates existing methods with
respect to both temporal stability and accuracy. We find that methods based on
XGBoost and LSTM neural networks exhibit the highest temporal stability. We
then show that temporal stability can be enhanced by hyperparameter-optimizing
random forests and XGBoost classifiers with respect to inter-run stability.
Finally, we show that time series smoothing techniques can further enhance
temporal stability at the expense of slightly lower accuracy.


  We consider strings moving in the R_t x S^3_\eta subspace of the
\eta-deformed AdS_5 x S^5 and obtain a class of solutions depending on several
parameters. They are characterized by the string energy and two angular
momenta. Finite-size dyonic giant magnon belongs to this class of solutions.
Further on, we restrict ourselves to the case of giant magnon with one nonzero
angular momentum, and obtain the leading finite-size correction to the
dispersion relation.


  Uranus and Neptune have intrinsic magnetic fields generated via convection in
a molten H2O layer, where the field strength is determined by its electrical
conductivity (EC) along with convection size and velocity. Previous shock
experiments reported that the EC of molten H2O is high enough to generate
magnetic fields of these ice giant planets with adiabatic thermal structures.
Here we measured the EC of ionic H2O fluid for the first time by static
compression experiments up to 45 GPa and 2,750 K. The EC determined is lower by
a few orders of magnitude than earlier data by shock compression measurements
and not capable of generating a magnetic field with the conventional interior
thermal structures. Our results necessitate recently-suggested fewfold hotter
interiors of Uranus and Neptune to explain their magnetic fields.